diff --git "a/5313.jsonl" "b/5313.jsonl" new file mode 100644--- /dev/null +++ "b/5313.jsonl" @@ -0,0 +1,234 @@ +{"seq_id":"74489622646","text":"import torch\nimport submodules.ddpm_sr3.data as Data\nimport submodules.ddpm_sr3.model as Model\nimport argparse\nimport logging\nimport submodules.ddpm_sr3.core.logger as Logger\nimport submodules.ddpm_sr3.core.metrics as Metrics\nimport numpy as np\nimport wandb\n\nfrom tqdm import tqdm\n\nfrom ml_collections import ConfigDict\n\nfrom src.constants import BaseCheckpoint\n\n\ndef parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--config', default='src/sr_ddpm/sr_config.yaml')\n parser.add_argument('-p', '--phase', choices=('train', 'val'), default='train')\n parser.add_argument('-b', '--name', default='sr-diffusion')\n parser.add_argument('-g', '--gpu_ids', type=int, default=None)\n parser.add_argument('--debug', action='store_true')\n parser.add_argument('--enable_wandb', action='store_true')\n parser.add_argument('--log_wandb_ckpt', action='store_true')\n parser.add_argument('--log_eval', action='store_true')\n return parser.parse_args()\n\n\ndef configure_dataloader(dataset_opt, phase):\n val_set = Data.create_dataset(dataset_opt, phase)\n return Data.create_dataloader(val_set, dataset_opt, phase)\n\n\ndef train(args):\n opt = ConfigDict(Logger.parse(args))\n opt.name = args.name\n\n opt.path.checkpoint = BaseCheckpoint.SR3 / opt.name\n\n torch.backends.cudnn.enabled = True\n torch.backends.cudnn.benchmark = True\n logging.basicConfig(level=logging.INFO)\n logging.info('Initial Dataset Finished')\n\n # model\n diffusion = Model.create_model(opt)\n logging.info('Initial Model Finished')\n\n train_loader = configure_dataloader(opt[\"datasets\"][\"train\"], \"train\")\n val_loader = configure_dataloader(opt[\"datasets\"][\"val\"], \"val\")\n\n # Train\n n_iter = opt.train.n_iter\n\n diffusion.set_new_noise_schedule(\n opt['model']['beta_schedule'][opt['phase']], schedule_phase=opt['phase'])\n\n valid_data_batch = next(iter(val_loader))\n wandb_config = dict(\n project=opt.project,\n name=opt.name,\n config=dict(opt)\n )\n\n with wandb.init(**wandb_config) as run:\n\n current_step, current_epoch = 0, 0\n while current_step < n_iter:\n\n pbar = tqdm(train_loader, desc=f\"Epoch {current_epoch}\", total=len(train_loader))\n for train_batch in pbar:\n\n current_step += 1\n\n diffusion.feed_data(train_batch)\n diffusion.optimize_parameters()\n run.log(diffusion.get_current_log())\n\n if current_step % opt.train.val_freq == 0:\n with torch.no_grad():\n diffusion.set_new_noise_schedule(\n opt.model.beta_schedule.val,\n schedule_phase='val'\n )\n diffusion.feed_data(valid_data_batch)\n logging.info(\"START SAMPLING FOR VALIDATION\")\n diffusion.test(continous=False)\n logging.info(\"END SAMPLING FOR VALIDATION\")\n\n visuals = diffusion.get_current_visuals()\n sr_img = Metrics.tensor2img(visuals['SR']) # uint8\n hr_img = Metrics.tensor2img(visuals['HR']) # uint8\n fake_img = Metrics.tensor2img(visuals['INF'])\n\n image_array = np.concatenate((fake_img, sr_img, hr_img), axis=1)\n wandb_image = wandb.Image(\n image_array,\n caption=f\"sample at {current_step} steps: FAKE SR | DDPM SR | TRUE HR\"\n )\n run.log({f\"Validation images\": wandb_image})\n\n diffusion.set_new_noise_schedule(opt.model.beta_schedule.train, 'train')\n\n if current_step % opt.train.save_checkpoint_freq == 0:\n diffusion.save_network(current_epoch, current_step)\n\n\ndef main():\n args = parse_arguments()\n train(args)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Kirill-Tamogashev/SR-Project","sub_path":"src/sr_ddpm/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"17621675552","text":"# coding: UTF-8\nimport tensorflow as tf\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 创建一个图来存放google调整好的模型 inception_pretrain\\classify_image_graph_def.pb\n# 结果数组与C:\\Users\\admin\\PycharmProjects\\TensorFlowTestNew\\TensorFlow\\inception利用\\output_labels.txt文件中的顺序要一致\nres = ['daisy','dandelion']\nwith tf.gfile.FastGFile('output_graph.pb', 'rb') as f:\n graph_def = tf.GraphDef()\n graph_def.ParseFromString(f.read())\n tf.import_graph_def(graph_def, name='')\n\nwith tf.Session() as sess:\n softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')#获取新模���最后的输出节点叫做final_result，可以从tensorboard中的graph中看到，其中名字后面的’:’之后接数字为EndPoints索引值（An operation allocates memory for its outputs, which are available on endpoints :0, :1, etc, and you can think of each of these endpoints as a Tensor.），通常情况下为0，因为大部分operation都只有一个输出。\n # 遍历目录\n for root, dirs, files in os.walk('testImage/'):#预测图片的位置\n for file in files:\n image_data = tf.gfile.FastGFile(os.path.join(root, file), 'rb').read()#Returns the contents of a file as a string.\n predictions = sess.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data})#tensorboard中的graph中可以看到DecodeJpeg/contents是模型的输入变量名字\n predictions = np.squeeze(predictions)\n\n image_path = os.path.join(root, file)\n print(image_path)\n #展示图片\n # img = plt.imread(image_path)#只能读png图,所以不能显示其他图片，训练非png图时把这段注释掉，他只是一个显示作用\n # plt.imshow(img)\n # plt.axis('off')\n # plt.show()\n\n top_k = predictions.argsort()[-2:][::-1]#概率最高的后2个，然后在倒排一下\n for node_id in top_k:\n score = predictions[node_id]\n print('%s (score=%.5f)' % (res[node_id], score))\n print()\n","repo_name":"18515350435/TensorFlowTest","sub_path":"TensorFlow/inception利用/测试利用inception-v3训练好的新模型.py","file_name":"测试利用inception-v3训练好的新模型.py","file_ext":"py","file_size_in_byte":2116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"5590021026","text":"import time\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nfrom tqdm import tqdm\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import mean_squared_error\nimport lightgbm as lgb\nimport re\nimport optuna\nsns.set_theme(style=\"darkgrid\")\n\ntrain_df = pd.read_csv('./input/train.csv')\ntest_df = pd.read_csv('./input/test.csv')\n\n#괄호와 괄호안내용 제거\nregex = \"\\(.*\\)|\\s-\\s.*\"\nfor i in tqdm(range(len(train_df))):\n train_df.at[i, 'apt'] = re.sub(regex, '', train_df.at[i, 'apt'])\nfor i in tqdm(range(len(test_df))):\n test_df.at[i, 'apt'] = re.sub(regex, '', test_df.at[i, 'apt']) \n\n# 같은 아파트 이름을 갖는 수를 저장하는 apt_counts 열 생성\ntrain_df['apt_counts'] = 0\ntrain_df.groupby('apt')['apt_counts'].count()\ntrain_df = pd.merge(train_df, train_df.groupby('apt')['apt_counts'].count(), on='apt', how='left').drop('apt_counts_x', axis=1).rename(columns={'apt_counts_y':'apt_counts'})\n\ntest_df['apt_counts'] = 0\ntest_df.groupby('apt')['apt_counts'].count()\ntest_df = pd.merge(test_df, test_df.groupby('apt')['apt_counts'].count(), on='apt', how='left').drop('apt_counts_x', axis=1).rename(columns={'apt_counts_y':'apt_counts'})\n\ntrain_df.head(3)\n\n# top 10 시공사 아파트 여부를 나타내는 컬럼 생성\ntrain_df['top10'] = 0\ntest_df['top10'] = 0\ntop10 = ['자이', '푸르지오', '더샵', '롯데캐슬', '이편한|e편한|e-편한',\n '힐스테이트', '아이파크', '래미안', 'sk|SK|에스케이', '데시앙']\n\ntrain_df['apt'] = train_df['apt'].fillna('others')\n# top 10 시공사면 1, 아니면 0\nfor i, brand in enumerate(top10):\n train_df.loc[train_df['apt'].str.contains(brand), 'top10'] = 1\n test_df.loc[test_df['apt'].str.contains(brand), 'top10'] = 1\n\n# 데이터에 많이 있는 아파트 대표 25개 리스트\napt_names = ['그레이스', '양지', '쌍용', '현대', '한신', '삼성', '대우', '신동아', '두산', '주공',\n '우성', '벽산', '동원로얄듀크','경남', '삼환', '쌍용', '삼익', '대림', '코오롱', '파크리오',\n '엘지', '성원', '잠실', '동궁리치웰', '동성']\n\n# top 10 시공사 키워드와 25개 리스트를 통합\napt_names_list = top10 + apt_names\n\n# `apt_names_list`의 키워드에 해당하는 아파트명이 있는지 여부를 나타내는 새로운 컬럼 생성\ntrain_df['transformed'] = False\ntest_df['transformed'] = False\n\n# `apt_names_list`의 키워드에 아파트명이 포함되면 해당 키워드로 아파트명을 통일함\n# 그리고 `transformed` 컬럼값을 True로 변경\nfor a in tqdm(apt_names_list):\n train_df.loc[train_df['apt'].str.contains(a), 'apt'] = a\n test_df.loc[test_df['apt'].str.contains(a), 'apt'] = a\n train_df.loc[train_df['apt'].str.contains(a), 'transformed'] = True\n test_df.loc[test_df['apt'].str.contains(a), 'transformed'] = True\n\n# 아파트 이름이 변경되지 않았을 경우(`transformed=False` 일 경우) 아파트명을 'others'로 변경\nfor a in tqdm(apt_names):\n train_df.loc[~train_df['transformed'], 'apt'] = 'others'\n test_df.loc[~test_df['transformed'] , 'apt'] = 'others'\n\n# 아파트별 가격의 ��균 내림차순 정렬\napt_price = train_df.groupby('apt')['transaction_real_price'].agg('mean').sort_values(ascending=False)\nprint('변환전\\n', apt_price[:5])\n\nfor i, a in enumerate(list(apt_price.index)):\n train_df.loc[train_df['apt'] == a, 'apt'] = i # 라벨 인코딩\n test_df.loc[test_df['apt'] == a, 'apt'] = i\napt_price = train_df.groupby('apt')['transaction_real_price'].agg('mean').sort_values(ascending=False)\nprint('변환후\\n', apt_price[:5])\n\n# test 시작 거래연월인 인덱스 저장\ntest_start = train_df.loc[train_df['transaction_year_month'] == 201701, 'transaction_year_month'].index[0]\n\n# 완공연도에서 최소연도를 뺌으로써 완공연도 라벨인코딩\nprint('변환전\\n', train_df['year_of_completion'].unique()[:5])\ntrain_df['year_of_completion'] = train_df['year_of_completion'] - train_df['year_of_completion'].min()\ntest_df['year_of_completion'] = test_df['year_of_completion'] - test_df['year_of_completion'].min()\nprint('변환후\\n', train_df['year_of_completion'].unique()[:5])\n\n# 연월 증가하는 순으로 라벨 인코딩\nprint('train 변환전\\n', train_df['transaction_year_month'].unique()[:5])\nprint('test 변환전\\n', test_df['transaction_year_month'].unique()[:5])\nle = LabelEncoder()\ntrain_df['transaction_year_month'] = le.fit_transform(train_df['transaction_year_month'])\n# test는 다음과 같이 처리\ntest_df['transaction_year_month'] = test_df['transaction_year_month'] - test_df['transaction_year_month'].min() + train_df.at[test_start, 'transaction_year_month']\nprint('train 변환후\\n', train_df['transaction_year_month'].unique()[:5])\nprint('test 변환후\\n', test_df['transaction_year_month'].unique()[:5])\n\n# 필요없는 열 제거\ntrain_df = train_df.drop(['jibun', 'transaction_date', 'addr_kr'], axis=1)\ntest_df = test_df.drop(['jibun', 'transaction_date', 'addr_kr'], axis=1)\n\nseoul_set = set(train_df.loc[train_df['city']=='서울특별시', 'dong'])\nbusan_set = set(train_df.loc[train_df['city']=='부산광역시', 'dong'])\nsame_dong = seoul_set & busan_set \nprint(same_dong)\n\nseoul_set = set(test_df.loc[test_df['city']=='서울특별시', 'dong'])\nbusan_set = set(test_df.loc[test_df['city']=='부산광역시', 'dong'])\nsame_dong = seoul_set & busan_set \nprint(same_dong)\n\nfor d in same_dong:\n train_df.loc[(train_df['city']=='서울특별시') & (train_df['dong']==d), 'dong'] = '서울' + d\n train_df.loc[(train_df['city']=='부산광역시') & (train_df['dong']==d), 'dong'] = '부산' + d\n test_df.loc[(test_df['city']=='서울특별시') & (test_df['dong']==d), 'dong'] = '서울' + d\n test_df.loc[(test_df['city']=='부산광역시') & (test_df['dong']==d), 'dong'] = '부산' + d\n \n\nseoul_set = set(train_df.loc[train_df['city']=='서울특별시', 'dong'])\nbusan_set = set(train_df.loc[train_df['city']=='부산광역시', 'dong'])\nsame_dong = seoul_set & busan_set\nprint(same_dong) \n\ntrain_df.loc[train_df['dong'].str.contains('장충동')].groupby('dong')['transaction_real_price'].\\\nagg('mean')\n\ndong_price = train_df.groupby('dong')['transaction_real_price'].agg('mean').sort_values(ascending=False)\ndong_price[:20]\n\n# 가격기준으로 동을 정렬한 리스트를 바탕으로 dong에 대해 라벨 인코딩 진행 - 477 it.\nfor i, d in tqdm(enumerate(list(dong_price.index)), total=len(dong_price)):\n train_df.loc[train_df['dong'] == d, 'dong'] = i\n test_df.loc[test_df['dong'] == d, 'dong'] = i\ntrain_df.head()\n\n# 최소값이 -4이므로 4를 더해서 음수를 없애고 순서형범주처리\nprint('변환전\\n', train_df['floor'].values[:5])\ntrain_df['floor'] = train_df['floor'].map(lambda x: x+4)\ntest_df['floor'] = test_df['floor'].map(lambda x: x+1)\nprint('변환후\\n', train_df['floor'].values[:5])\n\n# 가격 로그 변환 후 원래 가격 따로 저장\ntrain_df['log_price'] = np.log1p(train_df['transaction_real_price'])\nreal_price = train_df['transaction_real_price'] # 원래 가격\ntrain_df.drop('transaction_real_price', axis=1, inplace=True)\n\n# 면적 로그 변환 후 원래 면적 따로 저장\ntrain_df['log_area'] = np.log1p(train_df['exclusive_use_area'])\ntest_df['log_area'] = np.log1p(test_df['exclusive_use_area'])\narea = train_df['exclusive_use_area'] # 원래 가격\ntrain_df.drop('exclusive_use_area', axis=1, inplace=True)\ntest_df.drop('exclusive_use_area', axis=1, inplace=True)\n\ndrop_col = ['transaction_id', 'apartment_id', 'apt_counts', 'transformed']\n\ntrain_df['city'] = train_df['city'].map(lambda x: 1 if x == '서울특별시' else 0)\ntest_df['city'] = test_df['city'].map(lambda x: 1 if x == '서울특별시' else 0)\n\ntrain_df.drop(drop_col, axis=1, inplace=True)\ntest_df.drop(drop_col, axis=1, inplace=True)\n\ntrain_df['dong'] = train_df['dong'].astype('int64')\ntrain_df['apt'] = train_df['apt'].astype('int64')\ntest_df['dong'] = test_df['dong'].astype('int64')\ntest_df['apt'] = test_df['apt'].astype('int64')\ntrain_df.info()\n\ndef RMSE(y, y_pred):\n rmse = mean_squared_error(y, y_pred) ** 0.5\n return rmse\n\ncut = int(len(train_df)*0.8)\nh_train = train_df[:cut]\nh_valid = train_df[cut:]\n\nh_train_X = h_train.drop('log_price', axis=1)\nh_train_y = h_train['log_price']\nh_valid_X = h_valid.drop('log_price', axis=1)\nh_valid_y = h_valid['log_price']\nprint(h_train_X.shape, h_train_y.shape, h_valid_X.shape, h_valid_y.shape)\n\nfrom optuna.samplers import TPESampler\n\nsampler = TPESampler(seed=10)\n\ndef objective(trial):\n dtrain = lgb.Dataset(h_train_X, label=h_train_y)\n dtest = lgb.Dataset(h_valid_X, label=h_valid_y)\n\n param = {\n 'objective': 'regression', # 회귀\n 'verbose': -1,\n 'num_threads': 6,\n 'device': 'gpu',\n 'metric': 'rmse', \n 'max_depth': trial.suggest_int('max_depth',3, 15),\n 'learning_rate': trial.suggest_loguniform(\"learning_rate\", 1e-8, 1e-2),\n 'n_estimators': trial.suggest_int('n_estimators', 100, 3000),\n 'min_child_samples': trial.suggest_int('min_child_samples', 5, 100),\n 'subsample': trial.suggest_loguniform('subsample', 0.4, 1),\n }\n\n model = lgb.LGBMRegressor(**param)\n lgb_model = model.fit(h_train_X, h_train_y, eval_set=[(h_valid_X, h_valid_y)], verbose=0, early_stopping_rounds=25)\n rmse = RMSE(h_valid_y, lgb_model.predict(h_valid_X))\n return rmse\n \nstart = time.time()\nstudy_lgb = optuna.create_study(direction='minimize', sampler=sampler)\nstudy_lgb.optimize(objective, n_trials=100)\n\nprint(\"Tuning Time(sec):\", time.time() - start)\ntrial = study_lgb.best_trial\ntrial_params = trial.params\nprint('Best Trial: score {},\\nparams {}'.format(trial.value, trial_params))","repo_name":"Haebuk/Apartment","sub_path":"my_method.py","file_name":"my_method.py","file_ext":"py","file_size_in_byte":9768,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"} +{"seq_id":"71657350006","text":"from camos.tasks.opening import Opening\nimport camos.utils.apptools as apptools\n\nimport pickle\n\n\nclass OpenImage(Opening):\n analysis_name = \"Open View\"\n\n def __init__(self, *args, **kwargs):\n super(OpenImage, self).__init__(extensions=\"cms File (*.cms)\", *args, **kwargs)\n\n def _run(self):\n # Added so we can load CMOS chip image\n dp = pickle.load(open(self.filename, \"rb\"))\n self.populateModel(dp)\n\n def populateModel(self, model):\n for i in range(len(model.images)):\n self.model.add_image(\n model.images[i],\n name=model.names[i],\n cmap=model.colormaps[i],\n scale=model.scales[i],\n translation=model.translation[i],\n samplingrate=model.samplingrate[i],\n )\n","repo_name":"danilexn/camos","sub_path":"camos/plugins/openview/openview.py","file_name":"openview.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"} +{"seq_id":"10581079298","text":"import sys\ninput = sys.stdin.readline\n\nN = int(input())\nT = 0\nboard = []\ni, j = 1, 0\nfor _ in range(N):\n r = list(map(int, input().split()))\n for v in r:\n j = max(v, j)\n T += v\n board.append(r)\n\nres = 0\nwhile i <= j:\n m = (i+j) // 2\n\n tmp = 0\n for x in range(N):\n for y in range(N):\n tmp += board[x][y] if board[x][y] <= m else m\n \n if tmp / T >= 0.5:\n res = m\n j = m-1\n else:\n i = m+1\n\nprint(res)","repo_name":"97Kzone/CodeTest_practice","sub_path":"Implement/17245.py","file_name":"17245.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"35449424450","text":"# Prolog\n# Author: David Yurek\n# Email: dayure2@g.uky.edu\n# Section: 012\n# Sept. 29, 2012\n# Programming Assignment 1\n#\n# ______________________________________________________________________________\n#\n# Purpose: The purpose of this assignment is to take an input from the user of\n# altitude of a satellite and compute the velocity, acceleration and\n# time of orbit completion. The output format was predetermined by\n# a standard provided in the assignment.\n#\n# Preconditions: The only input from the user is the altitude of the satellite.\n#\n# Post-conditions: The outputs of the program are the velocity, acceleration, and\n# time of orbit completion. The outputs are formatting befitting\n# the standard provided in the assignment.\n#_______________________________________________________________________________\n\n# Imports the math library.\nimport math\n\n# Defines main.\ndef main():\n\n # Displays the introductory message.\n print(' Satellite Orbital Calculations')\n print(' by David Yurek')\n print(' ')\n\n # Prompts user for satellite altitude.\n sat_altitude_km = float(input(\"Please enter the altitude of the satellite \"\n \"in kilometers from the Earth's surface \"))\n\n # Defines constant variables.\n earth_radius_km = 6378.1370 # Earth radius in kilometers.\n earth_GM = float(3.986005 * (10 ** 14)) # Earth GM in m^3/s^2.\n orbit_radius_km = (sat_altitude_km + earth_radius_km) # Orbit radius in kilometers.\n orbit_radius_meters = orbit_radius_km * 1000 # Reassigns orbit radius > meters.\n\n # Calculations from provided equations.\n sat_velocity = math.sqrt(earth_GM / orbit_radius_meters) # Velocity calculated in m/s.\n acceleration = earth_GM / (orbit_radius_meters ** 2) # Acceleration calculated > m/s^2.\n orbit_time_seconds = math.sqrt(((4 * math.pi ** 2) # Orbit time in seconds.\n * orbit_radius_meters ** 3) / earth_GM)\n orbit_time_minutes = orbit_time_seconds / 60 # Orbit time converted to minutes.\n orbit_time_minutes = round(orbit_time_minutes, 2) # Orbit time in minutes rounded.\n\n # Prints output in assigned format.\n print(' ')\n print('The satellite is travelling at', sat_velocity, 'meters / second.')\n print('The satellite is accelerating at', acceleration,\n 'meters / second squared.')\n print('The satellite takes', orbit_time_seconds,\n 'seconds for one orbit or', orbit_time_minutes, 'minutes.')\n\n# Calls main.\nmain()","repo_name":"millidavids/cs115","sub_path":"program1.py","file_name":"program1.py","file_ext":"py","file_size_in_byte":2896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"5693139408","text":"#opener\nprint()\nimport urllib.request\nimport http.cookiejar\n\ncj = http.cookiejar.MozillaCookieJar()\nopener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cj))\nopener.addheaders = [('User-agent', 'Mozilla/5.0')]\n\n#loop setup\nkeepgoing = True \nURLstart = \"http://en.wikipedia.org/wiki/\"\nscore = 0\nplays = 0\ntutorial= input(\"Welcome to the zodiac game! Before we start, do you need a list of the signs? (Y/N) \")\nif tutorial == \"Y\":\n print(\"The signs are: Aries, Taurus, Gemini, Cancer, Leo, Virgo, Libra, Scorpio, Sagittarius, Capricorn, Aquarius and Pisces!\")\n print()\n#game loop\nwhile keepgoing == True:\n plays = plays + 1 \n person = input(\"Whose sign do you want to guess?\" + \"\\n\" + \"Public figures only, I'm not psychic! \")\n if person.lower() == \"zendaya\":\n print(\"ZENDAYA IS MEECHEE!\")\n URLend = person.replace(\" \",\"_\")\n completedURL = URLstart + URLend\n websitefile = opener.open(completedURL)\n decodedfile = websitefile.read().decode('utf-8')\n bdayindex = decodedfile.find('\"bday\">')\n if bdayindex > -1:\n bday = decodedfile[bdayindex+12:bdayindex+17]\n month = decodedfile[bdayindex+12:bdayindex+14]\n day = decodedfile[bdayindex+15:bdayindex+17]\n if month == \"01\":\n if int(day) <= 19:\n zodiac = \"Capricorn\"\n else:\n zodiac = \"Aquarius\"\n elif month == \"02\":\n if int(day) <= 18:\n zodiac = \"Aquarius\"\n else:\n zodiac = \"Pisces\"\n elif month == \"03\":\n if int(day) <= 20:\n zodiac = \"Pisces\"\n else:\n zodiac = \"Aries\"\n elif month == \"04\":\n if int(day) <= 19:\n zodiac = \"Aries\"\n else:\n zodiac = \"Taurus\"\n elif month == \"05\":\n if int(day) <= 20:\n zodiac = \"Taurus\"\n else:\n zodiac = \"Gemini\"\n elif month == \"06\":\n if int(day) <= 20:\n zodiac = \"Gemini\"\n else:\n zodiac = \"Cancer\"\n elif month == \"07\":\n if int(day) <= 21:\n zodiac = \"Cancer\"\n else:\n zodiac = \"Leo\"\n elif month == \"08\":\n if int(day) <= 22:\n zodiac = \"Leo\"\n else:\n zodiac = \"Virgo\"\n elif month == \"09\":\n if int(day) <= 22:\n zodiac = \"Virgo\"\n else:\n zodiac = \"Libra\"\n elif month == \"10\":\n if int(day) <= 22:\n zodiac = \"Libra\"\n else:\n zodiac = \"Scorpio\"\n elif month == \"11\":\n if int(day) <= 21:\n zodiac = \"Scorpio\"\n else:\n zodiac = \"Sagittarius\"\n elif month == \"12\":\n if int(day) <= 21:\n zodiac = \"Sagittarius\"\n else:\n zodiac = \"Capricorn\"\n else:\n print(\"Birthday unknown, sorry!\")\n zodiacguess = input(\"Guess their sign: \")\n if zodiac.lower() == zodiacguess.lower():\n print(\"Congrats! You got it right!\")\n print()\n score = score + 1\n else:\n print(\"Sorry, that's not correct.\")\n print(\"The correct answer was \" + zodiac + \".\")\n print()\n stopper = input(\"Would you like to play again? (Y/N) \")\n if stopper.lower() == \"n\":\n keepgoing = False\n print()\n print(\"Bye! Thanks for playing!\")\n print(\"Your final score was: \" + str(score))\n accrating = score/plays\n accpercent = (score/plays)*100\n print(\"You had an accuracy rating of \" + str(accpercent) + \" percent!\")\n if accpercent <= 25:\n print(\"Come on, even Costar can do better!\")\n elif accpercent in range(26,51):\n print(\"Not bad, keep working on your ESP!\")\n elif accpercent in range(51,76):\n print(\"Very impressive!\")\n elif accpercent in range(76,100):\n print(\"Are you psychic or something?\")\n elif accpercent == 100:\n print(\"A perfect score!!! Sure you didn't cheat?\")\n \n \n","repo_name":"sambee33/Zodiac-Guessing-Game","sub_path":"zodiacgame.py","file_name":"zodiacgame.py","file_ext":"py","file_size_in_byte":4181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"42207584080","text":"#!/usr/bin/python\n\"\"\"\"\nThis file was created by Jahan Kuruvilla Cherian on 01/21/16.\nThis file runs the POSIX command comm to compare two files and output\nthe differences and similarities in the files. This modified version also\nsupports unsorted comparisons.\n\"\"\"\nimport sys, string, locale\nfrom optparse import OptionParser\n\nclass compare:\n\tdef __init__(self,filename1, filename2):\n\t\ttry:\n\t\t\tif filename1 == \"-\":\n\t\t\t\tf1 = sys.stdin\n\t\t\t\tf2 = open(filename2, 'r')\n\t\t\telif filename2 == \"-\":\n\t\t\t\tf2 = sys.stdin\n\t\t\t\tf1 = open(filename1, 'r')\n\t\t\telif filename1 == \"-\" and filename2 == \"-\":\n\t\t\t\tprint(\"Error! Can't read both files from STDIN\")\n\t\t\t\texit()\n\t\t\telse:\n\t\t\t\tf1 = open(filename1, 'r')\n\t\t\t\tf2 = open(filename2, 'r')\n\t\t\tself.lines1 = f1.read().split('\\n')\n\t\t\tself.lines2 = f2.read().split('\\n')\n\t\t\tdel self.lines1[len(self.lines1)-1]\n\t\t\tdel self.lines2[len(self.lines2)-1]\n\t\t\tself.list1 = []\n\t\t\tself.list2 = []\n\t\t\tself.list3 = []\n\t\t\tf1.close()\n\t\t\tf2.close()\n\t\texcept IOError as e:\n\t\t\terrno = e.errno\n\t\t\tstrerror = e.strerror\n\t\t\tparser.error(\"I/O error({0}): {1}\".\n\t\t\t\t\t\t format(errno, strerror))\n\n\tdef add_Newline(self, file):\n\t\tfor i in range(len(file)):\n\t\t\tif file[i] == '':\n\t\t\t\tfile[i] = '\\n'\n\t\t\tif file[i].count(' ') >= 1 and file[i].isspace():\n\t\t\t \tfile[i] = '\\n' * file[i].count(' ')\n\n\tdef modify(self):\n\t\tself.add_Newline(self.lines1)\n\t\tself.add_Newline(self.lines2)\n\n\tdef u_comparison(self):\n\t\tself.modify()\n\t\tfor i in range(len(self.lines1)):\n\t\t\tfor j in range(len(self.lines2)):\n\t\t\t\tif self.lines1[i] == self.lines2[j]:\n\t\t\t\t\tself.list3.append(self.lines1[i])\n\t\t\t\t\tself.list1.append(\" \")\n\t\t\t\t\tself.list2.append(\" \")\n\t\t\t\t\tdel self.lines2[j]\n\t\t\t\t\tsimilar = True\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t similar = False\n\t\t\tif similar == False:\n\t\t\t\tself.list1.append(self.lines1[i])\n\t\t\t\tself.list2.append(\"\")\n\t\t\t\tself.list3.append(\"\")\n\t\textra_spaces = [\" \"] * len(self.lines2)\n\t\textra_null = [\"\"] * len(self.lines2)\n\t\tself.list1 += extra_spaces\n\t\tself.list3 += extra_null\n\t\tself.list2 += self.lines2\n\n\tdef is_Sorted(self, file, file_num):\n\t\tfor i in range(len(file) - 1):\n\t\t\tif locale.strcoll(file[i],file[i+1]) > 0:\n\t\t\t\tprint (\"File %s is not sorted\") % file\n\t\t\t\texit()\n\t\treturn True\n\n\tdef s_comparison(self):\n\t\tif self.is_Sorted(self.lines1, 1) and self.is_Sorted(self.lines2, 2):\n\t\t\tself.modify()\n\t\t\ti = j = 0\n\t\t\twhile i < len(self.lines1) and j < len(self.lines2):\n\t\t\t\tif self.lines1[i] == self.lines2[j]:\n\t\t\t\t\tself.list3.append(self.lines1[i])\n\t\t\t\t\tself.list1.append(\" \")\n\t\t\t\t\tself.list2.append(\" \")\n\t\t\t\t\tself.lines1[i] = ''\n\t\t\t\t\tself.lines2[j] = ''\n\t\t\t\t\ti += 1\n\t\t\t\t\tj += 1\n\t\t\t\telif self.lines1[i] > self.lines2[j]:\n\t\t\t\t\tself.list2.append(self.lines2[j])\n\t\t\t\t\tself.list1.append(\" \")\n\t\t\t\t\tself.list3.append(\"\")\n\t\t\t\t\tself.lines2[j] = ''\n\t\t\t\t\tj += 1\n\t\t\t\telif self.lines1[i] < self.lines2[j]:\n\t\t\t\t\tself.list1.append(self.lines1[i])\n\t\t\t\t\tself.list2.append(\"\")\n\t\t\t\t\tself.list3.append(\"\")\n\t\t\t\t\tself.lines1[i] = ''\n\t\t\t\t\ti +=1\n\t\t\tif i > j: \n\t\t\t extra_spaces = [\" \"] * len(self.lines2)\n\t\t\t extra_null = [\"\"] * len(self.lines2)\n\t\t\t self.list2 += self.lines2\n\t\t\t self.list1 += extra_spaces\n\t\t\t self.list3 += extra_null\n\t\t\telif i < j:\n\t\t\t extra_spaces = [\"\"] * len(self.lines1)\n\t\t\t self.list1 += self.lines1\n\t\t\t self.list2 += extra_spaces\n\t\t\t self.list3 += extra_spaces\n\n\n\tdef printer(self, option1, option2, option3 ):\n\t\tif option1 == True:\n\t\t\tself.list1 = [''] * len(self.list1)\n\t\tif option2 == True:\n\t\t\tself.list2 = [''] * len(self.list2)\n\t\tif option3 == True:\n\t\t\tself.list3 = [''] * len(self.list3)\n\t\tfinal_list = []\n\t\tfor i in range(len(self.list1)):\n\t\t\tfinal_list.append(self.list1[i] + self.list2[i] + self.list3[i])\n\t\tfor j in range(len(final_list)):\n\t\t\tfor k in range(len(final_list[j])):\n\t\t\t\tif final_list[j][k] != \" \":\n\t\t\t\t if final_list[j][k] == \"\\n\":\n\t\t\t\t\t str = final_list[j]\n\t\t\t\t\t for i in range(len(str)):\n\t\t\t\t\t\t if str[i] == '\\n':\n\t\t\t\t\t\t\t sys.stdout.write(\" \")\n\t\t\t\t\t\t else:\n\t\t\t\t\t\t\t sys.stdout.write(str[i])\n\t\t\t\t\t print('')\n\t\t\t\t\t break\n\t\t\t\t else:\n\t\t\t\t\t print(final_list[j])\n\t\t\t\t\t break\n\ndef main():\n\tlocale.setlocale(locale.LC_ALL, 'C')\n\tversion_msg = \"%prog 1.0\"\n\tusage_msg = \"\"\"%prog [OPTION]... FILE1 FILE2\n\n\tOutput the comparison results between FILE1 and FILE2 in three columns.\"\"\"\n\n\tparser = OptionParser(version=version_msg, usage=usage_msg)\n\tparser.add_option(\"-1\", action=\"store_true\", dest=\"sup1\",\n\t\t\t\t\t default = False,\n\t\t\t\t\t help=\"suppress column 1 (lines unique to FILE1)\")\n\tparser.add_option(\"-2\", action=\"store_true\", dest=\"sup2\",\n\t\t\t\t\t default = False,\n\t\t\t\t\t help=\"suppress column 2 (lines unique to FILE2)\")\n\tparser.add_option(\"-3\", action=\"store_true\", dest=\"sup3\",\n\t\t\t\t\t default = False,\n\t\t\t\t\t help=\"suppress column 3 (lines that appear in both files)\")\n\tparser.add_option(\"-u\", action=\"store_true\", dest=\"unsort\",\n\t\t\t\t\t default = False,\n\t\t\t\t\t help=\"run comparison on unsorted files line by line.\" )\n\toptions, args = parser.parse_args(sys.argv[1:])\n\ttry:\n\t\tsup1 = bool(options.sup1)\n\t\tsup2 = bool(options.sup2)\n\t\tsup3 = bool(options.sup3)\n\t\tunsort = bool(options.unsort)\n\texcept:\n\t\tparser.error(\"invalid option type: {0}\".\n\t\t\t\t\tformat(options.sup1))\n\tif len(args) != 2:\n\t\tparser.error(\"missing option arguments\")\n\tinput_file1 = args[0]\n\tinput_file2 = args[1]\n\ttry:\n\t\tcomparator = compare(input_file1, input_file2)\n\t\tif unsort:\n\t\t\tcomparator.u_comparison()\n\t\telse:\n\t\t\tcomparator.s_comparison()\n\t\tcomparator.printer(sup1, sup2, sup3)\n\texcept IOError as e:\n\t\terrno = e.errno\n\t\tstrerror = e.strerror\n\t\tparser.error(\"I/O error({0}): {1}\".\n\t\t\t\t\t format(errno, strerror))\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"jcherianucla/UCLA-CS-35L","sub_path":"Lab 3/comm.py","file_name":"comm.py","file_ext":"py","file_size_in_byte":5601,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"} +{"seq_id":"21610937477","text":"#open file to list\r\n\r\nhashtags = []\r\n\r\nwith open('jneymarjr5hashtagsALL.txt', 'r', encoding=\"utf8\") as filehandle:\r\n\tfor line in filehandle:\r\n\t\tcurrentPlace = line[:-1]\r\n\t\thashtags.append(currentPlace)\r\n\r\nprint(len(hashtags))\r\nlist_set = set(hashtags)\r\nhashtagsNew = (list(list_set))\r\nprint(len(hashtagsNew))","repo_name":"sotiriszogos/Online-Social-Networks-and-Media","sub_path":"Προγράμματα/listToSet.py","file_name":"listToSet.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"24819967458","text":"import RPi.GPIO as GPIO\nimport time\nimport json\nfrom datetime import datetime, timedelta\n\nGPIO.setmode(GPIO.BOARD)\n\ndef plant_control_function(MQTTClient, plant_config):\n\n plant_control_function.stop = False # Function attribute used for stopping thread\n plant_id = plant_config[\"plant_id\"]\n sms_pin = plant_config['sms_pin'] # Pin for taking sensor inputs\n output_pin = plant_config['output_pin'] # Pin for giving output for water pump motors\n GPIO.setup(sms_pin, GPIO.IN)\n GPIO.setup(output_pin, GPIO.OUT, initial=GPIO.HIGH)\n\n updated_mode = \"AUTO\" # Default\n current_mode = \"AUTO\"\n\n Mode_To_GPIO_Signal = {\n \"ON\": GPIO.LOW,\n \"OFF\": GPIO.HIGH,\n }\n\n stop_motor_on = datetime.min\n cooldown = datetime.min\n\n pour_water_time = 5 # in seconds\n cooldown_time = 30 # in seconds\n\n def on_plant_signal_received(client, userdata, message):\n nonlocal updated_mode\n try:\n payload = json.loads(message.payload)\n updated_mode = payload.get(\"mode\", \"AUTO\")\n print(payload.get(\"message\"))\n except Exception as err:\n print(f\"Payload Object has an error.\\nPayload: {payload}\\nException Error: {err}\")\n\n def auto_mode(sms_pin):\n nonlocal stop_motor_on, cooldown, pour_water_time, cooldown_time, plant_id\n try:\n if stop_motor_on >= datetime.now():\n # print(f\"PlantID {plant_id} Keeping motor on till time specified\")\n return GPIO.LOW\n\n if cooldown >= datetime.now():\n # print(f\"PlantID {plant_id} On cooldown for {(cooldown-datetime.now()).total_seconds()} seconds\")\n return GPIO.HIGH\n\n elif GPIO.input(sms_pin):\n print(f\"PlantID {plant_id} Water Inadequate, no cooldown period detected, pour water for {pour_water_time} seconds\")\n stop_motor_on = datetime.now() + timedelta(seconds=pour_water_time)\n cooldown = stop_motor_on + timedelta(seconds=cooldown_time)\n return GPIO.LOW\n\n else:\n print(f\"PlantID {plant_id} Water Adequate\")\n return GPIO.HIGH\n\n except Exception as err:\n print(\"Error while receiving input from soil moisture sensor.\\n Exception:\", err)\n\n return GPIO.HIGH\n\n MQTTClient.subscribe(\n topic=f\"PLANT_MODE_CONTROL/{plant_config['plant_id']}\", QoS=1, callback=on_plant_signal_received)\n print(\"Subscribed to topic 'PLANT_MODE_CONTROL' ...\")\n\n while not plant_control_function.stop:\n if updated_mode == \"AUTO\":\n current_mode = \"AUTO\"\n GPIO.output(output_pin, auto_mode(sms_pin))\n\n elif current_mode != updated_mode:\n current_mode = updated_mode\n GPIO.output(output_pin, Mode_To_GPIO_Signal[current_mode])\n \n time.sleep(1)\n\n print(f\"Stopped thread for plant-{plant_config['plant_id']}\")\n\n\n# GPIO.setmode(GPIO.BOARD)\n# GPIO.setup(8,GPIO.IN)\n# GPIO.setup(10,GPIO.OUT)\n\n# while True:\n# if (GPIO.input(8)):\n# print(\"Water Inadequate\")\n# GPIO.output(10,GPIO.HIGH)\n# else:\n# print(\"Water Adequate\")\n# GPIO.output(10,GPIO.LOW)\n# time.sleep(2)\n","repo_name":"MihirHundiwala/smart-society-rpi-server","sub_path":"reference/plants_copy.py","file_name":"plants_copy.py","file_ext":"py","file_size_in_byte":3295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"9126798121","text":"from __future__ import annotations\r\nimport json\r\nimport string\r\nfrom Elements.Player import Player\r\nfrom Elements.Pieces.Pieces import Pieces\r\nfrom Elements.Pieces.PiecesDeclaration import LISTEPIECES\r\nimport os.path as path\r\n\r\n# Exemple de fonction pour gérer les Json\r\n\r\nclass fonctionJson:\r\n \"\"\"Classe principale qui est l'application qui garantie la gestion de la logique et des vue et\r\n donc de la communication entre les différents élèments de l'application\r\n \"\"\"\r\n def __init__(self: fonctionJson) -> None:\r\n self.val : int\r\n self.chemin = path.join(\"Highscore\", \"highscore.json\")\r\n\r\n def JsonAjout(self, donne):\r\n \"\"\"Fonction permettant de\r\n\r\n Args:\r\n donne (_type_): _description_\r\n \"\"\"\r\n with open(self.chemin, mode = \"r\") as mon_fichier:\r\n data = json.load(mon_fichier)\r\n val_test = \"Game1\"\r\n num = 1\r\n existe = True\r\n while existe == True:\r\n if val_test not in data:\r\n existe = False\r\n else:\r\n num += 1\r\n val_test = val_test[:4] + str(num)\r\n data[val_test] = donne\r\n \r\n \r\n with open(self.chemin, mode = \"w\") as mon_fichier: \r\n json.dump(data, mon_fichier) \r\n\r\n def getJSON(self):\r\n \"\"\"Fonction permettant de\r\n\r\n Args:\r\n donne (_type_): _description_\r\n \"\"\"\r\n with open(self.chemin, mode = \"r\") as mon_fichier:\r\n data = json.load(mon_fichier)\r\n return data\r\n \r\n def getPlayers(self, partie):\r\n \"\"\"Fonction permettant de récupérer la liste des joueurs de la partie\r\n\r\n Args:\r\n Partie: Le nom de la partie (Game+numéro)\r\n \"\"\"\r\n listjoueurs = []\r\n with open(self.chemin, mode = \"r\") as mon_fichier:\r\n data = json.load(mon_fichier)\r\n joueurs = data[partie][0]['joueurs']\r\n\r\n for i in range(len(joueurs)):\r\n listjoueurs.append(Player(i, data[partie][0]['joueurs'][i]))\r\n\r\n return listjoueurs\r\n\r\n def getNbGames(self):\r\n \"\"\"Fonction permettant de récupérer le nombre de partie\r\n \"\"\"\r\n with open(self.chemin, mode = \"r\") as mon_fichier:\r\n data = json.load(mon_fichier)\r\n return len(data)\r\n\r\n def getWinners(self, partie):\r\n \"\"\"Fonction permettant de récupérer la liste des joueurs de la partie\r\n\r\n Args:\r\n Partie: Le nom de la partie (Game+numéro)\r\n \"\"\"\r\n with open(self.chemin, mode = \"r\") as mon_fichier:\r\n data = json.load(mon_fichier)\r\n joueurs = data[partie][0]['winners']\r\n\r\n return joueurs\r\n\r\n def getPieces(self, partie):\r\n \"\"\"Fonction permettant de récupérer la liste des joueurs de la partie\r\n\r\n Args:\r\n Partie: Le nom de la partie (Game+numéro)\r\n \"\"\"\r\n pieces = []\r\n joueurs = []\r\n tableau = []\r\n with open(self.chemin, mode = \"r\") as mon_fichier:\r\n data = json.load(mon_fichier)\r\n for i in range (len(data[partie])):\r\n pieces.append(LISTEPIECES[f\"{data[partie][i]['num_piece']}\"])\r\n joueurs.append(Player(data[partie][i]['joueur'], data[partie][0]['joueurs'][data[partie][i]['joueur']]))\r\n tableau = data[partie][0]['tableau']\r\n return pieces, joueurs, tableau\r\n","repo_name":"la-ref/blokus-amazing","sub_path":"HighScore/fonctionJson.py","file_name":"fonctionJson.py","file_ext":"py","file_size_in_byte":3468,"program_lang":"python","lang":"fr","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"} +{"seq_id":"30239717664","text":"# import sklearn.naive_bayes\nimport numpy as np\nimport csv\nimport math\nimport random\nimport copy\n\n\ndef readFile(file):\n\tcsvfile = open(file, newline='')\n\trows = np.array(list(csv.reader(csvfile, delimiter=',', quotechar='\"')))\n\tnewRows = []\n\n\tfor r in rows:\n\t\tif r[6] == '': #if midsem not there, discard entry\n\t\t\tcontinue\n\t\tif r[4] == '': #if cg not given, map to zero for easier processing\n\t\t\tr[4] = '0'\n\n\t\tnewRows.append([r[1],r[3],math.ceil(float(r[4])),r[5],grademap(r[6]),r[7],grademap(r[-3])])\n\n\treturn newRows\n\ndef splitData(data, splitFraction = 0.8):\n\t'''splits data into training and testing by a factor of splitFraction'''\n\ttest = copy.deepcopy(data)\n\trandom.shuffle(test)\n\tl1 = math.ceil(len(data)*splitFraction)\n\ttrain = np.array(test[:l1])\n\ttest = np.array(test[l1:])\n\tX_train = train[:,:-1]\n\tY_train = train[:,-1]\n\tX_test = test[:,:-1]\n\tY_test = test[:,-1]\n\n\treturn X_train,Y_train,X_test,Y_test\n\ndef grademap(grade):\n\t'''maps grade to gradepoint'''\n\tgradep=0\n\tif grade=='A':\n\t\tgradep=10\n\telif grade=='A-':\n\t\tgradep=9\n\telif grade=='B':\n\t\tgradep=8\n\telif grade=='B-':\n\t\tgradep=7\n\telif grade=='C':\n\t\tgradep=6\n\telif grade=='D':\n\t\tgradep=5\n\telif grade=='D-':\n\t\tgradep=4\n\telif grade=='E':\n\t\tgradep=2\n\telif grade=='NC':\n\t\tgradep=0\n\n\treturn gradep\n","repo_name":"prithviking98/ML-project-metric-learning","sub_path":"code/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"} +{"seq_id":"19049616322","text":"from dataclasses import dataclass\nfrom typing import Optional, Sequence\n\nfrom biocframe import BiocFrame\nfrom numpy import float64, ndarray, array\n\nfrom .. import _cpphelpers as lib\nfrom .._utils import process_block\nfrom ._utils import check_custom_thresholds\n\n\n@dataclass\nclass SuggestCrisprQcFiltersOptions:\n \"\"\"Optional arguments for :py:meth:`~scranpy.quality_control.suggest_crispr_qc_filters.suggest_crispr_qc_filters`.\n\n Attributes:\n block:\n Block assignment for each cell.\n Thresholds are computed within each block to avoid inflated variances from\n inter-block differences.\n\n If provided, this should have length equal to the number of cells, where\n cells have the same value if and only if they are in the same block.\n Defaults to None, indicating all cells are part of the same block.\n\n num_mads:\n Number of median absolute deviations for computing an outlier threshold.\n Larger values will result in a less stringent threshold.\n Defaults to 3.\n\n custom_thresholds:\n Data frame containing one or more columns with the same names as those in the return value of\n :py:meth:`~scranpy.quality_control.suggest_crispr_qc_filters.suggest_crispr_qc_filters`.\n If a column is present, it should contain custom thresholds for the corresponding metric\n and will override any suggested thresholds in the final BiocFrame.\n\n If ``block = None``, this data frame should contain one row.\n Otherwise, the number of rows should be equal to the number of blocks,\n where each row contains a block-specific threshold for the relevant metrics.\n The identity of each block should be stored in the row names.\n \"\"\"\n\n block: Optional[Sequence] = None\n num_mads: int = 3\n custom_thresholds: Optional[BiocFrame] = None\n\n\ndef suggest_crispr_qc_filters(\n metrics: BiocFrame,\n options: SuggestCrisprQcFiltersOptions = SuggestCrisprQcFiltersOptions(),\n) -> BiocFrame:\n \"\"\"Suggest filter thresholds for CRISPR-based per-cell quality control (QC) metrics. This identifies outliers on the\n low tail of the distribution of the count for the most abundant guide across cells, aiming to remove cells that have\n low counts due to failed transfection. (Multiple transfections are not considered undesirable at this point.)\n\n Args:\n metrics: A data frame containing QC metrics for each cell,\n see the output of :py:meth:`~scranpy.quality_control.per_cell_crispr_qc_metrics.per_cell_crispr_qc_metrics`\n for the expected format.\n\n options: Optional parameters.\n\n Raises:\n ValueError, TypeError: if provided ``inputs`` are incorrect type or do\n not contain expected metrics.\n\n Returns:\n A data frame containing one row per block and the following fields -\n ``\"max_count\"``, the suggested (lower) threshold on the maximum count.\n\n If ``options.block`` is None, all cells are assumed to belong to a single\n block, and the output BiocFrame contains a single row.\n \"\"\"\n if not isinstance(metrics, BiocFrame):\n raise TypeError(\"'metrics' is not a `BiocFrame` object.\")\n\n num_cells = metrics.shape[0]\n use_block, num_blocks, block_names, block_info, block_offset = process_block(\n options.block, num_cells\n )\n\n sums = array(metrics.column(\"sums\"), dtype=float64, copy=False)\n max_prop = array(metrics.column(\"max_proportion\"), dtype=float64, copy=False)\n max_count_out = ndarray((num_blocks,), dtype=float64)\n\n lib.suggest_crispr_qc_filters(\n num_cells,\n sums,\n max_prop,\n num_blocks,\n block_offset,\n max_count_out,\n options.num_mads,\n )\n\n custom_thresholds = check_custom_thresholds(\n num_blocks, block_names, options.custom_thresholds\n )\n if custom_thresholds is not None:\n if custom_thresholds.has_column(\"max_count\"):\n max_count_out = custom_thresholds.column(\"max_count\")\n\n return BiocFrame(\n {\n \"max_count\": max_count_out,\n },\n number_of_rows=num_blocks,\n row_names=block_names,\n )\n","repo_name":"BiocPy/scranpy","sub_path":"src/scranpy/quality_control/suggest_crispr_qc_filters.py","file_name":"suggest_crispr_qc_filters.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"} +{"seq_id":"41024524909","text":"from flask import Flask\nfrom flask_testing import TestCase\nfrom sqlalchemy import exc\nfrom backend.test import test_utils\nfrom backend.app import db, app\nfrom backend.app import connection_manager as cm\n\n\nclass ConnectionManagerTest(TestCase):\n\n def create_app(self):\n app = Flask(__name__)\n app.config.from_object(test_utils.Config())\n db.init_app(app)\n return app\n\n def setUp(self):\n self.client = app.test_client()\n db.create_all()\n\n def tearDown(self):\n db.session.remove()\n db.drop_all()\n\n def create_db_with_test_data(self):\n conn = test_utils.create_connection(label='test_conn', db_type='sqlite', host='/tmp')\n connection = cm.create_connection(conn)\n connection.execute('DROP TABLE IF EXISTS \"TABLE1\"')\n connection.execute('CREATE TABLE \"TABLE1\" ('\n 'id INTEGER NOT NULL,'\n 'name VARCHAR, '\n 'PRIMARY KEY (id));')\n\n connection.execute('INSERT INTO \"TABLE1\" '\n '(id, name) '\n 'VALUES (1,\"raw1\"), (2,\"raw2\"), (3,\"raw3\"), (4,\"raw4\")')\n return conn\n\n def test_create_engine_with_valid_input(self):\n conn = test_utils.create_connection(label='test_conn', db_type='sqlite', host='/tmp')\n engine = cm.create_engine(conn)\n\n assert engine\n\n def test_create_connection_with_valid_input(self):\n conn = test_utils.create_connection(label='test_conn', db_type='sqlite', host='/tmp')\n connection = cm.create_connection(conn)\n\n assert connection\n\n def test_create_connection_with_invalid_input(self):\n conn = test_utils.create_connection(label='test_conn', db_type='sqlite', host='bananas')\n\n try:\n connection = cm.create_connection(conn)\n assert not connection\n except exc.ArgumentError:\n pass\n except:\n assert False\n\n def test_get_db_metadata(self):\n conn = self.create_db_with_test_data()\n meta = cm.get_db_metadata(conn)\n assert isinstance(meta, list)\n\n def test_execute_select_statement_with_valid_input(self):\n conn = self.create_db_with_test_data()\n sql = 'select * from TABLE1'\n result = cm.execute_select_statement(conn=conn, raw_sql=sql)\n assert isinstance(result, list)\n\n def test_execute_select_statement_with_unknown_table(self):\n conn = self.create_db_with_test_data()\n sql = 'select * from TABLE12'\n try:\n result = cm.execute_select_statement(conn=conn, raw_sql=sql)\n assert not isinstance(result, list)\n except exc.OperationalError:\n pass\n except:\n assert False\n\n def test_execute_select_statement_with_invalid_input(self):\n conn = self.create_db_with_test_data()\n sql = 'delete all the tables'\n try:\n result = cm.execute_select_statement(conn=conn, raw_sql=sql)\n assert not result\n except AssertionError:\n pass\n except:\n assert False\n\n def test_execute_query_object_with_valid_input(self):\n conn = self.create_db_with_test_data()\n sql = 'select * from TABLE1'\n query = test_utils.create_query(label='testQ', raw_sql=sql)\n result = cm.execute_query_object(conn=conn, query=query)\n assert isinstance(result, list)\n\n","repo_name":"Nunie123/narratus","sub_path":"backend/test/test_connection_manager.py","file_name":"test_connection_manager.py","file_ext":"py","file_size_in_byte":3444,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"} +{"seq_id":"40953761766","text":"'''\n@desc: FG群文件处理\n@author: Martin Huang\n@time: created on 2020/4/4 15:04\n@修改记录:\n'''\nimport nonebot\nimport time\nimport os\nfrom cn.acmsmu.FG import Timer\nfrom Utils.JsonUtils import JsonUtils\nfrom Utils.IOUtils import IOUtils\n\nconfiguration = JsonUtils.json2Dict(os.path.join(os.getcwd(),'cn','acmsmu','FG','data','config.json'))\ngroupInfo = configuration['groupInfo']\nfor each in groupInfo:\n fpath = os.path.join(os.getcwd(),'cn','acmsmu','FG','data',each['groupId'])\n try:\n dataDict = dict()\n dataDict['flag'] = True\n dataDict['file'] = 'chatA.txt'\n IOUtils.mkdir(fpath)\n IOUtils.serializeObj2Pkl(dataDict, fpath + '/var.pkl')\n except FileExistsError:\n continue\nbot = nonebot.get_bot()\nprint('初始化完成')\n\n@bot.on_message('group')\nasync def handleGroupMsg(session):\n groupInfo = configuration['groupInfo']\n for each in groupInfo:\n if each['groupId'] == str(session['group_id']):\n # 读取每个群文件夹的pkl\n dataDict = IOUtils.deserializeObjFromPkl(os.path.join(os.getcwd(),'cn','acmsmu','FG','data',each['groupId'],'var.pkl'))\n # 确定flag的值\n flag = dataDict['flag']\n # 确定要往哪一个文件中写入聊天记录\n msg = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime()) + ' ' + str(session['user_id']) + '\\n' + session['raw_message'] + '\\n'\n if flag:\n with open(os.path.join(os.getcwd(),'cn','acmsmu','FG','data',each['groupId'],'chatA.txt'), 'a', encoding='utf-8') as fileA:\n fileA.write(msg)\n else:\n with open(os.path.join(os.getcwd(),'cn','acmsmu','FG','data',each['groupId'],'chatB.txt'), 'a', encoding='utf-8') as fileB:\n fileB.write(msg)\n break\n","repo_name":"mgsky1/FG","sub_path":"cn/acmsmu/FG/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":94,"dataset":"github-code","pt":"76"} +{"seq_id":"32201039772","text":"from threading import Thread, Event, Lock\nimport ctypes\n\nfrom bleson.interfaces.adapter import Adapter\nfrom bleson.core.types import Advertisement, UUID16, UUID128\nfrom bleson.core.hci.constants import *\nfrom bleson.logger import log\nfrom bleson.core.hci.type_converters import bytearray_to_hexstring\n\n# Work around for Sphinx\ntry:\n import objc\n from Foundation import *\n from PyObjCTools import AppHelper\n import CoreBluetooth\nexcept (ImportError, AttributeError) as e:\n import sys\n if 'sphinx' in sys.modules:\n log.warning(\"macOS modules not found, if this is a documentation build all is good\")\n else:\n log.error(\"macOS modules not found, but this doesn't look like a documentation build\")\n raise e\n\n\n######################################\n# Dispatch Queue support\n\n# see: https://bitbucket.org/ronaldoussoren/pyobjc/issues/215/starting-runconsoleeventloop-from-a\n\n# Opaque structure use to pass areound 'dispatch_queue_t' C type\n# see: https://stackoverflow.com/questions/5030730/is-it-acceptable-to-subclass-c-void-p-in-ctypes\n#class dispatch_queue_t(ctypes.Structure):\n# pass\n# The above is not used as the PyObcC 4.0.1 doesn't accept it as a type to create the pointer to the dispatch_queu_\n\nNULL_PTR = ctypes.POINTER(ctypes.c_int)()\n_lib = None\n\n# lazy load the library, helps Sphinx.\ndef dispatch_queue_create(name):\n global _lib, _dispatch_queue_create\n if not _lib:\n # Load the dispatch library, once\n _lib = ctypes.cdll.LoadLibrary(\"/usr/lib/system/libdispatch.dylib\")\n\n _dispatch_queue_create = _lib.dispatch_queue_create\n _dispatch_queue_create.argtypes = [ctypes.c_char_p, ctypes.c_void_p] # 2nd param is stuct, but we don't use it.\n _dispatch_queue_create.restype = ctypes.c_void_p\n # https://developer.apple.com/documentation/dispatch/1453030-dispatch_queue_create\n\n b_name = name.encode('utf-8')\n c_name = ctypes.c_char_p(b_name)\n return _dispatch_queue_create(c_name, NULL_PTR)\n\n\n# The adapter is a singleton because of the (singleton) nature of the underlying native NSApp runtime.\nclass Singleton(type):\n _instances = {}\n __singleton_lock = Lock()\n\n def __call__(cls, *args, **kwargs):\n if cls not in cls._instances:\n with cls.__singleton_lock:\n cls._instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)\n return cls._instances[cls]\n\n\nclass CoreBluetoothAdapter(Adapter, metaclass=Singleton):\n\n # states\n poweredOn = 5\n\n def __init__(self, device_id=0):\n self.device_id = device_id\n self.on_advertising_data = None\n self.connected = False\n self._keep_running = True\n self._socket_poll_thread = Thread(target=self._runloop_thread, name='BlesonObjCRunLoop')\n self._socket_poll_thread.setDaemon(True)\n self._dispatch_queue = dispatch_queue_create('blesonq')\n self._manager = None\n self._peripheral_manager = None\n self._runloop_started_lock = Event()\n\n self._socket_poll_thread.start()\n\n def open(self):\n self.wait_for_event_timeout(self._runloop_started_lock)\n #self._runloop_started_lock.set()\n\n def on(self):\n log.debug(\"TODO: adatper on\")\n\n def off(self):\n log.debug(\"TODO: adatper off\")\n\n def wait_for_event_timeout(self, e, t=5):\n while not e.isSet():\n log.debug('wait_for_event_timeout starting')\n event_is_set = e.wait(t)\n log.debug('event set: %s', event_is_set)\n\n\n def start_scanning(self):\n self.wait_for_event_timeout(self._runloop_started_lock)\n if self._manager:\n self._manager.scanForPeripheralsWithServices_options_(None, None)\n\n def stop_scanning(self):\n log.debug(\"\")\n if self._manager:\n self._manager.stopScan()\n\n\n def start_advertising(self, advertisement, scan_response=None):\n log.warning(\"TODO\")\n return\n adv_data = {\n 'CBAdvertisementDataLocalNameKey': 'bleson',\n #'CBAdvertisementDataServiceUUIDsKey': CBUUID.UUIDWithString_(u'6E400001-B5A3-F393-E0A9-E50E24DCCA9E')\n 'CBAdvertisementDataServiceUUIDsKey': CBUUID.UUIDWithString_(u'0xFFEF')\n }\n self._peripheral_manager.startAdvertising_(adv_data)\n\n def stop_advertising(self):\n log.warning(\"TODO\")\n return\n adv_data = {\n 'CBAdvertisementDataLocalNameKey': 'bleson',\n #'CBAdvertisementDataServiceUUIDsKey': CBUUID.UUIDWithString_(u'6E400001-B5A3-F393-E0A9-E50E24DCCA9E')\n 'CBAdvertisementDataServiceUUIDsKey': CBUUID.UUIDWithString_(u'0xFFEF')\n }\n if self._peripheral_manager.isAdvertising:\n self._peripheral_manager.stopAdvertising()\n\n\n\n\n # https://pythonhosted.org/pyobjc/core/intro.html#working-with-threads\n def _runloop_thread(self):\n try:\n with objc.autorelease_pool():\n queue_ptr = objc.objc_object(c_void_p=self._dispatch_queue)\n\n self._manager = CoreBluetooth.CBCentralManager.alloc()\n self._manager.initWithDelegate_queue_options_(self, queue_ptr, None)\n\n #self._peripheral_manager = CoreBluetooth.CBPeripheralManager.alloc()\n #self._peripheral_manager.initWithDelegate_queue_options_(self, queue_ptr, None)\n self._runloop_started_lock.set()\n AppHelper.runConsoleEventLoop(installInterrupt=True)\n except Exception as e:\n log.exception(e)\n log.info(\"Exiting runloop\")\n\n\n # CoreBluetooth PeripheralManager Protocol\n\n def peripheralManagerDidUpdateState_(self, manager):\n state = manager.state()\n log.debug(\"State: {}\".format(state))\n\n def peripheralManagerDidStartAdvertising_error_(self, peripheral, error):\n print(\"peripheralManagerDidStartAdvertising_error_ {} {}\".format(peripheral, error))\n\n\n # CoreBluetooth CentralManager Protocol\n\n def centralManagerDidUpdateState_(self, manager):\n state = manager.state()\n log.debug(\"State: {}\".format(state))\n\n\n def centralManager_didDiscoverPeripheral_advertisementData_RSSI_(self, manager, peripheral, data, rssi):\n try:\n log.debug('Found: name={} rssi={} data={} '.format(peripheral.name(), rssi, data))\n\n if self.on_advertising_data:\n advertisement = Advertisement()\n advertisement.flags = 0 # Not available\n advertisement.name = peripheral.name()\n advertisement.rssi = rssi\n advertisement.raw_data = data\n\n if 'kCBAdvDataTxPowerLevel' in data:\n advertisement.tx_pwr_lvl = int(data['kCBAdvDataTxPowerLevel'])\n\n if data['kCBAdvDataIsConnectable']:\n # TODO: handle: kCBAdvDataIsConnectable correctly\n advertisement.type = 0x01 # BLE_GAP_ADV_TYPE_ADV_DIRECT_IND\n\n if 'kCBAdvDataServiceUUIDs' in data:\n log.debug('kCBAdvDataServiceUUIDs:')\n for cbuuid in data['kCBAdvDataServiceUUIDs']:\n uuid_bytes = cbuuid.data().bytes().tobytes()\n\n if 2 == len(uuid_bytes):\n uuid = UUID16(uuid_bytes, little_endian=False)\n advertisement.uuid16s.append(uuid)\n\n elif 16 == len(uuid_bytes):\n uuid = UUID128(uuid_bytes, little_endian=False)\n advertisement.uuid128s.append(uuid)\n else:\n log.error(\"Unsupporten UUID length for UUID bytes={}\".format(uuid_bytes))\n\n log.debug('Service UUID: {} {}'.format(type(cbuuid), cbuuid))\n\n if 'kCBAdvDataManufacturerData' in data:\n mfg_data=data['kCBAdvDataManufacturerData']\n log.debug('kCBAdvDataManufacturerData={}'.format(mfg_data))\n advertisement.mfg_data=mfg_data\n\n self.on_advertising_data(advertisement)\n\n except Exception as e:\n log.exception(e)\n\n\n def centralManager_didConnectPeripheral_(self, manager, peripheral):\n log.debug('Connected: ' + peripheral.name())\n self.connected = True\n self.peripheral.setDelegate_(self)\n self.peripheral.readRSSI()\n #self.peripheral.discoverServices_([CBUUID(...)])\n\n def centralManager_didFailToConnectPeripheral_error_(self, manager, peripheral, error):\n log.error(repr(error))\n\n def centralManager_didDisconnectPeripheral_error_(self, manager, peripheral, error):\n log.debug(\"centralManager_didDisconnectPeripheral_error_\")\n self.connected = False\n #AppHelper.stopEventLoop()\n\n def peripheral_didDiscoverServices_(self, peripheral, error):\n log.debug(\"peripheral_didDiscoverServices_\")\n if (error == None):\n self.service = self.peripheral.services()[0]\n #self.peripheral.discoverCharacteristics_forService_([CBUUD(...)], self.service)\n\n def peripheral_didDiscoverCharacteristicsForService_error_(self, peripheral, service, error):\n log.debug(\"peripheral_didDiscoverCharacteristicsForService_error_\")\n\n for characteristic in self.service.characteristics():\n if characteristic.UUID().UUIDString() == crtp_characteristic.UUIDString():\n self.crtp_characteristic = characteristic\n self.peripheral.setNotifyValue_forCharacteristic_(True, self.crtp_characteristic)\n\n def peripheral_didWriteValueForCharacteristic_error_(self, peripheral, characteristic, error):\n log.debug(\"peripheral_didWriteValueForCharacteristic_error_\")\n\n if error != None:\n log.error(repr(error))\n\n def peripheral_didUpdateNotificationStateForCharacteristic_error_(self, peripheral, characteristic, error):\n log.debug(\"peripheral_didUpdateNotificationStateForCharacteristic_error_\")\n\n\n def peripheral_didUpdateValueForCharacteristic_error_(self, peripheral, characteristic, error):\n log.debug(\"peripheral_didUpdateValueForCharacteristic_error_\")\n log.debug(repr(characteristic.value().bytes().tobytes()))\n","repo_name":"TheCellule/python-bleson","sub_path":"bleson/providers/macos/macos_adapter.py","file_name":"macos_adapter.py","file_ext":"py","file_size_in_byte":10248,"program_lang":"python","lang":"en","doc_type":"code","stars":114,"dataset":"github-code","pt":"76"} +{"seq_id":"6814517022","text":"# Definition for singly-linked list.\n\nclass ListNode:\n def __init__(self, val=0, next=None):\n self.val = val\n self.next = next\n\nclass Solution:\n def mergeTwoLists(self, list1: ListNode | None, list2: ListNode | None) -> ListNode | None:\n '''\n cur = dummy = ListNode()\n while list1 and list2: \n if list1.val < list2.val:\n cur.next = list1\n list1, cur = list1.next, list1\n else:\n cur.next = list2\n list2, cur = list2.next, list2\n \n if list1 or list2:\n cur.next = list1 if list1 else list2\n \n return dummy.next\n '''\n\n head = None\n next1 = list1\n next2 = list2\n\n if (next1 == None):\n return next2\n if (next2 == None):\n return next1\n\n if (next1.val <= next2.val):\n head = list1\n else:\n head = list2\n \n while (next1 != None and next2 != None):\n if (next1.val <= next2.val):\n temp = next1.next\n next1.next = next2\n next1 = temp\n else:\n temp = next2.next\n next2.next = next1\n next2 = temp\n \n if (next1 == None):\n next1 = next2\n else:\n next2 = next1\n\n return head\n \n '''\n if not list1 and not list2:\n return list1\n if not list1 or not list2:\n return list1 if not list2 else list2\n seek, target = (list1, list2) if list1.val < list2.val else (list2, list1)\n head = seek\n while seek and target:\n while seek.next and seek.next.val < target.val:\n seek = seek.next\n seek.next, target = target, seek.next\n seek = seek.next\n return head\n '''\n\nlist1 = ListNode()\nlist1.val = 1\nlist1.next = None\nlist2 = ListNode()\nlist2.val = 2\nlist2.next = None\n\nprint(Solution().mergeTwoLists(list1, list2))","repo_name":"hsiaotingluv/NeetCode150","sub_path":"NeetCode/Linked_list/Merge_two_sorted_lists.py","file_name":"Merge_two_sorted_lists.py","file_ext":"py","file_size_in_byte":2068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"3815358154","text":"import boto\n# import mimetypes\nimport json\nfrom django.http import HttpResponse\nimport os\nfrom dotenv import load_dotenv\nfrom .models import Resume\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nload_dotenv()\n\nif not boto.config.get('s3', 'use-sigv4'):\n boto.config.add_section('s3')\n boto.config.set('s3', 'use-sigv4', 'True')\nboto.config.set('s3', 'host', 's3.amazonaws.com')\n\nconn = boto.connect_s3(os.getenv(\"AWSAccessKeyId\"), os.getenv(\"AWSSecretKey\"))\n\n\ndef sign_s3_upload_cv(request):\n print(\"===== cv sign api called =======\")\n object_name = request.GET['objectName']\n content_type = request.GET['contentType']\n # content_type = mimetypes.guess_type(object_name)[0]\n # content_type = content_type + \";codecs=vp8,opus\" ### ATTENTION: this added part is required if upload dirctly from the browser. If used for uploading local files, comment this line out.###\n\n signed_url = conn.generate_url(\n 300,\n \"PUT\",\n os.getenv(\"CV_Bucket\"),\n object_name,\n headers={'Content-Type': content_type, 'x-amz-acl': 'public-read'})\n\n return HttpResponse(json.dumps({'signedUrl': signed_url}))\n\n@api_view(['POST'])\ndef delete_resume(request):\n id = request.data[\"id\"]\n Resume.objects.filter(id=id).delete()\n return Response({\"deleted_cv_id\": id})\n","repo_name":"dankleying/hire_beat_react_django","sub_path":"hirebeat/resume/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"12741052391","text":"from typing import List, Set, Dict\nfrom ..models import SyntaxAnalyzerDefinition, Production, Terminal, NonTerminal, StackSymbol, Symbol\n\n\ndef mark_empty_nonterminals(definiton: SyntaxAnalyzerDefinition):\n for production in definiton.productions:\n if len(production.right_side) == 1 and production.right_side[0] is None:\n production.left_side.empty = True\n\n changed = True\n while changed:\n changed = False\n for production in definiton.productions:\n if not production.left_side.empty:\n production.left_side.empty = True\n for symbol in production.right_side:\n if isinstance(symbol, NonTerminal) and not symbol.empty or symbol.name != '$':\n production.left_side.empty = False\n break\n else:\n changed = True\n\n\ndef find_direct_firsts(definition: SyntaxAnalyzerDefinition):\n for production in definition.productions:\n for symbol in production.right_side:\n if isinstance(symbol, Terminal) and symbol.name != '$':\n production.left_side.direct_first.add(symbol)\n break\n if isinstance(symbol, NonTerminal):\n production.left_side.direct_first.add(symbol)\n if not symbol.empty:\n break\n\n\ndef find_firsts(definition: SyntaxAnalyzerDefinition):\n changed = True\n while changed:\n changed = False\n for production in definition.productions:\n for direct_first in production.left_side.direct_first:\n if isinstance(direct_first, Terminal):\n if direct_first not in production.left_side.first:\n production.left_side.first.add(direct_first)\n changed = True\n elif isinstance(direct_first, NonTerminal):\n for first in direct_first.first:\n if first not in production.left_side.first:\n production.left_side.first.add(first)\n changed = True\n\n\ndef find_firsts_for_suffix(\n suffix: List[Symbol]) -> (Set[Terminal], bool):\n firsts = set()\n can_be_empty = False\n\n for next_symbol in suffix:\n if isinstance(next_symbol, Terminal) and next_symbol.name != '$':\n firsts.add(next_symbol)\n break\n elif isinstance(next_symbol, NonTerminal):\n sub_firsts = next_symbol.first\n firsts.update(sub_firsts)\n if not next_symbol.empty:\n break\n else:\n can_be_empty = True\n\n return firsts, can_be_empty or len(suffix) == 0\n","repo_name":"DiMorrison/projects","sub_path":"ppj/Labos_2/analizator/packages/util/auxilary_tables.py","file_name":"auxilary_tables.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"9409465946","text":"import einops\nimport torch\nimport torch.nn as nn\nfrom flash_attn.flash_attention import FlashAttention\n\n\nclass FlashAttentionWrapper(nn.Module):\n \"\"\" timm.vision_transformer.Attention but with FlashAttention \"\"\"\n\n def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):\n super().__init__()\n assert dim % num_heads == 0, \"dim should be divisible by num_heads\"\n self.num_heads = num_heads\n head_dim = dim // num_heads\n self.scale = head_dim ** -0.5\n\n # restriction from flash_attn.flash_attention.FlashMHA (could change in the future)\n assert head_dim % 8 == 0 and head_dim <= 128, \"Only support head_dim <= 128 and divisible by 8\"\n\n self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n self.attn = FlashAttention(softmax_scale=head_dim ** -0.5, attention_dropout=attn_drop)\n self.proj = nn.Linear(dim, dim)\n self.proj_drop = nn.Dropout(proj_drop)\n\n # attention dropout for non FlashAttention forward\n self.attn_drop = nn.Dropout(attn_drop)\n\n def forward(self, x):\n B, N, C = x.shape\n qkv = self.qkv(x)\n\n if qkv.dtype in [torch.float16, torch.bfloat16]:\n qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads)\n x = self.attn(qkv)[0]\n x = einops.rearrange(x, \"bs l n_heads head_dim -> bs l (n_heads head_dim)\")\n else:\n # not all operations are mixed precision (e.g. automatic shape inferences)\n # copy pasted from timm.vision_transformer.Attention\n qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)\n q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)\n attn = (q @ k.transpose(-2, -1)) * self.attn.softmax_scale\n attn = attn.softmax(dim=-1)\n attn = self.attn_drop(attn)\n x = (attn @ v).transpose(1, 2).reshape(B, N, C)\n\n x = self.proj(x)\n x = self.proj_drop(x)\n return x\n","repo_name":"ml-jku/MAE-CT","sub_path":"models/modules/flash_attention_wrapper.py","file_name":"flash_attention_wrapper.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"76"} +{"seq_id":"364833277","text":"import imp\nimport math\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom inspect import isfunction\nfrom .condition_deal import condition_net\nfrom .util import TimeEmbedding, Swish, ResnetBlocWithAttn, Downsample, Upsample, Block, default, exists\n\n\nclass UNet(nn.Module):\n def __init__(\n self,\n in_channel=6,\n out_channel=3,\n # inner_channel=32,\n inner_channel=64,\n norm_groups=32,\n # channel_mults=(1, 2, 4, 8, 8),\n channel_mults=(1, 2, 4, 4, 8, 8),\n # attn_res=(8,),\n attn_res=(16,),\n res_blocks=3,\n dropout=0,\n with_time_emb=True,\n image_size=256,\n ):\n super().__init__()\n\n if with_time_emb:\n time_dim = inner_channel\n self.time_mlp = nn.Sequential(\n TimeEmbedding(inner_channel),\n nn.Linear(inner_channel, inner_channel * 4),\n Swish(),\n nn.Linear(inner_channel * 4, inner_channel)\n )\n else:\n time_dim = None\n self.time_mlp = None\n\n self.condition_conv = condition_net(time_dim=inner_channel, condition_dim=56)\n\n num_mults = len(channel_mults)\n pre_channel = inner_channel\n feat_channels = [pre_channel]\n now_res = image_size\n downs = [nn.Conv2d(in_channel, inner_channel,\n kernel_size=3, padding=1)]\n for ind in range(num_mults):\n is_last = (ind == num_mults - 1)\n use_attn = (now_res in attn_res)\n channel_mult = inner_channel * channel_mults[ind]\n for _ in range(0, res_blocks):\n downs.append(ResnetBlocWithAttn(\n pre_channel, channel_mult, time_emb_dim=time_dim, norm_groups=norm_groups, dropout=dropout, with_attn=use_attn))\n feat_channels.append(channel_mult)\n pre_channel = channel_mult\n if not is_last:\n downs.append(Downsample(pre_channel))\n feat_channels.append(pre_channel)\n now_res = now_res//2\n self.downs = nn.ModuleList(downs)\n\n self.mid = nn.ModuleList([\n ResnetBlocWithAttn(pre_channel, pre_channel, time_emb_dim=time_dim, norm_groups=norm_groups,\n dropout=dropout, with_attn=True),\n ResnetBlocWithAttn(pre_channel, pre_channel, time_emb_dim=time_dim, norm_groups=norm_groups,\n dropout=dropout, with_attn=False)\n ])\n\n ups = []\n for ind in reversed(range(num_mults)):\n is_last = (ind < 1)\n use_attn = (now_res in attn_res)\n channel_mult = inner_channel * channel_mults[ind]\n for _ in range(0, res_blocks+1):\n ups.append(ResnetBlocWithAttn(\n pre_channel+feat_channels.pop(), channel_mult, time_emb_dim=time_dim, dropout=dropout, norm_groups=norm_groups, with_attn=use_attn))\n pre_channel = channel_mult\n if not is_last:\n ups.append(Upsample(pre_channel))\n now_res = now_res*2\n\n self.ups = nn.ModuleList(ups)\n\n self.final_conv = Block(pre_channel, default(out_channel, in_channel), groups=norm_groups)\n\n def forward(self, x, time, condition_input):\n t = self.time_mlp(time) if exists(self.time_mlp) else None\n warp_s = condition_input['warping']\n g = condition_input['generated']\n attr = condition_input['attribute']\n c, color_loss = self.condition_conv(warp_source=warp_s, color_cond=g, attribute=attr, t=t)\n x = torch.cat((c, x), dim=1)\n\n feats = []\n for layer in self.downs:\n if isinstance(layer, ResnetBlocWithAttn):\n x = layer(x, t)\n else:\n x = layer(x)\n feats.append(x)\n\n for layer in self.mid:\n if isinstance(layer, ResnetBlocWithAttn):\n x = layer(x, t)\n else:\n x = layer(x)\n\n for layer in self.ups:\n if isinstance(layer, ResnetBlocWithAttn):\n x = layer(torch.cat((x, feats.pop()), dim=1), t)\n else:\n x = layer(x)\n\n return self.final_conv(x), color_loss\n","repo_name":"zengbohan0217/FADM","sub_path":"modules/diffusion/core_net.py","file_name":"core_net.py","file_ext":"py","file_size_in_byte":4289,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"76"} +{"seq_id":"45558264223","text":"# pylint: disable=unused-variable,invalid-name\n\"\"\"\nDecorator and utilities for the integration with TOPI and NNVM\n\n\"\"\"\nimport warnings\nimport logging\n\n\nfrom ... import tensor, placeholder, create_schedule, target as _target\n\nfrom ..util import get_const_tuple\nfrom .task import create, register\n\nlogger = logging.getLogger('autotvm')\n\ndef serialize_args(args):\n \"\"\"serialize arguments of a topi function to a hashable tuple.\n\n Parameters\n ----------\n args: list of hashable or Tensor\n \"\"\"\n ret = []\n for t in args:\n if isinstance(t, tensor.Tensor):\n ret.append(('TENSOR', get_const_tuple(t.shape), t.dtype))\n else:\n ret.append(t)\n return tuple(ret)\n\n\ndef deserialize_args(args):\n \"\"\"The inverse function of :code:`serialize_args`.\n\n Parameters\n ----------\n args: list of hashable or Tensor\n \"\"\"\n ret = []\n for t in args:\n if isinstance(t, tuple) and t[0] == 'TENSOR':\n ret.append(placeholder(shape=t[1], dtype=t[2]))\n else:\n ret.append(t)\n return ret\n\n\n# Task extractor for nnvm graph\nclass TaskExtractEnv:\n \"\"\"Global environment for extracting tuning tasks from nnvm graph\"\"\"\n current = None\n\n def __init__(self):\n import topi\n import nnvm\n\n # NOTE: To add more symbols, you only need to change the following lists\n # nnvm symbol -> topi compute\n self.symbol2topi = {\n nnvm.sym.conv2d: [topi.nn.conv2d, topi.nn.depthwise_conv2d_nchw],\n nnvm.sym.conv2d_transpose: [topi.nn.conv2d_transpose_nchw],\n nnvm.sym.dense: [topi.nn.dense],\n }\n\n # topi compute -> autotvm task name\n self.topi_to_task = {\n topi.nn.conv2d: \"topi_nn_conv2d\",\n topi.nn.depthwise_conv2d_nchw: \"topi_nn_depthwise_conv2d_nchw\",\n topi.nn.conv2d_transpose_nchw: \"topi_nn_conv2d_transpose_nchw\",\n topi.nn.dense: \"topi_nn_dense\",\n }\n\n self.topi_to_schedule = {\n topi.nn.conv2d: [topi.generic.schedule_conv2d_nchw,\n topi.generic.schedule_conv2d_nhwc],\n topi.nn.depthwise_conv2d_nchw: [topi.generic.schedule_depthwise_conv2d_nchw,\n topi.generic.schedule_depthwise_conv2d_nhwc],\n topi.nn.conv2d_transpose_nchw: [topi.generic.schedule_conv2d_transpose_nchw],\n topi.nn.dense: [topi.generic.schedule_dense],\n }\n\n self._register_tracing()\n self._register_topi_task()\n self.task_collection = []\n self.wanted_topi_funcs = list(self.topi_to_task.keys())\n\n def _register_tracing(self):\n \"\"\"Register tracing function to track the topi function call\"\"\"\n # register topi compute for \"tracing\" target\n for topi_compute in self.topi_to_task:\n def _local_scope(compute_func):\n \"\"\"start a scope to hold the local function in for loop\"\"\"\n\n @compute_func.register(\"tracing\", )\n def _tracing_topi_compute(*args, **kwargs):\n assert not kwargs, \"Do not support extracting tuning tasks when\" \\\n \"kwargs is used in TOPI function call.\" \\\n \"Please modify it to use only positional args.\"\n\n if compute_func in self.wanted_topi_funcs: # record this call\n key = (self.topi_to_task[compute_func], serialize_args(args))\n if key not in self.task_collection:\n self.task_collection.append(key)\n\n return compute_func.fdefault(*args)\n _local_scope(topi_compute)\n\n # register topi schedule for \"tracing\" target\n for topi_compute in self.topi_to_task:\n for topi_schedule in self.topi_to_schedule[topi_compute]:\n def _local_scope_(schedule_func):\n \"\"\"start a scope to hold the local function in for loop\"\"\"\n\n @schedule_func.register(\"tracing\", )\n def _tracing_topi_compute(outs):\n outs = [outs] if isinstance(outs, tensor.Tensor) else outs\n return create_schedule([x.op for x in outs])\n _local_scope_(topi_schedule)\n\n def _register_topi_task(self):\n \"\"\"register tuning wrapper for topi function\"\"\"\n import topi\n\n # Tuning wrapper for topi functions\n @register(\"topi_nn_conv2d\")\n def _topi_nn_conv2d(*args, **kwargs):\n assert not kwargs, \"Do not support kwargs in template function call\"\n args = deserialize_args(args)\n A, W = args[:2]\n layout = args[-2]\n assert layout == 'NCHW', \"only support NCHW currently\"\n C = topi.nn.conv2d(*args, **kwargs)\n s = topi.generic.schedule_conv2d_nchw([C])\n return s, [A, W, C]\n\n @register(\"topi_nn_depthwise_conv2d_nchw\")\n def _topi_nn_depthwise_conv2d_nchw(*args, **kwargs):\n assert not kwargs, \"Do not support kwargs in template function call\"\n args = deserialize_args(args)\n A, W = args[:2]\n C = topi.nn.depthwise_conv2d_nchw(*args, **kwargs)\n s = topi.generic.schedule_depthwise_conv2d_nchw([C])\n return s, [A, W, C]\n\n @register(\"topi_nn_conv2d_transpose_nchw\")\n def _topi_nn_conv2d_transpose_nchw(*args, **kwargs):\n assert not kwargs, \"Do not support kwargs in template function call\"\n args = deserialize_args(args)\n A, W = args[:2]\n C = topi.nn.conv2d_transpose_nchw(*args, **kwargs)\n s = topi.generic.schedule_conv2d_transpose_nchw([C])\n return s, [A, W, C]\n\n @register(\"topi_nn_dense\")\n def _topi_nn_dense(*args, **kwargs):\n assert not kwargs, \"Do not support kwargs in template function call\"\n args = deserialize_args(args)\n data, weight, bias = args\n C = topi.nn.dense(*args, **kwargs)\n s = topi.generic.schedule_dense([C])\n if bias is not None:\n return s, [data, weight, bias, C]\n return s, [data, weight, C]\n\n def reset(self, wanted_topi_funcs):\n \"\"\"Reset task collections\n\n Parameters\n ----------\n wanted_topi_funcs: List of function\n The topi function to be extracted\n \"\"\"\n self.task_collection = []\n self.wanted_topi_funcs = wanted_topi_funcs\n\n def get_tasks(self):\n \"\"\"Get collected tasks\n\n Returns\n -------\n tasks: List of tuple(name, args)\n A list of tasks extracted from the nnvm graph\n \"\"\"\n return self.task_collection\n\n @staticmethod\n def get():\n \"\"\"Get the single instance of TaskExtractEnv\n\n Returns\n -------\n env: TaskExtractEnv\n The single instance of TaskExtractEnv\n \"\"\"\n if not TaskExtractEnv.current:\n TaskExtractEnv.current = TaskExtractEnv()\n return TaskExtractEnv.current\n\n\ndef extract_from_graph(graph, shape, dtype, target, symbols, target_host=None):\n \"\"\" Extract tuning tasks from a nnvm graph.\n\n This function collects tuning tasks by building the graph\n with a \"tracing\" target and tracing all the calls to topi.\n\n Parameters\n ----------\n graph : Graph\n The graph to tune\n shape : dict of str to tuple\n The input shape to the graph\n dtype : str or dict of str to str\n The input types to the graph\n target: tvm.target.Target\n The compilation target\n symbols : Array of nnvm.symbol\n Array of nnvm symbols want to be tuned\n target_host: tvm.target.Target\n The host compilation target\n\n Returns\n -------\n task: Array of autotvm.task.Task\n collected tasks\n \"\"\"\n import nnvm.compiler\n\n env = TaskExtractEnv.get()\n\n topi_funcs = []\n for sym_name in symbols:\n if sym_name in env.symbol2topi:\n topi_funcs.extend(env.symbol2topi[sym_name])\n else:\n warnings.warn(\"Symbol %s is not tunable, ignored\" % sym_name)\n\n # run compiler to collect all TOPI calls during compilation\n env.reset(topi_funcs)\n\n # disable logger temporarily\n old_state = logger.disabled\n logger.disabled = True\n\n # use a \"tracing\" target to do a fake compile for collecting topi calls\n tracing_target = _target.create(\"llvm -device=tracing\")\n nnvm.compiler.engine.clear_cache()\n nnvm.compiler.build(graph, target=tracing_target, shape=shape, dtype=dtype)\n\n logger.disabled = old_state\n\n # create tasks for target\n tasks = []\n for task_name, args in env.get_tasks():\n tasks.append(create(task_name, args,\n target=target, target_host=target_host,\n template_key='direct'))\n\n return tasks\n\n\ndef extract_from_multiple_graph(graphs, shapes, dtypes, target, symbols, target_host=None):\n \"\"\" Extract tuning tasks from multiple nnvm graphs.\n\n This function is the multiple graph version of extract_from_graph\n\n Parameters\n ----------\n graphs : List of Graph\n The list of graphs to tune\n shapes : List of dict of str to tuple\n The input shape to the graph\n dtypes : List of str or dict of str to str\n The input types to the graph\n target: tvm.target.Target\n The compilation target\n symbols : Array of nnvm.symbol\n Array of nnvm symbols want to be tuned\n target_host: tvm.target.Target\n The host compilation target\n\n Returns\n -------\n task: Array of autotvm.task.Task\n collected tasks\n \"\"\"\n import nnvm.compiler\n\n env = TaskExtractEnv.get()\n\n topi_funcs = []\n for sym_name in symbols:\n if sym_name in env.symbol2topi:\n topi_funcs.extend(env.symbol2topi[sym_name])\n else:\n warnings.warn(\"Symbol %s is not tunable, ignored\" % sym_name)\n\n # run compiler to collect all TOPI calls during compilation\n env.reset(topi_funcs)\n\n # disable logger temporarily\n old_state = logger.disabled\n logger.disabled = True\n\n # use a \"tracing\" target to do a fake compile for collecting topi calls\n tracing_target = _target.create(\"llvm -device=tracing\")\n\n nnvm.compiler.engine.clear_cache()\n for graph, shape, dtype in zip(graphs, shapes, dtypes):\n nnvm.compiler.build(graph, target=tracing_target, shape=shape, dtype=dtype)\n\n logger.disabled = old_state\n\n # create tasks for target\n tasks = []\n for task_name, args in env.get_tasks():\n tasks.append(create(task_name, args,\n target=target, target_host=target_host,\n template_key='direct'))\n\n return tasks\n","repo_name":"researchmm/tasn","sub_path":"tasn-mxnet/3rdparty/tvm/python/tvm/autotvm/task/nnvm_integration.py","file_name":"nnvm_integration.py","file_ext":"py","file_size_in_byte":10842,"program_lang":"python","lang":"en","doc_type":"code","stars":216,"dataset":"github-code","pt":"76"} +{"seq_id":"40510390450","text":"# -*- mode: python ; coding: utf-8 -*-\n\n\nblock_cipher = None\n\nadded_files = [\n ( 'res\\\\setting.json', '.'),\n ( 'res\\\\creds.json', '.')\n ]\na = Analysis(['main_eel.py', \n 'backend\\\\afd_docx.py', \n 'backend\\\\afd_ggl.py', \n 'backend\\\\afd_helps.py', \n 'backend\\\\afd_json.py', \n 'backend\\\\afd_pandas.py'\n ],\n pathex=['E:\\\\python\\\\fill_docs'],\n binaries=[],\n datas=[\n ('C:\\\\Users\\\\PTO88\\\\.virtualenvs\\\\fill_docs-RRYzcrO0\\\\lib\\\\site-packages\\\\eel\\\\eel.js', 'eel'), \n ('frontend', 'frontend'), \n ('res\\\\icon.ico', '.')],\n hiddenimports=['bottle_websocket', 'docxtpl', 'colorama', 'cryptography', 'gspread', 'oauth2client', 'pandas'],\n hookspath=[],\n runtime_hooks=[],\n excludes=[],\n win_no_prefer_redirects=False,\n win_private_assemblies=False,\n cipher=block_cipher,\n noarchive=False)\npyz = PYZ(a.pure, a.zipped_data,\n cipher=block_cipher)\nexe = EXE(pyz,\n a.scripts,\n a.binaries,\n a.zipfiles,\n a.datas,\n [],\n name='soft v1.1',\n debug=False,\n bootloader_ignore_signals=False,\n strip=False,\n upx=True,\n upx_exclude=[],\n runtime_tmpdir=None,\n console=False, \n icon='res\\\\icon.ico'\n )\n","repo_name":"saferq/fill_docs","sub_path":"main_eel.spec","file_name":"main_eel.spec","file_ext":"spec","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"11852150102","text":"from ctypes import *\n\nmsvcrt = cdll.msvcrt\n\ninput(\"Press any key.\")\n\n#Create 8-byte destination Buffer\nBuff = c_char_p(\"AA\")\n\n#The overflow string\n\noverflow = \"A\" * 100\n\nmsvcrt.strcpy(Buff, overflow)\n\nos.environ(Buff, overflow)\n","repo_name":"SirawichDev/Window_debuggers","sub_path":"buffer_overflow.py","file_name":"buffer_overflow.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"72338058487","text":"class Employee:\r\n # class attribute\r\n company = \"Msys\"\r\n # constructor\r\n def __init__(self, name, age, salary):\r\n # instance attributes\r\n self.name = name\r\n self.age = age\r\n self.salary = salary\r\n\r\n# creating objects\r\nemp1 = Employee(\"John\", 34, 50000)\r\nemp2 = Employee(\"Harry\", 30, 60000)\r\n\r\n# accessing class attributes using __class__ method\r\n# syntax is-- instance.__class__.attribute\r\nprint(f\"{emp1.name} and {emp2.name} work for {emp1.__class__.company}\")\r\n\r\n# accessing instance attributes\r\n# syntax is-- instance.instance_attribute\r\nprint(f\"{emp1.name}'s age is {emp1.age} and salary is {emp1.salary}\")\r\nprint(f\"{emp2.name}'s age is {emp2.age} and salary is {emp2.salary}\")\r\n","repo_name":"Madhusudhanreddy01/python_everything","sub_path":"classes_and_objects.py","file_name":"classes_and_objects.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"11737279027","text":"from openerp.osv import osv, fields\n\nclass course (osv.Model):\n _name = 'openacademy.course'\n \n _columns = {\n 'name': fields.char(string=\"Name\", size=128, required=True),\n 'description': fields.text(string=\"Description\"),\n 'responsible_id': fields.many2one('res.users', string=\"Responsible\", required=True, select=\"True\"),\n 'session_ids': fields.one2many('openacademy.session', 'course_id', string=\"Sessions\"),\n }\n \n _sql_constraints = [\n ('name_description_check',\n 'CHECK(name <> description)',\n 'The course title must be different from its description.'),\n \n ('name_unique',\n 'UNIQUE(name)',\n 'The course title must be unique.')\n ]\n \n def copy(self, cr, uid, id, default=None, context={}):\n course_brw = self.browse(cr, uid, id, context=context)\n new_name = course_brw.name\n while self.search(cr, uid, [('name', '=ilike', new_name)], count=True, context=context) != 0:\n new_name = \"%s (copy)\" % new_name\n default['name'] = new_name\n return super(course, self).copy(cr, uid, id, default, context=context)\n\nclass attendee (osv.Model):\n _name = 'openacademy.attendee'\n \n _rec_name = 'partner_id'\n _order = 'partner_id'\n \n _columns = {\n 'partner_id': fields.many2one('res.partner', string=\"Partner\"),\n 'session_id': fields.many2one('openacademy.session', string=\"Attended session\", ondelete=\"cascade\"),\n 'partner_id_mobile': fields.related('partner_id','mobile',string='Mobile',type=\"char\",readonly=True),\n 'partner_id_country': fields.related('partner_id','country_id','name',string='Country',type=\"char\",readonly=True),\n }\n \n _sql_constraints = [\n ('partner_session_unique',\n 'UNIQUE(partner_id, session_id)',\n 'You cannot add an attendee multiple times on the same session.')\n ]\n\nclass resPartner (osv.Model):\n #_name = \"res.partner\"\n _inherit = \"res.partner\"\n \n _columns = {\n 'instructor': fields.boolean(string=\"Instructor\"),\n 'attendee_ids': fields.one2many('openacademy.attendee', 'partner_id', string=\"Sessions\"),\n }\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"jesramirez/openacademy","sub_path":"openacademy.py","file_name":"openacademy.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"39388111686","text":"import os\nimport torch\nimport argparse\nimport numpy as np\nimport imageio\nimport sys\nfrom tqdm import tqdm\nfrom skimage import img_as_ubyte\nimport importlib\nsys.path.append('')\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\nfrom models.dataset import test_dataset as EvalDataset\n\n\n\nnet_list = ['UEDGNet_iterative_pvt_antiartifact_laplace']\nmodel_list = ['MyTrain']\n\nckp_path_list = []\nfor cur_model in model_list:\n ckp_path_list.append(cur_model + '/Net_epoch_best.pth')\n \ndef evaluator(model, val_root, map_save_path, trainsize=352):\n val_loader = EvalDataset(image_root=val_root + 'Imgs/',\n gt_root=val_root + 'GT/',\n testsize=trainsize)\n model.eval()\n with torch.no_grad():\n for i in tqdm(range(val_loader.size)):\n image, gt, name, _ = val_loader.load_data()\n gt = np.asarray(gt, np.float32)\n\n image = image.cuda()\n\n output = model(image)\n output = F.interpolate(output[0][3], size=gt.shape, mode='bilinear', align_corners=False)\n output = output.sigmoid().data.cpu().numpy().squeeze()\n output = (output - output.min()) / (output.max() - output.min() + 1e-8)\n\n imageio.imsave(map_save_path + name, img_as_ubyte(output)) #change\n #print('>>> prediction save at: {}'.format(map_save_path + name))\n \nfor cur_net, cur_model in zip(net_list, model_list):\n globals()['UEDG'] = importlib.import_module('models.' + cur_net)\n txt_save_path = './exp_result/{}/'.format(cur_model)\n cur_ckp = './log/' + cur_model + '/Net_epoch_best.pth'\n\n\ncnt = 0\nfor cur_net, cur_model in zip(net_list, model_list):\n cnt += 1\n print('{}/{}'.format(cnt, len(net_list)))\n \n cur_module = importlib.import_module('models.' + cur_net)\n txt_save_path = './exp_result/{}/'.format(cur_model)\n cur_ckp = './log/' + cur_model + '/Net_epoch_best.pth'\n os.makedirs(txt_save_path, exist_ok=True)\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n cudnn.benchmark = True\n\n model = cur_module.UEDGNet(channel=64, M=[8, 8, 8], N=[4, 8, 16])\n model.load_state_dict(torch.load(cur_ckp))\n model.eval()\n model.cuda() #change\n\n for data_name in ['CHAMELEON', 'CAMO', 'COD10K', 'NC4K']:\n map_save_path = txt_save_path + \"/{}/\".format(data_name)\n os.makedirs(map_save_path, exist_ok=True)\n evaluator(\n model=model,\n val_root='./dataset/TestDataset/' + data_name + '/',\n map_save_path=map_save_path,\n trainsize=352)\n","repo_name":"lyu-yx/UEDG","sub_path":"MyTest.py","file_name":"MyTest.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"76"} +{"seq_id":"5293241395","text":"import datetime\nfrom django.test import TestCase\nfrom django.utils import timezone\nimport cloudinary\nimport cloudinary.uploader\nfrom django.contrib.auth.models import User\nfrom qforum.models import Category, Thread, Comment\nfrom qblog.models import Comment as PostComment\nfrom qblog.models import Post\nfrom home.models import Contact\nfrom users.models import Profile\n\n\nclass CategoryModelTest(TestCase):\n \"\"\"\n Testing the category model in qforum\n \"\"\"\n @classmethod\n def setUpTestData(cls):\n user = User.objects.create(username='Aman',\n email='aman@mail.com',\n password='123someTest')\n Category.objects.create(name=user, subject='Finance',\n description='Financial analysis',\n created_on=datetime.date.today(),\n status=0, thread_count=0)\n\n def test_subject_max_length(self):\n category = Category.objects.get(id=1)\n max_length = category._meta.get_field('subject').max_length\n self.assertEqual(max_length, 50)\n\n def test_description_max_length(self):\n category = Category.objects.get(id=1)\n max_length = category._meta.get_field('description').max_length\n self.assertEqual(max_length, 255)\n\n def test_category_username(self):\n user = User.objects.get(id=1)\n category = Category.objects.get(id=1)\n username = category._meta.get_field('name').value_from_object(category)\n self.assertEqual(username, user.pk)\n\n def test_category_status(self):\n category = Category.objects.get(id=1)\n status = category._meta.get_field('status').value_from_object(category)\n self.assertEqual(status, 0)\n\n def test_no_of_threads(self):\n category = Category.objects.get(id=1)\n no_of_threads = category._meta.get_field('thread_count')\\\n .value_from_object(category)\n self.assertEqual(no_of_threads, 0)\n\n\nclass ThreadModelTest(TestCase):\n \"\"\"\n Testing the thread model in qforum\n \"\"\"\n @classmethod\n def setUpTestData(cls):\n user1 = User.objects.create(id=1, username='Aman',\n email='aman@mail.com',\n password='123someTest')\n category = Category.objects.create(id=1, name=user1,\n subject='Finance',\n description='Financial analysis',\n created_on=datetime.date.today(),\n status=0,\n thread_count=0)\n Thread.objects.create(id=1, name=user1,\n topic='Some topic',\n slug='some-topic',\n description='First test',\n category=category,\n status=1,\n created_on=datetime.date.today())\n\n def test_thread_username(self):\n user = User.objects.get(username='Aman')\n category = Category.objects.get(id=1)\n username = category._meta.get_field('name').value_from_object(category)\n self.assertEqual(username, user.pk)\n\n def test_thread_topic_max_length(self):\n thread = Thread.objects.get(id=1)\n max_length = thread._meta.get_field('topic').max_length\n self.assertEqual(max_length, 300)\n\n def test_thread_category(self):\n category = Category.objects.get(id=1)\n thread = Thread.objects.get(id=1)\n thread_category = thread._meta.get_field('category')\\\n .value_from_object(thread)\n self.assertEqual(thread_category, category.pk)\n\n def test_thread_status(self):\n thread = Thread.objects.get(id=1)\n status = thread._meta.get_field('status').value_from_object(thread)\n self.assertEqual(status, 1)\n\n def test_thread_slug_max_length(self):\n thread = Thread.objects.get(id=1)\n max_length = thread._meta.get_field('slug').max_length\n self.assertEqual(max_length, 300)\n\n def test_thread_description_max_length(self):\n thread = Thread.objects.get(id=1)\n max_length = thread._meta.get_field('description').max_length\n self.assertEqual(max_length, 500)\n\n\nclass PostModelTest(TestCase):\n \"\"\"\n Testing post model in qblog app\n \"\"\"\n @classmethod\n def setUpTestData(cls):\n user = User.objects.create(username='Aman',\n email='aman@mail.com',\n password='123someTest')\n Post.objects.create(id=1,\n title='First post',\n slug='first-post',\n author=user,\n last_updated=datetime.date.today(),\n content='This is a sample post. It has a content.',\n created_on=datetime.date.today(), status=0)\n\n def test_post_title_max_length(self):\n post = Post.objects.get(id=1)\n max_length = post._meta.get_field('title').max_length\n self.assertEqual(max_length, 200)\n\n def test_post_slug_max_length(self):\n post = Post.objects.get(id=1)\n max_length = post._meta.get_field('slug').max_length\n self.assertEqual(max_length, 200)\n\n def test_post_author(self):\n user = User.objects.get(username='Aman')\n post = Post.objects.get(id=1)\n author = post._meta.get_field('author').value_from_object(post)\n self.assertEqual(author, user.pk)\n\n def test_post_last_updated(self):\n post = Post.objects.get(id=1)\n last_updated = post._meta.get_field('last_updated')\\\n .value_from_object(post)\n self.assertEqual(last_updated.day, datetime.date.today().day)\n\n def test_post_content(self):\n post = Post.objects.get(id=1)\n content = post._meta.get_field('content').value_from_object(post)\n self.assertEqual(content, 'This is a sample post. It has a content.')\n\n def test_post_status(self):\n post = Post.objects.get(id=1)\n status = post._meta.get_field('status').value_from_object(post)\n self.assertEqual(status, 0)\n\n\nclass PostCommentModelTest(TestCase):\n \"\"\"\n Testing post comment model in qblog\n \"\"\"\n @classmethod\n def setUpTestData(cls):\n user = User.objects.create(username='Aman',\n email='aman@mail.com',\n password='123someTest')\n post = Post.objects.create(title='First post',\n slug='first-post',\n author=user,\n last_updated=datetime.date.today(),\n content='This is a sample post.\\\n It has a content.',\n created_on=datetime.date.today(), status=0)\n PostComment.objects.create(post=post,\n name=user,\n email=user.email,\n body='Nice post',\n created_on=datetime.date.today(),\n approved=True)\n\n def test_post_comment_post(self):\n comment = PostComment.objects.get(id=1)\n post = Post.objects.get(id=1)\n comment_post = comment._meta.get_field('post')\\\n .value_from_object(comment)\n self.assertEqual(comment_post, post.pk)\n\n def test_post_comment_name_max_length(self):\n comment = PostComment.objects.get(id=1)\n max_length = comment._meta.get_field('name').max_length\n self.assertEqual(max_length, 50)\n\n def test_post_comment_name_max_length(self):\n comment = PostComment.objects.get(id=1)\n max_length = comment._meta.get_field('name').max_length\n self.assertEqual(max_length, 50)\n\n def test_post_comment_author_email(self):\n user = User.objects.get(username='Aman')\n comment = PostComment.objects.get(id=1)\n email = comment._meta.get_field('email').value_from_object(comment)\n self.assertEqual(email, user.email)\n\n def test_post_comment_created(self):\n comment = PostComment.objects.get(id=1)\n created_on = comment._meta.get_field('created_on')\\\n .value_from_object(comment)\n self.assertEqual(created_on.day, datetime.date.today().day)\n\n def test_post_comment_status(self):\n comment = PostComment.objects.get(id=1)\n status = comment._meta.get_field('approved').value_from_object(comment)\n self.assertTrue(status)\n\n def test_post_comment_body_max_length(self):\n comment = PostComment.objects.get(id=1)\n max_length = comment._meta.get_field('body').max_length\n self.assertEqual(max_length, 200)\n\n def test_post_comment_body_content(self):\n comment = PostComment.objects.get(id=1)\n body = comment._meta.get_field('body').value_from_object(comment)\n self.assertEqual(body, 'Nice post')\n\n\nclass ThreadCommentModelTest(TestCase):\n \"\"\"\n Testing thread comment model in qforum app\n \"\"\"\n @classmethod\n def setUp(self):\n self.user1 = User.objects.create(id=1, username='Aman',\n email='aman@mail.com',\n password='123someTest')\n self.user2 = User.objects.create(id=2, username='Newuser',\n email='new@mail.com',\n password='123someTest')\n self.category = Category.objects.create(id=1, name=self.user1,\n subject='Finance',\n description='Test',\n created_on=datetime.date.today(),\n status=0,\n thread_count=0)\n self.thread = Thread.objects.create(id=1, name=self.user1,\n topic='Some topic',\n slug='some-topic',\n description='First test',\n category=self.category,\n status=1,\n created_on=datetime.date.today())\n Comment.objects.create(id=1, thread=self.thread,\n name=self.user2,\n content='Test comment',\n created=datetime.date.today(),\n active=False)\n\n def test_thread_comment(self):\n \"\"\" Test thread comment \"\"\"\n comment = Comment.objects.get(id=1)\n thread = self.thread\n comment_thread = comment._meta.get_field('thread')\\\n .value_from_object(comment)\n self.assertEqual(comment_thread, thread.pk)\n max_length = comment._meta.get_field('content').max_length\n self.assertEqual(max_length, 255)\n active = comment._meta.get_field('active').value_from_object(comment)\n self.assertFalse(active)\n created = comment._meta.get_field('created')\\\n .value_from_object(comment)\n self.assertEqual(created.day, datetime.date.today().day)\n parent = comment._meta.get_field('parent').value_from_object(comment)\n self.assertFalse(parent)\n\n\nclass ProfileModelTest(TestCase):\n \"\"\"\n Testing user profile model in users app\n \"\"\"\n @classmethod\n def setUpTestData(cls):\n User.objects.create(username='Aman',\n email='aman@mail.com',\n password='123someTest')\n\n def test_profile_creation(self):\n \"\"\" Signals will have created profile \"\"\"\n self.assertEqual(Profile.objects.count(), 1)\n user = User.objects.get(username='Aman')\n profile = Profile.objects.all()\n self.assertEqual(profile[0].user, user)\n\n def test_profile_update(self):\n \"\"\" Test profile update \"\"\"\n profile = Profile.objects.all()[0]\n profile.bio = \"I use quteba.\"\n profile.save()\n self.assertEqual(profile.bio, \"I use quteba.\")\n self.assertEqual(Profile.objects.count(), 1)\n\n def test_profile_str(self):\n \"\"\"Test profile string representation\"\"\"\n profile = Profile.objects.all()[0]\n self.assertEqual(str(profile), \"Aman\")\n\n\nclass ContactModelTest(TestCase):\n \"\"\"\n Testing contact model in the home app\n \"\"\"\n @classmethod\n def setUpTestData(cls):\n Contact.objects.create(name='Aman',\n email='aman@mail.com',\n subject='Test subject',\n message='Test message')\n\n def test_contact_creation(self):\n \"\"\" Test if message exists and its contents \"\"\"\n self.assertEqual(Contact.objects.count(), 1)\n contact = Contact.objects.get(id=1)\n self.assertEqual(contact.name, 'Aman')\n self.assertEqual(contact.email, 'aman@mail.com')\n self.assertEqual(contact.subject, 'Test subject')\n self.assertEqual(contact.message, 'Test message')\n","repo_name":"Amareteklay/quteba","sub_path":"tests/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":13512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"5177851156","text":"from aiogram import types, Dispatcher\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters.state import State, StatesGroup\nfrom pytube import YouTube\nfrom config import dp, bot\nfrom uuid import uuid4\nimport os\n\nclass Download(StatesGroup):\n download = State()\n\ndef download_video(url, type='audio'):\n yt = YouTube(url)\n audio_id = uuid4().fields[-1]\n if type == 'audio':\n yt.streams.filter(only_audio=True).first().download(\"audio\", f\"{audio_id}.mp3\")\n return f\"{audio_id}.mp3\"\n elif type == 'video':\n return f\"{audio_id}.mp4\"\n\n\nasync def start_dow(message: types.Message):\n await message.answer(text=f\"Привет, {message.from_user.full_name}, скинь ссылку на видео и я отправлю ее тебе ввиде аудио.\")\n await Download.download.set()\n\nasync def dowload(message: types.Message, state: FSMContext):\n title = download_video(message.text)\n audio = open(f'audio/{title}', 'rb')\n await message.answer(text=\" На, держи\")\n try:\n await bot.send_audio(message.chat.id, audio)\n await bot.send_message(message.chat.id, text='')\n except:\n return Exception\n os.remove(f'audio/{title}')\n await state.finish()\n\n\ndef register_handlers_yt(dp: Dispatcher):\n dp.register_message_handler(start_dow, commands='audio')\n dp.register_message_handler(dowload, state=Download.download)\n","repo_name":"aliiiiaa/hw1","sub_path":"handlers/yt_media.py","file_name":"yt_media.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"73987807286","text":"import copy\nimport inspect\n\nimport sentry_sdk\nfrom django.conf import settings\nfrom django.urls import resolve\n\n# Reexport sentry_sdk just in case we ever have to write another shim like we\n# did for raven\nfrom sentry_sdk import capture_exception, capture_message, configure_scope, push_scope # NOQA\nfrom sentry_sdk.client import get_options\nfrom sentry_sdk.transport import make_transport\nfrom sentry_sdk.utils import logger as sdk_logger\n\nfrom sentry import options\nfrom sentry.utils import metrics\nfrom sentry.utils.db import DjangoAtomicIntegration\nfrom sentry.utils.rust import RustInfoIntegration\n\nUNSAFE_FILES = (\n \"sentry/event_manager.py\",\n \"sentry/tasks/process_buffer.py\",\n \"sentry/ingest/ingest_consumer.py\",\n # This consumer lives outside of sentry but is just as unsafe.\n \"outcomes_consumer.py\",\n)\n\n# URLs that should always be sampled\nSAMPLED_URL_NAMES = {\n # codeowners\n \"sentry-api-0-project-codeowners\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-project-codeowners-details\": settings.SAMPLED_DEFAULT_RATE,\n # external teams POST, PUT, DELETE\n \"sentry-api-0-external-team\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-external-team-details\": settings.SAMPLED_DEFAULT_RATE,\n # external users POST, PUT, DELETE\n \"sentry-api-0-organization-external-user\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-organization-external-user-details\": settings.SAMPLED_DEFAULT_RATE,\n # integration platform\n \"external-issues\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-sentry-app-authorizations\": settings.SAMPLED_DEFAULT_RATE,\n # integrations\n \"sentry-extensions-jira-issue-hook\": 0.05,\n \"sentry-extensions-vercel-webhook\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-extensions-vercel-generic-webhook\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-extensions-vercel-configure\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-extensions-vercel-ui-hook\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-group-integration-details\": settings.SAMPLED_DEFAULT_RATE,\n # notification platform\n \"sentry-api-0-user-notification-settings\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-team-notification-settings\": settings.SAMPLED_DEFAULT_RATE,\n # releases\n \"sentry-api-0-organization-releases\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-organization-release-details\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-project-releases\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-project-release-details\": settings.SAMPLED_DEFAULT_RATE,\n # stats\n \"sentry-api-0-organization-stats\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-organization-stats-v2\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-project-stats\": 0.05, # lower rate because of high TPM\n # debug files\n \"sentry-api-0-assemble-dif-files\": 0.1,\n # scim\n \"sentry-api-0-organization-scim-member-index\": 0.1,\n \"sentry-api-0-organization-scim-member-details\": 0.1,\n \"sentry-api-0-organization-scim-team-index\": 0.1,\n \"sentry-api-0-organization-scim-team-details\": 0.1,\n # members\n \"sentry-api-0-organization-invite-request-index\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-organization-invite-request-detail\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-api-0-organization-join-request\": settings.SAMPLED_DEFAULT_RATE,\n # login\n \"sentry-login\": 0.1,\n \"sentry-auth-organization\": 0.2,\n \"sentry-auth-link-identity\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-auth-sso\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-logout\": 0.1,\n \"sentry-register\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry-2fa-dialog\": settings.SAMPLED_DEFAULT_RATE,\n # reprocessing\n \"sentry-api-0-issues-reprocessing\": settings.SENTRY_REPROCESSING_APM_SAMPLING,\n}\nif settings.ADDITIONAL_SAMPLED_URLS:\n SAMPLED_URL_NAMES.update(settings.ADDITIONAL_SAMPLED_URLS)\n\nSAMPLED_TASKS = {\n \"sentry.tasks.send_ping\": settings.SAMPLED_DEFAULT_RATE,\n \"sentry.tasks.store.symbolicate_event\": settings.SENTRY_SYMBOLICATE_EVENT_APM_SAMPLING,\n \"sentry.tasks.store.symbolicate_event_from_reprocessing\": settings.SENTRY_SYMBOLICATE_EVENT_APM_SAMPLING,\n \"sentry.tasks.store.process_event\": settings.SENTRY_PROCESS_EVENT_APM_SAMPLING,\n \"sentry.tasks.store.process_event_from_reprocessing\": settings.SENTRY_PROCESS_EVENT_APM_SAMPLING,\n \"sentry.tasks.assemble.assemble_dif\": 0.1,\n \"sentry.tasks.app_store_connect.dsym_download\": settings.SENTRY_APPCONNECT_APM_SAMPLING,\n \"sentry.tasks.app_store_connect.refresh_all_builds\": settings.SENTRY_APPCONNECT_APM_SAMPLING,\n \"sentry.tasks.process_suspect_commits\": settings.SENTRY_SUSPECT_COMMITS_APM_SAMPLING,\n \"sentry.tasks.post_process.post_process_group\": settings.SENTRY_POST_PROCESS_GROUP_APM_SAMPLING,\n \"sentry.tasks.reprocessing2.handle_remaining_events\": settings.SENTRY_REPROCESSING_APM_SAMPLING,\n \"sentry.tasks.reprocessing2.reprocess_group\": settings.SENTRY_REPROCESSING_APM_SAMPLING,\n \"sentry.tasks.reprocessing2.finish_reprocessing\": settings.SENTRY_REPROCESSING_APM_SAMPLING,\n \"sentry.tasks.relay.update_config_cache\": settings.SENTRY_RELAY_TASK_APM_SAMPLING,\n \"sentry.tasks.reports.prepare_organization_report\": 0.1,\n \"sentry.tasks.reports.deliver_organization_user_report\": 0.01,\n}\n\n\nUNSAFE_TAG = \"_unsafe\"\n\n\ndef is_current_event_safe():\n \"\"\"\n Tests the current stack for unsafe locations that would likely cause\n recursion if an attempt to send to Sentry was made.\n \"\"\"\n\n with configure_scope() as scope:\n\n # Scope was explicitly marked as unsafe\n if scope._tags.get(UNSAFE_TAG):\n return False\n\n project_id = scope._tags.get(\"processing_event_for_project\")\n\n if project_id and project_id == settings.SENTRY_PROJECT:\n return False\n\n for _, filename, _, _, _, _ in inspect.stack():\n if filename.endswith(UNSAFE_FILES):\n return False\n\n return True\n\n\ndef mark_scope_as_unsafe():\n \"\"\"\n Set the unsafe tag on the SDK scope for outgoing crashes and transactions.\n\n Marking a scope explicitly as unsafe allows the recursion breaker to\n decide early, before walking the stack and checking for unsafe files.\n \"\"\"\n with configure_scope() as scope:\n scope.set_tag(UNSAFE_TAG, True)\n\n\ndef set_current_event_project(project_id):\n \"\"\"\n Set the current project on the SDK scope for outgoing crash reports.\n\n This is a dedicated function because it is also important for the recursion\n breaker to work. You really should set the project in every task that is\n relevant to event processing, or that task may crash ingesting\n sentry-internal errors, causing infinite recursion.\n \"\"\"\n with configure_scope() as scope:\n scope.set_tag(\"processing_event_for_project\", project_id)\n scope.set_tag(\"project\", project_id)\n\n\ndef get_project_key():\n from sentry.models import ProjectKey\n\n if not settings.SENTRY_PROJECT:\n return None\n\n key = None\n try:\n if settings.SENTRY_PROJECT_KEY is not None:\n key = ProjectKey.objects.get(\n id=settings.SENTRY_PROJECT_KEY, project=settings.SENTRY_PROJECT\n )\n else:\n key = ProjectKey.get_default(settings.SENTRY_PROJECT)\n except Exception as exc:\n # if the relation fails to query or is missing completely, lets handle\n # it gracefully\n sdk_logger.warning(\n \"internal-error.unable-to-fetch-project\",\n extra={\n \"project_id\": settings.SENTRY_PROJECT,\n \"project_key\": settings.SENTRY_PROJECT_KEY,\n \"error_message\": str(exc),\n },\n )\n if key is None:\n sdk_logger.warning(\n \"internal-error.no-project-available\",\n extra={\n \"project_id\": settings.SENTRY_PROJECT,\n \"project_key\": settings.SENTRY_PROJECT_KEY,\n },\n )\n return key\n\n\ndef traces_sampler(sampling_context):\n # If there's already a sampling decision, just use that\n if sampling_context[\"parent_sampled\"] is not None:\n return sampling_context[\"parent_sampled\"]\n\n if \"celery_job\" in sampling_context:\n task_name = sampling_context[\"celery_job\"].get(\"task\")\n\n if task_name in SAMPLED_TASKS:\n return SAMPLED_TASKS[task_name]\n\n # Resolve the url, and see if we want to set our own sampling\n if \"wsgi_environ\" in sampling_context:\n try:\n match = resolve(sampling_context[\"wsgi_environ\"].get(\"PATH_INFO\"))\n if match and match.url_name in SAMPLED_URL_NAMES:\n return SAMPLED_URL_NAMES[match.url_name]\n except Exception:\n # On errors or 404, continue to default sampling decision\n pass\n\n # Default to the sampling rate in settings\n return float(settings.SENTRY_BACKEND_APM_SAMPLING or 0)\n\n\n# Patches transport functions to add metrics to improve resolution around events sent to our ingest.\n# Leaving this in to keep a permanent measurement of sdk requests vs ingest.\ndef patch_transport_for_instrumentation(transport, transport_name):\n _send_request = transport._send_request\n if _send_request:\n\n def patched_send_request(*args, **kwargs):\n metrics.incr(f\"internal.sent_requests.{transport_name}.events\")\n return _send_request(*args, **kwargs)\n\n transport._send_request = patched_send_request\n return transport\n\n\ndef configure_sdk():\n from sentry_sdk.integrations.celery import CeleryIntegration\n from sentry_sdk.integrations.django import DjangoIntegration\n from sentry_sdk.integrations.logging import LoggingIntegration\n from sentry_sdk.integrations.redis import RedisIntegration\n from sentry_sdk.integrations.threading import ThreadingIntegration\n\n assert sentry_sdk.Hub.main.client is None\n\n sdk_options = dict(settings.SENTRY_SDK_CONFIG)\n\n relay_dsn = sdk_options.pop(\"relay_dsn\", None)\n internal_project_key = get_project_key()\n upstream_dsn = sdk_options.pop(\"dsn\", None)\n sdk_options[\"traces_sampler\"] = traces_sampler\n sdk_options[\"release\"] = (\n f\"backend@{sdk_options['release']}\" if \"release\" in sdk_options else None\n )\n sdk_options[\"send_client_reports\"] = True\n\n if upstream_dsn:\n transport = make_transport(get_options(dsn=upstream_dsn, **sdk_options))\n upstream_transport = patch_transport_for_instrumentation(transport, \"upstream\")\n else:\n upstream_transport = None\n\n if relay_dsn:\n transport = make_transport(get_options(dsn=relay_dsn, **sdk_options))\n relay_transport = patch_transport_for_instrumentation(transport, \"relay\")\n elif settings.IS_DEV and not settings.SENTRY_USE_RELAY:\n relay_transport = None\n elif internal_project_key and internal_project_key.dsn_private:\n transport = make_transport(get_options(dsn=internal_project_key.dsn_private, **sdk_options))\n relay_transport = patch_transport_for_instrumentation(transport, \"relay\")\n else:\n relay_transport = None\n\n class MultiplexingTransport(sentry_sdk.transport.Transport):\n def capture_envelope(self, envelope):\n # Temporarily capture envelope counts to compare to ingested\n # transactions.\n metrics.incr(\"internal.captured.events.envelopes\")\n transaction = envelope.get_transaction_event()\n\n if transaction:\n metrics.incr(\"internal.captured.events.transactions\")\n\n # Assume only transactions get sent via envelopes\n if options.get(\"transaction-events.force-disable-internal-project\"):\n return\n\n self._capture_anything(\"capture_envelope\", envelope)\n\n def capture_event(self, event):\n if event.get(\"type\") == \"transaction\" and options.get(\n \"transaction-events.force-disable-internal-project\"\n ):\n return\n\n self._capture_anything(\"capture_event\", event)\n\n def _capture_anything(self, method_name, *args, **kwargs):\n\n # Upstream should get the event first because it is most isolated from\n # the this sentry installation.\n if upstream_transport:\n metrics.incr(\"internal.captured.events.upstream\")\n # TODO(mattrobenolt): Bring this back safely.\n # from sentry import options\n # install_id = options.get('sentry:install-id')\n # if install_id:\n # event.setdefault('tags', {})['install-id'] = install_id\n getattr(upstream_transport, method_name)(*args, **kwargs)\n\n if relay_transport and options.get(\"store.use-relay-dsn-sample-rate\") == 1:\n # If this is a envelope ensure envelope and it's items are distinct references\n if method_name == \"capture_envelope\":\n args_list = list(args)\n envelope = args_list[0]\n relay_envelope = copy.copy(envelope)\n relay_envelope.items = envelope.items.copy()\n args = [relay_envelope, *args_list[1:]]\n\n if is_current_event_safe():\n metrics.incr(\"internal.captured.events.relay\")\n getattr(relay_transport, method_name)(*args, **kwargs)\n else:\n metrics.incr(\n \"internal.uncaptured.events.relay\",\n skip_internal=False,\n tags={\"reason\": \"unsafe\"},\n )\n\n sentry_sdk.init(\n transport=MultiplexingTransport(),\n integrations=[\n DjangoAtomicIntegration(),\n DjangoIntegration(),\n CeleryIntegration(),\n LoggingIntegration(event_level=None),\n RustInfoIntegration(),\n RedisIntegration(),\n ThreadingIntegration(propagate_hub=True),\n ],\n **sdk_options,\n )\n\n\nclass RavenShim:\n \"\"\"Wrapper around sentry-sdk in case people are writing their own\n integrations that rely on this being here.\"\"\"\n\n def captureException(self, exc_info=None, **kwargs):\n with sentry_sdk.push_scope() as scope:\n self._kwargs_into_scope(scope, **kwargs)\n return capture_exception(exc_info)\n\n def captureMessage(self, msg, **kwargs):\n with sentry_sdk.push_scope() as scope:\n self._kwargs_into_scope(scope, **kwargs)\n return capture_message(msg)\n\n def tags_context(self, tags):\n with sentry_sdk.configure_scope() as scope:\n for k, v in tags.items():\n scope.set_tag(k, v)\n\n def _kwargs_into_scope(self, scope, extra=None, tags=None, fingerprint=None, request=None):\n for key, value in extra.items() if extra else ():\n scope.set_extra(key, value)\n for key, value in tags.items() if tags else ():\n scope.set_tag(key, value)\n if fingerprint is not None:\n scope.fingerprint = fingerprint\n\n\ndef bind_organization_context(organization):\n helper = settings.SENTRY_ORGANIZATION_CONTEXT_HELPER\n\n # XXX(dcramer): this is duplicated in organizationContext.jsx on the frontend\n with sentry_sdk.configure_scope() as scope:\n scope.set_tag(\"organization\", organization.id)\n scope.set_tag(\"organization.slug\", organization.slug)\n scope.set_context(\"organization\", {\"id\": organization.id, \"slug\": organization.slug})\n if helper:\n try:\n helper(scope=scope, organization=organization)\n except Exception:\n sdk_logger.exception(\n \"internal-error.organization-context\",\n extra={\"organization_id\": organization.id},\n )\n","repo_name":"gms-ws-sandbox/sentry","sub_path":"src/sentry/utils/sdk.py","file_name":"sdk.py","file_ext":"py","file_size_in_byte":15777,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"} +{"seq_id":"72394450804","text":"from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget ,QPushButton, QGroupBox , QHBoxLayout ,QVBoxLayout\nimport sys\n\nclass window(QWidget):\n def __init__(self):\n super(window,self).__init__()\n self.title = \"any application\"\n self.top = 12\n self.left = 300\n self.mainwindow()\n self.layout_alignment()\n vbox = QVBoxLayout()\n vbox.addWidget(self.groupbox)\n self.setLayout(vbox)\n self.show()\n\n def mainwindow(self):\n self.setWindowTitle(self.title)\n qw = QWidget()\n self.setGeometry(self.top,self.left,qw.maximumWidth(),qw.maximumHeight())\n\n\n def layout_alignment(self):\n self.groupbox = QGroupBox(\"Select a prefered Sports:\")\n self.groupbox.setGeometry(100,100,100,100)\n #widget = QWidget()\n layout = QHBoxLayout()\n \n button1 = QPushButton(\"Hockey\",self)\n layout.addWidget(button1)\n button2 = QPushButton(\"Cricket\",self)\n layout.addWidget(button2)\n button3 = QPushButton(\"Football\",self)\n layout.addWidget(button3)\n\n self.groupbox.setLayout(layout)\n\n\n\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n window = window()\n sys.exit(app.exec_())\n","repo_name":"parmveersingh/Machine-Learning","sub_path":"01.Python/Application/pyqt/layout.py","file_name":"layout.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"5480796331","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Apr 6 23:03:06 2019\r\n\r\n@author: IIST\r\n\"\"\"\r\nimport numpy as np\r\nimport csv\r\nimport cvxopt\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn.model_selection import train_test_split\r\n\r\ndef create_kernel_matrix(kernel_val,X,Y):\r\n if(kernel_val=='L'):\r\n k=X@Y.T\r\n elif(kernel_val=='P'):\r\n k=np.power(X@Y.T,2)\r\n elif(kernel_val=='E'):\r\n sigma =100\r\n k= np.exp((-1/(2*sigma**2))*np.linalg.norm(X[:,None]-Y, axis=2))\r\n elif(kernel_val=='H'):\r\n c=10\r\n k = 1/np.sqrt(np.linalg.norm(X[:,None]-Y, axis=2)+c)\r\n return k\r\n \r\ndef fit2(X,y,K,c):\r\n alphas=np.random.randn(X.shape[0])\r\n etas2=np.zeros(X.shape[0])\r\n alpha_new=np.zeros(X.shape[0])\r\n etas=np.diagonal(K)\r\n etas=1/etas\r\n err=1\r\n itere=0\r\n while itere<20:\r\n itere=itere+1\r\n for i in range(X.shape[0]): \r\n alph_y=np.multiply(alphas,y)\r\n temp=np.dot(alph_y,K[i])\r\n #print(temp)\r\n alpha_new[i]=alphas[i]+(etas[i]*(1-(y[i]*temp)))\r\n print(err)\r\n err=np.linalg.norm(alpha_new-alphas)\r\n alphas=np.copy(alpha_new)\r\n alphas[alphas<0]=0\r\n alphas[alphas>c]=c\r\n return alphas\r\n\r\ndef getdata1():\r\n reader = csv.reader(open(\"data6.csv\", \"rt\"), delimiter=\",\")\r\n x = list(reader)\r\n H = np.array(x).astype(\"float\")\r\n X=np.delete(H,-1,axis=1)\r\n y=np.take(H, -1, axis=1)\r\n for e in range(-5,1,2):\r\n c=2**e\r\n c=10**3\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, stratify=y,random_state=42)\r\n #Creation of inital Kernel Matrix\r\n K=create_kernel_matrix('L',X_train,X_train) \r\n #Get Alphas.\r\n alphas=fit2(X_train,y_train,K,c)\r\n y_train=y_train.reshape(y_train.shape[0],1)\r\n alpha_y= alphas*y_train\r\n f_train=np.dot(alpha_y.T,K)\r\n diff=y_train-f_train.T\r\n cnt = ((alphas >0) & (alphas int:\n n = len(strs[0])\n leng = len(strs)\n dp = [1 for _ in range(n)]\n res = 1\n for i in range(1, n):\n for j in range(i):\n judge = True\n for k in range(leng):\n if strs[k][j] > strs[k][i]:\n judge = False\n if judge:\n dp[i] = max(dp[i], dp[j]+1)\n res = max(res, dp[i])\n return n - res\n","repo_name":"jiangruofan/algorithm","sub_path":"960. Delete Columns to Make Sorted III.py","file_name":"960. Delete Columns to Make Sorted III.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"40029262567","text":"class Matrix:\n def __init__(self, list1):\n self.list1 = list1\n # self.list2 = list2\n\n def __str__(self):\n for i in range(len(self.list1)):\n print(*self.list1[i])\n return '' # костыль от NoneType ошибки =(\n\n def __add__(self, other):\n a = []\n b = []\n for i in range(len(self.list1)):\n for j in range(len(self.list1[i])):\n a += ([self.list1[i][j] + other.list1[i][j]])\n b.append(a)\n a = []\n for i in b:\n print(*i)\n return '' # костыль от NoneType ошибки =( кажется это дич, подскажите как сделать по-другому!\n\n\ntest1 = Matrix([[31, 22], [37, 43], [51, 86]])\ntest2 = Matrix([[3, 5], [2,4], [1 , 64]])\nprint(test1)\nprint(test2)\nprint(test1 + test2)\nprint('qwe')\n\n","repo_name":"Glebsan/GeekBrains_Python","sub_path":"Lesson7/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"27908273140","text":"# r914Test.py\r\n#\r\n# author: Alex Erf, Airspace, alex.erf@airspace.co\r\n# date created: 8/17/2018\r\n\r\nfrom CRAMmsg.unusedCRAMmsg.r914ProtectedAssetPolygon import r914ProtectedAssetPolygon\r\nfrom CRAMmsg.ElementUInt32 import ElementUInt32\r\n\r\nfrom Utilities.TimeUtils import millisSinceMidnight\r\nfrom Utilities.BytesToMessage import getMessageFromBytes\r\nfrom Utilities.JSONToMessage import getMessageFromJSON\r\n\r\nMSG_LENGTH = 76\r\nMSG_ID = 914\r\nKIND = 0\r\nPART_COUNT = 4\r\n\r\nASSET_ID = 0x9281\r\nNO_FIRE_ZONE = 0x01\r\nASSET_PRIORITY = 0x08\r\n# uint8 spare\r\nDELETE_FLAG = 0x01\r\n# uint16 spare\r\n# uint32 spare\r\n# uint32 spare\r\n# uint32 spare\r\n# uint32 spare\r\nASSET_HEIGHT = 0xE2771283\r\nASSET_ALT = -10 # 0xFFFFFFF6\r\nASSET_LATS = [-200000, 100000, 50, -73981] # [0xFFFCF2C0, 0x000186A0, 0x00000032, 0xFFFEDF03]\r\nASSET_LONGS = [80100000, -679000, -111111, -7321018] # [0x04C63AA0, 0xFFF5A3A8, 0xFFFE4DF9, 0xFF904A46]\r\n\r\n\r\nFAKE_TIME = 0xCCCCCCCC\r\n\r\nBYTES = bytearray([0x00, 0x00, 0x00, 0x4C, 0x03, 0x92, 0x00, 0x04, 0xCC, 0xCC, 0xCC, 0xCC,\r\n 0x92, 0x81, 0x01, 0x08, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\r\n 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\r\n 0xE2, 0x77, 0x12, 0x83, 0xFF, 0xFF, 0xFF, 0xF6, 0xFF, 0xFC, 0xF2, 0xC0,\r\n 0x04, 0xC6, 0x3A, 0xA0, 0x00, 0x01, 0x86, 0xA0, 0xFF, 0xF5, 0xA3, 0xA8,\r\n 0x00, 0x00, 0x00, 0x32, 0xFF, 0xFE, 0x4D, 0xF9, 0xFF, 0xFE, 0xDF, 0x03,\r\n 0xFF, 0x90, 0x4A, 0x46])\r\n\r\n\r\ndef run914Test():\r\n test914JSONSimple()\r\n test914FromBytes()\r\n test914ToBytes()\r\n print('914 Protected Asset Polygon: PASS')\r\n\r\n\r\ndef test914JSONSimple():\r\n msg = r914ProtectedAssetPolygon(ASSET_ID, NO_FIRE_ZONE, ASSET_PRIORITY, DELETE_FLAG, ASSET_HEIGHT, ASSET_ALT,\r\n ASSET_LATS, ASSET_LONGS)\r\n str1 = msg.toJSON()\r\n msg_copy = getMessageFromJSON(str1)\r\n assert msg == msg_copy, 'JSON failed: r914'\r\n\r\n\r\ndef test914ToBytes():\r\n msg = r914ProtectedAssetPolygon(ASSET_ID, NO_FIRE_ZONE, ASSET_PRIORITY, DELETE_FLAG, ASSET_HEIGHT, ASSET_ALT,\r\n ASSET_LATS, ASSET_LONGS)\r\n assert msg.header.transmitTime.data + 5000 > millisSinceMidnight(), 'Transmit time way off (>5 seconds): r914 toBytes'\r\n msg.header.transmitTime = ElementUInt32(FAKE_TIME)\r\n assert msg.getByteArray() == BYTES, 'Byte array failed: r914 toBytes'\r\n\r\n\r\ndef test914FromBytes():\r\n msg = getMessageFromBytes(BYTES)\r\n assert msg.header.messageLength.data == MSG_LENGTH, 'Message Length wrong: r914 fromBytes'\r\n assert msg.header.messageId.data == MSG_ID, 'Message ID wrong: r914 fromBytes'\r\n assert msg.header.interfaceKind.data == KIND, 'Interface Kind wrong: r914 fromBytes'\r\n assert msg.header.partCount.data == PART_COUNT, 'Part Count wrong: r914 fromBytes'\r\n assert msg.header.transmitTime.data == FAKE_TIME, 'Transmit Time wrong: r914 fromBytes'\r\n\r\n assert msg.assetId.data == ASSET_ID, 'Asset ID wrong: r914 fromBytes'\r\n assert msg.noFireZone.data == NO_FIRE_ZONE, 'No Fire Zone wrong: r914 fromBytes'\r\n assert msg.assetPriority.data == ASSET_PRIORITY, 'Asset Priority wrong: r914 fromBytes'\r\n assert msg.deleteFlag.data == DELETE_FLAG, 'Delete Flag wrong: r914 fromBytes'\r\n assert msg.assetHeight.data == ASSET_HEIGHT, 'Asset Height wrong: r914 fromBytes'\r\n assert msg.assetAlt.data == ASSET_ALT, 'Asset Altitude wrong: r914 fromBytes'\r\n \r\n assert msg.assetLats[0].data == ASSET_LATS[0], 'Asset Latitude 0 wrong: r914 fromBytes'\r\n assert msg.assetLongs[0].data == ASSET_LONGS[0], 'Asset Longitude 0 wrong: r914 fromBytes'\r\n assert msg.assetLats[1].data == ASSET_LATS[1], 'Asset Latitude 1 wrong: r914 fromBytes'\r\n assert msg.assetLongs[1].data == ASSET_LONGS[1], 'Asset Longitude 1 wrong: r914 fromBytes'\r\n assert msg.assetLats[2].data == ASSET_LATS[2], 'Asset Latitude 2 wrong: r914 fromBytes'\r\n assert msg.assetLongs[2].data == ASSET_LONGS[2], 'Asset Longitude 2 wrong: r914 fromBytes'\r\n assert msg.assetLats[3].data == ASSET_LATS[3], 'Asset Latitude 3 wrong: r914 fromBytes'\r\n assert msg.assetLongs[3].data == ASSET_LONGS[3], 'Asset Longitude 3 wrong: r914 fromBytes'\r\n","repo_name":"NTierSoftware/GSCRAM","sub_path":"Testing/ElementUnitTests/r914Test.py","file_name":"r914Test.py","file_ext":"py","file_size_in_byte":4243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"71814453686","text":"#CICLOS Y CONDICIONES\n\n#IF\n\nresultado = 10\n\nresultado = resultado > 10\n\nif resultado:\n print('La variable cumple con la condicion')\n\n# ELSE\n\"\"\"\nresultado = 50\n\nif resultado > 10 and resultado < 20:\n print('La variable cumple con la condicion')\nelse:\n print('No se cumplio')\n\n\"\"\"\n#ELIF\n\n\"\"\"\ncalificacion = 5\n\nif calificacion == 10: #bloque debe ir con 4 espacios segun la comunidad de pyth\n print('Felicidades aprobaste la materia con una calificacion perfecta')\nelif calificacion == 9 or calificacion == 8:\n print('Felicidades aprobaste la materia')\nelif calificacion == 7 or calificacion == 6:\n print('Aprobaste la materia')\nelse: #En caso contrario\n print('Reprobaste la materia')\n\"\"\"","repo_name":"fullmakeralchemist/SCI","sub_path":"Python Codes/excercise21_.py","file_name":"excercise21_.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"10190354040","text":"from uplogic.nodes import ULActionNode\nfrom uplogic.nodes import ULOutSocket\nfrom uplogic.utils import is_waiting\nfrom uplogic.utils import is_invalid\nfrom uplogic.utils import not_met\n\n\nclass ULApplyForce(ULActionNode):\n def __init__(self):\n ULActionNode.__init__(self)\n self.condition = None\n self.game_object = None\n self.force = None\n self.done = None\n self.OUT = ULOutSocket(self, self.get_done)\n\n def get_done(self):\n return self.done\n\n def evaluate(self):\n self.done = False\n condition = self.get_input(self.condition)\n if not_met(condition):\n return\n game_object = self.get_input(self.game_object)\n force = self.get_input(self.force)\n local = self.local\n if is_waiting(force):\n return\n if is_invalid(game_object):\n return\n self._set_ready()\n game_object.applyForce(force, local)\n self.done = True\n","repo_name":"AlexandreMuller/UPBGE-uplogic","sub_path":"uplogic/nodes/actions/applyforce.py","file_name":"applyforce.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"76"} +{"seq_id":"907581119","text":"# Задача 14: Требуется вывести все целые степени двойки (т.е. числа\n# вида 2k), не превосходящие числа N.\n# 10 -> 1 2 4 8\n\nn = int(input('Введите число N: '))\noutput_list_num = []\nnum = 2\nfor i in range(n):\n if num ** i <= n:\n output_list_num.append(num ** i)\n else:\n break\noutput_str_num = [str(s) for s in output_list_num]\nprint()\nprint(f'{n} -> {\" \".join(output_str_num)}')","repo_name":"isscom/python","sub_path":"python2.0/lesson2/task14.py","file_name":"task14.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"43718352697","text":"# 加上缓存\n\nimport re\nimport pylru\n\nclass LRUCache(object):\n def __init__(self, size=32):\n self.cache = pylru.lrucache(size)\n\n def has(self, key):\n return key in self.cache\n\n def get(self, key):\n return self.cache[key]\n\n def set(self, key, value):\n self.cache[key] = value\n\n\nclass SearchEngineBase(object):\n def __init__(self):\n pass\n\n def add_corpus(self, file_path):\n '''\n 添加语料(正常来说此处功能为爬虫，搜索器，提供语料)\n '''\n with open(file_path, mode='r') as f:\n text = f.read()\n self.process_corpus(file_path, text)\n\n def process_corpus(self, id, text):\n '''\n 索引器(为语料添加索引)\n '''\n raise Exception(u'未实现语料索引')\n\n def search(self, query):\n '''\n 检索器(用户查询关键词)\n '''\n raise Exception(u'未实现检索功能')\n\n\nclass BOWInvertedIndexEngine(SearchEngineBase):\n def __init__(self):\n super().__init__()\n self.inverted_index = {}\n\n def process_corpus(self, id, text):\n words = self.parse_text_to_words(text)\n for word in words:\n if word in self.inverted_index:\n self.inverted_index[word].append(id)\n self.inverted_index[word] = []\n\n def search(self, query):\n query_words = list(self.parse_text_to_words(query))\n query_words_index = []\n for query_word in query_words:\n query_words_index.append(0)\n\n # 如果某个查询单词的倒序索引为空， 就返回\n for query_word in query_words:\n if query_word not in self.inverted_index:\n return []\n\n result = []\n while True:\n # 获得当前状态下所有倒序索引的index\n current_ids = []\n\n for idx, query_word in enumerate(query_words):\n current_index = query_words_index[idx]\n current_inverted_list = self.inverted_index[query_word]\n\n # 已经遍历到某个倒序索引的结尾，结束搜索\n if current_index >= len(current_inverted_list):\n return result\n\n current_ids.append(current_inverted_list[current_index])\n\n # 如果 current_ids 的所有元素都一样，就表明这个单词在这个元素对应的文档中都出现了\n if all(x == current_ids[0] for x in current_ids):\n result.append(current_ids[0])\n query_words_index = [x + 1 for x in query_words_index]\n continue\n # 如果不是，就把最小的元素加一\n min_val = min(current_ids)\n min_val_pos = current_ids.index(min_val)\n query_words_index[min_val_pos] += 1\n\n @staticmethod\n def parse_text_to_words(text):\n # 使用正则去除标点符号和换行符\n text = re.sub(r'[^\\w]', ' ', text)\n # 转为小写\n text = text.lower()\n # 生成所有单词的列表\n word_list = text.split(' ')\n # 去除空白单词\n word_list = filter(None, word_list)\n # 返回单词的 set\n return set(word_list)\n\n\nclass BOWInvertedIndexEngineWithCache(BOWInvertedIndexEngine, LRUCache):\n def __init__(self):\n super(BOWInvertedIndexEngine, self).__init__()\n LRUCache.__init__(self)\n\n def search(self, query):\n if self.has(query):\n print('cache hit')\n return self.get(query)\n\n result = super(BOWInvertedIndexEngineWithCache, self).search(query)\n self.set(query, result)\n\n return result\n\n\ndef main(search_engine):\n for file_path in ['out.txt', 'moby_dict.txt', 'myProgramLog.txt']:\n search_engine.add_corpus(file_path)\n while True:\n query = input()\n results = search_engine.search(query)\n print('找到 {} 个结果'.format(len(results)))\n for res in results:\n print(res)\n\n\nif __name__ == '__main__':\n search_engine = BOWInvertedIndexEngineWithCache()\n main(search_engine)\n","repo_name":"pyl-10/python_scripts","sub_path":"bow_inverted_index_cache_search.py","file_name":"bow_inverted_index_cache_search.py","file_ext":"py","file_size_in_byte":4121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"5992469211","text":"from django.conf import settings\nfrom django.db import models\n\n\nclass MuscleGroup(models.Model):\n name = models.CharField(max_length=100,\n verbose_name=\"Название\")\n\n def __str__(self):\n return self.name\n\n class Meta:\n verbose_name = \"Группа мышц\"\n verbose_name_plural = \"Группы мышц\"\n ordering = ['id']\n\n\nclass Exercise(models.Model):\n name = models.CharField(max_length=255)\n image = models.ImageField(upload_to='workout_tracker/photos/', blank=True,\n null=True, verbose_name='Фото')\n muscle_groups = models.ManyToManyField(MuscleGroup,\n verbose_name=\"Группы мышц\")\n creator = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE,\n default=None,\n blank=True,\n null=True\n )\n\n def __str__(self):\n return self.name\n\n class Meta:\n verbose_name = \"Упражнение\"\n verbose_name_plural = \"Упражнения\"\n ordering = ['id']\n\n\nclass Workout(models.Model):\n time = models.DateField(auto_now_add=True,\n verbose_name=\"День тренировки\")\n user = models.ForeignKey(\n settings.AUTH_USER_MODEL,\n on_delete=models.CASCADE,\n default=None,\n blank=True,\n null=True\n )\n muscle_groups = models.ManyToManyField(MuscleGroup,\n verbose_name=\"Группы мышц\",\n default=None,\n blank=True,\n null=True)\n\n def __str__(self):\n return str(self.time)\n\n class Meta:\n verbose_name = \"Тренировка\"\n verbose_name_plural = \"Тренировки\"\n ordering = ['id']\n\n\nclass Set(models.Model):\n exercise = models.ForeignKey(Exercise,\n on_delete=models.CASCADE)\n workout = models.ForeignKey(Workout,\n on_delete=models.CASCADE)\n weight = models.FloatField(verbose_name=\"Вес\")\n reps = models.IntegerField(verbose_name=\"Повторения\")\n\n def __str__(self):\n return f\"{self.exercise} {self.weight}Кг на {self.reps} повт.\"\n\n class Meta:\n verbose_name = \"Подход\"\n verbose_name_plural = \"Подходы\"\n ordering = ['id']\n\n def get_int_weight(self):\n return int(self.weight)\n\n def get_weight(self):\n if self.weight % 1 == 0:\n return int(self.weight)\n return self.weight\n","repo_name":"Artemoskalenko/self_development","sub_path":"workout_tracker/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"26180020832","text":"import random\nfrom celleClass import Celle\nimport math\nimport MlClass \n\n\nclass Brett:\n\tdef __init__(self, rad, kol, tilfeldig):\n\t\tself._rad = rad\n\t\tself._kol = kol\n\t\tself._score = 0\n\t\tself._slange = [[25, 25], [25, 24]]\n\t\tself._IkkeOK = False\n\t\tself._eple = self.lagEple()\n\t\tself._lengde = 10\n\t\tself._brett = self._lagBrett()\n\t\tself._tilfeldig = tilfeldig\n\t\tself._ML = MlClass.MachinLearning(self._tilfeldig)\n\t\tself._moves = 250\n\n\tdef __str__(self):\n\t\treturn str(self._slange)\n\n\n\tdef _lagBrett(self):\n\t\tbrett = []\n\n\t\t#Lager antall rader som verdien til variablen _rader\n\t\tfor i in range(0, self._rad):\n\t\t\tbrett.append([])\n\t\t\t#Legger til antall \"celle\" objekter som verdien til variablen _kolonner\n\t\t\tfor n in range(0, self._kol):\n\t\t\t\tbrett[i].append(Celle())\n\n\t\tfor celle in brett[0]:\n\t\t\tcelle.settVegg()\n\t\tfor celle in brett[self._rad - 1]:\n\t\t\tcelle.settVegg()\n\t\tfor i in range(0, self._rad):\n\t\t\tbrett[i][0].settVegg()\n\t\t\tbrett[i][self._kol - 1].settVegg()\n\n\n\t\tfor index in range(0, len(self._slange)):\n\t\t\tposX = self._slange[index][0]\n\t\t\tposY = self._slange[index][1]\n\t\t\tif brett[posX][posY].settVegg():\n\t\t\t\tself._IkkeOK = True\n\n\t\tself._score += 1\n\t\tself._moves -= 1\n\t\tif self._moves <= 0:\n\t\t\tself._IkkeOK = True\n\t\tbrett[self._eple[0]][self._eple[1]].settEple()\n\n\t\treturn brett\n\n\n\tdef LagInputs(self):\n\t\tinn = []\n\t\tinn.append(self._brett[self._slange[0][0] + 1][self._slange[0][1]].hentStatus())\n\t\tinn.append(self._brett[self._slange[0][0] - 1][self._slange[0][1]].hentStatus())\n\t\tinn.append(self._brett[self._slange[0][0]][self._slange[0][1] + 1].hentStatus())\n\t\tinn.append(self._brett[self._slange[0][0]][self._slange[0][1] - 1].hentStatus())\n\n\t\tif self.dist(self._slange[0][0], self._slange[0][1]) > self.dist(self._slange[0][0] + 1, self._slange[0][1]):\n\t\t\tinn.append(1)\n\t\telse:\n\t\t\tinn.append(0)\n\n\t\tif self.dist(self._slange[0][0], self._slange[0][1]) > self.dist(self._slange[0][0] - 1, self._slange[0][1]):\n\t\t\tinn.append(1)\n\t\telse:\n\t\t\tinn.append(0)\n\n\t\tif self.dist(self._slange[0][0], self._slange[0][1]) > self.dist(self._slange[0][0], self._slange[0][1] + 1):\n\t\t\tinn.append(1)\n\t\telse:\n\t\t\tinn.append(0)\n\n\t\tif self.dist(self._slange[0][0], self._slange[0][1]) > self.dist(self._slange[0][0], self._slange[0][1] - 1):\n\t\t\tinn.append(1)\n\t\telse:\n\t\t\tinn.append(0)\n\n\t\treturn inn\n\n\n\tdef bevege(self):\n\t\tretning = self._ML.styre(self.LagInputs())\n\t\tif self._ML.getIngen():\n\t\t\tself._score -= 30\n\n\n\t\tif retning == \"ned\":\n\t\t\tpos1 = [self._slange[0][0] + 1, self._slange[0][1]]\n\n\t\telif retning == \"opp\":\n\t\t\tpos1 = [self._slange[0][0] - 1, self._slange[0][1]]\n\n\t\telif retning == \"venstre\":\n\t\t\tpos1 = [self._slange[0][0], self._slange[0][1] - 1]\n\n\t\telif retning == \"hoyre\":\n\t\t\tpos1 = [self._slange[0][0], self._slange[0][1] + 1]\n\n\t\tself._slange.insert(0, pos1)\n\t\tif len(self._slange) > self._lengde:\n\t\t\tself._slange.pop(-1)\n\n\t\tself._brett = self._lagBrett()\n\n\t\tif self.dist(self._slange[0][0], self._slange[0][1]) == 0:\n\t\t\tself._eple = self.lagEple()\n\t\t\tself._lengde += 3\n\n\tdef lagEple(self):\n\t\tpos = [random.randint(1, self._kol - 2), random.randint(1, self._rad - 2)]\n\t\tself._score += 100\n\t\tself._moves = 500\n\t\treturn pos\n\n\tdef dist(self, koX, koY):\n\t\tliste = [self._eple[0] - koX, self._eple[1] - koY]\n\t\treturn math.sqrt((liste[0] ** 2) + liste[1] ** 2)\n\n\n\tdef erIkkeOK(self):\n\t\treturn self._IkkeOK\n\n\tdef tegne(self):\n\t\treturn self._brett\n\n\tdef getBias(self):\n\t\treturn self._ML.getBias()\n\n\tdef getScore(self):\n\t\treturn self._score\n","repo_name":"tobiaslo/Projects","sub_path":"Python_SnakeAI/brettClass.py","file_name":"brettClass.py","file_ext":"py","file_size_in_byte":3428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"4857205750","text":"from data.vision_dataset import VisionDataset\nfrom PIL import Image\nimport os\nimport os.path\nimport torchvision.transforms.functional as F\nimport numpy\nimport random\nimport copy\n\nfrom train.helpers import *\n\n\nclass CocoDetection(VisionDataset):\n \"\"\"`MS Coco Detection `_ Dataset.\n\n Args:\n root (string): Root directory where images are downloaded to.\n annFile (string): Path to json annotation file.\n transform (callable, optional): A function/transform that takes in an PIL image\n and returns a transformed version. E.g, ``transforms.ToTensor``\n target_transform (callable, optional): A function/transform that takes in the\n target and transforms it.\n transforms (callable, optional): A function/transform that takes input sample and its target as entry\n and returns a transformed version.\n\n We are using the COCO API on top of which we build our custom data processing\n \"\"\"\n\n def __init__(self, root, annFile, transform=None, target_transform=None, transforms=None, augmentation=True):\n super().__init__(root, transforms, transform, target_transform)\n from pycocotools.coco import COCO\n self.coco = COCO(annFile)\n self.ids = list(sorted(self.coco.imgs.keys()))\n self.augmentation = augmentation\n\n def __getitem__(self, batched_indices):\n \"\"\"\n return B x C x H x W image tensor and [B x img_bboxes, B x img_classes]\n \"\"\"\n\n imgs, targets_bboxes, targets_classes, image_info = [], [], [], []\n for index in batched_indices:\n coco = self.coco\n img_id = self.ids[index]\n ann_ids = coco.getAnnIds(imgIds=img_id)\n target = coco.loadAnns(ann_ids)\n path = coco.loadImgs(img_id)[0]['file_name']\n img = Image.open(os.path.join(self.root, path)).convert('RGB')\n\n # target[0] = tensor of bboxes of objects in image\n # target[1] = tensor of class ids in image\n target = prepare_gt(img, target)\n\n width, height = img.size\n\n img = F.resize(img, size=(320, 320), interpolation=2)\n if self.augmentation:\n img, target = self.augment_data(img, target)\n\n # C x H x W\n img = F.to_tensor(img)\n img = F.normalize(img, mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n\n imgs.append(img)\n targets_bboxes.append(target[0])\n targets_classes.append(target[1])\n image_info.append((img_id, (height, width)))\n\n # B x C x H x W\n batch_images = torch.stack(imgs)\n\n # batch_targets[0] = list of bboxes tensors for each image\n # batch_targets[1] = list of class id tensors for each image\n batch_targets = [targets_bboxes, targets_classes]\n\n return batch_images, batch_targets, image_info\n\n def __len__(self):\n return len(self.ids)\n\n def augment_data(self, img, target):\n # random flip\n if random.random() > 0.5:\n img = F.hflip(img)\n self.flip_gt_bboxes(target[0])\n\n # color jitter\n img = F.adjust_brightness(img, random.uniform(0.85, 1.15))\n img = F.adjust_contrast(img, random.uniform(0.85, 1.15))\n img = F.adjust_saturation(img, random.uniform(0.85, 1.15))\n img = F.adjust_hue(img, random.uniform(-0.08, 0.08))\n\n return img, target\n\n def flip_gt_bboxes(self, image_bboxes):\n image_bboxes[:, 1] = 1 - image_bboxes[:, 1]\n image_bboxes[:, 3] = 1 - image_bboxes[:, 3]\n\n # don't forget to also swap second and fourth columns to keep format\n temp = copy.deepcopy(image_bboxes[:, 1])\n image_bboxes[:, 1] = image_bboxes[:, 3]\n image_bboxes[:, 3] = temp\n","repo_name":"lauradiosan/MIRPR-2019-2020","sub_path":"StudProjects/team12/data/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":3860,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"75"} +{"seq_id":"5962567173","text":"import math\n\nx = int(input(\"entre número entero \"))\nif x % 2 == 0:\n print(x, \"No es primo, es numero par\")\n exit(0)\n\ni = 2\nfor i in range(3, int(x**(.5))+1, 2):\n print(x, \"NO es primo, es divisible por\", i)\n break\nif x % i !=0: \n print(x, \"es primo\")\n\n\n \n","repo_name":"rCristianH/python-edu","sub_path":"Finalizados/Programas/Primo.py","file_name":"Primo.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"28660746746","text":"def pancakes(s):\n\tif s==\"--\" or s == \"-\" or s==\"-+\":\n\t\treturn 1\n\telif s == \"++\" or s==\"+\" or s==\"\":\n\t\treturn 0\n\telif s== \"+-\":\n\t\treturn 2\n\telse:\n\t\ts = list(s)\n\t\ts = s[::-1]\n\t\tindex = 0\n\t\tflag = 0\n\t\tfor i in s:\n\t\t\tif i == \"-\":\n\t\t\t\tflag = 1\n\t\t\t\tindex = s.index(i)\n\t\t\t\twhile index < len(s):\n\t\t\t\t\tif s[index]==\"-\":\n\t\t\t\t\t\tindex+=1\n\t\t\t\t\telse:\n\t\t\t\t\t\tbreak\n\t\t\t\tbreak\n\t\tif flag == 1:\n\t\t\tq = []\n\t\t\tfor i in s[index:]:\n\t\t\t\tif i==\"+\":\n\t\t\t\t\tq.append(\"-\")\n\t\t\t\telse:\n\t\t\t\t\tq.append(\"+\")\n\t\t\tq=\"\".join(q[::-1])\n\t\t\treturn pancakes(q)+1\n\t\telse:\n\t\t\treturn 0\nwith open(\"B-large.in\",'r') as f:\n\to = open('B-large.txt','a')\n\ttest_cases = []\n\tfor line in f:\n\t\tline = line.replace(\"\\n\",\"\")\n\t\ttest_cases.append(line)\n\tfor t in xrange(1,int(test_cases[0])+1):\n\t\ts = test_cases[t]\n\t\to.write(\"Case #\"+str(t)+\": \"+str(pancakes(s))+\"\\n\")\n","repo_name":"DaHuO/Supergraph","sub_path":"codes/CodeJamCrawler/16_0_2_neat/16_0_2_gopesht_pancakes.py","file_name":"16_0_2_gopesht_pancakes.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"39231233633","text":"import sqlite3\n\nconexion = sqlite3.connect(\"restaurante.db\")\ncursor = conexion.cursor()\n\n\nclass Pedido:\n def __init__(self):\n self.pedidos = {}\n self.contador_pedido = 1\n\n def crear_pedido(self, cliente, producto, precio):\n self.pedidos[self.contador_pedido] = {\n 'cliente': cliente,\n 'producto': producto,\n 'precio': precio\n }\n self.contador_pedido += 1\n\n cursor.execute(\"INSERT INTO Pedido (cliente, producto, precio) VALUES (?, ?, ?)\", (cliente, producto, precio))\n conexion.commit()\n\n #Agregamos el codigo para simular la impresion de un ticket en un archivo \n with open(\"ticket.txt\", \"a\") as ticket_file:\n ticket_file.write(f\"Cliente: {cliente}\\nProducto: {producto}\\nPrecio: ${precio:.2f}\\n\\n\")\n\n def cancelar_pedido(self, numero_pedido):\n if numero_pedido in self.pedidos:\n del self.pedidos[numero_pedido]\n# docstring\n\"\"\"\nMódulo y Clase de Pedido\n\nEsta clase permite gestionar la información de los pedidos realizados.\n\nFunciones:\n- crear_pedido(cliente, producto, precio)\n- cancelar_pedido(numero_pedido)\n\"\"\"\n\n#Aqui insertamos el codigo de nuestra base de datos\ncursor.execute('''\nCREATE TABLE IF NOT EXISTS Pedido (\n pedido INTEGER PRIMARY KEY,\n cliente TEXT,\n producto TEXT,\n precio REAL,\n FOREIGN KEY (cliente) REFERENCES clientes(clave),\n FOREIGN KEY (producto) REFERENCES Menu(clave) \n)\n''')\nconexion.commit()","repo_name":"felipelugo1988/proyecto-final-python","sub_path":"pedido.py","file_name":"pedido.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"36294541882","text":"\"\"\"\nName: company_complaints.py\nAuthor: Ari Bernstein\nDescription: Prints contents of instances of complaint classes in a clear, concise, and pretty way.\nPre-conditions, utilities.py works correctly and is the in same directory, csv files are in\nsubdirectory labeled 'data'\n\"\"\"\n\nfrom utilities import *\n\ndef eightSpace(slotName, value):\n \"\"\"\n Helper function print slot's name, indent first line of each slot's value, and print UNKNOWN if slot is empty\n :param slotName: Name of slot as string eg.Company, Product, State, etc.\n :param value: value of complaint slot\n :return: Not applicable\n \"\"\"\n if value is not \"\":\n\n return slotName + \"\\n \" + value\n else:\n return slotName + \" UNKNOWN\"\n\ndef returnClassFromID(dictionary, id):\n \"\"\"\n Helper function to return Complaint class instance given its ID\n :param dictionary: (returned from read_complaint_data in utilities.py) a dictionary whose keys are complaint IDs and\n values are the associated instance of the complaint class\n\n :param id: Complaint ID of a given Complaint Class as string or int\n\n :return:\n \"\"\"\n if id == \"\":\n print(\"Complaint ID field is empty.\")\n return None\n else:\n id = int(id)\n for key in dictionary:\n if key == id:\n return dictionary[key]\n print(str(id) + \" is not in dataset.\")\n\n\ndef formatChars(slot):\n \"\"\"\n Formats long lines so that they stack every 67 characters\n :param slot: String from the value of a slot in an instance of complaint class\n :return: string formatted so that it has a newLine character every 67 characters\n \"\"\"\n if slot is not '':\n slot = slot.split()\n line = ''\n fullString = ''\n for i in slot:\n if len(line) + len(i) < 66:\n line += i + ' '\n else:\n fullString += line + '\\n' + (' ' * 8)\n line = ''\n fullString += line + (' ' * 8)\n return fullString\n else:\n return slot\n\n\n\n\ndef display_complaint(complaint):\n \"\"\"\n\n :param complaint: instance of Complaint class\n :return: Not Applicable\n printed output: All data from an instance of the complaint class in a pretty manner\n \"\"\"\n if complaint is not None:\n print(eightSpace(\"Date_received :\", complaint.Date_received))\n print(eightSpace(\"Product :\", formatChars(complaint.Product)))\n print(eightSpace(\"Sub_product :\", formatChars(complaint.Sub_product)))\n print(eightSpace(\"Issue :\", formatChars(complaint.Issue)))\n print(eightSpace(\"Sub_issue :\", formatChars(complaint.Sub_issue)))\n print(eightSpace(\"Consumer_complaint_narrative :\", formatChars(complaint.Consumer_complaint_narrative)))\n print(eightSpace(\"Company_public_response :\", formatChars(complaint.Company_public_response)))\n print(eightSpace(\"Company :\", formatChars(complaint.Company)))\n print(eightSpace(\"State :\", complaint.State))\n print(eightSpace(\"ZIP_code :\", complaint.ZIP_code))\n print(eightSpace(\"Tags :\", formatChars(complaint.Tags)))\n print(eightSpace(\"Consumer_consent_provided :\", complaint.Consumer_consent_provided))\n print(eightSpace(\"Submitted_via :\", formatChars(complaint.Submitted_via)))\n print(eightSpace(\"Date_sent_to_company :\", complaint.Date_sent_to_company))\n print(eightSpace(\"Company_response_to_consumer :\", formatChars(complaint.Company_response_to_consumer)))\n print(eightSpace(\"Timely_response :\", complaint.Timely_response))\n print(eightSpace(\"Consumer_disputed :\", complaint.Consumer_disputed))\n print(eightSpace(\"Complaint_ID :\", complaint.Complaint_ID))\n\n elif complaint is None:\n return\n else:\n print(str(complaint) + \" not in dataset\")\n\n\ndef main():\n complaintIDList = []\n filePath = \"./data/\" + input(\"Enter CSV file name: \")\n complaints = read_complaint_data(filePath)\n\n id = input(\"Enter a Complaint_ID (e.g. 13002) or press ENTER key to stop: \")\n complaintIDList.append(id)\n while id is not \"\":\n id = input(\"Enter a Complaint_ID (e.g. 13002) or press ENTER key to stop: \")\n if id is not \"\":\n complaintIDList.append(id)\n print(\"===================================================================\")\n\n\n for i in complaintIDList:\n complaintInstance = returnClassFromID(complaints, i)\n display_complaint(complaintInstance)\n print(\"===================================================================\")\n\nif __name__ == '__main__':\n main()\n","repo_name":"AriBernstein/CSV_Complaint_Parser","sub_path":"display_complaints.py","file_name":"display_complaints.py","file_ext":"py","file_size_in_byte":4582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"40853198653","text":"import setuptools\n\n\ndef def_requirements():\n \"\"\" Check PIP Requirements \"\"\"\n with open('requirements.txt') as f:\n pip_lines = f.read().splitlines()\n return pip_lines\n\n\ndef def_readme():\n \"\"\" Check Readme RST \"\"\"\n readme = ''\n with open('README.rst') as f:\n readme = f.read()\n return readme\n\n\ndef def_short():\n \"\"\" Check Readme MDL \"\"\"\n readme = ''\n with open('README.md') as f:\n readme = f.read()\n return readme\n\n\nsetuptools.setup(\n name=\"lichesspy\",\n version=\"0.0.4\",\n author=\"Zeyecx\",\n author_email=\"zeyecx@gmail.com\",\n description=\"Python wrapper for lichess\",\n long_description=def_readme(),\n long_description_content_type=\"text/markdown\",\n url=\"https://github.com/Zeyecx/lichesspy\",\n packages=['lichesspy'],\n package_data={\"lichesspy\": [\"VERSION\"]},\n include_package_data=True,\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n \"Operating System :: OS Independent\",\n ],\n python_requires=\">=3.8\",\n install_requires=def_requirements(),\n)\n","repo_name":"Donbur4156/lichesspy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"10139389434","text":"#\n# Views for issueing announcements to all active members.\n#\nimport smtplib\nfrom datetime import date\n\nfrom django.shortcuts import render\nfrom django.conf import settings\nfrom django.contrib.admin.views.decorators import staff_member_required\nimport django.forms as forms\nfrom django.core.mail import send_mail\n\nfrom members.models import get_active_members, ContactInfo\n\n\nclass AnnouncementForm(forms.Form):\n subject = forms.CharField(required=True, label=\"Thema\", max_length=40)\n body = forms.CharField(required=True, label=\"Mitteilung\",\n widget=forms.Textarea)\n to = forms.ChoiceField(required=True, label=\"An\",\n choices=(('collection', 'collection'), ('all', 'all'),))\n\n\n@staff_member_required\ndef announce(request):\n form = AnnouncementForm(request.POST or None)\n if not request.POST or not form.is_valid():\n context = {'form': form, 'user': request.user}\n return render(request, 'announce/write_message.html', context)\n # Valid message: send it!\n users = get_active_members()\n if form.cleaned_data['to'] != 'all':\n users = users.filter(paymentinfo__bank_collection_allowed=True)\\\n .filter(paymentinfo__bank_collection_mode__id=4)\n for u in users:\n debt = u.contactinfo.get_debt_for_month(date.today())\n if debt == 0:\n users = users.exclude(pk=u.pk)\n\n for user in users:\n ci = ContactInfo.objects.get(user=user)\n try:\n send_mail(form.cleaned_data['subject'],\n form.cleaned_data['body'],\n settings.HOS_ANNOUNCE_FROM,\n [user.email],\n fail_silently=False)\n ci.last_email_ok = True\n ci.save()\n except smtplib.SMTPException as e:\n f = open(settings.HOS_ANNOUNCE_LOG, 'a')\n f.write('\\n\\n'+user.email)\n f.write('\\n'+repr(e))\n ci.last_email_ok = False\n ci.save()\n f.close()\n\n context = {'form': form, 'user': request.user, 'users': users}\n return render(request, 'announce/message_sent.html', context)\n","repo_name":"hop/mos","sub_path":"announce/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"30324915401","text":"def solution(n, p, q):\n prime_check = [0] * 2 + [1] * (n - 1)\n i = 2\n while i * i <= n:\n if prime_check[i] == 1:\n j = 2\n while i * j < n + 1:\n prime_check[i * j] = 0\n j += 1\n i += 1\n\n semiprime_check = [0] * (n + 1)\n i = 2\n while i < n + 1:\n if prime_check[i] == 0:\n i += 1\n continue\n j = i\n while j < n + 1:\n if prime_check[j] == 0:\n j += 1\n continue\n elif i * j > n:\n break\n else:\n semiprime_check[i * j] = 1\n j += 1\n i += 1\n\n prefix_count = [0] * (n + 1)\n i = 0\n cnt = 0\n while i < n + 1:\n cnt += semiprime_check[i]\n prefix_count[i] = cnt\n i += 1\n\n m = len(p)\n ret = []\n for i in xrange(m):\n ret.append(prefix_count[q[i]] - prefix_count[p[i]-1])\n return ret\n","repo_name":"limitDM/codility","sub_path":"11_sieve_of_eratosthenes/count_semiprimes.py","file_name":"count_semiprimes.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"13624937946","text":"#!/usr/bin/env python\nfrom web3 import Web3, IPCProvider, WebsocketProvider\nfrom web3.middleware import geth_poa_middleware\n\nimport json\nimport logging\nfrom aux import print_table, mapping_id_keys, l2d\n\nlogging.basicConfig(format='[%(levelname)s %(name)s] %(message)s')\nlogger = logging.getLogger(__name__)\n\n\ndef init_web3(__ip=None):\n\n w3 = None\n\n if __ip:\n provider = WebsocketProvider('ws://'+__ip+':8545')\n else:\n provider = IPCProvider('/root/.ethereum/devchain/geth.ipc')\n\n w3 = Web3(provider)\n w3.provider = provider\n w3.middleware_onion.inject(geth_poa_middleware, layer=0)\n w3.geth.personal.unlockAccount(w3.eth.coinbase, \"\", 0)\n w3.eth.defaultAccount = w3.eth.coinbase\n w3.key = w3.eth.coinbase\n w3.enode = w3.geth.admin.nodeInfo().enode\n\n logger.info('VERSION: %s', w3.clientVersion)\n logger.info('ADDRESS: %s', w3.key)\n logger.info('ENODE: %s', w3.enode)\n\n return w3\n\n\ndef registerSC(w3):\n sc = None\n\n abiPath = '/root/contracts/deploy.abi'\n abi = json.loads(open(abiPath).read())\n addressPath = '/root/contracts/contractAddress.txt'\n address = '0x' + open(addressPath).read().rstrip()\n\n sc = w3.eth.contract(abi=abi, address=address)\n return sc\n\n\ndef call(show_points=True, raw=False):\n block = w3.eth.getBlock('latest')\n points = getPoints()\n clusters = getClusters()\n balance, usable = getBalance(points, clusters)\n unclustered_points = []\n\n if not raw:\n for point in points:\n if point['cluster'] >= 0:\n p1 = point['position']\n p2 = clusters[point['cluster']]['position']\n # point['RME'] = round(colourBGRDistance(p1,p2)/1e5, 2)\n # point['MAN'] = round(manhattan_distance(p1,p2)/1e5, 2)\n # point['CHS'] = round(chebyshev_distance(p1,p2)/1e5, 2)\n point['position'] = [round(i/1e5) for i in point['position']]\n point['credit'] //= 1e16\n # point['sender'] = point['sender'][0:5]\n point['sender'] = key_to_id[point['sender'].lower()]\n\n for idx, cluster in enumerate(clusters):\n del cluster['life']\n cluster['outlier_senders'] = len(cluster['outlier_senders'])\n cluster['position'] = [round(i/1e5) for i in cluster['position']]\n cluster['sup_position'] = [round(i/1e5)\n for i in cluster['sup_position']]\n cluster['total_credit'] //= 1e16\n cluster['total_credit_food'] //= 1e16\n cluster['total_credit_outlier'] //= 1e16\n # cluster['init_reporter'] = cluster['init_reporter'][0:5]\n cluster['init_reporter'] = key_to_id[cluster['init_reporter'].lower()]\n\n if show_points:\n cluster['points'] = [\n point for point in points if point['cluster'] == idx]\n unclustered_points = [\n point for point in points if point['cluster'] == -1]\n print_table(clusters)\n print()\n print(\n f\"LAST BLOCK: block# (hash) {block['number']} ({block['hash'][0:8]})\")\n print(f\"MY BALANCE: usable (balance) {usable:.2f} ({balance:.2f})\")\n if show_points:\n print_table(unclustered_points, indent=2)\n\ndef getClusters():\n cluster_list = sc.functions.getClusters().call()\n cluster_dict = [l2d(c, cluster_keys) for c in cluster_list]\n return cluster_list, cluster_dict\n\n\ndef getPoints():\n point_list = sc.functions.getPoints().call()\n point_dict = [l2d(c, point_keys) for c in point_list]\n return point_list, point_dict\n\n\ndef getBalance(allpoints, allclusters):\n # check all my balance, including those frozen in unverified clusters.\n myUsableBalance = float(w3.fromWei(\n w3.eth.getBalance(w3.eth.coinbase), \"ether\")) - 1\n myBalance = myUsableBalance\n # _, allpoints = getPoints()\n # _, allclusters = getClusters()\n for idx, cluster in enumerate(allclusters):\n if cluster['verified'] == 0:\n for point in allpoints:\n if point['sender'] == w3.key and int(point['cluster']) == idx:\n myBalance += float(point['credit']) / 1e18\n return round(myBalance, 2), myUsableBalance\n\n\nif __name__ == '__main__':\n\n w3 = init_web3()\n sc = registerSC(w3)\n\n _, key_to_id = mapping_id_keys(\"/home/eksander/geth-argos/argos-blockchain-sm/geth/files/keystores/\", limit=200)\n point_keys = sc.functions.getPointKeys().call()\n cluster_keys = sc.functions.getClusterKeys().call()","repo_name":"teksander/geth-argos","sub_path":"FraudForaging3D/controllers/docker/console.py","file_name":"console.py","file_ext":"py","file_size_in_byte":4511,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"44335621689","text":"\nfrom typing import List\nimport numpy as np\n\nfrom .base import Controller, PotentialField, Predictor\nfrom manipulators.base import Manipulator, ManipulatorState\nimport logging\nfrom typing import Tuple\nimport copy\n\n\nclass PDController(Controller):\n \"\"\"PD Controller.\"\"\"\n def __init__(self, p_gain: float, d_gain: float, max_velocity: float):\n \"\"\" Constructor\n\n Args:\n p_gain: Proportional gain\n d_gain: Derivative gain\n max_velocity: Maximum velocity\n \"\"\"\n self.p_gain = p_gain\n self.d_gain = d_gain\n self.max_velocity = max_velocity\n\n def set_initial_position(self, position):\n self.position = position\n self.desired_position = position\n\n def set_initial_velocity(self, velocity):\n self.velocity = velocity\n\n def update(self, position, velocity, desired_position):\n self.position = position\n self.velocity = velocity\n self.desired_position = desired_position\n\n def get_control_signal(self, error):\n # Compute error\n position_error = np.array([desired_position - current_position for desired_position, current_position in zip(self.desired_position, self.position)])\n velocity_error = np.array([desired_velocity - current_velocity for desired_velocity, current_velocity in zip(position_error, self.velocity)])\n # Compute control signal\n control_signal = np.array([self.p_gain * position_error + self.d_gain * velocity_error for position_error, velocity_error in zip(position_error, velocity_error)])\n # Clip control signal\n control_signal = np.array([max(min(control_signal, self.max_velocity), -self.max_velocity) for control_signal in control_signal])\n return control_signal\n\n\nclass AttractivePotentialField(PotentialField):\n\n def __init__(self, p_gain: float, d_gain: float, max_force: float):\n \"\"\" Constructor\n\n Args:\n p_gain: Proportional gain\n d_gain: Derivative gain\n \"\"\"\n self.p_gain = p_gain\n self.d_gain = d_gain\n\n def calculate_force(self, position, velocity, desired_position):\n position_error = desired_position - position\n velocity_error = -velocity\n return self.p_gain * position_error + self.d_gain * velocity_error\n\n\nclass RepulsivePotentialField(PotentialField):\n \"\"\"Repulsive potential field.\"\"\"\n def __init__(self, p_gain: float, d_gain: float, max_force: float):\n \"\"\" Constructor\n\n Args:\n p_gain: Proportional gain\n d_gain: Derivative gain\n \"\"\"\n self.p_gain = p_gain\n self.d_gain = d_gain\n self.repulsive_range = 2\n self.repulsive_gain = self.p_gain\n\n def calculate_force(self, position, other_shuttles_positions):\n repulsive_force = np.zeros_like(position)\n for other_position in other_shuttles_positions:\n other_position = np.array(other_position)\n distance_vector = position - other_position\n distance = np.linalg.norm(distance_vector)\n if distance < self.repulsive_range:\n force_magnitude = self.repulsive_gain / (distance**2 + 0.00001)\n force_direction = distance_vector / distance\n repulsive_force += force_magnitude * force_direction\n return repulsive_force\n\n\nclass ForceFieldController(Controller):\n def __init__(self, attractive_field, repulsive_field, max_force):\n self.attractive_field = attractive_field\n self.repulsive_field = repulsive_field\n self.max_force = max_force\n\n def set_initial_position(self, position):\n self.position = np.array(position)\n self.desired_position = np.array(position)\n\n def set_initial_velocity(self, velocity):\n self.velocity = np.array(velocity)\n\n def update(self, position, velocity, desired_position):\n self.position = np.array(position)\n self.velocity = np.array(velocity)\n self.desired_position = np.array(desired_position)\n\n def get_control_signal(self, error):\n attractive_force = self.attractive_field.calculate_force(self.position, self.velocity, self.desired_position)\n repulsive_force = self.repulsive_field.calculate_force(self.position, self.get_other_shuttles_positions())\n control_signal = attractive_force + repulsive_force\n\n control_signal_magnitude = np.linalg.norm(control_signal)\n if control_signal_magnitude > self.max_force:\n control_signal = (control_signal / control_signal_magnitude) * self.max_force\n\n return control_signal\n\n def set_other_shuttles_positions(self, other_shuttles_positions):\n self.other_shuttles_positions = other_shuttles_positions\n\n def get_other_shuttles_positions(self):\n return self.other_shuttles_positions\n\n\nclass ShuttlePredictor(Predictor):\n \"\"\" Class for predicting future states of shuttles, and add potential to the states if they are in collision with other shuttles.\n\n Args:\n shuttles: List of shuttles\n dt: Time step\n timesteps: Number of time steps to predict\n\n Attributes:\n predicted_states: List of predicted states\n predicted_times: List of predicted times\n shuttles: List of shuttles\n states: List of current states\n \"\"\"\n def __init__(self, shuttles: List[Manipulator], dt: float, timesteps: int):\n \"\"\"ShuttlePredictor constructor\"\"\"\n # self.predicted_times = [dt * i for i in range(timesteps)] # Initialize predicted times\n self.dt = dt\n self.time_range = timesteps\n self.shuttles = shuttles\n self.states: List[ManipulatorState] = [shuttle.get_state() for shuttle in shuttles]\n\n def predict(self, potentials: np.ndarray = None) -> np.ndarray:\n \"\"\" Predict future states of shuttles, and add potential to the states if they are in collision with other shuttles.\"\"\"\n\n # Initialize variables\n is_finished = False\n\n if potentials is None:\n potentials = np.zeros((len(self.shuttles), 2))\n\n first_control_signal = np.zeros((len(self.shuttles), 2))\n counter = 0\n # Predict future states until no collisions detected\n while not is_finished:\n # Get attributes of the class\n time_range = copy.deepcopy(self.time_range)\n states = copy.deepcopy(self.states)\n collision = False\n\n # Predict future states\n for i in range(time_range):\n for idx, shuttle in enumerate(self.shuttles):\n current_state = states[idx] # Get current state of the shuttle\n new_state = shuttle.get_next_state(dt=self.dt, current_state=current_state, additional_force=potentials[idx]) # Get next state of the shuttle\n states[idx] = new_state # Update the current state of the shuttle\n if i == 0:\n direction_vector = shuttle.get_desired_position() - new_state.get_position()\n velocity_magnitude = np.linalg.norm(new_state.get_velocity()) * 0.25\n velocity_command = new_state.get_velocity()# + direction_vector * velocity_magnitude\n first_control_signal[idx] = velocity_command\n\n # Check if there is a collision, and calculate potential field.\n collision, potential = self.potentialFieldChecker(self.shuttles, states)\n potentials += potential\n \n\n # If collision detected, then stop predicting, and add potential to the states, and try again with different potential field\n if collision:\n # import time\n \n # print(f'Collision detected at time {i} for shuttle {shuttle.get_idx()}')\n # time.sleep(2)\n # logging.info(f'Collision detected at time {i} for shuttle {shuttle.get_idx()}')\n break\n\n # If no collisions detected for all the shuttles at the end of the time range, then the prediction is finished\n if not collision:\n is_finished = True\n \n counter += 1\n #print(counter)\n print(f'Potentials: {potentials[0]}')\n # print(f) \n return first_control_signal, potentials\n\n def potentialFieldChecker(self, shuttles, states: List[ManipulatorState]) -> Tuple[bool, np.ndarray]:\n \"\"\" Check if there is a collision between shuttles, and calculate the potential field.\n\n Args:\n shuttles: List of shuttles\n states: List of states of the shuttles\n\n Returns:\n collision: Boolean indicating if there is a collision between shuttles\n potential: Repulsive potential field\n \"\"\"\n repulsive_gain = 1\n repulsive_force = np.zeros((len(shuttles), 2), dtype=float)\n collision = False\n # Check if there is a collision between shuttles\n for i in range(len(shuttles)):\n for j in range(len(shuttles)):\n if i != j:\n # Calculate the distance between the shuttles, using the infinite norm, and check if it is less than 2\n pos1 = states[i].get_position()\n pos2 = states[j].get_position()\n L_inifnite_norm = np.linalg.norm(states[i].get_position() - states[j].get_position(), np.inf)\n if L_inifnite_norm < 1:\n collision = True\n # If there is a collision, then calculate the repulsive force\n force_magnitude = repulsive_gain / (L_inifnite_norm**2 + 0.00001)\n #force_magnitude = np.clip(force_magnitude, -2, 2)\n force_direction = (states[i].get_position() - states[j].get_position()) / L_inifnite_norm\n repulsive_force[i] += force_magnitude * force_direction\n\n \n # If there is no collision, then return False, and 0 as the potential\n if not collision:\n return False, repulsive_force\n else:\n return True, repulsive_force\n\n\nif __name__ == '__main__':\n pass\n","repo_name":"abmoRobotics/MAPs","sub_path":"shuttle_simulator/controllers/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":10289,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"42738571385","text":"import csv\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport os\n\ncwd = os.getcwd()\nDATA_PATH = '../feedback/csv/data.csv'\n\nAF = 'Accept and fix'\nAC = 'Accept'\nNI = 'Not interested'\nFP = 'Marked as fp'\nMR = 'Marked as resolved but did not reply'\npattern_dict = dict()\n\n\ndef load_pattern_dict():\n global pattern_dict\n csv_table = [row for row in csv.reader(open(DATA_PATH, 'r'))]\n for line_no, row in enumerate(csv_table):\n if line_no == 0:\n continue\n else:\n\n note = row[7]\n if 'delete' in note:\n continue\n feedback = row[8]\n if '404 Not Found' in feedback:\n continue\n pattern = row[4]\n if any(key == feedback for key in (AF, AC, NI, FP, MR)) and pattern not in pattern_dict:\n pattern_dict[pattern] = {'AF': 0, 'AC': 0, 'NI': 0, 'FP': 0, 'MR': 0}\n if feedback == AF:\n pattern_dict[pattern]['AF'] += 1\n elif feedback == AC:\n pattern_dict[pattern]['AC'] += 1\n elif feedback == NI:\n pattern_dict[pattern]['NI'] += 1\n elif feedback == FP:\n pattern_dict[pattern]['FP'] += 1\n elif feedback == MR:\n pattern_dict[pattern]['MR'] += 1\n pattern_dict = sorted(pattern_dict.items(), key=lambda x: (x[1]['AF'] + x[1]['AC'] + x[1]['NI'] + x[1]['FP'] + x[1]['MR']))\n sums = [['AF', 'AC', 'NI', 'MR', 'FP'], [0]*5]\n for pattern in pattern_dict:\n sums[1] = [sums[1][0] + pattern[1]['AF'], sums[1][1] + pattern[1]['AC'], sums[1][2] + pattern[1]['NI'], \n sums[1][3] + pattern[1]['MR'], sums[1][4] + pattern[1]['FP']]\n print('total_feedback', str(sum(sums[1])))\n print('AF+AC', str((sums[1][0] + sums[1][1])))\n print('(AF+AC) / total', str(round((sums[1][0] + sums[1][1]) * 100/sum(sums[1]), 2)))\n print(sums[0])\n print(sums[1])\n\ndef draw_staked_bar():\n load_pattern_dict()\n patterns = [''] * len(pattern_dict)\n FPs = [0] * len(patterns)\n NIs = [0] * len(patterns)\n MRs = [0] * len(patterns)\n ACs = [0] * len(patterns)\n AFs = [0] * len(patterns)\n for idx, content in enumerate(pattern_dict):\n patterns[idx] = content[0]\n AFs[idx] = content[1]['AF']\n ACs[idx] = content[1]['AC']\n NIs[idx] = content[1]['NI']\n FPs[idx] = content[1]['FP']\n MRs[idx] = content[1]['MR']\n print('patterns', patterns)\n print('FPs', FPs, str(sum(FPs)))\n print('NIs', NIs, str(sum(NIs)))\n print('MRs', MRs, str(sum(MRs)))\n print('ACs', ACs, str(sum(ACs)))\n print('AFs', AFs, str(sum(AFs)))\n bar_height = 0.8\n\n print('len patterns', len(patterns))\n plt.barh(patterns, FPs, color='black', alpha=1, edgecolor=\"k\", height=bar_height)\n plt.barh(patterns, NIs, hatch='|||||', color='white', alpha=1, edgecolor=\"k\", left=FPs, height=bar_height)\n plt.barh(patterns, MRs, hatch='----', color='white', alpha=1, edgecolor=\"k\",\n left=[FPs[i] + NIs[i] for i in range(len(patterns))], height=bar_height)\n plt.barh(patterns, ACs, hatch='/////', color='white', alpha=1, edgecolor=\"k\",\n left=[FPs[i] + NIs[i] + MRs[i] for i in range(len(patterns))], height=bar_height)\n plt.barh(patterns, AFs, color='white', alpha=1, edgecolor=\"k\",\n left=[FPs[i] + NIs[i] + MRs[i] + ACs[i] for i in range(len(patterns))], height=bar_height)\n xc = [x * 5 for x in range(int(max([FPs[i] + NIs[i] + MRs[i] + ACs[i] + AFs[i] for i in range(len(patterns))]) / 5) + 1)]\n plt.xticks(xc, fontsize=13)\n plt.yticks(fontsize=13)\n plt.tight_layout()\n labels = ['FP', 'NI', 'MR', 'AC', 'AF']\n plt.legend(labels, loc='center right', bbox_to_anchor=(0.5, 0., 0.5, 0.5), frameon=False,\n fontsize='xx-large')\n for x in xc:\n plt.axvline(x=x, color='black', linewidth='0.4')\n # top value\n for i, v in enumerate([FPs[i] + NIs[i] + MRs[i] + ACs[i] + AFs[i] for i in range(len(patterns))]):\n plt.text(v, i, \" \" + str(v), color='black', va='center')\n plt.show()\n\n\ndef parse_url(html_url):\n tokens = html_url.split('/')\n owner = tokens[3]\n repo = tokens[4]\n pull_number = tokens[6]\n return owner, repo, pull_number\n\n\ndef cnt_total_pr_comment():\n csv_table = [row for row in csv.reader(open(DATA_PATH, 'r'))]\n total_pr_comment = set()\n for line_no, row in enumerate(csv_table):\n if line_no == 0:\n continue\n else:\n html_url = row[0]\n note = row[7]\n if any(key in note for key in ('delete', '404 Not Found')):\n continue\n owner, repo, pull_number = parse_url(html_url)\n key = ','.join([owner, repo, pull_number])\n if key not in total_pr_comment:\n total_pr_comment.add(key)\n print('total_pr_comment', len(total_pr_comment))\n\n\nif __name__ == '__main__':\n cnt_total_pr_comment()\n draw_staked_bar()\n","repo_name":"codegex-analysis/codegex-evaluation","sub_path":"result/pull-request/scripts/analyze-fb.py","file_name":"analyze-fb.py","file_ext":"py","file_size_in_byte":4988,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"31527522862","text":"from controls.input_controls import Controls, general_controls, menu_controls\nfrom menus.baseMenu import BaseMenu, MenuOption\nfrom viewer.colors import Color\n\n\nclass PostGameMenu(BaseMenu):\n def __init__(self, game):\n BaseMenu.__init__(self, game)\n self.name = \"PostGameMenu\"\n self.options = {\n \"restart\": MenuOption(\"restart\", function=self.restart_function),\n \"options\": MenuOption(\"options\", function=self.options_function),\n \"quit\": MenuOption(\"quit\", function=self.quit_function),\n }\n self.selected_option = self.options[\"restart\"]\n\n def restart_function(self):\n self.quit_menu()\n self.game.restart_game()\n\n def options_function(self):\n self.game.menuHandler.options_menu()\n\n def quit_function(self):\n self.quit_menu()\n self.game.quit_game()\n","repo_name":"IvoSte/MultiplayerSnake","sub_path":"src/menus/postGameMenu.py","file_name":"postGameMenu.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"43212062342","text":"from django.db import models\n\nfrom django.utils.translation import ugettext_lazy as _\nimport datetime\n\nclass Propaganda(models.Model):\n\t\n\tnombre = models.CharField(\n\t\tmax_length = 120,\n\t\tverbose_name = _('Nombre')\n\t)\n\n\tdescripcion = models.TextField(\n\t\tverbose_name = _('Descripcion')\n\t)\n\n\tfecha_inicio = models.DateField()\n\t\n\tfecha_fin = models.DateField()\n\n\turl = models.URLField(\n\t\tverbose_name = _('URL Propaganda'),\n\t\tmax_length = 500\n\t)\n\n\thabilitado = models.BooleanField(\n\t\tdefault = False,\n\t\tverbose_name = _('Habilitado')\n\t)\n\n\tdef __str__(self):\n\t\treturn self.nombre\n\n\tdef estadofecha(self):\n\t\thoy = datetime.date.today()\n\t\tdias = (self.fecha_fin - hoy).days\n\t\treturn dias\n\n\tclass Meta:\n\t\tverbose_name = _('Propaganda')\n\t\tverbose_name_plural = _('Propagandas')","repo_name":"Alfredynho/sisventas","sub_path":"Mbot/apps/propaganda/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"36881265700","text":"import music21 as m21\nfrom itertools import product\n\npitchToZ12 = dict(zip([\"C\",\"C#\",\"D\",\"D#\",\"E\",\"F\",\"F#\",\"G\",\"G#\",\"A\",\"A#\",\"B\"],range(12)))\nZ12ToPitch = dict(zip(range(12),[\"C\",\"C#\",\"D\",\"D#\",\"E\",\"F\",\"F#\",\"G\",\"G#\",\"A\",\"A#\",\"B\"]))\n\nimport numpy as np\ndef kernPause(a1,a2):\n return 1*(a1==a2)\n\ndef kernPitch(k1,k2):\n q = getRational(k2-k1)\n a,b = q.numerator(),q.denominator()\n #print(a,b)\n return gcd(a,b)**2/(a*b)\n\ndef kernDuration(k1,k2):\n return min(k1,k2)/max(k1,k2)\n\ndef kernVolume(k1,k2):\n return min(k1,k2)/max(k1,k2)\n\ndef getRational(k):\n alpha = 2**(1/12.0)\n x = RDF(alpha**k).n(50)\n return x.nearby_rational(max_error=0.01*x)\n\ndef kern(t1,t2):\n import numpy as np\n pitch1,duration1,volume1,isPause1 = t1\n pitch2,duration2,volume2,isPause2 = t2\n weights = [1,2,3,4]\n #weights = np.array(weights)/np.sum(weights)\n #print(weights)\n tt = list(zip(t1,t2))\n kerns = [kernPitch, kernDuration, kernVolume, kernPause]\n x = np.sum([weights[i]*kerns[i](tt[i][0],tt[i][1]) for i in range(4)])\n #print(x)\n return x\n\ndef kernChord(c1,c2):\n return 1/(len(c2)*len(c1))*sum([kern(x,y) for x in c1 for y in c2])\n\ndef findNearestDuration(duration,durationslist):\n return sorted([(abs(duration-nv),nv) for nv in durationslist])[0][1]\n\ndef xml_to_list(xml):\n xml_data = m21.converter.parse(xml)\n score = []\n for part in xml_data.parts:\n parts = []\n for note in part.recurse().notesAndRests:\n if note.isRest:\n start = note.offset\n duration = float(note.quarterLength)/4.0\n vol = 32 #note.volume.velocity\n pitches= tuple([-1])\n parts.append(tuple([pitches,duration,vol,1]))\n elif type(note)==m21.chord.Chord:\n pitches = sorted([e.pitch.midi for e in note]) # todo: think about chords\n vol = int(note[0].volume.velocity)\n duration = float(note.duration.quarterLength)/4.0\n parts.append(tuple([tuple(pitches),duration,vol,0]))\n else:\n #print(note)\n start = note.offset\n duration = float(note.quarterLength)/4.0\n pitches = tuple([note.pitch.midi])\n #print(pitch,duration,note.volume)\n vol = note.volume.velocity\n if vol is None:\n vol = int(note.volume.realized * 127)\n parts.append(tuple([pitches,duration,vol,0]) )\n score.append(parts) \n return score\n\n\n\ndef getNearestVolume(vol):\n return sorted([(abs(vol-v),v) for v in volumelist])[0][1]\n\ndef parseXml(fp):\n return xml_to_list(fp)\n\ndef ngrams(inp, n):\n output = []\n for i in range(len(inp)-n+1):\n output.append(tuple(inp[i:(i+n)]))\n return output\n\n#print(ngrams(list(range(7)),3))\n\n\nfrom collections import Counter\nimport numpy as np\n\n\ndef zeroMatDict(ll):\n from itertools import product\n possible_ll = list(product(ll,ll))\n dd = dict([])\n for x in possible_ll:\n dd[x] = 0\n return dd \n \n \n\ndef getCountValue(counter,possibilities,key):\n x,y = key\n if x in possibilities and y in possibilities:\n if key in counter.keys():\n return counter[key]\n else:\n return 0\n #print(possibilities,key) \n return 0 \n\n\n\ndef getProbValue(counter,possibilities,key):\n x,y = key\n #print(x,y,key)\n cntv = getCountValue(counter,possibilities,key)\n s = sum([getCountValue(counter,possibilities,(x,Y)) for Y in possibilities])\n if s ==0:\n s=1\n val = float(cntv*1.0/s)\n return val\n\ndef getProbValues(counter,possibilities,x):\n return [getProbValue(counter,possibilities,(x,y)) for y in possibilities]\n\n\n\n#print(bigram_pitches)\n#print(bigram_durations)\ndef power(a,b):\n return np.exp(b*np.log(a))\n\n\ndef generateNext(counter,possibilities,current):\n probs = getProbValues(counter,possibilities,current)\n #print(current,probs)\n N = len(probs)\n if abs(np.sum(probs))<0.01:\n probs = [1.0/N for n in range(N)]\n #print(probs) \n ch = np.random.choice(range(N),p=probs)\n x = possibilities[ch]\n #if type(x)==type((1,2)) and len(x)>1:\n # while current[-1]!=x[0]:\n # ch = np.random.choice(range(N),p=probs)\n # x = possibilities[ch]\n # #print(x)\n return x\n \n\n\ndef getPossibilities(ll,NMarkov=2):\n from itertools import product\n return [tuple(x) for x in list(product(*((NMarkov)*[ll])))]\n\ndef getNextWithKnn(bigram,possibilities,start_notes,NMarkov,NCandidates=10,rev=False):\n nxts = []\n possibs = [ p for p in possibilities if start_notes[-(NMarkov-1):]==p[0:(NMarkov-1)]]\n inv_possibs = [ p for p in possibilities if start_notes[-(NMarkov-1):]!=p[0:(NMarkov-1)]]\n for k in range(NCandidates):\n print(len(possibs))\n if len(possibs)>0:\n nxt = generateNext(bigram,possibs,start_notes)\n else:\n nxt = choice(inv_possibs)\n nxts.append(nxt)\n return sorted([(kernChord(nxt,start_notes),nxt) for nxt in nxts],reverse=rev)[0][1]\n\ndef getNext(bigram,possibilities,start_notes,NMarkov):\n import numpy as np\n possibs = [ p for p in possibilities if start_notes[-(NMarkov-1):]==p[0:(NMarkov-1)]]\n inv_possibs = [ p for p in possibilities if start_notes[-(NMarkov-1):]!=p[0:(NMarkov-1)]]\n print(len(possibs))\n if len(possibs)>0:\n nxt = generateNext(bigram,possibs,start_notes)\n else:\n rng = range(len(inv_possibs))\n rng_nxt = np.random.choice(rng)\n nxt = inv_possibs[rng_nxt]\n return nxt\n \n\ndef chordOrNoteOrRest(pitches):\n r = pitches\n if len(r)==1 and r[0]!=-1:\n pitch = r[0]%12\n octave = r[0]//12\n n0 = m21.note.Note(Z12ToPitch[pitch]+str(octave))\n elif len(r)==1 and r[0]==-1:\n n0 = m21.note.Rest()\n else:\n octave = r[0]//12\n n0 = m21.chord.Chord([Z12ToPitch[rr%12]+str(octave) for rr in r])\n return n0 \n\ndef generate_from_file(NMarkov=2, tempo=70,nrMinutes=12,inputfn=\"./midi/una_mattina.mid\",outputfn=\"./midi/markov.mid\"):\n import music21 as m21\n scores = parseXml(inputfn)\n from itertools import product\n\n score = m21.stream.Score()\n tm = m21.tempo.MetronomeMark(number=tempo)\n score.append(tm)\n \n for j in range(len(scores)):\n\n sc = [tuple(x) for x in scores[j]]\n #print(ngrams(sc,NMarkov))\n possibilities = list(sorted(frozenset(ngrams(sc,NMarkov))))\n \n bigram = Counter(ngrams(sc,NMarkov+1))\n #print(bigram)\n start_notes = tuple(sc[0:(NMarkov)])\n \n print(start_notes)\n #counters = [bigram_pitches,bigram_octaves,bigram_durations,bigram_volumes,bigram_pauses]\n #possibs = [pitchlist,octavelist,durationslist,volumelist,pauseslist]\n\n ll = []\n\n minBars = int(tempo*nrMinutes/4)\n sumBars = 0\n while sumBars < minBars:\n note = []\n Note = []\n nxt = getNext(bigram,possibilities,start_notes,NMarkov)\n #nxt = generateNext(bigram,possibilities,start_notes)\n #print(start_notes,nxt)\n start_notes = nxt\n duration = nxt[-1][1]\n print(j,duration,sumBars)\n sumBars += duration\n ll.append(nxt[-1]) \n #print(ll) \n\n lh = m21.stream.Part()\n lh.append(m21.instrument.Piano()) \n notesLH = []\n for i in range(len(ll)):\n #print(ll[i])\n pitches,duration,volume,pause = ll[i]\n n0 = chordOrNoteOrRest(pitches)\n n0.duration = m21.duration.Duration(duration*4.0)\n if not type(n0)==m21.note.Rest:\n n0.volume.velocity = int(volume)\n #n0.duration.quarterLength = float(choice([0.5,0.25,1]))\n notesLH.append(n0)\n for n in notesLH:\n lh.append(n)\n score.append(lh)\n print(len(score.parts)) \n score_name = outputfn\n score.write('midi', fp=score_name) \n score.write(\"musicxml\",fp=score_name+\".xml\")\n\nimport sys\nNMarkov, tempo, nrMinutes, inputfn, outputfn = sys.argv[1:]\ngenerate_from_file(NMarkov=int(NMarkov),tempo=int(tempo),nrMinutes=int(nrMinutes),inputfn=inputfn,outputfn=outputfn) ","repo_name":"githubuser1983/music_accompainment_python","sub_path":"markov_chain.py","file_name":"markov_chain.py","file_ext":"py","file_size_in_byte":8291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"30938797539","text":"\"\"\"\n@author: Nono Saha Cyrille Merleau, email: nonosaha@mis.mpg.de/csaha@aims.edu.gh\n\"\"\"\n\nimport os\nfrom numpy import array\nfrom RNA import fold, read_parameter_file\nfrom uuid import uuid4\n\nROOT_LOG_FOLDER = os.getcwd()+\"/../data/Log\"\n\ndef ppRNAfold(listOfSeqs, nrj_param) :\n if (nrj_param) :\n read_parameter_file(\"../params/energy/rna_turner1999.par\")\n\n rst = array ([list(fold(seq)) for seq in listOfSeqs])\n return rst[:,0].tolist(), array(rst[:,1],dtype=float).tolist()\n\ndef fold_with_ipknot(seq) :\n\n out_file = str(uuid4())\n cmd= \"echo '>\"+out_file+\"\\n\"+seq+\"'>\"+ROOT_LOG_FOLDER+\"/tmp/\"+out_file+\".fa\"\n os.system(cmd)\n ipknot_cmd = os.environ.get(\"IPKNOT\")+\"/./ipknot \"+ROOT_LOG_FOLDER+\"/tmp/\"+out_file+\".fa\"\n p = os.popen(ipknot_cmd)\n rst = p.read().split()\n p.close()\n os.remove(ROOT_LOG_FOLDER+\"/tmp/\"+out_file+\".fa\")\n if len(rst) > 0 :\n return rst[-1]\n else :\n print(\"ERROR during the folding with ipknot\")\n return None\n\ndef ppipknot(listOfSeqs) :\n result = []\n for s in listOfSeqs :\n result.append(fold_with_ipknot(s))\n result = array(result)\n\n return result, [0]*len(result)\n\ndef pKiss(seq) :\n\n cmd= \"pKiss --strategy 'P' --mode='mfe' {} 2>/dev/null\".format(seq)\n p = os.popen(cmd)\n rst = p.read().split()\n if len(rst) > 0 :\n return (rst[-1],rst[-2])\n else :\n print(\"ERROR during the folding with pKiss\")\n return None\n\ndef hotknots(seq) :\n\n cmd= os.environ.get(\"HOTKNOTS_ROOT\")+\"./bin/HotKnots -s {} -m {} 2>/dev/null\".format(seq, \"CC\")\n p = os.popen(cmd)\n rst = p.read().split('\\n')\n rst = rst[2].split()\n if len(rst) > 0 :\n return (rst[-2],rst[-1])\n else :\n print(\"ERROR during the folding with Hotknots\")\n return None\n\ndef ppHotknots(listOfSeqs) :\n\n result = []\n for s in listOfSeqs :\n result.append(hotknots(s))\n result = array(result)\n return list(result[:,0]),list(result[:,1])\n","repo_name":"strevol-mpi-mis/aRNAque","sub_path":"src/utilities/folding_wrapper.py","file_name":"folding_wrapper.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"11171561265","text":"import cv2\nimport numpy as np\n\nfrom api.Code.ImageSegmentation import ImageSegmentation\n\n\nclass WordSegmentation(ImageSegmentation):\n def __init__(self, thinned_image, original_image):\n super(WordSegmentation,self).__init__(thinned_image,original_image)\n\n def get_first_empty_line(self, loop_range):\n emptyRowIndex = -1\n for row in loop_range:\n whiteBixels = 0\n for col in range(self.width):\n if self.thinned_image[row, col] == 255:\n whiteBixels += 1\n\n if whiteBixels == 0:\n emptyRowIndex = row\n break\n return emptyRowIndex\n\n def remove_empty_lines(self, begin, end):\n self.thinned_image[begin:end, :] = 0\n self.original_image[begin:end, :] = 0\n\n def remove_horizontal_lines(self):\n\n # remove below horizontal lines\n emptyRowsBelow = self.get_first_empty_line(range(round(self.height / 2), self.height))\n if (emptyRowsBelow != -1):\n self.remove_empty_lines(emptyRowsBelow, self.height)\n else:\n self.remove_empty_lines(self.height - 2, self.height)\n\n # remove above horizontal lines\n emptyRowsAbove = self.get_first_empty_line(reversed(range(0, round(self.height / 2))))\n if (emptyRowsAbove != -1):\n self.remove_empty_lines(0, emptyRowsAbove)\n else:\n self.remove_empty_lines(0, 2)\n\n #TODO Refactor_function\n # def remove_vertical_lines(self, thiningImage):\n #\n # # cv2.imshow(\"before\",image)\n # lineColsRight = []\n # lineColsleft = []\n # NoOfPixelsCol = []\n #\n # # remove left Lines\n # for col in range(len(thiningImage[0])):\n # whiteBixels = 0\n # for row in range(len(thiningImage)):\n # if thiningImage[row, col] == 255:\n # whiteBixels += 1\n #\n # if whiteBixels == 0:\n # break\n # else:\n # lineColsleft.append(col)\n # NoOfPixelsCol.append(whiteBixels)\n # for col, NoOFPixel in zip(lineColsleft, NoOfPixelsCol):\n # if NoOFPixel > 3:\n # thiningImage[:, col] = 0\n #\n # # remove right Lines\n # for col in reversed(range(len(thiningImage[0]))):\n # whiteBixels = 0\n # for row in range(len(thiningImage)):\n # if thiningImage[row, col] == 255:\n # whiteBixels += 1\n #\n # if whiteBixels == 0:\n # break\n # else:\n # lineColsRight.append(col)\n # NoOfPixelsCol.append(whiteBixels)\n # for col, NoOFPixel in zip(lineColsRight, NoOfPixelsCol):\n # if NoOFPixel > 3:\n # thiningImage[:, col] = 0\n #\n # return thiningImage\n\n def removeVerticalLines(self,image):\n # remove left Lines\n image[:, 0:2] = 0\n # remove right Lines\n image[:, len(image[0]) - 2:len(image[0])] = 0\n\n return image\n\n\n def get_empty_sequential_cols_count(self, col_white_pixels, phrase_beginning, phrase_ending):\n empty_sequential_cols_count = []\n counter = 0\n for colIndex in range(phrase_beginning, phrase_ending):\n if col_white_pixels[colIndex] == 0:\n counter = counter + 1\n else:\n if counter > 0:\n empty_sequential_cols_count.append(counter)\n counter = 0\n return empty_sequential_cols_count\n\n def get_segmentation_threshold(self):\n\n col_white_pixels = np.empty(self.width, dtype=np.int8)\n\n # step1 White Pixel count\n for colIndex in range(self.width):\n white_pixels = 0\n for rowIndex in range(self.height):\n if self.thinned_image[rowIndex, colIndex] == 255:\n white_pixels += 1\n # arr[colIndex] = white_pixels if white_pixels < 1 else 2\n col_white_pixels[colIndex] = white_pixels\n\n phrase_beginning, phrase_ending = self.get_boundaries(col_white_pixels)\n\n countArr = self.get_empty_sequential_cols_count(col_white_pixels, phrase_beginning,\n phrase_ending)\n\n countArr.sort()\n\n threshold = 0\n x = 0\n for i in reversed(range(len(countArr))):\n if countArr[i] - countArr[i - 1] > 15 and i > 1:\n threshold = countArr[i]\n x = 1\n\n if x == 1:\n return threshold\n else:\n return 1000\n\n def get_dialation_image(self):\n segmentation_threshold=self.get_segmentation_threshold()\n kernel = np.ones((20,segmentation_threshold ), np.uint8)\n return cv2.dilate(self.thinned_image, kernel, iterations=1)\n\n def word_segmentaion(self):\n\n self.thinned_image = self.removeVerticalLines(self.thinned_image)\n self.remove_horizontal_lines()\n\n # dilation\n img_dilation = self.get_dialation_image()\n\n # find contours\n im2, ctrs, hier = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n # sort contours\n sorted_ctrs = sorted(ctrs, key=lambda ctr: cv2.boundingRect(ctr)[0])\n\n Words = []\n for i, ctr in enumerate(sorted_ctrs):\n\n # Get bounding box\n x, y, w, h = cv2.boundingRect(ctr)\n\n # Getting ROI \"Region of intersts\"\n roi_original = self.original_image[y:y + h, x:x + w]\n roi2_thining = self.thinned_image[y:y + h, x:x + w]\n\n # show ROI\n\n height,width = roi_original.shape\n if (height > 8 and width > 8):\n # cv2.imwrite(\"output\\\\wordSegmentation\\\\\" + str(counter()) + \".png\", roi_original)\n Words.append((roi2_thining,roi_original))\n #characterSegmentation(roi2_thining, roi_original)\n return Words","repo_name":"the-squad/excelify","sub_path":"api/Code/WordSegmentation.py","file_name":"WordSegmentation.py","file_ext":"py","file_size_in_byte":5990,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"75"} +{"seq_id":"25663005519","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n # 1. 演示创建xhr\n url(r'^01-createxhr/$', views.create_views),\n # 2. 演示使用ajax发送get请求的步骤\n url(r'^02-server/$', views.server02_views),\n url(r'^02-ajax-get/$', views.ajaxget_views),\n # 3. 演示使用ajax发送get请求并附带参数\n url(r'^03-ajax-get-params/$', views.getparams_views),\n url(r'^03-server/$', views.server03_views),\n # 4. 使用 AJAX 完成注册操作\n url(r'^04-register/$',views.register_views),\n url(r'^04-checkuname/$',views.checkuname_views),\n url(r'^04-reguser/$',views.reguser_views),\n url(r'^04-regpost/$',views.regpost_views),\n # 5. 使用AJAX发送post请求\n url(r'^05-ajax-post/$',views.post_views),\n url(r'^05-server/$',views.server05_views),\n # 6. 使用AJAX读取数据\n url(r'^06-ajax-users/$',views.users_views),\n url(r'^06-server/$',views.server06_views),\n # 7. 在前端中处理JSON格式字符串\n url(r'^07-json/$',views.json_views),\n # 8. 在服务器端中处理JSON字符串\n url(r'^08-json-server/$',views.jsonserver_views),\n # 9. 在服务器端中,读取Users表中的数据再转换成JSON串\n url(r'^09-json-users/$',views.jsonusers_views),\n # 10. 读取Users信息并显示在网页上(使用JSON)\n url(r'^10-users/$',views.jsonusers_views),\n url(r'^10-server/$',views.server10_views),\n # 11. 前端中将JS对象转换成JSON串\n url(r'^11-front-json/$',views.front_views),\n url(r'^11-server-json/$',views.serverjson_views),\n # 12. 通过JSON完成注册操作\n url(r'^12-register-json/$',views.regjson_views),\n url(r'^12-server/$',views.server12_views),\n # 13. 演示jquery中的$obj.load() 的作用\n url(r'^13-head/$',views.head_views),\n url(r'^13-index/$',views.index_views),\n # 14. 演示jquery中的$.get()的作用\n url(r'^14-jq-get/$',views.jqget_views),\n # 15. 通过$.get()完成搜索建议\n url(r'^15-search/$',views.search_views),\n url(r'^15-server/$',views.server15_views),\n # 16. 通过$.ajax() 完成自定义的ajax请求\n url(r'^16-jq-ajax/$',views.jqajax_views),\n]\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Jack-HFK/hfklswn","sub_path":"pychzrm course/AJAX/AJAX-XHR-JSON-JQuery/Day03_PM/Day03/AjaxDemo01/ajax/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"29780585832","text":"RESOURCES = [\"Air\", \"Water\", \"Food\"]\n\nfrom random import choice\n\nclass Location(object):\n\n def __init__(self, x=0, y=0, capacity=15, resources=[], desc=\"An empty room\"):\n \"\"\"\n Create a location\n \"\"\"\n self.x = x\n self.y = y\n self.desc = desc\n self.capacity = capacity\n self.things = []\n self.resources = [choice(RESOURCES)]\n def is_full(self):\n if(len(self.things == self.capacity)):\n return True\n return False\n\n def update(self,filled):\n self.space_filled = space_filled\n\n","repo_name":"nguyenml/PracticePython","sub_path":"src/locations.py","file_name":"locations.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"37218088824","text":"from Preprocessor import Preprocessor\nfrom Lexer import Lexer\nfrom Parser import Parser\n\ntext_input = \"\"\"\nprint(4 - 4 + 2);\n\"\"\"\n\nlexer = Lexer().get_lexer()\ntokens = lexer.lex(text_input)\n\ntext_input = \"\"\"\na = 4 - (5 + 1);\nprint(a);\nb = a * 2;\nc = b;\nprint(3 + c);\nprint(a == b);\n\"\"\"\n\nraw_input_text = \"\"\"\na = 2;\nfunction apple(a, b, c) {\n a = 2;\n}\nif a == 2 {\n nothing; ## This to show that we are doing nothing here\n}\n## This is also a raw input file\nelse if a == 3 {\n writeln(5 == 4, \"Rahul\", 3);\n} \nwriteln(\"hello \", 5);\n\"\"\"\n\npreprocessor = Preprocessor(raw_input_text)\ntext_input = preprocessor.get_processed_input()\n\nlexer = Lexer().get_lexer()\ntokens = lexer.lex(text_input)\n\n# for token in tokens:\n# print(token)\n\npg = Parser()\npg.parse()\nparser = pg.get_parser()\nparser.parse(tokens).eval()\n","repo_name":"rahul-jha98/Toy-Compiler","sub_path":"Interpreted/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"34319492769","text":"# 프로그래머스 모음사전 level 2\nword = input()\nanswer = 0\nwordarr = ['A','E','I','O','U']\n\ndef dfs(string):\n global answer\n answer += 1\n if string == word:\n return True\n if len(string) == 5:\n return False\n for i in wordarr:\n if dfs(string+i):\n return True\n\nfor i in wordarr:\n if dfs(i):\n print(answer)\n break","repo_name":"meohyeon/Python","sub_path":"py41.py","file_name":"py41.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"4916853960","text":"def triangle_description(side1, side2, side3):\n if (\n not side1 + side2 > side3\n or not side2 + side3 > side1\n or not side3 + side1 > side2\n ):\n return \"Não é um triângulo!\"\n if side1 == side2 == side3:\n return \"Triângulo equilátero.\"\n elif side1 == side2 or side1 == side3:\n return \"Triângulo isósceles.\"\n else:\n return \"Triângulo escaleno.\"\n\n\nprint(triangle_description(5, 3, 2))\n","repo_name":"rangel20/Trybe-exercises","sub_path":"exercises/33_1/exercise6.py","file_name":"exercise6.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"26916505092","text":"from .template import Settings, Processor\nfrom .CONSTANT import *\n\n\nclass HTSeq(Processor):\n\n sorted_bam: str\n gtf: str\n out_counts: str\n\n def __init__(self, settings: Settings):\n super().__init__(settings=settings)\n\n def main(self,\n sorted_bam: str,\n gtf: str) -> str:\n\n self.sorted_bam = sorted_bam\n self.gtf = gtf\n\n self.set_output_path()\n self.htseq()\n\n return self.out_counts\n\n def set_output_path(self):\n self.out_counts = f'{self.outdir}/out_counts.csv'\n\n def htseq(self):\n cmd = f'htseq-count \\\n --format bam \\\n --order name \\\n --stranded {STANDARD_SPECIFIC_ASSAY} \\\n -a {SKIP_LOWER_QUALITY_READ} \\\n --type exon \\\n --idattr gene_id \\\n --mode {MODE_TO_HANDLE_READ_OVERLAPPING} \\\n {self.sorted_bam} \\\n {self.gtf} \\\n > {self.out_counts}'\n self.call(cmd)\n","repo_name":"kobe2103/RNA_seq_analysis","sub_path":"counting.py","file_name":"counting.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"74323770163","text":"#!/usr/bin/env python3\n\"\"\"Hypermedia pagination\"\"\"\n\nimport csv\nimport math\nfrom typing import List, Tuple, Dict, Union\n\n\nclass Server:\n \"\"\"Server class to paginate a database of popular baby names.\n \"\"\"\n DATA_FILE = \"Popular_Baby_Names.csv\"\n\n def __init__(self):\n self.__dataset = None\n\n def dataset(self) -> List[List]:\n \"\"\"Cached dataset\n \"\"\"\n if self.__dataset is None:\n with open(self.DATA_FILE) as f:\n reader = csv.reader(f)\n dataset = [row for row in reader]\n self.__dataset = dataset[1:]\n\n return self.__dataset\n\n def index_range(self, page: int, page_size: int) -> Tuple[int, int]:\n \"\"\"\n Gets start index and end index\n\n Args:\n page (int): number of page\n page_size (int): size of page\n Returns:\n Tuple[int, int]: (start index, end index)\n \"\"\"\n start = (page - 1) * page_size\n end = start + page_size\n return (start, end)\n\n def get_page(self, page: int = 1, page_size: int = 10) -> List[List]:\n \"\"\"get page\n Args:\n page (int, optional): number of page. Default eq 1.\n page_size (int, optional): number of row in page. Defaults eq 10.\n Returns:\n List[List]: List of dataset rows by range\n \"\"\"\n assert isinstance(page, int) and isinstance(page_size, int)\n assert page > 0 and page_size > 0\n\n data_set = self.dataset()\n start, end = self.index_range(page, page_size)\n\n if end > len(data_set):\n return []\n return [data_set[start:end]]\n\n def get_hyper(self, page: int = 1, page_size: int = 10) -> Dict[str, int]:\n \"\"\"gets hypermedia\n Args:\n page (int, optional): number of page. Defaults to 1.\n page_size (int, optional): number of row in page. Defaults to 10.\n Returns:\n Dict[ str, int]HATEOAS\n \"\"\"\n totalPages = math.ceil(len(self.dataset()) / page_size)\n previousPage = page - 1 if page > 1 else None\n nextPage = page + 1 if page < totalPages else None\n data = self.get_page(page, page_size)\n\n hypermedia = {\n 'page_size': page_size,\n 'page': page,\n 'data': data,\n 'next_page': nextPage,\n 'prev_page': previousPage,\n 'total_pages': totalPages\n }\n\n return hypermedia\n","repo_name":"gims-inc/alx-backend","sub_path":"0x00-pagination/2-hypermedia_pagination.py","file_name":"2-hypermedia_pagination.py","file_ext":"py","file_size_in_byte":2466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"3978619547","text":"\"\"\"\n使用alexNet实现数字分类\n\"\"\"\nimport sys; print('Python %s on %s' % (sys.version, sys.platform))\nsys.path.extend(['C:\\\\Users\\\\Administrator\\\\Desktop\\\\detection', 'C:/Users/Administrator/Desktop/detection'])\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.examples.tutorials.mnist import input_data\nfrom AlexNet.read_CIFAR import *\nimport matplotlib.pyplot as plt\n\n# mnist=input_data.read_data_sets(\"MNIST_DATA\",one_hot=True)\n\ntf.set_random_seed(1)\n#1.定义卷积神经网络\ntf_x=tf.placeholder(tf.float32,[None,227,227,3])\ntf_y=tf.placeholder(tf.float32,[None,10])\n\nconv1=tf.layers.conv2d(tf_x,filters=96,kernel_size=11,strides=4,activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))#55*55*96\npool1=tf.layers.max_pooling2d(conv1,3,2)#27*27*96\nlrn=tf.nn.lrn(pool1,depth_radius=5,bias=2,alpha=1e-4,beta=0.75)\n\nconv2=tf.layers.conv2d(lrn,256,5,padding='same',activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))#27*27*256\npool2=tf.layers.max_pooling2d(conv2,3,2)#13*13*256\nlrn2=tf.nn.lrn(pool2,depth_radius=5,bias=2,alpha=1e-4,beta=0.75)\n\nconv3=tf.layers.conv2d(lrn2,384,3,padding='same',activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))#13*13*384\nlrn3=tf.nn.lrn(conv3,depth_radius=5,bias=2,alpha=1e-4,beta=0.75)\n\nconv4=tf.layers.conv2d(lrn3,384,3,padding='same',activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))#13*13*384\nlrn4=tf.nn.lrn(conv4,depth_radius=5,bias=2,alpha=1e-4,beta=0.75)\n\nconv5=tf.layers.conv2d(lrn4,256,3,padding='same',activation=tf.nn.relu,kernel_initializer=tf.truncated_normal_initializer(stddev=0.1,dtype=tf.float32))#13*13*256\npool5=tf.layers.max_pooling2d(conv5,3,2)#6*6*256\nlrn5=tf.nn.lrn(pool5,depth_radius=5,bias=2,alpha=1e-4,beta=0.75)\n\nflat=tf.reshape(lrn5,[-1,9216])\nfc1=tf.layers.dense(flat,4096,tf.nn.relu)\nfc2=tf.layers.dense(fc1,1024,tf.nn.relu)\noutput=tf.layers.dense(fc2,10,tf.nn.softmax)\n\nloss=tf.losses.softmax_cross_entropy(onehot_labels=tf_y,logits=output)\ntrain_op=tf.train.AdamOptimizer(1*1e-3).minimize(loss)\naccury=tf.metrics.accuracy(labels=tf_y,predictions=output)[1]\n\nsess=tf.Session()\nsess.run([tf.global_variables_initializer(),tf.local_variables_initializer()])\n\nif __name__=='__main__':\n saver = tf.train.Saver(max_to_keep=2)\n # modelfile=r'C:\\Users\\Administrator\\Desktop\\detection\\AlexNet\\model\\AlexNet-11600'\n # saver.restore(sess,modelfile)\n print(\"开始训练\")\n accuries=[]\n losses=[]\n losses_test=[]\n plt.ion()\n for i in range(200001):\n bx,by=get_traindata(64)\n train_op_,loss_=sess.run([train_op,loss],{tf_x:bx,tf_y:by})\n if i%100==0:\n losses.append(loss_)\n bxt,byt=get_testdata(64)\n loss_,accury_=sess.run([loss,accury],{tf_x:bxt,tf_y:byt})\n accuries.append(accury_)\n losses_test.append(loss_)\n plt.subplot(2,1,1)\n plt.plot(range(len(losses)),losses,'r-')\n plt.plot(range(len(losses_test)), losses_test, 'g-')\n plt.subplot(2, 1, 2)\n plt.plot(range(len(accuries)),accuries,'g-')\n plt.pause(0.1)\n\n print(\"准确度：\",accury_)\n \n if i%1000==0:\n saver.save(sess, r\"F:\\models\\AlexNet\", global_step=i)\n\n\n print(\"网络训练完成\")\n plt.ioff()\n plt.show()","repo_name":"msdnqqy/detection","sub_path":"AlexNet/alexNet.py","file_name":"alexNet.py","file_ext":"py","file_size_in_byte":3464,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"25254273472","text":"import json\nimport psycopg2\nfrom psycopg2 import sql\nfrom verify_emch_number import verify_emch_number_function\n\n\n#input will be emch as emch_number\n#needs to handle error\ndatabase_config = {\n \"user\": 'postgres',\n \"password\": '9812376024',\n \"host\": '172.19.0.2',\n \"port\": 5432,\n \"dbname\": 'postgres'\n}\n\ndef lambda_handler(event, context):\n body = json.loads(event.get(\"body\", \"{}\"))\n emch_number = body['emch']\n if verify_emch_number_function(emch_number):\n with psycopg2.connect(**database_config) as conn: \n with conn.cursor() as cur:\n dbquery = sql.SQL(\"INSERT INTO vehicles(emch) VALUES (%s)\")\n cur.execute(dbquery, (emch_number,))\n conn.commit()\n\n\n msg = {\n \"message\": \"Vehicle Added Successfully with following EMCH number: %s\"%(emch_number)\n }\n else:\n msg={\n \"message\": \"Vehicle Already Registered\"\n }\n\n return {\n \"statusCode\": 200,\n 'headers': {\n 'X-Custom-Header': \"application/json, text/plain\",\n 'Content-Type': \"application/json, text/plain\",\n 'Access-Control-Allow-Headers': \"content-type,X-Requested-With\",\n 'Access-Control-Allow-Origin': \"http://localhost:4004\",\n 'Access-Control-Allow-Methods': \"GET, POST, OPTIONS, PUT, PATCH, DELETE\"\n },\n \"body\": json.dumps(msg)\n }\n","repo_name":"atulyaduvanshieuler/sam-api","sub_path":"register_vehicle/register_vehicle_function.py","file_name":"register_vehicle_function.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"22328457164","text":"import json\r\nimport locale\r\n\r\n\r\nlocale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\r\n\r\n\r\nclass Movie:\r\n\r\n def __init__(self, dic, transform=True):\r\n if transform:\r\n # all fields are declared here, although they may change value\r\n self.title = dic['Title']\r\n self.year = int(dic['Year'][:4])\r\n self.rated = dic['Rated']\r\n self.genre = dic['Genre']\r\n self.director = dic['Director']\r\n self.writer = dic['Writer']\r\n self.actors = dic['Actors']\r\n self.plot = dic['Plot']\r\n self.language = dic['Language']\r\n self.country = dic['Country']\r\n self.awards = dic['Awards']\r\n self.poster = dic['Poster']\r\n self.type = dic['Type']\r\n self.runtime = 0\r\n self.ratings = []\r\n self.average_rating = 0\r\n self.box_office = 0\r\n self.lpmdb_id = 0\r\n\r\n self.setRuntime(dic['Runtime'])\r\n self.setRatings(dic['Ratings'])\r\n self.setAverageRating()\r\n self.setBoxOffice(dic.get('BoxOffice', 'N/A'))\r\n self.setlpmdbID(dic['imdbID'])\r\n\r\n else:\r\n self.__dict__ = dic\r\n\r\n def __str__(self):\r\n return (\"Title: {}\\n\".format(self.title)\r\n + \"Year: {}\\n\".format(self.year)\r\n + \"Rated: {}\\n\".format(self.rated)\r\n + \"Genre: {}\\n\".format(self.genre)\r\n + \"Director: {}\\n\".format(self.director)\r\n + \"Writer: {}\\n\".format(self.writer)\r\n + \"Actors: {}\\n\".format(self.actors)\r\n + \"Plot: {}\\n\".format(self.plot)\r\n + \"Language: {}\\n\".format(self.language)\r\n + \"Country: {}\\n\".format(self.country)\r\n + \"Awards: {}\\n\".format(self.awards)\r\n + \"Poster: {}\\n\".format(self.poster)\r\n + \"Type: {}\\n\".format(self.type)\r\n + \"Runtime: {} min\\n\".format(self.runtime)\r\n + \"Ratings: {}\\n\".format(self.print_ratings())\r\n + \"Average Rating: {}\\n\".format(self.average_rating)\r\n + \"Box Office: {}\\n\".format(locale.currency(self.box_office, grouping=True))\r\n + \"lpmdbID: {}\\n\".format(self.lpmdb_id))\r\n\r\n def print_ratings(self):\r\n out = '[Internet Movie Database: {}, Rotten Tomatoes: {}, Metacritic: {}]'\r\n length_r = len(self.ratings)\r\n\r\n if length_r != 3:\r\n x = 3 - length_r\r\n self.ratings = self.ratings + x * [self.average_rating]\r\n\r\n out = out.format(*self.ratings)\r\n\r\n return out\r\n\r\n def setRuntime(self, string):\r\n self.runtime = int(string[:-4])\r\n\r\n @staticmethod\r\n def load(dic):\r\n return Movie(dic, False)\r\n\r\n def setRatings(self, list_of_ratings):\r\n\r\n for rating in list_of_ratings:\r\n if rating['Source'] == 'Internet Movie Database':\r\n imdb = rating['Value']\r\n imdb = int(imdb[:-3].replace('.',''))\r\n self.ratings.append(imdb)\r\n elif rating['Source'] == 'Rotten Tomatoes':\r\n rotten = rating['Value']\r\n rotten = int(rotten[:-1])\r\n self.ratings.append(rotten)\r\n elif rating['Source'] == 'Metacritic':\r\n metacritic = rating['Value']\r\n metacritic = int(metacritic[:-4])\r\n self.ratings.append(metacritic)\r\n\r\n def setAverageRating(self):\r\n self.average_rating = sum(self.ratings)/len(self.ratings)\r\n\r\n def setBoxOffice(self, string):\r\n number = string[1:].replace(',', '')\r\n try:\r\n self.box_office = int(number)\r\n except ValueError: # sometimes the string is 'N/A'\r\n self.box_office = 0\r\n\r\n def setlpmdbID(self, string):\r\n self.lpmdb_id = int(string[2:])\r\n\r\n def dumps(self):\r\n return json.dumps(self.__dict__)\r\n","repo_name":"PedroCardouzo/LPMDB","sub_path":"lib/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":3944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"6965576716","text":"import numpy as np\n\n\ndef bpsk_demod(qamSeq):\n \"\"\"\n Cette fonction prend en entrée une séquence de symboles QPSK\n modulée avec une démodulation BPSK pour retourner la séquence\n de bits correspondante.\n\n Args:\n qamSeq (ndarray): Une séquence de symboles QPSK.\n\n Returns:\n ndarray: Une séquence de bits.\n \"\"\"\n output = []\n\n for elem in qamSeq:\n if np.real(elem) > 0:\n bit = 1\n else:\n bit = 0\n output.append(bit)\n\n return output\n\n\ndef hamming748_decode(bitSeq):\n \"\"\"\n An error corrector capable of detecting and correcting single bit errors. \n The three coded bits are chosen so that each bit position in the code word is covered \n by a unique combination of bits, as well as a parity bit.\n \"\"\"\n H = np.array([[0, 0, 0, 1, 1, 1, 1], [\n 0, 1, 1, 0, 0, 1, 1], [1, 0, 1, 0, 1, 0, 1]])\n res = []\n for i in range(0, len(bitSeq), 8):\n bit_group = bitSeq[i:i+8]\n syndrom = np.dot(H, bit_group[:len(bit_group)-1])\n syndrom = syndrom % 2\n\n if np.array_equal(syndrom, np.array([0, 0, 0])):\n res += bit_group[:4]\n else:\n sum_parity = np.sum(bit_group[:len(bit_group)-1])\n sum_parity = np.mod(sum_parity, 2)\n if sum_parity == bit_group[7]:\n res += bit_group[:4]\n else:\n binary = syndrom[0]*4 + syndrom[1]*2 + syndrom[2]*1\n bit_group[binary-1] = np.mod(bit_group[binary-1] + 1, 2)\n res += bit_group[:4]\n return res\n\n\ndef bin2dec(nb):\n \"\"\"\n Transform a binary list to an integer\n \"\"\"\n n = \"0b\"\n for b in nb:\n n = n + str(b)\n return int(n, 2)\n\n\ndef qpsk_demod(qamSeq):\n \"\"\"\n Implement a QPSK demodulator\n it takes a complex-valued input qamSeq and produces a binary sequence as output. \n \"\"\"\n res = []\n for i in range(qamSeq.shape[0]):\n if qamSeq[i].real > 0:\n if qamSeq[i].imag > 0:\n res += [1, 1]\n else:\n res += [1, 0]\n else:\n if qamSeq[i].imag > 0:\n res += [0, 1]\n else:\n res += [0, 0]\n return res\n\n\ndef pdcchu_decode(stream, MCS):\n if MCS == 0:\n return bpsk_demod(stream[:72])\n elif MCS == 2:\n return qpsk_demod(stream[:72])\n","repo_name":"paulbretonpro/wirelessnetwork","sub_path":"decode.py","file_name":"decode.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"30879838188","text":"\"\"\"Preprocess a directory with S_0000.tif, S_0001.tif, ... S_*.tif into trakem2 import text file\"\"\"\nfrom __future__ import print_function, division\nimport os\nimport glob\nimport tifffile\nimport numpy as np\nfrom matplotlib.pyplot import *\nimport argparse\nimport shutil\nimport sys\nimport cv2\n#import magic\nimport re\nfrom PIL import Image\nfrom ast import literal_eval as make_tuple\nfrom tqdm import tqdm\nfrom pprint import pprint\nclass EM_preprocessor(object):\n def __init__(self, input_dir, output):\n self.input_dir = input_dir\n self.output = output\n output_dir = os.path.dirname(self.output)\n os.makedirs(output_dir, exist_ok=True)\n\n self.flist = None\n self.MAX_ROW = 0\n self.MAX_COL = 0\n\n self.TILE_ROW = 0\n self.TILE_COL = 0\n self.TILE_MIN = 0\n self.TILE_MAX = 0\n self.DTYPE = 0\n\n def test_one_image(self):\n f_dummy = glob.glob(os.path.join(self.input_dir, '*.tif*'))[0]\n dummy_data = cv2.imread(f_dummy, flags=cv2.IMREAD_GRAYSCALE)\n print(dummy_data.shape)\n self.TILE_ROW, self.TILE_COL = dummy_data.shape\n self.TILE_MIN, self.TILE_MAX = np.min(dummy_data[:]), np.max(dummy_data[:])\n print(self.TILE_ROW, self.TILE_COL, self.TILE_MIN,\n self.TILE_MAX, dummy_data.dtype)\n if dummy_data.dtype == np.uint8:\n print('8bit')\n self.DTYPE = 0\n elif dummy_data.dtype == np.uint16:\n print('16bit')\n self.DTYPE = 1\n\n def prepare_align_txt(self):\n #f_align_txt = os.path.join(self.output_dir, 'align.txt')\n\n flist = np.asarray(glob.glob(os.path.abspath(\n os.path.join(self.input_dir, '*.tif*'))))\n inds = [int(re.search('.*_([0-9]*)', f.split('/')[-1]).group(1))\n for f in flist]\n flist = flist[np.argsort(inds)]\n with open(self.output, 'w') as output:\n for i, f in enumerate(flist):\n command = '{0} \\t {1} \\t {2} \\t {3} \\t {4} \\t {5} \\t {6} \\t {7} \\t {8} \\n'.format(\n f, 0, 0, i, self.TILE_COL, self.TILE_ROW, self.TILE_MIN, self.TILE_MAX, self.DTYPE)\n print(command)\n output.write(command)\n output.close()\n\n def run(self):\n print(\"Input:\", self.input_dir)\n print(\"Output:\", self.output)\n\n self.flist = glob.glob(os.path.join(self.input_dir, 'S_*'))\n pprint(self.flist)\n self.test_one_image()\n self.prepare_align_txt()\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('input')\n parser.add_argument('output')\n args = parser.parse_args()\n emp = EM_preprocessor(args.input, args.output)\n emp.run()\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Hanyu-Li/klab_utils","sub_path":"klab_utils/trakem2/preprocess_stack.py","file_name":"preprocess_stack.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"29609609277","text":"class Solution:\n def countBits(self, n: int) -> List[int]:\n def cnt(x):\n res = 0\n while x:\n x = x&(x-1)\n res+=1\n return res\n res = [cnt(x) for x in range(n+1)]\n return res\n\n\n","repo_name":"CA2528357431/leetcode-note","sub_path":"LIST2/0923 剑指 Offer II 003.py","file_name":"0923 剑指 Offer II 003.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"75"} +{"seq_id":"25307777424","text":"import pickle\n\nimport torch\nfrom tqdm import tqdm\nfrom transformers import RobertaTokenizer, RobertaModel\n\nfrom SourceCodeTools.models.Embedder import Embedder\nfrom SourceCodeTools.nlp.codebert.codebert import CodeBertModelTrainer, load_typed_nodes\nfrom SourceCodeTools.nlp.entity.type_prediction import get_type_prediction_arguments, ModelTrainer\nfrom SourceCodeTools.nlp.entity.utils.data import read_data\n\n\nclass CodeBertModelTrainer2(CodeBertModelTrainer):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def set_type_ann_edges(self, path):\n self.type_ann_edges = path\n\n def get_batcher(self, *args, **kwargs):\n kwargs.update({\"tokenizer\": \"codebert\"})\n return self.batcher(*args, **kwargs)\n\n def train_model(self):\n # graph_emb = load_pkl_emb(self.graph_emb_path) if self.graph_emb_path is not None else None\n\n typed_nodes = load_typed_nodes(self.type_ann_edges)\n\n decoder_mapping = RobertaTokenizer.from_pretrained(\"microsoft/codebert-base\").decoder\n tok_ids, words = zip(*decoder_mapping.items())\n vocab_mapping = dict(zip(words, tok_ids))\n batcher = self.get_batcher(\n self.train_data + self.test_data, self.batch_size, seq_len=self.seq_len,\n graphmap=None,\n wordmap=vocab_mapping, tagmap=None,\n class_weights=False, element_hash_size=1\n )\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n model = RobertaModel.from_pretrained(\"microsoft/codebert-base\")\n model.to(device)\n\n node_ids = []\n embeddings = []\n\n for ind, batch in enumerate(tqdm(batcher)):\n # token_ids, graph_ids, labels, class_weights, lengths = b\n token_ids = torch.LongTensor(batch[\"tok_ids\"])\n lens = torch.LongTensor(batch[\"lens\"])\n\n token_ids[token_ids == len(vocab_mapping)] = vocab_mapping[\"\"]\n\n def get_length_mask(target, lens):\n mask = torch.arange(target.size(1)).to(target.device)[None, :] < lens[:, None]\n return mask\n\n mask = get_length_mask(token_ids, lens)\n with torch.no_grad():\n embs = model(input_ids=token_ids, attention_mask=mask)\n\n for s_emb, s_repl in zip(embs.last_hidden_state, batch[\"replacements\"]):\n unique_repls = set(list(s_repl))\n repls_for_ann = [r for r in unique_repls if r in typed_nodes]\n\n for r in repls_for_ann:\n position = s_repl.index(r)\n if position > 512:\n continue\n node_ids.append(r)\n embeddings.append(s_emb[position])\n\n all_embs = torch.stack(embeddings, dim=0).numpy()\n embedder = Embedder(dict(zip(node_ids, range(len(node_ids)))), all_embs)\n pickle.dump(embedder, open(\"codebert_embeddings.pkl\", \"wb\"), fix_imports=False)\n print(node_ids)\n\n\ndef main():\n # device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n # tokenizer = RobertaTokenizer.from_pretrained(\"microsoft/codebert-base\")\n model = RobertaModel.from_pretrained(\"microsoft/codebert-base\")\n # model.to(device)\n args = get_type_prediction_arguments()\n\n # allowed = {'str', 'bool', 'Optional', 'None', 'int', 'Any', 'Union', 'List', 'Dict', 'Callable', 'ndarray',\n # 'FrameOrSeries', 'bytes', 'DataFrame', 'Matcher', 'float', 'Tuple', 'bool_t', 'Description', 'Type'}\n\n train_data, test_data = read_data(\n open(args.data_path, \"r\").readlines(), normalize=True, allowed=None, include_replacements=True,\n include_only=\"entities\",\n min_entity_count=args.min_entity_count, random_seed=args.random_seed\n )\n\n trainer = CodeBertModelTrainer(train_data, test_data, params={}, seq_len=512)\n trainer.set_type_ann_edges(args.type_ann_edges)\n trainer.train_model()\n\n\n\nif __name__ == \"__main__\":\n main()","repo_name":"VitalyRomanov/method-embedding","sub_path":"SourceCodeTools/nlp/codebert/deprecated/codebert_training.py","file_name":"codebert_training.py","file_ext":"py","file_size_in_byte":3990,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"75"} +{"seq_id":"21191601288","text":"from cells import Cell\nimport time\nimport random\n\nclass Maze:\n def __init__(self, x1, y1, num_rows, num_cols, cell_size, win, animation_speed=0.01, seed=None):\n self.__x1 = x1\n self.__y1 = y1\n self.__num_rows = num_rows\n self.__num_cols = num_cols\n self.__cell_size = cell_size\n self.__win = win\n self.__animation_speed = animation_speed\n if seed is not None:\n random.seed(seed)\n # self.__cells is a list of lists of cells. Each inner list is a column of cells\n self.__cells = []\n self.__create_cells()\n\n def __create_cells(self):\n for col in range(self.__num_cols):\n self.__cells.append([])\n for row in range(self.__num_rows):\n x1 = self.__x1 + col * self.__cell_size\n y1 = self.__y1 + row * self.__cell_size\n x2 = x1 + self.__cell_size\n y2 = y1 + self.__cell_size\n self.__cells[col].append(Cell(x1, y1, x2, y2, self.__win))\n self.__draw_maze()\n \n def __draw_maze(self):\n for col in self.__cells:\n for cell in col:\n cell.draw()\n self.__animate()\n self.__break_entrance_and_exit()\n self.__break_walls_r(self.__cells[0][0])\n self.__reset_cells_visited()\n self.solve()\n\n def __animate(self):\n self.__win.redraw()\n time.sleep(self.__animation_speed)\n\n def __break_entrance_and_exit(self):\n # In each maze the entrace is the top wall of the top-left cell \n # and the exit is the bottom wall of the bottom-right cell\n\n # Break the top wall of the top-left cell\n top_left_cell = self.__cells[0][0]\n top_left_cell.has_top_wall = False\n top_left_cell.draw()\n self.__animate()\n\n # Break the bottom wall of the bottom-right cell\n bottom_right_cell = self.__cells[-1][-1]\n bottom_right_cell.has_bottom_wall = False\n bottom_right_cell.draw()\n self.__animate()\n\n def __break_walls_r(self, cell):\n cell.is_visited = True\n while True:\n # Create a list to store the unvisited neighbours of the cell\n unvisited_neighbours = []\n\n # Get the neighbours of the cell\n neighbours = self.__get_neighbours(cell)\n\n # Iterate through the neighbours\n for neighbour in neighbours:\n # If the neighbour has not been visited, add it to the unvisited_neighbours list\n if not neighbour.is_visited:\n unvisited_neighbours.append(neighbour)\n\n # If there are no unvisited neighbours, return\n if len(unvisited_neighbours) == 0:\n return\n\n # Choose a random unvisited neighbour\n random_neighbour = random.choice(unvisited_neighbours)\n\n # Break the wall between the cell and the random neighbour\n self.__break_wall(cell, random_neighbour)\n\n # Recursively call the __break_walls_r method with the random neighbour as the argument\n self.__break_walls_r(random_neighbour)\n\n def __get_neighbours(self, cell):\n # Create a list to store the neighbours\n neighbours = []\n\n # Get the row and column of the cell\n row = self.__get_row(cell)\n col = self.__get_col(cell)\n\n # If the cell is not in the first row, add the cell above it to the neighbours list\n if row > 0:\n neighbours.append(self.__cells[col][row - 1])\n # If the cell is not in the last row, add the cell below it to the neighbours list\n if row < self.__num_rows - 1:\n neighbours.append(self.__cells[col][row + 1])\n # If the cell is not in the first column, add the cell to the left of it to the neighbours list\n if col > 0:\n neighbours.append(self.__cells[col - 1][row])\n # If the cell is not in the last column, add the cell to the right of it to the neighbours list\n if col < self.__num_cols - 1:\n neighbours.append(self.__cells[col + 1][row])\n\n # Return the neighbours list\n return neighbours\n \n def __get_row(self, cell) -> int:\n # Get the column of the cell\n col = self.__get_col(cell)\n\n # Iterate through the cells in the column\n for row in range(self.__num_rows):\n # If the cell is found, return its row\n if self.__cells[col][row] == cell:\n return row\n \n # If the cell is not found, raise an AssertionError\n assert False, \"Cell not found\"\n \n def __get_col(self, cell) -> int:\n # Iterate through the columns\n for col in range(self.__num_cols):\n # Iterate through the cells in the column\n for row in range(self.__num_rows):\n # If the cell is found, return its column\n if self.__cells[col][row] == cell:\n return col\n \n # If the cell is not found, raise an AssertionError\n assert False, \"Cell not found\"\n \n def __break_wall(self, cell1, cell2):\n # Get the row and column of cell1\n row1 = self.__get_row(cell1)\n col1 = self.__get_col(cell1)\n\n # Get the row and column of cell2\n row2 = self.__get_row(cell2)\n col2 = self.__get_col(cell2)\n\n # If cell1 is to the left of cell2\n if col1 < col2:\n # Break the right wall of cell1 and the left wall of cell2\n cell1.has_right_wall = False\n cell2.has_left_wall = False\n # If cell1 is to the right of cell2\n elif col1 > col2:\n # Break the left wall of cell1 and the right wall of cell2\n cell1.has_left_wall = False\n cell2.has_right_wall = False\n # If cell1 is above cell2\n elif row1 < row2:\n # Break the bottom wall of cell1 and the top wall of cell2\n cell1.has_bottom_wall = False\n cell2.has_top_wall = False\n # If cell1 is below cell2\n elif row1 > row2:\n # Break the top wall of cell1 and the bottom wall of cell2\n cell1.has_top_wall = False\n cell2.has_bottom_wall = False\n\n # Draw the walls\n cell1.draw()\n self.__animate()\n cell2.draw()\n self.__animate()\n \n def __reset_cells_visited(self):\n for col in self.__cells:\n for cell in col:\n cell.is_visited = False\n\n def solve(self):\n # Create a list to store the cells that are part of the path\n path = []\n\n # Get the entrance cell\n entrance_cell = self.__cells[0][0]\n\n # Add the entrance cell to the path\n path.append(entrance_cell)\n\n # Call the __solve_r method with the entrance cell and the path as the arguments\n return self.__solve_r(entrance_cell, path)\n\n def __solve_r(self, cell, path):\n # This function will move through cells that dont have a path between and try to reach the exit\n # If we find a deadend it will retrace back to the last cell that had a path between and try to find another path\n # If we find the exit it will return True\n # If we find a deadend and there is no cell in the path that has a path between it will return False\n # It will draw the path while moving with the draw_move method\n\n # If the cell is the exit, return True\n if self.__is_exit(cell):\n return True\n \n # Get the neighbours of the cell\n neighbours = self.__get_neighbours_without_wall_between(cell)\n\n # Iterate through the neighbours\n for neighbour in neighbours:\n # If the neighbour is not in the path\n if neighbour not in path:\n # Add the neighbour to the path\n path.append(neighbour)\n\n # Draw the move from the cell to the neighbour\n cell.draw_move(neighbour)\n self.__animate()\n\n # Call the __solve_r method with the neighbour and the path as the arguments\n if self.__solve_r(neighbour, path):\n return True\n\n # If the __solve_r method returns False, remove the neighbour from the path\n path.remove(neighbour)\n\n # Draw the move from the neighbour to the cell\n neighbour.draw_move(cell, True)\n self.__animate()\n\n # If the function has not returned True, return False\n return False\n \n \n def __is_exit(self, cell):\n # Get the row and column of the cell\n row = self.__get_row(cell)\n col = self.__get_col(cell)\n\n # If the cell is in the last column and the last row, return True\n if col == self.__num_cols - 1 and row == self.__num_rows - 1:\n return True\n \n # If the cell is not in the last column or the last row, return False\n return False\n\n def __get_neighbours_without_wall_between(self, cell):\n # Create a list to store the neighbours\n neighbours = []\n\n # Get the row and column of the cell\n row = self.__get_row(cell)\n col = self.__get_col(cell)\n\n # If the cell is not in the first row and the top wall of the cell is broken, add the cell above it to the neighbours list\n if row > 0 and not cell.has_top_wall:\n neighbours.append(self.__cells[col][row - 1])\n\n # If the cell is not in the last row and the bottom wall of the cell is broken, add the cell below it to the neighbours list\n if row < self.__num_rows - 1 and not cell.has_bottom_wall:\n neighbours.append(self.__cells[col][row + 1])\n\n # If the cell is not in the first column and the left wall of the cell is broken, add the cell to the left of it to the neighbours list\n if col > 0 and not cell.has_left_wall:\n neighbours.append(self.__cells[col - 1][row])\n\n # If the cell is not in the last column and the right wall of the cell is broken, add the cell to the right of it to the neighbours list\n if col < self.__num_cols - 1 and not cell.has_right_wall:\n neighbours.append(self.__cells[col + 1][row])\n\n # Return the neighbours list\n return neighbours\n ","repo_name":"Umbrasyl/mazeSolver","sub_path":"maze.py","file_name":"maze.py","file_ext":"py","file_size_in_byte":10375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"27541986510","text":"lst=[]\r\nn=int(input(\"enter the range\"))\r\nfor i in range(n+1):\r\n lst.append(i)\r\nprint(\"sum of n natural numbers is \",sum(lst)) \r\ncount=0\r\nm=int(input(\"enter the range\")) \r\nfor i in range(m+1):\r\n count=count+(i**2)\r\nprint(\"sum of squares is\",count)\r\ncount=0\r\nm=int(input(\"enter the range\")) \r\nfor i in range(m+1):\r\n count=count+(i**3)\r\nprint(\"sum of the cubes is\",count)\r\ncount=0\r\nm=int(input(\"enter the range\")) \r\nfor i in range(m+1):\r\n if i%2!=0:\r\n count=count+(i)\r\nprint(\"sum of the cubes is\",count)\r\ncount=0\r\nm=int(input(\"enter the range\")) \r\nfor i in range(m+1):\r\n if i%2==0:\r\n count=count+(i)\r\nprint(\"sum of the cubes is\",count)\r\nfactorial=1\r\nm=int(input(\"enter the range\"))\r\nfor i in range(1,m+1):\r\n factorial=factorial*i\r\nprint(f\"factorial of {m} is {factorial}\")\r\ncount=0\r\nm=input(\"enter a number\")\r\nfor i in m:\r\n count=count+1\r\nprint(\"digits count in given nbr is\",count)\r\ncount=[]\r\nm=(input(\"enter a number\"))\r\nfor i in m:\r\n count.append(int(i))\r\nprint(\"sum of digits in given nbr is\",sum(count))\r\n#get input and initialize variables\r\ndecimal = int(input(\"Enter a decimal number \\n\"))\r\nbinary = 0\r\nctr = 0\r\ntemp = decimal \r\nwhile(temp > 0):\r\n binary = ((temp%2)*(10**ctr)) + binary\r\n temp = int(temp/2)\r\n ctr += 1 \r\nprint(\"Binary of {x} is: {y}\".format(x=decimal,y=binary))\r\ndecimal = int(input(\"Enter a decimal number :\"))\r\n\r\nprint(\"The octal equivalent is :\",decimal_to_octal(decimal))\r\n\r\ndef decimal_to_octal(decimal):\r\n octal = 0\r\n i = 1\r\n while (decimal != 0):\r\n octal = octal + (decimal % 8) * i\r\n decimal = int(decimal / 8)\r\n i = i * 10\r\n return octal","repo_name":"Shivaprasad431/Shiva-s","sub_path":"assignment11.py","file_name":"assignment11.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"16101959969","text":"#!/usr/bin/env blender --python\n\nimport bpy\nimport os\nfrom math import pi\n\ndef create_empty(name):\n #obj = bpy.data.objects.new(name, None)\n #bpy.context.collection.objects.link(obj)\n bpy.ops.object.empty_add(type=\"PLAIN_AXES\", radius=0.1)\n #obj.scale = (0.1, 0.1, 0.1)\n #bpy.ops.object.transform_apply()\n #return obj\n bpy.context.object.name = name\n return bpy.context.object\n\n# def import_stl(filepath):\n# parent = create_empty(filepath)\n# bpy.ops.import_mesh.stl(filepath=filepath)\n# for i in bpy.context.selected_objects:\n# i.parent = parent\n# return parent\n\n# def import_obj(filepath):\n# parent = create_empty(filepath)\n# bpy.ops.import_scene.obj(filepath=filepath)\n# for i in bpy.context.selected_objects:\n# if i.type == \"MESH\":\n# # Set each mesh's parent\n# i.parent = parent\n# else:\n# # delete lights, cameras, and any other non-mesh objects\n# bpy.data.objects.remove(i, do_unlink=True)\n# return parent\n\n# def import_collada(filepath):\n# parent = create_empty(filepath)\n# bpy.ops.wm.collada_import(filepath=filepath)\n# for i in bpy.context.selected_objects:\n# if i.type == \"MESH\":\n# # Set each mesh's parent\n# i.parent = parent\n# else:\n# # delete lights, cameras, and any other non-mesh objects\n# bpy.data.objects.remove(i, do_unlink=True)\n# return parent\n\n\ndef import_mesh(name, filepath):\n parent = create_empty(name)\n\n _, ext = os.path.splitext(filepath)\n if \".dae\" == ext.lower():\n bpy.ops.wm.collada_import(filepath=filepath)\n elif \".stl\" == ext.lower():\n bpy.ops.import_mesh.stl(filepath=filepath)\n elif \".obj\" == ext.lower():\n bpy.ops.import_scene.obj(filepath=filepath)\n else:\n return None\n\n for i in bpy.context.selected_objects:\n if i.type == \"MESH\":\n # Set each mesh's parent\n i.parent = parent\n i.name = name + '.' + i.name\n else:\n # delete lights, cameras, and any other non-mesh objects\n bpy.data.objects.remove(i, do_unlink=True)\n\n bpy.context.view_layer.objects.active = parent\n parent.select_set(True)\n return parent\n\nclass Gripper:\n def __init__(self):\n self._links = {}\n self._joints = {}\n self._mesh_path = \"robotiq_2f_85_gripper_visualization/meshes/\"\n self._grasp_point = create_empty(\"grasp_point\")\n self._robotiq_arg2f_85()\n base = self._links[\"robotiq_arg2f_base_link\"]\n base.parent = self._grasp_point\n base.location = (0, 0, -0.125)\n\n def _robotiq_arg2f_85(self):\n self._robotiq_arg2f_base_link()\n self._finger_links(fingerprefix=\"left\", stroke=85)\n self._finger_links(fingerprefix=\"right\", stroke=85)\n self._finger_joint()\n self._right_outer_knuckle_joint()\n self._robotiq_arg2f_transmission()\n\n def _link(self, name, visual, scale, color):\n mesh = import_mesh(name, self._mesh_path + visual)\n bpy.ops.transform.resize(value=(scale, scale, scale))\n bpy.ops.object.transform_apply()\n self._links[name] = mesh\n\n def _joint(self, name, type, parent, child, axis=(0.0, 0.0, 1.0), xyz=(0.0, 0.0, 0.0), rpy=(0.0, 0.0, 0.0), q_min=0.0, q_max=0.0):\n origin = create_empty(name+\"_origin\")\n origin.parent = self._links[parent]\n origin.location = xyz\n origin.rotation_euler = rpy\n joint = create_empty(name)\n joint.parent = origin\n joint.rotation_mode = 'AXIS_ANGLE'\n joint.rotation_axis_angle = (0.0, axis[0], axis[1], axis[2])\n self._links[child].parent = joint\n self._joints[name] = { \"type\": type, \"axis\": axis, \"q_min\": q_min, \"q_max\": q_max, \"joint\": joint }\n\n def _robotiq_arg2f_base_link(self):\n self._link(\n name=\"robotiq_arg2f_base_link\",\n visual=\"visual/robotiq_arg2f_85_base_link.dae\",\n scale=0.001,\n color=(0.1, 0.1, 0.1, 1.0))\n\n def _finger_links(self, fingerprefix, stroke):\n self._outer_knuckle(fingerprefix=fingerprefix, stroke=stroke)\n self._outer_finger(fingerprefix=fingerprefix, stroke=stroke)\n self._inner_finger(fingerprefix=fingerprefix, stroke=stroke)\n self._inner_finger_pad(fingerprefix=fingerprefix)\n self._inner_knuckle(fingerprefix=fingerprefix)\n\n def _outer_knuckle(self, fingerprefix, stroke):\n self._link(\n name=fingerprefix+\"_outer_knuckle\",\n visual=\"visual/robotiq_arg2f_85_outer_knuckle.dae\",\n scale=0.001,\n color=(0.792156862745098, 0.819607843137255, 0.933333333333333, 1.0))\n\n def _outer_finger(self, fingerprefix, stroke):\n self._link(\n name=fingerprefix+\"_outer_finger\",\n visual=\"visual/robotiq_arg2f_85_outer_finger.dae\",\n scale=0.001,\n color=(0.1, 0.1, 0.1, 1.0))\n\n def _inner_finger(self, fingerprefix, stroke):\n self._link(\n name=fingerprefix+\"_inner_finger\",\n visual=\"visual/robotiq_arg2f_85_inner_finger.dae\",\n scale=0.001,\n color=(0.1, 0.1, 0.1, 1.0))\n\n def _inner_finger_pad(self, fingerprefix):\n # TODO: \n # TODO: \n name=fingerprefix+\"_inner_finger_pad\"\n bpy.ops.mesh.primitive_cube_add(size=1.0, enter_editmode=False)\n bpy.context.object.name = name\n self._links[name] = bpy.context.object\n bpy.ops.transform.resize(value=(0.022, 0.00635, 0.0375))\n bpy.ops.object.transform_apply()\n\n def _inner_knuckle(self, fingerprefix):\n self._link(\n name=fingerprefix+\"_inner_knuckle\",\n visual=\"visual/robotiq_arg2f_85_inner_knuckle.dae\",\n scale=0.001,\n color=(0.1, 0.1, 0.1, 1.0))\n\n def _finger_joint(self):\n self._joint(\n name=\"finger_joint\",\n type=\"revolute\",\n parent=\"robotiq_arg2f_base_link\",\n child=\"left_outer_knuckle\",\n axis=(1.0, 0.0, 0.0),\n xyz=(0.0, -0.0306011, 0.054904),\n rpy=(0.0, 0.0, pi),\n q_min=0, q_max=0.8)\n self._finger_joints(fingerprefix=\"left\", reflect=1.0)\n\n def _right_outer_knuckle_joint(self):\n self._joint(\n name=\"right_outer_knuckle_joint\",\n type=\"revolute\",\n parent=\"robotiq_arg2f_base_link\",\n child=\"right_outer_knuckle\",\n axis=(1.0, 0.0, 0.0),\n xyz=(0.0, 0.0306011, 0.054904),\n rpy=(0.0, 0.0, 0.0),\n q_min=0.0, q_max=0.81)\n self._finger_joints(fingerprefix=\"right\", reflect=-1.0)\n\n def _finger_joints(self, fingerprefix, reflect):\n self._outer_finger_joint(fingerprefix)\n self._inner_knuckle_joint(fingerprefix, reflect)\n self._inner_finger_joint(fingerprefix)\n self._inner_finger_pad_joint(fingerprefix)\n\n def _outer_finger_joint(self, fingerprefix):\n self._joint(\n name=fingerprefix+\"_outer_finger_joint\",\n type=\"fixed\",\n parent=fingerprefix+\"_outer_knuckle\",\n child=fingerprefix+\"_outer_finger\",\n xyz=(0.0, 0.0315, -0.0041))\n\n def _inner_knuckle_joint(self, fingerprefix, reflect):\n self._joint(\n name=fingerprefix+\"_inner_knuckle_joint\",\n type=\"revolute\",\n xyz=(0.0, reflect * -0.0127, 0.06142),\n rpy=(0.0, 0.0, (1 + reflect) * pi / 2),\n parent=\"robotiq_arg2f_base_link\",\n child=fingerprefix+\"_inner_knuckle\",\n axis=(1.0, 0.0, 0.0),\n q_min=0.0, q_max=0.8757)\n\n def _inner_finger_joint(self, fingerprefix):\n self._joint(\n name=fingerprefix+\"_inner_finger_joint\",\n type=\"revolute\",\n xyz=(0.0, 0.0061, 0.0471),\n parent=fingerprefix+\"_outer_finger\",\n child=fingerprefix+\"_inner_finger\",\n axis=(1.0, 0.0, 0.0),\n q_min=0.0, q_max=0.8757)\n\n def _inner_finger_pad_joint(self, fingerprefix):\n self._joint(\n name=fingerprefix+\"_inner_finger_pad_joint\",\n type=\"fixed\",\n xyz=(0.0, -0.0220203446692936, 0.03242),\n parent=fingerprefix+\"_inner_finger\",\n child=fingerprefix+\"_inner_finger_pad\")\n\n def _robotiq_arg2f_transmission(self):\n pass\n\n def set_config(self, q):\n self._joints[\"finger_joint\"][\"joint\"].rotation_axis_angle[0] = q\n self._joints[\"right_inner_knuckle_joint\"][\"joint\"].rotation_axis_angle[0] = q\n self._joints[\"left_inner_knuckle_joint\"][\"joint\"].rotation_axis_angle[0] = q\n self._joints[\"right_inner_finger_joint\"][\"joint\"].rotation_axis_angle[0] = -q\n self._joints[\"left_inner_finger_joint\"][\"joint\"].rotation_axis_angle[0] = -q\n self._joints[\"right_outer_knuckle_joint\"][\"joint\"].rotation_axis_angle[0] = q\n\n def set_material(self, mat):\n def recur(obj):\n if \"MESH\" == obj.type:\n obj.active_material = mat\n print(obj)\n \n for c in obj.children:\n recur(c)\n \n for link in self._links.values():\n recur(link)\n\ndef create_shadeless_material():\n #tree = bpy.context.scene.node_tree\n mat = bpy.data.materials.new(name=\"Flat\")\n mat.use_nodes = True\n out = mat.node_tree.nodes[0]\n rgb = mat.node_tree.nodes.new(type=\"ShaderNodeRGB\")\n rgb.outputs[0].default_value = (1, 0, 0, 1) # RGBA = red\n mat.node_tree.links.new(rgb.outputs[0], out.inputs[0])\n return mat\n\n\nif \"__main__\" == __name__:\n # Delete the starting cube\n bpy.ops.object.delete(use_global=False)\n\n mat = create_shadeless_material()\n gripper = Gripper()\n gripper.set_material(mat)\n #gripper.set_config(0.5)\n\n z_near = 0.5\n z_far = 1.5\n\n\n # position the camera 1 meter above the view plane.\n # Objects at the origin will be centered in the rendered view\n camera = bpy.data.objects['Camera']\n camera.location = (0.0, 0.0, 1.0)\n camera.rotation_euler = (0.0, 0.0, 0.0)\n camera.data.clip_start = z_near\n camera.data.clip_end = z_far\n","repo_name":"BerkeleyAutomation/Orienting_Novel_3D_Objects","sub_path":"blender/gripper.py","file_name":"gripper.py","file_ext":"py","file_size_in_byte":10282,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"34492341408","text":"from mtcnn.mtcnn import MTCNN\r\nimport numpy as np\r\nfrom PIL import Image\r\nfrom tensorflow.keras.models import load_model\r\nimport cv2\r\nfrom sklearn.preprocessing import Normalizer\r\n\r\n\r\npessoa = [\"caio\",\"desconhecido\"]\r\n\r\nnum_classes = len(pessoa)\r\n\r\ncap = cv2.VideoCapture(0)\r\n\r\ndetector = MTCNN()\r\nfacenet = load_model(\"facenet_keras.h5\")\r\nmodel = load_model(\"faces_d.h5\")\r\n\r\ndef extract_face(image, box, required_size=(160, 160)):\r\n pixels = np.asarray(image)\r\n \r\n x1, y1, width, height = box\r\n \r\n x2, y2 = x1 + width, y1 + height\r\n \r\n face = pixels[y1:y2, x1:x2]\r\n \r\n image = Image.fromarray(face)\r\n image = image.resize(required_size)\r\n \r\n return np.asarray(image)\r\n\r\ndef get_embedding(facenet, face_pixels):\r\n face_pixels = face_pixels.astype('float32')\r\n \r\n mean, std = face_pixels.mean(), face_pixels.std()\r\n face_pixels = (face_pixels - mean)/ std\r\n \r\n samples = np.expand_dims(face_pixels, axis=0)\r\n \r\n yhat = facenet.predict(samples)\r\n return yhat[0]\r\n\r\nwhile True:\r\n _, frame= cap.read()\r\n \r\n faces = detector.detect_faces(frame)\r\n \r\n for face in faces:\r\n confidence = face['confidence'] * 100\r\n \r\n if confidence >= 98:\r\n x1, y1, w, h = face['box']\r\n face = extract_face(frame, face['box'])\r\n face = face.astype(\"float32\")/255\r\n \r\n emb = get_embedding(facenet, face)\r\n \r\n tensor = np.expand_dims(emb, axis=0)\r\n norm = Normalizer(norm=\"l2\")\r\n tensor = norm.transform(tensor)\r\n \r\n classe = model.predict_classes(tensor)[0]\r\n prob = model.predict_proba(tensor)\r\n prob = prob[0][classe] * 100\r\n \r\n if prob>=98:\r\n #COR EM (BGR)\r\n if classe==2:\r\n color = (224, 43, 100)\r\n else:\r\n color = (192, 255, 119)\r\n \r\n user = str(pessoa[classe]).upper()\r\n \r\n \r\n \r\n cv2.rectangle(frame, (x1,y1), (x1+w, y1+h), color, 2)\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n font_scale = 0.5\r\n cv2.putText(frame, user, (x1, y1-10), font, font_scale, color, \r\n thickness=1)\r\n \r\n \r\n cv2.imshow(\"Face Recognittion\", frame)\r\n \r\n key = cv2.waitKey(1)\r\n \r\n if key==27: #ESC\r\n break\r\n\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"CaioCamilo/Reconhecimento-Facial-tensorflow","sub_path":"face_recognition.py","file_name":"face_recognition.py","file_ext":"py","file_size_in_byte":2574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"8353108384","text":"from flask import Flask\nfrom datetime import datetime\nfrom flask import request\nfrom flask import render_template_string, render_template\nfrom flask import jsonify\nimport uuid\nimport firebase_client\nimport process\nimport json\nfrom collections import OrderedDict\nimport HTMLParser\nimport os\nfrom flask_caching import Cache\nimport requests\nfrom jinja2 import Template\nimport data_struct_utils as du\nimport flask\nimport urllib\n\napp = Flask(__name__)\ncache = Cache(app, config={'CACHE_TYPE': 'simple'})\n\n@app.route('/')\ndef index():\n return \"track release status \"\n\n@app.route('/createplan',methods=['POST'])\ndef createReleasePlan():\n plan_data= request.data\n FBC=firebase_client.Firebase_Client()\n uniq= str(uuid.uuid4())\n try:\n FBC.putvalue(uniq,plan_data)\n return uniq\n except:\n return 'error'\n\n@app.route('//createplan',methods=['POST'])\ndef createReleasePlanByProduct(product):\n plan_data=request.data\n FBC=firebase_client.Firebase_Client()\n rs=requests.post('https://releasetracker.herokuapp.com/createplan',data=plan_data)\n id=rs.content\n try:\n FBC.putvalue('Latest_'+product,id)\n return id\n except:\n return 'error'\n\n\n\n@app.route('/track',methods=['GET'])\n@cache.cached(timeout=25)\ndef get_tracking_status():\n key=request.args.get('id')\n FBC=firebase_client.Firebase_Client()\n plan=FBC.getdb().child(key).get().val()\n p1=process.processor(json.loads(plan, object_pairs_hook=OrderedDict))\n return render_template_string(p1)\n\n@app.route('//latest', methods=['GET'])\n@cache.cached(timeout=25)\ndef getTrackingStatusByProduct(product):\n FBC=firebase_client.Firebase_Client()\n id=FBC.getdb().child('Latest_'+product).get().val()\n plan=FBC.getdb().child(id).get().val()\n p1=process.processor(json.loads(plan, object_pairs_hook=OrderedDict))\n return render_template_string(p1)\n\n\n@app.route('/release_history_backend/', methods=['GET'])\n@cache.cached(timeout=25)\ndef releasehistorypage(product):\n return jsonify( process.process_historical_releases(product_name=product))\n\n\n\n@app.route('/release_history/', methods=['GET'])\ndef releasehistory(product,methods=['GET']):\n rs=requests.get('https://releasetracker.herokuapp.com/release_history_backend/'+product)\n data=rs.json()\n return render_template('releasehistory.html', data= data , product = product, monthSorter= du.monthSorter)\n\n\n@app.route('/release_history///',methods=['GET'])\ndef release_history_year(product,year,month):\n rs=requests.get('https://releasetracker.herokuapp.com/release_history_backend/'+product)\n data=rs.json()\n return render_template('releasehistoryDetail.html', data=data[year][month], dateformat=du.date_formater )\n \n\n\nif __name__ == '__main__':\n from os import environ\n app.run(debug=False , host='0.0.0.0', port=environ.get(\"PORT\", 5000), threaded=True)\n #DFNG\n ","repo_name":"praveenkumar-s/MB-Release_tracker","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"21557478949","text":"import numpy as np\nfrom skimage.filters import threshold_otsu\nfrom skimage.io import imread\nfrom skimage.color import rgb2gray, rgb2hsv, hsv2rgb\nfrom skimage.morphology import square, closing, opening\nfrom skimage.segmentation import clear_border\nfrom skimage import measure\nimport useful_wsi as usi\nimport os\n\n\n#modif : selection of one of the biggest connected components.\n\ndef main(image, out, mask_level):\n slide = usi.open_image(image)\n if mask_level < 0:\n mask_level = len(slide.level_dimensions) + mask_level\n im = usi.get_whole_image(slide, level=mask_level, numpy=True) \n num_histo, _ = os.path.splitext(os.path.basename(image))\n im_gray = rgb2gray(im)\n im_gray = clear_border(im_gray, prop=30)\n size = im_gray.shape\n im_gray = im_gray.flatten()\n pixels_int = im_gray[np.logical_and(im_gray > 0.1, im_gray < 0.98)]\n t = threshold_otsu(pixels_int)\n mask = opening(closing(np.logical_and(im_gray0.1).reshape(size), selem=square(2)), selem=square(2))\n print( 'mask ',mask.sum())\n mask_path = os.path.join(out, num_histo+'.npy')\n final_mask = mask\n #if mask.sum() >= (mask.shape[0]*mask.shape[1])/25:\n # labeled_c = measure.label(mask, background=0, connectivity=2)\n # size = [(labeled_c==(x+1)).sum() for x in range(labeled_c.max())]\n # biggest_cc = (labeled_c == np.argmax(size)+1).astype(int)\n # print('biggest cc ',biggest_cc.sum())\n # final_mask = biggest_cc\n np.save(mask_path, final_mask)\n\ndef clear_border(mask, prop):\n r, c = mask.shape\n pr, pc = r//prop, c//prop\n mask[:pr, :] = 0\n mask[r-pr:, :] = 0\n mask[:,:pc] = 0\n mask[:,c-pc:] = 0\n return mask\n","repo_name":"trislaz/tile_image","sub_path":"pkg/tiler_wsi/tile_image/auto_cc_masks.py","file_name":"auto_cc_masks.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"11286916451","text":"# This file cannot be imported from until Django sets up\ntry:\n # Django 1.11\n from django.test.utils import setup_databases, teardown_databases # noqa: F401\nexcept ImportError:\n # In Django prior to 1.11, teardown_databases is only available as a method on DiscoverRunner\n from django.test.runner import ( # noqa: F401\n setup_databases,\n DiscoverRunner as _DiscoverRunner,\n )\n\n def teardown_databases(db_cfg, verbosity):\n _DiscoverRunner(verbosity=verbosity, interactive=False).teardown_databases(\n db_cfg\n )\n","repo_name":"thisishoon/django-script-slide","sub_path":"venv2/lib/python3.7/site-packages/pytest_django/compat.py","file_name":"compat.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"75"} +{"seq_id":"36645132195","text":"from . import views\nfrom django.conf.urls import url, include\nfrom rest_framework import routers\nfrom rest_framework_bulk.routes import BulkRouter\n\nrouter = routers.DefaultRouter()\nrouter.register(r'users', views.UserViewSet, 'user')\nrouter.register(r'groups', views.GroupViewSet)\nrouter.register(r'profiles', views.ProfileViewSet)\nrouter.register(r'user-subscriptions', views.UserSubscriptionViewSet)\nrouter.register(r'user-contents', views.UserContentViewSet)\nrouter.register(r'user-logs', views.UserLogViewSet)\n\n# Wire up our API using automatic URL routing.\n# Additionally, we include login URLs for the browsable API.\nurlpatterns = [\n url(r'^', include(router.urls)),\n #url(r'^createuser', views.CreateUser),\n #url(r'^usersubscriptions/manual/create', views.CreateUserSubscription),\n url(r'^integrations/user-subscriptions', views.IntegrateUserSubscription),\n url(r'^integrations/user-watchlist', views.ShowWatchlistFromMessengerId),\n url(r'^integrations/update-user-content', views.UpdateUserContent),\n url(r'^integrations/add-to-watchlist-from-messenger-id', views.add_to_watchlist_from_messenger_id_and_content_id),\n url(r'^integrations/fb-webhooks', views.FacebookWebhook.as_view()),\n #url(r'^custom-views/content-blocks', views.GetContentBlocksFromTags),\n #url(r'^usercontents/manual/update', views.UpdateUserContent),\n #url(r'^custom-views/show-watchlist', views.ShowWatchlist),\n #url(r'^custom-views/get-watchlist-from-messenger-id', views.ShowWatchlistFromMessengerId),\n #url(r'^getcontentblocks', views.GetContentBlocks),\n #url(r'^test/$', views.Test),\n]\n","repo_name":"jlech42/chump_django","sub_path":"user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"9657042839","text":"#This script is to get a list of discovered type VM's, check if the plan id auto assigned by morpheus is still an existing plan, \n# if not then remove the discovered VM without removing infrastructure. \n# and let the cloud discovery get the VM again with an existing plan.\n\nimport requests\n\n# define vars for API\nhost=morpheus['morpheus']['applianceHost']\ntoken=morpheus['morpheus']['apiAccessToken']\nheaders = {\"Content-Type\":\"application/json\",\"Accept\":\"application/json\",\"Authorization\": \"BEARER \" + (token)}\n\n# Get all discovered VM\ndef getalldiscoveredvms():\n print(\"Get a list of discovered VM's\\n\")\n url=\"https://%s/api/servers?managed=false&serverType=Vmware+VM&max=1\" % (host)\n r = requests.get(url, headers=headers, verify=False)\n data = r.json()\n l = len(data['servers'])\n if l is None:\n print(\"No discovered servers found\")\n else:\n print(\"Total number of discovered servers \"+ str(l) + \".\\n\")\n for i in range(0, l):\n existingPlans=['12','13']\n if str(data['servers'][i]['plan']['id']) in existingPlans:\n print(\"VM \" + data['servers'][i] + \" is running with an existing plan.\")\n else:\n print(\"Plan for VM \"+ data['servers'][i]['name'] + \" is \" + str(data['servers'][i]['plan']['name']) + \" and the id of the plan is \" + str(data['servers'][i]['plan']['id']) + \".\\nRemoving the discovered VM \" + data['servers'][i]['name'] + \" from morpheus without deleting the VM infrastructure. Upon Cloud sync the VM will be back in morpheus as discovered type VM.\" )\n url=\"https://%s/api/servers/%s?removeResources=off\" % (host,data['servers'][i]['id'])\n r = requests.delete(url, headers=headers, verify=False)\n rdata = r.json()\n if rdata['success'] == True:\n print(\"VM \"+ data['servers'][i]['name'] + \" successfully deleted.\\n\")\n\ndef main():\n getalldiscoveredvms()\n\nif __name__ == \"__main__\":\n main() \n","repo_name":"gomorpheus/morpheus-automation-examples","sub_path":"Python/Service Plans/assignPlanstoDiscoveredVM.py","file_name":"assignPlanstoDiscoveredVM.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"75"} +{"seq_id":"34072507650","text":"from datetime import datetime\nimport logging\n\nfrom sqlalchemy import Column\nfrom sqlalchemy.exc import SQLAlchemyError\nfrom sqlalchemy.sql import ColumnCollection\nfrom sqlalchemy.sql.elements import BinaryExpression\nfrom sqlalchemy.sql.expression import CompoundSelect\nfrom sqlalchemy.types import TypeDecorator\n\nfrom .json import JSON\n\n\nlog = logging.getLogger(__name__)\n\n\ndef get_exception_message(e):\n \"\"\"Extract the error message from the given exception.\n\n :param e: an Exception instance\n :rtype: a unicode string\n \"\"\"\n\n msg = str(e)\n if isinstance(msg, bytes):\n for enc in ('utf-8', 'latin1'):\n try:\n msg = msg.decode(enc)\n except UnicodeDecodeError:\n pass\n else:\n break\n else:\n msg = msg.decode('utf-8', errors='replace')\n return msg\n\n\ndef get_column_type(column):\n \"\"\"Return the concrete type of a column.\"\"\"\n\n ctype = column.type\n if isinstance(ctype, TypeDecorator):\n ctype = ctype.impl\n return ctype\n\n\ndef col_by_name(query, colname):\n \"Helper: find the (first) column with the given name.\"\n\n is_compound = isinstance(query, CompoundSelect)\n columns = query.columns._all_columns if is_compound else query.inner_columns\n\n # First look in the selected columns\n for c in columns:\n try:\n if c.name == colname:\n return c\n except AttributeError:\n if isinstance(c, BinaryExpression):\n l, r = c._orig\n if (isinstance(l, Column) and l.name == colname\n or\n isinstance(r, Column) and r.name == colname):\n return c\n else:\n log.warning('Unhandled inner column type: %r', type(c).__name__)\n\n if is_compound:\n # In a compound statement we cannot go farther\n return None\n\n # Then in the froms\n for f in query.froms:\n columns = f.columns\n\n # The FROM may not be a plain table: this happens for example when we are selecting\n # from a PG function; in that case, there's little we can do here\n\n if not isinstance(columns, ColumnCollection):\n continue\n\n c = columns.get(colname)\n if c is not None:\n return c\n\n papables = [c for c in columns if c.key == colname]\n if len(papables)>=1:\n c = papables[0]\n if len(papables)>1:\n log.warning('Ambiguous column name \"%s\" in %s:'\n ' selecting \"%s\"', colname, str(query), c)\n return c\n\n papables = [c for c in columns if c.name.endswith('_'+colname)]\n if len(papables)>=1:\n c = papables[0]\n if len(papables)>1:\n log.warning('Ambiguous column name \"%s\" in %s:'\n ' selecting \"%s\"', colname, str(query), c)\n return c\n\n\ndef create_change_saver(adaptor=None, save_changes=None,\n modified_slot_name='modified_records',\n deleted_slot_name='deleted_records',\n inserted_ids_slot='inserted_ids',\n modified_ids_slot='modified_ids',\n deleted_ids_slot='deleted_ids',\n result_slot='root',\n success_slot='success',\n message_slot='message'): # pragma: nocover\n \"\"\"Function factory to implement the standard POST handler for a proxy.\n\n :param adaptor: a function that adapts the changes before application\n :param save_changes: the function that concretely applies the changes\n :param modified_slot_name: a string, by default 'modified_records'\n :param deleted_slot_name: a string, by default 'deleted_records'\n :param inserted_ids_slot: a string, by default 'inserted_ids'\n :param modified_ids_slot: a string, by default 'modified_ids'\n :param deleted_ids_slot: a string, by default 'deleted_ids'\n :param result_slot: a string, by default 'root'\n :param success_slot: a string, by default 'success'\n :param message_slot: a string, by default 'message'\n :returns: a dictionary, with a boolean `success` slot with a\n ``True`` value if the operation was completed without errors,\n ``False`` otherwise: in the latter case the `message` slot\n contains the reason for the failure. Three other slots carry\n lists of dictionaries with the ids of the *inserted*,\n *modified* and *deleted* records.\n\n This implements the generic behaviour we need to save changes back to\n the database.\n\n The `adaptor` function takes four arguments, respectively the SA\n session, the request, a list of added/modified records and a list\n of deleted records; it must return two (possibly modified) lists,\n one containing added/modified records and the other with the\n records to delete, e.g.::\n\n def adaptor(sa_session, request, modified_recs, deleted_recs):\n # do any step to adapt incoming data\n return modified_recs, deleted_recs\n \"\"\"\n\n def workhorse(sa_session, request, **args):\n mr = JSON.decode(args[modified_slot_name])\n dr = JSON.decode(args[deleted_slot_name])\n\n if adaptor is not None:\n try:\n mr, dr = adaptor(sa_session, request, mr, dr)\n except Exception as e:\n log.critical('Could not adapt changes: %s', e, exc_info=True)\n return {\n success_slot: False,\n message_slot: 'Internal error, consult application log'\n }\n\n try:\n iids, mids, dids = save_changes(sa_session, request, mr, dr)\n status = True\n statusmsg = \"Ok\"\n except SQLAlchemyError as e:\n msg = get_exception_message(e)\n log.error('Could not save changes to the database: %s', msg)\n status = False\n statusmsg = msg.split('\\n')[0]\n iids = mids = dids = None\n except Exception as e:\n msg = get_exception_message(e)\n log.critical('Could not save changes to the database: %s',\n msg, exc_info=True)\n status = False\n statusmsg = 'Internal error, consult application log.'\n iids = mids = dids = None\n\n return { success_slot: status,\n message_slot: statusmsg,\n inserted_ids_slot: iids,\n modified_ids_slot: mids,\n deleted_ids_slot: dids,\n }\n\n return workhorse\n\n\ndef csv_to_list(csv):\n \"\"\"Build a list of strings from a CSV or JSON array.\n\n :param csv: a string containing either a ``CSV`` or a JSON array\n :rtype: a Python list\n\n This is very simplicistic: since its used to transfer a list of field names, that is plain\n ASCII strings, JSON escapes are not even considered.\n\n `csv` may be either a plain CSV string such as ``first,second,third`` or a JSON array, such\n as ``[\"first\",\"second\",\"third\"]``.\n \"\"\"\n\n if csv.startswith('[') and csv.endswith(']'):\n res = [v[1:-1] for v in csv[1:-1].split(',')]\n else:\n res = [v.strip() for v in csv.split(',')]\n return res\n","repo_name":"arbindtechguy/SecureTradeUsingBlockchain","sub_path":"venv/lib/python3.6/site-packages/metapensiero/sqlalchemy/proxy/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"71236845683","text":"bug_message = state_change[\"object\"]\n\n# Bug Information\nbug = bug_message.getParentValue()\nbody = \"\"\"\n Bug : %s\n Status : %s\n Date : %s\n Link : %s/view\n\"\"\" % (bug.getTitle(''), bug.getSimulationStateTitle(),\n bug.getStartDate(''), bug.getAbsoluteUrl())\n\nif bug.getSourceTitle() is not None:\n body += \"\"\" Requester : %s\n Assignee : %s\n\"\"\" % (bug.getDestinationTitle(''), bug.getSourceTitle(''),)\n\nif bug.getSourceTradeTitle() is not None:\n body += \"\"\" Reporter : %s\n\"\"\" % (bug.getSourceTradeTitle(''),)\n\nif bug.getSourceDecisionTitle() is not None:\n body += \"\"\" Supervisor : %s\n\"\"\" % (bug.getSourceDecisionTitle(''),)\n\nif bug.getDestinationProjectTitle() is not None:\n body += \"\"\" Request Project : %s\n\"\"\" % bug.getDestinationProjectTitle()\n\nif bug.getSourceProjectTitle() is not None:\n body += \"\"\" Assigned Project : %s\n\"\"\" % bug.getSourceProjectTitle()\n\nbody += \"\"\"\n Description:\n\n%s\n\n\"\"\" % (bug.getDescription(''))\n\nattachment_list = bug.Base_getRelatedDocumentList(\n portal_type=bug.getPortalDocumentTypeList())\nif attachment_list:\n body += \"\"\"Attachments:\n\n %s\n\n\"\"\" % ('\\n '.join(['%s %s/view' % (a.getTitle(), a.absolute_url()) for a in attachment_list]))\nbody += \"\"\" Messages :\n\"\"\"\n# Messages Information\nsimulation_state = ('delivered', 'started')\nbug_message_list = [bug_message]\nlines_list = bug.searchFolder(portal_type='Bug Line', sort_on=((\"id\", \"DESC\"),),\n simulation_state=simulation_state)\nbug_message_list.extend(lines_list)\nmessage_count = len(bug_message_list)+1\nfor message in bug_message_list:\n message_count -= 1\n text = message.asText()\n body += \"\"\"\n++++++ Message #%s submitted by %s on %s ++++++\n%s\n\"\"\" % (message_count, message.getSourceTitle(''),\n message.getStartDate(), text )\n\nrecipient_list = bug_message.BugLine_getRecipientValueList()\nif not recipient_list: return\n\nportal = bug_message.getPortalObject()\nportal.portal_notifications.sendMessage(sender=bug_message.getSourceValue(),\n recipient=recipient_list,\n subject=\"[Bug %s] %s\" % (bug.getReference(), bug.getTitle()),\n message=body)\n","repo_name":"Nexedi/erp5","sub_path":"bt5/erp5_forge/WorkflowTemplateItem/portal_workflow/bug_event_workflow/script_BugEvent_sendNotification.py","file_name":"script_BugEvent_sendNotification.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":171,"dataset":"github-code","pt":"75"} +{"seq_id":"21235292345","text":"import json\nimport MeCab\nimport sys\nimport numpy as np\nimport pickle\nfrom scipy import sparse\nimport glob\nfrom scipy.sparse import csr_matrix\nimport gzip\nif '--shrinkage' in sys.argv:\n m = MeCab.Tagger('-Owakati')\n for line in open('reveiws.json'):\n line = line.strip()\n obj = json.loads(line)\n title = obj['reviewTitle'].strip()\n review = obj['review'].strip()\n star = obj['stars']\n if 'ネタバレ' in review:\n continue\n if len(title) > 50:\n continue\n if len(review) > 500:\n continue\n print( json.dumps( (star, title, review), ensure_ascii=False ) )\n\nif '--to_index' in sys.argv:\n chars = set()\n for line in open('shrinkage.json'):\n line = line.strip()\n star, title, review = json.loads(line)\n [chars.add(char) for char in list(title)]\n [chars.add(char) for char in list(review)]\n\n char_index, index_char = {}, {}\n for index, char in enumerate(chars):\n print(char, index)\n char_index[char] = index\n index_char[index] = char\n\n open('char_index.json','w').write( json.dumps(char_index, indent=2, ensure_ascii=False) )\n open('index_char.json','w').write( json.dumps(index_char, indent=2, ensure_ascii=False) )\n\nif '--to_array' in sys.argv:\n char_index = json.loads(open('char_index.json','r').read() )\n index_char = json.loads(open('index_char.json','r').read() )\n\n for index, line in enumerate(open('shrinkage.json')):\n print('now iter', index)\n line = line.strip()\n star, ts, rs = json.loads(line)\n ts, rs = list(ts), list(rs)\n tv = [0.0]*50\n rv = [0.0]*500\n for i,t in enumerate(ts):\n tv[i] = char_index[t]\n for i,r in enumerate(rs):\n #print(i, r)\n rv[i] = char_index[r]\n \n tv = np.array(tv, dtype=np.int)\n rv = np.array(rv, dtype=np.int)\n open('dataset/{index}.pkl'.format(index=index), 'wb').write( gzip.compress( pickle.dumps( ( star, tv, rv) ) ) )\n\n","repo_name":"GINK03/keras-cnn-text-classify","sub_path":"keras2-star-predictor/prepare.py","file_name":"prepare.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"75"} +{"seq_id":"41065878124","text":"import os\n\nfrom app.common import SAMPLE_API\nfrom tests.common.utils import dict2obj\n\n\ngraylog_expected_result = ''\nmysq_orm_engine_expected_result = \"Engine(mysql+pymysql://{user}:***@{host}/{db})\".format(user=os.environ['MYSQL_DATABASE_USER'],\n host=os.environ['MYSQL_DATABASE_HOST'],\n db=os.environ['MYSQL_DATABASE_DB'])\napi_route_expected_result = \" 1:\n with Pool(16) as p:\n p.map(main_work, map(lambda x: int(x), sys.argv[1:]))\n return\n\n aid = read_start()\n while aid <= latest_aid:\n range_end = min(aid + concurrent, latest_aid + 1)\n with Pool(16) as p:\n p.map(main_work, list(range(aid, range_end)))\n\n # 更新 aid\n aid = range_end\n update_start(aid)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"Yesterday17/bilibili-jinping","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"73297060402","text":"\"\"\"Integration tests for charm-k8s-openldap.\"\"\"\n\nimport asyncio\nimport logging\nfrom pathlib import Path\n\nimport pytest\nimport yaml\nfrom ops.model import ActiveStatus, WaitingStatus\nfrom pytest_operator.plugin import OpsTest\n\nlogger = logging.getLogger(__name__)\n\nAPP_NAME = \"openldap-k8s\"\nPSQL = \"postgresql-k8s\"\n\n\n@pytest.mark.abort_on_fail\nasync def test_build_and_deploy(ops_test: OpsTest):\n openldap_charm = await ops_test.build_charm(\".\")\n openldap_image = yaml.safe_load(Path(\"metadata.yaml\").read_text())[\"resources\"][\"openldap-image\"][\"upstream-source\"]\n await asyncio.gather(\n ops_test.model.deploy(\"postgresql-k8s\", application_name=PSQL, num_units=1),\n ops_test.model.deploy(\n openldap_charm,\n resources={'openldap-image': openldap_image},\n application_name=APP_NAME,\n num_units=1,\n ),\n )\n await ops_test.model.wait_for_idle(apps=[APP_NAME, PSQL])\n assert ops_test.model.applications[APP_NAME].status == WaitingStatus.name\n assert ops_test.model.applications[PSQL].status == ActiveStatus.name\n\n await ops_test.model.add_relation(APP_NAME + \":db\", PSQL + \":db\")\n await ops_test.model.wait_for_idle(apps=[APP_NAME, PSQL])\n assert ops_test.model.applications[APP_NAME].status == ActiveStatus.name\n assert ops_test.model.applications[PSQL].status == ActiveStatus.name\n\n\n@pytest.mark.abort_on_fail\nasync def test_maintenance_without_postgresql(ops_test: OpsTest):\n await asyncio.gather(ops_test.model.applications[PSQL].remove())\n await ops_test.model.wait_for_idle(apps=[APP_NAME])\n assert ops_test.model.applications[APP_NAME].status == WaitingStatus.name\n","repo_name":"canonical/openldap-k8s-operator","sub_path":"tests/integration/test_charm.py","file_name":"test_charm.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"7786868607","text":"#!/bin/bash/env python3\nn = int(input('Enter the demission of mitrix:'))\nprint('Enter values of the mitrix A')\na = []\nfor i in range(n):\n a.append([int(x) for x in input().split()])\nprint('Enter values of mitrix B')\nb = []\nfor i in range(n):\n b.append([int(x) for x in input().split()])\nc = []\nfor i in range(n):\n c.append([a[i][j] * b[i][j] for j in range(n)])\nprint('After matrix multipliaction')\nprint('-' * 7 * n)\nfor x in c:\n #print(x)\n for y in x:\n print(str(y).rjust(5), end = ' ')\n print()\nprint('-' * 7 * n)\n","repo_name":"melinmmmm/shiyanlou-code","sub_path":"matrixmul.py","file_name":"matrixmul.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"8870467939","text":"from feet.entities.nlp import Parser\nimport unittest\n\n\nclass NLPEntitiesTests(unittest.TestCase):\n def setup(self):\n pass\n\n def test_extract_entities(self):\n \"\"\"\n Test\n \"\"\"\n text = 'The United Nations (UN) is an intergovernmental organization '\\\n 'to promote international co-operation. A replacement for the '\\\n 'ineffective League of Nations, the organization was established '\\\n 'on 24 October 1945 after World War II in order to prevent '\\\n 'another such conflict. At its founding, the UN had 51 member '\\\n 'states; there are now 193. The headquarters of the United '\\\n 'Nations is in Manhattan, New York City, and experiences '\\\n 'extraterritoriality. Further main offices are situated in '\\\n 'Geneva, Nairobi, and Vienna. The organization is financed by '\\\n 'assessed and voluntary contributions from its member states. '\\\n 'Its objectives include maintaining international peace and '\\\n 'security, promoting human rights, fostering social and economic '\\\n 'development, protecting the environment, and providing '\\\n 'humanitarian aid in cases of famine, natural disaster, and armed '\\\n 'conflict.'\n # probleme with World War II not concatenated\n # we may take also NN and NNS with combination of CC and IN\n grammar = 'NE : {*?+}'\n result = Parser().extract_entities(text, grammar)\n self.assertEqual(result[0][1], 'english')\n self.assertTrue('intergovernmental organization' in result[0][0])\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"Altarika/feet","sub_path":"tests/test_entities.py","file_name":"test_entities.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"75"} +{"seq_id":"30321520540","text":"import numpy as np\n\nQ = [0] * 40\nstate = 0\n\n\nclass control_learn():\n\n global Q #each state has two actions: drift/no drift\n global state\n\n def getQ(self):\n return Q\n\n def setQ(self,index, value):\n Q[index] = value\n\n def getState(self,aim):\n if(aim<-0.9):\n return 0\n elif(aim<-0.8):\n return 1\n elif(aim<-0.7):\n return 2\n elif(aim<-0.6):\n return 3\n elif(aim<-0.5):\n return 4\n elif(aim<-0.4):\n return 5\n elif(aim<-0.3):\n return 6\n elif(aim<-0.2):\n return 7\n elif(aim<-0.1):\n return 8\n elif(aim<-0):\n return 9\n elif(aim<0.1):\n return 10\n elif(aim<0.2):\n return 11\n elif(aim<0.3):\n return 12\n elif(aim<0.4):\n return 13\n elif(aim<0.5):\n return 14\n elif(aim<0.6):\n return 15\n elif(aim<0.7):\n return 16\n elif(aim<0.8):\n return 17\n elif(aim<0.9):\n return 18\n else:\n return 19\n\n def computeQ(self,state):\n if(Q[state*2] < Q[state*2+1]):\n return Q[state*2+1]\n else:\n return Q[state*2]\n\n def getAction(self,state):\n if(Q[state*2] < Q[state*2+1]):\n return 1\n else:\n return 0\n \n def update(self, action, nextState):\n if(np.abs(nextState - 4.5) < np.abs(state - 4.5)):\n reward = 1\n elif(np.abs(nextState - 4.5) == np.abs(state - 4.5)):\n reward = 0\n else:\n reward = -1\n\n Q[state*2+action] = Q[state*2+action] + 0.2 * (reward + 0.05*Q[nextState*2 + self.getAction(nextState)] - Q[state*2+action])","repo_name":"yixiuzhu/EC400_RL_FinalProject","sub_path":"homework5_for_python_3/homework/control_learn.py","file_name":"control_learn.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"33526311147","text":"import os\nimport json\n\n\n\ndef load_all_conversations(path_to_conversations):\n conversations = []\n files_names = os.listdir(path_to_conversations)\n files_names.sort()\n for f in files_names:\n file_d = open(f\"{path_to_conversations}/{f}\", 'r')\n text = file_d.read()\n jsn = json.loads(text)\n conversations.append(jsn)\n file_d.close()\n\n return conversations\n\n\ndef dict_to_json(dct, json_filename):\n r = json.dumps(dct)\n loaded_r = json.loads(r)\n with open(f\"tests_results/{json_filename}.json\", \"w\", encoding='utf-8') as file:\n json.dump(loaded_r, file, ensure_ascii=False, indent=4)","repo_name":"carbonbug/nlu_v2","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"17949903811","text":"#!/usr/bin/python3\n# coding: utf-8\n\nimport time, sys, json, selenium\nfrom redis import Redis\nfrom selenium import webdriver\nfrom rq import Connection, Worker, Queue\nimport logging\n\nlogging.getLogger('rq.worker').addHandler(logging.FileHandler(\"rq.log\"))\nlogging.getLogger('rq.worker').addHandler(logging.StreamHandler(sys.stdout))\n\n\nFLAG = 'hitcon{var flag.map(arbitrary-file-upload-to-XSS-again!)}'\nCONFIG = json.load(open('oStyle/config.json', 'rb'))\nURL_BASE = CONFIG.get('URL_BASE')\nREDIS_PASS = CONFIG.get('REDIS_PASS')\n\ndef add(url):\n chrome_options = webdriver.ChromeOptions()\n chrome_options.add_argument('--headless')\n chrome_options.add_argument('--disable-gpu')\n chrome_options.add_argument('--disable-dev-shm-usage')\n chrome_options.add_experimental_option(\"prefs\", {\n 'download.prompt_for_download': True, \n 'download.default_directory': '/dev/null'\n })\n # chrome_options.add_argument('--no-sandbox')\n\n client = webdriver.Chrome('./chromedriver', chrome_options=chrome_options)\n client.set_page_load_timeout(5)\n client.set_script_timeout(5)\n\n client.get(URL_BASE)\n client.add_cookie({\n 'name': 'this-is-for-you', \n 'value': FLAG, \n })\n\n with open('save_url.log', 'a+') as fp:\n print(url)\n fp.write(url + '\\n')\n client.get(url)\n i = 0\n while 1:\n try:\n client.switch_to_alert().accept()\n i += 1\n if i > 1000:\n break\n except selenium.common.exceptions.NoAlertPresentException:\n break\n\n time.sleep(2)\n client.quit()\n\nif __name__ == '__main__':\n if 'test' in sys.argv:\n add(sys.argv[2])\n exit()\n\n with Connection(connection=Redis(host=\"127.0.0.1\", password=REDIS_PASS)):\n w = Worker('default', log_job_description=True, serializer=json)\n w.work()\n","repo_name":"orangetw/My-CTF-Web-Challenges","sub_path":"hitcon-ctf-2020/oStyle/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","stars":2550,"dataset":"github-code","pt":"75"} +{"seq_id":"5750925972","text":"\"\"\"\n-----------------------------MAP()------------------------------\n\nmap(function, iterable, ...) returns an iterator that applies\nfunction to every item of iterable, yielding the results.\nIf additional iterable arguments are passed, function must take\nthat many arguments and is applied to the items from all\niterables in parallel. With multiple iterables, the iterator\nstops when the shortest iterable is exhausted.\n\nReturns an iterator.\n\"\"\"\n\n# Example 1\nprint(list(map(lambda *a: a, range(3))))\n\n# Example 2\nprint(list(map(lambda *a: a, range(3), 'abc')))\n\n# Example 3\nprint(list(map(lambda *a: a, (1, 2, 3, 4), 'abc')))\n\n# Full Example\n\nstudents = [\n dict(id=0, credits=dict(math=9, physics=6, history=7)),\n dict(id=1, credits=dict(math=6, physics=7, latin=10)),\n dict(id=2, credits=dict(history=8, physics=9, chemistry=10)),\n dict(id=3, credits=dict(math=5, physics=5, geography=7)),\n]\n\ndef decorate(student):\n # Create a 2-tuple (sum of credits, student) from student dict\n return (sum(student['credits'].values()), student)\n\ndef undecorate(decorated_student):\n # Discard sum of credits, return original student dict\n return decorated_student[1]\n\ndef print_students(students):\n for student in students:\n print(student)\n\nstudents = sorted(map(decorate, students), reverse=True)\nprint(\"\\nStudents after sorting them by their sum\", print_students(students))\nstudents = list(map(undecorate, students))\nprint(\"\\nStudents' Scores\", students)\n\n\n\n\n\"\"\"\n-----------------------------FILTER()------------------------------\n\nfilter(function, iterable) construct an iterator from those elements\nof iterable for which function returns True.\nIterable may be either a sequence, a container which supports\niteration or an iterator. \nIf function is None, the identity function is assumed, that is,\nall elements of iterable that are false are removed.\n\nReturns an iterator.\n\"\"\"\n\n# Example\ntest = [2, 5, 8, 0, 0, 1, 0]\nprint(list(filter(None, test)))\nprint(list(filter(lambda x: x, test)))\nprint(list(filter(lambda x: x > 4, test))) # keep only items > 4\n\n\n\n\"\"\"\n-----------------------------ZIP()------------------------------\n\nzip(*iterables) returns an iterator of tuples, where the i-th\ntuple contains the i-th element from each of the argument sequences\nor iterables. The iterator stops when the shortest input iterable\nis exhausted.\nWith a single iterable argument, it returns an iterator of 1-tuples.\nWith no arguments, it returns an empty iterator.\n\nReturns an iterator.\n\"\"\"\n\n# Example\n\ngrades = [18, 23, 30, 27, 15, 9, 22]\navgs = [22, 21, 29, 24, 18, 18, 24]\n\nprint(list(zip(avgs, grades)))\n\na = [5, 9, 2, 4, 7]\nb = [3, 7, 1, 9, 2]\nc = [6, 8, 0, 5, 3]\nmaxs = map(lambda n: max(*n), zip(a, b, c))\n\nprint(list(maxs))","repo_name":"ngchrbn/DS-Roadmap","sub_path":"DS Code/1. Programming/Python/Functions/map_filter_zip.py","file_name":"map_filter_zip.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"32154370877","text":"# Given two strings s and t, return true if t is an anagram of s, and false otherwise.\n\n# An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.\n\nclass Solution:\n def isAnagram(self, s: str, t: str) -> bool:\n return to_dict(s) == to_dict(t)\n\ndef to_dict(s):\n dict = {}\n\n for char in s:\n if char in dict:\n dict[char] += 1\n else:\n dict[char] = 1\n return dict\n","repo_name":"Dhaaaf/Leetcoding","sub_path":"242.validAnagram.py","file_name":"242.validAnagram.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"17001275387","text":"from json import loads\n\n\n\n#随机取名和头像\ndef txname2(dicts):\n name4 = choice(dicts)\n txname1 = name4['name']\n return txname1 #返回昵称\ndef imgfile(t):\n pwd = getcwd()\n txfile = f'{t}.jpg'\n\n txfile = join(pwd, 'tx','3', txfile)\n return txfile #返回头像路径\n\ndef main():\n with open('userjson7.json', 'r', encoding='utf-8') as fs:\n t3 = fs.read()\n dicts = loads(t3)\n\n return dicts\n\n#先调用输出一个名单列表\n#再调用输出名字和图片路径","repo_name":"sheenblue/fanbookv2","sub_path":"venv/fanbook-v3/txname.py","file_name":"txname.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"36343731219","text":"from openprocurement.tender.core.procedure.views.contract_items_unit_value import ContractItemsUnitValueResource\nfrom openprocurement.tender.openua.procedure.state.contract import OpenUAContractState\nfrom openprocurement.tender.competitivedialogue.constants import STAGE_2_EU_TYPE, STAGE_2_UA_TYPE\nfrom cornice.resource import resource\n\n\n@resource(\n name=f\"{STAGE_2_EU_TYPE}:Tender Contract Items Unit Value\",\n path=\"/tenders/{tender_id}/contracts/{contract_id}/items/{item_id}/unit/value\",\n procurementMethodType=STAGE_2_EU_TYPE,\n description=\"Tender contract items unit value\",\n)\nclass EU2ContractItemsUnitValueResource(ContractItemsUnitValueResource):\n state_class = OpenUAContractState\n\n\n@resource(\n name=f\"{STAGE_2_UA_TYPE}:Tender Contract Items Unit Value\",\n path=\"/tenders/{tender_id}/contracts/{contract_id}/items/{item_id}/unit/value\",\n procurementMethodType=STAGE_2_UA_TYPE,\n description=\"Tender contract items unit value\",\n)\nclass UA2ContractItemsUnitValueResource(ContractItemsUnitValueResource):\n state_class = OpenUAContractState\n","repo_name":"ProzorroUKR/openprocurement.api","sub_path":"src/openprocurement/tender/competitivedialogue/procedure/views/stage2/contract_items_unit_value.py","file_name":"contract_items_unit_value.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"75"} +{"seq_id":"75343681203","text":"import random\r\ndef roll():\r\n#随机三个数字相加，用户猜大小\r\n times=3\r\n points = []\r\n print('<<<<< GAME STARTS! >>>>>')\r\n user_point=input('Big or Small:')\r\n print('<<<<< ROLE THE DICE! >>>>>')\r\n while times>0:\r\n point=random.randrange(1,7)\r\n points.append(point)\r\n times=times-1\r\n total=sum(points)\r\n isBig=11<=total<=18\r\n isSmall=3<=total<=10\r\n if (isBig and user_point=='Big') or (isSmall and user_point=='Small'):\r\n print(points,'You Win!')\r\n roll()\r\n else:\r\n print(points,'You Lose!')\r\n roll()\r\nroll()\r\n\r\n","repo_name":"oreaxp/python","sub_path":"P75_Roll.py","file_name":"P75_Roll.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"2740149005","text":"\"\"\"app URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Import the include() function: from django.urls import include, path\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, reverse_lazy\nfrom django.urls.conf import include\nfrom django.views.generic.base import RedirectView\nfrom drf_spectacular.views import (\n SpectacularAPIView,\n SpectacularRedocView,\n SpectacularSwaggerView,\n)\n\n_DJANGO_URL_PATTERNS = [\n path('admin/', admin.site.urls),\n]\n\n_THIRD_PARTY_URL_PATTERNS = [\n # Django Oauth Toolkit\n path('auth/', include('oauth2_provider.urls', namespace='oauth2_provider')),\n # Rosetta\n path('rosetta/', include('rosetta.urls')),\n # DRF Spectacular\n path('schema/', SpectacularAPIView.as_view(), name='schema'),\n path(\n 'schema/swagger/',\n SpectacularSwaggerView.as_view(url_name='schema'),\n name='swagger',\n ),\n path(\n 'schema/redoc/',\n SpectacularRedocView.as_view(url_name='schema'),\n name='redoc',\n ),\n]\n\n_MY_URL_PATTERNS = [\n path('', RedirectView.as_view(url=reverse_lazy('admin:index'))),\n path('', include('services.users.urls')),\n path('', include('services.account_recovery.urls')),\n]\n\nurlpatterns = _DJANGO_URL_PATTERNS + _THIRD_PARTY_URL_PATTERNS + _MY_URL_PATTERNS\n","repo_name":"Almogo97/django-rest-template","sub_path":"src/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"20979677862","text":"from os import environ\nimport requests\nimport time\nimport datetime\nfrom boto3.dynamodb import Attr\n\ntiingo_token = environ['tiingo_token']\n\ndynamodb = boto3.resource('dynamodb', region_name='us-east-3')\nsymbol_outlook_table = dynamodb.Table('Symbol_Outlook')\nsymbol_price_table = dynamodb.Table('Symbol_Price')\n\nheaders = {\n 'Content-Type': 'application/json',\n 'Authorization' : f'Token {tiingo_token}'\n }\n\nrequestResponse = requests.get(\"https://api.tiingo.com/api/test/\",\n headers=headers)\n\ndef lambda_daily_price_fetcher(event, context):\n ticker_list = get_daily_tickers()\n\n price_list = get_stock_prices(ticker_list)\n\n store_prices(price_list)\n\n return\n\ndef get_daily_tickers():\n date = current_day_utc()\n response = symbol_outlook_table.scan(\n FilterExpression=Attr('date').eq(date)\n )\n symbols = []\n for item in response['Items']:\n for key in item.keys():\n symbols.append(key)\n\n return symbols\n\n\n# get stock prices for those tickers\ndef get_stock_prices(symbols: list) -> list:\n price_list=[]\n\n for symbol in symbols:\n url = generate_url(symbol)\n response = requests.get(url=url, headers=headers)\n price = response.json()\n price['symbol'] = symbol\n price_list.append(price)\n\n\ndef generate_url(symbol: str) -> str:\n return f\"https://api.tiingo.com/tiingo/daily/{symbol}/prices\"\n\n\ndef store_prices(prices: list):\n with symbol_outlook_table.batch_write() as batch:\n for price in prices:\n batch.put_item(\n Item=tiingo_serialize(price)\n )\n\n\ndef tiingo_serialize(item):\n return {\n 'symbol': item['symbol'],\n 'date': current_day_utc(),\n 'open': item['open'],\n 'close': item['close'],\n 'high': item['high'],\n 'low': item['low'],\n 'volume': item['volume']\n }\n\n\ndef current_day_utc() -> int:\n \"\"\"\n Returns the date period to check for\n \"\"\"\n utc_current_datetime = datetime.now(timezone.utc)\n return int(utc_current_datetime.strptime('%Y%m%d'))","repo_name":"dtaivpp/BetsBot","sub_path":"lambdas/lambda_daily_price_fetcher/lambda_daily_price_fetcher.py","file_name":"lambda_daily_price_fetcher.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"828605331","text":"from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom flasgger import Swagger\nfrom flask_cors import CORS\n\napp = Flask(__name__, instance_relative_config=True)\nCORS(app)\n#load the config file\napp.config.from_object('config')\ndb = SQLAlchemy(app)\nmigrate = Migrate(app, db)\n\n#load the routes\n#from app import api\n\nswagger = Swagger(app, \n template={\n \"consumes\":[\n \"application/json\"\n ],\n \"produces\":[\n \"application/json\"\n ],\n \"Accept\":[\n \"application/json\"\n ]\n }\n)\n\nfrom .categories import mod\nfrom .auth import mod\nfrom .recipes import mod\n\napp.register_blueprint(categories.mod, url_prefix='/api-v0')\napp.register_blueprint(auth.mod, url_prefix='/api-v0')\napp.register_blueprint(recipes.mod, url_prefix='/api-v0')\n","repo_name":"samachola/chumvi_api","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"24744069239","text":"import math\nfrom itertools import permutations, product\nfrom typing import List, Union\n\nimport numpy as np\nfrom MEDimage.MEDimage import MEDimage\nfrom MEDimage.utils.image_volume_obj import image_volume_obj\nfrom scipy.signal import fftconvolve\n\nfrom .utils import convolve, pad_imgs\n\n\nclass Laws():\n \"\"\"\n The Laws filter class\n \"\"\"\n\n def __init__(\n self,\n config: List = None,\n energy_distance: int = 7,\n rot_invariance: bool = False,\n padding: str = \"symmetric\"):\n \"\"\"The constructor of the Laws filter\n\n Args:\n config (str): A string list of every 1D filter used to create the Laws kernel. Since the outer product is\n not commutative, we need to use a list to specify the order of the outer product. It is not\n recommended to use filter of different size to create the Laws kernel.\n energy_distance (float): The distance that will be used to create the energy_kernel.\n rot_invariance (bool): If true, rotation invariance will be done on the kernel.\n padding (str): The padding type that will be used to produce the convolution\n\n Returns:\n None\n \"\"\"\n\n ndims = len(config)\n\n self.config = config\n self.energy_dist = energy_distance\n self.dim = ndims\n self.padding = padding\n self.rot = rot_invariance\n self.energy_kernel = None\n self.create_kernel()\n self.__create_energy_kernel()\n\n @staticmethod\n def __get_filter(name,\n pad=False) -> np.ndarray:\n \"\"\"This method create a 1D filter according to the given filter name.\n\n Args:\n name (float): The filter name. (Such as L3, L5, E3, E5, S3, S5, W5 or R5)\n pad (bool): If true, add zero padding of length 1 each side of kernel L3, E3 and S3\n\n Returns:\n ndarray: A 1D filter that is needed to construct the Laws kernel.\n \"\"\"\n\n if name == \"L3\":\n ker = np.array([0, 1, 2, 1, 0]) if pad else np.array([1, 2, 1])\n return 1/math.sqrt(6) * ker\n elif name == \"L5\":\n return 1/math.sqrt(70) * np.array([1, 4, 6, 4, 1])\n elif name == \"E3\":\n ker = np.array([0, -1, 0, 1, 0]) if pad else np.array([-1, 0, 1])\n return 1 / math.sqrt(2) * ker\n elif name == \"E5\":\n return 1 / math.sqrt(10) * np.array([-1, -2, 0, 2, 1])\n elif name == \"S3\":\n ker = np.array([0, -1, 2, -1, 0]) if pad else np.array([-1, 2, -1])\n return 1 / math.sqrt(6) * ker\n elif name == \"S5\":\n return 1 / math.sqrt(6) * np.array([-1, 0, 2, 0, -1])\n elif name == \"W5\":\n return 1 / math.sqrt(10) * np.array([-1, 2, 0, -2, 1])\n elif name == \"R5\":\n return 1 / math.sqrt(70) * np.array([1, -4, 6, -4, 1])\n else:\n raise Exception(f\"{name} is not a valid filter name. \"\n \"Choose between : L3, L5, E3, E5, S3, S5, W5 or R5\")\n\n def __verify_padding_need(self) -> bool:\n \"\"\"Check if we need to pad the kernels\n \n Returns: \n bool: A boolean that indicate if a kernel is smaller than at least one other.\n \"\"\"\n\n ker_length = np.array([int(name[-1]) for name in self.config])\n\n return not(ker_length.min == ker_length.max)\n\n def create_kernel(self) -> np.ndarray:\n \"\"\"Create the Laws by computing the outer product of 1d filter specified in the config attribute.\n Kernel = config[0] X config[1] X ... X config[n]. Where X is the outer product.\n\n Returns:\n ndarray: A numpy multi-dimensional arrays that represent the Laws kernel.\n \"\"\"\n\n pad = self.__verify_padding_need()\n filter_list = np.array([[self.__get_filter(name, pad) for name in self.config]])\n\n if self.rot:\n filter_list = np.concatenate((filter_list, np.flip(filter_list, axis=2)), axis=0)\n prod_list = [prod for prod in product(*np.swapaxes(filter_list, 0, 1))]\n\n perm_list = []\n for i in range(len(prod_list)):\n perm_list.extend([perm for perm in permutations(prod_list[i])])\n\n filter_list = np.unique(perm_list, axis=0)\n\n kernel_list = []\n for perm in filter_list:\n kernel = perm[0]\n shape = kernel.shape\n\n for i in range(1, len(perm)):\n sub_kernel = perm[i]\n shape += np.shape(sub_kernel)\n kernel = np.outer(sub_kernel, kernel).reshape(shape)\n if self.dim == 3:\n kernel_list.extend([np.expand_dims(np.flip(kernel, axis=(1, 2)), axis=0)])\n else:\n kernel_list.extend([np.expand_dims(np.flip(kernel, axis=(0, 1)), axis=0)])\n\n self.kernel = np.unique(kernel_list, axis=0)\n\n def __create_energy_kernel(self) -> np.ndarray:\n \"\"\"Create the kernel that will be used to generate Laws texture energy images\n\n Returns:\n ndarray: A numpy multi-dimensional arrays that represent the Laws energy kernel.\n \"\"\"\n\n # Initialize the kernel as tensor of zeros\n kernel = np.zeros([self.energy_dist*2+1 for _ in range(self.dim)])\n\n for k in product(range(self.energy_dist*2 + 1), repeat=self.dim):\n position = np.array(k)-self.energy_dist\n kernel[k] = 1 if np.max(abs(position)) <= self.energy_dist else 0\n\n self.energy_kernel = np.expand_dims(kernel/np.prod(kernel.shape), axis=(0, 1))\n\n def __compute_energy_image(self,\n images: np.ndarray) -> np.ndarray:\n \"\"\"Compute the Laws texture energy images as described in (Ref 1).\n\n Args:\n images (ndarray): A n-dimensional numpy array that represent the filtered images\n\n Returns:\n ndarray: A numpy multi-dimensional array of the Laws texture energy map.\n \"\"\"\n # If we have a 2D kernel but a 3D images, we swap dimension channel with dimension batch.\n images = np.swapaxes(images, 0, 1)\n\n # absolute image intensities are used in convolution\n result = fftconvolve(np.abs(images), self.energy_kernel, mode='valid') \n\n if self.dim == 2:\n return np.swapaxes(result, axis1=0, axis2=1)\n else:\n return np.squeeze(result, axis=1)\n\n def convolve(self,\n images: np.ndarray,\n orthogonal_rot=False,\n energy_image=False):\n \"\"\"Filter a given image using the Laws kernel defined during the construction of this instance.\n\n Args:\n images (ndarray): A n-dimensional numpy array that represent the images to filter\n orthogonal_rot (bool): If true, the 3D images will be rotated over coronal, axial and sagittal axis\n energy_image (bool): If true, return also the Laws Texture Energy Images\n\n Returns:\n ndarray: The filtered image\n \"\"\"\n images = np.swapaxes(images, 1, 3)\n\n if orthogonal_rot:\n raise NotImplementedError\n\n result = convolve(self.dim, self.kernel, images, orthogonal_rot, self.padding)\n result = np.amax(result, axis=1) if self.dim == 2 else np.amax(result, axis=0)\n\n if energy_image:\n # We pad the response map\n result = np.expand_dims(result, axis=1) if self.dim == 3 else result\n ndims = len(result.shape)\n\n padding = [self.energy_dist for _ in range(2 * self.dim)]\n pad_axis_list = [i for i in range(ndims - self.dim, ndims)]\n\n response = pad_imgs(result, padding, pad_axis_list, self.padding)\n\n # Free memory\n del result\n\n # We compute the energy map and we squeeze the second dimension of the energy maps.\n energy_imgs = self.__compute_energy_image(response)\n\n return np.swapaxes(energy_imgs, 1, 3)\n else:\n return np.swapaxes(result, 1, 3)\n\ndef apply_laws(\n input_images: Union[np.ndarray, image_volume_obj],\n MEDimg: MEDimage = None,\n config: List[str] = [],\n energy_distance: int = 7,\n padding: str = \"symmetric\",\n rot_invariance: bool = False,\n orthogonal_rot: bool = False,\n energy_image: bool = False,\n ) -> np.ndarray:\n \"\"\"Apply the mean filter to the input image\n\n Args:\n input_images (ndarray): The images to filter.\n MEDimg (MEDimage, optional): The MEDimage object that will provide the filter parameters.\n config (List[str], optional): A string list of every 1D filter used to create the Laws kernel. Since the outer product is\n not commutative, we need to use a list to specify the order of the outer product. It is not\n recommended to use filter of different size to create the Laws kernel.\n energy_distance (int, optional): The distance of the Laws energy map from the center of the image.\n padding (str, optional): The padding type that will be used to produce the convolution. Check options \n here: `numpy.pad `__.\n rot_invariance (bool, optional): If true, rotation invariance will be done on the kernel.\n orthogonal_rot (bool, optional): If true, the 3D images will be rotated over coronal, axial and sagittal axis.\n energy_image (bool, optional): If true, will compute and return the Laws Texture Energy Images.\n\n Returns:\n ndarray: The filtered image.\n \"\"\"\n # Check if the input is a numpy array or a Image volume object\n spatial_ref = None\n if type(input_images) == image_volume_obj:\n spatial_ref = input_images.spatialRef\n input_images = input_images.data\n \n # Convert to shape : (B, W, H, D)\n input_images = np.expand_dims(input_images.astype(np.float64), axis=0) \n\n if MEDimg:\n # Initialize filter class instance\n _filter = Laws(\n config=MEDimg.params.filter.laws.config, \n energy_distance=MEDimg.params.filter.laws.energy_distance,\n rot_invariance=MEDimg.params.filter.laws.rot_invariance,\n padding=MEDimg.params.filter.laws.padding\n )\n # Run convolution\n result = _filter.convolve(\n input_images, \n orthogonal_rot=MEDimg.params.filter.laws.orthogonal_rot,\n energy_image=MEDimg.params.filter.laws.energy_image\n )\n elif config:\n # Initialize filter class instance\n _filter = Laws(\n config=config, \n energy_distance=energy_distance,\n rot_invariance=rot_invariance,\n padding=padding\n )\n # Run convolution\n result = _filter.convolve(\n input_images, \n orthogonal_rot=orthogonal_rot,\n energy_image=energy_image\n )\n else:\n raise ValueError(\"Either MEDimg or config must be provided\")\n \n if spatial_ref:\n return image_volume_obj(np.squeeze(result), spatial_ref)\n else:\n return np.squeeze(result)\n","repo_name":"MahdiAll99/MEDimage","sub_path":"MEDimage/filters/laws.py","file_name":"laws.py","file_ext":"py","file_size_in_byte":11345,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"75"} +{"seq_id":"39092596414","text":"from argparse import ArgumentParser\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader,random_split\nfrom lr_scheduler import CosineAnnealingWarmUpRestarts\nfrom torchvision import transforms\n\nimport PIL.Image as Image\n\nfrom sklearn.metrics import classification_report\nfrom sklearn.exceptions import UndefinedMetricWarning\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=UndefinedMetricWarning) \n\nfrom model import DeepLab_ResNet50,FCN_ResNet50\nfrom data import PCategoryDataset,NPCategoryDataset\n\nfrom pytorch_lightning.core.lightning import LightningModule\nfrom pytorch_lightning.callbacks import LearningRateLogger,ModelCheckpoint\nfrom pytorch_lightning import Trainer,loggers,seed_everything\n\n\nclass Model(LightningModule):\n \n @staticmethod\n def add_model_specific_args(parent_parser):\n parser = ArgumentParser(parents=[parent_parser], add_help=False)\n\n parser.add_argument('--image_dir', type=str, default='../../data/np/rgbd')\n parser.add_argument('--category_dir', type=str, default='../../data/np/category')\n parser.add_argument('--person', type=str, default='np')\n\n parser.add_argument('--batch_size', type=int, default=30)\n\n parser.add_argument('--optimizer', type=str, default='adamw')\n parser.add_argument('--scheduler', type=str, default='plateau')\n\n parser.add_argument('--learning_rate', type=float, default=1e-3)\n parser.add_argument('--lr_decay', type=float, default=0.5) # sqrt(2)=0.7 1/2=0.5 1/3=0.33\n\n parser.add_argument('--num_inputs', type=int, default=4)\n parser.add_argument('--num_classes', type=int, default=60)\n\n return parser\n\n def __init__(self, hparams):\n super().__init__()\n self.hparams = hparams\n self.batch_size = self.hparams.batch_size\n self.model = DeepLab_ResNet50(self.hparams.num_inputs,self.hparams.num_classes,pretrained=False,aux_loss=True)\n\n def forward(self, x):\n return self.model(x)\n\n def _shared_step(self, batch, batch_idx, prefix):\n x,y_true = batch\n output = self(x)\n out_loss = F.cross_entropy(output['out'],y_true,ignore_index=0)\n aux_loss = F.cross_entropy(output['aux'],y_true,ignore_index=0)\n loss = 0.6*out_loss+0.4*aux_loss\n\n return {'loss': loss, 'out_loss':out_loss.item(),'aux_loss':aux_loss.item()}\n \n def _shared_step_end(self,outputs,prefix):\n avg_loss = torch.stack([o[f'loss'] for o in outputs]).mean()\n out_loss = torch.tensor([o[f'out_loss'] for o in outputs]).mean()\n aux_loss = torch.tensor([o[f'aux_loss'] for o in outputs]).mean()\n\n tensorboard_logs = {f'{prefix}_loss': avg_loss,f'{prefix}_out_loss':out_loss,f'{prefix}_aux_loss':aux_loss}\n\n return {f'loss': avg_loss, f'{prefix}_loss':avg_loss, 'log': tensorboard_logs}\n\n def training_step(self, batch, batch_idx):\n return self._shared_step(batch,batch_idx,'train')\n \n def training_epoch_end(self, outputs):\n return self._shared_step_end(outputs,'train') \n\n def validation_step(self, batch, batch_idx):\n return self._shared_step(batch,batch_idx,'val')\n\n def validation_epoch_end(self, outputs):\n return self._shared_step_end(outputs,'val')\n\n def test_step(self, batch, batch_idx):\n return self._shared_step(batch,batch_idx,'test')\n\n def test_epoch_end(self, outputs):\n return self._shared_step_end(outputs,'test')\n\n def configure_optimizers(self):\n opt = self.hparams.optimizer\n sch = self.hparams.scheduler\n lr = self.hparams.learning_rate if sch!='cosine2' else 1e-6\n\n if opt == 'sgd':\n optimizer = torch.optim.SGD(self.parameters(),lr=lr)\n elif opt == 'adamw':\n optimizer = torch.optim.AdamW(self.parameters(),lr=lr)\n else:\n optimizer = torch.optim.Adam(self.parameters(),lr=lr)\n \n if sch == 'cosine1':\n scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 10, T_mult=2, eta_min=1e-6)\n elif sch == 'cosine2':\n scheduler = CosineAnnealingWarmUpRestarts(optimizer, T_0=10, T_mult=2, T_up=5, eta_max=self.hparams.learning_rate, gamma=self.hparams.lr_decay)\n elif sch == 'step':\n scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 200, gamma=0.1, last_epoch=-1)\n elif sch == 'plateau':\n scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,factor=self.hparams.lr_decay)\n else:\n scheduler = None\n\n if scheduler is None:\n return optimizer\n else:\n return [optimizer],[scheduler]\n\n def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_i, second_order_closure=None):\n # update params\n optimizer.step()\n optimizer.zero_grad()\n\n def prepare_data(self):\n dst_cls = PCategoryDataset if self.hparams.person == 'p' else NPCategoryDataset\n self.train_dataset = dst_cls(self.hparams.image_dir,self.hparams.category_dir,'../../data/ids.train.csv')\n self.valid_dataset = dst_cls(self.hparams.image_dir,self.hparams.category_dir,'../../data/ids.valid.csv')\n self.test_dataset = dst_cls(self.hparams.image_dir,self.hparams.category_dir,'../../data/ids.test.csv')\n\n def train_dataloader(self):\n return DataLoader(self.train_dataset, batch_size=self.hparams.batch_size, num_workers=8, shuffle=True, drop_last=True)\n\n def val_dataloader(self):\n return DataLoader(self.valid_dataset, batch_size=self.hparams.batch_size, num_workers=8, shuffle=False, drop_last=True)\n\n def test_dataloader(self):\n return DataLoader(self.test_dataset, batch_size=self.hparams.batch_size, num_workers=8, shuffle=False, drop_last=True)\n\ndef main(hparams):\n seed_everything(hparams.seed)\n model = Model(hparams)\n \n if hparams.version is None:\n hparams.version = f'{hparams.optimizer}_{hparams.scheduler}'\n tb_logger = loggers.TensorBoardLogger(hparams.save_dir,name=hparams.name,version=hparams.version)\n lr_logger = LearningRateLogger()\n\n if hparams.saver=='best':\n saver = ModelCheckpoint(prefix='best_')\n else:\n saver = ModelCheckpoint(save_top_k=-1,period=1,prefix='period_')\n trainer = Trainer.from_argparse_args(hparams,checkpoint_callback=saver)\n\n trainer.logger = tb_logger\n trainer.callbacks.append(lr_logger)\n\n trainer.fit(model)\n #trainer.test(model)\n\nif __name__ == \"__main__\":\n parser = ArgumentParser()\n parser.add_argument('--seed', type=int, default=22)\n parser.add_argument('--saver', type=str, default='best')\n parser.add_argument('--save_dir', type=str, default='../../logs/')\n parser.add_argument('--name', type=str, default='seg')\n parser.add_argument('--version', type=str, default=None)\n parser = Model.add_model_specific_args(parser)\n parser = Trainer.add_argparse_args(parser)\n hparams = parser.parse_args()\n main(hparams)\n","repo_name":"zzilch/Functionality-Learning","sub_path":"train/prediction/train_seg.py","file_name":"train_seg.py","file_ext":"py","file_size_in_byte":7026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"546483438","text":"from datetime import datetime, timedelta\nimport os\nfrom airflow import DAG\nfrom airflow.operators.dummy_operator import DummyOperator\nfrom airflow.operators import (StageToRedshiftOperator, LoadFactOperator,\n LoadDimensionOperator, DataQualityOperator)\nfrom helpers import SqlQueries\n\n\n#The DAG is scheduled to run once a day at 7 am, email on retry is set to false, retry every 5 minutes for a maximum of 3 times\ndefault_args = {\n 'owner': 'udacity',\n 'depends_on_past': False,\n 'retries': 3,\n 'retry_delay': timedelta(minutes=5),\n 'email_on_retry': False,\n 'start_date': datetime(2019, 1, 12),\n}\n\n#Catchup is turned off\ndag = DAG('etl_dag',\n default_args=default_args,\n description='ETL from S3 to Redshift',\n catchup=False,\n schedule_interval='@daily'\n )\n\n#Begin_execution task in the DAG\nstart_operator = DummyOperator(task_id='Begin_execution', dag=dag)\n\n#Stage_Stock_Price task in the DAG for copying price data from S3 storage to Redshift staging_stock_price table\nstage_price_to_redshift = StageToRedshiftOperator(\n task_id='Stage_Stock_Price',\n dag=dag,\n table=\"staging_stock_price\",\n redshift_conn_id=\"redshift\",\n aws_credentials_id=\"aws_credentials\",\n s3_bucket=\"akcapstone\",\n s3_key=\"priceinfo\",\n format=\"csv IGNOREHEADER 1\"\n)\n\n#Stage_Stock_Information task in the DAG for copying stock description data from S3 storage to Redshift staging_stock_info table\nstage_info_to_redshift = StageToRedshiftOperator(\n task_id='Stage_Stock_Information',\n dag=dag,\n table=\"staging_stock_info\",\n redshift_conn_id=\"redshift\",\n aws_credentials_id=\"aws_credentials\",\n s3_bucket=\"akcapstone\",\n s3_key=\"descinfo\",\n format=\"json 'auto'\"\n)\n\n#Load_fact_stock_price_table task in the DAG to load data from the staging tables to the fact_stock_price fact table\nload_price_fact_table = LoadFactOperator(\n task_id='Load_fact_stock_price_table',\n dag=dag,\n redshift_conn_id=\"redshift\",\n selection_sql=SqlQueries.fact_stock_price_table_insert,\n fact_table='fact_stock_price'\n)\n\n#load stock industry dimension table\nload_industry_dim_table = LoadDimensionOperator(\n task_id='Load__dim_stock_industry_table',\n dag=dag,\n redshift_conn_id=\"redshift\",\n selection_sql=\"\"\"SELECT distinct industry, ticker FROM staging_stock_info\"\"\",\n dim_table='dim_stock_industry',\n reload=True\n)\n\n#load stock exchange dimension table\nload_exchange_dim_table = LoadDimensionOperator(\n task_id='Load_dim_stock_exchange_table',\n dag=dag,\n redshift_conn_id=\"redshift\",\n selection_sql=\"\"\"SELECT distinct exchange, ticker FROM staging_stock_info\"\"\",\n dim_table='dim_stock_exchange',\n reload=True\n)\n\n#load info dimension table\nload_info_dim_table = LoadDimensionOperator(\n task_id='Load_dim_stock_information_table',\n dag=dag,\n redshift_conn_id=\"redshift\",\n selection_sql=\"\"\"SELECT distinct name, ticker FROM staging_stock_info\"\"\",\n dim_table='dim_stock_information',\n reload=True\n)\n#load date dimension table\nload_date_dim_table = LoadDimensionOperator(\n task_id='Load_dim_stock_date_table',\n dag=dag,\n redshift_conn_id=\"redshift\",\n selection_sql=\"\"\"SELECT distinct date as date, cast(DATE_PART(y,date) as integer) as year, cast(DATE_PART(mon,date) as integer) as month,cast(DATE_PART(d,date) as integer) as day FROM staging_stock_price\"\"\",\n dim_table='dim_stock_date',\n reload=True\n)\n\n#load sector dimension table\nload_sector_dim_table = LoadDimensionOperator(\n task_id='Load_dim_stock_sector_table',\n dag=dag,\n redshift_conn_id=\"redshift\",\n selection_sql=\"\"\"SELECT distinct sector, ticker FROM staging_stock_info\"\"\",\n dim_table='dim_stock_sector',\n reload=True\n)\n\n#Data quality check function that fails if a table is empty (zero records)\ndef check_greater_than_zero(redshift_hook,table,logging):\n records = redshift_hook.get_records(f\"SELECT COUNT(*) FROM {table}\")\n if len(records) < 1 or len(records[0]) < 1:\n raise ValueError(f\"Has records check failed. {table} returned no results\")\n num_records = records[0][0]\n if num_records < 1:\n raise ValueError(f\"Has records check failed. {table} contained 0 rows\")\n logging.info(f\"Has records check on table {table} passed with {records[0][0]} records\")\n\n#Data quality check function that checks if fact_stock_price record count is over million\ndef check_greater_than_equal_million(redshift_hook,table,logging):\n if table == 'fact_stock_price':\n records = redshift_hook.get_records(f\"SELECT COUNT(*) FROM {table}\")\n if len(records) < 1 or len(records[0]) < 1:\n raise ValueError(f\"Has million records check failed. {table} returned no results\")\n num_records = records[0][0]\n if num_records < 1000000:\n raise ValueError(f\"Has million records check failed. {table} {num_records} contained less than 1000000 rows\")\n logging.info(f\"Has million records check on table {table} passed with {records[0][0]} records\")\n\n#Quality check operator that performs two checks on the fact and dimension tables\nrun_quality_checks = DataQualityOperator(\n task_id='Run_data_quality_checks',\n dag=dag,\n redshift_conn_id=\"redshift\",\n tables=['fact_stock_price','dim_stock_industry','dim_stock_exchange','dim_stock_information','dim_stock_date','dim_stock_sector'],\n check_functions=[check_greater_than_zero,check_greater_than_equal_million]\n)\n\nend_operator = DummyOperator(task_id='Stop_execution', dag=dag)\n\n#sequence the tasks for dependency\nstart_operator >> stage_price_to_redshift\nstart_operator >> stage_info_to_redshift\nstage_price_to_redshift >> load_price_fact_table\nstage_info_to_redshift >> load_industry_dim_table\nload_price_fact_table >> load_date_dim_table\nstage_info_to_redshift >> load_sector_dim_table\nstage_info_to_redshift >> load_info_dim_table\nstage_info_to_redshift >> load_exchange_dim_table\nload_industry_dim_table >> run_quality_checks\nload_date_dim_table >> run_quality_checks\nload_sector_dim_table >> run_quality_checks\nload_info_dim_table >> run_quality_checks\nload_exchange_dim_table >> run_quality_checks\nload_price_fact_table >> run_quality_checks\nrun_quality_checks >> end_operator\n","repo_name":"akomandooru/Data-Warehouse","sub_path":"dags/etl_dag.py","file_name":"etl_dag.py","file_ext":"py","file_size_in_byte":6247,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"41126093770","text":"#__author__ = 'richeng'\r\n\r\nfrom django.urls import path,re_path\r\nfrom . import views,views1\r\nfrom . import views\r\n\r\napp_name = 'hello'\r\nurlpatterns = [\r\n path('', views.index, name='index'), #If this pattern is targeted in an include(), ensure the include() pattern has a trailing '/'.\r\n path('useradd/', views1.useradd, name='useradd'),\r\n path('userlist/', views1.userlist, name='userlist'),\r\n re_path('modify/(?P[0-9]+)?/', views1.modify, name='modify'),\r\n re_path('userdel/(?P[0-9]+)?/', views1.userdel, name='userdel'),\r\n]","repo_name":"MagePY27/P27N21-zhangsheng","sub_path":"day02/devops/hello/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"14549959166","text":"from odoo import models, fields\n\nCATEGORY_SELECTION = [\n ('required', 'Required'),\n ('optional', 'Optional'),\n ('no', 'None')]\n\n\nclass ApprovalCategory(models.Model):\n _inherit = 'approval.category'\n\n has_scm = fields.Selection(CATEGORY_SELECTION, string=\"Has SCM\", default=\"no\", required=True)\n","repo_name":"hafiz9w1/ihhg_scm","sub_path":"ihhg_approval/models/approval_category.py","file_name":"approval_category.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"15425290464","text":"import LinkedList\nfrom LinkedList import Node\n\nnode1 = Node(\"3\")\nnode2 = Node(\"7\")\nnode3 = Node(\"10\")\n\nnode1.nextNode = node2\nnode2.nextNode = node3\nhead = node1\ncurrentNode = head\n\nwhile currentNode is not None:\n print(currentNode.value + \"->\")\n currentNode = currentNode.nextNode\n","repo_name":"ShivangDave/Python-Programs","sub_path":"LinkedListTest.py","file_name":"LinkedListTest.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"19347439515","text":"from smbus2 import SMBus\r\nimport time\r\n\r\nadr = 0x0d\r\nbus = SMBus(1)\r\n\r\nclass QMC5883L(object):\r\n\r\n\tdef __init__(self):\r\n\t\tself.bus = SMBus(1)\r\n\t\tself.offset_X = 0\r\n\t\tself.offset_Y = 0\r\n\t\tself.offset_Z = 0\r\n\t \r\n\t\r\n\tdef set_config(self):\r\n\t\tbus.write_byte_data(adr,0x0b,0x01)\r\n\t\tbus.write_byte_data(adr,0x09,0x0d)\r\n\t\r\n\tdef get_status(self):\r\n\t\tready=bus.read_byte_data(adr,0x06)\r\n\t\tprint(\"status={}\".format(ready))\r\n\t\t\r\n\tdef _convert_data(self, data):\r\n\t\tmagval = ((data[1] << 8) + data[0])\r\n\t\tif magval > (2 ** 15) - 1:\r\n\t\t magval = magval - (2 **16)\r\n\t\tmagval = float(magval) * 2 / 2 ** 15\r\n\t\treturn magval\r\n\t\t\r\n\tdef get_data(self):\r\n\t#print the data X, Y, Z converted\r\n\t\tdata = bus.read_i2c_block_data(adr,0x00,6)\r\n\t\tX_data = self._convert_data(data[0:2])-self.offset_X\t\t\r\n\t\tY_data = self._convert_data(data[2:4])-self.offset_Y\t\t\r\n\t\tZ_data = self._convert_data(data[4:6])-self.offset_Z\r\n\t\tdata = [X_data, Y_data, Z_data]\r\n\t\treturn data\r\n\t\t\r\n\t\t\t\t\r\n\tdef disp_data(self):\r\n\t\ti = 0\r\n\t\twhile i < 1:\r\n\t\t\tdata= self.get_data()\r\n\t\t\tprint(\"data X = {} G\".format(data[0]))\r\n\t\t\tprint(\"data Y = {} G\".format(data[1]))\r\n\t\t\tprint(\"data Z = {} G\".format(data[2]))\r\n\t\t\ttime.sleep(1)\r\n\t\t\ti += 1\r\n\t\r\n\t\t\r\n\tdef calibration(self):\r\n\t\tdata = self.get_data()\r\n\t\tself.offset_X = data[0]\r\n\t\tself.offset_Y = data[1]\r\n\t\tself.offset_Z = data[2]\r\n\t\t\r\n\t\t","repo_name":"Petipa/Localizer","sub_path":"QMC5883L.py","file_name":"QMC5883L.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"71050621681","text":"# -*- coding:utf-8 -*-\nfrom __future__ import unicode_literals\n\nimport os\nfrom django.db import models\n\nREVIEWER_INITIAL = 'I'\nREVIEWER_AVAILABLE = 'A'\nREVIEWER_SELECTED = 'S'\nREVIEWER_UNAVAILABLE = 'N'\nREVIEWER_STATE_CHOICE = (\n (REVIEWER_INITIAL, u\"初始\"),\n (REVIEWER_AVAILABLE, u\"候选\"),\n (REVIEWER_SELECTED, u\"选中\"),\n (REVIEWER_UNAVAILABLE, u\"无法参加\"),\n)\n\nREVIEW_STATE_NOTSTART = 'N'\nREVIEW_STATE_ONGOING = 'O'\nREVIEW_STATE_COMMITTED = 'F'\nREVIEW_STATE_CHOICE = {\n REVIEW_STATE_NOTSTART: u\"尚未开始\",\n REVIEW_STATE_ONGOING: u\"进行中\",\n REVIEW_STATE_COMMITTED: u\"已提交\",\n}\n\nGENDER_CHOICE = (\n (u\"男\", u\"男\"),\n (u\"女\", u\"女\"),\n)\n\nZHUANJIALEIXING_CHOICES = (\n (u\"科技界专家\", u\"科技界专家\"),\n (u\"产业界专家\", u\"产业界专家\"),\n (u\"经济界专家\", u\"经济界专家\"),\n)\n\nCHENGHAO_DEFAULT = u\"无\"\nCHENGHAO_CHOICES = (\n (u\"甘肃省领军人才第一层次\", u\"甘肃省领军人才第一层次\"),\n (u\"甘肃省领军人才第二层次\", u\"甘肃省领军人才第二层次\"),\n (u\"甘肃省特聘科技专家\", u\"甘肃省特聘科技专家\"),\n (u\"甘肃省优秀专家\", u\"甘肃省优秀专家\"),\n (u\"甘肃省优秀科技工作者\", u\"甘肃省优秀科技工作者\"),\n (u\"中国科学院院士\", u\"中国科学院院士\"),\n (u\"中国工程院院士\", u\"中国工程院院士\"),\n (u\"长江学者特聘教授\", u\"长江学者特聘教授\"),\n (u\"长江学者奖励计划人选\", u\"长江学者奖励计划人选\"),\n (u\"国务院政府特殊津贴专家\", u\"国务院政府特殊津贴专家\"),\n (u\"国家杰出青年科学基金获得者\", u\"国家杰出青年科学基金获得者\"),\n (u\"国家优秀青年科学基金获得者\", u\"国家优秀青年科学基金获得者\"),\n (u\"教育部“新世纪优秀人才支持计划”人选\", u\"教育部“新世纪优秀人才支持计划”人选\"),\n (u\"国家“千人计划”人选\", u\"国家“千人计划”人选\"),\n (u\"国家“百千万人才工程”人选\", u\"国家“百千万人才工程”人选\"),\n (u\"中科院“百人计划”人选\", u\"中科院“百人计划”人选\"),\n (u\"国家有突出贡献专家\", u\"国家有突出贡献专家\"),\n (u\"国家有突出贡献中青年专家\", u\"国家有突出贡献中青年专家\"),\n (u\"国家科技重大专项首席专家\", u\"国家科技重大专项首席专家\"),\n (u\"国家重大工程项目首席技术专家和管理专家\", u\"国家重大工程项目首席技术专家和管理专家\"),\n (u\"国家自然科学基金重大项目首席专家\", u\"国家自然科学基金重大项目首席专家\"),\n (u\"国家自然科学基金创新研究群体带头人\", u\"国家自然科学基金创新研究群体带头人\"),\n (u\"全国优秀科技工作者，教育部创新团队带头人\", u\"全国优秀科技工作者，教育部创新团队带头人\"),\n (u\"省级重点科技创新团队带头人\", u\"省级重点科技创新团队带头人\"),\n (u\"国家级科技奖励获得者\", u\"国家级科技奖励获得者\"),\n (u\"省级科技奖励获得者（二等奖以上）\", u\"省级科技奖励获得者（二等奖以上）\"),\n (u\"科技部科技领军人才\", u\"科技部科技领军人才\"),\n (u\"科技部中青年科技领军人才\", u\"科技部中青年科技领军人才\"),\n (u\"国家级重点实验室（工程技术中心）带头人\", u\"国家级重点实验室（工程技术中心）带头人\"),\n (u\"教育部重点实验室（工程技术中心）带头人\", u\"教育部重点实验室（工程技术中心）带头人\"),\n (u\"省级重点实验室（工程技术中心）带头人\", u\"省级重点实验室（工程技术中心）带头人\"),\n (u\"国际顶级学术期刊审稿人\", u\"国际顶级学术期刊审稿人\"),\n (CHENGHAO_DEFAULT, u\"无\"),\n #(u\"A1\", u\"甘肃省领军人才第一层次\"),\n #(u\"A2\", u\"甘肃省领军人才第二层次\"),\n #(u\"A3\", u\"甘肃省特聘科技专家\"),\n #(u\"A4\", u\"甘肃省优秀专家\"),\n #(u\"A5\", u\"甘肃省优秀科技工作者\"),\n #(u\"A6\", u\"中国科学院院士\"),\n #(u\"A7\", u\"中国工程院院士\"),\n #(u\"A8\", u\"长江学者特聘教授\"),\n #(u\"A9\", u\"长江学者奖励计划人选\"),\n #(u\"B1\", u\"国务院政府特殊津贴专家\"),\n #(u\"B2\", u\"国家杰出青年科学基金获得者\"),\n #(u\"B3\", u\"国家优秀青年科学基金获得者\"),\n #(u\"B4\", u\"教育部“新世纪优秀人才支持计划”人选\"),\n #(u\"B5\", u\"国家“千人计划”人选\"),\n #(u\"B6\", u\"国家“百千万人才工程”人选\"),\n #(u\"B7\", u\"中科院“百人计划”人选\"),\n #(u\"B8\", u\"国家有突出贡献专家\"),\n #(u\"B9\", u\"国家有突出贡献中青年专家\"),\n #(u\"C1\", u\"国家科技重大专项首席专家\"),\n #(u\"C2\", u\"国家重大工程项目首席技术专家和管理专家\"),\n #(u\"C3\", u\"国家自然科学基金重大项目首席专家\"),\n #(u\"C4\", u\"国家自然科学基金创新研究群体带头人\"),\n #(u\"C5\", u\"全国优秀科技工作者，教育部创新团队带头人\"),\n #(u\"C6\", u\"省级重点科技创新团队带头人\"),\n #(u\"C7\", u\"国家级科技奖励获得者\"),\n #(u\"C8\", u\"省级科技奖励获得者（二等奖以上）\"),\n #(u\"C9\", u\"科技部科技领军人才\"),\n #(u\"D1\", u\"科技部中青年科技领军人才\"),\n #(u\"D2\", u\"国家级重点实验室（工程技术中心）带头人\"),\n #(u\"D3\", u\"教育部重点实验室（工程技术中心）带头人\"),\n #(u\"D4\", u\"省级重点实验室（工程技术中心）带头人\"),\n #(u\"D5\", u\"国际顶级学术期刊审稿人\"),\n #(u\"NULL\", u\"无\"),\n)\n\n# Follow iso-3166-1全球国家名称代码\nCOUNTRY_CHOICE = (\n ('CHN', u\"中国（China）\"),\n ('HKG', u\"中国香港（Hong Kong, China）\"),\n ('MAC', u\"中国澳门（Macao, China）\"),\n ('TWN', u\"中国台湾（Taiwan, China）\"),\n ('AND', u\"安道尔（Andorra）\"),\n ('ARE', u\"阿联酋（United Arab Emirates）\"),\n ('AFG', u\"阿富汗（Afghanistan）\"),\n ('ATG', u\"安提瓜和巴布达（Antigua & Barbuda）\"),\n ('AIA', u\"安圭拉（Anguilla）\"),\n ('ALB', u\"阿尔巴尼亚（Albania）\"),\n ('ARM', u\"亚美尼亚（Armenia）\"),\n ('AGO', u\"安哥拉（Angola）\"),\n ('ATA', u\"南极洲（Antarctica）\"),\n ('ARG', u\"阿根廷（Argentina）\"),\n ('ASM', u\"美属萨摩亚（American Samoa）\"),\n ('AUT', u\"奥地利（Austria）\"),\n ('AUS', u\"澳大利亚（Australia）\"),\n ('ABW', u\"阿鲁巴（Aruba）\"),\n ('ALA', u\"奥兰群岛（?aland Island）\"),\n ('AZE', u\"阿塞拜疆（Azerbaijan）\"),\n ('BIH', u\"波黑（Bosnia & Herzegovina）\"),\n ('BRB', u\"巴巴多斯（Barbados）\"),\n ('BGD', u\"孟加拉（Bangladesh）\"),\n ('BEL', u\"比利时（Belgium）\"),\n ('BFA', u\"布基纳法索（Burkina）\"),\n ('BGR', u\"保加利亚（Bulgaria）\"),\n ('BHR', u\"巴林（Bahrain）\"),\n ('BDI', u\"布隆迪（Burundi）\"),\n ('BEN', u\"贝宁（Benin）\"),\n ('BLM', u\"圣巴泰勒米岛（Saint Barthélemy）\"),\n ('BMU', u\"百慕大（Bermuda）\"),\n ('BRN', u\"文莱（Brunei）\"),\n ('BOL', u\"玻利维亚（Bolivia）\"),\n ('BES', u\"荷兰加勒比区（Caribbean Netherlands）\"),\n ('BRA', u\"巴西（Brazil）\"),\n ('BHS', u\"巴哈马（The Bahamas）\"),\n ('BTN', u\"不丹（Bhutan）\"),\n ('BVT', u\"布韦岛（Bouvet Island）\"),\n ('BWA', u\"博茨瓦纳（Botswana）\"),\n ('BLR', u\"白俄罗斯（Belarus）\"),\n ('BLZ', u\"伯利兹（Belize）\"),\n ('CAN', u\"加拿大（Canada）\"),\n ('CCK', u\"科科斯群岛（Cocos (Keeling) Islands）\"),\n ('CAF', u\"中非（Central African Republic）\"),\n ('CHE', u\"瑞士（Switzerland）\"),\n ('CHL', u\"智利（Chile）\"),\n ('CMR', u\"喀麦隆（Cameroon）\"),\n ('COL', u\"哥伦比亚（Colombia）\"),\n ('CRI', u\"哥斯达黎加（Costa Rica）\"),\n ('CUB', u\"古巴（Cuba）\"),\n ('CPV', u\"佛得角（Cape Verde）\"),\n ('CXR', u\"圣诞岛（Christmas Island）\"),\n ('CYP', u\"塞浦路斯（Cyprus）\"),\n ('CZE', u\"捷克（Czech Republic）\"),\n ('DEU', u\"德国（Germany）\"),\n ('DJI', u\"吉布提（Djibouti）\"),\n ('DNK', u\"丹麦（Denmark）\"),\n ('DMA', u\"多米尼克（Dominica）\"),\n ('DOM', u\"多米尼加（Dominican Republic）\"),\n ('DZA', u\"阿尔及利亚（Algeria）\"),\n ('ECU', u\"厄瓜多尔（Ecuador）\"),\n ('EST', u\"爱沙尼亚（Estonia）\"),\n ('EGY', u\"埃及（Egypt）\"),\n ('ESH', u\"西撒哈拉（Western Sahara）\"),\n ('ERI', u\"厄立特里亚（Eritrea）\"),\n ('ESP', u\"西班牙（Spain）\"),\n ('FIN', u\"芬兰（Finland）\"),\n ('FJI', u\"斐济群岛（Fiji）\"),\n ('FLK', u\"马尔维纳斯群岛（ 福克兰）（Falkland Islands）\"),\n ('FSM', u\"密克罗尼西亚联邦（Federated States of Micronesia）\"),\n ('FRO', u\"法罗群岛（Faroe Islands）\"),\n ('FRA', u\"法国（France）\"),\n ('GAB', u\"加蓬（Gabon）\"),\n ('GRD', u\"格林纳达（Grenada）\"),\n ('GEO', u\"格鲁吉亚（Georgia）\"),\n ('GUF', u\"法属圭亚那（French Guiana）\"),\n ('GHA', u\"加纳（Ghana）\"),\n ('GIB', u\"直布罗陀（Gibraltar）\"),\n ('GRL', u\"格陵兰（Greenland）\"),\n ('GIN', u\"几内亚（Guinea）\"),\n ('GLP', u\"瓜德罗普（Guadeloupe）\"),\n ('GNQ', u\"赤道几内亚（Equatorial Guinea）\"),\n ('GRC', u\"希腊（Greece）\"),\n ('SGS', u\"南乔治亚岛和南桑威奇群岛（South Georgia and the South Sandwich Islands）\"),\n ('GTM', u\"危地马拉（Guatemala）\"),\n ('GUM', u\"关岛（Guam）\"),\n ('GNB', u\"几内亚比绍（Guinea-Bissau）\"),\n ('GUY', u\"圭亚那（Guyana）\"),\n ('HMD', u\"赫德岛和麦克唐纳群岛（Heard Island and McDonald Islands）\"),\n ('HND', u\"洪都拉斯（Honduras）\"),\n ('HRV', u\"克罗地亚（Croatia）\"),\n ('HTI', u\"海地（Haiti）\"),\n ('HUN', u\"匈牙利（Hungary）\"),\n ('IDN', u\"印尼（Indonesia）\"),\n ('IRL', u\"爱尔兰（Ireland）\"),\n ('ISR', u\"以色列（Israel）\"),\n ('IMN', u\"马恩岛（Isle of Man）\"),\n ('IND', u\"印度（India）\"),\n ('IOT', u\"英属印度洋领地（British Indian Ocean Territory）\"),\n ('IRQ', u\"伊拉克（Iraq）\"),\n ('IRN', u\"伊朗（Iran）\"),\n ('ISL', u\"冰岛（Iceland）\"),\n ('ITA', u\"意大利（Italy）\"),\n ('JEY', u\"泽西岛（Jersey）\"),\n ('JAM', u\"牙买加（Jamaica）\"),\n ('JOR', u\"约旦（Jordan）\"),\n ('JPN', u\"日本（Japan）\"),\n ('KHM', u\"柬埔寨（Cambodia）\"),\n ('KIR', u\"基里巴斯（Kiribati）\"),\n ('COM', u\"科摩罗（The Comoros）\"),\n ('KWT', u\"科威特（Kuwait）\"),\n ('CYM', u\"开曼群岛（Cayman Islands）\"),\n ('LBN', u\"黎巴嫩（Lebanon）\"),\n ('LIE', u\"列支敦士登（Liechtenstein）\"),\n ('LKA', u\"斯里兰卡（Sri Lanka）\"),\n ('LBR', u\"利比里亚（Liberia）\"),\n ('LSO', u\"莱索托（Lesotho）\"),\n ('LTU', u\"立陶宛（Lithuania）\"),\n ('LUX', u\"卢森堡（Luxembourg）\"),\n ('LVA', u\"拉脱维亚（Latvia）\"),\n ('LBY', u\"利比亚（Libya）\"),\n ('MAR', u\"摩洛哥（Morocco）\"),\n ('MCO', u\"摩纳哥（Monaco）\"),\n ('MDA', u\"摩尔多瓦（Moldova）\"),\n ('MNE', u\"黑山（Montenegro）\"),\n ('MAF', u\"法属圣马丁（Saint Martin (France)）\"),\n ('MDG', u\"马达加斯加（Madagascar）\"),\n ('MHL', u\"马绍尔群岛（Marshall islands）\"),\n ('MKD', u\"马其顿（Republic of Macedonia (FYROM)）\"),\n ('MLI', u\"马里（Mali）\"),\n ('MMR', u\"缅甸（Myanmar (Burma)）\"),\n ('MTQ', u\"马提尼克（Martinique）\"),\n ('MRT', u\"毛里塔尼亚（Mauritania）\"),\n ('MSR', u\"蒙塞拉特岛（Montserrat）\"),\n ('MLT', u\"马耳他（Malta）\"),\n ('MDV', u\"马尔代夫（Maldives）\"),\n ('MWI', u\"马拉维（Malawi）\"),\n ('MEX', u\"墨西哥（Mexico）\"),\n ('MYS', u\"马来西亚（Malaysia）\"),\n ('NAM', u\"纳米比亚（Namibia）\"),\n ('NER', u\"尼日尔（Niger）\"),\n ('NFK', u\"诺福克岛（Norfolk Island）\"),\n ('NGA', u\"尼日利亚（Nigeria）\"),\n ('NIC', u\"尼加拉瓜（Nicaragua）\"),\n ('NLD', u\"荷兰（Netherlands）\"),\n ('NOR', u\"挪威（Norway）\"),\n ('NPL', u\"尼泊尔（Nepal）\"),\n ('NRU', u\"瑙鲁（Nauru）\"),\n ('OMN', u\"阿曼（Oman）\"),\n ('PAN', u\"巴拿马（Panama）\"),\n ('PER', u\"秘鲁（Peru）\"),\n ('PYF', u\"法属波利尼西亚（French polynesia）\"),\n ('PNG', u\"巴布亚新几内亚（Papua New Guinea）\"),\n ('PHL', u\"菲律宾（The Philippines）\"),\n ('PAK', u\"巴基斯坦（Pakistan）\"),\n ('POL', u\"波兰（Poland）\"),\n ('PCN', u\"皮特凯恩群岛（Pitcairn Islands）\"),\n ('PRI', u\"波多黎各（Puerto Rico）\"),\n ('PSE', u\"巴勒斯坦（Palestinian territories）\"),\n ('PLW', u\"帕劳（Palau）\"),\n ('PRY', u\"巴拉圭（Paraguay）\"),\n ('QAT', u\"卡塔尔（Qatar）\"),\n ('REU', u\"留尼汪（Réunion）\"),\n ('ROU', u\"罗马尼亚（Romania）\"),\n ('SRB', u\"塞尔维亚（Serbia）\"),\n ('RUS', u\"俄罗斯（Russian Federation）\"),\n ('RWA', u\"卢旺达（Rwanda）\"),\n ('SLB', u\"所罗门群岛（Solomon Islands）\"),\n ('SYC', u\"塞舌尔（Seychelles）\"),\n ('SDN', u\"苏丹（Sudan）\"),\n ('SWE', u\"瑞典（Sweden）\"),\n ('SGP', u\"新加坡（Singapore）\"),\n ('SVN', u\"斯洛文尼亚（Slovenia）\"),\n ('SJM', u\"斯瓦尔巴群岛和 扬马延岛（Template:Country data SJM Svalbard）\"),\n ('SVK', u\"斯洛伐克（Slovakia）\"),\n ('SLE', u\"塞拉利昂（Sierra Leone）\"),\n ('SMR', u\"圣马力诺（San Marino）\"),\n ('SEN', u\"塞内加尔（Senegal）\"),\n ('SOM', u\"索马里（Somalia）\"),\n ('SUR', u\"苏里南（Suriname）\"),\n ('SSD', u\"南苏丹（South Sudan）\"),\n ('STP', u\"圣多美和普林西比（Sao Tome & Principe）\"),\n ('SLV', u\"萨尔瓦多（El Salvador）\"),\n ('SYR', u\"叙利亚（Syria）\"),\n ('SWZ', u\"斯威士兰（Swaziland）\"),\n ('TCA', u\"特克斯和凯科斯群岛（Turks & Caicos Islands）\"),\n ('TCD', u\"乍得（Chad）\"),\n ('TGO', u\"多哥（Togo）\"),\n ('THA', u\"泰国（Thailand）\"),\n ('TKL', u\"托克劳（Tokelau）\"),\n ('TLS', u\"东帝汶（Timor-Leste (East Timor)）\"),\n ('TUN', u\"突尼斯（Tunisia）\"),\n ('TON', u\"汤加（Tonga）\"),\n ('TUR', u\"土耳其（Turkey）\"),\n ('TUV', u\"图瓦卢（Tuvalu）\"),\n ('TZA', u\"坦桑尼亚（Tanzania）\"),\n ('UKR', u\"乌克兰（Ukraine）\"),\n ('UGA', u\"乌干达（Uganda）\"),\n ('USA', u\"美国（United States of America (USA)）\"),\n ('URY', u\"乌拉圭（Uruguay）\"),\n ('VAT', u\"梵蒂冈（Vatican City (The Holy See)）\"),\n ('VEN', u\"委内瑞拉（Venezuela）\"),\n ('VGB', u\"英属维尔京群岛（British Virgin Islands）\"),\n ('VIR', u\"美属维尔京群岛（United States Virgin Islands）\"),\n ('VNM', u\"越南（Vietnam）\"),\n ('WLF', u\"瓦利斯和富图纳（Wallis and Futuna）\"),\n ('WSM', u\"萨摩亚（Samoa）\"),\n ('YEM', u\"也门（Yemen）\"),\n ('MYT', u\"马约特（Mayotte）\"),\n ('ZAF', u\"南非（South Africa）\"),\n ('ZMB', u\"赞比亚（Zambia）\"),\n ('ZWE', u\"津巴布韦（Zimbabwe）\"),\n ('COG', u\"刚果（布）（Republic of the Congo）\"),\n ('COD', u\"刚果（金）（Democratic Republic of the Congo）\"),\n ('MOZ', u\"莫桑比克（Mozambique）\"),\n ('GGY', u\"根西岛（Guernsey）\"),\n ('GMB', u\"冈比亚（Gambia）\"),\n ('MNP', u\"北马里亚纳群岛（Northern Mariana Islands）\"),\n ('ETH', u\"埃塞俄比亚（Ethiopia）\"),\n ('NCL', u\"新喀里多尼亚（New Caledonia）\"),\n ('VUT', u\"瓦努阿图（Vanuatu）\"),\n ('ATF', u\"��属南部领地（French Southern Territories）\"),\n ('NIU', u\"纽埃（Niue）\"),\n ('UMI', u\"美国本土外小岛屿（United States Minor Outlying Islands）\"),\n ('COK', u\"库克群岛（Cook Islands）\"),\n ('GBR', u\"英国（Great Britain (United Kingdom; England)）\"),\n ('TTO', u\"特立尼达和多巴哥（Trinidad & Tobago）\"),\n ('VCT', u\"圣文森特和格林纳丁斯（St. Vincent & the Grenadines）\"),\n ('NZL', u\"新西兰（New Zealand）\"),\n ('SAU', u\"沙特阿拉伯（Saudi Arabia）\"),\n ('LAO', u\"老挝（Laos）\"),\n ('PRK', u\"朝鲜（北朝鲜）（North Korea）\"),\n ('KOR', u\"韩国（南朝鲜）（South Korea）\"),\n ('PRT', u\"葡萄牙（Portugal）\"),\n ('KGZ', u\"吉尔吉斯斯坦（Kyrgyzstan）\"),\n ('KAZ', u\"哈萨克斯坦（Kazakhstan）\"),\n ('TJK', u\"塔吉克斯坦（Tajikistan）\"),\n ('TKM', u\"土库曼斯坦（Turkmenistan）\"),\n ('UZB', u\"乌兹别克斯坦（Uzbekistan）\"),\n ('KNA', u\"圣基茨和尼维斯（St. Kitts & Nevis）\"),\n ('SPM', u\"圣皮埃尔和密克隆（Saint-Pierre and Miquelon）\"),\n ('SHN', u\"圣赫勒拿（St. Helena & Dependencies）\"),\n ('LCA', u\"圣卢西亚（St. Lucia）\"),\n ('MUS', u\"毛里求斯（Mauritius）\"),\n ('CIV', u\"科特迪瓦（C?te d'Ivoire）\"),\n ('KEN', u\"肯尼亚（Kenya）\"),\n ('MNG', u\"蒙古国（Mongolia）\"),\n)\n\nDOMAIN_TYPE = (\n (u\"国家科技部领域\", u\"国家科技部领域\"),\n (u\"国家基金委领域\", u\"国家基金委领域\"),\n (u\"行业分类\", u\"行业分类\"),\n (u\"中图分类\", u\"中图分类\"),\n)\n\nMINZU_CHOICE = (\n (u\"汉族\", u\"汉族\"),\n (u\"壮族\", u\"壮族\"),\n (u\"满族\", u\"满族\"),\n (u\"回族\", u\"回族\"),\n (u\"苗族\", u\"苗族\"),\n (u\"维吾尔族\", u\"维吾尔族\"),\n (u\"土家族\", u\"土家族\"),\n (u\"彝族\", u\"彝族\"),\n (u\"蒙古族\", u\"蒙古族\"),\n (u\"藏族\", u\"藏族\"),\n (u\"布依族\", u\"布依族\"),\n (u\"侗族\", u\"侗族\"),\n (u\"瑶族\", u\"瑶族\"),\n (u\"朝鲜族\", u\"朝鲜族\"),\n (u\"白族\", u\"白族\"),\n (u\"哈尼族\", u\"哈尼族\"),\n (u\"哈萨克族\", u\"哈萨克族\"),\n (u\"黎族\", u\"黎族\"),\n (u\"傣族\", u\"傣族\"),\n (u\"畲族\", u\"畲族\"),\n (u\"傈僳族\", u\"傈僳族\"),\n (u\"仡佬族\", u\"仡佬族\"),\n (u\"东乡族\", u\"东乡族\"),\n (u\"高山族\", u\"高山族\"),\n (u\"拉祜族\", u\"拉祜族\"),\n (u\"水族\", u\"水族\"),\n (u\"佤族\", u\"佤族\"),\n (u\"纳西族\", u\"纳西族\"),\n (u\"羌族\", u\"羌族\"),\n (u\"土族\", u\"土族\"),\n (u\"仫佬族\", u\"仫佬族\"),\n (u\"锡伯族\", u\"锡伯族\"),\n (u\"柯尔克孜族\", u\"柯尔克孜族\"),\n (u\"达斡尔族\", u\"达斡尔族\"),\n (u\"景颇族\", u\"景颇族\"),\n (u\"毛南族\", u\"毛南族\"),\n (u\"撒拉族\", u\"撒拉族\"),\n (u\"布朗族\", u\"布朗族\"),\n (u\"塔吉克族\", u\"塔吉克族\"),\n (u\"阿昌族\", u\"阿昌族\"),\n (u\"普米族\", u\"普米族\"),\n (u\"鄂温克族\", u\"鄂温克族\"),\n (u\"怒族\", u\"怒族\"),\n (u\"京族\", u\"京族\"),\n (u\"基诺族\", u\"基诺族\"),\n (u\"德昂族\", u\"德昂族\"),\n (u\"保安族\", u\"保安族\"),\n (u\"俄罗斯族\", u\"俄罗斯族\"),\n (u\"裕固族\", u\"裕固族\"),\n (u\"乌孜别克族\", u\"乌孜别克族\"),\n (u\"门巴族\", u\"门巴族\"),\n (u\"鄂伦春族\", u\"鄂伦春族\"),\n (u\"独龙族\", u\"独龙族\"),\n (u\"塔塔尔族\", u\"塔塔尔族\"),\n (u\"赫哲族\", u\"赫哲族\"),\n (u\"珞巴族\", u\"珞巴族\"),\n)\n\nXUELI_CHOICE = (\n (u\"博士研究生\", u\"博士研究生\"),\n (u\"硕士研究生\", u\"硕士研究生\"),\n (u\"本科生\", u\"本科生\"),\n (u\"大专\", u\"大专\"),\n (u\"中专\", u\"中专\"),\n (u\"其他\", u\"其他\"),\n)\n\nXUEWEI_CHOICE = (\n (u\"博士\", u\"博士\"),\n (u\"硕士\", u\"硕士\"),\n (u\"学士\", u\"学士\"),\n (u\"其他\", u\"其他\"),\n)\n\nZHENGJIANLEIXING_CHOICE = {\n (u\"身份证\", u\"身份证\"),\n (u\"其他\", u\"其他\"),\n}\n\nBASE_DIR = 'upload'\nPICTURE_BASE = 'pictures'\nATTACHMENT_BASE = 'attachments'\nREVIEW_BASE = 'review'\n\ndef picture_directory_path(instance, filename):\n ext = filename.split('.')[-1]\n if len(ext) > 4:\n ext = \"jpg\"\n filename = \"picture.%s\" % ext\n filepath = os.path.join(BASE_DIR, str(instance.id), PICTURE_BASE, filename)\n if os.path.isfile(filepath):\n os.remove(filepath)\n return filepath\n\ndef attachment_directory_path(instance, filename):\n return os.path.join(BASE_DIR, str(instance.expert.id), ATTACHMENT_BASE, filename)\n\ndef review_attachment_directory_path(instance, filename):\n return os.path.join(BASE_DIR, str(instance.project.id), REVIEW_BASE, str(instance.expert.id), filename)\n\n\n# Step 1 ~ Step 7\nclass Expert(models.Model):\n USERNAME_FIELD = 'keystone_username'\n # 账号信息 -- Step 1\n expertname = models.CharField(max_length=32, verbose_name=u\"*中文姓名\", help_text=u\"专家姓名\", null=True, blank=True)\n keystone_uuid = models.CharField(max_length=64, verbose_name=u\"UUID\", help_text=u\"账号UUID\", null=True, blank=True)\n keystone_username = models.CharField(max_length=64, verbose_name=u\"*用户名\", help_text=u\"专家账号用户名\")\n keystone_initial_pwd = models.CharField(max_length=128, verbose_name=u\"*初始密码\", help_text=u\"输入初始密码\")\n email = models.EmailField(verbose_name=u\"*邮箱\", help_text=u\"电子邮箱，例如：abc123@example.com\")\n email_verified = models.BooleanField(default=False, verbose_name=u\"邮箱地址是否已验证\", help_text=u\"是否已经验证邮箱地址\")\n email_verified_words = models.CharField(max_length=128, verbose_name=u\"邮箱验证秘钥\", help_text=u\"邮箱地址验证秘钥，用于验证邮箱是否有效\", null=True)\n\n # 基本信息 -- Step 2\n picture = models.ImageField(verbose_name=u\"*照片\", upload_to=picture_directory_path, help_text=u\"照片（小于1M）\", null=True, blank=True)\n cengyong_name = models.CharField(max_length=32, verbose_name=u\"曾用名\", help_text=u\"曾用名\", null=True, blank=True)\n pinyin_name = models.CharField(max_length=32, verbose_name=u\"拼音或英文名\", help_text=u\"拼音或英文名\", null=True, blank=True)\n gender = models.CharField(max_length=16, choices=GENDER_CHOICE, verbose_name=u\"*性别\", help_text=u\"性别\", null=True, blank=True)\n born = models.DateField(auto_now_add=False, verbose_name=u\"*出生日期\", help_text=u\"出生日期，年月日\", null=True, blank=True)\n chushengdi = models.CharField(max_length=32, verbose_name=u\"出生地\", help_text=u\"出生地，例如：甘肃兰州\", null=True, blank=True)\n guoji = models.CharField(max_length=32, choices=COUNTRY_CHOICE, verbose_name=u\"*国籍\", help_text=u\"国籍，例如：中国\", null=True, blank=True)\n jiguan = models.CharField(max_length=32, verbose_name=u\"*籍贯\", help_text=u\"籍贯，例如：甘肃兰州\", null=True, blank=True)\n minzu = models.CharField(max_length=32, choices=MINZU_CHOICE, verbose_name=u\"*民族\", help_text=u\"民族，例如：汉族\", null=True, blank=True)\n zhengzhimianmao = models.CharField(max_length=32, verbose_name=u\"政治面貌\", help_text=u\"政治面貌，例如：党员\", null=True, blank=True)\n zhengjianleixing = models.CharField(max_length=32, choices=ZHENGJIANLEIXING_CHOICE, verbose_name=u\"*证件类型\", help_text=u\"证件类型：例如：身份证\", null=True, blank=True)\n zhengjianhaoma = models.CharField(max_length=64, verbose_name=u\"*证件号码\", help_text=u\"证件号码：允许数字、英文字母\", null=True, blank=True)\n\n #银行账号信息 -- Step 3\n kaihuyinhang = models.CharField(max_length=32, verbose_name=u\"*开户银行\", help_text=u\"开户银行，例如：中国银行\", null=True, blank=True)\n kaihuhuming = models.CharField(max_length=64, verbose_name=u\"*开户户名\", help_text=u\"户名，需与本人姓名一致\", null=True, blank=True)\n kaihuzhanghao = models.CharField(max_length=64, verbose_name=u\"*开户帐号\", help_text=u\"账号，允许数字\", null=True, blank=True)\n suoshuzhihang = models.CharField(max_length=64, verbose_name=u\"*所属支行\", help_text=u\"支行，例如：中国银行兰州市城关中心支行\", null=True, blank=True)\n\n #所在单位信息 -- Step 4\n suozaidaiwei = models.CharField(max_length=64, verbose_name=u\"所在单位\", help_text=u\"所在单位，括号请用半角括号\", null=True, blank=True)\n danweixinzhi = models.CharField(max_length=64, verbose_name=u\"*单位性质\", help_text=u\"单位性质：例如：事业单位\", null=True, blank=True)\n suozaibumen = models.CharField(max_length=64, verbose_name=u\"所在二级单位（部门）\", help_text=u\"所在二级单位（部门）信息\", null=True, blank=True)\n danweisuozaisheng = models.CharField(max_length=32, verbose_name=u\"*单位所在省\", help_text=u\"例如：甘肃省\", null=True, blank=True)\n danweisuozaishi = models.CharField(max_length=32, verbose_name=u\"*单位所在市\", help_text=u\"例如：兰州市\", null=True, blank=True)\n tongxundizhi = models.CharField(max_length=256, verbose_name=u\"*通讯地址\", help_text=u\"该地址用于接收函件\", null=True, blank=True)\n youzhengbianma = models.CharField(max_length=32, verbose_name=u\"邮政编码\", help_text=u\"邮编，例如：730030\", null=True, blank=True)\n zhiwu = models.CharField(max_length=32, verbose_name=u\"*职务\", help_text=u\"担任之职务\", null=True, blank=True)\n zhiwujibie = models.CharField(max_length=32, verbose_name=u\"职务级别\", help_text=u\"该职务所属的级别\", null=True, blank=True)\n zhicheng = models.CharField(max_length=32, verbose_name=u\"*职称\", help_text=u\"职称\", null=True, blank=True)\n gongzuoxingzhi = models.CharField(max_length=64, verbose_name=u\"*工作性质\", help_text=u\"工作性质，例如：全职\", null=True, blank=True)\n\n #学历信息 -- Step 5\n zuigaoxueli = models.CharField(max_length=64, choices=XUELI_CHOICE, verbose_name=u\"*最高学历\", help_text=u\"最高学历，例如：博士研究生\", null=True, blank=True)\n zuigaoxuewei = models.CharField(max_length=64, choices=XUEWEI_CHOICE, verbose_name=u\"*最高学位\", help_text=u\"最高学位，例如：博士学位\", null=True, blank=True)\n xueweishouyudiqu = models.CharField(max_length=64, choices=COUNTRY_CHOICE, verbose_name=u\"最高学位授予国或地区\", help_text=u\"授予最高学位的院校所在的国家或地区\", null=True, blank=True)\n xueweishouyuriqi = models.CharField(max_length=32, verbose_name=u\"最高学位授予年份\", help_text=u\"授予最高学位的年份，如：1997\", null=True, blank=True)\n biyeyuanxiao = models.CharField(max_length=64, verbose_name=u\"毕业院校\", help_text=u\"获取最高学历学位的毕业院校名称\", null=True, blank=True)\n xueweishouyuyuanxiao = models.CharField(max_length=64, verbose_name=u\"*获得最高学位院校\", help_text=u\"授予最高学位的院校名称\", null=True, blank=True)\n suoxuezhuanye = models.CharField(max_length=64, verbose_name=u\"*所学专业\", help_text=u\"所学专业的名称\", null=True, blank=True)\n xianzhuanye = models.CharField(max_length=64, verbose_name=u\"*现从事专业\", help_text=u\"现从事的专业名称\", null=True, blank=True)\n isBodao = models.BooleanField(default=False, verbose_name=u\"是否博士生导师\", help_text=u\"是否为博士生导师，默认为否\")\n isLiangYuanYuanshi = models.BooleanField(default=False, verbose_name=u\"*是否两院院士\", help_text=u\"是否为两院院士，默认为否\")\n\n #其他 -- Step 6\n mobile = models.CharField(max_length=32, verbose_name=u\"*移动电话\", help_text=u\"移动电话，请去除空格、保留国家码，例如：+8613912341234\", null=True, blank=True)\n mobile_verified = models.BooleanField(default=False, verbose_name=u\"手机号是否已验证\", help_text=u\"是否已经验证手机号码\")\n workphone = models.CharField(max_length=32, verbose_name=u\"*办公电话\", help_text=u\"办公室电话，请去除空格，保留国家、地区码，例如：+869311234567\", null=True, blank=True)\n homephone = models.CharField(max_length=32, verbose_name=u\"家庭电话\", help_text=u\"家庭电话，请去除空格，保留国家、地区码，例如：+869311234567\", null=True, blank=True)\n fax = models.CharField(max_length=32, verbose_name=u\"传真\", help_text=u\"传真号码，请去除空格，保留国家、地区码，例如：+869311234567\", null=True, blank=True)\n personal_web = models.URLField(verbose_name=u\"个人学术网址\", help_text=u\"个人学术网址，例如：http://abc.example.com\", null=True, blank=True)\n weibo = models.CharField(max_length=64, verbose_name=u\"微博\", help_text=u\"微博名或微博号或微博网址，例如：故宫博物院 或者：https://weibo.com/gugongweb\", null=True, blank=True)\n jinjilianxiren = models.CharField(max_length=32, verbose_name=u\"紧急联系人\", help_text=u\"紧急联系人姓名\", null=True, blank=True)\n jinjilianxidianhua = models.CharField(max_length=32, verbose_name=u\"紧急联系人电话\", help_text=u\"紧急联系人电话，允许手机或固定电话，请去除>空格、保留国家、地区码，例如：+8613912341234\", null=True, blank=True)\n zhiyezizhi = models.CharField(max_length=64, verbose_name=u\"职业资质\", help_text=u\"职业资质\", null=True, blank=True)\n\n #学术专长或研究方向 -- Step 7\n zhuanchangfangxiang = models.TextField(max_length=4096, verbose_name=u\"*学术专长或研究方向\", help_text=u\"学术专长或研究方向（限500字以内）\", null=True, blank=True)\n\n #个人简介 -- Step 7\n gerenjianjie = models.TextField(max_length=4096, verbose_name=u\"*个人简介\", help_text=u\"个人简历（限1000字）\", null=True, blank=True)\n\n #享受特殊津贴、荣誉称号、其他需要特别说明事宜 -- Step 7\n teshujingtie = models.TextField(max_length=4096, verbose_name=u\"享受特殊津贴\", help_text=u\"享受特殊津贴(限500字) \", null=True, blank=True)\n rongyuchenghao = models.TextField(max_length=4096, verbose_name=u\"荣誉称号\", help_text=u\"荣誉称号（限500字）\", null=True, blank=True)\n others = models.TextField(max_length=4096, verbose_name=u\"其他需要特别说明事宜\", help_text=u\"其他需要特别说明事宜（限500字）\", null=True, blank=True)\n\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n deleted_at = models.DateTimeField(auto_now_add=False, null=True)\n state = models.CharField(max_length=32, verbose_name=u\"状态\", help_text=u\"状态\")\n\n\n#人才称号 -- Step 8\nclass ExpertTitle(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n rencaichenghao = models.CharField(max_length=64, choices=CHENGHAO_CHOICES, default=CHENGHAO_DEFAULT, verbose_name=u\"人才称号\", help_text=u\"称号，多选\", null=True, blank=True)\n\n\n#专家类型 -- Step 9\nclass ExpertClass(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n zhuanjialeixing = models.CharField(max_length=32, choices=ZHUANJIALEIXING_CHOICES, verbose_name=u\"*专家类型（多选）\", help_text=u\"专家类型（多选）\", null=True, blank=True)\n\n\n#研究领域 -- Step 10\nclass ExpertDomain(models.Model):\n domainserial = models.IntegerField(verbose_name=u\"*序号\", help_text=u\"序号\", blank=True)\n domainname = models.CharField(max_length=32, verbose_name=u\"*学科名称\", help_text=u\"在该研究领域研究的学科名称\", blank=True)\n domainkeywords = models.CharField(max_length=256, verbose_name=u\"*中文关键字\", help_text=u\"中文关键字，若有多个请用空格分开\", blank=True)\n domaintype = models.CharField(max_length=32, choices=DOMAIN_TYPE, verbose_name=u\"*领域分类\", help_text=u\"研究领域所属分类\", blank=True)\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n\n\n#工作履历 -- Step 11\nclass FormalJob(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n formaljob_serial = models.IntegerField(verbose_name=u\"*序号\", help_text=u\"序号\", blank=True)\n formaljob_start = models.DateField(verbose_name=u\"*起始时间\", help_text=u\"起始时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", blank=True)\n formaljob_end = models.DateField(verbose_name=u\"结束时间\", help_text=u\"填写结束时间，或者至今；至今则留空。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n formaljob_country = models.CharField(max_length=32, verbose_name=u\"*国家\", choices=COUNTRY_CHOICE, default=\"CHN\", help_text=u\"工作地点所属国家\", blank=True)\n formaljob_danwei = models.CharField(max_length=64, verbose_name=u\"*工作单位\", help_text=u\"工作单位名称\", blank=True)\n formaljob_zhiwu = models.CharField(max_length=64, verbose_name=u\"*职务\", help_text=u\"担任之职务\", blank=True)\n formaljob_zhiwujibie = models.CharField(max_length=64, verbose_name=u\"职务级别\", help_text=u\"该职务所属的级别\", null=True, blank=True)\n formaljob_zhichen = models.CharField(max_length=64, verbose_name=u\"职称\", help_text=u\"职称\", null=True, blank=True)\n formaljob_yanjiufangxiang = models.CharField(max_length=64, verbose_name=u\"*研究方向\", help_text=u\"研究方向\", blank=True)\n formaljob_gongzuoneirong = models.TextField(max_length=4096, verbose_name=u\"*工作内容\", help_text=u\"工作内容描述（限500字）\", blank=True)\n formaljob_gongzuoxingzhi = models.CharField(max_length=64, verbose_name=u\"*工作性质\", help_text=u\"工作性质\", null=True, blank=True)\n\n\n#教育信息 -- Step 12\nclass Education(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n education_serial = models.IntegerField(verbose_name=u\"序号\", help_text=u\"序号\", blank=True)\n education_start = models.DateField(verbose_name=u\"*起始时间\", help_text=u\"起始时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", blank=True)\n education_end = models.DateField(verbose_name=u\"结束时间\", help_text=u\"填写结束时间，或者至今；至今则留空。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n education_country = models.CharField(max_length=32, verbose_name=u\"国家\", choices=COUNTRY_CHOICE, default=\"CHN\", help_text=u\"接受教育院校所属国家\", blank=True)\n education_yuanxiaomingcheng = models.CharField(max_length=64, verbose_name=u\"院校名称\", help_text=u\"院校名称\", blank=True)\n education_zhuanye = models.CharField(max_length=64, verbose_name=u\"专业\", help_text=u\"专业名称\", blank=True)\n education_xueli = models.CharField(max_length=64, choices=XUELI_CHOICE, verbose_name=u\"学历\", help_text=u\"所取得的学历，若未取得学历，请留空。例如：博士研究生\", null=True, blank=True)\n education_xuewei = models.CharField(max_length=64, choices=XUEWEI_CHOICE, verbose_name=u\"学位\", help_text=u\"所取得的学位，若未取得学位，请留空。例如：博士\", null=True, blank=True)\n education_peixunjinxiu = models.CharField(max_length=64, verbose_name=u\"培训进修\", help_text=u\"培训进修经历\", null=True, blank=True)\n education_zhidaojiaoshi = models.CharField(max_length=32, verbose_name=u\"指导教师\", help_text=u\"指导教师姓名，若无情留空\", null=True, blank=True)\n\n\n#学术兼职信息 -- Step 13\nclass PartTimeJob(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n parttimejob_serial = models.IntegerField(verbose_name=u\"*序号\", help_text=u\"序号\", blank=True)\n parttimejob_start = models.DateField(verbose_name=u\"*起始时间\", help_text=u\"起始时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", blank=True)\n parttimejob_end = models.DateField(verbose_name=u\"结束时间\", help_text=u\"填写结束时间，或者至今；至今则留空。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n parttimejob_danwei = models.CharField(max_length=64, verbose_name=u\"*兼职单位\", help_text=u\"学术兼职单位名称\", blank=True)\n parttimejob_zhiwu = models.CharField(max_length=64, verbose_name=u\"*职务\", help_text=u\"担任之职务\", blank=True)\n parttimejob_sessionid = models.CharField(max_length=32, verbose_name=u\"届次\", help_text=u\"此次学术兼职活动所属届次，如没有请留空\", null=True, blank=True)\n\n\n#学术评审信息 -- Step 14\nclass ReviewHistory(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n reviewhistory_serial = models.IntegerField(verbose_name=u\"*序号\", help_text=u\"序号\", blank=True)\n reviewhistory_start = models.DateField(verbose_name=u\"*起始时间\", help_text=u\"起始时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", blank=True)\n reviewhistory_end = models.DateField(verbose_name=u\"结束时间\", help_text=u\"填写结束时间，或者至今；至今则留空。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n reviewhistory_content = models.TextField(max_length=4096, verbose_name=u\"*评审内容\", help_text=u\"评审内容简介\", blank=True)\n reviewhistory_weituojigou = models.CharField(max_length=64, verbose_name=u\"*评审委托机构\", help_text=u\"评审委托机构\", blank=True)\n\n\n#承担项目情况 -- Step 15\nclass XiangmuInfo(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n xiangmuinfo_serial = models.IntegerField(verbose_name=u\"*序号\", help_text=u\"序号\", blank=True)\n xiangmuinfo_name = models.CharField(max_length=256, verbose_name=u\"*项目名称\", help_text=u\"项目名称\", blank=True)\n xiangmuinfo_jibie = models.CharField(max_length=64, verbose_name=u\"项目级别\", help_text=u\"项目级别\", null=True, blank=True)\n xiangmuinfo_paiming = models.CharField(max_length=64, verbose_name=u\"本人排名\", help_text=u\"本人排名\", null=True, blank=True)\n xiangmuinfo_bianhao = models.CharField(max_length=256, verbose_name=u\"项目编号\", help_text=u\"项目编号\", null=True, blank=True)\n xiangmuinfo_zizhuleibie = models.CharField(max_length=256, verbose_name=u\"资助类别\", help_text=u\"资助类别\", null=True, blank=True)\n xiangmuinfo_jingfei = models.DecimalField(max_digits=31, decimal_places=6, verbose_name=u\"*项目经费(万元)\", help_text=u\"项目经费(万元)\", blank=True)\n xiangmuinfo_start = models.DateField(verbose_name=u\"*起始时间\", help_text=u\"起始时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", blank=True)\n xiangmuinfo_end = models.DateField(verbose_name=u\"结束时间\", help_text=u\"填写结束时间，或者至今；至今则留空。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n\n#附件 -- Step 16\nclass Attachment(models.Model):\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n attachment_serial = models.IntegerField(verbose_name=u\"*序号\", help_text=u\"序号\", blank=True)\n attachment_name = models.CharField(max_length=256, verbose_name=u\"附件名称\", help_text=u\"附件名称（默认与上传附件文件名一致）\", blank=True)\n attachment_type = models.CharField(max_length=64, verbose_name=u\"*附件类型\", help_text=u\"附件类型描述\", blank=True)\n attachment_file = models.FileField(upload_to=attachment_directory_path, verbose_name=u\"附件\", help_text=u\"上传附件\", blank=True)\n\nclass Project(models.Model):\n projectname = models.CharField(max_length=256, verbose_name=u\"项目名称\", help_text=u\"项目名称\")\n serial_no = models.CharField(max_length=64, verbose_name=u\"项目编号\", help_text=u\"项目编号\")\n shenbaodanwei = models.CharField(max_length=256, verbose_name=u\"项目申报单位\", help_text=u\"项目申报单位\")\n shenbaoriqi = models.DateField(verbose_name=u\"项目申报日期\", help_text=u\"项目申报日期，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n leibie = models.CharField(max_length=64, verbose_name=u\"项目类别\", help_text=u\"项目类别\", null=True, blank=True)\n fuzeren = models.CharField(max_length=64, verbose_name=u\"项目负责人\", help_text=u\"项目负责人\", null=True, blank=True)\n fuzeren_dianhua = models.CharField(max_length=64, verbose_name=u\"项目负责人电话\", help_text=u\"项目负责人电话\", null=True, blank=True)\n yewuchushi = models.CharField(max_length=128, verbose_name=u\"业务处室\", help_text=u\"业务处室\", null=True, blank=True)\n yewuchushi_dianhua = models.CharField(max_length=64, verbose_name=u\"业务处室电话\", help_text=u\"业务处室电话\", null=True, blank=True)\n pingshengshijian = models.DateField(verbose_name=u\"项目评审或验收时间\", help_text=u\"项目评审时间，或项目验收时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n yanshoushijian = models.DateField(verbose_name=u\"项目验收时间\", help_text=u\"项目验收时间，精确到日。日期格式：YYYY-MM-DD；例如：1998-01-31\", null=True, blank=True)\n pingshengdidian = models.CharField(max_length=128, verbose_name=u\"项目评审或验收地点\", help_text=u\"项目评审地点，或项目验收地点\", null=True, blank=True)\n yanshoudidian = models.CharField(max_length=128, verbose_name=u\"项目验收地点\", help_text=u\"项目验收地点\", null=True, blank=True)\n chouqurenshu = models.IntegerField(verbose_name=u\"抽取人数\", help_text=u\"抽取评审专家人数\", null=True, blank=True)\n chouquyaoqiu = models.TextField(max_length=4096, verbose_name=u\"*抽取专家要求\", help_text=u\"抽取专家要求\", null=True)\n startreviewdescription = models.TextField(max_length=4096, verbose_name=u\"*项目评审说明\", help_text=u\"项目评审启动说明\", null=True)\n state = models.CharField(max_length=32, verbose_name=u\"状态\")\n current_bindrec = models.CharField(max_length=32, null=True)\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n deleted_at = models.DateTimeField(auto_now_add=False, null=True)\n started_at = models.DateField(verbose_name=u\"项目评审启动时间\", help_text=u\"项目评审启动时间，默认为今日\", null=True)\n finished_at = models.DateField(verbose_name=u\"项目评审结束时间\", help_text=u\"项目评审结束时间，默认为今日\", null=True)\n reviewers = models.ManyToManyField(\n Expert, \n through='Reviewer',\n through_fields=('project', 'expert'),\n )\n\nclass Reviewer(models.Model):\n project = models.ForeignKey(Project, on_delete=models.CASCADE)\n expert = models.ForeignKey(Expert, on_delete=models.CASCADE)\n state = models.CharField(max_length=2, choices=REVIEWER_STATE_CHOICE, default=REVIEWER_INITIAL, verbose_name=u\"状态\")\n isPrioritized = models.BooleanField(default=False, verbose_name=u\"是否优先抽取\", help_text=u\"是否优先抽取\")\n isnotified = models.BooleanField(default=False, verbose_name=u\"是否已被通知\", help_text=u\"是否已被通知\")\n comments = models.TextField(max_length=65536, verbose_name=u\"评审内容\", help_text=u\"专家评审内容\", null=True, blank=True)\n comments_attachments = models.FileField(upload_to=review_attachment_directory_path, verbose_name=u\"评审附件\", help_text=u\"上传评审附件，多个附件请打包后上传\", null=True, blank=True)\n #comments_committed = models.BooleanField(default=False, verbose_name=u\"评审内容是否已提交\", help_text=u\"是否已经提交评审内容\")\n review_state = models.CharField(max_length=2, default=REVIEW_STATE_NOTSTART, verbose_name=u\"评审状态\", help_text=u\"该专家对项目评审的状态\", null=True, blank=True)\n comments_on_comments = models.TextField(max_length=65536, verbose_name=u\"评审评价\", help_text=u\"对专家评审质量之评价、评论\", null=True)\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n deleted_at = models.DateTimeField(auto_now_add=False, null=True)\n selected_at = models.DateTimeField(auto_now_add=False, null=True)\n\nclass BindRecord(models.Model):\n project = models.ForeignKey(Project, on_delete=models.CASCADE)\n conditions = models.CharField(max_length=1024, verbose_name=u\"Bind Conditions\", help_text=u\"Json Object For Python internal usage\", null=True)\n bind_number = models.IntegerField(verbose_name=u\"抽取人数\", help_text=u\"抽取评审专家人数\", null=True, blank=True)\n candidate_number = models.IntegerField(verbose_name=u\"匹配人数\", help_text=u\"匹配人数\", null=True, blank=True)\n reviewer_number = models.IntegerField(verbose_name=u\"实际抽取人数\", help_text=u\"实际抽取人数\", null=True, blank=True)\n bind_comments = models.TextField(max_length=65536, verbose_name=u\"抽取说明\", help_text=u\"抽取说明\", null=True, blank=True)\n created_at = models.DateTimeField(auto_now_add=True)\n binded_at = models.DateTimeField(null=True)\n rebinded_at = models.DateTimeField(null=True)\n finished_at = models.DateTimeField(null=True)\n\n","repo_name":"NZtechpapa/expert-system","sub_path":"openstack_dashboard/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":45019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"17670090015","text":"import requests\nfrom pprint import pprint\n\n\ndef credits(title):\n BASE_URL = 'https://api.themoviedb.org/3'\n path = '/search/movie'\n params = {\n 'api_key': '1f4e35b537297dc83bf23024d8160334',\n 'region': 'KR',\n 'language': 'ko',\n 'query': title\n }\n response = requests.get(BASE_URL+path, params=params).json()\n\n movie_id = 0\n for search in response.get('results'):\n movie_id = search.get('id')\n\n new_response = requests.get(\n f'{BASE_URL}/movie/{movie_id}/credits', params=params).json()\n\n if new_response.get('cast') == None:\n return None\n\n actor_list = []\n director_list = []\n result = {}\n for i in new_response.get('cast'):\n if i.get('cast_id') < 10:\n actor_list.append(i.get('name'))\n\n for j in new_response.get('crew'):\n if j.get('known_for_department') == 'Directing':\n director_list.append(j.get('name'))\n\n result = {'cast': actor_list, 'crew': director_list}\n return result\n\n\nif __name__ == '__main__':\n \"\"\"\n 제목에 해당하는 영화가 있으면\n 해당 영화 id를 통해 영화 상세정보를 검색하여\n 주연배우 목록(cast)과 제작진(crew).\n 영화 id검색에 실패할 경우 None.\n \"\"\"\n pprint(credits('기생충'))\n # => {'cast': ['Song Kang-ho', 'Lee Sun-kyun', ..., 'Jang Hye-jin'], 'crew': ['Bong Joon-ho', 'Park Hyun-cheol', ..., 'Yoon Young-woo']}\n pprint(credits('검색할 수 없는 영화'))\n # => None\n","repo_name":"qooktree1/SSAFY_PJT","sub_path":"pjt02/problem_e.py","file_name":"problem_e.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"11086793909","text":"def factorization(v):\n \"\"\"素因数分解 O(√N)\n\n Args:\n v (int): 対象の値\n\n Returns:\n list: 素因数とその個数の組み合わせの列挙\n \"\"\"\n res = []\n fact = 2\n while fact ** 2 <= v:\n if v % fact == 0:\n cnt = 0\n while v % fact == 0:\n cnt += 1\n v //= fact\n res.append([fact, cnt])\n fact += 1\n\n if v > 1:\n res.append([v, 1])\n\n return res\n\n\n# Driver Code\nif __name__ == \"__main__\":\n print(factorization(24))\n # [[2, 3], [3, 1]]","repo_name":"ishikawa16/library","sub_path":"math/factorization.py","file_name":"factorization.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"72127086963","text":"# receba o ano de nascimento de um atleta e encaixe ele na classificação correta\n# até 9 anos: mirin, até 14 anos: infantil, até 19 anos: junior, até 20 anos: sênior, acima: master.\nprint('\\033[35m='*30)\nprint('\\033[mCATEGORIZADOR DE ATLETAS')\nprint('\\033[35m=\\033[m'*30)\nimport datetime\nanonascimento = int(input('Qual seu ano de nascimento? '))\ndata = datetime.datetime.today()\nanoatual = int(data.year)\nidade = anoatual - anonascimento\nif idade <= 9:\n print('Como você tem {} anos, ficará na categoria MIRIN.'\n .format(idade))\nelif 9 < idade <= 14:\n print('Como você tem {} anos, ficará na categoria INFANTIL.'\n .format(idade))\nelif 14 < idade <= 19:\n print('Como voce tem {} anos, ficará na categoria JUNIOR.'\n .format(idade))\nelif 19 < idade <= 20:\n print('Como você tem {} anos. ficará na categoria SÊNIOR.'\n .format(idade))\nelif idade > 20:\n print('Como você tem {} anos, ficará na categoria MASTER.'\n .format(idade))\n\n\n","repo_name":"EricaFederowicz/Desafios-feitos-Curso-em-Video-Python","sub_path":"exercícios/Erica/desafio41.py","file_name":"desafio41.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"73100878642","text":"# coding=utf-8\nfrom unittest.case import TestCase\n\nfrom calculator.core.calculator import Calculator\nfrom calculator.exceptions import VariableError, VariableRemoveRestrictError, VariableNameError\n\n__author__ = \"Josef Kolář, Martin Omacht\"\n__copyright__ = \"Copyright 2017, /dej/uran/dom team\"\n__credits__ = [\"Josef Kolář\", \"Son Hai Nguyen\", \"Martin Omacht\", \"Robert Navrátil\"]\n__license__ = \"GNU GPL Version 3\"\n\n\nclass CalculatorTest(TestCase):\n _default_variable_definition = (\n Calculator.DEFAULT_VARIABLE_TYPE(),\n '= {}'.format(str(Calculator.DEFAULT_VARIABLE_TYPE())),\n set()\n )\n\n def assertDictEqual(self, d1, d2, msg=None):\n return super().assertDictEqual(dict(d1), dict(d2), msg)\n\n def setUp(self):\n self.calculator = Calculator()\n\n def test_default_ans(self):\n self.assertTupleEqual(\n self.calculator.variables.get(Calculator.ANSWER_VARIABLE_NAME),\n self._default_variable_definition,\n 'Default answer is 0 from source \"0\" with no dependencies.'\n )\n\n def test_simple_calculation(self):\n result, variables = self.calculator.process('1 + 2')\n self.assertEqual(\n 1 + 2,\n result,\n 'Result of simple calculation.'\n )\n self.assertDictEqual(\n variables,\n {Calculator.ANSWER_VARIABLE_NAME: (result, '1 + 2', set())},\n 'Answer variable in variables mapping.'\n )\n\n def test_variable_assign(self):\n result, variables = self.calculator.process('a = 42')\n self.assertIsNone(\n result,\n 'Result of assign should be None.'\n )\n self.assertDictEqual(\n variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n 'a': (42, 'a = 42', set())\n },\n 'Between variables should be added a new variable after assign (and Ans should stay same).'\n )\n\n def test_new_variables_from_expression(self):\n result, variables = self.calculator.process('a = b + c')\n self.assertIsNone(\n result,\n 'Result of assign should be None.'\n )\n self.assertDictEqual(\n variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n 'a': (Calculator.DEFAULT_VARIABLE_TYPE(), 'a = b + c', {'b', 'c'}),\n 'b': self._default_variable_definition,\n 'c': self._default_variable_definition\n },\n 'New variable from another two variables.'\n )\n\n def test_expression_from_existing_variables(self):\n self.calculator.process('a = 8')\n self.calculator.process('b = (2 + a) * a')\n result, variables = self.calculator.process('b / a')\n self.assertEqual(\n result,\n 10,\n 'Result from two combined variables.'\n )\n self.assertDictEqual(\n variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: (10, 'b / a', {'a', 'b'}),\n 'a': (8, 'a = 8', set()),\n 'b': (80, 'b = (2 + a) * a', {'a'})\n },\n 'Generated variables list after three operations with calculator.'\n )\n\n def test_invalid_assign(self):\n with self.assertRaises(SyntaxError, msg='No syntax support for multiple assign.'):\n self.calculator.process('a, b = 8, 8')\n\n with self.assertRaises(SyntaxError, msg='No syntax support for index assign.'):\n self.calculator.process('f[4] = 5')\n\n with self.assertRaises(VariableError, msg='Self assign is not supported.'):\n self.calculator.process('c = c')\n\n self.calculator.process('d = 5')\n self.calculator.process('e = d')\n with self.assertRaises(VariableError, msg='Circular reference in variables definition is not supported.'):\n self.calculator.process('d = e')\n\n self.assertDictEqual(\n self.calculator.variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n 'd': (5, 'd = 5', set()),\n 'e': (5, 'e = d', set('d'))\n },\n 'After circular invalid assign, variables should stay without changes.'\n )\n\n def test_variable_updating(self):\n self.calculator.process('a = b + 42')\n result, variables = self.calculator.process('b = 2 * 21')\n\n self.assertIsNone(\n result,\n 'Result of assign should be None.'\n )\n\n self.assertDictEqual(\n variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n 'a': (84, 'a = b + 42', {'b'}),\n 'b': (42, 'b = 2 * 21', set())\n },\n 'Generated variables list after two operations with calculator.'\n )\n\n def test_ans_in_expr(self):\n self.calculator.process('5*5')\n result, variables = self.calculator.process('9 + Ans')\n\n self.assertEqual(\n result,\n 34,\n 'Result of expression with Ans'\n )\n\n self.assertDictEqual(\n variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: (34, '9 + Ans', {'Ans'})\n },\n 'Ans source expression and dependency'\n )\n\n result, variables = self.calculator.process('9 + Ans')\n\n self.assertEqual(\n result,\n 43,\n 'Result of second expression with Ans'\n )\n\n self.assertDictEqual(\n variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: (43, '9 + Ans', {'Ans'})\n },\n 'Ans source expression and dependency after second compute with Ans'\n )\n\n def test_remove_variable(self):\n self.calculator.process('a = 5')\n self.calculator.remove_variable('a')\n self.assertDictEqual(\n self.calculator.variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition\n },\n 'Removed a not in variables.'\n )\n\n self.calculator.process('b = c + 42')\n with self.assertRaises(VariableRemoveRestrictError, msg='Error when removing depending variable.'):\n self.calculator.remove_variable('c')\n self.assertDictEqual(\n self.calculator.variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n 'b': (42, 'b = c + 42', {'c'}),\n 'c': self._default_variable_definition\n },\n 'Variables after invalid remove.'\n )\n\n with self.assertRaises(VariableError, msg='Remove of unknown variable.'):\n self.calculator.remove_variable('x')\n\n def test_variable_process(self):\n result, variables = self.calculator.process_variable('a', '42')\n\n self.assertIsNone(result, 'None is result of assign.')\n\n self.assertDictEqual(\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n 'a': (42, 'a = 42', set())\n },\n variables,\n 'Variables after variable process.'\n )\n\n def test_too_long_variable_name(self):\n var_name = 'a' * (self.calculator.MAX_VARIABLE_NAME_LEN + 1)\n msg = 'Error when creating variable with name longer than {}'.format(self.calculator.MAX_VARIABLE_NAME_LEN)\n with self.assertRaises(VariableNameError, msg=msg):\n self.calculator.process(\"{} = 42\".format(var_name))\n\n self.assertDictEqual(\n self.calculator.variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition\n },\n 'No new variables should be created.'\n )\n\n def test_too_long_variable_name_in_expr(self):\n var_name = 'a' * (self.calculator.MAX_VARIABLE_NAME_LEN + 1)\n msg = 'Error when creating variable with name longer ' \\\n 'than {} in expression with default value.'.format(self.calculator.MAX_VARIABLE_NAME_LEN)\n with self.assertRaises(VariableNameError, msg=msg):\n self.calculator.process(\"5 + {}\".format(var_name))\n\n self.assertDictEqual(\n self.calculator.variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition\n },\n 'No new variables should be created.'\n )\n\n def test_assign_with_long_var_in_expr(self):\n var_name = 'a' * (self.calculator.MAX_VARIABLE_NAME_LEN + 1)\n msg = 'Error when creating variable with name longer ' \\\n 'than {} in assign expression.'.format(self.calculator.MAX_VARIABLE_NAME_LEN)\n with self.assertRaises(VariableNameError, msg=msg):\n self.calculator.process(\"a = 5 + {}\".format(var_name))\n\n self.assertDictEqual(\n self.calculator.variables,\n {\n Calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition\n },\n 'No new variables should be created.'\n )\n\n def test_variable_with_max_name_len(self):\n var_name = 'a' * self.calculator.MAX_VARIABLE_NAME_LEN\n result, variables = self.calculator.process(\"{} = 42\".format(var_name))\n\n self.assertIsNone(result, 'None is result of assign.')\n self.assertDictEqual(\n variables,\n {\n self.calculator.ANSWER_VARIABLE_NAME: self._default_variable_definition,\n var_name: (42, \"{} = 42\".format(var_name), set())\n },\n 'New variable {} should be created.'.format(var_name)\n )\n\n def test_variable_with_max_name_in_expr(self):\n var_name = 'a' * self.calculator.MAX_VARIABLE_NAME_LEN\n result, variables = self.calculator.process(\"5 + {}\".format(var_name))\n\n self.assertEqual(result, 5, 'Result should be calculated.')\n self.assertDictEqual(\n variables,\n {\n self.calculator.ANSWER_VARIABLE_NAME: (5, '5 + {}'.format(var_name), {var_name}),\n var_name: self._default_variable_definition\n }\n )\n","repo_name":"thejoeejoee/IVS-VUT-BIT-2016-2017","sub_path":"src/tests/calculator/core/calculator/calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":10323,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"75"} +{"seq_id":"15120596781","text":"import pyautogui\nimport time\nimport argparse\nimport logging\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.options import Options\n\npyautogui.FAILSAFE = False\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nCLOSE_NOTIFICATION = (413, 83)\n\n\ndef goto_class(driver):\n driver.find_element_by_id('btn_guest').click()\n\n\ndef close_chrome_notification():\n pyautogui.click(*CLOSE_NOTIFICATION)\n\n\ndef force_refresh(driver):\n driver.execute_script('window.onbeforeunload = function() {};')\n driver.refresh()\n\n\ndef try_s(func):\n for retry_number in range(10):\n try:\n func()\n break\n except Exception as e:\n logger.exception(e)\n\n\ndef get_vc(driver, args):\n driver.switch_to.window(driver.window_handles[-1])\n driver.get(args.url)\n\n\ndef login(driver, i, usernames):\n force_refresh(driver)\n goto_class(driver)\n if i == 0:\n driver.find_element_by_xpath(\"//input[@class='full-width']\")\n driver.execute_script(\n \"document.querySelector('.dlg-nickname .full-width').value\"\n f\" = '{usernames[i].strip()}';\")\n driver.execute_script(\"document.querySelector('.dlg-nickname .btn').click();\")\n else:\n driver.find_element_by_id(\"app_menu\").click()\n driver.find_element_by_xpath(\"//*[contains(text(), 'اطلاعات کاربری')]\").click()\n driver.find_element_by_xpath('//*[@title=\"ویرایش نام نمایشی\"]').click()\n input1 = driver.find_element_by_xpath(\"//input[@class='box-shrink']\")\n input1.clear()\n input1.send_keys(usernames[i].strip())\n driver.find_element_by_xpath('//*[@type=\"submit\"]').click()\n driver.find_element_by_xpath('//*[@class=\"box-shrink close-button-container\"]').click()\n driver.find_element_by_xpath('//*[@title=\"خروجی صدا\"]').click()\n text_message = driver.find_element_by_xpath('//*[@placeholder=\"پیام خود را وارد کنید\"]')\n text_message.click()\n text_message.send_keys(\"سلام\")\n text_message.send_keys(\"\\n\")\n\n\ndef exit_from_vc(driver):\n driver.switch_to.window(driver.window_handles[-1])\n text_message = driver.find_element_by_xpath('//*[@placeholder=\"پیام خود را وارد کنید\"]')\n text_message.click()\n text_message.send_keys(\"خسته نباشید\")\n text_message.send_keys(\"\\n\")\n driver.find_element_by_id(\"app_menu\").click()\n driver.find_element_by_xpath(\"//*[contains(text(), 'خروج')]\").click()\n driver.close()\n\n\ndef main():\n parser = argparse.ArgumentParser(description='This command will record a VClass page')\n parser.add_argument('-u', '--url', type=str, default='', help='URL of vclass')\n parser.add_argument('-d', '--duration', type=float, default=0, help='Duration of class in minutes')\n parser.add_argument('-a', '--username', type=str, default='./usernames.txt', help='file Username of skyroom user')\n args = parser.parse_args()\n f = open(args.username, 'r', encoding=\"utf-8\")\n usernames = f.readlines()\n n = len(usernames)\n if args.url == '':\n return\n chrome_options = Options()\n chrome_options.add_argument(\"--incognito\")\n logger.info('Opening google chrome')\n driver = None\n for retry_number in range(10):\n try:\n if driver:\n logger.info('Driver is not none, close it.')\n driver.close()\n driver = webdriver.Chrome(options=chrome_options)\n driver.implicitly_wait(10)\n driver.maximize_window()\n break\n except Exception as e:\n logger.exception(e)\n for i in range(n):\n if i != 0:\n try_s(lambda: driver.execute_script(f\"window.open('about:blank', 'tab_{i}');\"))\n logger.info(f'Open vc for tab {i + 1}')\n try_s(lambda: get_vc(driver, args))\n logger.info(f'Login as guest for {i + 1}')\n try_s(lambda: login(driver, i, usernames))\n close_chrome_notification()\n\n time.sleep(60 * args.duration)\n for i in range(n):\n logger.info(f\"exit tab{i + 1}\")\n try_s(lambda: exit_from_vc(driver))\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"samssh/skyroom_spammer","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"27667095721","text":"from PyQt6 import QtCore, QtWidgets\n\nfrom crdesigner.common.config.gui_config import gui_config\nfrom crdesigner.common.config.settings_config import CONFIGS_TO_RENDER\nfrom crdesigner.common.config.settings_config import settings as settings_model\nfrom crdesigner.common.logging import logger\nfrom crdesigner.ui.gui.view.settings.settings_ui import SettingsUI\n\n\nclass SettingsController:\n \"\"\"\n Controller for the settings window. Mainly provides the logic for the buttons. And\n connects the settings window to the settings models. Also sets up the UI.\n \"\"\"\n\n def __init__(self, parent):\n \"\"\"\n Initialize the settings controller. As well as the settings UI.\n \"\"\"\n self.cr_designer = parent.mwindow_ui\n self.scenario_model = parent.scenario_model\n self.canvas = parent.animated_viewer_wrapper.cr_viewer.dynamic\n self.parent = parent\n\n self.settings_ui = SettingsUI(self.cr_designer)\n\n for config in CONFIGS_TO_RENDER:\n tab_name = config.__class__.__name__.replace(\"Config\", \"\").upper()\n self.settings_ui._add_tab(tab_name, config.LAYOUT)\n\n self.settings_ui.update_window()\n self.settings_ui._retranslate_ui(self.settings_ui.settings)\n QtCore.QMetaObject.connectSlotsByName(self.settings_ui.settings)\n\n self.connect_events()\n\n def connect_events(self):\n \"\"\"\n Connect buttons to callables.\n \"\"\"\n self.settings_ui.button_select_directory.clicked.connect(lambda: _select_directory())\n self.settings_ui.button_ok.clicked.connect(lambda: self.apply_close())\n self.settings_ui.button_cancel.clicked.connect(lambda: self.close())\n self.settings_ui.button_set_to_default.clicked.connect(lambda: _set_default())\n\n @logger.log\n def close(self):\n \"\"\"\n Reset the settings to their previous values and close the settings window.\n \"\"\"\n _restore_all_settings()\n\n self._hide_and_update()\n\n @logger.log\n def apply_close(self):\n \"\"\"\n Save the settings to their respective files and close the settings window.\n \"\"\"\n if not _validate_all():\n return\n _save_all_to_yaml()\n self._hide_and_update()\n if self.scenario_model.scenario_created():\n self.scenario_model.notify_all()\n self.parent.mwindow_ui.set_stylesheet(gui_config.get_stylesheet())\n self.parent.mwindow_ui.update_window()\n for config in CONFIGS_TO_RENDER:\n config.notify_all()\n\n def show(self):\n \"\"\"\n Backup the current settings and show the settings window.\n \"\"\"\n _backup_settings()\n self.settings_ui.settings.show()\n\n def _hide_and_update(self):\n self.settings_ui.settings.hide()\n\n\n@logger.log\ndef _set_default():\n \"\"\"\n Sets the default settings for all settings models.\n \"\"\"\n for config in CONFIGS_TO_RENDER:\n config.reset_settings()\n\n\n@logger.log\ndef _select_directory():\n \"\"\"\n Opens a file dialog to select a directory. If a directory is selected, the\n CUSTOM_SETTINGS_DIR attribute of the settings model is set to the selected directory.\n (This triggers the _load_custom_settings function in the settings model.)\n \"\"\"\n directory = QtWidgets.QFileDialog.getExistingDirectory(caption=\"Select Directory\")\n if directory:\n settings_model.CUSTOM_SETTINGS_DIR = directory\n settings_model.notify_all()\n\n\ndef _validate_all() -> bool:\n valid = True\n for config in CONFIGS_TO_RENDER:\n if not config.validate_all_settings():\n valid = False\n return valid\n\n\ndef _backup_settings():\n for config in CONFIGS_TO_RENDER:\n config.backup_settings()\n\n\ndef _save_all_to_yaml():\n for config in CONFIGS_TO_RENDER:\n config.save_to_yaml_file()\n settings_model.save_to_yaml_file()\n\n\ndef _restore_all_settings():\n for config in CONFIGS_TO_RENDER:\n config.restore_settings()\n config.notify_all()\n","repo_name":"CommonRoad/commonroad-scenario-designer","sub_path":"crdesigner/ui/gui/controller/settings/settings_controller.py","file_name":"settings_controller.py","file_ext":"py","file_size_in_byte":4006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"8466419177","text":"import numpy as np\nfrom copy import deepcopy\nfrom event_parser import data_set\nimport basic_plotter as plotter\nimport vmath\nimport physics as phys\nimport time\n\n#Note: OS is implicit here\ndef list_choices():\n print(\"Set filter by inputting an array of choices\")\n print(\"Event:0:trig\")\n print(\"Event:1:lxy_cut\")\n print(\"Pair:2:iso_cut\")\n print(\"Pair:3:dphi_muonsDV_cut\")\n print(\"Pair:4:dphi_muons_cut\")\n print(\"Pair:5:pileup_cut\")\n print(\"Pair:6:dxy_cut\")\n\ndef print_cut_selection(arr):\n print(\"Cut choices\")\n if(arr[0]):\n print(\"True:trig\")\n else:\n print(\"False:trig\")\n \n if(arr[1]):\n print(\"True:lxy_cut\")\n else:\n print(\"False:lxy_cut\")\n \n if(arr[2]):\n print(\"True:iso_cut\")\n else:\n print(\"False:iso_cut\")\n\n if(arr[3]):\n print(\"True:dphi_muonsDV_cut\")\n else:\n print(\"False:dphi_muonsDV_cut\")\n\n if(arr[4]):\n print(\"True:dphi_muons_cut\")\n else:\n print(\"False:dphi_muons_cut\")\n \n if(arr[5]):\n print(\"True:pileup_cut\")\n else:\n print(\"False:pileup_cut\")\n \n if(arr[6]):\n print(\"True:dxy_cut\")\n else:\n print(\"False:dxy_cut\")\n\ndef trigA(mu, amu):\n #Calculate muon deltaR\n muon_deltaR = phys.deltaR(mu, amu)\n cut_pT = (mu.pT > 4.) & (amu.pT > 4.)\n cut_deltaR = muon_deltaR < 1.2\n cut = cut_pT & cut_deltaR\n return cut\n\ndef trigB(mu, amu):\n muon_deltaR = phys.deltaR(mu, amu)\n cut_eta = (np.abs(mu.eta) < 1.4) & (np.abs(amu.eta) < 1.4)\n cut_deltaR = muon_deltaR < 1.4\n cut = cut_eta & cut_deltaR\n return cut\n\ndef trigC(mu, amu):\n c1 = mu.pT > 15.\n c2 = mu.pT > 7.\n c3 = amu.pT > 15.\n c4 = amu.pT > 7.\n\n cut = (c1 & c4) | (c2 & c3) \n return cut\n\ndef trig(mu, amu):\n cA = trigA(mu, amu)\n cB = trigB(mu, amu)\n cC = trigC(mu, amu)\n cD = (mu.pT > 3.) & (amu.pT > 3.) & (np.abs(mu.eta) < 2.4) & (np.abs(amu.eta) < 2.4)\n\n cut = (cA | cB | cC) & cD\n return cut\n\ndef lxy_cut(mu):\n #Note: Both mu and amu have the same lxy\n lxy = phys.lxy(mu)\n cut = lxy < 110. #mm here, CMS paper is in cm\n return cut\n\ndef dphi_muonsDV_cut(mu, amu, n):\n max_dphi = 0.\n\n if(n == 1):\n max_dphi = 0.02\n elif(n == 2):\n max_dphi = 0.01\n else:\n print(\"Not a valid muon n\")\n \n DV = deepcopy(mu.xi)\n DV[:,3] = 0.\n pmm = (mu.p + amu.p)\n pmm[:,3] = 0.\n dphi = vmath.vec4_dphi(DV, pmm)\n cut = dphi < max_dphi \n return cut\n\ndef iso_cut(mu, amu, tr, jets, n): \n R = 0.3\n mu_iso = phys.iso(mu, tr, R, 'track')\n amu_iso = phys.iso(amu, tr, R, 'track')\n if(n == 1):\n iso_cut = (mu_iso > 0.1*mu.pT) | (amu_iso > 0.1*amu.pT)\n elif(n == 2):\n iso_cut = (mu_iso > 0.2*mu.pT) | (amu_iso > 0.2*amu.pT)\n\n mu_jets = phys.iso(mu, jets, R, 'jet').astype(bool)\n amu_jets = phys.iso(amu, jets, R, 'jet').astype(bool)\n jet_cut = ~mu_jets | ~amu_jets\n\n cut = jet_cut | iso_cut\n return cut\n\ndef dphi_muons_cut(mu, amu):\n dphi = vmath.dphi(mu.phi, amu.phi)\n cut = dphi < 2.8\n return cut\n\ndef pileup_cut(mu, amu):\n pileup = phys.pileup(mu,amu)\n cut = pileup < 1.25\n return cut\n\ndef dxy_cut(sigma, mu, amu, n):\n ##See the bottom of CMS paper page 5\n sig = sigma # 1mm sigma\n mu_dxy = phys.dxy(mu)\n mu_lxy = phys.lxy(mu)\n amu_dxy = phys.dxy(amu)\n amu_lxy = phys.lxy(amu)\n \n min_vbl = 0.\n min_dxysig = 0.\n\n if(n == 1):\n min_vbl = 0.1\n min_dxysig = 2.\n elif(n == 2):\n min_vbl = 0.05\n min_dxysig = 1.\n else:\n print(\"Not a valid n\")\n\n cut1 = (np.abs(mu_dxy)/sig) > min_dxysig\n \n m_12 = vmath.vec4_invar(mu.p + amu.p) #Check\n p_sum = deepcopy(mu.p) + deepcopy(amu.p)\n p_sum[:,3] = 0.\n pT_12 = vmath.vec4_norm(p_sum)\n mu_vbl = mu_dxy/(mu_lxy*m_12/pT_12)\n amu_vbl = amu_dxy/(amu_lxy*m_12/pT_12)\n cut2 = (mu_vbl > min_vbl) & (amu_vbl > min_vbl)\n\n cut = cut1 & cut2\n return cut\n\ndef event_cut(arr, mu, amu):\n cut = np.ones(mu.size()).astype(bool)\n \n if (arr[0]):\n cut = cut & trig(mu, amu) \n if (arr[1]):\n cut = cut & lxy_cut(mu)\n return cut\n\ndef pair_cut(sigma, arr, mu, amu, tracks, jets, n):\n cut = np.ones(mu.size()).astype(bool)\n\n if (arr[2]):\n cut = cut & iso_cut(mu, amu, tracks, jets, n)\n if (arr[3]):\n cut = cut & dphi_muonsDV_cut(mu, amu, n)\n if (arr[4]):\n cut = cut & dphi_muons_cut(mu, amu)\n if (arr[5]):\n cut = cut & pileup_cut(mu, amu)\n if (arr[6]):\n cut = cut & dxy_cut(sigma, mu, amu, n)\n\n return cut\n\ndef isolate_repeats(data):#Should move this function into evt_parser\n #Find all pairs of repeats\n repeats = np.zeros(dp.size(),dtype=bool)\n\n for i in range(data.size()-1):\n if data.event_index[i] == data.event_index[i+1]:\n repeats[i] = True\n repeats[i+1] = True\n i = i+2\n\n return repeats\n\ndef prep_dataset(my_set):\n #Note that this function will need to be edited again if you allow decays to non-muons\n\n #Split out the dark photons\n dark_photons = my_set.get_pid(999999)\n\n #Get just muons and antimuons, + the set of aligned dark photons\n daughters = my_set.get_type(1)\n muons = daughters.get_pid(13)\n antimuons = daughters.get_pid(-13)\n\n #Get other entries \n tracks = my_set.get_type(2)\n jets = my_set.get_type(3)\n jets.eta = phys.calc_pseudo_rap(jets)\n\n return dark_photons, muons, antimuons, tracks, jets\n\ndef get_cms_eff(sigma, dark_photons, muons, antimuons, tracks, jets, arr):\n #Function splits out full cut and re-ordered darkphotons, muons, antimuons\n #For efficiency correlogram\n\n #Load dataset from filename \n start_time = time.time()\n \n init = muons.size()\n n = 1 #First/second muon pair - for non-repeat events, all are first\n \n #Find all repeat event entries and split sets accordingly\n #r_entries = dark_photons.find_repeats()\n r_entries = muons.find_repeats()\n r_dp, s_dp = dark_photons.split_set(r_entries)\n r_mu, s_mu = muons.split_set(r_entries)\n print(\"r: \" + str(r_mu.size()))\n print(\"s: \" + str(s_mu.size()))\n r_amu, s_amu = antimuons.split_set(r_entries)\n\n #Run all single entries through cuts \n s_evt_cut = event_cut(arr, s_mu, s_amu)\n s_pair_cut = pair_cut(sigma, arr, s_mu, s_amu, tracks, jets, 1)\n s_cut = s_evt_cut & s_pair_cut\n \n final_dp, final_mu, final_amu = s_dp, s_mu, s_amu\n final_cut = s_cut\n final_evt_cut = s_evt_cut\n \n #Split double events along even and odd entries\n eo_split = r_dp.create_even_odd_split()\n er_dp, or_dp = r_dp.split_set(eo_split)\n er_mu, or_mu = r_mu.split_set(eo_split)\n er_amu, or_amu = r_amu.split_set(eo_split)\n \n #Create cut where one of even or odd passes the event cut\n #The other is included even if it doesn't pass cut criteria\n er_cut = event_cut(arr, er_mu, er_amu)\n or_cut = event_cut(arr, or_mu, or_amu)\n evt_cut = er_cut | or_cut\n \n #Split muon/antimuon set into closer/farther from beamline\n a = vmath.vec3_norm(er_mu.xi)\n b = vmath.vec3_norm(or_mu.xi)\n mask = a > b\n ar_mu = er_mu.get(mask).append(or_mu.get(~mask))\n br_mu = er_mu.get(~mask).append(or_mu.get(mask))\n ar_amu = er_amu.get(mask).append(or_amu.get(~mask))\n br_amu = er_amu.get(~mask).append(or_amu.get(mask))\n ar_dp = er_dp.get(mask).append(or_dp.get(~mask))\n br_dp = er_dp.get(~mask).append(or_dp.get(mask))\n evt_cutA = np.append(evt_cut[mask], evt_cut[~mask])\n evt_cutB = np.append(evt_cut[~mask], evt_cut[mask])\n\n #Next run er events through with stringent conditions, \n #and or through with loosened conditions\n ar_pair_cut = pair_cut(sigma, arr, ar_mu, ar_amu, tracks, jets, 1)\n br_pair_cut = pair_cut(sigma, arr, br_mu, br_amu, tracks, jets, 2)\n final_cut = np.append(final_cut, (ar_pair_cut & evt_cutA))\n final_cut = np.append(final_cut, (br_pair_cut & evt_cutB))\n final_evt_cut = np.append(final_evt_cut, evt_cutA)\n final_evt_cut = np.append(final_evt_cut, evt_cutB)\n \n #Finally, append all particles\n final_dp = final_dp.append(ar_dp)\n final_dp = final_dp.append(br_dp)\n final_mu = final_mu.append(ar_mu)\n final_mu = final_mu.append(br_mu)\n final_amu = final_amu.append(ar_amu)\n final_amu = final_amu.append(br_amu)\n \n print(\"Final: \" + str(final_cut[final_cut].shape[0]) + \" muon pairs\")\n final = final_cut[final_cut].shape[0]\n print(\"Ratio: \" + str(final/init))\n ratio = final/init\n print(\"Analyze time: --- %s seconds ---\" % (time.time()-start_time))\n\n return final_dp, final_mu, final_amu, final_cut, final_evt_cut\n","repo_name":"sborn95/dark_showers-CMS_mockup","sub_path":"modules/CMS_analysis.py","file_name":"CMS_analysis.py","file_ext":"py","file_size_in_byte":8679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"29360224836","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 10 20:49:03 2018\n\n@author: dawnstear\n\"\"\"\n# Other packages youll need: CUDAtoolkit and CUDAdriver\nimport numpy as np\nfrom timeit import default_timer as timer\nfrom numbapro import vectorize, cuda, float32 \n# nvprof \n# One way to tell the compiler to generate an accelerated version \n# of our VectorAdd function is with a decorator:\n\n# The first input parameter to this decorator is a list of strings containing the signature\n# of the function to be accelerated. A function signature (or type signature, or method\n# signature) defines input and output of functions or methods.\n\n# Remember this function will be compiled to the gpu machine code and this compiler needs\n# to know the data types of the input and output, lets assume float32 for this example\n# decorator([\"output datatype\", \"input datatypes\"], target)\n\n@vectorize([\"float32(float32, float32)\"], target='gpu') # by defult vectorize() uses a single threaded cpu\ndef VectorAdd(a,b): # target can also be cuda/parallel\n # for i in xrange(a.size):\n # c[i] = a[i] + b[i]\n return a+b # no need to pass in c for parallelization\n # now the numbapro compiler can apply the scalar function\n # automatically across the numpy arrays on gpu\n \n \ndef main():\n N = 32000000\n \n A = np.ones(N,dtype=np.float32) \n B = np.ones(N,dtype=np.float32)\n C = np.zeros(N,dtype=np.float32)\n \n start = timer()\n # VectorAdd(A,B,C) # the only other step is to then change how we call VectorAdd\n C = VectorAdd(A,B)\n VectorAdd_time = timer() - start\n \n print(\"C[:5] = \" + str(C[:5]))\n print(\"C[-:5] = \" + str(C[-5:]))\n print(\"VectorAdd took %f seconds\" % VectorAdd_time)\n \nif __name__ == '__main__':\n main()\n","repo_name":"benstear/GPU-parallel-computing","sub_path":"VectorAdd_parallel.py","file_name":"VectorAdd_parallel.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"71542039923","text":"from flask import Flask, Response\nfrom flask_restplus import Api\nfrom flask_socketio import SocketIO, send, emit\nimport pdb\nimport eventlet\neventlet.monkey_patch()\n\napp = Flask(__name__)\napi = Api(app)\nsocketio = SocketIO(app)\n\n@app.route('/')\ndef get_domain():\n return Response('OK')\n\n@socketio.on('connect')\ndef handle_connect():\n print('client connected')\n\n\n@socketio.on('message')\ndef handle_message(message):\n emit('message', message, broadcast=True, json=True)\n\n\n@socketio.on('disconnect')\ndef handle_disconnect():\n print('client disconnected')\n\n\nif __name__ == '__main__':\n socketio.run(app, host='0.0.0.0', port=5000)\n","repo_name":"ashokkumarprajapati/flasksocketiodocker","sub_path":"api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"33388857683","text":"from config import MONGODB_DB_NAME\nfrom controllers.users import get_user\nfrom utils import format_ids\nfrom mongodb import get_nosql_db\nfrom models import RoomInDB, User\nimport logging\nimport json\nfrom bson import ObjectId\n\nlogger = logging.getLogger(__name__)\n\n\nasync def upload_message_to_room(data):\n message_data = json.loads(data)\n client = await get_nosql_db()\n db = client[MONGODB_DB_NAME]\n try:\n room = await get_room(message_data[\"room_name\"])\n user = await get_user(message_data[\"user\"][\"username\"])\n message_data[\"user\"] = user\n message_data.pop(\"room_name\", None)\n collection = db.rooms\n collection.update_one({\"_id\": ObjectId(room[\"_id\"])}, {\"$push\": {\"messages\": message_data}})\n return True\n except Exception as e:\n logger.error(f\"Error adding message to DB: {type(e)} {e}\")\n return False\n\n\nasync def insert_room(username, room_name, collection):\n room = {}\n room[\"room_name\"] = room_name\n user = await get_user(username)\n room[\"members\"] = [user] if user is not None else []\n dbroom = RoomInDB(**room)\n response = collection.insert_one(dbroom.dict())\n res = collection.find_one({\"_id\": response.inserted_id})\n res[\"_id\"] = str(res[\"_id\"])\n return res\n\n\nasync def get_rooms(filter_list: list = None):\n client = await get_nosql_db()\n db = client[MONGODB_DB_NAME]\n collection = db.rooms\n if filter_list is None:\n rows = collection.find()\n else:\n rows = collection.find({\"room_name\": {\"$in\": filter_list}})\n\n row_list = []\n for row in rows:\n f_row = format_ids(row)\n row_list.append(f_row)\n return row_list\n\n\nasync def get_room(room_name) -> RoomInDB:\n client = await get_nosql_db()\n db_client = client[MONGODB_DB_NAME]\n db = db_client.rooms\n row = db.find_one({\"room_name\": room_name})\n if row is not None:\n row = format_ids(row)\n return row\n else:\n return None\n\n\nasync def add_user_to_room(username: str, room_name: str):\n client = await get_nosql_db()\n db = client[MONGODB_DB_NAME]\n try:\n room = await get_room(room_name)\n user = await get_user(username)\n\n collection = db.rooms\n username_list = [m[\"username\"] for m in room[\"members\"]]\n if user[\"username\"] not in username_list:\n logger.info(f\"Adding {user['username']} to members\")\n collection.update_one({\"_id\": ObjectId(room[\"_id\"])}, {\"$push\": {\"members\": user}})\n return True\n else:\n logger.info(f\"{user['username']} is already a member\")\n return True\n except Exception as e:\n logger.error(f\"Error updating members: {e}\")\n return None\n\n\nasync def remove_user_from_room(user: User, room_name: str, username=None):\n client = await get_nosql_db()\n db = client[MONGODB_DB_NAME]\n try:\n room = await get_room(room_name)\n if username is not None and user is None:\n user = await get_user(username)\n\n collection = db.rooms\n username_list = [m[\"username\"] for m in room[\"members\"]]\n if user[\"username\"] in username_list:\n logger.info(f\"Removing {user['username']} from {room_name} members\")\n collection.update_one(\n {\"_id\": ObjectId(room[\"_id\"])}, {\"$pull\": {\"members\": {\"username\": user[\"username\"]}}}\n )\n return True\n else:\n logger.info(f\"{user['username']} is already out of the room\")\n return True\n except Exception as e:\n logger.error(f\"Error updating members: {e}\")\n return False\n\n\nasync def set_room_activity(room_name, activity_bool):\n client = await get_nosql_db()\n db_client = client[MONGODB_DB_NAME]\n db = db_client.rooms\n room = await get_room(room_name)\n if room is not None:\n _id = room[\"_id\"]\n try:\n result = db.update_one({\"_id\": ObjectId(_id)}, {\"$set\": {\"active\": activity_bool}})\n logger.info(f\"Updated room activity {result}\")\n except Exception as e:\n logger.error(f\"ERROR SETTING ACTIVITY: {e}\")\n new_doc = await get_room(room_name)\n return new_doc\n else:\n return None\n\n\nasync def get_user_favorites(user_name):\n user = await get_user(user_name)\n favs = user[\"favorites\"]\n favorite_rooms = await get_rooms(favs)\n return favorite_rooms\n","repo_name":"jmoussa/chat-app-be","sub_path":"controllers/rooms.py","file_name":"rooms.py","file_ext":"py","file_size_in_byte":4401,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"75"} +{"seq_id":"35808940311","text":"from __future__ import division\nfrom __future__ import print_function\n\nfrom easydict import EasyDict as edict\nimport numpy as np\n\n\n__C = edict()\ncfg = __C\n\n__C.CONFIG_NAME = 'ConNet'\n__C.DATASET_NAME = 'SVHN'\n__C.SEED = 1234\n\n# Training options\n__C.TRAIN = edict()\n__C.TRAIN.DATASET_SPLIT = 'train'\n__C.TRAIN.VALID_SPLIT = 0.8\n__C.TRAIN.SAMPLE_SIZE = 100\n__C.TRAIN.BATCH_SIZE = 32\n__C.TRAIN.NUM_EPOCHS = 5\n__C.TRAIN.LR = [0.001, 0.1]\n__C.TRAIN.L2 = [0.0001, 0.01]\n__C.TRAIN.DROPOUT = 0.25\n__C.TRAIN.MOM = [0.75, 0.99]\n__C.TRAIN.SCHEDULER_PATIENCE = 5\n__C.TRAIN.TRACK_MISCLASSIFIED = False\n\n\ndef _merge_a_into_b(a, b):\n '''\n Merge config dictionary a into config dictionary b, clobbering the\n options in b whenever they are also specified in a.\n\n Parameters\n ----------\n a : dict\n Config dictionary a.\n b : dict\n Config dictionary b.\n\n '''\n if type(a) is not edict:\n return\n\n for k, v in a.items():\n # a must specify keys that are in b\n if k not in b:\n # raise KeyError('{} is not a valid config key'.format(k))\n b[k] = v\n\n else:\n # the types must match, too\n old_type = type(b[k])\n if old_type is not type(v):\n if isinstance(b[k], np.ndarray):\n v = np.array(v, dtype=b[k].dtype)\n else:\n raise ValueError(('Type mismatch ({} vs. {}) '\n 'for config key: {}').format(type(b[k]),\n type(v), k))\n\n # recursively merge dicts\n if type(v) is edict:\n try:\n _merge_a_into_b(a[k], b[k])\n except Exception as e:\n print('Error under config key: {}'.format(k))\n raise e\n else:\n b[k] = v\n\n\ndef cfg_from_file(filename):\n '''\n Load a config file and merge it into the default options.\n\n Parameters\n ----------\n filename : string\n Path to filename.\n\n '''\n import yaml\n with open(filename, 'r') as f:\n yaml_cfg = edict(yaml.load(f))\n\n _merge_a_into_b(yaml_cfg, __C)\n","repo_name":"josephdviviano/humanware","sub_path":"utils/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"32800689183","text":"\"\"\"Tests for simple h5 format.\"\"\"\nfrom __future__ import annotations\n\nimport shutil\n\nimport pytest\nimport tables\n\nimport dascore as dc\nfrom dascore.utils.downloader import fetch\n\n\nclass TestH5Simple:\n \"\"\"Tests for h5simple that aren't covered in common tests.\"\"\"\n\n @pytest.fixture(scope=\"class\")\n def h5simple_path(self):\n \"\"\"Get the path to a h5 simple file.\"\"\"\n return fetch(\"h5_simple_1.h5\")\n\n @pytest.fixture(scope=\"class\")\n def h5simple_with_dim_attrs_path(self, tmp_path_factory):\n \"\"\"Create a h5_simpl which has dimensions specified.\"\"\"\n basic_path = fetch(\"h5_simple_2.h5\")\n new_path = tmp_path_factory.mktemp(\"h5simple_dim_attrs\") / \"simple.h5\"\n\n shutil.copy2(basic_path, new_path)\n with tables.open_file(new_path, \"a\") as h5:\n h5.root._v_attrs[\"dims\"] = \"distance,time\"\n return new_path\n\n def test_no_snap(self, h5simple_path):\n \"\"\"Ensure when snap is not used it still reads patch.\"\"\"\n patch = dc.read(h5simple_path, file_format=\"h5simple\", snap=False)[0]\n assert isinstance(patch, dc.Patch)\n\n def test_dims_in_attrs(self, h5simple_with_dim_attrs_path):\n \"\"\"Ensure if 'dims' is in attrs it gets used.\"\"\"\n patch = dc.spool(h5simple_with_dim_attrs_path, file_format=\"h5simple\")[0]\n assert isinstance(patch, dc.Patch)\n","repo_name":"DASDAE/dascore","sub_path":"tests/test_io/test_h5simple/test_h5simple.py","file_name":"test_h5simple.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"75"} +{"seq_id":"33965190033","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\n# useful for handling different item types with a single interface\n# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\n# useful for handling different item types with a single interface\nfrom itemadapter import ItemAdapter\nimport scrapy\nimport os\nfrom scrapy.pipelines.images import ImagesPipeline\nfrom pymongo import MongoClient\nfrom scrapy.utils.python import to_bytes\nimport hashlib\n\nclass InstaparserPipeline:\n# Создаём подключение к db\n def __init__(self):\n client = MongoClient('localhost', 27017)\n self.mongo_base = client.instagram_SUPER\n\n def write_to_db(self, item, collection_name):\n collection = self.mongo_base[collection_name]\n try:\n collection.insert_one(item)\n except Exception as e:\n print(e, item)\n pass\n\n def process_item(self, item, spider):\n self.write_to_db(item, spider.name)\n return item\n\n# Обработка фотографий\nclass InstagramImagesPipeline(ImagesPipeline):\n\n def get_media_requests(self, item, info):\n if item['photo']:\n try:\n yield scrapy.Request(item['photo'], meta=item)\n except Exception as e:\n print(e)\n if item['photo_followers']:\n try:\n yield scrapy.Request(item['photo_followers'], meta=item)\n except Exception as e:\n print(e)\n if item['photo_following']:\n try:\n yield scrapy.Request(item['photo_following'], meta=item)\n except Exception as e:\n print(e)\n\n def file_path(self, request, response=None, info=None):\n item = request.meta\n name = item['user_name']\n url = request.url\n media_guid = hashlib.sha1(to_bytes(url)).hexdigest()\n media_ext = os.path.splitext(url)[1]\n return f'full/{name}/%s%s.jpg' % (media_guid, media_ext)\n\n def item_completed(self, results, item, info):\n if results:\n item['photo'] = [itm[1] for itm in results if itm[0]]\n elif results:\n item['photo_followers'] = [itm[1] for itm in results if itm[0]]\n elif results:\n item['photo_following'] = [itm[1] for itm in results if itm[0]]\n return item\n# Далее смотрим лист base_filter\n","repo_name":"Multik838/Instagram_db","sub_path":"pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"7206076091","text":"import math\nimport heapq\n\ndef solution(N, road, K):\n heap = []\n # 현 위치에서 연결된 관계 그래프 형태 배열 만들기\n dist = [math.inf for _ in range(N+1)]\n # 방문기록\n delivery = [[] for _ in range(N+1)]\n\n for x, y, z in road:\n delivery[x].append([y, z])\n delivery[y].append([x, z])\n\n # [[], \n # [[2, 1], [4, 2]], \n # [[1, 1], [3, 3], [5, 2]], \n # [[2, 3], [5, 1]], \n # [[1, 2], [5, 2]], \n # [[2, 2], [3, 1], [4, 2]]] \n # return answer\n\n # 시작지점인 1 : 0으로 초기화\n dist[1] = 0\n\n # 우선순위큐에 방문기록 저장 [1, 0]\n # 설명 : 1번동네까지 거리 = 0\n # heapq.heappush(heap, [1, dist[1]])\n heap.append([1, dist[1]])\n\n while len(heap) > 0:\n loc = heapq.heappop(heap)\n # deliver[loc[0]] = [[2, 1], [4, 2]]\n for next in delivery[loc[0]]:\n # print(f'loc[0] : {next}')\n \n if dist[next[0]] > loc[1] + next[1]:\n dist[next[0]] = loc[1] + next[1]\n # heapq.heappush(heap, [next[0], dist[next[0]]])\n heap.append([next[0], dist[next[0]]])\n \n count = 0\n for i in range(1, len(dist)):\n # print(f'count : {i}')\n if dist[i] <= K:\n count += 1\n \n return count\n\n","repo_name":"henrynoowah/Algorithms","sub_path":"programmers/다익스트라/python/배달.py","file_name":"배달.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"6057421951","text":"from ...database.models import *\nfrom ...core.methods import *\nfrom fastapi import APIRouter\nfrom fastapi import HTTPException\n\nrouter = APIRouter(tags=[\"bloques\"])\n\n\n@router.get(\"/bloques\")\nasync def getBlocks():\n result = await get_blocks()\n return result\n\n\n@router.get(\"/bloque/{bloque_id}\")\nasync def getBlock(bloque_id: int):\n result = await get_block(bloque_id)\n return result\n\n\n@router.post(\"/bloques\")\nasync def newBlock(bloque: Bloque):\n result = await newblock(bloque)\n return result\n\n\n@router.delete(\"/bloques/{bloque_id}\")\nasync def deleteBlock(bloque_id: int):\n result = await delete_block(bloque_id)\n return result\n","repo_name":"esuazo2601/LaboFriend","sub_path":"backend/src/api/routers/bloques.py","file_name":"bloques.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"73494967921","text":"import codecs\nimport nltk\nimport pickle\n\nentrada=codecs.open(\"fragmento-wikicorpus-tagged-spa.txt\",\"r\",encoding=\"utf-8\")\n\ntagged_words=[]\ntagged_sents=[]\ntagged_sents_per_unigrams=[]\ncont=0\nfor linia in entrada:\n #cont+=1\n #if cont==10000:\n # break\n linia=linia.rstrip()\n if linia.startswith(\"<\") or len(linia)==0:\n #nova linia\n if len(tagged_words)>0:\n tagged_sents.append(tagged_words)\n tagged_sents_per_unigrams.append(tagged_words)\n tagged_words=[]\n else:\n camps=linia.split(\" \")\n forma=camps[0]\n lema=camps[1]\n etiqueta=camps[2]\n tupla=(forma,etiqueta)\n tagged_words.append(tupla)\n\nif len(tagged_words)>0:\n tagged_sents.append(tagged_words)\n tagged_sents_per_unigrams.append(tagged_words)\n tagged_words=[]\n \ndiccionario=codecs.open(\"diccionario-freeling-spa.txt\",\"r\",encoding=\"utf-8\")\n\ncont=0\nfor linia in diccionario:\n #cont+=1\n #if cont==10000:\n # break\n linia=linia.rstrip()\n camps=linia.split(\":\")\n if len(camps)>=3:\n forma=camps[0]\n lema=camps[1]\n etiqueta=camps[2]\n tupla=(forma,etiqueta)\n tagged_words.append(tupla)\ntagged_sents_per_unigrams.append(tagged_words)\n\n\ndefault_tagger=nltk.DefaultTagger(\"NP00000\")\naffix_tagger=nltk.AffixTagger(tagged_sents_per_unigrams, affix_length=-3, min_stem_length=2,backoff=default_tagger)\nunigram_tagger_diccionari=nltk.UnigramTagger(tagged_sents_per_unigrams,backoff=affix_tagger)\nunigram_tagger=nltk.UnigramTagger(tagged_sents,backoff=unigram_tagger_diccionari)\nbigram_tagger=nltk.BigramTagger(tagged_sents,backoff=unigram_tagger)\ntrigram_tagger=nltk.TrigramTagger(tagged_sents,backoff=bigram_tagger)\n\nsortida=open('etiquetador-spa.pkl', 'wb')\npickle.dump(trigram_tagger, sortida, -1)\nsortida.close()\n","repo_name":"aoliverg/python","sub_path":"programas5-spa/programa-5-15.py","file_name":"programa-5-15.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"42023356790","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 27 17:15:01 2016\n\n@author: marioromero\n\"\"\"\n\nx = [8, 2, 3, 2, 2]\ny = [8, 2, 3, 2, 9]\n\n#Cuantos elementos hay en x si se eliminan los repetidos?\nprint(\"En x hay {} elementos si eliminamos los repetidos\\n\".format(len(set(x))))\n\n#Una lista que contenga la concatenacion de ambas listas.\na = x.copy()\nprint(\"Concatenamos las listas x: {} e y: {}: {}\\n\".format(x,y,a.extend(y)))\n\n#Una lista que contenga la union de ambas listas, sin duplicados.\nprint(\"Unimos las listas x: {} e y: {}, elminando los repetidos: {}\\n\".format(x,y,set(x).union(set(y))))\n\n#Un conjunto que tenga la interseccion de ambas listas.\nprint(\"Interseccio de las listas x: {} e y: {}: {}\\n\".format(x,y,set(x).intersection(set(y))))\n\n#Un diccionario en el que para cada entero de la lista x se almacena su \n#cuadrado.\ndiccionario = {}\nfor num in set(x):\n diccionario[num] = num**2 \nprint (\"El diccionario con los numeros y sus cuadrados de la lista {} es: {}\\n\".format(set(x),diccionario))\n\n#Un diccionario en el que se almacena el numero de veces que cada entero \n#aparece en la lista y.\ndiccionario2 = {}\nfor num in y:\n #diccionario2[num] = diccionario2.get(num,0)+1\n cont = 1\n if num in diccionario2:\n cont = diccionario2[num] + 1 \n diccionario2[num] = cont\nprint (\"El diccionario con los numeros y sus cuadrados de la lista {} es: {}\\n\".format(set(x),diccionario2))","repo_name":"donmariolo/Introduction-to-Python","sub_path":"tema8_2.py","file_name":"tema8_2.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"23597497089","text":"import os\nimport json\nimport random\nimport config\nimport numpy as np\n\nimport torch\nif 'roformer' in config.bert_config[\"bert_path\"]:\n print('using roformer...')\n from transformers.models.bert.modeling_bert import BertConfig\n from roformer import RoFormerModel, RoFormerConfig\nelse:\n from transformers import BertModel, AutoModel\n\nfrom utils.data_util import NewBertTokenizer\nfrom model.casrel import ERENet\nfrom train import Trainer\nfrom data_loader import Reader, Feature\n\n\ndef build_dataset(fold:int, reader:Reader, test_max_len:int, tokenizer:NewBertTokenizer, test_batch_size:int, rel2id:dict):\n\n \n data_path = os.path.join(config.data_in_dir, 'duee_test2.json')\n\n with open(data_path, 'r') as f:\n test_data = [json.loads(line) for line in f.readlines()]\n\n test_examples = reader.read_examples(test_data, 'test', config.eval_config[\"max_len\"])\n\n convert_examples_to_features = Feature(max_len=test_max_len, tokenizer=tokenizer)\n\n test_features = convert_examples_to_features(test_examples, rel2id, 'test')\n\n test_dataloader = test_features.get_dataloader(test_batch_size, num_workers=2, shuffle=False)\n\n data_loaders = test_dataloader\n eval_examples = test_examples\n return data_loaders, eval_examples\n\ndef main():\n\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"6\"\n \n random.seed(config.common[\"seed\"])\n np.random.seed(config.common[\"seed\"])\n torch.manual_seed(config.common[\"seed\"])\n\n \n with open(os.path.join(config.data_out_dir, config.common[\"exp_name\"], 'tag2id.json'), 'r') as f:\n rel2id = json.load(f)\n\n tokenizer = NewBertTokenizer.from_pretrained(config.bert_config[\"bert_path\"], add_special_tokens = False, do_lower_case = True)\n tokenize = tokenizer.tokenize\n tok2char_span = lambda text: tokenizer.get_offset_mappings(text)\n\n reader = Reader(tokenize, tok2char_span, rel2id)\n\n data_loaders, eval_examples = build_dataset(fold=0, \n reader=reader, \n test_max_len=config.eval_config[\"max_len\"],\n tokenizer=tokenizer,\n test_batch_size=config.eval_config[\"batch_size\"],\n rel2id=rel2id\n )\n\n if 'nezha' in config.bert_config[\"bert_path\"]:\n bert_config = BertConfig.from_json_file(config.bert_config[\"bert_path\"]+'/config.json')\n bert_model = BertModel(bert_config)\n torch_init_model(bert_model, config.bert_config[\"bert_path\"]+'/pytorch_model.bin')\n elif 'electra' in config.bert_config[\"bert_path\"]:\n print('using ernie or electra')\n bert_model = AutoModel.from_pretrained(config.bert_config[\"bert_path\"])\n elif 'roformer' in config.bert_config[\"bert_path\"]:\n print('using roformer...')\n bert_model = RoFormerModel.from_pretrained(config.bert_config[\"bert_path\"])\n else:\n bert_model = BertModel.from_pretrained(config.bert_config[\"bert_path\"])\n encoder = ERENet(bert_model, classes_num=len(rel2id))\n\n trainer = Trainer(encoder=encoder,\n data_loaders = data_loaders, \n examples = eval_examples, \n spo_conf=rel2id, \n seed=config.common[\"seed\"], \n device_id=config.common[\"device_num\"], \n output_dir=config.common[\"model_output_path\"]\n )\n res = trainer.eval_data_set(chosen='test')\n\n answer_path = os.path.join('/data2/liuyaduo/casrel_lstm_ee/test2', 'duee_electra_base_9.json')\n with open(answer_path, 'w') as f:\n for line in res:\n f.write(json.dumps(line, ensure_ascii = False ) + '\\n')\n print(\"---------写入完毕---------\")\n\nif __name__ == '__main__':\n\n main()","repo_name":"neukg/MultiIE","sub_path":"EE/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3880,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"75"} +{"seq_id":"73708452723","text":"\"\"\"\nDistributed Proximal Policy Optimization (Distributed PPO or DPPO) continuous\nversion implementation with distributed Tensorflow and Python’s multiprocessing\npackage. This implementation uses normalized running rewards with GAE. The code\nis tested with Gym’s continuous action space environment, Pendulum-v0 on Colab.\n\"\"\"\n\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n#!pip install -q tf-nightly\n\nimport tensorflow as tf\ntf.reset_default_graph()\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport gym\nimport time\nfrom multiprocessing import Process\n\n# The following class is adapted from OpenAI's baseline:\n# https://github.com/openai/baselines/blob/master/baselines/common/running_mean_std.py\n# https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm\n# This class is used for the normalization of rewards in this program before GAE computation.\nclass RunningStats(object):\n def __init__(self, epsilon=1e-4, shape=()):\n self.mean = np.zeros(shape, 'float64')\n self.var = np.ones(shape, 'float64')\n self.std = np.ones(shape, 'float64')\n self.count = epsilon\n\n def update(self, x):\n batch_mean = np.mean(x, axis=0)\n batch_var = np.var(x, axis=0)\n batch_count = x.shape[0]\n self.update_from_moments(batch_mean, batch_var, batch_count)\n\n def update_from_moments(self, batch_mean, batch_var, batch_count):\n delta = batch_mean - self.mean\n new_mean = self.mean + delta * batch_count / (self.count + batch_count)\n m_a = self.var * self.count\n m_b = batch_var * batch_count\n M2 = m_a + m_b + np.square(delta) * self.count * batch_count / (self.count + batch_count)\n new_var = M2 / (self.count + batch_count)\n\n self.mean = new_mean\n self.var = new_var\n self.std = np.maximum(np.sqrt(self.var), 1e-6)\n self.count = batch_count + self.count\n\nclass PPO(object):\n def __init__(self, scope, sess, env, global_PPO=None):\n self.sess = sess\n self.env = env\n #OPT_A = tf.train.AdamOptimizer(A_LR, beta1=0.99, beta2=0.999, name='OPT_A')\n #OPT_C = tf.train.AdamOptimizer(C_LR, beta1=0.99, beta2=0.999, name='OPT_C')\n OPT_A = tf.train.AdamOptimizer(A_LR, name='OPT_A')\n OPT_C = tf.train.AdamOptimizer(C_LR, name='OPT_C')\n\n with tf.variable_scope(scope): # scope is either global or wid\n self.state = tf.placeholder(tf.float32, [None, S_DIM], 'state')\n\n # critic\n with tf.variable_scope('critic'):\n h1 = tf.layers.dense(self.state, hidden, tf.nn.relu, name='hidden', trainable=True)\n self.val = tf.layers.dense(h1, 1, name='val', trainable=True)\n self.critic_params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/critic')\n self.discounted_r = tf.placeholder(tf.float32, [None, 1], 'discounted_r')\n self.advantage = self.discounted_r - self.val\n self.closs = tf.reduce_mean(tf.square(self.advantage))\n self.ctrain_op = OPT_C.minimize(self.closs)\n with tf.variable_scope('cgrads'):\n self.critic_grad_op = tf.gradients(self.closs, self.critic_params)\n\n # actor\n self.pi, self.pi_params = self._build_anet(scope, 'pi', self.env, trainable=True)\n self.oldpi, self.oldpi_params = self._build_anet(scope, 'oldpi', self.env, trainable=True) # originally trainable=False\n with tf.variable_scope('sample_action'):\n self.sample_op = tf.squeeze(self.pi.sample(1), axis=0) # choosing action\n with tf.variable_scope('update_oldpi'):\n self.update_oldpi_op = [oldp.assign(p) for p, oldp in zip(self.pi_params, self.oldpi_params)]\n self.act = tf.placeholder(tf.float32, [None, A_DIM], 'action')\n self.adv = tf.placeholder(tf.float32, [None, 1], 'advantage')\n with tf.variable_scope('loss'):\n with tf.variable_scope('surrogate'):\n ratio = self.pi.prob(self.act) / self.oldpi.prob(self.act)\n surr = ratio * self.adv\n self.aloss = -tf.reduce_mean(tf.minimum(surr, tf.clip_by_value(ratio, 1.-epsilon, 1.+epsilon)*self.adv))\n with tf.variable_scope('atrain'):\n self.atrain_op = OPT_A.minimize(self.aloss)\n with tf.variable_scope('agrads'):\n self.pi_grad_op = tf.gradients(self.aloss, self.pi_params)\n\n if scope != net_scope: # not global\n with tf.name_scope('params'): # push/pull from local/worker perspective\n with tf.name_scope('push_to_global'):\n self.push_actor_pi_params = OPT_A.apply_gradients(zip(self.pi_grad_op, global_PPO.pi_params))\n self.push_critic_params = OPT_C.apply_gradients(zip(self.critic_grad_op, global_PPO.critic_params))\n with tf.name_scope('pull_fr_global'):\n self.pull_actor_pi_params = [local_params.assign(global_params) for local_params, global_params in zip(self.pi_params, global_PPO.pi_params)]\n self.pull_critic_params = [local_params.assign(global_params) for local_params, global_params in zip(self.critic_params, global_PPO.critic_params)]\n\n def update(self, s, a, r, adv):\n self.sess.run(self.update_oldpi_op)\n\n for _ in range(A_EPOCH): # train actor\n self.sess.run(self.atrain_op, {self.state: s, self.act: a, self.adv: adv})\n # update actor\n self.sess.run([self.push_actor_pi_params,\n self.pull_actor_pi_params],\n {self.state: s, self.act: a, self.adv: adv})\n for _ in range(C_EPOCH): # train critic\n # update critic\n self.sess.run(self.ctrain_op, {self.state: s, self.discounted_r: r})\n self.sess.run([self.push_critic_params,\n self.pull_critic_params],\n {self.state: s, self.discounted_r: r})\n\n def _build_anet(self, scope, name, env, trainable):\n with tf.variable_scope(name):\n h1 = tf.layers.dense(self.state, hidden, tf.nn.relu, name='hidden', trainable=trainable)\n mu = self.env.action_space.high * tf.layers.dense(h1, A_DIM, tf.nn.tanh, name='mu', trainable=trainable)\n sigma = tf.layers.dense(h1, A_DIM, tf.nn.softplus, name='sigma', trainable=trainable)\n norm_dist = tf.distributions.Normal(loc=mu, scale=sigma)\n params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/' + name)\n return norm_dist, params\n\n def choose_action(self, s):\n s = s[None, :]\n a = self.sess.run(self.sample_op, {self.state: s})[0]\n return np.clip(a, self.env.action_space.low, self.env.action_space.high)\n\n def get_val(self, s):\n if s.ndim < 2: s = s[None, :]\n return self.sess.run(self.val, {self.state: s})[0, 0]\n\n # This function is adapted from OpenAI's Baseline\n # GAE computation\n # returns TD lamda return & advantage\n def add_vtarg_and_adv(self, R, done, V, v_s_, gamma, lam):\n # Compute target value using TD(lambda) estimator, and advantage with GAE(lambda)\n # last element is only used for last vtarg, but we already zeroed it if last new = 1\n done = np.append(done, 0)\n V_plus = np.append(V, v_s_)\n T = len(R)\n adv = gaelam = np.empty(T, 'float32')\n lastgaelam = 0\n for t in reversed(range(T)):\n nonterminal = 1-done[t+1]\n delta = R[t] + gamma * V_plus[t+1] * nonterminal - V_plus[t]\n gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam\n #print(\"adv=\", adv.shape)\n #print(\"V=\", V.shape)\n #print(\"V_plus=\", V_plus.shape)\n tdlamret = np.vstack(adv) + V\n #print(\"tdlamret=\", tdlamret.shape)\n return tdlamret, adv # tdlamret is critic_target or Qs\n\nclass Worker(object):\n def __init__(self, wid, GLOBAL_PPO, GLOBAL_EP, GLOBAL_RUNNING_R, sess):\n self.wid = wid\n self.env = gym.make(GAME).unwrapped\n self.g_ppo = GLOBAL_PPO\n self.ppo = PPO(wid, sess, self.env, GLOBAL_PPO)\n self.running_stats_r = RunningStats()\n self.sess = sess\n self.GLOBAL_EP = GLOBAL_EP\n self.GLOBAL_RUNNING_R = GLOBAL_RUNNING_R\n\n def work(self):\n T = 0\n t = 0\n SESS = self.sess\n GLOBAL_EP = self.GLOBAL_EP\n GLOBAL_RUNNING_R = self.GLOBAL_RUNNING_R\n\n while SESS.run(GLOBAL_EP) < EP_MAX:\n s = self.env.reset()\n buffer_s, buffer_a, buffer_r, buffer_done, buffer_V = [], [], [], [], []\n ep_r = 0\n for t in range(EP_LEN):\n a = self.ppo.choose_action(s)\n s_, r, done, _ = self.env.step(a)\n buffer_s.append(s)\n buffer_a.append(a)\n buffer_r.append(r)\n buffer_done.append(done)\n\n v = self.ppo.get_val(s)\n buffer_V.append(v)\n\n s = s_\n ep_r += r\n\n # update ppo\n if (t+1) % BATCH == 0 or t == EP_LEN-1:\n\n self.running_stats_r.update(np.array(buffer_r))\n buffer_r = np.clip( (np.array(buffer_r) - self.running_stats_r.mean) / self.running_stats_r.std, -stats_CLIP, stats_CLIP )\n\n v_s_ = self.ppo.get_val(s_)\n\n tdlamret, adv = self.ppo.add_vtarg_and_adv(np.vstack(buffer_r), np.vstack(buffer_done), np.vstack(buffer_V), v_s_, GAMMA, lamda)\n\n bs, ba, br, b_adv = np.vstack(buffer_s), np.vstack(buffer_a), tdlamret, np.vstack(adv)\n buffer_s, buffer_a, buffer_r, buffer_done, buffer_V = [], [], [], [], []\n\n self.ppo.update(bs, ba, br, b_adv)\n\n SESS.run(GLOBAL_EP.assign_add(1.0))\n qe = GLOBAL_RUNNING_R.enqueue(ep_r)\n SESS.run(qe)\n\nGAME = 'Pendulum-v0'\nenv = gym.make(GAME).unwrapped\nnet_scope = 'global'\n\nEP_MAX = 500 #500 # max number of episodes\nEP_LEN = 200 # episode length\nGAMMA = 0.9\n\nlamda = 0.95 #0.95\n\nhidden = 50 #100\n\nA_LR = 0.0001 # actor's learning rate\nC_LR = 0.0002 # critic's learning rate\nBATCH = 32 # minibatch size\nA_EPOCH = 10 # number of epoch\nC_EPOCH = 10 # number of epoch\nS_DIM, A_DIM = 3, 1 # state, action dimension\nstats_CLIP = 10 # upper bound of RunningStats\nepsilon=0.2\n\ncluster = tf.train.ClusterSpec({\n \"worker\": [\"localhost:3331\",\n \"localhost:3332\",\n \"localhost:3333\",\n \"localhost:3334\"\n ],\n \"ps\": [\"localhost:3330\"]\n})\n\ndef parameter_server():\n #tf.reset_default_graph()\n\n server = tf.train.Server(cluster,\n job_name=\"ps\",\n task_index=0)\n sess = tf.Session(target=server.target)\n\n with tf.device(\"/job:ps/task:0\"):\n GLOBAL_PPO = PPO(net_scope, sess, env, global_PPO=None) # only need its params\n GLOBAL_EP = tf.Variable(0.0, name='GLOBAL_EP') # num of global episodes\n # a queue of ep_r\n GLOBAL_RUNNING_R = tf.FIFOQueue(EP_MAX, tf.float32, shared_name=\"GLOBAL_RUNNING_R\")\n\n print(\"Parameter server: waiting for cluster connection...\")\n sess.run(tf.report_uninitialized_variables())\n print(\"Parameter server: cluster ready!\")\n\n print(\"Parameter server: initializing variables...\")\n sess.run(tf.global_variables_initializer())\n print(\"Parameter server: variables initialized\")\n\n while True:\n time.sleep(1.0)\n if sess.run(GLOBAL_RUNNING_R.size()) >= EP_MAX: # GLOBAL_EP starts from 0, hence +1 to max_global_episodes\n time.sleep(10.0)\n GLOBAL_RUNNING_R_list = []\n ep_r_prev = 0.0\n for i in range(sess.run(GLOBAL_RUNNING_R.size())):\n ep_r = sess.run(GLOBAL_RUNNING_R.dequeue())\n if i==0:\n GLOBAL_RUNNING_R_list.append(ep_r) # for display\n else:\n GLOBAL_RUNNING_R_list.append(GLOBAL_RUNNING_R_list[-1]*0.9 + ep_r*0.1) # for display\n break\n\n # display\n plt.plot(np.arange(len(GLOBAL_RUNNING_R_list)), GLOBAL_RUNNING_R_list)\n plt.xlabel('episode')\n plt.ylabel('reward')\n plt.show()\n\n #print(\"Parameter server: blocking...\")\n #server.join() # currently blocks forever\n print(\"Parameter server: ended...\")\n\ndef worker(worker_n):\n #tf.reset_default_graph()\n\n server = tf.train.Server(cluster,\n job_name=\"worker\",\n task_index=worker_n)\n sess = tf.Session(target=server.target)\n\n with tf.device(\"/job:ps/task:0\"):\n GLOBAL_PPO = PPO(net_scope, sess, env, global_PPO=None) # only need its params\n GLOBAL_EP = tf.Variable(0.0, name='GLOBAL_EP') # num of global episodes\n # a queue of ep_r\n GLOBAL_RUNNING_R = tf.FIFOQueue(EP_MAX, tf.float32, shared_name=\"GLOBAL_RUNNING_R\")\n \"\"\"\n with tf.device(tf.train.replica_device_setter(\n worker_device='/job:worker/task:' + str(worker_n),\n cluster=cluster)):\n \"\"\"\n print(\"Worker %d: waiting for cluster connection...\" % worker_n)\n sess.run(tf.report_uninitialized_variables())\n print(\"Worker %d: cluster ready!\" % worker_n)\n\n #while sess.run(tf.report_uninitialized_variables()):\n while (sess.run(tf.report_uninitialized_variables())).any(): # ********** .any() .all() **********\n print(\"Worker %d: waiting for variable initialization...\" % worker_n)\n time.sleep(1.0)\n print(\"Worker %d: variables initialized\" % worker_n)\n\n w = Worker(str(worker_n), GLOBAL_PPO, GLOBAL_EP, GLOBAL_RUNNING_R, sess)\n print(\"Worker %d: created\" % worker_n)\n\n sess.run(tf.global_variables_initializer()) # got to initialize after Worker creation\n w.work()\n print(\"Worker %d: w.work()\" % worker_n)\n\n #print(\"Worker %d: blocking...\" % worker_n)\n server.join() # currently blocks forever\n print(\"Worker %d: ended...\" % worker_n)\n\nstart_time = time.time()\n\nps_proc = Process(target=parameter_server, daemon=True)\nw1_proc = Process(target=worker, args=(0, ), daemon=True)\nw2_proc = Process(target=worker, args=(1, ), daemon=True)\nw3_proc = Process(target=worker, args=(2, ), daemon=True)\nw4_proc = Process(target=worker, args=(3, ), daemon=True)\n\nps_proc.start()\nw1_proc.start()\nw2_proc.start()\nw3_proc.start()\nw4_proc.start()\n\n# if not join, parent will terminate before children\n# & children will terminate as well cuz children are daemon\nps_proc.join()\n#w1_proc.join()\n#w2_proc.join()\n#w3_proc.join()\n#w4_proc.join()\n\nfor proc in [w1_proc,\n w2_proc,\n w3_proc,\n w4_proc,\n ps_proc]:\n proc.terminate() # only way to kill server is to kill it's process\n\nprint('All done.')\n\nprint(\"--- %s seconds ---\" % (time.time() - start_time))\n","repo_name":"ChuaCheowHuan/reinforcement_learning","sub_path":"DPPO/dppo_cont_gae_dist_gpu.py","file_name":"dppo_cont_gae_dist_gpu.py","file_ext":"py","file_size_in_byte":15053,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"75"} +{"seq_id":"28681231102","text":"import logging\n\nfrom django.conf import settings\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404, redirect, render\nfrom django.urls import reverse\nfrom django.views.decorators.http import require_http_methods\n\nfrom authn.decorators.auth import require_auth\nfrom club.exceptions import AccessDenied, RateLimitException\nfrom comments.forms import CommentForm, ReplyForm, BattleCommentForm\nfrom comments.models import Comment, CommentVote\nfrom common.request import parse_ip_address, parse_useragent\nfrom authn.decorators.api import api\nfrom posts.models.linked import LinkedPost\nfrom posts.models.post import Post\nfrom posts.models.subscriptions import PostSubscription\nfrom posts.models.views import PostView\nfrom search.models import SearchIndex\n\nlog = logging.getLogger(__name__)\n\n\n@require_auth\ndef create_comment(request, post_slug):\n post = get_object_or_404(Post, slug=post_slug)\n if not post.is_commentable and not request.me.is_moderator:\n raise AccessDenied(\n title=\"Комментарии к этому посту закрыты\",\n data={\"saved_text\": request.POST.get(\"text\")},\n )\n\n if request.POST.get(\"reply_to_id\"):\n ProperCommentForm = ReplyForm\n elif post.type == Post.TYPE_BATTLE:\n ProperCommentForm = BattleCommentForm\n else:\n ProperCommentForm = CommentForm\n\n comment_order = request.POST.get(\"post_comment_order\", \"created_at\")\n\n if request.method == \"POST\":\n form = ProperCommentForm(request.POST)\n if form.is_valid():\n is_ok = Comment.check_rate_limits(request.me)\n if not is_ok:\n raise RateLimitException(\n title=\"🙅‍♂️ Вы комментируете слишком часто\",\n message=\"Подождите немного, вы достигли своего лимита на комментарии в день.\",\n data={\"saved_text\": request.POST.get(\"text\")},\n )\n\n comment = form.save(commit=False)\n comment.post = post\n if not comment.author:\n comment.author = request.me\n\n comment.ipaddress = parse_ip_address(request)\n comment.useragent = parse_useragent(request)\n comment.save()\n\n # subscribe to top level comments\n if form.cleaned_data.get(\"subscribe_to_post\"):\n PostSubscription.subscribe(\n user=request.me,\n post=post,\n type=PostSubscription.TYPE_ALL_COMMENTS if post.author_id == request.me.id\n else PostSubscription.TYPE_TOP_LEVEL_ONLY\n )\n\n # update the shitload of counters :)\n request.me.update_last_activity()\n Comment.update_post_counters(post)\n PostView.increment_unread_comments(comment)\n PostView.register_view(\n request=request,\n user=request.me,\n post=post,\n )\n SearchIndex.update_comment_index(comment)\n LinkedPost.create_links_from_text(post, comment.text)\n return redirect(\n reverse(\"show_post\", kwargs={\n \"post_type\": post.type,\n \"post_slug\": post.slug\n }) + f\"?comment_order={comment_order}#comment-{comment.id}\"\n )\n else:\n log.error(f\"Comment form error: {form.errors}\")\n return render(request, \"error.html\", {\n \"title\": \"Какая-то ошибка при публикации комментария 🤷‍♂️\",\n \"message\": f\"Мы уже получили оповещение и скоро пофиксим. \"\n f\"Ваш коммент мы сохранили чтобы вы могли скопировать его и запостить еще раз:\",\n \"data\": {\"saved_text\": form.cleaned_data.get(\"text\")}\n }, status=500)\n\n raise Http404()\n\n\ndef show_comment(request, post_slug, comment_id):\n post = get_object_or_404(Post, slug=post_slug)\n return redirect(\n reverse(\"show_post\", kwargs={\"post_type\": post.type, \"post_slug\": post.slug}) + f\"#comment-{comment_id}\"\n )\n\n\n@require_auth\ndef edit_comment(request, comment_id):\n comment = get_object_or_404(Comment, id=comment_id)\n\n if not request.me.is_moderator:\n if comment.author != request.me:\n raise AccessDenied()\n\n if comment.is_deleted:\n raise AccessDenied(\n title=\"Нельзя редактировать удаленный комментарий\",\n message=\"Сначала тот, кто его удалил, должен его восстановить\"\n )\n\n if not comment.is_editable:\n hours = int(settings.COMMENT_EDITABLE_TIMEDELTA.total_seconds() // 3600)\n raise AccessDenied(\n title=\"Время вышло\",\n message=f\"Комментарий можно редактировать только в течение {hours} часов после создания\"\n )\n\n if not comment.post.is_visible or not comment.post.is_commentable:\n raise AccessDenied(title=\"Комментарии к этому посту закрыты\")\n\n post = comment.post\n\n if request.method == \"POST\":\n form = CommentForm(request.POST, instance=comment)\n if form.is_valid():\n comment = form.save(commit=False)\n comment.is_deleted = False\n comment.html = None # flush cache\n comment.ipaddress = parse_ip_address(request)\n comment.useragent = parse_useragent(request)\n comment.save()\n\n SearchIndex.update_comment_index(comment)\n\n return redirect(\"show_comment\", post.slug, comment.id)\n else:\n form = CommentForm(instance=comment)\n\n return render(request, \"comments/edit.html\", {\n \"comment\": comment,\n \"post\": post,\n \"form\": form\n })\n\n\n@require_auth\ndef delete_comment(request, comment_id):\n comment = get_object_or_404(Comment, id=comment_id)\n\n if not request.me.is_moderator:\n # only comment author, post author or moderator can delete comments\n if comment.author != request.me and request.me != comment.post.author:\n raise AccessDenied(\n title=\"Нельзя!\",\n message=\"Только автор комментария, поста или модератор может удалить комментарий\"\n )\n\n if not comment.is_deletable_by(request.me):\n raise AccessDenied(\n title=\"Время вышло\",\n message=\"Комментарий можно удалять только в первые дни после создания. \"\n \"Потом только автор или модератор может это сделать.\"\n )\n\n if not comment.post.is_visible:\n raise AccessDenied(\n title=\"Пост скрыт!\",\n message=\"Нельзя удалять комментарии к скрытому посту\"\n )\n\n if not comment.is_deleted:\n # delete comment\n comment.delete(deleted_by=request.me)\n PostView.decrement_unread_comments(comment)\n else:\n # undelete comment\n if comment.deleted_by == request.me.id or request.me.is_moderator:\n comment.undelete()\n PostView.increment_unread_comments(comment)\n else:\n raise AccessDenied(\n title=\"Нельзя!\",\n message=\"Только тот, кто удалил комментарий, может его восстановить\"\n )\n\n Comment.update_post_counters(comment.post, update_activity=False)\n\n return redirect(\"show_comment\", comment.post.slug, comment.id)\n\n\n@require_auth\ndef pin_comment(request, comment_id):\n comment = get_object_or_404(Comment, id=comment_id)\n\n if not request.me.is_moderator and comment.post.author != request.me:\n raise AccessDenied(\n title=\"Нельзя!\",\n message=\"Только автор поста или модератор может пинить посты\"\n )\n\n if comment.reply_to:\n raise AccessDenied(\n title=\"Нельзя!\",\n message=\"Можно пинить только комменты первого уровня\"\n )\n\n comment.is_pinned = not comment.is_pinned # toggle pin/unpin\n comment.save()\n\n return redirect(\"show_comment\", comment.post.slug, comment.id)\n\n\n@api(require_auth=True)\n@require_http_methods([\"POST\"])\ndef upvote_comment(request, comment_id):\n comment = get_object_or_404(Comment, id=comment_id)\n\n post_vote, is_created = CommentVote.upvote(\n user=request.me,\n comment=comment,\n request=request,\n )\n\n return {\n \"comment\": {\n \"upvotes\": comment.upvotes + (1 if is_created else 0)\n },\n \"upvoted_timestamp\": int(post_vote.created_at.timestamp() * 1000) if post_vote else 0\n }\n\n\n@api(require_auth=True)\n@require_http_methods([\"POST\"])\ndef retract_comment_vote(request, comment_id):\n comment = get_object_or_404(Comment, id=comment_id)\n\n is_retracted = CommentVote.retract_vote(\n request=request,\n user=request.me,\n comment=comment,\n )\n\n return {\n \"success\": is_retracted,\n \"comment\": {\n \"upvotes\": comment.upvotes - (1 if is_retracted else 0)\n }\n }\n","repo_name":"vas3k/vas3k.club","sub_path":"comments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9741,"program_lang":"python","lang":"ru","doc_type":"code","stars":685,"dataset":"github-code","pt":"75"} +{"seq_id":"17916225008","text":"import pathlib\nimport re\nimport json\nimport timeit\nimport functools\n\n\nfrom net_parser.utils import get_logger, first_candidate_or_none, re_search, re_search_lines, re_match, PATTERN_TYPE, compile_regex\n\n\nclass BaseConfigLine(object):\n\n PATTERN_TYPE = PATTERN_TYPE\n _parent_indent_regex = re.compile(pattern=r\"[^! ]\", flags=re.MULTILINE)\n _interface_regex = re.compile(pattern=r\"^interface\\s(\\S+)\", flags=re.MULTILINE)\n comment_regex = re.compile(pattern=r\"^\\s*!.*\", flags=re.MULTILINE)\n\n def __init__(self, number, text, config: 'BaseConfigParser', verbosity=3, name=\"BaseConfigLine\"):\n \"\"\"\n **This class is not meant to be instantiated directly, but only from BaseConfigParser instance.**\n\n Args:\n number (int): Index of line in config\n text (str): Text of the config line\n config (:obj:`BaseConfigParser`): Reference to the parent BaseConfigParser object\n verbosity (:obj:`int`, optional): Logging output level, defaults to 3: Warning\n\n \"\"\"\n self._name = name\n self.logger = get_logger(name=name, verbosity=verbosity)\n #print(self.logger.handlers)\n self.config = config\n self.config_lines_obj = self.config.lines\n self.number = number\n self.text = text\n self.indent = len(self.text) - len(self.text.lstrip(\" \"))\n self.type = None\n # self.logger.debug(\"Parsing line: #{}: '{}'\".format(self.number, self.text))\n\n\n def return_obj(self):\n return self\n\n # def __eq__(self, other) -> bool:\n # return self.get_line == other.get_line\n\n # def __ne__(self, other) -> bool:\n # return not self.__eq__(other)\n\n def compile_regex(self, pattern: str, flags=re.MULTILINE):\n return compile_regex(pattern=pattern, flags=flags, logger=self.logger, raise_exc=True)\n\n def get_children(self, max_depth: int = None):\n \"\"\"\n Return all children lines (all following lines with larger indent)\n\n Returns:\n list: List of child config lines (objects)\n\n \"\"\"\n children = []\n line_num = int(self.number) + 1\n #print(len(self.config.lines))\n while line_num <= len(self.config.lines) - 1: # Avoid IndexError\n if self.config.lines[line_num].indent <= self.indent:\n break\n else:\n if max_depth and self.config.lines[line_num].indent > self.indent + max_depth:\n pass\n else:\n children.append(self.config.lines[line_num])\n line_num += 1\n return children\n\n @property\n def parent(self):\n if not self.is_child:\n self.logger.debug(\"Line is not a child, therefore has no parent. Line: {}\".format(self.text))\n return None\n else:\n line_num = int(self.number) - 1\n line = self.config.lines[line_num]\n while line.indent >= self.indent and line_num > 0:\n line_num -= 1\n line = self.config.lines[line_num]\n return line\n\n @functools.lru_cache()\n def get_parents(self):\n start = timeit.default_timer()\n parents = []\n if not self.is_child:\n self.logger.debug(\"Line is not a child, therefore has no parent. Line: {}\".format(self.text))\n pass\n else:\n parents.insert(0, self.parent)\n while parents[0].parent is not None:\n parents.insert(0, parents[0].parent)\n stop = timeit.default_timer()\n self.logger.debug(\"Getting parents of line {} took {} ms\".format(str(self), (stop-start)*10e3))\n return parents\n\n @property\n def get_path(self):\n path = [x.text for x in self.get_parents()]\n if len(path):\n return path\n else:\n return None\n\n @property\n def get_line(self):\n line = None\n if self.get_path is not None:\n line = list(self.get_path)\n else:\n line = list()\n line.append(self.text)\n return line\n\n\n def re_search_children(self, regex, group=None):\n \"\"\"\n Search all children for given regex.\n\n Args:\n regex (:obj:`re.Pattern` or ``str``): Regex to search for\n group (:obj:`str` or ``int``, optional): Return only specific (named or numbered) group of given regex.\n If set to \"ALL\", return value will be a dictionary with all named groups of the regex.\n\n Returns:\n list: List of all child object which match given regex, or, if `group` was provided, returns\n list containing matched group across all children.\n\n Example::\n\n # Given following config section, interface line stored in `line` variable\n config = '''\n interface Ethernet0/0\n description Test Interface\n ip address 10.0.0.1 255.255.255.0\n ip address 10.0.1.1 255.255.255.0 secondary\n !\n '''\n pattern = r\"^ ip address (?P\\S+) (?P\\S+)\"\n\n result = line.re_search_children(regex=pattern)\n print(result)\n # Returns: [\n # [BaseConfigLine #2 (child): ip address 10.0.0.1 255.255.255.0],\n # [BaseConfigLine #3 (child): ip address 10.0.1.1 255.255.255.0 secondary]\n # ]\n\n result = line.re_search_children(regex=pattern, group=\"ip\")\n print(result)\n # Returns: [\n # \"10.0.0.1\",\n # \"10.0.1.1\"\n # ]\n\n result = line.re_search_children(regex=pattern, group=\"ALL\")\n print(result)\n # Returns: [\n # {\"ip\": \"10.0.0.1\", \"mask\": \"255.255.255.0\"},\n # {\"ip\": \"10.0.1.1\", \"mask\": \"255.255.255.0\"}\n # ]\n\n\n \"\"\"\n pattern = None\n if not isinstance(regex, self.PATTERN_TYPE):\n pattern = self.compile_regex(pattern=regex)\n else:\n pattern = regex\n if not pattern:\n return []\n children = self.get_children()\n return re_search_lines(lines=children, regex=regex, group=group)\n\n # TODO: Add Tests\n # TODO: Add Examples\n def re_search_children_multipattern(self, regexes: list, group=None, deduplicate: bool = True) -> list:\n \"\"\"\n Wrapper function for ``self.re_search_children()`` allowing to use multiple patterns\n\n Args:\n regexes (``list``): List of patterns to search\n group (``str`` or ``int``, optional): Return only specific (named or numbered) group of given regex.\n If set to \"ALL\", return value will be a dictionary with all named groups of the regex.\n deduplicate (``bool``, optional): When set to ``True`` (default), results will not contain duplicate line objects in\n cases where multiple patterns match the same line.\n\n Returns:\n list: List of all child object which match given regex, or, if `group` was provided, returns\n list containing matched group across all children.\n\n \"\"\"\n results = []\n for regex in regexes:\n for result in [x for x in self.re_search_children(regex=regex, group=group)]:\n if result in results:\n if deduplicate:\n continue\n else:\n results.append(result)\n else:\n results.append(result)\n return results\n\n def re_search(self, regex, group=None):\n return re_search(line=self, regex=regex, group=group)\n\n def re_match(self, regex, group=None):\n return re_match(line=self, regex=regex, group=group)\n\n @property\n def get_type(self):\n \"\"\"\n Return `types` of config line. Used mostly for filtering purposes.\n\n Currently available values are:\n\n - ``parent``\n - ``child``\n - ``interface``\n - ``comment``\n\n Returns:\n list: List of types\n\n \"\"\"\n types = []\n if re.match(self.comment_regex, self.text):\n types.append(\"comment\")\n # If line is comment, it's comment only\n return types\n if self.is_parent:\n types.append(\"parent\")\n if self.is_child:\n types.append(\"child\")\n return types\n\n @functools.cached_property\n def is_comment(self):\n return bool(re.match(self.comment_regex, self.text))\n\n @property\n def is_parent(self):\n \"\"\"\n Check whether this line is a parent\n\n Returns:\n bool: True if line is a parent line, False otherwise\n\n \"\"\"\n if self.number < len(self.config.lines)-1:\n if self.config.lines[self.number+1].indent > self.indent:\n return True\n else:\n return False\n else:\n return False\n\n @property\n def is_child(self):\n \"\"\"\n Check whether this line is a child\n\n Returns:\n bool: True if line is a child line, False otherwise\n\n \"\"\"\n if self.indent > 0:\n return True\n else:\n return False\n\n def _val_to_bool(self, entry: dict, keys: list):\n if not isinstance(keys, list):\n keys = list(keys)\n for key in keys:\n if entry[key]:\n entry[key] = True\n else:\n entry[key] = False\n return entry\n\n def first_candidate_or_none(self, candidates: list, wanted_type=None):\n return first_candidate_or_none(candidates=candidates, logger=self.logger, wanted_type=wanted_type)\n\n def __str__(self):\n return f\"[{self.__class__.__name__} #{self.number}\\t({self.get_type}): '{self.text}']\"\n\n def __repr__(self):\n return self.__str__()\n\n\n","repo_name":"mihudec/net_parser","sub_path":"net_parser/config/BaseConfigLine.py","file_name":"BaseConfigLine.py","file_ext":"py","file_size_in_byte":10098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"7250377851","text":"# Write your solution here\ndef search(phonebook):\n name = input(\"name: \")\n if name in phonebook:\n print(phonebook[name])\n else:\n print(\"no number\")\n\ndef add(phonebook):\n name = input(\"name: \")\n number = input(\"number: \")\n phonebook[name] = number\n print(\"ok!\")\n\ndef main():\n phonebook = {}\n while True:\n command = int(input(\"command (1 search, 2 add, 3 quit): \"))\n\n if command == 1:\n search(phonebook)\n if command == 2:\n add(phonebook)\n if command == 3:\n break\n print(\"quitting...\")\n\nmain()","repo_name":"P4r1nc3/Python_Programming_MOOC_2023_I","sub_path":"part05/part05-17_phone_book_v1/src/phone_book_v1.py","file_name":"phone_book_v1.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"75"} +{"seq_id":"16674591022","text":"#!/usr/bin/python3\nfrom random import random\n\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport copy\n\n\nclass RRT_solver:\n\n def __init__(self, map2d, discrete_num=5):\n self.map2d = map2d\n self.discrete_num = discrete_num\n\n def path_plan(self, start_point, end_point, num_vertices, incremental_distance, tol_dist):\n for k in range(num_vertices):\n candidate = self.single_find()\n\n def single_find(self, from_point, dist):\n while True:\n rand_vec = np.array([random(), random()])\n q_rand = rand_vec / np.linalg.norm(rand_vec) * dist\n dest = np.floor(from_point + q_rand)\n if self.valid_path(from_point, dest):\n return dest\n\n def valid_path(self, start, end):\n direction = (end - start) / (self.discrete_num + 1.0)\n for k in range(self.discrete_num + 1):\n pt = start + direction * (k + 1)\n if not self.map2d.is_blank(pt[0], pt[1]):\n return False\n return True\n","repo_name":"imbaguanxin/pythonPathSmooth","sub_path":"a_star/rrt_solver.py","file_name":"rrt_solver.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"36227555498","text":"from random import randint\nfrom flask import Flask\nfrom flask import render_template\nfrom collections import Counter\nfrom random import randint\nfrom random import shuffle\nfrom flask import request, redirect\nfrom jinja2 import Template\nfrom flask_sqlalchemy import SQLAlchemy\nfrom sqlalchemy import MetaData, create_engine\nfrom sqlalchemy import text\nfrom sqlalchemy.orm import sessionmaker\n\nengine = create_engine('sqlite:///./Gra.db', connect_args={'check_same_thread': False})\n\nMETA_DATA = MetaData(bind=engine)\n\nc = engine.connect()\nMETA_DATA.reflect()\n\n\n# select kart z tabel\nhero = META_DATA.tables['Hero_1']\nhero_laczenie = META_DATA.tables['Hero_laczenie']\nreka = META_DATA.tables['Reka']\n\nSession = sessionmaker(bind = engine)\nsession = Session()\nreka_select = reka.select()\na = engine.execute(reka_select)\nresult2 = a.fetchall()\n\n#result dla wszystkich kart\nhero_select = hero.select()\nc=engine.execute(hero_select)\nresult= c.fetchall()\n\n\nclass Gracz:\n def __init__(self, imie='Nieznane'):\n self.nazwa = imie\n self.talia = [i[0] for i in result]\n self.reka = []\n self.odrzucone = []\n self.wszystkie = []\n self.kupione=[]\n self.zielone = []\n self.lista = []\n self.monety = 0\n self.atak = 0\n self.zycie = 10\n self.dict = {}\n self.talia_gracz()\n self.potasuj()\n self.wszystkie_karty()\n self.kolor_talia()\n\n\n def talia_gracz(self):\n del self.talia[:54]\n #self.talia.append[30, 31, 44, 45, 46, 47,1,8,5,17,18]\n\n def potasuj(self):\n shuffle(self.talia)\n\n def wyloz_karty(self):\n # self.reka= [30, 31, 44, 45, 46, 47,1,8,5,17,18]\n self.reka = self.talia[:5]\n del self.talia[:5]\n #self.dobierz_karte()\n\n def zdjecie_wyswietl(self):\n qur2 = session.query(hero).filter(hero.c.ID.in_(self.reka)).all()\n qur2 = sorted(qur2, key=lambda o: self.reka.index(o.ID))\n zdjecie = ([i.Zdjecie for i in qur2])\n return zdjecie\n\n def zobacz_karty(self):\n qur2 = session.query(hero).filter(hero.c.ID.in_(self.wszystkie)).all()\n qur2 = sorted(qur2, key=lambda o: self.wszystkie.index(o.ID))\n zdjecie = ([i.Zdjecie for i in qur2])\n return zdjecie\n\n def odrzuc_karte(self):\n self.odrzucone.extend(self.talia[1])\n del self.talia[1]\n\n def koniec_tury(self):\n self.odrzucone.extend(self.reka[:])\n del self.reka[:]\n\n def koniec_talii(self):\n shuffle(self.odrzucone)\n self.talia.extend(self.odrzucone[:])\n del self.odrzucone[:]\n #shuffle(self.talia)\n\n def kup(self, sprzedane):\n if sprzedane != None:\n qur3 = session.query(hero).filter(hero.c.ID.in_(sprzedane)).all()\n # qur3 = sorted(qur3, key=lambda o: sprzedane.index(o.ID))\n cena = sum([i.Cena for i in qur3])\n if self.monety >= cena:\n self.monety = self.monety - cena\n for x in sprzedane:\n self.odrzucone.append(x)\n for x in sprzedane:\n self.kupione.append(x)\n return self.kupione\n else:\n return self.kupione\n\n def sumuj_monety(self):\n qry = session.query(hero).filter(hero.c.ID.in_(self.reka)).all()\n monety = sum([i.Monety for i in qry])\n nazwy = ([i.Nazwa for i in qry])\n self.monety = monety\n return self.monety\n\n def sumuj_atak(self):\n qry = session.query(hero).filter(hero.c.ID.in_(self.reka)).all()\n atak = sum([i.Atak for i in qry])\n self.atak = atak\n return self.atak\n\n def sumuj_zdrowie(self):\n qry = session.query(hero).filter(hero.c.ID.in_(self.reka)).all()\n zdrowie = sum([i.Zdrowie for i in qry])\n self.zycie = self.zycie + zdrowie\n\n def id(self, slownik):\n i = list(slownik.keys())\n return i\n\n def atakuj(self, atak):\n self.zycie = self.zycie - atak\n return self.zycie\n\n def slownik(self, i, atak, d={}):\n d[i] = atak\n return (d)\n\n def suma(self, slownik):\n slownik_1 = slownik\n print('SLOWNIK', slownik_1)\n atak = list(slownik_1.values())\n print('atak', atak)\n suma = 0\n for x in atak:\n suma += x\n # print('suma',suma)\n return suma\n\n def odejmij_atak(self,slownik):\n self.atak = self.atak - self.suma(slownik)\n #print(suma)\n\n\n def kolor(self):\n qur2 = session.query(hero).filter(hero.c.ID.in_(self.reka)).all()#where(hero.c.Kolor == 'Zielony')\n qur2 = sorted(qur2, key=lambda o: self.reka.index(o.ID))\n id = ([i.ID for i in qur2])\n zielony = 0\n czerwony=0\n niebieski=0\n zloty=0\n #print(kolor)\n d={}\n for x in id:\n qur = session.query(hero).filter(hero.c.ID.in_(id)).filter(hero.c.ID == x)\n kolor =([i.Kolor for i in qur])\n d[x]=kolor\n\n #print('slownik',d)\n for x in d:\n if 'Zielony' in d[x]:\n zielony+=1\n if 'Niebieski' in d[x]:\n niebieski+=1\n if 'Czerwony' in d[x]:\n czerwony+=1\n if 'Zloty' in d[x]:\n zloty+=1\n lista = []\n for x in id:\n if zielony > 1 and 'Zielony' in d[x]:\n lista.append(x)\n if niebieski > 1 and 'Niebieski' in d[x]:\n lista.append(x)\n if czerwony > 1 and 'Czerwony' in d[x]:\n lista.append(x)\n if zloty > 1 and 'Zloty' in d[x]:\n lista.append(x)\n #return d[x]\n #print(zielony, niebieski, czerwony, zloty)\n self.lista =lista\n return(lista)\n\n def cena_kolor(self):\n pass\n\n def wszystkie_karty(self):\n self.wszystkie.extend(self.reka[:])\n self.wszystkie.extend(self.odrzucone[:])\n self.wszystkie.extend(self.talia[:])\n #print(self.wszystkie)\n\n def kolor_talia(self):\n #self.wszytkie = self.reka + self.odrzucone + self.talia()\n qur2 = session.query(hero).filter(hero.c.ID.in_(self.wszystkie)).all()#where(hero.c.Kolor == 'Zielony')\n qur2 = sorted(qur2, key=lambda o: self.wszystkie.index(o.ID))\n id = ([i.ID for i in qur2])\n zielony = 0\n czerwony=0\n niebieski=0\n zloty=0\n a = None\n\n d={}\n for x in id:\n qur = session.query(hero).filter(hero.c.ID.in_(id)).filter(hero.c.ID == x)\n #print(qur)\n kolor =([i.Kolor for i in qur])\n d[x]=kolor\n\n #print('slownik',d)\n for x in d:\n if 'Zielony' in d[x]:\n zielony+=1\n if 'Niebieski' in d[x]:\n niebieski+=1\n if 'Czerwony' in d[x]:\n czerwony+=1\n if 'Zloty' in d[x]:\n zloty+=1\n\n maxi = max(zielony, zloty, niebieski, czerwony)\n if zielony is maxi:\n a = 1 #zielony\n if zloty is maxi:\n a = 2 #zloty\n if niebieski is maxi:\n a = 3 #'niebieski'\n if czerwony is maxi:\n a = 4 # 'czerwony'\n\n # print(zielony, zloty, niebieski, czerwony)\n #print(\"AA\", a)\n return(a)\n\n def hero_laczenie(self):\n qry = session.query(hero_laczenie).filter(hero_laczenie.c.ID.in_(self.lista)).all()\n qry = sorted(qry, key=lambda o: self.lista.index(o.ID))\n atak_laczenie = sum([i.Atak for i in qry])\n monety_laczenie = sum([i.Monety for i in qry])\n zdrowie_laczenie = sum([i.Zdrowie for i in qry])\n zdolnosci = ([i.Inne_zdolnosci for i in qry])\n self.atak = self.atak + atak_laczenie\n self.monety = self.monety + monety_laczenie\n self.zycie = self.zycie + zdrowie_laczenie\n\n def dobierz_karte(self):\n qur2 = session.query(hero).filter(hero.c.ID.in_(self.reka)).all()\n qur2 = sorted(qur2, key=lambda o: self.reka.index(o.ID))\n zdolnosci = ([i.Inne_zdolnosci for i in qur2])\n x = 0\n for i in zdolnosci:\n if i == 'Dobierz karte':\n x+=1\n if i == 'Dobierz 2 karty':\n x += 2\n\n if len(self.talia) >= x+5:\n self.reka.extend(self.talia[:x])\n del self.talia[:x]\n else:\n self.reka.extend(self.odrzucone[:x])\n del self.odrzucone[:x]\n print(zdolnosci, x)\n\n '''if x != 0:\n if len(self.talia) >= x:\n self.reka.append(self.talia[:1])\n del self.talia[:x]\n else:\n self.reka.append(self.odrzucone[:2])\n del self.talia[:x]\n '''\n def hero_laczenie_dobranie(self):\n qry = session.query(hero_laczenie).filter(hero_laczenie.c.ID.in_(self.lista)).all()\n qry = sorted(qry, key=lambda o: self.lista.index(o.ID))\n\n zdolnosci = ([i.Inne_zdolnosci for i in qry])\n x = 0\n for i in zdolnosci:\n if i == 'Dobierz karte':\n x += 1\n if i == 'Dobierz 2 karty':\n x += 2\n if len(self.talia)>= x:\n self.reka.extend(self.talia[:x])\n del self.talia[:x]\n else:\n self.reka.extend(self.odrzucone[:x])\n del self.odrzucone[:x]\n print(zdolnosci, x)\n\n def komputer(self):\n if len(self.talia) < 5:\n self.koniec_talii()\n self.wyloz_karty()\n self.dobierz_karte()\n self.kolor()\n self.wszystkie_karty()\n self.kolor_talia()\n self.hero_laczenie_dobranie()\n self.sumuj_monety()\n self.sumuj_atak()\n self.sumuj_zdrowie()\n self.hero_laczenie()\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"aleks-gita/hero","sub_path":"gracz.py","file_name":"gracz.py","file_ext":"py","file_size_in_byte":9791,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"73386861043","text":"class Solution:\r\n def maximumGap(self, nums):\r\n \"\"\"\r\n :type nums: List[int]\r\n :rtype: int\r\n \"\"\"\r\n\r\n if len(nums) < 2:\r\n return 0\r\n\r\n nums.sort()\r\n\r\n maximum = 0\r\n for i in range(len(nums) - 1):\r\n if nums[i + 1] - nums[i] > maximum:\r\n maximum = nums[i + 1] - nums[i]\r\n\r\n return maximum\r\n \r\nsol = Solution()\r\nprint(sol.maximumGap([3,6,9,1]))\r\n","repo_name":"MrGouIsTaken/LeetCode-Solutions","sub_path":"code/ex164.py","file_name":"ex164.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"37832291141","text":"# pylint: skip-file\nimport pandas as pd\n\ndf = pd.DataFrame([[1]])\nsr = pd.Series([1])\n\n\ndf_concat = pd.concat([df, df])\nsr_concat = pd.concat([sr, sr])\nsr_axis1_concat = pd.concat([sr, sr], axis=1)\n\n# mypy error without pandera plugin\ndf_generator_concat: pd.DataFrame = pd.concat((df for _ in range(3)))\n\n# mypy error without pandera plugin\nsr_generator_concat: pd.Series = pd.concat((sr for _ in range(3)))\n","repo_name":"unionai-oss/pandera","sub_path":"tests/mypy/modules/pandas_concat.py","file_name":"pandas_concat.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":2685,"dataset":"github-code","pt":"75"} +{"seq_id":"36435443929","text":"import datetime\nimport unittest\n\nfrom airflow import DAG, configuration\nfrom airflow.contrib.operators.sqoop_operator import SqoopOperator\nfrom airflow.exceptions import AirflowException\n\n\nclass TestSqoopOperator(unittest.TestCase):\n _config = {\n 'conn_id': 'sqoop_default',\n 'cmd_type': 'export',\n 'table': 'target_table',\n 'query': 'SELECT * FROM schema.table',\n 'target_dir': '/path/on/hdfs/to/import',\n 'append': True,\n 'file_type': 'avro',\n 'columns': 'a,b,c',\n 'num_mappers': 22,\n 'split_by': 'id',\n 'export_dir': '/path/on/hdfs/to/export',\n 'input_null_string': '\\n',\n 'input_null_non_string': '\\t',\n 'staging_table': 'target_table_staging',\n 'clear_staging_table': True,\n 'enclosed_by': '\"',\n 'escaped_by': '\\\\',\n 'input_fields_terminated_by': '|',\n 'input_lines_terminated_by': '\\n',\n 'input_optionally_enclosed_by': '\"',\n 'batch': True,\n 'relaxed_isolation': True,\n 'direct': True,\n 'driver': 'com.microsoft.jdbc.sqlserver.SQLServerDriver',\n 'create_hcatalog_table': True,\n 'hcatalog_database': 'hive_database',\n 'hcatalog_table': 'hive_table',\n 'properties': {\n 'mapred.map.max.attempts': '1'\n },\n 'extra_import_options': {\n 'hcatalog-storage-stanza': \"\\\"stored as orcfile\\\"\",\n 'show': ''\n },\n 'extra_export_options': {\n 'update-key': 'id',\n 'update-mode': 'allowinsert'\n }\n }\n\n def setUp(self):\n configuration.load_test_config()\n args = {\n 'owner': 'airflow',\n 'start_date': datetime.datetime(2017, 1, 1)\n }\n self.dag = DAG('test_dag_id', default_args=args)\n\n def test_execute(self):\n \"\"\"\n Tests to verify values of the SqoopOperator match that passed in from the config.\n \"\"\"\n operator = SqoopOperator(\n task_id='sqoop_job',\n dag=self.dag,\n **self._config\n )\n\n self.assertEqual(self._config['conn_id'], operator.conn_id)\n self.assertEqual(self._config['query'], operator.query)\n self.assertEqual(self._config['cmd_type'], operator.cmd_type)\n self.assertEqual(self._config['table'], operator.table)\n self.assertEqual(self._config['target_dir'], operator.target_dir)\n self.assertEqual(self._config['append'], operator.append)\n self.assertEqual(self._config['file_type'], operator.file_type)\n self.assertEqual(self._config['num_mappers'], operator.num_mappers)\n self.assertEqual(self._config['split_by'], operator.split_by)\n self.assertEqual(self._config['input_null_string'], operator.input_null_string)\n self.assertEqual(self._config['input_null_non_string'], operator.input_null_non_string)\n self.assertEqual(self._config['staging_table'], operator.staging_table)\n self.assertEqual(self._config['clear_staging_table'], operator.clear_staging_table)\n self.assertEqual(self._config['batch'], operator.batch)\n self.assertEqual(self._config['relaxed_isolation'], operator.relaxed_isolation)\n self.assertEqual(self._config['direct'], operator.direct)\n self.assertEqual(self._config['driver'], operator.driver)\n self.assertEqual(self._config['properties'], operator.properties)\n self.assertEqual(self._config['hcatalog_database'], operator.hcatalog_database)\n self.assertEqual(self._config['hcatalog_table'], operator.hcatalog_table)\n self.assertEqual(self._config['create_hcatalog_table'], operator.create_hcatalog_table)\n self.assertEqual(self._config['extra_import_options'], operator.extra_import_options)\n self.assertEqual(self._config['extra_export_options'], operator.extra_export_options)\n\n # the following are meant to be more of examples\n SqoopOperator(\n task_id='sqoop_import_using_table',\n cmd_type='import',\n conn_id='sqoop_default',\n table='company',\n verbose=True,\n num_mappers=8,\n hcatalog_database='default',\n hcatalog_table='import_table_1',\n create_hcatalog_table=True,\n extra_import_options={'hcatalog-storage-stanza': \"\\\"stored as orcfile\\\"\"},\n dag=self.dag\n )\n\n SqoopOperator(\n task_id='sqoop_import_using_query',\n cmd_type='import',\n conn_id='sqoop_default',\n query='select name, age from company where $CONDITIONS',\n split_by='age',\n # the mappers will pass in values to the $CONDITIONS based on the field you select to split by\n verbose=True,\n num_mappers=None,\n hcatalog_database='default',\n hcatalog_table='import_table_2',\n create_hcatalog_table=True,\n extra_import_options={'hcatalog-storage-stanza': \"\\\"stored as orcfile\\\"\"},\n dag=self.dag\n )\n\n SqoopOperator(\n task_id='sqoop_import_with_partition',\n cmd_type='import',\n conn_id='sqoop_default',\n table='company',\n verbose=True,\n num_mappers=None,\n hcatalog_database='default',\n hcatalog_table='import_table_3',\n create_hcatalog_table=True,\n extra_import_options={\n 'hcatalog-storage-stanza': \"\\\"stored as orcfile\\\"\",\n 'hive-partition-key': 'day',\n 'hive-partition-value': '2017-10-18'},\n dag=self.dag\n )\n\n SqoopOperator(\n task_id='sqoop_export_tablename',\n cmd_type='export',\n conn_id='sqoop_default',\n table='rbdms_export_table_1',\n verbose=True,\n num_mappers=None,\n hcatalog_database='default',\n hcatalog_table='hive_export_table_1',\n extra_export_options=None,\n dag=self.dag\n )\n\n SqoopOperator(\n task_id='sqoop_export_tablepath',\n cmd_type='export',\n conn_id='sqoop_default',\n table='rbdms_export_table_2',\n export_dir='/user/hive/warehouse/export_table_2',\n direct=True, # speeds up for data transfer\n verbose=True,\n num_mappers=None,\n extra_export_options=None,\n dag=self.dag\n )\n\n def test_invalid_cmd_type(self):\n \"\"\"\n Tests to verify if the cmd_type is not import or export, an exception is raised.\n \"\"\"\n operator = SqoopOperator(task_id='sqoop_job', dag=self.dag,\n cmd_type='invalid')\n with self.assertRaises(AirflowException):\n operator.execute({})\n\n def test_invalid_import_options(self):\n \"\"\"\n Tests to verify if a user passes both a query and a table then an exception is raised.\n \"\"\"\n import_query_and_table_configs = self._config.copy()\n import_query_and_table_configs['cmd_type'] = 'import'\n operator = SqoopOperator(\n task_id='sqoop_job',\n dag=self.dag,\n **import_query_and_table_configs\n )\n with self.assertRaises(AirflowException):\n operator.execute({})\n\n\nif __name__ == '__main__':\n unittest.main()\n","repo_name":"BigDataMatrix/DataPipeline","sub_path":"tests/contrib/operators/test_sqoop_operator.py","file_name":"test_sqoop_operator.py","file_ext":"py","file_size_in_byte":7398,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"75"} +{"seq_id":"17120770492","text":"#coding:utf-8\nimport unittest\nimport time\nfrom Model.BaseSet import Helper\nfrom Model.baseNumber import DataHlper\nfrom ways.Systest import SysPage\nfrom ways.wms import wmsPage\n\nclass Store(Helper, DataHlper, wmsPage, SysPage):\n def test_001(self):\n '''不勾选店铺，是否可以批量启用'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickBatchStore()\n self.clickStartStore()\n self.assertEqual(u'请选择需要启用的店铺', self.getPrompt())\n\n def test_002(self):\n '''不勾选店铺，是否可以批量停用'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickBatchStore()\n self.clickEndStore()\n self.assertEqual(u'请选择需要停用的店铺', self.getPrompt())\n\n def test_003(self):\n '''勾选店铺，批量停用是否有效'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickBatchStore()\n self.clickAllSelectStore()\n self.clickBatchStore()\n self.clickEndStore()\n time.sleep(1)\n self.assertEqual(u'无效', self.checkStateStore())\n\n def test_004(self):\n '''勾选店铺，批量启用是否有效'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickBatchStore()\n self.clickAllSelectStore()\n self.clickBatchStore()\n self.clickStartStore()\n time.sleep(1)\n self.assertEqual(u'有效', self.checkStateStore())\n\n def test_005(self):\n '''验证编辑店铺页面，显示店铺残次类型'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickEditButtonStore()\n self.assertEqual(u'店铺残次类型设置', self.getDefectTypeStore())\n\n def test_006(self):\n '''验证编辑店铺页面，显示店铺残次原因'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickEditButtonStore()\n self.assertEqual(u'店铺残次原因设置', self.getDefectReasonStore())\n\n def test_007(self):\n '''验证编辑店铺页面，显示物理仓列表'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickEditButtonStore()\n self.assertEqual(u'物理仓列表', self.getWarehouseStore())\n\n def test_008(self):\n '''验证单个停用'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickSingleEditStoreButton()\n self.clickSingleEndStore()\n self.assertEqual(u'无效', self.checkStateStore())\n\n def test_009(self):\n '''验证单个启用'''\n self.login(self.readFile(1), self.readFile(2))\n self.clickBaseMenue()\n self.clickBaseStore()\n self.clickSingleEditStoreButton()\n self.clickSingleStartStore()\n self.assertEqual(u'有效', self.checkStateStore())\n\n\nif __name__ == '__main__':\n suite = unittest.TestSuite(unittest.makeSuite(Store))\n unittest.TextTestRunner(verbosity=2).run(suite)\n","repo_name":"ZhangShuqian/webdriverHq","sub_path":"Testcase/test_store.py","file_name":"test_store.py","file_ext":"py","file_size_in_byte":3423,"program_lang":"python","lang":"fa","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"19202556110","text":"import sys\n\ninput = sys.stdin.readline\n\nX = input().rstrip()\n\nnum_string = \"\"\nN = len(X)\nfor i in range(1, (len(X) + 1) * 10):\n num_string += str(i)\n\nfind = 0\ni = 0\nj = 0\nwhile find != len(X):\n if num_string[i] == X[j]:\n find += 1\n j += 1\n i += 1\n\ncur_idx = 0\ncur_num = 1\nwhile cur_idx < i:\n cur_idx += len(str(cur_num))\n cur_num += 1\nprint(cur_num - 1)\n","repo_name":"B2SIC/CodeStorage","sub_path":"백준/1515.py","file_name":"1515.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"18940466870","text":"\"\"\"\nGiven an array of integers, remove the duplicate numbers in it.\n\nYou should:\n\n1) Do it in place in the array.\n2) Move the unique numbers to the front of the array.\n3) Return the total number of the unique numbers.\nExample\nExample 1:\n\nInput:\nnums = [1,3,1,4,4,2]\nOutput:\n[1,3,4,2,?,?]\n4\nExplanation:\n\n1) Move duplicate integers to the tail of nums => nums = [1,3,4,2,?,?].\n2) Return the number of unique integers in nums => 4.\nActually we don't care about what you place in ?, we only care about the part which has no duplicate integers.\nExample 2:\n\nInput:\nnums = [1,2,3]\nOutput:\n[1,2,3]\n3\n\n\"\"\"\nclass Solution:\n \"\"\"\n @param nums: an array of integers\n @return: the number of unique integers\n \"\"\"\n def deduplication(self, nums):\n\n left, right = 0, 0\n\n #need to create visited set so we can compare the elements\n visited, duplicates = set(), []\n\n\n #start moving the right pointer\n while right < len(nums):\n #if the current element at the right pointer is not in the visited set then add it to the visited set\n if nums[right] not in visited:\n visited.add(nums[right])\n #checks if numbers arent the same. if they are the same do nothing and increase the left pointer to one more, otherwise set the left to th the right pointers element and then increment the left\n if nums[left] != nums[right]:\n nums[left] = nums[right]\n left += 1\n else:\n #if its in the visited set just append it to the duplicate list\n duplicates.append(nums[right])\n\n #after the rest of the codes executes increment the right side by one\n right += 1\n\n #since out left pointer is already set at the elements that arrent replaced we can add a placeholder for that variable to return at the end\n position = left\n\n #while the left pointer isnt at the end of the list iterate over the duplicate list and check each left eleemnt one at a time with the current element and then increment the left\n while left < len(nums):\n for i in duplicates:\n nums[left] = i\n left += 1\n\n return position\n","repo_name":"charlessokolowski/Problems","sub_path":"Two Pointers/remove_duplicates.py","file_name":"remove_duplicates.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"23531670101","text":"from PyQt5 import QtCore, QtGui, QtWidgets\n\n\nclass Ui_MainWindow(object):\n def setupUi(self, MainWindow):\n # Main window\n MainWindow.setObjectName(\"MainWindow\")\n MainWindow.resize(892, 640)\n MainWindow.setMinimumSize(QtCore.QSize(892, 640))\n MainWindow.setMaximumSize(QtCore.QSize(892, 640))\n MainWindow.setToolButtonStyle(QtCore.Qt.ToolButtonIconOnly)\n self.centralwidget = QtWidgets.QWidget(MainWindow)\n self.centralwidget.setObjectName(\"centralwidget\")\n\n # Tabs hub \n self.tabHub = QtWidgets.QTabWidget(self.centralwidget)\n self.tabHub.setEnabled(True)\n self.tabHub.setGeometry(QtCore.QRect(10, 80, 871, 481))\n font = QtGui.QFont()\n font.setPointSize(17)\n self.tabHub.setFont(font)\n self.tabHub.setTabShape(QtWidgets.QTabWidget.Rounded)\n self.tabHub.setObjectName(\"tabHub\")\n\n\n # - - - - - - - - TAB LOCATION - - - - - - - - #\n self.tab_location = QtWidgets.QWidget()\n self.tab_location.setObjectName(\"tab_location\")\n\n # LineEdit location\n self.loc_lineEdit = QtWidgets.QLineEdit(self.tab_location)\n self.loc_lineEdit.setGeometry(QtCore.QRect(120, 60, 301, 51))\n self.loc_lineEdit.setObjectName(\"loc_lineEdit\")\n\n # Button search\n self.loc_button_search = QtWidgets.QPushButton(self.tab_location)\n self.loc_button_search.setGeometry(QtCore.QRect(540, 60, 191, 51))\n self.loc_button_search.setObjectName(\"loc_button_search\")\n\n # Label Enter location\n self.loc_label_enter_location = QtWidgets.QLabel(self.tab_location)\n self.loc_label_enter_location.setGeometry(QtCore.QRect(120, 30, 301, 20))\n font = QtGui.QFont()\n font.setPointSize(13)\n self.loc_label_enter_location.setFont(font)\n self.loc_label_enter_location.setAlignment(QtCore.Qt.AlignCenter)\n self.loc_label_enter_location.setObjectName(\"loc_label_enter_location\")\n\n # Frame choose location\n self.loc_frame_choose = QtWidgets.QFrame(self.tab_location)\n self.loc_frame_choose.setGeometry(QtCore.QRect(110, 210, 631, 181))\n self.loc_frame_choose.setFrameShape(QtWidgets.QFrame.WinPanel)\n self.loc_frame_choose.setFrameShadow(QtWidgets.QFrame.Raised)\n self.loc_frame_choose.setObjectName(\"loc_frame_choose\")\n\n # Combobox location\n self.loc_combo = QtWidgets.QComboBox(self.loc_frame_choose)\n self.loc_combo.setGeometry(QtCore.QRect(10, 60, 301, 51))\n self.loc_combo.setObjectName(\"loc_combo\")\n font = QtGui.QFont()\n font.setPointSize(13)\n self.loc_combo.setFont(font)\n\n # Button OK\n self.loc_button_OK = QtWidgets.QPushButton(self.loc_frame_choose)\n self.loc_button_OK.setGeometry(QtCore.QRect(430, 60, 191, 51))\n self.loc_button_OK.setObjectName(\"loc_button_OK\")\n\n # Label choose location\n self.loc_label_choose_location = QtWidgets.QLabel(self.loc_frame_choose)\n self.loc_label_choose_location.setGeometry(QtCore.QRect(10, 30, 301, 20))\n font = QtGui.QFont()\n font.setPointSize(13)\n self.loc_label_choose_location.setFont(font)\n self.loc_label_choose_location.setAlignment(QtCore.Qt.AlignCenter)\n self.loc_label_choose_location.setObjectName(\"loc_label_choose_location\")\n\n # Horizontal line\n self.loc_line = QtWidgets.QFrame(self.tab_location)\n self.loc_line.setGeometry(QtCore.QRect(-10, 150, 891, 20))\n self.loc_line.setFrameShape(QtWidgets.QFrame.HLine)\n self.loc_line.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.loc_line.setObjectName(\"loc_line\")\n\n\n # - - - - - - - - TAB CURRENT WEATHER - - - - - - - - #\n self.tabHub.addTab(self.tab_location, \"\")\n self.tab_current = QtWidgets.QWidget()\n self.tab_current.setEnabled(True)\n self.tab_current.setObjectName(\"tab_current\")\n\n # - - - - Frame temperature\n self.curr_frame_temp = QtWidgets.QFrame(self.tab_current)\n self.curr_frame_temp.setGeometry(QtCore.QRect(10, 50, 381, 181))\n self.curr_frame_temp.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_frame_temp.setFrameShadow(QtWidgets.QFrame.Raised)\n self.curr_frame_temp.setObjectName(\"curr_frame_temp\")\n\n # Label temperature\n self.curr_label_temp = QtWidgets.QLabel(self.curr_frame_temp)\n self.curr_label_temp.setGeometry(QtCore.QRect(0, 30, 211, 81))\n font = QtGui.QFont()\n font.setPointSize(61)\n self.curr_label_temp.setFont(font)\n self.curr_label_temp.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_temp.setObjectName(\"curr_label_temp\")\n\n # Label temperature title frame\n self.curr_label_temp_title = QtWidgets.QLabel(self.curr_frame_temp)\n self.curr_label_temp_title.setGeometry(QtCore.QRect(10, 10, 141, 16))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.curr_label_temp_title.setFont(font)\n self.curr_label_temp_title.setObjectName(\"curr_label_temp_title\")\n\n # Label temperature more info\n self.curr_label_temp_more = QtWidgets.QLabel(self.curr_frame_temp)\n self.curr_label_temp_more.setGeometry(QtCore.QRect(240, 120, 141, 61))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.curr_label_temp_more.setFont(font)\n self.curr_label_temp_more.setScaledContents(False)\n self.curr_label_temp_more.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignTop)\n self.curr_label_temp_more.setWordWrap(False)\n self.curr_label_temp_more.setObjectName(\"curr_label_temp_more\")\n\n # Label temperature description\n self.curr_label_temp_description = QtWidgets.QLabel(self.curr_frame_temp)\n self.curr_label_temp_description.setGeometry(QtCore.QRect(30, 120, 161, 21))\n font = QtGui.QFont()\n font.setPointSize(13)\n self.curr_label_temp_description.setFont(font)\n self.curr_label_temp_description.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_temp_description.setObjectName(\"curr_label_temp_description\")\n\n # Icon current weather\n self.curr_icon_temp = QtWidgets.QLabel(self.curr_frame_temp)\n self.curr_icon_temp.setGeometry(QtCore.QRect(250, 10, 101, 101))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_temp.setFont(font)\n self.curr_icon_temp.setText(\"\")\n self.curr_icon_temp.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_temp.setScaledContents(True)\n self.curr_icon_temp.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_temp.setObjectName(\"curr_icon_temp\")\n\n # - - - - Frame wind\n self.curr_frame_wind = QtWidgets.QFrame(self.tab_current)\n self.curr_frame_wind.setGeometry(QtCore.QRect(470, 50, 381, 181))\n self.curr_frame_wind.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_frame_wind.setFrameShadow(QtWidgets.QFrame.Raised)\n self.curr_frame_wind.setObjectName(\"curr_frame_wind\")\n\n # Label wind speed\n self.curr_label_wind_speed = QtWidgets.QLabel(self.curr_frame_wind)\n self.curr_label_wind_speed.setGeometry(QtCore.QRect(20, 50, 161, 61))\n font = QtGui.QFont()\n font.setPointSize(30)\n self.curr_label_wind_speed.setFont(font)\n self.curr_label_wind_speed.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_wind_speed.setObjectName(\"curr_label_wind_speed\")\n\n # Label wind title\n self.curr_label_wind_title = QtWidgets.QLabel(self.curr_frame_wind)\n self.curr_label_wind_title.setGeometry(QtCore.QRect(10, 10, 141, 16))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.curr_label_wind_title.setFont(font)\n self.curr_label_wind_title.setObjectName(\"curr_label_wind_title\")\n\n # Label wind direction\n self.curr_label_wind_dir = QtWidgets.QLabel(self.curr_frame_wind)\n self.curr_label_wind_dir.setGeometry(QtCore.QRect(260, 50, 111, 61))\n font = QtGui.QFont()\n font.setPointSize(30)\n self.curr_label_wind_dir.setFont(font)\n self.curr_label_wind_dir.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_wind_dir.setObjectName(\"curr_label_wind_dir\")\n\n # Label wind more info\n self.curr_label_wind_more = QtWidgets.QLabel(self.curr_frame_wind)\n self.curr_label_wind_more.setGeometry(QtCore.QRect(20, 114, 161, 41))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.curr_label_wind_more.setFont(font)\n self.curr_label_wind_more.setAlignment(QtCore.Qt.AlignHCenter|QtCore.Qt.AlignTop)\n self.curr_label_wind_more.setObjectName(\"curr_label_wind_more\")\n\n # Label wind cardinal point\n self.curr_label_wind_cardinal = QtWidgets.QLabel(self.curr_frame_wind)\n self.curr_label_wind_cardinal.setGeometry(QtCore.QRect(248, 120, 131, 21))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.curr_label_wind_cardinal.setFont(font)\n self.curr_label_wind_cardinal.setAlignment(QtCore.Qt.AlignHCenter|QtCore.Qt.AlignTop)\n self.curr_label_wind_cardinal.setObjectName(\"curr_label_wind_cardinal\")\n \n # - - - - Frame more info\n self.curr_frame_more_info = QtWidgets.QFrame(self.tab_current)\n self.curr_frame_more_info.setGeometry(QtCore.QRect(10, 240, 381, 181))\n self.curr_frame_more_info.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_frame_more_info.setFrameShadow(QtWidgets.QFrame.Raised)\n self.curr_frame_more_info.setObjectName(\"curr_frame_more_info\")\n \n # Label more info title\n self.curr_label_more_title = QtWidgets.QLabel(self.curr_frame_more_info)\n self.curr_label_more_title.setGeometry(QtCore.QRect(10, 10, 141, 16))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.curr_label_more_title.setFont(font)\n self.curr_label_more_title.setObjectName(\"curr_label_more_title\")\n \n # Label more : pressure ...\n self.curr_label_more_info_press = QtWidgets.QLabel(self.curr_frame_more_info)\n self.curr_label_more_info_press.setGeometry(QtCore.QRect(30, 50, 141, 101))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.curr_label_more_info_press.setFont(font)\n self.curr_label_more_info_press.setScaledContents(False)\n self.curr_label_more_info_press.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignTop)\n self.curr_label_more_info_press.setWordWrap(False)\n self.curr_label_more_info_press.setObjectName(\"curr_label_more_info_press\")\n \n # Vertical line\n self.curr_line_more_info = QtWidgets.QFrame(self.curr_frame_more_info)\n self.curr_line_more_info.setGeometry(QtCore.QRect(180, 54, 20, 81))\n self.curr_line_more_info.setFrameShape(QtWidgets.QFrame.VLine)\n self.curr_line_more_info.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.curr_line_more_info.setObjectName(\"curr_line_more_info\")\n \n # Label sunrise/sunset\n self.curr_label_sun = QtWidgets.QLabel(self.curr_frame_more_info)\n self.curr_label_sun.setGeometry(QtCore.QRect(230, 60, 111, 61))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.curr_label_sun.setFont(font)\n self.curr_label_sun.setScaledContents(False)\n self.curr_label_sun.setAlignment(QtCore.Qt.AlignLeading|QtCore.Qt.AlignLeft|QtCore.Qt.AlignTop)\n self.curr_label_sun.setWordWrap(False)\n self.curr_label_sun.setObjectName(\"curr_label_sun\")\n\n # - - - - Frame next hours\n self.curr_frame_next_hours = QtWidgets.QFrame(self.tab_current)\n self.curr_frame_next_hours.setGeometry(QtCore.QRect(470, 240, 381, 181))\n self.curr_frame_next_hours.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_frame_next_hours.setFrameShadow(QtWidgets.QFrame.Raised)\n self.curr_frame_next_hours.setObjectName(\"curr_frame_next_hours\")\n \n # Label next hours title\n self.label_next_hours_title = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.label_next_hours_title.setGeometry(QtCore.QRect(10, 10, 141, 16))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.label_next_hours_title.setFont(font)\n self.label_next_hours_title.setObjectName(\"label_next_hours_title\")\n \n # Icon H1\n self.curr_icon_h1 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_icon_h1.setGeometry(QtCore.QRect(12, 70, 51, 51))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_h1.setFont(font)\n self.curr_icon_h1.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_icon_h1.setText(\"\")\n self.curr_icon_h1.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_h1.setScaledContents(True)\n self.curr_icon_h1.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_h1.setObjectName(\"curr_icon_h1\")\n\n # Icon H2\n self.curr_icon_h2 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_icon_h2.setGeometry(QtCore.QRect(75, 70, 51, 51))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_h2.setFont(font)\n self.curr_icon_h2.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_icon_h2.setText(\"\")\n self.curr_icon_h2.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_h2.setScaledContents(True)\n self.curr_icon_h2.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_h2.setObjectName(\"curr_icon_h2\")\n\n # Icon H3\n self.curr_icon_h3 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_icon_h3.setGeometry(QtCore.QRect(136, 70, 51, 51))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_h3.setFont(font)\n self.curr_icon_h3.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_icon_h3.setText(\"\")\n self.curr_icon_h3.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_h3.setScaledContents(True)\n self.curr_icon_h3.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_h3.setObjectName(\"curr_icon_h3\")\n\n # Icon H4\n self.curr_icon_h4 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_icon_h4.setGeometry(QtCore.QRect(197, 70, 51, 51))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_h4.setFont(font)\n self.curr_icon_h4.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_icon_h4.setText(\"\")\n self.curr_icon_h4.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_h4.setScaledContents(True)\n self.curr_icon_h4.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_h4.setObjectName(\"curr_icon_h4\")\n\n # Icon H5\n self.curr_icon_h5 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_icon_h5.setGeometry(QtCore.QRect(257, 70, 51, 51))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_h5.setFont(font)\n self.curr_icon_h5.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_icon_h5.setText(\"\")\n self.curr_icon_h5.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_h5.setScaledContents(True)\n self.curr_icon_h5.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_h5.setObjectName(\"curr_icon_h5\")\n\n # Icon H6\n self.curr_icon_h6 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_icon_h6.setGeometry(QtCore.QRect(317, 70, 51, 51))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.curr_icon_h6.setFont(font)\n self.curr_icon_h6.setFrameShape(QtWidgets.QFrame.Box)\n self.curr_icon_h6.setText(\"\")\n self.curr_icon_h6.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.curr_icon_h6.setScaledContents(True)\n self.curr_icon_h6.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_icon_h6.setObjectName(\"curr_icon_h6\")\n\n # Label H1\n self.curr_label_h1 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h1.setGeometry(QtCore.QRect(14, 50, 47, 13))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h1.setFont(font)\n self.curr_label_h1.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h1.setObjectName(\"curr_label_h1\")\n \n # Label H2\n self.curr_label_h2 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h2.setGeometry(QtCore.QRect(76, 50, 47, 13))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h2.setFont(font)\n self.curr_label_h2.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h2.setObjectName(\"curr_label_h2\")\n\n # Label H3\n self.curr_label_h3 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h3.setGeometry(QtCore.QRect(138, 50, 47, 13))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h3.setFont(font)\n self.curr_label_h3.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h3.setObjectName(\"curr_label_h3\")\n\n # Label H4\n self.curr_label_h4 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h4.setGeometry(QtCore.QRect(200, 50, 47, 13))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h4.setFont(font)\n self.curr_label_h4.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h4.setObjectName(\"curr_label_h4\")\n\n # Label H5\n self.curr_label_h5 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h5.setGeometry(QtCore.QRect(260, 50, 47, 13))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h5.setFont(font)\n self.curr_label_h5.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h5.setObjectName(\"curr_label_h5\")\n\n # Label H6\n self.curr_label_h6 = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h6.setGeometry(QtCore.QRect(320, 50, 47, 13))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h6.setFont(font)\n self.curr_label_h6.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h6.setObjectName(\"curr_label_h6\")\n\n # Label H1 weather\n self.curr_label_h1_info = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h1_info.setGeometry(QtCore.QRect(15, 125, 47, 31))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h1_info.setFont(font)\n self.curr_label_h1_info.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h1_info.setObjectName(\"curr_label_h1_info\")\n \n # Label H2 weather\n self.curr_label_h2_info = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h2_info.setGeometry(QtCore.QRect(77, 125, 47, 31))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h2_info.setFont(font)\n self.curr_label_h2_info.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h2_info.setObjectName(\"curr_label_h2_info\")\n \n # Label H3 weather\n self.curr_label_h3_info = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h3_info.setGeometry(QtCore.QRect(140, 125, 47, 31))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h3_info.setFont(font)\n self.curr_label_h3_info.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h3_info.setObjectName(\"curr_label_h3_info\")\n \n # Label H4 weather\n self.curr_label_h4_info = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h4_info.setGeometry(QtCore.QRect(200, 125, 47, 31))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h4_info.setFont(font)\n self.curr_label_h4_info.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h4_info.setObjectName(\"curr_label_h4_info\")\n \n # Label H5 weather\n self.curr_label_h5_info = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h5_info.setGeometry(QtCore.QRect(262, 125, 47, 31))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h5_info.setFont(font)\n self.curr_label_h5_info.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h5_info.setObjectName(\"curr_label_h5_info\")\n \n # Label H6 weather\n self.curr_label_h6_info = QtWidgets.QLabel(self.curr_frame_next_hours)\n self.curr_label_h6_info.setGeometry(QtCore.QRect(322, 125, 47, 31))\n font = QtGui.QFont()\n font.setPointSize(8)\n self.curr_label_h6_info.setFont(font)\n self.curr_label_h6_info.setAlignment(QtCore.Qt.AlignCenter)\n self.curr_label_h6_info.setObjectName(\"curr_label_h6_info\")\n \n # Label recap (on main tab)\n self.curr_label_recap = QtWidgets.QLabel(self.tab_current)\n self.curr_label_recap.setGeometry(QtCore.QRect(10, 10, 680, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.curr_label_recap.setFont(font)\n self.curr_label_recap.setObjectName(\"curr_label_recap\")\n\n\n # - - - - - - - - TAB FORECAST - - - - - - - - #\n self.tabHub.addTab(self.tab_current, \"\")\n self.tab_forecast = QtWidgets.QWidget()\n self.tab_forecast.setObjectName(\"tab_forecast\")\n\n # - - - - FRAME TODAY - - - -\n self.for_frame_today = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_today.setGeometry(QtCore.QRect(20, 10, 401, 91))\n self.for_frame_today.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_today.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_today.setObjectName(\"for_frame_today\")\n\n # Label today title\n self.for_label_today = QtWidgets.QLabel(self.for_frame_today)\n self.for_label_today.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_today.setFont(font)\n self.for_label_today.setObjectName(\"for_label_today\")\n\n # Label min\n self.for_label_today_min = QtWidgets.QLabel(self.for_frame_today)\n self.for_label_today_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_today_min.setFont(font)\n self.for_label_today_min.setObjectName(\"for_label_today_min\")\n \n # Label minimum temperature today\n self.for_label_today_minTemp = QtWidgets.QLabel(self.for_frame_today)\n self.for_label_today_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_today_minTemp.setFont(font)\n self.for_label_today_minTemp.setObjectName(\"for_label_today_minTemp\")\n \n # Label max\n self.for_label_today_max = QtWidgets.QLabel(self.for_frame_today)\n self.for_label_today_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_today_max.setFont(font)\n self.for_label_today_max.setObjectName(\"for_label_today_max\")\n \n # Label maximum temperature today\n self.for_label_today_maxTemp = QtWidgets.QLabel(self.for_frame_today)\n self.for_label_today_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_today_maxTemp.setFont(font)\n self.for_label_today_maxTemp.setObjectName(\"for_label_today_maxTemp\")\n \n # Label description weather\n self.for_label_today_descr = QtWidgets.QLabel(self.for_frame_today)\n self.for_label_today_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_today_descr.setFont(font)\n self.for_label_today_descr.setObjectName(\"for_label_today_descr\")\n \n # Label icon today\n self.for_icon_today = QtWidgets.QLabel(self.for_frame_today)\n self.for_icon_today.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_today.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_today.setText(\"\")\n self.for_icon_today.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_today.setScaledContents(True)\n self.for_icon_today.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_today.setObjectName(\"for_icon_today\")\n\n # - - - - FRAME DAY+1 - - - -\n self.for_frame_d1 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d1.setGeometry(QtCore.QRect(440, 10, 401, 91))\n self.for_frame_d1.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d1.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d1.setObjectName(\"for_frame_d1\")\n\n # Label day+1 title\n self.for_label_d1 = QtWidgets.QLabel(self.for_frame_d1)\n self.for_label_d1.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d1.setFont(font)\n self.for_label_d1.setObjectName(\"for_label_d1\")\n\n # Label min\n self.for_label_d1_min = QtWidgets.QLabel(self.for_frame_d1)\n self.for_label_d1_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d1_min.setFont(font)\n self.for_label_d1_min.setObjectName(\"for_label_d1_min\")\n\n # Label minimum temperature D+1\n self.for_label_d1_minTemp = QtWidgets.QLabel(self.for_frame_d1)\n self.for_label_d1_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d1_minTemp.setFont(font)\n self.for_label_d1_minTemp.setObjectName(\"for_label_d1_minTemp\")\n \n # Label max\n self.for_label_d1_max = QtWidgets.QLabel(self.for_frame_d1)\n self.for_label_d1_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d1_max.setFont(font)\n self.for_label_d1_max.setObjectName(\"for_label_d1_max\")\n\n # Label maximum temperature D+1\n self.for_label_d1_maxTemp = QtWidgets.QLabel(self.for_frame_d1)\n self.for_label_d1_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d1_maxTemp.setFont(font)\n self.for_label_d1_maxTemp.setObjectName(\"for_label_d1_maxTemp\")\n \n # Label D+1 description\n self.for_label_d1_descr = QtWidgets.QLabel(self.for_frame_d1)\n self.for_label_d1_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d1_descr.setFont(font)\n self.for_label_d1_descr.setObjectName(\"for_label_d1_descr\")\n \n # Icon D+1\n self.for_icon_d1 = QtWidgets.QLabel(self.for_frame_d1)\n self.for_icon_d1.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d1.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d1.setText(\"\")\n self.for_icon_d1.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d1.setScaledContents(True)\n self.for_icon_d1.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d1.setObjectName(\"for_icon_d1\")\n\n # - - - - FRAME D+2 - - - -\n self.for_frame_d2 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d2.setGeometry(QtCore.QRect(20, 120, 401, 91))\n self.for_frame_d2.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d2.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d2.setObjectName(\"for_frame_d2\")\n\n # Label day+2 title\n self.for_label_d2 = QtWidgets.QLabel(self.for_frame_d2)\n self.for_label_d2.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d2.setFont(font)\n self.for_label_d2.setObjectName(\"for_label_d2\")\n\n # Label min\n self.for_label_d2_min = QtWidgets.QLabel(self.for_frame_d2)\n self.for_label_d2_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d2_min.setFont(font)\n self.for_label_d2_min.setObjectName(\"for_label_d2_min\")\n\n # Label minimum temperature\n self.for_label_d2_minTemp = QtWidgets.QLabel(self.for_frame_d2)\n self.for_label_d2_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d2_minTemp.setFont(font)\n self.for_label_d2_minTemp.setObjectName(\"for_label_d2_minTemp\")\n \n # Label max\n self.for_label_d2_max = QtWidgets.QLabel(self.for_frame_d2)\n self.for_label_d2_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d2_max.setFont(font)\n self.for_label_d2_max.setObjectName(\"for_label_d2_max\")\n \n # Label maximum temperature D+2\n self.for_label_d2_maxTemp = QtWidgets.QLabel(self.for_frame_d2)\n self.for_label_d2_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d2_maxTemp.setFont(font)\n self.for_label_d2_maxTemp.setObjectName(\"for_label_d2_maxTemp\")\n \n # Label description D+2\n self.for_label_d2_descr = QtWidgets.QLabel(self.for_frame_d2)\n self.for_label_d2_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d2_descr.setFont(font)\n self.for_label_d2_descr.setObjectName(\"for_label_d2_descr\")\n \n # Icon D+2\n self.for_icon_d2 = QtWidgets.QLabel(self.for_frame_d2)\n self.for_icon_d2.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d2.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d2.setText(\"\")\n self.for_icon_d2.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d2.setScaledContents(True)\n self.for_icon_d2.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d2.setObjectName(\"for_icon_d2\")\n\n # - - - - FRAME D+3 - - - -\n self.for_frame_d3 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d3.setGeometry(QtCore.QRect(440, 120, 401, 91))\n self.for_frame_d3.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d3.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d3.setObjectName(\"for_frame_d3\")\n \n # Label D+3 title\n self.for_label_d3 = QtWidgets.QLabel(self.for_frame_d3)\n self.for_label_d3.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d3.setFont(font)\n self.for_label_d3.setObjectName(\"for_label_d3\")\n\n # Label min\n self.for_label_d3_min = QtWidgets.QLabel(self.for_frame_d3)\n self.for_label_d3_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d3_min.setFont(font)\n self.for_label_d3_min.setObjectName(\"for_label_d3_min\")\n\n # Label minimum temperature D+3\n self.for_label_d3_minTemp = QtWidgets.QLabel(self.for_frame_d3)\n self.for_label_d3_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d3_minTemp.setFont(font)\n self.for_label_d3_minTemp.setObjectName(\"for_label_d3_minTemp\")\n \n # Label max\n self.for_label_d3_max = QtWidgets.QLabel(self.for_frame_d3)\n self.for_label_d3_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d3_max.setFont(font)\n self.for_label_d3_max.setObjectName(\"for_label_d3_max\")\n \n # Label maximum temperature D+3\n self.for_label_d3_maxTemp = QtWidgets.QLabel(self.for_frame_d3)\n self.for_label_d3_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d3_maxTemp.setFont(font)\n self.for_label_d3_maxTemp.setObjectName(\"for_label_d3_maxTemp\")\n \n # Label description D+3\n self.for_label_d3_descr = QtWidgets.QLabel(self.for_frame_d3)\n self.for_label_d3_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d3_descr.setFont(font)\n self.for_label_d3_descr.setObjectName(\"for_label_d3_descr\")\n \n # Icon D+3\n self.for_icon_d3 = QtWidgets.QLabel(self.for_frame_d3)\n self.for_icon_d3.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d3.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d3.setText(\"\")\n self.for_icon_d3.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d3.setScaledContents(True)\n self.for_icon_d3.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d3.setObjectName(\"for_icon_d3\")\n\n # - - - - FRAME D+4 - - - -\n self.for_frame_d4 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d4.setGeometry(QtCore.QRect(20, 230, 401, 91))\n self.for_frame_d4.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d4.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d4.setObjectName(\"for_frame_d4\")\n\n # Label D+4 title\n self.for_label_d4 = QtWidgets.QLabel(self.for_frame_d4)\n self.for_label_d4.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d4.setFont(font)\n self.for_label_d4.setObjectName(\"for_label_d4\")\n\n # Label min\n self.for_label_d4_min = QtWidgets.QLabel(self.for_frame_d4)\n self.for_label_d4_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d4_min.setFont(font)\n self.for_label_d4_min.setObjectName(\"for_label_d4_min\")\n\n # Label minimum temperature D+4\n self.for_label_d4_minTemp = QtWidgets.QLabel(self.for_frame_d4)\n self.for_label_d4_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d4_minTemp.setFont(font)\n self.for_label_d4_minTemp.setObjectName(\"for_label_d4_minTemp\")\n \n # Label max\n self.for_label_d4_max = QtWidgets.QLabel(self.for_frame_d4)\n self.for_label_d4_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d4_max.setFont(font)\n self.for_label_d4_max.setObjectName(\"for_label_d4_max\")\n\n # Label maximum temperature D+4\n self.for_label_d4_maxTemp = QtWidgets.QLabel(self.for_frame_d4)\n self.for_label_d4_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d4_maxTemp.setFont(font)\n self.for_label_d4_maxTemp.setObjectName(\"for_label_d4_maxMin\")\n \n # Label D+4 description\n self.for_label_d4_descr = QtWidgets.QLabel(self.for_frame_d4)\n self.for_label_d4_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d4_descr.setFont(font)\n self.for_label_d4_descr.setObjectName(\"for_label_d4_descr\")\n \n # Icon D+4\n self.for_icon_d4 = QtWidgets.QLabel(self.for_frame_d4)\n self.for_icon_d4.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d4.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d4.setText(\"\")\n self.for_icon_d4.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d4.setScaledContents(True)\n self.for_icon_d4.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d4.setObjectName(\"for_icon_d4\")\n\n # - - - - FRAME D+5 - - - -\n self.for_frame_d5 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d5.setGeometry(QtCore.QRect(440, 230, 401, 91))\n self.for_frame_d5.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d5.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d5.setObjectName(\"for_frame_d5\")\n\n # Label D+5 title\n self.for_label_d5 = QtWidgets.QLabel(self.for_frame_d5)\n self.for_label_d5.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d5.setFont(font)\n self.for_label_d5.setObjectName(\"for_label_d5\")\n\n # Label min\n self.for_label_d5_min = QtWidgets.QLabel(self.for_frame_d5)\n self.for_label_d5_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d5_min.setFont(font)\n self.for_label_d5_min.setObjectName(\"for_label_d5_min\")\n\n # Label minimum temperature D+5\n self.for_label_d5_minTemp = QtWidgets.QLabel(self.for_frame_d5)\n self.for_label_d5_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d5_minTemp.setFont(font)\n self.for_label_d5_minTemp.setObjectName(\"for_label_d5_minTemp\")\n \n # Label max\n self.for_label_d5_max = QtWidgets.QLabel(self.for_frame_d5)\n self.for_label_d5_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d5_max.setFont(font)\n self.for_label_d5_max.setObjectName(\"for_label_d5_max\")\n \n # Label maximum temperature D+5\n self.for_label_d5_maxTemp = QtWidgets.QLabel(self.for_frame_d5)\n self.for_label_d5_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d5_maxTemp.setFont(font)\n self.for_label_d5_maxTemp.setObjectName(\"for_label_d5_maxTemp\")\n \n # Label D+5 description\n self.for_label_d5_descr = QtWidgets.QLabel(self.for_frame_d5)\n self.for_label_d5_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d5_descr.setFont(font)\n self.for_label_d5_descr.setObjectName(\"for_label_d5_descr\")\n \n # Icon D+5\n self.for_icon_d5 = QtWidgets.QLabel(self.for_frame_d5)\n self.for_icon_d5.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d5.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d5.setText(\"\")\n self.for_icon_d5.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d5.setScaledContents(True)\n self.for_icon_d5.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d5.setObjectName(\"for_icon_d5\")\n\n # - - - - FRAME D+6 - - - -\n self.for_frame_d6 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d6.setGeometry(QtCore.QRect(20, 340, 401, 91))\n self.for_frame_d6.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d6.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d6.setObjectName(\"for_frame_d6\")\n\n # Label D+6 title\n self.for_label_d6 = QtWidgets.QLabel(self.for_frame_d6)\n self.for_label_d6.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d6.setFont(font)\n self.for_label_d6.setObjectName(\"for_label_d6\")\n\n # Label min\n self.for_label_d6_min = QtWidgets.QLabel(self.for_frame_d6)\n self.for_label_d6_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d6_min.setFont(font)\n self.for_label_d6_min.setObjectName(\"for_label_d6_min\")\n\n # Label minimum temperature D+6\n self.for_label_d6_minTemp = QtWidgets.QLabel(self.for_frame_d6)\n self.for_label_d6_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d6_minTemp.setFont(font)\n self.for_label_d6_minTemp.setObjectName(\"for_label_d6_minTemp\")\n \n # Label max\n self.for_label_d6_max = QtWidgets.QLabel(self.for_frame_d6)\n self.for_label_d6_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d6_max.setFont(font)\n self.for_label_d6_max.setObjectName(\"for_label_d6_max\")\n\n # Label maximum temperature D+6\n self.for_label_d6_maxTemp = QtWidgets.QLabel(self.for_frame_d6)\n self.for_label_d6_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d6_maxTemp.setFont(font)\n self.for_label_d6_maxTemp.setObjectName(\"for_label_d6_maxTemp\")\n \n # Label D+6 description\n self.for_label_d6_descr = QtWidgets.QLabel(self.for_frame_d6)\n self.for_label_d6_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d6_descr.setFont(font)\n self.for_label_d6_descr.setObjectName(\"for_label_d6_descr\")\n \n # Icon D+6\n self.for_icon_d6 = QtWidgets.QLabel(self.for_frame_d6)\n self.for_icon_d6.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d6.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d6.setText(\"\")\n self.for_icon_d6.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d6.setScaledContents(True)\n self.for_icon_d6.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d6.setObjectName(\"for_icon_d6\")\n\n # - - - - FRAME D+7 - - - -\n self.for_frame_d7 = QtWidgets.QFrame(self.tab_forecast)\n self.for_frame_d7.setGeometry(QtCore.QRect(440, 340, 401, 91))\n self.for_frame_d7.setFrameShape(QtWidgets.QFrame.Box)\n self.for_frame_d7.setFrameShadow(QtWidgets.QFrame.Raised)\n self.for_frame_d7.setObjectName(\"for_frame_d7\")\n\n # Label D+7 title\n self.for_label_d7 = QtWidgets.QLabel(self.for_frame_d7)\n self.for_label_d7.setGeometry(QtCore.QRect(10, 10, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(11)\n self.for_label_d7.setFont(font)\n self.for_label_d7.setObjectName(\"for_label_d7\")\n\n # Label min\n self.for_label_d7_min = QtWidgets.QLabel(self.for_frame_d7)\n self.for_label_d7_min.setGeometry(QtCore.QRect(20, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d7_min.setFont(font)\n self.for_label_d7_min.setObjectName(\"for_label_d7_min\")\n\n # Label minimum temperature D+7\n self.for_label_d7_minTemp = QtWidgets.QLabel(self.for_frame_d7)\n self.for_label_d7_minTemp.setGeometry(QtCore.QRect(60, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d7_minTemp.setFont(font)\n self.for_label_d7_minTemp.setObjectName(\"for_label_d7_minTemp\")\n \n # Label max\n self.for_label_d7_max = QtWidgets.QLabel(self.for_frame_d7)\n self.for_label_d7_max.setGeometry(QtCore.QRect(120, 61, 31, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d7_max.setFont(font)\n self.for_label_d7_max.setObjectName(\"for_label_d7_max\")\n\n # Label maximum temperature D+7\n self.for_label_d7_maxTemp = QtWidgets.QLabel(self.for_frame_d7)\n self.for_label_d7_maxTemp.setGeometry(QtCore.QRect(160, 49, 61, 41))\n font = QtGui.QFont()\n font.setPointSize(16)\n self.for_label_d7_maxTemp.setFont(font)\n self.for_label_d7_maxTemp.setObjectName(\"for_label_d7_maxTemp\")\n \n # Label D+7 description\n self.for_label_d7_descr = QtWidgets.QLabel(self.for_frame_d7)\n self.for_label_d7_descr.setGeometry(QtCore.QRect(223, 61, 91, 16))\n font = QtGui.QFont()\n font.setPointSize(9)\n self.for_label_d7_descr.setFont(font)\n self.for_label_d7_descr.setObjectName(\"for_label_d7_descr\")\n \n # Icon D+7\n self.for_icon_d7 = QtWidgets.QLabel(self.for_frame_d7)\n self.for_icon_d7.setGeometry(QtCore.QRect(315, 5, 81, 81))\n self.for_icon_d7.setFrameShape(QtWidgets.QFrame.Box)\n self.for_icon_d7.setText(\"\")\n self.for_icon_d7.setPixmap(QtGui.QPixmap(\"10d@4x.png\"))\n self.for_icon_d7.setScaledContents(True)\n self.for_icon_d7.setAlignment(QtCore.Qt.AlignCenter)\n self.for_icon_d7.setObjectName(\"for_icon_d7\")\n\n\n # - - - - - - - - TAB OPTION - - - - - - - - #\n self.tabHub.addTab(self.tab_forecast, \"\")\n self.tab_option = QtWidgets.QWidget()\n self.tab_option.setObjectName(\"tab_option\")\n\n # LineEdit API\n self.opt_lineEdit_api = QtWidgets.QLineEdit(self.tab_option)\n self.opt_lineEdit_api.setGeometry(QtCore.QRect(230, 20, 341, 41))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_lineEdit_api.setFont(font)\n self.opt_lineEdit_api.setObjectName(\"opt_lineEdit_api\")\n\n # Label API title\n self.opt_label_apiTitle = QtWidgets.QLabel(self.tab_option)\n self.opt_label_apiTitle.setGeometry(QtCore.QRect(140, 14, 81, 51))\n font = QtGui.QFont()\n font.setPointSize(7)\n self.opt_label_apiTitle.setFont(font)\n self.opt_label_apiTitle.setAlignment(QtCore.Qt.AlignCenter)\n self.opt_label_apiTitle.setObjectName(\"opt_label_apiTitle\")\n \n # Button verify\n self.opt_button_verify = QtWidgets.QPushButton(self.tab_option)\n self.opt_button_verify.setGeometry(QtCore.QRect(590, 20, 121, 41))\n self.opt_button_verify.setObjectName(\"opt_button_verify\")\n \n # GroupBox temperature unit\n self.opt_groupBox_tempUnit = QtWidgets.QGroupBox(self.tab_option)\n self.opt_groupBox_tempUnit.setGeometry(QtCore.QRect(140, 80, 571, 91))\n font = QtGui.QFont()\n font.setPointSize(13)\n self.opt_groupBox_tempUnit.setFont(font)\n self.opt_groupBox_tempUnit.setObjectName(\"opt_groupBox_tempUnit\")\n \n # Radio Celcius\n self.opt_radio_celcius = QtWidgets.QRadioButton(self.opt_groupBox_tempUnit)\n self.opt_radio_celcius.setGeometry(QtCore.QRect(30, 50, 101, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_radio_celcius.setFont(font)\n self.opt_radio_celcius.setChecked(True)\n self.opt_radio_celcius.setObjectName(\"opt_radio_celcius\")\n\n # Radio Kelvin\n self.opt_radio_kelvin = QtWidgets.QRadioButton(self.opt_groupBox_tempUnit)\n self.opt_radio_kelvin.setGeometry(QtCore.QRect(450, 50, 81, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_radio_kelvin.setFont(font)\n self.opt_radio_kelvin.setObjectName(\"opt_radio_kelvin\")\n\n # Radio Farhenheit\n self.opt_radio_farh = QtWidgets.QRadioButton(self.opt_groupBox_tempUnit)\n self.opt_radio_farh.setGeometry(QtCore.QRect(210, 50, 121, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_radio_farh.setFont(font)\n self.opt_radio_farh.setObjectName(\"opt_radio_farh\")\n\n # GroupBox wind unit\n self.opt_groupBox_windUnit = QtWidgets.QGroupBox(self.tab_option)\n self.opt_groupBox_windUnit.setGeometry(QtCore.QRect(140, 190, 571, 91))\n font = QtGui.QFont()\n font.setPointSize(13)\n self.opt_groupBox_windUnit.setFont(font)\n self.opt_groupBox_windUnit.setObjectName(\"opt_groupBox_windUnit\")\n \n # Radio km/h\n self.opt_radio_kmh = QtWidgets.QRadioButton(self.opt_groupBox_windUnit)\n self.opt_radio_kmh.setGeometry(QtCore.QRect(30, 50, 101, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_radio_kmh.setFont(font)\n self.opt_radio_kmh.setChecked(True)\n self.opt_radio_kmh.setObjectName(\"opt_radio_kmh\")\n\n # Radio mph\n self.opt_radio_mph = QtWidgets.QRadioButton(self.opt_groupBox_windUnit)\n self.opt_radio_mph.setGeometry(QtCore.QRect(450, 50, 81, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_radio_mph.setFont(font)\n self.opt_radio_mph.setObjectName(\"opt_radio_mph\")\n\n # Radio m/s\n self.opt_radio_ms = QtWidgets.QRadioButton(self.opt_groupBox_windUnit)\n self.opt_radio_ms.setGeometry(QtCore.QRect(260, 50, 61, 21))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_radio_ms.setFont(font)\n self.opt_radio_ms.setObjectName(\"opt_radio_ms\")\n\n # GroupBox refresh\n self.opt_groupBox_refresh = QtWidgets.QGroupBox(self.tab_option)\n self.opt_groupBox_refresh.setGeometry(QtCore.QRect(140, 300, 571, 91))\n font = QtGui.QFont()\n font.setPointSize(13)\n self.opt_groupBox_refresh.setFont(font)\n self.opt_groupBox_refresh.setObjectName(\"opt_groupBox_refresh\")\n \n # SpinBox refresh\n self.opt_spinBox_refresh = QtWidgets.QSpinBox(self.opt_groupBox_refresh)\n self.opt_spinBox_refresh.setGeometry(QtCore.QRect(113, 30, 61, 41))\n self.opt_spinBox_refresh.setAlignment(QtCore.Qt.AlignCenter)\n self.opt_spinBox_refresh.setMinimum(20)\n self.opt_spinBox_refresh.setMaximum(300)\n self.opt_spinBox_refresh.setProperty(\"value\", 20)\n self.opt_spinBox_refresh.setObjectName(\"opt_spinBox_refresh\")\n \n # Label every\n self.opt_label_every = QtWidgets.QLabel(self.opt_groupBox_refresh)\n self.opt_label_every.setGeometry(QtCore.QRect(40, 38, 61, 20))\n self.opt_label_every.setObjectName(\"opt_label_every\")\n \n # Label minutes\n self.opt_label_minutes = QtWidgets.QLabel(self.opt_groupBox_refresh)\n self.opt_label_minutes.setGeometry(QtCore.QRect(190, 40, 81, 20))\n self.opt_label_minutes.setObjectName(\"opt_label_minutes\")\n \n # Vertical line\n self.opt_lineVert = QtWidgets.QFrame(self.opt_groupBox_refresh)\n self.opt_lineVert.setGeometry(QtCore.QRect(280, 10, 20, 80))\n self.opt_lineVert.setFrameShape(QtWidgets.QFrame.VLine)\n self.opt_lineVert.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.opt_lineVert.setObjectName(\"opt_lineVert\")\n \n # Label countdown\n self.opt_label_countDown = QtWidgets.QLabel(self.opt_groupBox_refresh)\n self.opt_label_countDown.setGeometry(QtCore.QRect(330, 40, 191, 20))\n self.opt_label_countDown.setAlignment(QtCore.Qt.AlignCenter)\n self.opt_label_countDown.setObjectName(\"opt_label_countDown\")\n \n # Button Save all\n self.opt_button_saveAll = QtWidgets.QPushButton(self.tab_option)\n self.opt_button_saveAll.setGeometry(QtCore.QRect(376, 400, 111, 31))\n font = QtGui.QFont()\n font.setPointSize(14)\n self.opt_button_saveAll.setFont(font)\n self.opt_button_saveAll.setObjectName(\"opt_button_saveAll\")\n \n self.tabHub.addTab(self.tab_option, \"\")\n\n # - - - - - - - - MAIN WINDOW - - - - - - - - #\n # Label status\n self.main_label_status = QtWidgets.QLabel(self.centralwidget)\n self.main_label_status.setGeometry(QtCore.QRect(10, 580, 861, 41))\n font = QtGui.QFont()\n font.setPointSize(12)\n self.main_label_status.setFont(font)\n self.main_label_status.setFrameShape(QtWidgets.QFrame.Box)\n self.main_label_status.setFrameShadow(QtWidgets.QFrame.Raised)\n self.main_label_status.setAlignment(QtCore.Qt.AlignCenter)\n self.main_label_status.setObjectName(\"main_label_status\")\n \n # Label app title\n self.main_label_title = QtWidgets.QLabel(self.centralwidget)\n self.main_label_title.setGeometry(QtCore.QRect(10, 9, 871, 61))\n font = QtGui.QFont()\n font.setPointSize(17)\n self.main_label_title.setFont(font)\n self.main_label_title.setAlignment(QtCore.Qt.AlignCenter)\n self.main_label_title.setObjectName(\"main_label_title\")\n \n MainWindow.setCentralWidget(self.centralwidget)\n\n self.retranslateUi(MainWindow)\n self.tabHub.setCurrentIndex(2)\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"WhatsTheWeather\"))\n self.loc_button_search.setText(_translate(\"MainWindow\", \"Search\"))\n self.loc_label_enter_location.setText(_translate(\"MainWindow\", \"Enter a city name\"))\n self.loc_button_OK.setText(_translate(\"MainWindow\", \"OK\"))\n self.loc_label_choose_location.setText(_translate(\"MainWindow\", \"Choose a location\"))\n self.tabHub.setTabText(self.tabHub.indexOf(self.tab_location), _translate(\"MainWindow\", \"Location\"))\n\n\n self.curr_label_temp.setText(_translate(\"MainWindow\", \".°C\"))\n self.curr_label_temp_title.setText(_translate(\"MainWindow\", \"Temperature\"))\n self.curr_label_temp_more.setText(_translate(\"MainWindow\", \"Max temp : .°C\\n\"\n\"Min temp : .°C\\n\"\n\"Feels : .°C\"))\n self.curr_label_temp_description.setText(_translate(\"MainWindow\", \". . .\"))\n self.curr_label_wind_speed.setText(_translate(\"MainWindow\", \". km/h\"))\n self.curr_label_wind_title.setText(_translate(\"MainWindow\", \"Wind\"))\n self.curr_label_wind_dir.setText(_translate(\"MainWindow\", \".°\"))\n self.curr_label_wind_more.setText(_translate(\"MainWindow\", \"Beaufort scale : ./12\\n\"\n\"...\"))\n self.curr_label_wind_cardinal.setText(_translate(\"MainWindow\", \"...\"))\n self.curr_label_more_title.setText(_translate(\"MainWindow\", \"More info\"))\n self.curr_label_more_info_press.setText(_translate(\"MainWindow\", \"Pressure : . hP\\n\"\n\"\\n\"\n\"Visibility : . km\\n\"\n\"\\n\"\n\"Humidity : .%\"))\n self.curr_label_sun.setText(_translate(\"MainWindow\", \"Sunrise : 00:00\\n\"\n\"\\n\"\n\"Sunset : 00:00\"))\n self.label_next_hours_title.setText(_translate(\"MainWindow\", \"Next hours\"))\n self.curr_label_h1.setText(_translate(\"MainWindow\", \"00:00\"))\n self.curr_label_h2.setText(_translate(\"MainWindow\", \"00:00\"))\n self.curr_label_h3.setText(_translate(\"MainWindow\", \"00:00\"))\n self.curr_label_h4.setText(_translate(\"MainWindow\", \"00:00\"))\n self.curr_label_h5.setText(_translate(\"MainWindow\", \"00:00\"))\n self.curr_label_h6.setText(_translate(\"MainWindow\", \"00:00\"))\n self.curr_label_h1_info.setText(_translate(\"MainWindow\", \".°C\\n\"\n\". km/h\"))\n self.curr_label_h2_info.setText(_translate(\"MainWindow\", \".°C\\n\"\n\". km/h\"))\n self.curr_label_h3_info.setText(_translate(\"MainWindow\", \".°C\\n\"\n\". km/h\"))\n self.curr_label_h4_info.setText(_translate(\"MainWindow\", \".°C\\n\"\n\". km/h\"))\n self.curr_label_h5_info.setText(_translate(\"MainWindow\", \".°C\\n\"\n\". km/h\"))\n self.curr_label_h6_info.setText(_translate(\"MainWindow\", \".°C\\n\"\n\". km/h\"))\n self.curr_label_recap.setText(_translate(\"MainWindow\", \"\"))\n self.tabHub.setTabText(self.tabHub.indexOf(self.tab_current), _translate(\"MainWindow\", \"Current weather\"))\n\n\n self.for_label_today.setText(_translate(\"MainWindow\", \"Today\"))\n self.for_label_today_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_today_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_today_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_today_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_today_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.for_label_d1.setText(_translate(\"MainWindow\", \"Day +1\"))\n self.for_label_d1_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d1_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d1_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d1_maxTemp.setText(_translate(\"MainWindow\", \"...°C\"))\n self.for_label_d1_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.for_label_d2.setText(_translate(\"MainWindow\", \"Day +2\"))\n self.for_label_d2_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d2_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d2_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d2_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d2_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.for_label_d3.setText(_translate(\"MainWindow\", \"Day +3\"))\n self.for_label_d3_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d3_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d3_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d3_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d3_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.for_label_d4.setText(_translate(\"MainWindow\", \"Day +4\"))\n self.for_label_d4_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d4_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d4_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d4_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d4_descr.setText(_translate(\"MainWindow\", \".\"))\n self.for_label_d5.setText(_translate(\"MainWindow\", \"Day +5\"))\n self.for_label_d5_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d5_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d5_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d5_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d5_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.for_label_d6.setText(_translate(\"MainWindow\", \"Day +6\"))\n self.for_label_d6_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d6_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d6_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d6_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d6_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.for_label_d7.setText(_translate(\"MainWindow\", \"Day +7\"))\n self.for_label_d7_min.setText(_translate(\"MainWindow\", \"Min :\"))\n self.for_label_d7_minTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d7_max.setText(_translate(\"MainWindow\", \"Max :\"))\n self.for_label_d7_maxTemp.setText(_translate(\"MainWindow\", \".°C\"))\n self.for_label_d7_descr.setText(_translate(\"MainWindow\", \"...\"))\n self.tabHub.setTabText(self.tabHub.indexOf(self.tab_forecast), _translate(\"MainWindow\", \"Forecast\"))\n\n\n self.opt_lineEdit_api.setText(_translate(\"MainWindow\", \"1def0c78689f22035176fc71c68b106c\"))\n self.opt_label_apiTitle.setText(_translate(\"MainWindow\", \"OpenWeatherMap\\n\"\n\"API key :\"))\n self.opt_button_verify.setText(_translate(\"MainWindow\", \"Verify\"))\n self.opt_groupBox_tempUnit.setTitle(_translate(\"MainWindow\", \"Temperature unit\"))\n self.opt_radio_celcius.setText(_translate(\"MainWindow\", \"Celcius\"))\n self.opt_radio_kelvin.setText(_translate(\"MainWindow\", \"Kelvin\"))\n self.opt_radio_farh.setText(_translate(\"MainWindow\", \"Fahrenheit\"))\n self.opt_groupBox_windUnit.setTitle(_translate(\"MainWindow\", \"Wind unit\"))\n self.opt_radio_kmh.setText(_translate(\"MainWindow\", \"km/h\"))\n self.opt_radio_mph.setText(_translate(\"MainWindow\", \"mph\"))\n self.opt_radio_ms.setText(_translate(\"MainWindow\", \"m/s\"))\n self.opt_groupBox_refresh.setTitle(_translate(\"MainWindow\", \"Refresh\"))\n self.opt_label_every.setText(_translate(\"MainWindow\", \"Every :\"))\n self.opt_label_minutes.setText(_translate(\"MainWindow\", \"minutes\"))\n self.opt_label_countDown.setText(_translate(\"MainWindow\", \"Next refresh in : 00:00\"))\n self.opt_button_saveAll.setText(_translate(\"MainWindow\", \"Save all\"))\n self.tabHub.setTabText(self.tabHub.indexOf(self.tab_option), _translate(\"MainWindow\", \"Option\"))\n self.main_label_status.setText(_translate(\"MainWindow\", \"\"))\n self.main_label_title.setText(_translate(\"MainWindow\", \"WhatsTheWeather\"))\n\nif __name__ == \"__main__\":\n import sys\n app = QtWidgets.QApplication(sys.argv)\n MainWindow = QtWidgets.QMainWindow()\n ui = Ui_MainWindow()\n ui.setupUi(MainWindow)\n MainWindow.show()\n sys.exit(app.exec_())","repo_name":"Kartmaan/WeatherApp","sub_path":"weather_window.py","file_name":"weather_window.py","file_ext":"py","file_size_in_byte":61045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"37125821746","text":"import tkinter as tk\r\nimport tkinter.messagebox as messagebox\r\nclass MBTI_Test:\r\n def __init__(self,questions,responses):\r\n self.questions = questions\r\n self.responses = responses\r\n self.index=0\r\n def add_response(self, response):\r\n self.responses.append(response)\r\n \r\n def calculate_type(self):\r\n traits = {\r\n \"E\": 0,\r\n \"I\": 0,\r\n \"S\": 0,\r\n \"N\": 0,\r\n \"T\": 0,\r\n \"F\": 0,\r\n \"J\": 0,\r\n \"P\": 0\r\n }\r\n\r\n for response in self.responses:\r\n if response == \"e\":\r\n traits[\"E\"] += 1\r\n elif response == \"i\":\r\n traits[\"I\"] += 1\r\n elif response == \"s\":\r\n traits[\"S\"] += 1\r\n elif response == \"n\":\r\n traits[\"N\"] += 1\r\n elif response == \"t\":\r\n traits[\"T\"] += 1\r\n elif response == \"f\":\r\n traits[\"F\"] += 1\r\n elif response == \"j\":\r\n traits[\"J\"] += 1\r\n elif response == \"p\":\r\n traits[\"P\"] += 1\r\n\r\n mbti_type = \"\"\r\n\r\n if traits[\"E\"] > traits[\"I\"]:\r\n mbti_type += \"E\"\r\n else:\r\n mbti_type += \"I\"\r\n\r\n if traits[\"S\"] > traits[\"N\"]:\r\n mbti_type += \"S\"\r\n else:\r\n mbti_type += \"N\"\r\n\r\n if traits[\"T\"] > traits[\"F\"]:\r\n mbti_type += \"T\"\r\n else:\r\n mbti_type += \"F\"\r\n\r\n if traits[\"J\"] > traits[\"P\"]:\r\n mbti_type += \"J\"\r\n else:\r\n mbti_type += \"P\"\r\n\r\n return mbti_type\r\n\r\n def display_type(self,mbti_type):\r\n messagebox.showinfo(\"MBTI 테스트결과\", f\"당신의 mbti 성격 유형은 {mbti_type}입니다!\")\r\n\r\n def select_option(self,option):\r\n self.add_response(option)\r\n print(option)\r\n if len(self.responses) == len(self.questions)/2:\r\n mbti_type = self.calculate_type()\r\n print(mbti_type)\r\n self.display_type(mbti_type)\r\n else:\r\n question_label.configure(text=self.questions[2*len(self.responses)])\r\n self.index=len(self.responses)\r\n a_button.configure(command= lambda: test.select_option(self.questions[self.index*2+1][0]))\r\n b_button.configure(command= lambda: test.select_option(self.questions[self.index*2+1][1]))\r\n print(len(responses))\r\n print(len(questions))\r\n print(self.index)\r\n\r\nroot = tk.Tk()\r\nroot.title(\"MBTI Test\")\r\nroot.geometry(\"700x450\")\r\n\r\nquestions = [\r\n \"1. 나에게 새로운 친구를 만드는것은 쉬운 일이다\",['e','i'] #e\r\n ,\"2. 자유시간을 다양한 관심사를 찾는데 사용한다\",['n','s'] #n\r\n ,\"3. 다급하거나 힘든 상황에도 평정심을 유지하려고 노력한다\",['t','f'] #t\r\n ,\"4. 일이나 공부를 대비해 여러 계획을 세우려고 한다\",['j','p'] #j\r\n ,\"5. 혼자시간을 보내도 심심하지 않은 편이다\",['i','e'] #i\r\n ,\"6. 영화에 대한 감상이나 토론을 하는일에는 관심이 없다\",['s','n'] #s\r\n ,\"7. 나는 스스로 어떠한 감성을 즐긴다고 생각한다\",['f','t'] #f\r\n ,\"8. 하나의 일을 완전히 끝내고 다른 일을 시작한다\",['j','p'] #j\r\n ,\"9. 어떤 사람에게 관심이 생기면 먼저 다가가서 대화를 시작하는 편이다\",['e','i'] #e\r\n ,\"10. 열린 결말처럼 창작물을 자신의 방식대로 해석하는것을 좋아한다\",['n','s'] #n\r\n ,\"11. 자신보다 남의 일에 도움을 주는것에 더 만족감을 느낀다\",['f','t'] #f\r\n ,\"12. 삶의 만족은 내가 즉흥적으로 하고 싶은 일을 하는것에서 온다\",['p','j']# p\r\n ,\"13. 단체활동에 참여하는 일을 즐긴다 \",['e','i'] #e\r\n ,\"14. 체험을 하며 이론을 먼저 듣는거 보다 먼저 해보며 느끼고 싶다\",['s','n'] #s\r\n ,\"15. 자신과 삶의 배경이 다른 사람의 삶은 공감하지 못하는 것이 당연하다\",['t','f'] #t\r\n ,\"16. 일을 할때 절차나 규칙을 지키는 것보다 내가 어느정도 스스로 판단해서 진행하는게 좋다 \",['p','j']# p\r\n]\r\nresponses = []\r\ntest = MBTI_Test(questions,responses)\r\nquestion_label = tk.Label(root,text=questions[0])\r\nquestion_label.pack(pady=20)\r\na_button = tk.Button(root, text=\"a) 동의\", command= lambda: test.select_option(\"e\"))\r\na_button.pack(pady=20)\r\nb_button = tk.Button(root, text=\"b) 비동의\", command= lambda:test.select_option(\"i\"))\r\nb_button.pack(pady=20)\r\n'''if test.index==1:\r\n c_button = tk.Button(root, text=\"c) 동의\", command=lambda: test.select_option(\"c\"))\r\n c_button.pack(pady=20)\r\n d_button = tk.Button(root, text=\"d) 비동의\", command=lambda: test.select_option(\"d\"))\r\n d_button.pack(pady=20)\r\nif test.index==2:\r\n e_button = tk.Button(root, text=\"e) 동의\", command=lambda: test.select_option(\"e\"))\r\n e_button.pack(pady=20)\r\n f_button = tk.Button(root, text=\"f) 비동의\", command=lambda: test.select_option(\"f\"))\r\n f_button.pack(pady=20)\r\nif test.index==3:\r\n g_button = tk.Button(root, text=\"g) 동의\", command=lambda: test.select_option(\"g\"))\r\n g_button.pack(pady=20)\r\n h_button = tk.Button(root, text=\"h) 비동의\", command=lambda: test.select_option(\"h\"))\r\n h_button.pack(pady=20)'''\r\nroot.mainloop()\r\n","repo_name":"woosy123/python-project","sub_path":"김반석/파이썬 수정중.py","file_name":"파이썬 수정중.py","file_ext":"py","file_size_in_byte":5373,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"34543644942","text":"from simtk.openmm import *\nfrom simtk.openmm.app import *\nfrom simtk.unit import *\n\npdb = PDBFile('villin.pdb')\ntopology = pdb.topology\npositions = pdb.positions\n\nplatform = Platform.getPlatformByName('Reference')\nff = ForceField('ff14SB.xml')\nsystem = ff.createSystem(topology)\nintegrator = VerletIntegrator(1*femtosecond)\n\nsimulation = Simulation(topology, system, integrator, platform)\nsimulation.context.setPositions(positions)\n\nprint(\"The ffxml potential energy is: %s\" % simulation.context.getState(getEnergy=True).getPotentialEnergy())\n\nprmtop = AmberPrmtopFile('ff14SB.top')\ninpcrd = AmberInpcrdFile('ff14SB.crd')\n\nsystem2 = prmtop.createSystem()\nintegrator2 = VerletIntegrator(1*femtosecond)\n\nsimulation2 = Simulation(prmtop.topology, system2, integrator2, platform)\nsimulation2.context.setPositions(inpcrd.positions)\n\nprint(\"The AMBER potential energy is: %s\" % simulation2.context.getState(getEnergy=True).getPotentialEnergy())\n","repo_name":"rafwiewiora/FFConv","sub_path":"compareEnergies.py","file_name":"compareEnergies.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"1106595315","text":"from converter.ena.classes import AttributeType\nfrom converter.ena.classes.sra_common import PlatformType, RefObjectType, TypeIlluminaModel\nfrom converter.ena.classes.sra_experiment import Experiment, ExperimentSet, LibraryDescriptorType, LibraryType, SampleDescriptorType, TypeLibrarySelection, TypeLibrarySource, TypeLibraryStrategy\nfrom converter.ena.base import EnaModel, XMLType\nfrom converter.ena.ena_receipt import EnaReceipt\nimport logging\n\n\nclass EnaExperiment(EnaModel):\n\n def __init__(self, study_ref, alias_prefix=\"\"):\n self.study_ref = study_ref\n self.alias_prefix = alias_prefix\n\n def archive(self, assay):\n experiment = self.create(assay)\n is_update = True if experiment.accession else False\n input_xml = self.xml_str(experiment)\n receipt_xml = self.post(XMLType.EXPERIMENT, input_xml, update=is_update)\n accessions = EnaReceipt(XMLType.EXPERIMENT, input_xml, receipt_xml).process_receipt()\n if accessions and len(accessions) == 1:\n (alias, accession) = accessions[0]\n return accession, is_update\n raise EnaArchiveException('Ena archive no accession returned.')\n\n def create_set(self, assays):\n experiment_set = ExperimentSet()\n for assay in assays:\n experiment_set.experiment.append(self.create(assay))\n\n return experiment_set\n\n def create(self, assay):\n\n sequencing_protocol = assay[\"sequencing_protocol\"]\n library_preparation_protocols = assay[\"library_preparation_protocols\"]\n input_biomaterials = assay[\"input_biomaterials\"]\n\n experiment = Experiment()\n experiment.experiment_attributes = Experiment.ExperimentAttributes()\n\n protocol_desc = sequencing_protocol.get(\"content\", {}).get(\"protocol_core\", {}).get(\"protocol_description\")\n if protocol_desc:\n experiment.experiment_attributes.experiment_attribute.append(AttributeType(tag=\"Description\", value=protocol_desc))\n\n experiment_accession = self.get_experiment_accession(assay)\n if experiment_accession:\n experiment.accession = experiment_accession\n logging.info(f\"EXISTING insdc_experiment accession {experiment.accession}\")\n else:\n experiment.alias = self.alias_prefix + assay.get(\"content\", {}).get(\"process_core\", {}).get(\"process_id\")\n logging.info(f\"NEW experiment alias {experiment.alias}\")\n\n experiment.title = sequencing_protocol.get(\"content\", {}).get(\"protocol_core\", {}).get(\"protocol_name\", \"Untitled\")\n\n experiment.design = LibraryType()\n experiment.design.library_descriptor = LibraryDescriptorType()\n experiment.design.library_descriptor.library_strategy = TypeLibraryStrategy.OTHER\n experiment.design.library_descriptor.library_source = TypeLibrarySource.TRANSCRIPTOMIC_SINGLE_CELL\n experiment.design.library_descriptor.library_selection = self.ena_library_selection(library_preparation_protocols)\n experiment.design.library_descriptor.library_layout = self.ena_library_layout(sequencing_protocol)\n experiment.design.design_description = ''\n\n library_name, sample_accession = self.ena_library_name_and_accession(input_biomaterials)\n experiment.design.library_descriptor.library_name = library_name\n\n experiment.design.sample_descriptor = SampleDescriptorType()\n experiment.design.sample_descriptor.accession = sample_accession\n\n experiment.study_ref = RefObjectType()\n experiment.study_ref.accession = self.study_ref\n\n experiment.platform = self.ena_platform_type(sequencing_protocol)\n return experiment\n\n def get_experiment_accession(self, assay):\n return assay.get(\"content\", {}).get(\"insdc_experiment\", {}).get(\"insdc_experiment_accession\")\n\n def ena_library_name_and_accession(self, input_biomaterials):\n # unlikely to have multiple biomaterial inputs it is still possible, ena takes one accession only, so\n # use accepted ERS or SAM accession only.\n library_name = None\n sample_accession = None\n for input_biomaterial in input_biomaterials:\n biosamples_accession = input_biomaterial[\"content\"][\"biomaterial_core\"][\"biosamples_accession\"]\n if biosamples_accession.startswith(\"ERS\") or biosamples_accession.startswith(\"SAM\"):\n sample_accession = biosamples_accession\n library_name = input_biomaterial[\"content\"][\"biomaterial_core\"][\"biomaterial_id\"]\n break\n return library_name, sample_accession\n\n def ena_library_selection(self, library_preparation_protocols):\n primers = []\n for library_preparation_protocol in library_preparation_protocols:\n if library_preparation_protocol[\"content\"][\"primer\"]:\n primers.append(library_preparation_protocol[\"content\"][\"primer\"])\n\n if primers:\n if all(p == primers[0] for p in primers):\n return HcaEnaMapping.LIBRARY_SELECTION_MAPPING.get(primers[0], None)\n else:\n raise EnaArchiveException('Library preparation protocols have different primer.')\n else:\n return None\n\n def ena_library_layout(self, sequencing_protocol):\n library_layout = LibraryDescriptorType.LibraryLayout()\n if sequencing_protocol[\"content\"][\"paired_end\"]:\n library_layout.paired = LibraryDescriptorType.LibraryLayout.Paired()\n library_layout.paired.nominal_length = 0\n library_layout.paired.nominal_sdev = 0\n else:\n library_layout.single = ''\n return library_layout\n\n def ena_platform_type(self, sequencing_protocol):\n platform_type = PlatformType()\n if sequencing_protocol[\"content\"][\"instrument_manufacturer_model\"]:\n instrument_manufacturer_model = sequencing_protocol[\"content\"][\"instrument_manufacturer_model\"][\"text\"]\n instrument_model = HcaEnaMapping.INSTRUMENT_MANUFACTURER_MODEL_MAPPING.get(instrument_manufacturer_model.lower(), None)\n if not instrument_model:\n instrument_manufacturer_model = sequencing_protocol[\"content\"][\"instrument_manufacturer_model\"][\"ontology_label\"]\n instrument_model = HcaEnaMapping.INSTRUMENT_MANUFACTURER_MODEL_MAPPING.get(instrument_manufacturer_model.lower(), None)\n if not instrument_model:\n instrument_model = TypeIlluminaModel.UNSPECIFIED\n\n platform_type.illumina = PlatformType.Illumina()\n platform_type.illumina.instrument_model = instrument_model\n\n return platform_type\n\n\nclass HcaEnaMapping:\n\n # Library selection mappings\n LIBRARY_SELECTION_MAPPING = {\n \"poly-dT\": TypeLibrarySelection.OLIGO_D_T,\n \"random\": TypeLibrarySelection.RANDOM\n }\n\n # ENA Instrument Model and Platform Type Mappings\n INSTRUMENT_MANUFACTURER_MODEL_MAPPING = {\n 'illumina genome analyzer': TypeIlluminaModel.ILLUMINA_GENOME_ANALYZER,\n 'illumina genome analyzer ii': TypeIlluminaModel.ILLUMINA_GENOME_ANALYZER_II,\n 'illumina genome analyzer iix': TypeIlluminaModel.ILLUMINA_GENOME_ANALYZER_IIX,\n 'illumina hiseq 2500': TypeIlluminaModel.ILLUMINA_HI_SEQ_2500,\n 'illumina hiseq 2000': TypeIlluminaModel.ILLUMINA_HI_SEQ_2000,\n 'illumina hiseq 1500': TypeIlluminaModel.ILLUMINA_HI_SEQ_1500,\n 'illumina hiseq 1000': TypeIlluminaModel.ILLUMINA_HI_SEQ_1000,\n 'illumina miseq': TypeIlluminaModel.ILLUMINA_MI_SEQ,\n 'illumina hiscansq': TypeIlluminaModel.ILLUMINA_HI_SCAN_SQ,\n 'hiseq x ten': TypeIlluminaModel.HI_SEQ_X_TEN,\n 'illumina hiseq x 10': TypeIlluminaModel.HI_SEQ_X_TEN,\n 'nextseq 500': TypeIlluminaModel.NEXT_SEQ_500,\n 'illumina nextseq 500': TypeIlluminaModel.NEXT_SEQ_500,\n 'hiseq x five': TypeIlluminaModel.HI_SEQ_X_FIVE,\n 'illumina hiseq 3000': TypeIlluminaModel.ILLUMINA_HI_SEQ_3000,\n 'illumina hiseq 4000': TypeIlluminaModel.ILLUMINA_HI_SEQ_4000,\n 'nextseq 550': TypeIlluminaModel.NEXT_SEQ_550,\n 'illumina novaseq 6000': TypeIlluminaModel.ILLUMINA_NOVA_SEQ_6000,\n }\n","repo_name":"ebi-ait/ingest-archiver","sub_path":"converter/ena/ena_experiment.py","file_name":"ena_experiment.py","file_ext":"py","file_size_in_byte":8168,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"75"} +{"seq_id":"4787564492","text":"#\n#\n#\n#\n#\n\nimport tensorflow as tf\n\nmodel_dir = \".\"\nmodel_name = \"model.ckpt\"\npath = model_dir + \"/\" + model_name\n\ndef model_fn():\n\tvar1 = tf.get_variable(name=\"var1\",shape=[1],initializer=tf.zeros_initializer())\n\tinc_var1 = var1.assign(var1 + 1)\n\treturn inc_var1\n\ndef restore_model_fn():\n\tvar1 = tf.get_variable(name=\"var1\",shape=[1])\n\tinc_var1 = var1.assign(var1 + 1)\n\treturn inc_var1\n\n","repo_name":"dano000/WatermelonUI","sub_path":"Examples/Tensorflow/SavableNetwork/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"74792353522","text":"from random import randint\nfrom gameFunctions import gameVars\n\n# #These are our choices\n# choices=[\"rock\", \"paper\", \"scissors\"]\n\n# #adding lives\n# player_lives = 5\n# computer_lives = 5\n\n# #let the ai make a choice\n# computer=choices[randint(0,2)]\n\n# #set up a game loop here so we dont have to keep restarting\n# player = False\n\ndef winorlose(status):\n\t#print(\"called win or lose function\", status, \"\\n\")\n\tprint(\"You\", status, \"! Would you like to play again?\")\n\tchoice = input(\"Y / N?\")\n\n\tif choice == \"Y\" or choice == \"y\":\n\t\tgameVars.player_lives = 5\n\t\tgameVars.computer_lives = 5\n\t\tgameVars.player = False\n\t\tgameVars.computer= gameVars.choices[randint(0,2)]\n#reset the game\n\telif choice == \"N\" or choice == \"n\":\n\t\tprint(\"You Decide to Leave... Come back Soon!\")\n\t\texit()\n\telse:\n\t\tprint(\"Make a valid choice. Yes or no!\")\n\t\t#recursive -> calling a function from inside itself\n\t\twinorlose(status)","repo_name":"NathanJennex/Jennex_N_PythonRPS","sub_path":"gameFunctions/winlose.py","file_name":"winlose.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"20415746115","text":"\"\"\"\nSupport for Axis binary sensors.\n\nFor more details about this platform, please refer to the documentation at\nhttps://home-assistant.io/components/binary_sensor.axis/\n\"\"\"\nfrom datetime import timedelta\nimport logging\n\nfrom homeassistant.components.axis import AxisDeviceEvent\nfrom homeassistant.components.binary_sensor import BinarySensorDevice\nfrom homeassistant.const import CONF_TRIGGER_TIME\nfrom homeassistant.helpers.event import track_point_in_utc_time\nfrom homeassistant.util.dt import utcnow\n\nDEPENDENCIES = ['axis']\n\n_LOGGER = logging.getLogger(__name__)\n\n\ndef setup_platform(hass, config, add_devices, discovery_info=None):\n \"\"\"Set up the Axis binary devices.\"\"\"\n add_devices([AxisBinarySensor(hass, discovery_info)], True)\n\n\nclass AxisBinarySensor(AxisDeviceEvent, BinarySensorDevice):\n \"\"\"Representation of a binary Axis event.\"\"\"\n\n def __init__(self, hass, event_config):\n \"\"\"Initialize the Axis binary sensor.\"\"\"\n self.hass = hass\n self._state = False\n self._delay = event_config[CONF_TRIGGER_TIME]\n self._timer = None\n AxisDeviceEvent.__init__(self, event_config)\n\n @property\n def is_on(self):\n \"\"\"Return true if event is active.\"\"\"\n return self._state\n\n def update(self):\n \"\"\"Get the latest data and update the state.\"\"\"\n self._state = self.axis_event.is_tripped\n\n def _update_callback(self):\n \"\"\"Update the sensor's state, if needed.\"\"\"\n self.update()\n\n if self._timer is not None:\n self._timer()\n self._timer = None\n\n if self._delay > 0 and not self.is_on:\n # Set timer to wait until updating the state\n def _delay_update(now):\n \"\"\"Timer callback for sensor update.\"\"\"\n _LOGGER.debug(\"%s called delayed (%s sec) update\",\n self._name, self._delay)\n self.schedule_update_ha_state()\n self._timer = None\n\n self._timer = track_point_in_utc_time(\n self.hass, _delay_update,\n utcnow() + timedelta(seconds=self._delay))\n else:\n self.schedule_update_ha_state()\n","repo_name":"jest-community/jest-pytest","sub_path":"src/__tests__/integration/home-assistant/homeassistant/components/binary_sensor/axis.py","file_name":"axis.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"75"} +{"seq_id":"570002259","text":"#!/usr/bin/env python3\nimport pytest\n\n# Define the list of tests\ntests_process = [\"test_process_models.py\"]\ntests_grid = [\"test_grid_models.py\"]\ntest_combustion = [\"test_models.py\"]\ntest_other = [\"test_utilities.py\"]\ntests_list = tests_process + tests_grid + test_combustion + test_other\n\n# Run pytest when this script is executed\npytest.main(tests_list)\n","repo_name":"RoberAgro/HES-OFF","sub_path":"hes_off/core/deprecated/tests/run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"16968142579","text":"import logging\nimport math\nimport numpy as np\nimport gc\n\nfrom numba import njit\n\nEPS = 1e-8\nNAN = -42.\nk = 0.5\nMINFLOAT = float('-inf')\n\nlog = logging.getLogger(__name__)\n\n\nclass MCTS():\n \"\"\"\n This class handles the MCTS tree.\n \"\"\"\n\n def __init__(self, game, nnet, args, dirichlet_noise=False, batch_info=None):\n self.game = game\n self.nnet = nnet\n self.args = args\n self.dirichlet_noise = dirichlet_noise\n\n # Contains tuple of Es, Vs, Ps, Ns, Qsa, Nsa\n # Es stores game.getGameEnded ended for board s\n # Vs stores game.getValidMoves for board s\n # Ps stores initial policy (returned by neural net) \n # Ns stores #times board s was visited\n # Qsa stores Q values for s,a (as defined in the paper)\n # Nsa stores #times edge s,a was visited\n # r stores round number\n # Qs stores Q value for s\n self.nodes_data = {} # stores data for each nodes in a single dictionary\n self.Qsa_default = np.full (self.game.getActionSize(), NAN, dtype=np.float64)\n self.Nsa_default = np.zeros(self.game.getActionSize() , dtype=np.int64)\n\n self.rng = np.random.default_rng()\n self.step = 0\n self.last_cleaning = 0\n self.batch_info = batch_info\n\n def getActionProb(self, canonicalBoard, temp=1, force_full_search=False):\n \"\"\"\n This function performs numMCTSSims simulations of MCTS starting from\n canonicalBoard.\n\n Returns:\n probs: a policy vector where the probability of the ith action is\n proportional to Nsa[(s,a)]**(1./temp)\n \"\"\"\n is_full_search = force_full_search or (self.rng.random() < self.args.prob_fullMCTS)\n nb_MCTS_sims = self.args.numMCTSSims if is_full_search else self.args.numMCTSSims // self.args.ratio_fullMCTS\n forced_playouts = (is_full_search and self.args.forced_playouts)\n for self.step in range(nb_MCTS_sims):\n dir_noise = (self.step == 0 and is_full_search and self.dirichlet_noise)\n self.search(canonicalBoard, dirichlet_noise=dir_noise, forced_playouts=forced_playouts)\n\n s = self.game.stringRepresentation(canonicalBoard)\n counts = [self.nodes_data[s][5][a] for a in range(self.game.getActionSize())] # Nsa\n\n # Compute Q at root node\n q_player0 = self.nodes_data[s][7]\n q = [q_player0 if n == 0 else -q_player0/(self.game.num_players-1) for n in range(self.game.num_players)]\n\n # Policy target pruning\n if forced_playouts:\n best_count = max(counts)\n Psas = [self.nodes_data[s][2][a] for a in range(self.game.getActionSize())] # Ps[a]\n adjusted_counts = [Nsa-int(math.sqrt(k*Psa*nb_MCTS_sims)) if Nsa != best_count else Nsa for (Nsa, Psa) in zip(counts, Psas)]\n adjusted_counts = [c if c > 1 else 0 for c in adjusted_counts]\n counts = adjusted_counts\n\n probs = np.array(counts)\n probs = probs / probs.sum()\n\n # Clean search tree from very old moves = less memory footprint and less keys to search into\n if not self.args.no_mem_optim:\n r = self.game.getRound(canonicalBoard)\n if r > self.last_cleaning + 20:\n for node in [n for n in self.nodes_data.keys() if self.nodes_data[n][6] < r-5]:\n del self.nodes_data[node]\n self.last_cleaning = r\n\n if temp == 0:\n bestAs = np.array(np.argwhere(counts == np.max(counts))).flatten()\n bestA = np.random.choice(bestAs)\n probs = [0] * len(counts)\n probs[bestA] = 1\n return probs, q, is_full_search\n\n counts = [x ** (1. / temp) for x in counts]\n counts_sum = float(sum(counts))\n probs = [x / counts_sum for x in counts]\n return probs, q, is_full_search\n\n def search(self, canonicalBoard, dirichlet_noise=False, forced_playouts=False):\n \"\"\"\n This function performs one iteration of MCTS. It is recursively called\n till a leaf node is found. The action chosen at each node is one that\n has the maximum upper confidence bound as in the paper.\n\n Once a leaf node is found, the neural network is called to return an\n initial policy P and a value v for the state. This value is propagated\n up the search path. In case the leaf node is a terminal state, the\n outcome is propagated up the search path. The values of Ns, Nsa, Qsa are\n updated.\n\n NOTE: the return values are the negative of the value of the current\n state. This is done since v is in [-1,1] and if v is the value of a\n state for the current player, then its value is -v for the other player.\n\n Returns:\n v: the negative of the value of the current canonicalBoard\n \"\"\"\n\n s = self.game.stringRepresentation(canonicalBoard)\n Es, Vs, Ps, Ns, Qsa, Nsa, r, Qs = self.nodes_data.get(s, (None, )*8)\n if r is None:\n r = self.game.getRound(canonicalBoard)\n\n if Es is None:\n Es = self.game.getGameEnded(canonicalBoard, 0)\n if Es.any():\n # terminal node\n self.nodes_data[s] = (Es, Vs, Ps, Ns, Qsa, Nsa, r, Qs)\n return Es\n elif Es.any():\n # terminal node\n return Es\n\n if Ps is None:\n # First time that we explore state s\n Vs = self.game.getValidMoves(canonicalBoard, 0)\n if self.batch_info is None:\n Ps, v = self.nnet.predict(canonicalBoard, Vs)\n else:\n Ps, v = self.nnet.predict_client(canonicalBoard, Vs, self.batch_info)\n if dirichlet_noise:\n Ps = softmax(Ps, self.args.temperature[0])\n self.applyDirNoise(Ps, Vs)\n normalise(Ps)\n\n Ns, Qsa, Nsa = 0, self.Qsa_default.copy(), self.Nsa_default.copy()\n self.nodes_data[s] = (Es, Vs, Ps, Ns, Qsa, Nsa, r, v[0])\n return v\n\n if dirichlet_noise:\n # We already visited this node, adding dirichlet noise this time\n Ps = softmax(Ps, self.args.temperature[0])\n self.applyDirNoise(Ps, Vs)\n normalise(Ps)\n\n # pick the action with the highest upper confidence bound\n # get next state and get canonical version of it\n a, next_s, next_player = get_next_best_action_and_canonical_state(\n Es, Vs, Ps, Ns, Qsa, Nsa, Qs,\n self.args.cpuct,\n self.game.board,\n canonicalBoard,\n forced_playouts,\n self.step,\n self.args.fpu,\n )\n\n v = self.search(next_s)\n v = np_roll(v, next_player)\n\n Qsa[a] = (Nsa[a] * Qsa[a] + v[0]) / (Nsa[a] + 1) # if Qsa[a] is NAN, then Nsa is zero\n Qs = ((Ns+1) * Qs + v[0]) / (Ns+2) # Qs can't be None here\n Nsa[a] += 1\n Ns += 1\n\n self.nodes_data[s] = (Es, Vs, Ps, Ns, Qsa, Nsa, r, Qs)\n return v\n\n\n def applyDirNoise(self, Ps, Vs):\n dir_values = self.rng.dirichlet([self.args.dirichletAlpha] * np.count_nonzero(Vs))\n dir_idx = 0\n for idx in range(len(Ps)):\n if Vs[idx]:\n Ps[idx] = (0.75 * Ps[idx]) + (0.25 * dir_values[dir_idx])\n dir_idx += 1\n\n @staticmethod\n def reset_all_search_trees():\n for obj in [o for o in gc.get_objects() if type(o) is MCTS]: # dirtier than isinstance, but that would trigger a pytorch warning\n obj.nodes_data = {}\n obj.last_cleaning = 0\n \n@njit(cache=True, fastmath=True, nogil=True)\ndef np_roll(arr, n):\n return np.roll(arr, n)\n\n# pick the action with the highest upper confidence bound\n@njit(cache=True, fastmath=True, nogil=True)\ndef pick_highest_UCB(Es, Vs, Ps, Ns, Qsa, Nsa, Qs, cpuct, forced_playouts, n_iter, fpu):\n cur_best = MINFLOAT\n best_act = -1\n fpu_init = Qs-fpu if fpu > 0 else fpu\n\n for a, valid in enumerate(Vs):\n if valid:\n if forced_playouts:\n if Nsa[a] < int(math.sqrt(k * Ps[a] * n_iter)): # Nsa is zero when not set\n return a\n\n if Qsa[a] != NAN:\n u = Qsa[a] + cpuct * Ps[a] * math.sqrt(Ns) / (1 + Nsa[a])\n else:\n u = fpu_init + cpuct * Ps[a] * math.sqrt(Ns + EPS)\n\n if u > cur_best:\n cur_best, best_act = u, a\n\n return best_act\n\n\n@njit(fastmath=True, nogil=True) # no cache because it relies on jitclass which isn't compatible with cache\ndef get_next_best_action_and_canonical_state(Es, Vs, Ps, Ns, Qsa, Nsa, Qs, cpuct, gameboard, canonicalBoard, forced_playouts, n_iter, fpu):\n a = pick_highest_UCB(Es, Vs, Ps, Ns, Qsa, Nsa, Qs, cpuct, forced_playouts, n_iter, fpu)\n\n # Do action 'a'\n gameboard.copy_state(canonicalBoard, True)\n next_player = gameboard.make_move(a, 0, deterministic=True)\n # next_s = gameboard.get_state()\n\n # Get canonical form\n if next_player != 0:\n # gameboard.copy_state(next_s, True)\n gameboard.swap_players(next_player)\n next_s = gameboard.get_state()\n\n return a, next_s, next_player\n\n@njit(cache=True, fastmath=True, nogil=True)\ndef normalise(vector):\n sum_vector = np.sum(vector)\n vector /= sum_vector\n\n@njit(cache=True, fastmath=True, nogil=True)\ndef softmax(Ps, softmax_temp):\n if softmax_temp == 1.:\n return Ps\n result = Ps ** (1. / softmax_temp)\n normalise(result)\n return result.astype(np.float32)\n","repo_name":"cestpasphoto/alpha-zero-general","sub_path":"MCTS.py","file_name":"MCTS.py","file_ext":"py","file_size_in_byte":9579,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"75"} +{"seq_id":"18378372386","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.common.by import By\nfrom commons.func import load_list_from_file, ensure_dir, load_obj, save_obj\n# import requests\nimport urllib.request\nPDBBinding_DIR = \"/home/gpux1/Data/PDB/PDBinding\"\nLigand_DATA_DIR = \"%s/LigandData\" % PDBBinding_DIR\nensure_dir(Ligand_DATA_DIR)\n\nLigandPDBBatchMap_Path = \"%s/ligandbatch.dict\" % Ligand_DATA_DIR\n\nLIGAND_LIST = \"/home/gpux1/Data/PDB/PDBinding/PDBbind_v2020_plain_text_index/index/ligand_ids.txt\"\n\n\nBATCH_SIZE = 50\n\n\ndef getLigandList():\n ls = load_list_from_file(LIGAND_LIST)\n return ls\n\n# def downloadFromURL(url,targetPath):\n# response = requests.get(url)\n# fout = open(targetPath, \"w\")\n# fout.write(response.text)\n# fout.close()\n\n\ndef download_url(url, save_path):\n with urllib.request.urlopen(url) as dl_file:\n with open(save_path, 'wb') as out_file:\n out_file.write(dl_file.read())\n\ndef downloadBatch():\n\n browser = webdriver.Chrome()\n\n dLigandBatchRe = dict()\n try:\n dLigandBatchRe = load_obj(LigandPDBBatchMap_Path)\n except:\n dLigandBatchRe = dict()\n\n\n ligandList = getLigandList()\n nBatch = len(ligandList) // BATCH_SIZE + 1\n browser.get(\"https://www.rcsb.org/downloads/ligands\")\n time.sleep(2)\n inputLigand = browser.find_element(By.ID, \"ligandIdList\")\n submitButtion = browser.find_element(By.ID, \"submitBtn\")\n\n for i in range(nBatch):\n startId = i * BATCH_SIZE\n endId = min((i+1) * BATCH_SIZE, len(ligandList))\n ligandBatchIds = ligandList[startId:endId]\n ligandBatchIdsString = \",\".join(ligandBatchIds)\n batchName = \"%s-%s\" % (ligandBatchIds[0], ligandBatchIds[-1])\n batchName = batchName.replace(\"/\", \"__\")\n if batchName in dLigandBatchRe:\n continue\n inputLigand.clear()\n inputLigand.send_keys(ligandBatchIdsString)\n time.sleep(2)\n submitButtion.click()\n time.sleep(2)\n url = browser.find_element(By.XPATH, \"//a[starts-with(@href,'https://download.rcsb.org/batch/ccd/')]\")\n url = url.get_attribute('href')\n targetPath = \"%s/%s.zip\" % (Ligand_DATA_DIR, batchName)\n # print(url)\n download_url(url, targetPath)\n dLigandBatchRe[batchName] = targetPath\n print(i, targetPath)\n save_obj(dLigandBatchRe, LigandPDBBatchMap_Path)\n\n save_obj(dLigandBatchRe, LigandPDBBatchMap_Path)\n\n\nif __name__ == \"__main__\":\n downloadBatch()","repo_name":"anhnda/D3GlobalBind","sub_path":"data_preparation/get_ligand_pdbmap/getLigandNamePDB.py","file_name":"getLigandNamePDB.py","file_ext":"py","file_size_in_byte":2479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"28974680514","text":"class Codec:\n def __init__(self):\n self.s2l = {}\n self.l2s = {}\n\n def encode(self, longUrl):\n \"\"\"Encodes a URL to a shortened URL.\n :type longUrl: str\n :rtype: str\n \"\"\"\n if longUrl in self.l2s:\n return f\"http://tinyurl.com/{self.l2s[longUrl]}\"\n i = str(hex(id(longUrl)))[-6:]\n self.l2s[longUrl] = i\n self.s2l[i] = longUrl\n return f\"http://tinyurl.com/{i}\"\n\n def decode(self, shortUrl):\n \"\"\"Decodes a shortened URL to its original URL.\n :type shortUrl: str\n :rtype: str\n \"\"\"\n i = shortUrl[-6:]\n return self.s2l[i]\n\n\nif __name__ == '__main__':\n codec = Codec()\n s = \"https://leetcode.com/problems/design-tinyurl\"\n print(codec.encode(s))\n print(codec.decode(codec.encode(s)))\n print(codec.decode(codec.encode(s)))\n","repo_name":"scolphew/leetcode_python","sub_path":"leetcode/_535_EncodeandDecodeTinyURL.py","file_name":"_535_EncodeandDecodeTinyURL.py","file_ext":"py","file_size_in_byte":865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"38475214516","text":"from keras.applications import InceptionV3\nfrom keras import Model, Input\nfrom keras import backend as K\nfrom keras.layers import merge, Dense, Flatten\n\n\nclass inception_keras_oneshot():\n def __init__(self, dimensions):\n img_shape = tuple(dimensions)\n left_input = Input(img_shape, name='left_input')\n right_input = Input(img_shape, name='right_input')\n\n model = InceptionV3(include_top=False)\n model_augmented = Model(inputs=[model.input], outputs=[model.output])\n\n encoding_left = Flatten(name='flattened_left_input')(model_augmented(left_input))\n encoding_right = Flatten(name='flattened_right_input')(model_augmented(right_input))\n distance = lambda x: K.abs((x[0] - x[1]) ** 2)\n merged_vector = merge(inputs=[encoding_left, encoding_right], mode=distance, output_shape=lambda x: x[0])\n predict_layer = Dense(1, activation='sigmoid', name='main_output')(merged_vector)\n siamese_network = Model(inputs=[left_input, right_input], outputs=predict_layer)\n\n self.model = siamese_network\n\n def get_model(self):\n return self.model\n\n\nif __name__ == '__main__':\n obj = inception_keras_oneshot([224, 224, 3])\n print(obj.get_model().summary())\n","repo_name":"nishantb21/food","sub_path":"Team 1/models/inception_keras_oneshot.py","file_name":"inception_keras_oneshot.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"23129001315","text":"from matplotlib.mlab import find\r\nimport pyaudio\r\nimport numpy as np\r\n\r\nimport math\r\n#from KeyPress import*\r\nimport os\r\n\r\n\r\nimport ctypes\r\n\r\nLONG = ctypes.c_long\r\nDWORD = ctypes.c_ulong\r\nULONG_PTR = ctypes.POINTER(DWORD)\r\nWORD = ctypes.c_ushort\r\n\r\nclass MOUSEINPUT(ctypes.Structure):\r\n\t_fields_ = (('dx', LONG),\r\n\t\t\t\t('dy', LONG),\r\n\t\t\t\t('mouseData', DWORD),\r\n\t\t\t\t('dwFlags', DWORD),\r\n\t\t\t\t('time', DWORD),\r\n\t\t\t\t('dwExtraInfo', ULONG_PTR))\r\n\r\nclass KEYBDINPUT(ctypes.Structure):\r\n\t_fields_ = (('wVk', WORD),\r\n\t\t\t\t('wScan', WORD),\r\n\t\t\t\t('dwFlags', DWORD),\r\n\t\t\t\t('time', DWORD),\r\n\t\t\t\t('dwExtraInfo', ULONG_PTR))\r\n\r\nclass HARDWAREINPUT(ctypes.Structure):\r\n\t_fields_ = (('uMsg', DWORD),\r\n\t\t\t\t('wParamL', WORD),\r\n\t\t\t\t('wParamH', WORD))\r\n\r\nclass _INPUTunion(ctypes.Union):\r\n\t_fields_ = (('mi', MOUSEINPUT),\r\n\t\t\t\t('ki', KEYBDINPUT),\r\n\t\t\t\t('hi', HARDWAREINPUT))\r\n\r\nclass INPUT(ctypes.Structure):\r\n\t_fields_ = (('type', DWORD),\r\n\t\t\t\t('union', _INPUTunion))\r\n\r\ndef SendInput(*inputs):\r\n\tnInputs = len(inputs)\r\n\tLPINPUT = INPUT * nInputs\r\n\tpInputs = LPINPUT(*inputs)\r\n\tcbSize = ctypes.c_int(ctypes.sizeof(INPUT))\r\n\treturn ctypes.windll.user32.SendInput(nInputs, pInputs, cbSize)\r\n\r\nINPUT_MOUSE = 0\r\nINPUT_KEYBOARD = 1\r\nINPUT_HARDWARD = 2\r\n\r\ndef Input(structure):\r\n\tif isinstance(structure, MOUSEINPUT):\r\n\t\treturn INPUT(INPUT_MOUSE, _INPUTunion(mi=structure))\r\n\tif isinstance(structure, KEYBDINPUT):\r\n\t\treturn INPUT(INPUT_KEYBOARD, _INPUTunion(ki=structure))\r\n\traise TypeError('Cannot create INPUT structure!')\r\n\r\nWHEEL_DELTA = 120\r\nXBUTTON1 = 0x0001\r\nXBUTTON2 = 0x0002\r\nMOUSEEVENTF_ABSOLUTE = 0x8000\r\nMOUSEEVENTF_HWHEEL = 0x01000\r\nMOUSEEVENTF_MOVE = 0x0001\r\nMOUSEEVENTF_MOVE_NOCOALESCE = 0x2000\r\nMOUSEEVENTF_LEFTDOWN = 0x0002\r\nMOUSEEVENTF_LEFTUP = 0x0004\r\nMOUSEEVENTF_RIGHTDOWN = 0x0008\r\nMOUSEEVENTF_RIGHTUP = 0x0010\r\nMOUSEEVENTF_MIDDLEDOWN = 0x0020\r\nMOUSEEVENTF_MIDDLEUP = 0x0040\r\nMOUSEEVENTF_VIRTUALDESK = 0x4000\r\nMOUSEEVENTF_WHEEL = 0x0800\r\nMOUSEEVENTF_XDOWN = 0x0080\r\nMOUSEEVENTF_XUP = 0x0100\r\n\r\ndef MouseInput(flags, x, y, data):\r\n\treturn MOUSEINPUT(x, y, data, flags, 0, None)\r\n\r\nVK_LBUTTON = 0x01 # Left mouse button\r\nVK_RBUTTON = 0x02 # Right mouse button\r\nVK_CANCEL = 0x03 # Control-break processing\r\nVK_MBUTTON = 0x04 # Middle mouse button (three-button mouse)\r\nVK_XBUTTON1 = 0x05 # X1 mouse button\r\nVK_XBUTTON2 = 0x06 # X2 mouse button\r\nVK_BACK = 0x08 # BACKSPACE key\r\nVK_TAB = 0x09 # TAB key\r\nVK_CLEAR = 0x0C # CLEAR key\r\nVK_RETURN = 0x0D # ENTER key\r\nVK_SHIFT = 0x10 # SHIFT key\r\nVK_CONTROL = 0x11 # CTRL key\r\nVK_MENU = 0x12 # ALT key\r\nVK_PAUSE = 0x13 # PAUSE key\r\nVK_CAPITAL = 0x14 # CAPS LOCK key\r\nVK_KANA = 0x15 # IME Kana mode\r\nVK_HANGUL = 0x15 # IME Hangul mode\r\nVK_JUNJA = 0x17 # IME Junja mode\r\nVK_FINAL = 0x18 # IME final mode\r\nVK_HANJA = 0x19 # IME Hanja mode\r\nVK_KANJI = 0x19 # IME Kanji mode\r\nVK_ESCAPE = 0x1B # ESC key\r\nVK_CONVERT = 0x1C # IME convert\r\nVK_NONCONVERT = 0x1D # IME nonconvert\r\nVK_ACCEPT = 0x1E # IME accept\r\nVK_MODECHANGE = 0x1F # IME mode change request\r\nVK_SPACE = 0x20 # SPACEBAR\r\nVK_PRIOR = 0x21 # PAGE UP key\r\nVK_NEXT = 0x22 # PAGE DOWN key\r\nVK_END = 0x23 # END key\r\nVK_HOME = 0x24 # HOME key\r\nVK_LEFT = 0xCB # LEFT ARROW key\r\nVK_UP = 0xC8 # UP ARROW key\r\nVK_RIGHT = 0xCD # RIGHT ARROW key\r\nVK_DOWN = 0xD0 # DOWN ARROW key\r\nVK_SELECT = 0x29 # SELECT key\r\nVK_PRINT = 0x2A # PRINT key\r\nVK_EXECUTE = 0x2B # EXECUTE key\r\nVK_SNAPSHOT = 0x2C # PRINT SCREEN key\r\nVK_INSERT = 0x2D # INS key\r\nVK_DELETE = 0x2E # DEL key\r\nVK_HELP = 0x2F # HELP key\r\nVK_LWIN = 0x5B # Left Windows key (Natural keyboard)\r\nVK_RWIN = 0x5C # Right Windows key (Natural keyboard)\r\nVK_APPS = 0x5D # Applications key (Natural keyboard)\r\nVK_SLEEP = 0x5F # Computer Sleep key\r\nVK_NUMPAD0 = 0x60 # Numeric keypad 0 key\r\nVK_NUMPAD1 = 0x61 # Numeric keypad 1 key\r\nVK_NUMPAD2 = 0x62 # Numeric keypad 2 key\r\nVK_NUMPAD3 = 0x63 # Numeric keypad 3 key\r\nVK_NUMPAD4 = 0x64 # Numeric keypad 4 key\r\nVK_NUMPAD5 = 0x65 # Numeric keypad 5 key\r\nVK_NUMPAD6 = 0x66 # Numeric keypad 6 key\r\nVK_NUMPAD7 = 0x67 # Numeric keypad 7 key\r\nVK_NUMPAD8 = 0x68 # Numeric keypad 8 key\r\nVK_NUMPAD9 = 0x69 # Numeric keypad 9 key\r\nVK_MULTIPLY = 0x6A # Multiply key\r\nVK_ADD = 0x6B # Add key\r\nVK_SEPARATOR = 0x6C # Separator key\r\nVK_SUBTRACT = 0x6D # Subtract key\r\nVK_DECIMAL = 0x6E # Decimal key\r\nVK_DIVIDE = 0x6F # Divide key\r\nVK_F1 = 0x70 # F1 key\r\nVK_F2 = 0x71 # F2 key\r\nVK_F3 = 0x72 # F3 key\r\nVK_F4 = 0x73 # F4 key\r\nVK_F5 = 0x74 # F5 key\r\nVK_F6 = 0x75 # F6 key\r\nVK_F7 = 0x76 # F7 key\r\nVK_F8 = 0x77 # F8 key\r\nVK_F9 = 0x78 # F9 key\r\nVK_F10 = 0x79 # F10 key\r\nVK_F11 = 0x7A # F11 key\r\nVK_F12 = 0x7B # F12 key\r\nVK_F13 = 0x7C # F13 key\r\nVK_F14 = 0x7D # F14 key\r\nVK_F15 = 0x7E # F15 key\r\nVK_F16 = 0x7F # F16 key\r\nVK_F17 = 0x80 # F17 key\r\nVK_F18 = 0x81 # F18 key\r\nVK_F19 = 0x82 # F19 key\r\nVK_F20 = 0x83 # F20 key\r\nVK_F21 = 0x84 # F21 key\r\nVK_F22 = 0x85 # F22 key\r\nVK_F23 = 0x86 # F23 key\r\nVK_F24 = 0x87 # F24 key\r\nVK_NUMLOCK = 0x90 # NUM LOCK key\r\nVK_SCROLL = 0x91 # SCROLL LOCK key\r\nVK_LSHIFT = 0xA0 # Left SHIFT key\r\nVK_RSHIFT = 0xA1 # Right SHIFT key\r\nVK_LCONTROL = 0xA2 # Left CONTROL key\r\nVK_RCONTROL = 0xA3 # Right CONTROL key\r\nVK_LMENU = 0xA4 # Left MENU key\r\nVK_RMENU = 0xA5 # Right MENU key\r\nVK_BROWSER_BACK = 0xA6 # Browser Back key\r\nVK_BROWSER_FORWARD = 0xA7 # Browser Forward key\r\nVK_BROWSER_REFRESH = 0xA8 # Browser Refresh key\r\nVK_BROWSER_STOP = 0xA9 # Browser Stop key\r\nVK_BROWSER_SEARCH = 0xAA # Browser Search key\r\nVK_BROWSER_FAVORITES = 0xAB # Browser Favorites key\r\nVK_BROWSER_HOME = 0xAC # Browser Start and Home key\r\nVK_VOLUME_MUTE = 0xAD # Volume Mute key\r\nVK_VOLUME_DOWN = 0xAE # Volume Down key\r\nVK_VOLUME_UP = 0xAF # Volume Up key\r\nVK_MEDIA_NEXT_TRACK = 0xB0 # Next Track key\r\nVK_MEDIA_PREV_TRACK = 0xB1 # Previous Track key\r\nVK_MEDIA_STOP = 0xB2 # Stop Media key\r\nVK_MEDIA_PLAY_PAUSE = 0xB3 # Play/Pause Media key\r\nVK_LAUNCH_MAIL = 0xB4 # Start Mail key\r\nVK_LAUNCH_MEDIA_SELECT = 0xB5 # Select Media key\r\nVK_LAUNCH_APP1 = 0xB6 # Start Application 1 key\r\nVK_LAUNCH_APP2 = 0xB7 # Start Application 2 key\r\nVK_OEM_1 = 0xBA # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the ';:' key\r\nVK_OEM_PLUS = 0xBB # For any country/region, the '+' key\r\nVK_OEM_COMMA = 0xBC # For any country/region, the ',' key\r\nVK_OEM_MINUS = 0xBD # For any country/region, the '-' key\r\nVK_OEM_PERIOD = 0xBE # For any country/region, the '.' key\r\nVK_OEM_2 = 0xBF # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the '/?' key\r\nVK_OEM_3 = 0xC0 # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the '`~' key\r\nVK_OEM_4 = 0xDB # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the '[{' key\r\nVK_OEM_5 = 0xDC # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the '\\|' key\r\nVK_OEM_6 = 0xDD # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the ']}' key\r\nVK_OEM_7 = 0xDE # Used for miscellaneous characters; it can vary by keyboard.\r\n\t\t\t\t\t\t\t\t# For the US standard keyboard, the 'single-quote/double-quote' key\r\nVK_OEM_8 = 0xDF # Used for miscellaneous characters; it can vary by keyboard.\r\nVK_OEM_102 = 0xE2 # Either the angle bracket key or the backslash key on the RT 102-key keyboard\r\nVK_PROCESSKEY = 0xE5 # IME PROCESS key\r\nVK_PACKET = 0xE7 # Used to pass Unicode characters as if they were keystrokes. The VK_PACKET key is the low word of a 32-bit Virtual Key value used for non-keyboard input methods. For more information, see Remark in KEYBDINPUT, SendInput, WM_KEYDOWN, and WM_KEYUP\r\nVK_ATTN = 0xF6 # Attn key\r\nVK_CRSEL = 0xF7 # CrSel key\r\nVK_EXSEL = 0xF8 # ExSel key\r\nVK_EREOF = 0xF9 # Erase EOF key\r\nVK_PLAY = 0xFA # Play key\r\nVK_ZOOM = 0xFB # Zoom key\r\nVK_PA1 = 0xFD # PA1 key\r\nVK_OEM_CLEAR = 0xFE # Clear key\r\n\r\nKEYEVENTF_EXTENDEDKEY = 0x0001\r\nKEYEVENTF_KEYUP = 0x0002\r\nKEYEVENTF_SCANCODE = 0x0008\r\nKEYEVENTF_UNICODE = 0x0004\r\n\r\nKEY_0 = 0x30\r\nKEY_1 = 0x31\r\nKEY_2 = 0x32\r\nKEY_3 = 0x33\r\nKEY_4 = 0x34\r\nKEY_5 = 0x35\r\nKEY_6 = 0x36\r\nKEY_7 = 0x37\r\nKEY_8 = 0x38\r\nKEY_9 = 0x39\r\nKEY_A = 0x1E\r\nKEY_B = 0x42\r\nKEY_C = 0x43\r\nKEY_D = 0x20\r\nKEY_E = 0x12\r\nKEY_F = 0x46\r\nKEY_G = 0x47\r\nKEY_H = 0x48\r\nKEY_I = 0x49\r\nKEY_J = 0x4A\r\nKEY_K = 0x4B\r\nKEY_L = 0x4C\r\nKEY_M = 0x4D\r\nKEY_N = 0x4E\r\nKEY_O = 0x4F\r\nKEY_P = 0x50\r\nKEY_Q = 0x10\r\nKEY_R = 0x52\r\nKEY_S = 0x1F\r\nKEY_T = 0x54\r\nKEY_U = 0x55\r\nKEY_V = 0x56\r\nKEY_W = 0x11\r\nKEY_X = 0x2D\r\nKEY_Y = 0x59\r\nKEY_Z = 0x2C\r\n\r\ndef KeybdInput(code, flags):\r\n\treturn KEYBDINPUT(code, code, flags, 0, None)\r\n\r\ndef HardwareInput(message, parameter):\r\n\treturn HARDWAREINPUT(message & 0xFFFFFFFF,\r\n\t\t\t\t\t\t parameter & 0xFFFF,\r\n\t\t\t\t\t\t parameter >> 16 & 0xFFFF)\r\n\r\ndef Mouse(flags, x=0, y=0, data=0):\r\n\treturn Input(MouseInput(flags, x, y, data))\r\n\r\ndef Keyboard(code, flags=0):\r\n\treturn Input(KeybdInput(code, flags))\r\n\r\ndef Hardware(message, parameter=0):\r\n\treturn Input(HardwareInput(message, parameter))\r\n\r\n################################################################################\r\n\r\n\r\n\r\n\r\n\r\n\r\nimport time\r\n\r\nchunk = 1024\r\nFORMAT = pyaudio.paInt16\r\nCHANNELS = 2\r\nRATE = 44100\r\nRECORD_SECONDS = 10\r\n\r\n\r\ndef Pitch(signal):\r\n\tsignal = np.fromstring(signal, 'Int16');\r\n\tcrossing = [math.copysign(1.0, s) for s in signal]\r\n\tindex = find(np.diff(crossing)); \r\n\tf0=round(len(index) *RATE /(2*np.prod(len(signal))))\r\n\treturn f0;\r\n\r\ndef Pitches(signal):\r\n\tsignal = np.fromstring(signal, 'Int16');\r\n\r\n\tsignalL = signal[::2]\r\n\tcrossingL = [math.copysign(1.0, s) for s in signalL]\r\n\tindexL = find(np.diff(crossingL));\r\n\tf0L=round(len(indexL) *RATE /(2*np.prod(len(signalL))))\r\n\t\r\n\tsignalR = signal[1::2]\r\n\tcrossingR = [math.copysign(1.0, s) for s in signalR]\r\n\tindexR = find(np.diff(crossingR));\r\n\tf0R=round(len(indexR) *RATE /(2*np.prod(len(signalR))))\r\n\r\n\treturn [f0L,f0R];\r\n\r\n\r\np = pyaudio.PyAudio()\r\n\r\nstream = p.open(format = FORMAT,\r\nchannels = CHANNELS,\r\nrate = RATE,\r\ninput = True,\r\noutput = True,\r\nframes_per_buffer = chunk)\r\n\r\nnotes = {1184:\"d\", 1163:\"d\",1141:\"d\",\r\n 1098:\"c#\", 1077:\"c#\", 1120:\"c#\",\r\n 947:\"b\",969:\"b\",991:\"b\",495:\"b\",\r\n 883:\"a\", 861:\"a\", 1766:\"a\", 1744:\"a\",\r\n 775:\"g\",797:\"g\", 1550:\"g\", 1572:\"g\", 1593:\"g \",366:\"g\", 323:\"g\",345:\"g\",\r\n 711:\"f#\",732:\"f#\",1464:\"f#\",1486:\"f#\",1507:\"f#\",\r\n 646:\"e\",668:\"e\",689:\"e\",1292:\"e\",1314:\"e\",1335:\"e\",1357:\"e\",\r\n 581:\"Low D\", 560:\"Low D\", 603:\"Low D\"}\r\n#currentNote = \"\"\r\n#currentNote = \"\"\r\n#noteCounter = 0\r\n\r\ncurrentNote = [\"\",\"\"]\r\nnoteCounter = [0,0]\r\n\r\nfor i in range(0, int(RATE / chunk * RECORD_SECONDS)):\r\n\tdata = stream.read(chunk)\r\n\t'''Frequency=Pitch(data)\r\n\tif(Frequency in notes):\r\n\t\tprint(notes[Frequency])\r\n\t\tif(notes[Frequency]==currentNote and noteCounter > 2):\r\n\t\t\tif(notes[Frequency]==\"b\"):\r\n\t\t\t\tSendInput(Keyboard(KEY_X))\r\n\t\t\t\ttime.sleep(0.1)\r\n\t\t\t\tSendInput(Keyboard(KEY_X, KEYEVENTF_KEYUP))\r\n\t\t\telif(notes[Frequency]==\"a\"):\r\n\t\t\t\tSendInput(Keyboard(KEY_Z))\r\n\t\t\t\ttime.sleep(0.1)\r\n\t\t\t\tSendInput(Keyboard(KEY_Z, KEYEVENTF_KEYUP))\r\n\t\t\telif(notes[Frequency]==\"g\"):\r\n\t\t\t\tSendInput(Keyboard(VK_UP))\r\n\t\t\t\ttime.sleep(0.1)\r\n\t\t\t\tSendInput(Keyboard(VK_UP, KEYEVENTF_KEYUP))\r\n\t\t\telif(notes[Frequency]==\"f#\"):\r\n\t\t\t\tSendInput(Keyboard(VK_DOWN))\r\n\t\t\t\ttime.sleep(0.1)\r\n\t\t\t\tSendInput(Keyboard(VK_DOWN, KEYEVENTF_KEYUP))\r\n\t\t\telif(notes[Frequency]==\"e\"):\r\n\t\t\t\tSendInput(Keyboard(VK_RIGHT))\r\n\t\t\t\ttime.sleep(0.1)\r\n\t\t\t\tSendInput(Keyboard(VK_RIGHT, KEYEVENTF_KEYUP))\r\n\t\t\telif(notes[Frequency]==\"Low D\"):\r\n\t\t\t\tSendInput(Keyboard(VK_LEFT))\r\n\t\t\t\ttime.sleep(0.1)\r\n\t\t\t\tSendInput(Keyboard(VK_LEFT, KEYEVENTF_KEYUP))\r\n\t\tif(notes[Frequency]!=currentNote):\r\n\t\t\tnoteCounter = 0\r\n\t\tcurrentNote = notes[Frequency]\r\n\t\tnoteCounter += 1\r\n\telse:\r\n\t\tprint(\"Frequency: \",Frequency)\r\n\r\n\t\t'''\r\n\tFrequency = Pitches(data)\r\n\tsleep = False\r\n\tfor i in range(0,2):\r\n\t\tif(Frequency[i] in notes):\r\n\t\t\tprint(notes[Frequency[i]])\r\n\t\t\tif(notes[Frequency[i]]==currentNote[i] and noteCounter[i] > 2):\r\n\t\t\t\tif(notes[Frequency[i]]==\"b\"):\r\n\t\t\t\t\tif (i==0):\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_X))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_Q))\r\n\t\t\t\t\tsleep = True\r\n\t\t\t\telif(notes[Frequency[i]]==\"a\"):\r\n\t\t\t\t\tif(i==0):\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_Z))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_E))\r\n\t\t\t\t\tsleep = True\r\n\t\t\t\telif(notes[Frequency[i]]==\"g\"):\r\n\t\t\t\t\tif(i==0):\r\n\t\t\t\t\t\tSendInput(Keyboard(VK_UP))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_W))\r\n\t\t\t\t\tsleep = True\r\n\t\t\t\telif(notes[Frequency[i]]==\"f#\"):\r\n\t\t\t\t\tif(i==0):\r\n\t\t\t\t\t\tSendInput(Keyboard(VK_DOWN))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_S))\r\n\t\t\t\t\tsleep = True\r\n\t\t\t\telif(notes[Frequency[i]]==\"e\" or notes[Frequency[i]]==\"d\"):\r\n\t\t\t\t\tif(i==0):\r\n\t\t\t\t\t\tSendInput(Keyboard(VK_RIGHT))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_D))\r\n\t\t\t\t\tsleep = True\r\n\t\t\t\telif(notes[Frequency[i]]==\"Low D\" or notes[Frequency[i]]==\"c#\"):\r\n\t\t\t\t\tif(i==0):\r\n\t\t\t\t\t\tSendInput(Keyboard(VK_LEFT))\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\tSendInput(Keyboard(KEY_A))\r\n\t\t\t\t\tsleep = True\r\n\t\t\tif(notes[Frequency[i]]!=currentNote[i]):\r\n\t\t\t\tnoteCounter[i] = 0\r\n\t\t\tcurrentNote[i] = notes[Frequency[i]]\r\n\t\t\tnoteCounter[i] += 1\r\n\t\telse:\r\n\t\t\tprint(\"Frequency: \",Frequency[i])\r\n\tif(sleep):\r\n\t\tsleep = False\r\n\t\ttime.sleep(0.1)\r\n\t\tSendInput(Keyboard(VK_LEFT, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(VK_RIGHT, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(VK_DOWN, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(VK_UP, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_Z, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_X, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_Q, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_E, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_W, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_S, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_D, KEYEVENTF_KEYUP))\r\n\t\tSendInput(Keyboard(KEY_A, KEYEVENTF_KEYUP))\r\n","repo_name":"karl-zhu9981/SonicPlayers","sub_path":"soundtest Airsick.py","file_name":"soundtest Airsick.py","file_ext":"py","file_size_in_byte":15714,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"8144848434","text":"import urllib.parse as urlparse\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\ndef merge_dict(a, b):\n a = {k: v for k, v in a.items()}\n a.update(b)\n return a\n\n\nclass Hanjaro(object):\n\n HOST = \"hanjaro.juntong.or.kr\"\n PATH = \"/text_translater.aspx\"\n INPUT_FIELDS = {\n \"__VIEWSTATE\", \n \"__VIEWSTATEGENERATOR\", \n \"__EVENTVALIDATION\", \n \"TextBox3\"\n }\n TEXTAREA_FIELDS = {f\"TextBox{i}\" for i in range(1, 3)}\n OPTIONS = merge_dict({\n \"ddlLanguage\": \"1\",\n \"1\": \"RadioButton2\",\n \"ImageButton1.x\": \"120\",\n \"ImageButton1.y\": \"20\"\n }, {f\"CheckBox{i}\": \"on\" for i in range(1, 9)})\n\n def __init__(self):\n self.session = None\n self.data = None\n self.url = self.get_url(self.PATH)\n\n def open(self):\n self.session = requests.Session()\n\n def close(self):\n if self.session is not None:\n self.session.close()\n\n def __enter__(self):\n self.open()\n return self\n\n def get_url(self, path):\n return urlparse.urljoin(f\"http://{self.HOST}\", path)\n\n def request(self, data=None, post=False):\n assert self.session is not None, \\\n \"make sure to call `self.open()` beforehand\"\n method_kwargs = dict()\n if post:\n method = self.session.post\n if self.data is not None:\n if data is not None:\n method_kwargs = {\"data\": merge_dict(self.data, data)}\n else:\n method_kwargs = {\"data\": self.data}\n else:\n method = self.session.get\n resp = method(self.url, **method_kwargs)\n assert resp.status_code / 100 == 2, \\\n f\"something went wrong with the request. status code: {resp.status_code}\"\n bs = BeautifulSoup(resp.content.decode(), features=\"html.parser\")\n self.url = self.get_url(bs.find(id=\"form1\").attrs[\"action\"])\n self.data = dict()\n self.data.update({f: bs.find(id=f).attrs[\"value\"] \n for f in self.INPUT_FIELDS})\n self.data.update({f: bs.find(id=f).text\n for f in self.TEXTAREA_FIELDS})\n self.data.update(self.OPTIONS)\n tb2 = bs.find(id=\"TextBox2\")\n if tb2 and tb2.text:\n return tb2.text.strip()\n else:\n return\n\n def query(self, q):\n if self.data is None:\n self.request(post=False)\n return self.request(data=dict(TextBox1=q), post=True)\n\n def __exit__(self, *exc):\n self.close()\n\n","repo_name":"kaniblu/hanja-tagger","sub_path":"hanjatagger/hanjaro.py","file_name":"hanjaro.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"75"} +{"seq_id":"12573295975","text":"import secrets\n\nimport aiohttp\nimport discord\nfrom lxml import html\n\nfrom sigma.core.utilities.generic_responses import GenericResponse\n\n\nasync def cyanideandhappiness(_cmd, pld):\n \"\"\"\n :param _cmd: The command object referenced in the command.\n :type _cmd: sigma.core.mechanics.command.SigmaCommand\n :param pld: The payload with execution data and details.\n :type pld: sigma.core.mechanics.payload.CommandPayload\n \"\"\"\n comic_img_url = None\n comic_url = None\n tries = 0\n while not comic_img_url and tries < 3:\n comic_number = secrets.randbelow(6078) + 1\n comic_url = f'http://explosm.net/comics/{comic_number}/'\n async with aiohttp.ClientSession() as session:\n async with session.get(comic_url) as data:\n page = await data.text()\n root = html.fromstring(page)\n comic_element = root.cssselect('[class^=MainComic__ComicImage]')\n try:\n comic_img_url = comic_element[0][0][0].attrib.get('src')\n if comic_img_url.startswith('//'):\n comic_img_url = 'https:' + comic_img_url\n except IndexError as e:\n print(e)\n tries += 1\n if comic_img_url:\n response = discord.Embed(color=0xFF6600)\n response.set_image(url=comic_img_url)\n cnh_image = 'https://i.imgur.com/jJl7FoT.jpg'\n response.set_author(name='Cyanide and Happiness', icon_url=cnh_image, url=comic_url)\n else:\n response = GenericResponse('Failed to grab a comic, try again.').error()\n await pld.msg.channel.send(embed=response)\n","repo_name":"lu-ci/apex-sigma-core","sub_path":"sigma/modules/fun/comics/cyanideandhappiness.py","file_name":"cyanideandhappiness.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"75"} +{"seq_id":"18382087352","text":"import json\nimport time\nimport threading\nfrom getpass import getuser\nimport sys\n\nclass NameNode(threading.Thread):\n\n\tdef __init__(self, config=\"./config/test_config.json\",*args, **kwargs):\n\t\t\n\t\tsuper(NameNode, self).__init__(*args,**kwargs)\n\t\tself._stop = threading.Event()\n\t\twith open(config) as f:\n\t\t\tdata = json.load(f)\n\t\t\tself.path = data['path_to_namenodes'].replace('$USER',getuser())+\"namenode.txt\"\n\t\t\ttry:\n\t\t\t\tf = open(self.path,\"r\")\n\t\t\t\tf.close()\n\t\t\texcept:\n\t\t\t\tf = open(self.path,\"w\")\n\t\t\t\tf.close()\n\n\tdef stop(self):\n\t\tself._stop.set()\n\n\tdef stopped(self):\n\t\treturn self._stop.is_set()\n\n\tdef put(self,nnblock):\n\t\twith open(self.path,'a') as f:\n\t\t\tfor i in nnblock:\n\t\t\t\tf.write(i)\n\t\t\tprint(\"writing to namenode...done.\")\n\t\t\t\n\tdef fetch(self, name):\n\t\t#print(name)\n\t\twith open(self.path, 'r') as fp:\n\t\t\t\tfor countt, line in enumerate(fp):\n\t\t\t\t\t\tpass\n\t\t\t\tprint(countt)\n\t\t# mappper = MapReduce(Mapper, Reducer)#returning an oject of the class to mappper\n\t\tWidth = 6;#After removing irrelevent Widthords(Characters) the size of the Widthidth required\n\t\tMatrix = [[0 for x in range(Width)] for y in range(countt+1)]\n\t\t\n\t\ttry:\n\t\t\tfile1 = open(self.path,'r')\n\n\t\texcept OSError:\n\t\t\t\t#implement secondary namenode here\n\t\t\t\tprint(\"Could not open NameNode ....Terminating\");\n\t\t\t\tsys.exit(\"File not Open Error\");\n\n\t\tLines = file1.readlines();\n\t\tcount = 0;\n\t\tfor line in Lines:\n\t\t\t\tline = line.strip();\n\t\t\t\tline_array = line.split(' ');\n\t\t\t\tif line_array[0]==name:\n\t\t\t\t\tline_array.pop(1);\n\t\t\t\t\tline_array.pop(3);\n\t\t\t\t\tline_array.pop(4);\n\t\t\t\t\tline_array.pop(5);\n\n\t\t\t\t\tMatrix[count] = line_array;\n\t\t\t\t\tcount += 1;\n\t\t# print(Matrix)\n\t\treturn Matrix\n\n\t\t\t\t\n\n\tdef cat(self,name):\n\t\twith open(self.path,\"r\") as f:\n\t\t\t# print(\"lol\")\n\t\t\tfor i in f.readlines():\n\t\t\t\ts = i.split(' ')\n\t\t\t\tif s[0] != name: continue\n\t\t\t\ttry:\n\t\t\t\t\tf2 = open(s[3],\"r\")\n\t\t\t\t\t# pos = int(s[-1][0])\n\t\t\t\t\tpos = int(s[-3])\n\t\t\t\t\t# print(pos)\n\t\t\t\t\tf2.seek(pos)\n\t\t\t\t\tprint(f2.readline().rstrip('\\n'))\n\t\t\t\t\tf2.close\n\t\t\t\texcept Exception as e: print(e)\n\t\tpass\n\n\tdef run(self):\n\t\tcount = 0\n\t\t#print(self.path)\n\t\t\t#initialize\n\t\twith open(self.path) as f:\n\t\t\twhile True:\n\t\t\t\ttime.sleep(5)\n\t\t\n\n# t1 = NameNode(daemon = True)\n# t1.start()\n\n# answer = input()\n","repo_name":"Suhasr76/Yet_Another_Hadoop","sub_path":"namenode.py","file_name":"namenode.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"27575423288","text":"import argparse\nimport logging\nimport os\n\nfrom src.config import Config\nfrom src.coordinator import Coordinator\n\nlog_level = os.environ.get('LOGLEVEL', 'INFO').upper()\nlogging.basicConfig(level=log_level)\n\n\ndef run(args: argparse.Namespace):\n config = Config.read(args.config_path)\n\n Coordinator.run(config, args.seeds, args.nodes, args.base_directory, args.base_binary, args.fail_fast, args.json_output)\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Namada Load tester utility.')\n parser.add_argument(\"-bd\", \"--base-directory\", type=str, action='store', required=True,\n help='Path to binaries directory.')\n parser.add_argument(\"-s\", \"--seeds\", nargs='*', action='store', help='Space separated list of seeds.', required=True)\n parser.add_argument(\"-n\", \"--nodes\", nargs='*', action='store', help='Space separated list of nodes (ip:port).', required=True)\n parser.add_argument(\"-bb\", \"--base-binary\", type=str, action='store', help='Base binary.', default='namada')\n parser.add_argument(\"-c\", \"--config-path\", type=str, action='store', help='Path to config file.',\n default=\"configs/conf.yaml\")\n parser.add_argument(\"-ff\", \"--fail-fast\", action='store_true', help='Fail if any tx fail.', default=False)\n parser.add_argument(\"-j\", \"--json-output\", action='store_true', help='Dump result as json.', default=False)\n\n args = parser.parse_args()\n\n run(args)\n","repo_name":"heliaxdev/namada-load-testing","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"75"} +{"seq_id":"6987632021","text":"#!/usr/bin/env python3\n# _*_ coding: utf-8 _*_\n\nimport sys\nfrom package.add import get_plane\nfrom package.list import display_planes\nfrom package.select import select_planes\nfrom package.help import help\n\n\ndef main():\n \"\"\"\n Главная функция программы.\n \"\"\"\n # Список самолетов.\n planes = []\n\n # Организовать бесконечный цикл запроса команд.\n while True:\n # Запросить команду из терминала.\n command = input(\">>> \").lower()\n\n # Выполнить действие в соответствие с командой.\n if command == 'exit':\n break\n\n elif command == 'add':\n # Запросить данные о самолете.\n plane = get_plane()\n\n # Добавить словарь в список.\n planes.append(plane)\n # Отсортировать список в случае необходимости.\n if len(planes) > 1:\n planes.sort(key=lambda item: item.get('destination', ''))\n\n elif command == 'list':\n # Отобразить все самолеты.\n display_planes(planes)\n\n elif command.startswith('select '):\n # Разбить команду на части для выделения пункта назначения.\n part = command.split(' ', maxsplit=1)\n com = part[1]\n\n # Выбрать самолеты заданного типа\n selected = select_planes(planes, com)\n # Отобразить выбранные самолеты\n display_planes(selected)\n\n elif command == 'help':\n help()\n else:\n print(f\"Неизвестная команда {command}\", file=sys.stderr)\n\n\nif __name__ == '__main__':\n main()","repo_name":"dshayderov/lw_2.13","sub_path":"Project/Индивидуальное задание 2/ind_2.py","file_name":"ind_2.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"9585660925","text":"\"\"\"Script to write an EMu import file\"\"\"\nfrom minsci import xmu\n\n\n# XMuRecord is the generic subclass for writing EMu-friendly XML\nrec = xmu.XMuRecord({\n 'CatPrefix': 'G',\n 'CatNumber': '3551',\n 'CatSuffix': '00',\n})\n\n# Add a classification term from etaxonomy by irn or name. Note that because\n# IdeTaxonRef_tab is a grid, we provide the values as a list.\nrec['IdeTaxonRef_tab'] = [{'irn': 1004090}]\nrec['IdeTaxonRef_tab'] = [{'ClaScientificName': 'Diamond'}]\n\n# Linking by irn is a common operation and consistent across all modules, so\n# XMuRecord assumes that a list of integers assigned to a reference are irns.\nrec['IdeTaxonRef_tab'] = [1004090]\n\nrec.module = 'ecatalogue' # specify the module\nrec.expand() # fill out grids and references\nxmu.write('import.xml', [rec], 'ecatalogue') # create the import\n","repo_name":"adamancer/minsci","sub_path":"minsci/examples/write_update.py","file_name":"write_update.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"3629438793","text":"from tkinter import *\nfrom tkinter import ttk, messagebox\nimport pymysql\n\nclass Student():\n def __init__(self, root):\n self.root = root\n self.root.title(\"Fee Management System\")\n self.root.geometry(\"1576x900\")\n\n # All Variables\n self.name_var = StringVar()\n self.date_var = StringVar()\n self.instru_var = StringVar()\n self.amount_var = StringVar()\n\n self.type_var = StringVar()\n self.search_var = StringVar()\n\n name = Label(self.root, text='Fee Management System',\n font=('Courier New', 40, 'bold'), bg='cyan',\n fg='white', borderwidth=2, relief='solid')\n name.pack(side=TOP, fill=X)\n # ---------------- To fill details ----------------\n fillDetails = Frame(self.root, borderwidth=2, relief='solid', bg='red')\n fillDetails.place(x=10, y=75, width=400, height=715)\n # Title\n fillTitle = Label(fillDetails, text = \"Details\", font=('Courier New', 25, 'bold'))\n fillTitle.grid(row=0, columnspan=2, padx=125)\n # Name\n name_label = Label(fillDetails, text=\"Name: \",\n font=('Courier New', 20, 'bold'), bg='red', fg='white', pady=20)\n name_label.grid(row=1, column=0, sticky='w')\n name_entry = Entry(fillDetails, width=15, textvariable=self.name_var, \n font=('Courier New', 15, 'bold'), borderwidth=2, relief='solid')\n name_entry.grid(row=1, column=1, sticky='w')\n\n # Date\n date_label = Label(fillDetails, text='Date of Fee \\nSubmission: ',\n bg='red', fg='white', font=('Courier New', 15, 'bold'))\n date_label.grid(row=2, column=0, sticky='w')\n date_entry = Entry(fillDetails, width=15, textvariable = self.date_var,\n font=('Courier New', 15, 'bold'), borderwidth=2, relief='solid')\n date_entry.grid(row=2, column=1, sticky='w', pady= 40)\n\n # Amount\n fee_label = Label(fillDetails, text='Amount: ₹', bg='red',\n fg='white', font=('Courier New', 15, 'bold'))\n fee_label.grid(row=3, column=0, sticky='w')\n fee_entry = Entry(fillDetails, width=15,textvariable=self.amount_var,\n font=('Courier New', 15, 'bold'), borderwidth=2, relief='solid')\n fee_entry.grid(row=3, column=1, sticky='w', pady=40)\n\n # Instrument\n intru_label= Label(fillDetails, text='Instrument: ', bg='red',\n fg='white', font=('Courier New', 15, 'bold'))\n intru_label.grid(row=4, column=0, sticky='w')\n intru_entry = ttk.Combobox(fillDetails, textvariable=self.instru_var, font=('Courier New', 15, 'bold'),\n width=15, state='readonly')\n intru_entry['values'] = ('Guitar', 'Drum', 'Synthesizer', 'Vocal', 'Octapad',\n 'Harmonium', 'Others')\n intru_entry.grid(row=4, column=1, sticky='w', pady=40)\n\n # ------------- Buttons -----------------\n btn_frame = Frame(fillDetails, bg='red')\n btn_frame.place(x=10, y= 475, width=375, height=60) \n # Add\n Addbtn = Button(btn_frame, text='Add', \n width=6, font=('Courier New', 15, 'bold'),\n bg='cyan', fg='white', command=self.register)\n Addbtn.grid(row=0, column=0, sticky='w', padx=5, pady=10)\n #Clear\n clearbtn = Button(btn_frame, text='Clear', width=6, font=('Courier New', 15, 'bold'),\n bg='cyan', fg='white', command=self.clear)\n clearbtn.grid(row=0, column=1, sticky='w', padx=5, pady=10)\n # Update\n updatebtn = Button(btn_frame, text='Update', \n width=6, font=('Courier New', 15, 'bold'),\n bg='cyan', fg='white', command=self.update)\n updatebtn.grid(row=0, column=2, sticky='w', padx=5, pady=10)\n #Delete\n deletebtn = Button(btn_frame, text='Delete', \n width=6, font=('Courier New', 15, 'bold'),\n bg='cyan', fg='white', command=self.delete)\n deletebtn.grid(row=0, column=3, sticky='w', padx=5, pady=10)\n \n # ---------------- To show the stored Data --------------\n showDetails = Frame(self.root, borderwidth=2, relief='solid', bg='red')\n showDetails.place(x=420, y=75, width=1100, height=715)\n\n type_search = Label(showDetails, text='Search By: ', bg='red',\n fg='white', font=('Courier New', 20, 'bold'))\n type_search.grid(row=0, column=0, sticky='w')\n type_search_box = ttk.Combobox(showDetails, textvariable=self.type_var, font=('Courier New', 15, 'bold'),\n width=15, state='readonly')\n type_search_box['values'] = ('Name', 'Date', 'Instrument')\n type_search_box.grid(row=0, column=1, sticky='w')\n\n search_label = Label(showDetails, text='Search: ', bg='red',\n fg='white', font=('Courier New', 20, 'bold'))\n search_label.grid(row=1, column=0, sticky='w', pady=15, padx= 10)\n search_entry = Entry(showDetails, width=25,\n font=('Courier New', 20, 'bold'), borderwidth=2, relief='solid', textvariable=self.search_var)\n search_entry.grid(row=1, column=1, sticky='w')\n searchBtn = Button(showDetails, text='Search',\n font=('Courier New', 15, 'bold'), borderwidth=2, relief='solid', command=self.type_data)\n searchBtn.grid(row=1, column=2, padx= 10)\n showBtn = Button(showDetails, text='Show All',\n font=('Courier New', 15, 'bold'), borderwidth=2, relief='solid', command=self.fetch_data)\n showBtn.grid(row=1, column=3, padx= 10)\n\n # ------------- Table ---------------\n table_frame = Frame(showDetails, width=1070, height=550, borderwidth=2, relief='solid')\n table_frame.place(x=10, y= 125)\n\n scrollX = Scrollbar(table_frame, orient=HORIZONTAL)\n scrollY = Scrollbar(table_frame, orient=VERTICAL)\n\n self.details = ttk.Treeview(table_frame, columns=('Name', 'Submission Date',\n 'Instrument', 'Amount'), xscrollcommand= scrollX.set,\n yscrollcommand= scrollY.set)\n\n scrollX.pack(side=BOTTOM, fill=X)\n scrollY.pack(side=RIGHT, fill=Y)\n scrollX.config(command=self.details.xview)\n scrollY.config(command=self.details.yview)\n\n self.details.heading('Name', text='Name')\n self.details.heading('Submission Date', text='Submission Date')\n self.details.heading('Instrument', text='Instrument')\n self.details.heading('Amount', text='Amount')\n self.details['show'] = 'headings'\n self.details.column('Name', width=300)\n self.details.column('Submission Date', width=200)\n self.details.column('Instrument', width=300)\n self.details.column('Amount', width=200)\n self.details.pack(fill=BOTH, expand=1)\n self.details.bind('', self.get_cursor)\n self.fetch_data()\n \n def register(self):\n con = pymysql.connect(host='localhost', user='root', password='', database='fms')\n cur=con.cursor()\n cur.execute(\"insert into fees values(%s, %s, %s, %s)\", (self.name_var.get(),\n self.date_var.get(),\n self.instru_var.get(),\n self.amount_var.get()\n ))\n con.commit()\n self.fetch_data()\n self.clear()\n con.close()\n \n def fetch_data(self):\n con = pymysql.connect(host='localhost', user='root', password='', database='fms')\n cur=con.cursor()\n cur.execute(\"select * from fees\")\n rows= cur.fetchall()\n if len(rows) != 0:\n self.details.delete(*self.details.get_children())\n for row in rows:\n self.details.insert('', END, values=row)\n con.commit()\n con.close()\n\n def clear(self):\n self.name_var.set('')\n self.date_var.set('')\n self.amount_var.set('')\n self.instru_var.set('')\n\n def get_cursor(self, event):\n cursor_row = self.details.focus()\n content = self.details.item(cursor_row)\n row = content['values']\n self.name_var.set(row[0])\n self.date_var.set(row[1])\n self.instru_var.set(row[2])\n self.amount_var.set(row[3])\n \n\n def update(self):\n con = pymysql.connect(host='localhost', user='root', password='', database='fms')\n cur=con.cursor()\n cur.execute(\"update fees set Name=%s, Date=%s,Instrument=%s,Amount=%s where Name=%s\", (self.name_var.get(),\n self.date_var.get(),\n self.instru_var.get(),\n self.amount_var.get(),\n self.name_var.get()\n ))\n con.commit()\n self.fetch_data()\n self.clear()\n con.close()\n\n def delete(self):\n con = pymysql.connect(host='localhost', user='root', password='', database='fms')\n cur=con.cursor()\n cur.execute(\"delete from fees where name=%s\", self.name_var.get())\n con.commit()\n con.close()\n self.fetch_data()\n self.clear()\n\n def type_data(self):\n con = pymysql.connect(host='localhost', user='root', password='', database='fms')\n cur=con.cursor()\n cur.execute(\"select * from fees where \" + str(self.type_var.get()) + \" LIKE '%\"\n + str(self.search_var.get()) + \"%'\")\n rows= cur.fetchall()\n if len(rows) != 0:\n self.details.delete(*self.details.get_children())\n for row in rows:\n self.details.insert('', END, values=row)\n con.commit()\n con.close()\n self.search_var.set(\"\")\n\nroot = Tk()\nob = Student(root)\nroot.mainloop()","repo_name":"Aryan190902/Fee-Management-System","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"41141227710","text":"from django.shortcuts import render\nfrom hello.models import Asset\n# Create your views here.\nfrom django.http import HttpResponse,QueryDict,HttpResponseRedirect\n\n\ndef asset_add(request):\n if request.method == 'POST':\n data = request.POST\n ip = data.get('ip')\n hostname = data.get('hostname')\n data1 = {'ip': ip, 'hostname': hostname }\n # print(data1)\n try:\n Asset.objects.create(**data1)\n except Exception as e:\n return HttpResponse(e)\n return HttpResponseRedirect('/hello/')\n else:\n return render(request, 'hello/add.html')\n\n\ndef asset_update(request, asset_id):\n asset = Asset.objects.get(id=asset_id)\n if request.method == 'POST':\n data = request.POST\n # print(type(data))\n ip = data.get('ip')\n hostname = data.get('hostname')\n data1 = {'ip': ip, 'hostname': hostname}\n try:\n Asset.objects.filter(id=asset_id).update(**data1)\n except Exception as e:\n return HttpResponse(e)\n return HttpResponseRedirect('/hello/')\n else:\n return render(request, 'hello/update.html', {'Asset': asset})\n\n\n\ndef asset_delete(request, asset_id):\n # asset = Asset.objects.get(id=asset_id)\n if request.method == 'POST':\n try:\n Asset.objects.filter(id=int(asset_id)).delete()\n except Exception as e:\n return HttpResponse(e)\n return HttpResponseRedirect('/hello/')\n else:\n return HttpResponse('404')\n\n\n\ndef asset_list(request):\n asset = Asset.objects.all()\n return render(request, 'hello/list.html', {'Asset': asset})\n\n\n\n","repo_name":"MagePY27/P27M02","sub_path":"day02/ops/hello/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"75"} +{"seq_id":"28424941370","text":"# creare un set\n# della parola \"STRINGA\"\n# tramite set comprehension e tramite ciclo\n# tramite print visualizzare che non sono ordinati\n\ns = {char for char in \"STRINGA\"}\nprint(s)\n\ns2 = set()\nfor c in \"STRINGA\":\n s2.add(c)\nprint(s2)\n\n","repo_name":"docentemaurocasadei/corso-python","sub_path":"esercizi/5_set_nonordinati.py","file_name":"5_set_nonordinati.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"12592413819","text":"# Lab 7\r\n# Authors: @Min Ahn @Pierre Smith @Joe Harkins\r\n# October 8, 2020\r\n\r\n\r\nclass Board:\r\n def __init__(self, board, x_turn, turns):\r\n self.board = board\r\n self.x_turn = x_turn\r\n self.turns = turns\r\n\r\n def getMoves(self):\r\n possibleMoves = []\r\n index = 0\r\n while index < len(self.board):\r\n if self.board[index] == '-':\r\n possibleMoves.append(index)\r\n index += 1\r\n return possibleMoves\r\n\r\n def makeMove(self, move):\r\n if self.x_turn:\r\n self.board[move] = 'X'\r\n self.x_turn = False\r\n self.turns += 1\r\n else:\r\n self.board[move] = 'O'\r\n self.x_turn = True\r\n self.turns += 1\r\n\r\n def undo(self,move):\r\n self.board[move] = '-'\r\n self.x_turn = not self.x_turn\r\n self.turns -= 1\r\n\r\n def __repr__(self):\r\n boardString = \"\\n\"\r\n j = 0\r\n for i in range(0, 9): # for (i = 0; i < 9; i++)\r\n boardString += str(self.board[i])\r\n boardString += ' '\r\n j += 1\r\n if j % 3 == 0:\r\n boardString += \"\\n\"\r\n return boardString\r\n\r\ndef GetBestMove(current_board):\r\n bestScore = float(\"-inf\")\r\n bestMove = None\r\n for move in current_board.getMoves():\r\n current_board.makeMove(move)\r\n score = MiniMax(current_board)\r\n current_board.undo(move)\r\n if(score > bestScore):\r\n bestScore = score\r\n bestMove = move\r\n\r\n current_board.makeMove(bestMove)\r\n return bestMove\r\n\r\n\r\ndef GameOver(current_board):\r\n winning_combos = [(0,1,2),(3,4,5),(6,7,8),\r\n (0,3,6),(1,4,7),(2,5,8),\r\n (0,4,8),(2,4,6)]\r\n\r\n for combo in winning_combos:\r\n winner = current_board.board[combo[0]]\r\n if winner == 'X' or winner == 'O':\r\n if winner == current_board.board[combo[1]] and winner == current_board.board[combo[2]]:\r\n if winner == 'X':\r\n return 1\r\n else:\r\n return -1\r\n\r\n if current_board.turns == 9:\r\n return 0\r\n\r\n return '-'\r\n\r\ndef MiniMax(current_board):\r\n if GameOver(current_board) != '-':\r\n return GameOver(current_board)\r\n else:\r\n if current_board.x_turn:\r\n best = float('-inf')\r\n for move in current_board.getMoves():\r\n current_board.makeMove(move)\r\n best = max(best, MiniMax(current_board))\r\n current_board.undo(move)\r\n return best\r\n else:\r\n best = float('inf')\r\n for move in current_board.getMoves():\r\n current_board.makeMove(move)\r\n best = min(best, MiniMax(current_board))\r\n current_board.undo(move)\r\n return best\r\n\r\n\r\n#~~~~~~~~~~~~~~~~~~~~~~~~~MAIN~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#\r\nboard = Board(['-', '-', '-', '-', '-','-', '-', '-', '-'], True, 0)\r\nprint(board)\r\nprint (GetBestMove(board))\r\n\r\nboard = Board(['X', 'O', 'X', 'O', 'O','X', '-', '-', '-'], True, 0)\r\nprint(board)\r\nprint (GetBestMove(board))\r\n\r\n\r\n\r\n\r\n","repo_name":"JHarkins24/Python-Practice","sub_path":"MinMax Tic-Tac-Toe.py","file_name":"MinMax Tic-Tac-Toe.py","file_ext":"py","file_size_in_byte":3170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"1697531771","text":"import mindspore.nn as nn\nimport mindspore.ops as ops\nimport math\nfrom src.model_utils.config import config\nfrom scipy.stats import truncnorm\nimport mindspore.common.initializer as ini\nfrom mindspore import Tensor\nimport mindspore.common.dtype as mstype\nimport numpy as np\nclass eca_layer(nn.Cell):\n \"\"\"Constructs a ECA module.\n Args:\n channel: Number of channels of the input feature map\n k_size: Adaptive selection of kernel size\n \"\"\"\n def __init__(self, channel, k_size=3):\n super(eca_layer, self).__init__()\n self.avg_pool = ops.AdaptiveAvgPool2D(1)\n self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, has_bias=False,pad_mode='pad')\n self.sigmoid = nn.Sigmoid()\n self.squeeze=ops.Squeeze(-1)\n self.softmax = nn.Softmax(axis = 1)\n\n def construct(self, x):\n # feature descriptor on the global spatial information\n y = self.avg_pool(x)\n # Two different branches of ECA module\n y = ops.expand_dims(self.conv(self.squeeze(y).transpose(0,-1, -2)).transpose(0,-1, -2),-1)\n\n # Multi-scale information fusion\n # y = self.sigmoid(y)\n y = self.softmax(y)\n\n return x * y.expand_as(x) + x, y\n\nclass eca_layer_drop(nn.Cell):\n \"\"\"Constructs a ECA module.\n Args:\n channel: Number of channels of the input feature map\n k_size: Adaptive selection of kernel size\n \"\"\"\n def __init__(self, channel, k_size=3, mask_style = 'larger', p = 0.5):\n super(eca_layer_drop, self).__init__()\n self.avg_pool = ops.AdaptiveAvgPool2d(1)\n self.conv =nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, has_bias=False,pad_mode='pad')\n self.sigmoid = nn.Sigmoid()\n self.mask_style = mask_style\n self.squeeze=ops.Squeeze(axis=-1)\n self.drop_rate = p\n \n def dy_drop(self, y):\n b, c,_,_ = y.size() \n # mask = torch.zeros(size = y.shape) # cpu\n mask = ops.Zeros(size = y.shape).cuda() # gpu\n sort_sum, index = ops.Sort(y, dim=1, descending= True)\n if self.mask_style == 'uniform': \n mask_index = index[:,::2] \n # mask_index = index[:,::3] \n elif self.mask_style == 'larger':\n mask_index = index[:, :int(c * self.drop_rate)] \n else:\n assert 0 , 'please choose from (uniform, larger)' \n mask = mask.scatter_(1, mask_index, 1)\n y = y.mul(mask)\n y = y.view(b, c, 1, 1)\n return y \n\n\n def construct(self, x):\n # feature descriptor on the global spatial information\n y = self.avg_pool(x)\n\n # Two different branches of ECA module\n y = ops.expand_dims(self.conv(self.squeeze(y).transpose(0,-1, -2)).transpose(0,-1, -2),-1)\n\n # Multi-scale information fusion\n y = self.sigmoid(y)\n \n y = self.dy_drop(y)\n\n return x * y.expand_as(x)\n","repo_name":"Terror03/GCP-DropCov","sub_path":"representation/eca_layer.py","file_name":"eca_layer.py","file_ext":"py","file_size_in_byte":2929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"74874163765","text":"# 이것도 한 10분컷 가능하겠지.. 3시 8분 시작~\n# 풀기 전 생각!\n# 동 서 남 북 -> [1, 2, 3, 4] x +- 그리고 y - + 이렇게 주고 있음.\n# input 다 받아서, 어차피 다 합쳐질거기 때문에\n# 결국 작은 사각형, 큰 사각형 두개로 구성되는거라서, 사각형 성질을 이용해보자.\n# 가장 큰 변의 길이가 가로 길이일것이고(width), 그게 되는 두 변의 합이 가로 쪼개기가 될것이다.(a +b로 보자) 찾는건 brute force로 찾자~\n# 받은 list에서 a, b, width를 빼면, 그 뒤에 남는건 뻔하지. (length, 그리고 나머지것들 c + d 라고 하자.)\n\n#3시 14분. 자세히 보니까 방향이 동서남북으로 이어지면 그 선분들은 짤라먹는 선분이 아니다. => 이렇게 접근하면 답은 나오겠지만 더 복잡해진다. 심플하게 패턴을 찾자.\n\n# 이거는 idx에 loop가 돌게해서, end tip에 걸렸을때는 0으로 다시 갈 수 있도록 조치를 취해야한다.\n# if statement로 가능함.\n\ndensity = int(input())\ndirection_distance_in_list = [list(map(int, input().split())) for _ in range(6)]\nsmall_square = []\nlarge_square = []\nsmall_square_idx = []\nfor i in range(6): # idx -1 되는건 괜찮으니까 놔두고 6되는것만 막아주자.\n if i == 5: # a,b는 i의 앞,뒤 idx를 말하는것.\n a, b = 4, 0\n else:\n a, b = i-1, i+1\n\n if direction_distance_in_list[a][0] == direction_distance_in_list[b][0]: # 패턴발견. idx의 앞뒤 direction이 겹친다면~\n small_square.append(direction_distance_in_list[i][1]) # 해당 길이를 넣어준다.\n large_square.append(direction_distance_in_list[a][1] + direction_distance_in_list[b][1]) # 근데 앞뒤의 방향이 같다는게, 두 변을 합쳤을때 큰 변이 된다는 뜻이다. 문제의 패턴과 특성을 이용한다.\nprint((large_square[0] * large_square[1] - small_square[0]* small_square[1]) * density) # 이렇게 풀다니.. 어이가 없음.. 근데 간단한거를 25분 걸렸습니다.\n\n\n\n # small_square_idx.append(i) # 그리고 그 변이 있는 idx를 넣어서 계산 쉽게. # idx 두개를 더해서 같은 방향이 아니면 그게 긴 변이라는 뜻임.\n","repo_name":"CessnaJ/CessnaJ_TIL","sub_path":"Algorithm/Algorithm/SSAFY/SSAFY IM 대비/BOJ-2477 참외.py","file_name":"BOJ-2477 참외.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"3662894330","text":"import requests #requests library for HTTP requests\nimport json\nimport os\nimport time\n\ndef pixabay_Download():\n #PIXABAY API INFO\n payload = {\n \"key\" : \"API_KEY_GOES_HERE\", #API_KEY_GOES_HERE\n \"q\" : \"Wallpaper\",\n \"image_type\":\"photo\",\n #\"category\" : \"backgrounds\",\n \"editors_choice\" : \"True\",\n \"per_page\" : \"10\",\n \"safesearch\" : \"True\"\n }\n\n #REQUEST\n url = \"https://pixabay.com/api/\"\n response = requests.get(url,params = payload)\n pixabay_json_res = json.loads(response.text.encode(\"UTF-8\"))\n\n #SAVE PIXABAY JSON RESPONSE TO A FILE (not necessary, but good to look at for troubleshooting purposes)\n with open(\"./res/response.json\", \"w\") as resfile:\n resfile.write(json.dumps(pixabay_json_res, indent=4, sort_keys=True))\n print(\"JSON Response for Pixabay Written\")\n\n #CHECKS IF FILE EXISTS, IF IT DOES, SKIPS TO THE NEXT ONE\n for pictureURL in pixabay_json_res[\"hits\"]:\n if os.path.isfile(\"./res/img/\"+str(pictureURL[\"id\"])+\".jpg\"):\n print(str(pictureURL[\"id\"])+\".jpg\", \"already exists\")\n else:\n with open (\"./res/img/\"+str(pictureURL[\"id\"])+\".jpg\", \"wb\") as outfile:\n outfile.write(requests.get(pictureURL[\"webformatURL\"]).content)\n print (pictureURL[\"id\"], \"Successfully Douwnloaded!\" )\n print(\"Finished getting pixabay images\")\n\n#FIND CURRENT DIRECTORY\nworking_dir = os.getcwd()\nprint(\"Working Directory\",working_dir)\n\ntry:\n pixabay_Download()\nexcept:\n print (\"Can't reach Pixabay, check internet connection or API info\")\n\n#lIST THE FILES IN THE /res/img FOLDER\nbackground_Pictures_List = os.listdir(\"./res/img\") #Make sure you run this before calling API fuction.\n\n#SETTING THE BACKGROUND (GNOME)\nbackground_Index = 0\nwhile (True):\n print(\"picture\",background_Index)\n os.system(\"gsettings set org.gnome.desktop.background picture-uri file://\"+working_dir+\"/res/img/\"+background_Pictures_List[background_Index]) #set background image (GNOME)\n print(\"Background Set\")\n time.sleep(5) #Time to wait before setting the next background in seconds.\n background_Index+=1\n if background_Index == len(background_Pictures_List): #If at the end of list of pictures, start over.\n print(\"Back to 0\")\n background_Index = 0\n","repo_name":"justusenumbers/The_Background_Project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"32929887130","text":"from collections import namedtuple\nfrom typing import *\n\nDiceValues = List[int]\n\n\nclass CombState:\n def __init__(self, score: int, dice: DiceValues, applied: bool):\n self.score = score\n self.dice = dice\n self.applied = applied\n\n\ndef straight(info: CombState) -> CombState:\n if len(set(info.dice)) == 6:\n return CombState(info.score + 1500, [], True)\n\n return CombState(info.score, info.dice, False)\n\n\ndef high_straight(info: CombState) -> CombState:\n high = set([2, 3, 4, 5, 6])\n\n if len(set(info.dice) & high) == 5:\n new_dice = info.dice.copy()\n for die in high:\n new_dice.remove(die)\n return CombState(info.score + 750, new_dice, True)\n\n return CombState(info.score, info.dice, False)\n\n\ndef low_straight(info: CombState) -> CombState:\n low = set([1, 2, 3, 4, 5])\n\n if len(set(info.dice) & low) == 5:\n new_dice = info.dice.copy()\n for die in low:\n new_dice.remove(die)\n return CombState(info.score + 750, new_dice, True)\n\n return CombState(info.score, info.dice, False)\n\n\ndef __of_a_kind(info: CombState, count: int) -> CombState:\n counter = Counter(info.dice)\n priority = [1, 6, 5, 4, 3, 2]\n\n for number in priority:\n if counter[number] >= count:\n new_dice = info.dice.copy()\n for _ in range(count):\n new_dice.remove(number)\n\n bonus = 1000 if number == 1 else 100 * number\n shift = 2**(count-3)\n new_score = info.score + bonus * shift\n\n return CombState(new_score, new_dice, True)\n\n return CombState(info.score, info.dice, False)\n\n\ndef six_of_a_kind(info: CombState) -> CombState:\n return __of_a_kind(info, 6)\n\n\ndef five_of_a_kind(info: CombState) -> CombState:\n return __of_a_kind(info, 5)\n\n\ndef four_of_a_kind(info: CombState) -> CombState:\n return __of_a_kind(info, 4)\n\n\ndef three_of_a_kind(info: CombState) -> CombState:\n return __of_a_kind(info, 3)\n\n\ndef two_of_a_kind(info: CombState) -> CombState:\n counter = Counter(info.dice)\n priority = [1, 5]\n\n for number in priority:\n if counter[number] >= 2:\n new_dice = info.dice.copy()\n for _ in range(2):\n new_dice.remove(number)\n\n bonus = 200 if number == 1 else 100\n new_score = info.score + bonus\n return CombState(new_score, new_dice, True)\n\n return CombState(info.score, info.dice, False)\n\n\ndef single(info: CombState) -> CombState:\n priority = [1, 5]\n\n for number in priority:\n if number in info.dice:\n new_dice = info.dice.copy()\n new_dice.remove(number)\n\n bonus = 100 if number == 1 else 50\n new_score = info.score + bonus\n\n return CombState(new_score, new_dice, True)\n\n return CombState(info.score, info.dice, False)\n\n\nall_combinations = [\n straight,\n high_straight,\n low_straight,\n six_of_a_kind,\n five_of_a_kind,\n four_of_a_kind,\n three_of_a_kind,\n two_of_a_kind,\n single\n]\n","repo_name":"keenua/farkle","sub_path":"src/farkle/logic/combinations.py","file_name":"combinations.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"73318518004","text":"# int, float, complex Numeric Types\n\n# complex are used to define real numbers\n# used only when two real numbers are into action.\n\nvolt = complex(2345324534345340j)\nvolt1 = complex(20535354363545j)\nprint(volt1 - volt)\n\ncurrent = 12\nprint(current - volt)\n\n","repo_name":"aryanz-co-in/python-indentation-datatypes-tamil","sub_path":"data_types/numeric_type/complex_type.py","file_name":"complex_type.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"43219828461","text":"\"\"\"\n{sys.executable} scripts/torch_time_series.py multi \\\n --num_epochs 4 --window_size 10 --num_steps 500 --batch_size 100 --learning-rate .010\\\n--weight_decay 0.00 \\\n{input} {output}\n\"\"\"\nimport xarray as xr\nimport numpy as np\nimport torch\nfrom lib.torch import train_multistep_objective, TrainingData\nimport json, sys\nfrom contextlib import redirect_stdout\nimport logging\n\nhandlers = [logging.FileHandler(snakemake.log[0]), logging.StreamHandler()]\nlogging.basicConfig(level=logging.DEBUG, handlers=handlers)\n\nparams = snakemake.params[0]\ni = snakemake.input\n\nlogging.info(\"Starting training script\")\n\n# define paths\nfiles = [\n (i.tend, ('FQT', 'FSL')),\n (i.cent, ('QV', 'TABS', 'QN', 'QP', 'QRAD')),\n (i.stat, ('p', 'RHO')),\n (i['2d'], ('LHF', 'SHF', 'SOLIN')),\n]\n\n# get training region\ny = params.pop('y', np.r_[:64])\nlogging.info(f\"Training on y-indices: {y}\")\n\ndef safesel(da, **kwargs):\n kwargs['y'] = y\n sel = {dim: kwargs[dim] for dim in da.dims\n if dim in kwargs}\n return da.isel(**sel)\n\ndef _train(x):\n return safesel(x, x=slice(64, None))\n\ndef _test(x):\n return safesel(x, x=slice(0, 64))\n\n\nlogging.info(\"Loading training data\")\ntrain = TrainingData.from_var_files(files, post=_train)\nlogging.info(\"Size of training dataset: %.2f MB\"%(train.nbytes/1e6))\n\nlogging.info(\"Loading testing data\")\ntest = TrainingData.from_var_files(files, post=_test)\nlogging.info(\"Size of testing dataset: %.2f MB\"%(test.nbytes/1e6))\n\nlogging.info(\"Calling train_multistep_objective\")\noutput_dir = params.pop('output_dir')\ntrain_multistep_objective(train, test, output_dir, **params)\n","repo_name":"nbren12/nn_atmos_param","sub_path":"scripts/train_neural_network.py","file_name":"train_neural_network.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"} +{"seq_id":"39730839279","text":"# Perform a left rotation by k times\n\ndef left_rotate(arr, k):\n k = k % len(arr)\n\n reverse(0, k-1, arr)\n reverse(k, len(arr)-1, arr)\n reverse(0, len(arr)-1, arr)\n\n\ndef reverse(start, end, arr):\n i = start\n j = end\n\n while i <= j:\n swap(arr, i, j)\n i += 1\n j -= 1\n return arr\n\n\ndef swap(arr, i, j):\n temp = arr[i]\n arr[i] = arr[j]\n arr[j] = temp\n\nif __name__ == \"__main__\":\n arr = [1, 2, 3, 4, 5, 6]\n left_rotate(arr, 2)\n\n print(arr)","repo_name":"abuchi247/datastructures","sub_path":"arrays/questions/leftRotation.py","file_name":"leftRotation.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"31159008945","text":"# from django.contrib.auth import models\nfrom django.shortcuts import get_object_or_404\n# from .models import Student, Teacher, Attendance\nfrom django.utils import timezone\n\nfrom .forms import *\nfrom django.contrib import messages\n# from django.core.paginator import Paginator\nfrom django.contrib.auth import login as auth_login, logout as auth_logout, authenticate\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth.decorators import login_required\n# from django.contrib.auth.models import *\nfrom django.shortcuts import render, redirect\nfrom django.views import generic\nfrom django.urls import reverse_lazy\nimport sys\n\n\n# Create your views here.\ndef home(request):\n return render(request, 'Schools/home.html', {'Schools': home})\n\n\ndef logout(request):\n auth_logout(request)\n return redirect('/home')\n\n\ndef login(request):\n if request.method == \"POST\":\n form = AuthenticationForm(request, data=request.POST)\n if form.is_valid():\n username = form.cleaned_data['username']\n password = form.cleaned_data['password']\n user = authenticate(username=username, password=password)\n if user is not None:\n auth_login(request, user)\n # logger.info(\"123\")\n return redirect('/home')\n\n form = AuthenticationForm(request)\n return render(request, 'registration/login.html', {'form': form})\n\n\nclass SignUpView(generic.CreateView):\n form_class = UserCreationForm\n success_url = reverse_lazy('login')\n template_name = 'registration/signup.html'\n\n\n@login_required\ndef teacher_list(request):\n teachers = Teacher.objects.all()\n return render(request, \"Schools/teacher_list.html\", {'teachers': teachers})\n\n\n@login_required\ndef teacher_create(request):\n if request.method == \"POST\":\n form = TeacherForm(request.POST)\n if form.is_valid():\n teacher = form.save(commit=False)\n teacher.save()\n teachers = Teacher.objects.all()\n return render(request, 'Schools/teacher_list.html',\n {'teachers': teachers})\n else:\n form = TeacherForm()\n return render(request, \"Schools/teacher_create.html\", {'form': form})\n\n\n@login_required\ndef teacher_edit(request, pk):\n teacher = get_object_or_404(Teacher, pk=pk)\n\n if request.method == \"POST\":\n form = TeacherForm(request.POST, instance=teacher)\n if form.is_valid():\n teacher = form.save(commit=False)\n teacher.save()\n teachers = Teacher.objects.all()\n return render(request, 'Schools/teacher_list.html', {'teachers': teachers})\n\n else:\n form = TeacherForm(instance=teacher)\n return render(request, \"Schools/teacher_edit.html\", {'forms': form})\n\n\n@login_required\ndef teacher_delete(request, pk):\n teacher = get_object_or_404(Teacher, pk=pk)\n teacher.delete()\n return redirect('Schools:teacher_list')\n\n\n@login_required\ndef student_list(request):\n students = Student.objects.all()\n return render(request, \"Schools/student_list.html\", {'students': students})\n\n\n@login_required\ndef student_create(request):\n if request.method == \"POST\":\n form = StudentForm(request.POST)\n\n if form.is_valid():\n student = form.save(commit=False)\n student.save()\n students = Student.objects.all()\n return render(request, 'Schools/student_list.html',\n {'students': students})\n else:\n form = StudentForm()\n\n return render(request, \"Schools/student_create.html\", {'forms': form})\n\n\n@login_required\ndef student_edit(request, pk):\n student = get_object_or_404(Student, pk=pk)\n if request.method == \"POST\":\n form = StudentForm(request.POST, instance=student)\n\n if form.is_valid():\n student = form.save(commit=False)\n student.save()\n students = Student.objects.all()\n return render(request, 'Schools/student_list.html', {'students': students})\n else:\n form = StudentForm(instance=student)\n return render(request, \"Schools/student_edit.html\", {'form': form})\n\n\n@login_required\ndef student_delete(request, pk):\n student = get_object_or_404(Student, pk=pk)\n student.delete()\n return redirect(\"Schools:student_list\")\n\n\n@login_required\ndef attendance_list(request):\n if request.method == \"POST\":\n fromdate = request.POST.get('fromdate')\n todate = request.POST.get('todate')\n searchresult = Attendance.objects.filter(date__range=[fromdate, todate])\n return render(request, \"Schools/attendance_list.html\", {'attendances': searchresult})\n else:\n attendances = Attendance.objects.all()\n return render(request, \"Schools/attendance_list.html\", {'attendances': attendances})\n\n\n@login_required\ndef attendance_create(request):\n if request.method == \"POST\":\n form = AttendanceForm(request.POST)\n\n if form.is_valid():\n attendance = form.save(commit=False)\n attendance.save()\n attendances = Attendance.objects.all()\n return render(request, 'Schools/attendance_list.html',\n {'attendances': attendances})\n else:\n form = AttendanceForm()\n\n return render(request, \"Schools/attendance_create.html\", {'forms': form})\n\n\n@login_required\ndef attendance_edit(request, pk):\n attendance = get_object_or_404(Attendance, pk=pk)\n if request.method == \"POST\":\n form = AttendanceForm(request.POST, instance=attendance)\n\n if form.is_valid():\n attendance = form.save(commit=False)\n attendance.save()\n attendances = Attendance.objects.all()\n return render(request, 'Schools/attendance_list.html', {'attendances': attendances})\n else:\n form = AttendanceForm(instance=attendance)\n return render(request, \"Schools/attendance_edit.html\", {'form': form})\n\n\n@login_required\ndef attendance_delete(request, pk):\n attendance = get_object_or_404(Attendance, pk=pk)\n attendance.delete()\n return redirect(\"Schools:attendance_list\")\n","repo_name":"bkuduvar/SchoolMgmt","sub_path":"Schools/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"21424824451","text":"def extract_weapon_components_row_data(raw_data_row):\n result = []\n weapon_name = raw_data_row[0].strip()\n raw_data = raw_data_row[1]\n # print(name)\n if 'Research' in raw_data:\n raw_data = raw_data.split('Research')[0]\n\n if \"Manufacturing\" not in raw_data:\n print(\"Manufacturing keyword not found: \" + weapon_name)\n return []\n\n weapon_blueprint_data = raw_data.split('')\n n = 0\n for component_blueprint_data in weapon_blueprint_data:\n result_name = weapon_name\n \n if n > 0:\n component_name = component_blueprint_data.split('')[0])\n if component_data == 'Platinum':\n continue\n if len(component_data) < 2:\n continue\n component_name = component_data[0]\n component_amount = component_data[1]\n append_component(result_name, component_name, component_amount, components)\n # components.append([result_name, component_name, component_amount])\n return components\n\ndef append_component(result_name, component_name, component_amount, components):\n for component_row in components:\n row_component_name = component_row[1]\n if row_component_name == component_name:\n component_row[2] += component_amount\n return\n components.append([result_name, component_name, component_amount])\n\n# For some reason the wiki has two ways of formatting component display - \n# one where components are referred to as blueprints (e.g. neuroptics blueprint), \n# and one where they are simply referred to by their type (e.g. neuroptics).\n# This causes different algorithms to be required for each format.\n\ndef extract_component_data_without_blueprint_in_title(result_name, value):\n # component_name = value.split('title=\"')[1].split('\"')[0]\n # component_name = component_name.split('Weapon')[1]\n # component_name = result_name + component_name\n return extract_component_data(result_name, value)\n\ndef extract_component_data_with_blueprint_in_title(result_name, value):\n # component_name = value.split(\"Blueprint\")[0]\n return extract_component_data(result_name, value)\n\ndef extract_component_row(value):\n name = \"\"\n amount = 0\n split_data = value.split('')\n if len(split_data) < 2:\n return ''\n\n name_data_block = split_data[0]\n amount_data_block = split_data[1]\n if 'title' in name_data_block:\n name = name_data_block.split('title=\"')[1].split('\"')[0]\n elif 'img alt=\"' in name_data_block:\n name = name_data_block.split('alt=\"')[1].split('.png')[0]\n else:\n print('no title found')\n return ''\n amount = 1\n if '
' in amount_data_block:\n amount_string = amount_data_block.split(\"\")[0].split('
')[1].strip()\n amount_string = amount_string.strip()\n amount_string = amount_string.replace(',', '')\n amount = int(amount_string)\n return [name, amount]","repo_name":"JimmyHaglund/TennoTab","sub_path":"Data/data_extractor_weapon_blueprints.py","file_name":"data_extractor_weapon_blueprints.py","file_ext":"py","file_size_in_byte":3455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"71861850807","text":"sami = input().split()\nA = float(sami[0])\nB = float(sami[1])\nC = float(sami[2])\n\narea_triangulo = (A*C)/2\narea_circulo = (3.14159*C*C)\narea_trapezio = (A+B)*C/2\narea_quadrado = (B*B)\narea_retangulo = (A*B)\n\nprint(f\"TRIANGULO: {area_triangulo:.3f}\\nCIRCULO: {area_circulo:.3f}\\nTRAPEZIO: {area_trapezio:.3f}\\nQUADRADO: {area_quadrado:.3f}\\nRETANGULO: {area_retangulo:.3f}\")\n","repo_name":"GlauberBalsani/desafios-uri-judge","sub_path":"python/1012/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"35509911861","text":"import time\r\nimport re\r\nimport telebot\r\nfrom telebot.apihelper import edit_message_reply_markup\r\nfrom telebot.types import InlineKeyboardButton, InlineKeyboardMarkup\r\nfrom help_db import DBHelper\r\nfrom config import *\r\n\r\nbot = telebot.TeleBot(token)\r\n\r\n######################\r\n# GENERATE KEYBOARDS #\r\n######################\r\n\r\ndef get_topics_keyboard(state_button=4):\r\n with DBHelper() as db:\r\n topics = db.get_post_topics()\r\n \r\n keyboard = InlineKeyboardMarkup()\r\n\r\n for i in range(state_button - 4, state_button):\r\n if i < len(topics):\r\n keyboard.add(InlineKeyboardButton(text=topics[i], callback_data=f'topic_{topics[i]}'))\r\n else:\r\n break\r\n\r\n button_next = InlineKeyboardButton(text=\"»\", callback_data=\"next\")\r\n button_prev = InlineKeyboardButton(text=\"«\", callback_data=\"prev\")\r\n if state_button == 4 and state_button < len(topics):\r\n keyboard.add(button_next)\r\n elif state_button > 4 and state_button < len(topics):\r\n keyboard.add(button_prev, button_next)\r\n elif state_button > 4:\r\n keyboard.add(button_prev)\r\n \r\n return keyboard\r\n\r\n\r\ndef get_topic_links_keyboard(len_links, state_link=0):\r\n keyboard = InlineKeyboardMarkup()\r\n \r\n button_next = InlineKeyboardButton(text=\"»\", callback_data=\"next_link\")\r\n button_prev = InlineKeyboardButton(text=\"«\", callback_data=\"prev_link\")\r\n button_topics = InlineKeyboardButton(text=\"выбрать другую тему\", callback_data=\"alt_topic\")\r\n \r\n if state_link == 0 and state_link < len_links:\r\n keyboard.add(button_next)\r\n elif state_link > 0 and state_link < len_links:\r\n keyboard.add(button_prev, button_next)\r\n elif state_link > 0 and state_link == len_links:\r\n keyboard.add(button_prev)\r\n keyboard.add(button_topics)\r\n\r\n return keyboard\r\n\r\n\r\n######################\r\n######################\r\n######################\r\n\r\n@bot.message_handler(commands=['start'])\r\ndef start(message):\r\n text = ('Привет! Это бот с катологом статей канала [hello world](https://t.me/hw_code), '\r\n 'разделенным по темам для удобного поиска нужного материала.\\n\\n'\r\n '_Чтобы выбрать нужную тему нажмите_ /topics')\r\n bot.delete_message(message.chat.id, message.message_id)\r\n bot.send_message(message.chat.id, text, parse_mode='Markdown')\r\n \r\n with DBHelper() as db:\r\n db.add_user(message.chat.id)\r\n\r\n\r\n############################\r\n# TOPICS CALLBACK HENDLERS #\r\n############################\r\n\r\n@bot.message_handler(commands=['topics'])\r\ndef get_topics_hendler(message):\r\n keyboard = get_topics_keyboard()\r\n text = \"Выберите тему:\"\r\n\r\n bot.delete_message(message.chat.id, message.message_id - 1)\r\n bot.delete_message(message.chat.id, message.message_id)\r\n bot.send_message(message.chat.id, text=text, reply_markup=keyboard)\r\n \r\n with DBHelper() as db:\r\n db.set_user_state_links(message.chat.id, 0) #nulling position on links\r\n\r\n\r\ndef get_topics(message):\r\n keyboard = get_topics_keyboard()\r\n text = \"Выберите тему:\"\r\n\r\n bot.delete_message(message.chat.id, message.message_id - 1)\r\n bot.delete_message(message.chat.id, message.message_id)\r\n bot.send_message(message.chat.id, text=text, reply_markup=keyboard)\r\n \r\n with DBHelper() as db:\r\n db.set_user_state_links(message.chat.id, 0)\r\n\r\n\r\n\r\n\r\n@bot.callback_query_handler(func=lambda call: call.data in ['next', 'prev'])\r\ndef call_next_listTopics(call):\r\n if call.data == 'next':\r\n with DBHelper() as db:\r\n state = db.get_user_state_topics(call.from_user.id)\r\n db.set_user_state_topics(call.from_user.id, state + 4)\r\n keyboard = get_topics_keyboard(state + 4)\r\n else:\r\n with DBHelper() as db:\r\n state = db.get_user_state_topics(call.from_user.id)\r\n db.set_user_state_topics(call.from_user.id, state - 4)\r\n keyboard = get_topics_keyboard(state - 4)\r\n\r\n bot.edit_message_reply_markup(call.message.chat.id, call.message.message_id, reply_markup=keyboard)\r\n\r\n\r\n###########################\r\n# LINKS CALLBACK HENDLERS #\r\n###########################\r\n\r\n@bot.callback_query_handler(func=lambda call: call.data in re.findall(r'topic_[\\w\\s]+', call.data))\r\ndef call_next_listTopic_links(call):\r\n enter_topic = re.match(r'topic_([\\w\\s]+)', call.data)\r\n with DBHelper() as db:\r\n links = db.get_topic_links(enter_topic.group(1))\r\n db.set_user_state_topics(call.from_user.id, 4) #4 - inital state user in topics list\r\n db.update_user_enter_topic(call.from_user.id, enter_topic.group(1)) \r\n\r\n keyboard = get_topic_links_keyboard(len(links) - 1)\r\n bot.delete_message(call.message.chat.id, call.message.message_id)\r\n bot.send_message(call.message.chat.id, links[0], reply_markup=keyboard)\r\n\r\n\r\n@bot.callback_query_handler(func=lambda call: call.data in ['prev_link', 'next_link'])\r\ndef call_next_link(call): \r\n if call.data == 'prev_link':\r\n with DBHelper() as db:\r\n state = db.get_user_state_links(call.from_user.id)\r\n enter_topic = db.get_user_enter_topic(call.from_user.id)\r\n links = db.get_topic_links(enter_topic)\r\n db.set_user_state_links(call.from_user.id, state - 1)\r\n state -= 1\r\n else:\r\n with DBHelper() as db:\r\n state = db.get_user_state_links(call.from_user.id)\r\n enter_topic = db.get_user_enter_topic(call.from_user.id)\r\n links = db.get_topic_links(enter_topic)\r\n db.set_user_state_links(call.from_user.id, state + 1)\r\n state += 1\r\n\r\n keyboard = get_topic_links_keyboard(len(links) - 1, state)\r\n bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id, text=links[state], reply_markup=keyboard)\r\n\r\n\r\n@bot.callback_query_handler(func=lambda call: call.data == 'alt_topic')\r\ndef call_topics_list(call):\r\n #bot.register_next_step_handler(call.message, get_topics)\r\n keyboard = get_topics_keyboard()\r\n text = \"Выберите тему:\"\r\n\r\n bot.delete_message(call.message.chat.id, call.message.message_id)\r\n bot.send_message(call.message.chat.id, text=text, reply_markup=keyboard)\r\n \r\n with DBHelper() as db:\r\n db.set_user_state_links(call.message.chat.id, 0)\r\n \r\n\r\n#################\r\n# ADMINS FUNCKS #\r\n#################\r\nregex_link = 'https:\\/\\/[a-zA-Z0-9а-я\\-]+.[a-zA-Zа-я]?[a-zA-Zа-я]?\\/[@a-zA-Z0-9\\-\\/\\_]*'\r\nregex_topic = '[а-яА-Яa-zA-Z\\s]+'\r\n\r\n@bot.message_handler(content_types=['text'],\r\n func=lambda message: message.chat.id in admin_id and\r\n message.text.split(' ')[0] == 'set')\r\ndef set_link(message):\r\n \r\n res = re.match(rf'(set\\s{regex_link}\\s{regex_topic})', message.text)\r\n if res is not None and res.group(0) == message.text:\r\n result = re.findall(rf'set\\s({regex_link})\\s({regex_topic})', message.text)\r\n post_link = result[0][0]\r\n post_topic = result[0][1]\r\n with DBHelper() as db:\r\n res = db.set_post(post_link, post_topic)\r\n bot.send_message(message.chat.id, res)\r\n else:\r\n bot.send_message(message.chat.id, 'Incorrect format request')\r\n\r\n\r\n@bot.message_handler(content_types=['text'],\r\n func=lambda message: message.chat.id in admin_id and\r\n message.text.split(' ')[0] == 'update_link') \r\ndef update_link(message):\r\n res = re.match(rf'(update_link\\s{regex_link}\\s{regex_link})', message.text)\r\n if res is not None and res.group(0) == message.text:\r\n post_link = message.text.split(' ')[1]\r\n new_link = message.text.split(' ')[2]\r\n with DBHelper() as db:\r\n res = db.update_post_link(post_link, new_link)\r\n bot.send_message(message.chat.id, res)\r\n else:\r\n bot.send_message(message.chat.id, 'Incorrect format request')\r\n\r\n@bot.message_handler(content_types=['text'],\r\n func=lambda message: message.chat.id in admin_id and\r\n message.text.split(' ')[0] == 'update_topic')\r\ndef update_topic(message):\r\n res = re.match(rf'(update_topic\\s{regex_link}\\s{regex_topic}\\s{regex_topic})', message.text)\r\n if res is not None and res.group(0) == message.text:\r\n result = re.findall(rf'update_topic\\s({regex_link})\\s({regex_topic})\\s({regex_topic})', message.text)\r\n post_link = result[0][0]\r\n old_topic = result[0][1]\r\n new_topic = result[0][2]\r\n\r\n with DBHelper() as db:\r\n res = db.update_post_topic(post_link, old_topic, new_topic)\r\n bot.send_message(message.chat.id, res)\r\n else:\r\n bot.send_message(message.chat.id, 'Incorrect format request')\r\n\r\n\r\n@bot.message_handler(content_types=['text'],\r\n func=lambda message: message.chat.id in admin_id and\r\n message.text.split(' ')[0] == 'delete')\r\ndef delete_link(message):\r\n res = re.match(rf'(delete\\s{regex_link})', message.text)\r\n if res is not None and res.group(0) == message.text:\r\n res = re.search(rf'delete\\s({regex_link})', message.text)\r\n with DBHelper() as db:\r\n del_res = db.del_post_link(res.group(1))\r\n bot.send_message(message.chat.id, del_res)\r\n\r\n\r\n#################\r\n#################\r\n#################\r\n\r\ndef main():\r\n bot.infinity_polling(True)\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n","repo_name":"MercyFlesh/tg_library_bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"18705463101","text":"import glob\nimport pickle\nimport re\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n#from tensorflow import keras\n\n\nimport numpy as np\nfrom keras import optimizers\nfrom keras.applications.vgg16 import VGG16\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import Flatten\nfrom keras.models import Model\n\nfrom CQ500DataGenerator import DataGenerator\n\n# define our variables\n\nnum_slices_original = 28\nnum_slices_per_subject = 24 # always using 16 slices per subject\nstart_slice = (num_slices_original - num_slices_per_subject)/2\nend_slice = start_slice + num_slices_per_subject\n\nstart_slice = int(start_slice)\nend_slice = int(end_slice)\n\n# create list of IDs from all slices\n# data_dir = './'\n# all_IDs = set()\n# all_Slices = glob.glob(r\".\\Slices\\CQ500-CT-*\")\n#print(all_Slices)\n#原程序\n# for item in all_Slices:\n# subj_match = re.match(data_dir + \"Slices\\CQ500-CT-[0-9]+_Slice[0-9]+.npy\", item)\n# print(item)\n# print(subj_match)\n# subj_id = subj_match.group(1)\n# all_IDs = all_IDs.union([subj_id])\n#修改后\n\ndata_dir = './'\nall_IDs = set()\nall_Slices = glob.glob(r\".\\Slices\\CQ500-CT-*\")\n\nfor item in all_Slices:\n subj_match = re.match(r\".*CQ500-CT-([0-9]+)_Slice[0-9]+\\.npy\", item)\n subj_id = subj_match.group(1)\n #all_IDs= all_IDs.union(subj_id)\n all_IDs.add(subj_id)\n\n\n# use half of the IDs for testing\n# print(all_IDs.__sizeof__())\n# all_IDs.remove(0)\n# all_IDs.remove(6)\nall_IDs = list(all_IDs)\nhalf = int(np.floor(len(all_IDs)/10*9))\nall_IDs = all_IDs[0:half]\n\nall_IDs_slices = list()\nfor subj_id in all_IDs:\n for slice_num in range(start_slice, end_slice):\n all_IDs_slices.append(subj_id + \"_Slice\" + str(slice_num)) #一个元素样例 5_Slice22\n\n\n# # create a dict of labels for all slices\n# all_labels = dict()\n# label_files = glob.glob(data_dir + \"Labels/CQ500-CT-*\")\n# for item in label_files:\n# slice_match = re.match(data_dir + \"Labels/(CQ500-CT-[0-9]+)_Slice([0-9]+).npy\", item)\n# subj_id = slice_match.group(1)\n# slice_num = slice_match.group(2)\n# # data_obj = np.load(item)\n# data_dict = data_obj.item()\n# all_labels[subj_id + \"_Slice\" + slice_num] = int(data_dict[\"label\"]) # store labes as 1 or 0 for True or False\n\n\n# divide list into train and validation\npercentage_to_train = 0.8\ncutoff_index = int(np.floor(len(all_IDs) * percentage_to_train)) * num_slices_per_subject\ntraining_IDs = all_IDs_slices[0:cutoff_index]\nvalidation_IDs = all_IDs_slices[cutoff_index:]\n\ntraining_generator = DataGenerator(training_IDs)\nvalidation_generator = DataGenerator(validation_IDs)\n\n# num_train_set = 40\n\n\n#add our own fully connected layers for the final classification\n\n# create the base pre-trained model\nbase_model = VGG16(include_top=False, weights='imagenet', \n input_tensor=None, input_shape=(224, 224, 3), pooling=None)\n\n#now we can start to fine-tune the model\n# first: train only the top layers (which were randomly initialized)\n# i.e. freeze all convolutional InceptionV3 layers\nfor layer in base_model.layers:\n layer.trainable = False\n\nx = base_model.output\n#flatten it\nx = Flatten()(x)\nx = Dropout(0.2)(x)\n# let's add a fully-connected layer\nx = Dense(100, activation='relu')(x)\nx = Dropout(0.2)(x)\n#x = LeakyReLU(alpha=.01)(x)\nx = Dense(100, activation='relu')(x)\nx = Dropout(0.2)(x)\n\n#another fully-connected layer\n#x = Dense(200, activation='relu')(x)\n# and a logistic layer -- let's say we have 200 classes\npredictions = Dense(1, activation='softmax')(x)\n\n# this is the model we will train\nmodel = Model(inputs=base_model.input, outputs=predictions)\n\nprint(\"model summary:\")\nprint(model.summary())\nmy_optimizer=optimizers.Adam(lr=0.00001)\n\n# compile the model (should be done *after* setting layers to non-trainable)\n#model.compile(loss='categorical_crossentropy', optimizer=my_optimizer, metrics=['accuracy'])\nmodel.compile(loss='binary_crossentropy', optimizer=my_optimizer, metrics=['accuracy'])\nhistory = model.fit_generator(generator=training_generator, validation_data=validation_generator, epochs=5, use_multiprocessing=False)\n \nmodel.save('FifthModel.h5')\n\nwith open('FifthModel_trainHistoryDict', 'wb') as file_pi:\n pickle.dump(history.history, file_pi)\n\n","repo_name":"CosmoWood/CQ500_ConvOuch","sub_path":"Run_Imgenet_VGG16_Model.py","file_name":"Run_Imgenet_VGG16_Model.py","file_ext":"py","file_size_in_byte":4233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"35223421484","text":"'''\nMica search query.\n'''\n\nimport json\nimport sys\nfrom obiba_mica.core import MicaClient, UriBuilder\nimport csv\nfrom io import StringIO\n\nclass SearchService:\n\n def __init__(self, client: MicaClient, verbose: bool = False):\n self.client = client\n self.verbose = verbose\n\n def __make_request(self):\n request = self.client.new_request()\n request.fail_on_error()\n request.post()\n request.accept_json()\n if self.verbose:\n request.verbose()\n return request\n\n def send_search_request(self, ws, query):\n '''\n Create a new search request\n\n :param ws - REST endpoint (/variables/_rql)\n :param query - RQL query\n '''\n try:\n request = self.__make_request()\n response = request.resource(ws).content_type_form().form({'query': query}).send()\n return response.as_json()\n except Exception as e:\n print(e, file=sys.stderr)\n\n return None\n\n def __as_rql(self, name, args):\n return name + '(' + ','.join(args) + ')'\n\n def __append_rql(self, query, target, select, sort, start, limit, locale):\n _fields = self.__as_rql('fields(', select) + ')'\n _sort = self.__as_rql('sort', sort)\n _limit = self.__as_rql('limit', [str(start), str(limit)])\n statement = ','.join([_fields, _limit, _sort])\n # normalize\n q = query\n if q == None or q == '':\n q = target + '()'\n\n # hack: replace target call with statement\n if target + '()' in q:\n q = q.replace(target + '()', target + '(' + statement + ')')\n elif target + '(' in q:\n q = q.replace(target + '(', target + '(' + statement + ',')\n else:\n q = target + '(' + statement + '),' + q\n\n return q + ',locale(' + locale + ')'\n\n def __extract_label(self, labels, locale='en', locale_key='lang', value_key='value'):\n if not labels:\n return None\n label_und = None\n if labels:\n for label in labels:\n if label[locale_key] == locale:\n return label[value_key]\n if label[locale_key] == 'und':\n label_und = label[value_key]\n return label_und if label_und else ''\n\n def __new_writer(self, out, headers):\n file = sys.stdout\n if out:\n if isinstance(out, StringIO):\n file = out\n else:\n file = open(out, 'w')\n writer = csv.DictWriter(file, fieldnames=headers, escapechar='\"', quotechar='\"', quoting=csv.QUOTE_ALL)\n writer.writeheader()\n return writer\n\n def __to_string(self, value):\n if value == None:\n return ''\n return str(value)\n\n def __flatten(self, content, locale='en'):\n flat = {}\n for key in list(content.keys()):\n value = content[key]\n if type(value) is dict:\n fvalue = self.__flatten(value, locale)\n for k in fvalue:\n nk = key + '.' + k if k != locale else key\n flat[nk] = fvalue[k]\n elif type(value) is list:\n flat[key] = '|'.join(map(self.__to_string, value))\n else:\n flat[key] = self.__to_string(value)\n return flat\n\n def search_networks(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches all published networks matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n\n q = self.__append_rql(query, 'network', ['*'], ['id'], start, limit, locale)\n ws = UriBuilder(['networks', '_rql']).build()\n res = self.send_search_request(ws, q)\n if 'networkResultDto' in res and 'obiba.mica.NetworkResultDto.result' in res['networkResultDto'] and res['networkResultDto']['totalHits'] > 0:\n headers = ['id', 'name', 'acronym', 'description', 'studyIds']\n for item in res['networkResultDto']['obiba.mica.NetworkResultDto.result']['networks']:\n if 'content' in item:\n item['flat'] = self.__flatten(json.loads(item['content']), locale)\n for key in list(item['flat'].keys()):\n if key not in headers:\n headers.append(key)\n writer = self.__new_writer(out, headers)\n for item in res['networkResultDto']['obiba.mica.NetworkResultDto.result']['networks']:\n row = {\n 'id': item['id'],\n 'name': self.__extract_label(item['name'], locale),\n 'description': self.__extract_label(item['description'], locale) if 'description' in item else '',\n 'acronym': self.__extract_label(item['acronym'], locale),\n 'studyIds': '|'.join(item['studyIds']) if 'studyIds' in item else ''\n }\n if 'flat' in item:\n for key in item['flat']:\n row[key] = item['flat'][key]\n writer.writerow(row)\n\n def __search_studies(self, query='', start=0, limit=100, locale='en', out=None):\n q = self.__append_rql(query, 'study', ['acronym', 'name', 'objectives', 'model'], ['id'], start, limit, locale)\n ws = UriBuilder(['studies', '_rql']).build()\n res = self.send_search_request(ws, q)\n if 'studyResultDto' in res and 'obiba.mica.StudyResultDto.result' in res['studyResultDto'] and res['studyResultDto']['totalHits'] > 0:\n headers = ['id', 'name', 'acronym', 'objectives']\n for item in res['studyResultDto']['obiba.mica.StudyResultDto.result']['summaries']:\n if 'content' in item:\n item['flat'] = self.__flatten(json.loads(item['content']), locale)\n for key in list(item['flat'].keys()):\n if key not in headers:\n headers.append(key)\n writer = self.__new_writer(out, headers)\n for item in res['studyResultDto']['obiba.mica.StudyResultDto.result']['summaries']:\n row = {\n 'id': item['id'],\n 'name': self.__extract_label(item['name'], locale),\n 'objectives': self.__extract_label(item['objectives'], locale) if 'objectives' in item else '',\n 'acronym': self.__extract_label(item['acronym'], locale)\n }\n if 'flat' in item:\n for key in item['flat']:\n row[key] = item['flat'][key]\n writer.writerow(row)\n\n def search_studies(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches all published individual studies matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n typeQuery = self.__as_rql('study', [self.__as_rql('in', ['Mica_dataset.className', 'Study'])])\n theQuery = '%s,%s' % (typeQuery, query) if query is not None and len(query) > 0 else typeQuery\n self.__search_studies(theQuery, start, limit, locale, out)\n\n def search_initiatives(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches all published initiatives matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n typeQuery = self.__as_rql('study', [self.__as_rql('in', ['Mica_dataset.className', 'HarmonizationStudy'])])\n theQuery = '%s,%s' % (typeQuery, query) if query is not None and len(query) > 0 else typeQuery\n self.__search_studies(theQuery, start, limit, locale, out)\n\n\n def search_study_populations(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches the populations of a individual studies matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n q = self.__append_rql(query, 'study', ['populations.name', 'populations.description', 'populations.model'], ['id'], start, limit, locale)\n ws = UriBuilder(['studies', '_rql']).build()\n res = self.send_search_request(ws, q)\n if 'studyResultDto' in res and 'obiba.mica.StudyResultDto.result' in res['studyResultDto']:\n headers = ['id', 'name', 'description', 'studyId']\n for item in res['studyResultDto']['obiba.mica.StudyResultDto.result']['summaries']:\n if 'populationSummaries' in item:\n for pop in item['populationSummaries']:\n if 'content' in pop:\n pop['flat'] = self.__flatten(json.loads(pop['content']), locale)\n for key in list(pop['flat'].keys()):\n if key not in headers:\n headers.append(key)\n writer = self.__new_writer(out, headers)\n for item in res['studyResultDto']['obiba.mica.StudyResultDto.result']['summaries']:\n if 'populationSummaries' in item:\n for pop in item['populationSummaries']:\n row = {\n 'id': item['id'] + ':' + pop['id'],\n 'name': self.__extract_label(pop['name'], locale),\n 'description': self.__extract_label(pop['description'], locale) if 'description' in pop else '',\n 'studyId': item['id']\n }\n if 'flat' in pop:\n for key in pop['flat']:\n row[key] = pop['flat'][key]\n writer.writerow(row)\n\n def search_study_dces(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches all published data collection events of individual studies matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n q = self.__append_rql(query, 'study', ['populations.dataCollectionEvents'], ['id'], start, limit, locale)\n ws = UriBuilder(['studies', '_rql']).build()\n res = self.send_search_request(ws, q)\n if 'studyResultDto' in res and 'obiba.mica.StudyResultDto.result' in res['studyResultDto']:\n headers = ['id', 'name', 'description', 'studyId', 'populationId', 'start', 'end']\n for item in res['studyResultDto']['obiba.mica.StudyResultDto.result']['summaries']:\n if 'populationSummaries' in item:\n for pop in item['populationSummaries']:\n if 'dataCollectionEventSummaries' in pop:\n for dce in pop['dataCollectionEventSummaries']:\n if 'content' in dce:\n dce['flat'] = self.__flatten(json.loads(dce['content']), locale)\n for key in list(dce['flat'].keys()):\n if key not in headers:\n headers.append(key)\n writer = self.__new_writer(out, headers)\n for item in res['studyResultDto']['obiba.mica.StudyResultDto.result']['summaries']:\n if 'populationSummaries' in item:\n for pop in item['populationSummaries']:\n if 'dataCollectionEventSummaries' in pop:\n for dce in pop['dataCollectionEventSummaries']:\n row = {\n 'id': item['id'] + ':' + pop['id'] + ':' + dce['id'],\n 'name': self.__extract_label(dce['name'], locale),\n 'description': self.__extract_label(dce['description'], locale) if 'description' in dce else '',\n 'studyId': item['id'],\n 'populationId': item['id'] + ':' + pop['id'],\n 'start': dce['start'] if 'start' in dce else '',\n 'end': dce['end'] if 'end' in dce else ''\n }\n if 'flat' in dce:\n for key in dce['flat']:\n row[key] = dce['flat'][key]\n writer.writerow(row)\n\n def __search_datasets(self, query='', start=0, limit=100, locale='en', out=None):\n q = self.__append_rql(query, 'dataset', ['*'], ['id'], start, limit, locale)\n ws = UriBuilder(['datasets', '_rql']).build()\n res = self.send_search_request(ws, q)\n if 'datasetResultDto' in res and 'obiba.mica.DatasetResultDto.result' in res['datasetResultDto']:\n headers = ['id', 'name', 'acronym', 'description', 'variableType', 'entityType', 'studyId', 'populationId', 'dceId']\n for item in res['datasetResultDto']['obiba.mica.DatasetResultDto.result']['datasets']:\n if 'content' in item:\n item['flat'] = self.__flatten(json.loads(item['content']), locale)\n for key in list(item['flat'].keys()):\n if key not in headers:\n headers.append(key)\n writer = self.__new_writer(out, headers)\n for item in res['datasetResultDto']['obiba.mica.DatasetResultDto.result']['datasets']:\n study_id = ''\n population_id = ''\n dce_id = ''\n if 'obiba.mica.CollectedDatasetDto.type' in item:\n study_id = item['obiba.mica.CollectedDatasetDto.type']['studyTable']['studyId']\n population_id = study_id + ':' + item['obiba.mica.CollectedDatasetDto.type']['studyTable']['populationId']\n dce_id = item['obiba.mica.CollectedDatasetDto.type']['studyTable']['dceId']\n if 'obiba.mica.HarmonizedDatasetDto.type' in item:\n study_id = item['obiba.mica.HarmonizedDatasetDto.type']['harmonizationTable']['studyId']\n row = {\n 'id': item['id'],\n 'name': self.__extract_label(item['name'], locale),\n 'acronym': self.__extract_label(item['acronym'], locale),\n 'description': self.__extract_label(item['description'], locale) if 'description' in item else '',\n 'variableType': item['variableType'],\n 'entityType': item['entityType'],\n 'studyId': study_id,\n 'populationId': population_id,\n 'dceId': dce_id\n }\n if 'flat' in item:\n for key in item['flat']:\n row[key] = item['flat'][key]\n writer.writerow(row)\n\n def search_datasets(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches all published collected datasets matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n typeQuery = self.__as_rql('dataset', [self.__as_rql('in', ['Mica_dataset.className', 'StudyDataset'])])\n theQuery = '%s,%s' % (typeQuery, query) if query is not None and len(query) > 0 else typeQuery\n self.__search_datasets(theQuery, start, limit, locale, out)\n\n def search_protocols(self, query='', start=0, limit=100, locale='en', out=None):\n \"\"\"\n Searches all published harmonization protocols matching the given query\n\n :param query - RQL query\n :param start - starting index from which to retrieve data\n :param limit - length of data to be retrieved\n :param locale - default is 'en'\n :param out - output file, if ignored the result is send to STDOUT\n \"\"\"\n typeQuery = self.__as_rql('dataset', [self.__as_rql('in', ['Mica_dataset.className', 'HarmonizationDataset'])])\n theQuery = '%s,%s' % (typeQuery, query) if query is not None and len(query) > 0 else typeQuery\n self.__search_datasets(theQuery, start, limit, locale, out)\n\n def search_variables(self, query='', start=0, limit=100, locale='en', out=None):\n q = self.__append_rql(query, 'variable', ['*'], ['id'], start, limit, locale)\n ws = UriBuilder(['variables', '_rql']).build()\n res = self.send_search_request(ws, q)\n\n def category_label(category):\n if 'attributes' in category:\n labels = [self.__extract_label(label['values'], locale) for label in [a for a in category['attributes'] if a['name'] == 'label']]\n return labels[0] if len(labels) > 0 else ''\n else:\n return ''\n\n if 'variableResultDto' in res and 'obiba.mica.DatasetVariableResultDto.result' in res['variableResultDto']:\n headers = ['id', 'name', 'label', 'description', 'valueType', 'nature', 'categories', 'categories.missing', 'categories.label',\n 'datasetId', 'studyId', 'populationId', 'dceId',\n 'variableType', 'mimeType', 'unit', 'referencedEntityType', 'repeatable', 'occurrenceGroup']\n for item in res['variableResultDto']['obiba.mica.DatasetVariableResultDto.result']['summaries']:\n if 'annotations' in item:\n for annot in item['annotations']:\n key = annot['taxonomy'] + '.' + annot['vocabulary']\n if key not in headers:\n headers.append(key)\n writer = self.__new_writer(out, headers)\n for item in res['variableResultDto']['obiba.mica.DatasetVariableResultDto.result']['summaries']:\n row = {\n 'id': item['id'],\n 'name': item['name'],\n 'label': self.__extract_label(item['variableLabel'], locale) if 'variableLabel' in item else '',\n 'description': self.__extract_label(item['description'], locale) if 'description' in item else '',\n 'datasetId': item['datasetId'],\n 'studyId': item['studyId'],\n 'populationId': item['populationId'] if 'populationId' in item else '',\n 'dceId': item['dceId'] if 'dceId' in item else '',\n 'variableType': item['variableType'],\n 'valueType': item['valueType'] if 'valueType' in item else '',\n 'nature': item['nature'] if 'nature' in item else '',\n 'mimeType': item['mimeType'] if 'mimeType' in item else '',\n 'unit': item['unit'] if 'unit' in item else '',\n 'referencedEntityType': item['referencedEntityType'] if 'referencedEntityType' in item else '',\n 'repeatable': item['repeatable'] if 'repeatable' in item else '',\n 'occurrenceGroup': item['occurrenceGroup'] if 'occurrenceGroup' in item else ''\n }\n if 'categories' in item:\n row['categories'] = '|'.join([c['name'] for c in item['categories']])\n row['categories.missing'] = '|'.join([str(c['missing']) for c in item['categories']])\n row['categories.label'] = '|'.join(map(category_label, item['categories']))\n if 'annotations' in item:\n for annot in item['annotations']:\n key = annot['taxonomy'] + '.' + annot['vocabulary']\n row[key] = annot['value']\n writer.writerow(row)\n\n\n @classmethod\n def add_arguments(self, parser):\n '''\n Add tags command specific options\n\n :param parser - commandline args parser\n '''\n parser.add_argument('--out', '-o', required=False, help='Output file (default is stdout).')\n parser.add_argument('--target', '-t', required=True, choices=['variable', 'dataset', 'study', 'population', 'dce', 'network'],\n help='Document type to be searched for.')\n parser.add_argument('--query', '-q', required=False, help='Query that filters the documents. If not specified, no filter is applied.')\n parser.add_argument('--start', '-s', required=False, type=int, default=0, help='Start search at document position.')\n parser.add_argument('--limit', '-lm', required=False, type=int, default=100, help='Max number of documents.')\n parser.add_argument('--locale', '-lc', required=False, default='en', help='The language for labels.')\n\n @classmethod\n def do_command(self, args):\n '''\n Execute search command\n\n :param args - commandline args\n '''\n service = SearchService(MicaClient.build(MicaClient.LoginInfo.parse(args)), args.verbose)\n if args.target == 'network':\n service.search_networks(query=args.query, start=args.start, limit=args.limit, locale=args.locale, out=args.out)\n elif args.target == 'study':\n self.search_studies(query=args.query, start=args.start, limit=args.limit, locale=args.locale, out=args.out)\n elif args.target == 'population':\n self.search_study_populations(query=args.query, start=args.start, limit=args.limit, locale=args.locale, out=args.out)\n elif args.target == 'dce':\n self.search_study_dces(query=args.query, start=args.start, limit=args.limit, locale=args.locale, out=args.out)\n elif args.target == 'dataset':\n self.search_datasets(query=args.query, start=args.start, limit=args.limit, locale=args.locale, out=args.out)\n elif args.target == 'variable':\n service.search_variables(query=args.query, start=args.start, limit=args.limit, locale=args.locale, out=args.out)\n","repo_name":"obiba/mica-python-client","sub_path":"obiba_mica/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":22285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"20399095172","text":"# user_1\r\n# user_2\r\n\r\n# user_1 ==> harf ABCDEFGH\r\n# user_1 ==> son 12345678\r\n# user_2 ==> harf ABCDEFGH\r\n# user_2 ==> son 12345678\r\n\r\n# # in\r\n# string = 'abcd'\r\n# print('A' in string)\r\n\r\n# user_1_harf = input('Foydalanuvchi 1. Harfni kiriting...') # str\r\n# shaxmat_doskasi_harflari = 'ABCDEFGH'\r\n#\r\n# if user_1_harf in shaxmat_doskasi_harflari:\r\n# print(True)\r\n# else:\r\n# print(False)\r\n\r\n\r\nuser_1_word = input('1 - chi Foydalanuvchi Harfni kiriting: ')\r\nuser_1_number = int(input('1 - chi Foydalanuvchi Sonni kiriting: '))\r\n\r\nshaxmat_harflari = 'ABCDEFGH'\r\n\r\nuser_2_word = input('2 - chi Foydalanuvchi Harfni kiriting: ')\r\nuser_2_number = int(input('2 - chi Foydalanuvchi Sonni kriiting: '))\r\n\r\n\r\nif user_1_word not in shaxmat_harflari and user_2_word not in shaxmat_harflari:\r\n print('To`g`ri harf kiriting...')\r\nelse:\r\n if not user_1_number >= 1 and not user_1_number <= 8 and not user_2_number >= 1 and not user_2_number <= 8:\r\n print('To`g`ri son kiriting...')\r\n else:\r\n if user_1_word == user_2_word and user_1_number == user_2_number:\r\n print(True)\r\n else:\r\n print(False)","repo_name":"arifmasrab/Dasturchilar_darslar","sub_path":"6-dars_ProblemSolving.py","file_name":"6-dars_ProblemSolving.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"12180991843","text":"# 加载Python自带 或通过pip安装的模块\nimport json\nimport os\nfrom functools import partial\nimport multiprocessing as mp\n# import pathos.multiprocessing as mp\nimport numpy as np\nimport random\nimport time\n\n\ndef _do_attack(inp_lines, kw_freq_dict):\n import json\n import os\n from model import get_bert_inference_model, FastTextInferenceModel, get_fasttext_inference_model\n from attackers import ObscenityAttacker, RuleBasedAttacker\n from pytorch_pretrained_bert import BertTokenizer\n import jieba\n fasttext_model_path = 'data/materials/mini.ftz'\n # fasttext_model_path = ['data/materials/mini.ftz', 'data/materials/mini-explicit-labels.ftz']\n fasttext_model = FastTextInferenceModel(fasttext_model_path)\n\n # bert_model_folder = 'ckpt/clf/ernie_weibo'\n # bert_model = get_bert_inference_model(bert_model_folder, 128, 100)\n\n kw_identify_model = fasttext_model\n attack_model = fasttext_model\n\n bert_tokenizer = BertTokenizer.from_pretrained('data/chinese_vocab.txt', do_lower_case=True)\n tokenizer = lambda x: bert_tokenizer.basic_tokenizer.tokenize(x)\n # tokenizer = lambda x: list(jieba.cut(x))\n\n obs_attacker = ObscenityAttacker(kw_identify_model, attack_model, tokenizer)\n obs_attacker.kw_freq_dict = kw_freq_dict\n out_lines, local_scores = obs_attacker.attack(inp_lines, rounds=31, topK=10)\n return out_lines, local_scores\n\n\nif __name__ == '__main__':\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n if os.path.exists('/tcdata/benchmark_texts.txt'):\n inp_path = '/tcdata/benchmark_texts.txt'\n max_line = None\n else:\n inp_path = 'data/obscenities.txt'\n max_line = 100\n\n out_path = 'adversarial.txt'\n\n inp_lines = []\n with open(inp_path, 'r', encoding='utf-8') as f:\n for line in f:\n inp_lines.append(line.strip())\n if max_line is not None and len(inp_lines) >= max_line:\n break\n\n time0 = time.time()\n kw_freq_dict = mp.Manager().dict()\n n_cpu = mp.cpu_count()\n max_cpu = 4\n if max_cpu is not None:\n n_cpu = min(n_cpu, max_cpu)\n if n_cpu > 1:\n inp_lines = list(enumerate(inp_lines))\n random.shuffle(inp_lines)\n indices_map = {raw_idx: cur_idx for cur_idx, (raw_idx, line) in enumerate(inp_lines)}\n inp_lines = [line for idx, line in inp_lines]\n with mp.Pool(processes=n_cpu) as p:\n samples_split = np.array_split(inp_lines, n_cpu)\n pool_results = p.map(partial(_do_attack, kw_freq_dict=kw_freq_dict), samples_split)\n\n out_lines = list(np.concatenate([results[0] for results in pool_results]))\n out_lines = [out_lines[indices_map[idx]] for idx in range(len(out_lines))]\n local_scores = list(np.concatenate([results[1] for results in pool_results]))\n else:\n out_lines, local_scores = _do_attack(inp_lines, kw_freq_dict)\n print(sum(local_scores) / len(local_scores))\n print('Time:', time.time() - time0)\n try:\n target = json.dumps({'text': out_lines}, ensure_ascii=False)\n with open(out_path, 'w', encoding='utf-8') as f:\n f.write(target)\n except:\n from materials.preprocessing_module import preprocess_text\n\n out_lines = [preprocess_text(line) for line in out_lines]\n target = json.dumps({'text': out_lines}, ensure_ascii=False)\n with open(out_path, 'w', encoding='utf-8') as f:\n f.write(target)\n","repo_name":"sfzhou5678/TextualAdversarialAttack-Tianchi","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3233,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"76"} +{"seq_id":"344674277","text":"import math\nimport helper\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport random\nimport timeit\n\nif __name__ == \"__main__\":\n \n \n load_variable = True;\n #load_variable = False;\n if(load_variable!=True):\n test_images_mount, test_images_above, poses,testimageraw = helper.Get_TestData()\n with open('test.pickle', 'wb') as f:\n pickle.dump([test_images_mount, test_images_above, poses,testimageraw], f)\n else:\n with open('test.pickle', 'rb') as f:\n test_images_mount, test_images_above, poses,testimageraw = pickle.load(f)\n\n\n \n X_test1 = np.squeeze(np.array(test_images_mount))\n X_test2 = np.squeeze(np.array(test_images_above))\n y_test = np.squeeze(np.array(poses))\n \n #X_test=X_test[0:50,:]\n #y_test=y_test[0:50,:]\n\n import posenet\n from keras.optimizers import Adam\n\n\n model = posenet.create_posenet()\n model.load_weights('checkpoint_weights.h5')\n #adam = Adam(lr=0.01, beta_1=0.5, beta_2=0.999, epsilon=1e-06, decay=0.0, clipvalue=2.0)\n #model.compile(optimizer=adam, loss={'cls1_fc_pose_xyz': posenet.euc_loss1x, 'cls1_fc_pose_wpqr': posenet.euc_loss1q,'cls1_fc_fl': posenet.euc_loss1fl,\n # 'cls2_fc_pose_xyz': posenet.euc_loss2x, 'cls2_fc_pose_wpqr': posenet.euc_loss2q,'cls2_fc_fl': posenet.euc_loss2fl,\n # 'cls3_fc_pose_xyz': posenet.euc_loss3x, 'cls3_fc_pose_wpqr': posenet.euc_loss3q,'cls3_fc_fl': posenet.euc_loss3fl})\n\n start = timeit.default_timer()\n testPredict = model.predict([X_test1,X_test2])\n stop = timeit.default_timer()\n print('Time: ', (stop - start)/X_test1.shape[0]) \n\n valsx = testPredict[4]\n valsq = testPredict[5]\n #valsfl = testPredict[8]\n \n# Get results... :/\n results = np.zeros((len(test_images_mount),2))\n for i in range(len(test_images_mount)):\n\n pose_q= np.asarray(poses[i][3:7])\n pose_fl= np.asarray(poses[i][7:8])\n pose_x= np.asarray(poses[i][0:3])\n predicted_x = valsx[i]\n #predicted_fl = valsfl[i]\n predicted_q = valsq[i]\n \n pose_q=np.asarray(pose_q, dtype='float32')\n pose_x=np.asarray(pose_x, dtype='float32')\n predicted_q=np.asarray(predicted_q, dtype='float32')\n predicted_x=np.asarray(predicted_x, dtype='float32')\n\n pose_q =np.round(np.squeeze(pose_q),2)\n pose_x = np.round(np.squeeze(pose_x),2)\n predicted_q = np.round(np.squeeze(predicted_q),2)\n predicted_x = np.round(np.squeeze(predicted_x),2)\n\n #predicted_fl = np.round(np.squeeze(predicted_fl),1)\n #pose_fl = np.round(np.squeeze(pose_fl),1)\n\n #predicted_x[0] = predicted_x[0]*100\n #predicted_x[1] = predicted_x[1]*100\n #predicted_x[2] = predicted_x[2]*100\n\n fig, ax = plt.subplots()\n ax.set_xlabel(\" true values=>\"+str(pose_x)+\" , \"+str(pose_q)+'\\n'+\"predicted values=>\"+str(predicted_x)+\" , \"+str(predicted_q))\n plt.imshow(testimageraw[i]) \n manager = plt.get_current_fig_manager() \n manager.window.state('zoomed')\n plt.show()\n\n\n #Compute Individual Sample Error\n #q1 = pose_q / np.linalg.norm(pose_q)\n #q2 = np.round(predicted_q / np.linalg.norm(predicted_q),2)\n #d = abs(np.sum(np.multiply(q1,q2)))\n #theta = 2 * np.arccos(d) * 180/math.pi\n\n theta = np.linalg.norm(pose_q-predicted_q)\n theta = np.round(theta,3)\n error_x = np.round(np.linalg.norm(pose_x-predicted_x),3) \n #error_LDI = np.round(pose_fl-predicted_fl,2)\n\n results[i,:] = [error_x,theta]\n print('Iteration: '+str(i)+' Error XYZ (cm): '+str(error_x)+' Error Q: '+str(theta))\n \n median_result = np.median(results,axis=0)\n print('Median error ', median_result[0], 'cm and ', median_result[1], 'degrees.')","repo_name":"amirhoseintorabi/Tnet","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"7022593971","text":"import time\r\n\r\nimport bibtexparser\r\nfrom bibtexparser.bparser import BibTexParser\r\nfrom bs4 import BeautifulSoup\r\nfrom selenium.common.exceptions import NoSuchElementException\r\n\r\nimport saver\r\nimport scholarly\r\nimport utils\r\n\r\n_MAIN = \"https://scholar.google.com/scholar?&lookup=0&hl=en&q={0}\"\r\n_LIBRARY = 'https://scholar.google.com/scholar?scilib=1&hl=en&as_sdt=0,5'\r\n_CITES = 'https://scholar.google.com/scholar?cites={0}&as_sdt=2005&sciodt=0,5&hl=en'\r\n\r\n_EMAIL = 'swehgadsfgae@gmail.com'\r\n_PASSWORD = 'Sasha1999sas!'\r\n\r\n_PATH_TO_PROFILE = 'C:\\\\Users\\\\ScRiB\\\\AppData\\\\Local\\\\Google\\\\Chrome\\\\User Data 3'\r\n_PROFILE = 'Profile 5'\r\n_PATH_TO_DRIVER = 'C:\\\\Users\\\\ScRiB\\\\Desktop\\\\GChrome\\\\chromedriver.exe'\r\n\r\n_RESULT_FILE = 'results\\\\result.json'\r\n_CONTINUE_FILE = 'results\\\\continue.json'\r\n\r\n_current_page = 0\r\n_CONTINUE_INFO = {\r\n \"query\": \"\", # последний запрос\r\n \"main_page\": 0, # номер страницы главных публикаций\r\n \"cities_index\": 0, # индекс публикации в списке главных публикаций\r\n \"citations_page\": 1, # номер страницы цитируемых публикаций\r\n \"last_index_in_result\": 1 # следующий индекс в файле result\r\n}\r\n\r\n\r\ndef open_window(driver, page):\r\n driver.execute_script(\"window.open('\" + page + \"','_blank');\")\r\n global _current_page\r\n _current_page = _current_page + 1\r\n driver.switch_to.window(driver.window_handles[_current_page])\r\n\r\n\r\ndef close_window(driver):\r\n \"\"\"Закрыаем окно браузера и переключаемся на текущую вкладку\"\"\"\r\n driver.execute_script(\"window.close()\")\r\n global _current_page\r\n _current_page = _current_page - 1\r\n driver.switch_to.window(driver.window_handles[_current_page])\r\n time.sleep(1)\r\n\r\n\r\ndef get_all_pubs(soup):\r\n \"\"\"Получаем из HTML все публикации на текущей странице\"\"\"\r\n search_query = scholarly.search_scholar_soup(soup)\r\n pubs = []\r\n pub = utils.get_next_pub(search_query)\r\n i = 0\r\n while not (pub is None):\r\n pubs.append(pub)\r\n i += 1\r\n pub = utils.get_next_pub(search_query)\r\n\r\n if i == 0:\r\n print('Похоже Google заблокировал нас... Ну или таких публикаций не существует')\r\n else:\r\n print('На текущей странице получено {0} публикаций'.format(str(i)))\r\n\r\n return pubs\r\n\r\n\r\ndef refresh_library(driver):\r\n utils.login(driver, url, _EMAIL, _PASSWORD, open_window, close_window)\r\n time.sleep(2)\r\n driver.refresh()\r\n utils.check_captcha(driver)\r\n\r\n utils.set_page_in_20(driver)\r\n\r\n utils.check_captcha(driver)\r\n\r\n open_window(driver, _LIBRARY)\r\n utils.check_captcha(driver)\r\n\r\n if delete_pubs_in_lib(driver):\r\n close_window(driver)\r\n\r\n driver.refresh()\r\n add_pubs_in_lib(driver)\r\n\r\n\r\ndef add_pubs_in_lib(driver):\r\n \"\"\"Добавляем все имеющиеся на странице публикации в Личную библиотеку\"\"\"\r\n rows = driver.find_elements_by_class_name('gs_or')\r\n rows.reverse()\r\n for row in rows:\r\n databox = row.find_element_by_class_name('gs_ri')\r\n lowerlinks = databox.find_element_by_class_name('gs_fl')\r\n star = lowerlinks.find_element_by_class_name('gs_or_sav')\r\n time.sleep(0.4)\r\n star.click()\r\n if not utils.check_available_star(driver):\r\n refresh_library(driver)\r\n break\r\n\r\n\r\ndef get_pubs_from_lib(driver):\r\n \"\"\"Получаем все публикации из библиотеки\"\"\"\r\n open_window(driver, _LIBRARY)\r\n\r\n utils.check_captcha(driver)\r\n\r\n html = driver.page_source\r\n html = html.replace(u'\\xa0', ' ')\r\n soup = BeautifulSoup(html, 'html.parser')\r\n\r\n pubs = get_all_pubs(soup)\r\n\r\n menu = driver.find_element_by_id('gs_ab_md')\r\n checkbox = menu.find_element_by_class_name('gs_in_cbj')\r\n checkbox.click()\r\n\r\n downl = driver.find_element_by_id('gs_res_ab_exp-b')\r\n downl.click()\r\n\r\n bib = driver.find_element_by_id('gs_res_ab_exp-d')\r\n a = bib.find_elements_by_class_name('gs_md_li')[0]\r\n a.click()\r\n\r\n html = driver.page_source\r\n soup = BeautifulSoup(html, 'html.parser')\r\n text = soup.find('pre').contents[0]\r\n\r\n bibs = bibtexparser.loads(text, BibTexParser(common_strings=True)).entries\r\n bibs.reverse()\r\n\r\n for i in range(len(pubs)):\r\n pubs[i].bib.update(bibs[i])\r\n\r\n driver.back()\r\n delete_pubs_in_lib(driver)\r\n\r\n if utils.check_captcha(driver):\r\n time.sleep(5)\r\n driver.get(_LIBRARY)\r\n delete_pubs_in_lib(driver)\r\n\r\n return pubs\r\n\r\n\r\ndef get_cites_pubs_on_pub(driver, pub, main_index):\r\n \"\"\"Получаем все публикации, в которых цитируется указанная\"\"\"\r\n driver.get(_CITES.format(pub.id_scholarcitedby))\r\n utils.check_captcha(driver)\r\n utils.unchecked_citations(driver)\r\n\r\n for z in range(1, _CONTINUE_INFO['citations_page']):\r\n time.sleep(0.5)\r\n if not utils.next_page(driver):\r\n print('Такой страницы с публикациями нет')\r\n break\r\n\r\n i = _CONTINUE_INFO['citations_page'] - 1\r\n while True:\r\n i = i + 1\r\n _CONTINUE_INFO['citations_page'] = i\r\n _CONTINUE_INFO['last_index_in_result'] = saver.get_last_index()\r\n saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)\r\n add_pubs_in_lib(driver)\r\n pubs = get_pubs_from_lib(driver)\r\n close_window(driver)\r\n for citi in pubs:\r\n saver.save(_RESULT_FILE, citi, main_index)\r\n if not utils.next_page(driver) or i > 9:\r\n break\r\n\r\n\r\ndef get_pubs_with_cities(driver):\r\n \"\"\"Соединяем публикации с их цитирующими публикацями\"\"\"\r\n indexes = []\r\n pubs = get_pubs_from_lib(driver)\r\n for pub in pubs:\r\n indexes.append(saver.save(_RESULT_FILE, pub))\r\n\r\n _CONTINUE_INFO['last_index_in_result'] = saver.get_last_index()\r\n saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)\r\n\r\n for p in range(_CONTINUE_INFO['cities_index'], len(indexes)):\r\n _CONTINUE_INFO['cities_index'] = p\r\n saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)\r\n if pubs[p].citedby != 0:\r\n get_cites_pubs_on_pub(driver, pubs[p], indexes[p])\r\n _CONTINUE_INFO['citations_page'] = 1\r\n _CONTINUE_INFO['cities_index'] = 0\r\n\r\n\r\ndef delete_pubs_in_lib(driver):\r\n \"\"\"Удаляем публикации из библиотеки\"\"\"\r\n try:\r\n if utils.next_page(driver):\r\n utils.check_captcha(driver)\r\n delete_pubs_in_lib(driver)\r\n driver.get(_LIBRARY)\r\n menu = driver.find_element_by_id('gs_ab_md')\r\n checkbox = menu.find_element_by_class_name('gs_in_cbj')\r\n checkbox.click()\r\n\r\n delete = menu.find_element_by_id('gs_res_ab_del')\r\n delete.click()\r\n if utils.check_captcha(driver):\r\n driver.get(_LIBRARY)\r\n delete_pubs_in_lib(driver)\r\n return True\r\n except NoSuchElementException:\r\n close_window(driver)\r\n return False\r\n\r\n\r\ndef clear_lib(driver):\r\n open_window(driver, _LIBRARY)\r\n if delete_pubs_in_lib(driver):\r\n close_window(driver)\r\n\r\n\r\nif __name__ == '__main__':\r\n driver = None\r\n is_continue = -1\r\n while is_continue != 0 and is_continue != 1:\r\n try:\r\n is_continue = int(input(\"Начать работу заново(0) или продолжить(1): \"))\r\n except Exception:\r\n is_continue = -1\r\n\r\n if is_continue != 0 and is_continue != 1:\r\n print('Введите корректный режим работы (0 или 1)')\r\n continue\r\n page = 1\r\n\r\n if is_continue == 0:\r\n query = input(\"Введите запрос: \")\r\n _CONTINUE_INFO['query'] = query\r\n saver.init_file(_RESULT_FILE)\r\n else:\r\n file = None\r\n try:\r\n file = saver.read_file(_CONTINUE_FILE)\r\n except FileNotFoundError:\r\n print('Файл с информацией о продолжении не найден')\r\n exit(-1)\r\n if 'query' not in file \\\r\n or 'main_page' not in file \\\r\n or 'cities_index' not in file \\\r\n or 'citations_page' not in file \\\r\n or 'last_index_in_result' not in file:\r\n print('В файле с информацией о продолжении недостаточно данных')\r\n exit(-2)\r\n _CONTINUE_INFO = file\r\n query = _CONTINUE_INFO['query']\r\n page = _CONTINUE_INFO['main_page']\r\n saver.set_index(_CONTINUE_INFO['last_index_in_result'])\r\n print('Последний запрос: ', query)\r\n\r\n print(\"Запрос принят. Начинаем обработку\")\r\n\r\n url = _MAIN.format(str(query))\r\n driver = utils.get_driver(_PATH_TO_DRIVER, _PATH_TO_PROFILE, _PROFILE)\r\n driver.get(url)\r\n\r\n try:\r\n driver.find_element_by_id('gs_hdr_act_s')\r\n utils.login(driver, url, _EMAIL, _PASSWORD, open_window, close_window)\r\n except NoSuchElementException:\r\n pass\r\n\r\n utils.check_captcha(driver)\r\n\r\n utils.set_page_in_20(driver)\r\n utils.check_captcha(driver)\r\n clear_lib(driver)\r\n\r\n utils.unchecked_citations(driver)\r\n\r\n if is_continue == 1:\r\n for i in range(1, page):\r\n time.sleep(0.5)\r\n if not utils.next_page(driver):\r\n print('Такой страницы с главными публикациями нет')\r\n break\r\n\r\n i = page - 1\r\n try:\r\n while True:\r\n i = i + 1\r\n _CONTINUE_INFO['main_page'] = i\r\n saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)\r\n add_pubs_in_lib(driver)\r\n get_pubs_with_cities(driver)\r\n close_window(driver)\r\n if not utils.next_page(driver):\r\n break\r\n except Exception as e:\r\n _CONTINUE_INFO['last_index_in_result'] = saver.get_last_index()\r\n saver.save_in_file(_CONTINUE_INFO, _CONTINUE_FILE)\r\n\r\n driver.quit()\r\n print('Программа завершила работу')\r\n","repo_name":"ScRiB2/scholar","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"6088989192","text":"import sqlite3 as sql\r\n\r\n\r\ndb_EKB = 'usersEKB.db'\r\ndb_SPB = 'usersSPB.db'\r\ndb_KRD = 'usersKRD.db'\r\ndb_TVR = 'usersTVR.db'\r\ndb_CHLB = 'usersCHLB.db'\r\ndb_vacancies = 'vacancies.db'\r\ndb_users = 'users.db'\r\n\r\ntableJava = 'internJava'\r\ntableTester = 'internTester'\r\ntableAnalytics = 'internAnalytics'\r\ntableTechWriter = 'internTechwriter'\r\ntableUsers = 'users'\r\n\r\ndef check_user_in_db(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"SELECT `tg_id` from `%s`\" % tablename)\r\n rows = cur.fetchall()\r\n con.commit()\r\n cur.close()\r\n for row in rows:\r\n if (row[0] == tg_id):\r\n return True\r\n return False\r\n\r\ndef add_user_to_subscribers(tg_id):\r\n con = sql.connect('users.db')\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"INSERT INTO `subscribers` (tg_id) VALUES (?)\", (tg_id,))\r\n con.commit()\r\n cur.close()\r\n\r\ndef get_users_notfilled():\r\n con = sql.connect('users.db')\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"SELECT `tg_id` from `users` where `filling_progress` < 100 and `filling_form` == 1\")\r\n rows = cur.fetchall()\r\n con.commit()\r\n cur.close()\r\n results = []\r\n for row in rows:\r\n results.append(row[0])\r\n return results\r\n\r\ndef clear_table_vacancies(tablename):\r\n con = sql.connect(db_vacancies)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"DELETE FROM `%s`\" % tablename)\r\n con.commit()\r\n cur.close()\r\n\r\ndef change_vacancies(tablename, values):\r\n con = sql.connect(db_vacancies)\r\n with con:\r\n cur = con.cursor()\r\n for value in values:\r\n cur.execute(\"INSERT INTO `%s` (name_internship) VALUES (?)\" % tablename, (value,))\r\n con.commit()\r\n cur.close()\r\n\r\ndef get_parameters(db_name, tablename, parameter):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"SELECT %s FROM `%s`\" % (parameter, tablename))\r\n rows = cur.fetchall()\r\n con.commit()\r\n cur.close()\r\n results = []\r\n for row in rows:\r\n results.append(row[0])\r\n return results\r\n\r\n\r\ndef get_new_id(db_name, tablename):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"SELECT COUNT(id) FROM %s\" % tablename)\r\n length = cur.fetchone()[0]\r\n con.commit()\r\n cur.close()\r\n return length+1\r\n\r\ndef add_forminfo_table(db_name, tablename, td_id, vals):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n query_str = \"INSERT INTO `%s` \" % tablename\r\n query_str += \"(id, tg_id, surname_intern, name_intern, patronymics_intern, \"\r\n query_str += \"date_of_birth, city_living, email, telnumber, date_of_start, \"\r\n query_str += \"time_spend, work_after_internship, edu_type, edu_name, \"\r\n query_str += \"edu_year_start, edu_year_end, edu_faculty, edu_score, \"\r\n query_str += \"edu2_type, edu2_name, edu2_year_start, edu2_year_end, \"\r\n query_str += \"edu2_faculty, edu2_score, additive_edu, jobexp_exist, time1, place1, \"\r\n query_str += \"rank1, duty1, time2, place2, rank2, duty2, \"\r\n query_str += \"time3, place3, rank3, duty3, \"\r\n query_str += \"time4, place4, rank4, duty4, \"\r\n query_str += \"projects, naumen_eduprogs, key_skills, prof_interests, \"\r\n query_str += \"last_read_book, hobbies, expectations, future_rank, source_info_naumen, \"\r\n query_str += \"source_info_internship, recommendations_authors, summary_hhlink, task_link)\"\r\n query_str += \"VALUES (\"\r\n for i in range(54):\r\n query_str += \"?, \"\r\n query_str += \"?)\"\r\n cur.execute(query_str, vals)\r\n con.commit()\r\n cur.close()\r\n\r\ndef update_cell(db_name, tablename, tg_id, progress, cell_name, cell_value):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n vals = (progress, cell_value, tg_id)\r\n query_str = \"update `%s` set filling_progress=?, %s=? where tg_id=?\" % (tablename, cell_name)\r\n cur.execute(query_str, vals)\r\n con.commit()\r\n cur.close()\r\n\r\ndef update_progress(db_name, tablename, tg_id, progress):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n vals = (progress, tg_id)\r\n cur.execute(\"update `%s` set filling_progress=? where tg_id=?\" % tablename, vals)\r\n con.commit()\r\n cur.close()\r\n\r\ndef start_filling(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"update `%s` set filling_form=1, filling_progress=0 where tg_id=?\" % tablename, (tg_id,))\r\n con.commit()\r\n cur.close()\r\n\r\ndef end_filling(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"update `%s` set filling_form=0 where tg_id=?\" % tablename, (tg_id,))\r\n con.commit()\r\n cur.close()\r\n\r\ndef get_progress(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"select filling_progress from `%s` where tg_id=?\" % tablename, (tg_id,))\r\n result = cur.fetchone()\r\n con.commit()\r\n cur.close()\r\n return result[0]\r\n\r\ndef check_filling(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"select filling_form from `%s` where tg_id=?\" % tablename, (tg_id,))\r\n result = cur.fetchone()\r\n con.commit()\r\n cur.close()\r\n return result[0]\r\n\r\ndef add_user_to_db(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"SELECT COUNT(id) FROM %s\" % tablename)\r\n length = cur.fetchone()[0]\r\n vals = (length+1, tg_id, 1, 0)\r\n cur.execute(\"INSERT INTO `%s` (id, tg_id, filling_form, filling_progress) VALUES (?, ?, ?, ?)\" % tablename,\r\n vals)\r\n con.commit()\r\n cur.close()\r\n\r\ndef get_userdata(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"SELECT * FROM `%s` where tg_id=?\" % tablename, (tg_id,))\r\n results = cur.fetchone()\r\n con.commit()\r\n cur.close()\r\n return results\r\n \r\n\r\ndef delete_user_from_db(db_name, tablename, tg_id):\r\n con = sql.connect(db_name)\r\n with con:\r\n cur = con.cursor()\r\n cur.execute(\"DELETE FROM `%s` where tg_id = ?\" % tablename, (tg_id,))\r\n con.commit()\r\n cur.close()","repo_name":"IlyaProdma/LCDTEAM_BOT","sub_path":"db_funcs.py","file_name":"db_funcs.py","file_ext":"py","file_size_in_byte":6751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"6194719495","text":"import json\nfrom kivy.uix.button import ButtonBehavior\nfrom kivy.properties import *\nfrom kivy.app import App\nfrom appPublic.myTE import MyTemplateEngine\nfrom appPublic.jsonConfig import getConfig\nfrom .baseWidget import *\nfrom .datalist import DataList\nfrom .utils import *\nfrom .vplayer import VPlayer\nfrom .aplayer import APlayer\nfrom .toolbar import ToolPage\n\n\"\"\"\ndic format\n{\n\t\"widgettype\":\"xx\",\n\t\"bases\":[\n\t\t{\n\t\t\t\"base\":\"Button\",\n\t\t\t\"options\":{}\n\t\t}\n\t],\n\t\"properties\":[\n\t\t{\n\t\t\t\"name\":\"kef\",\n\t\t\t\"type\":\"NumericProperty()\"\n\t\t}\n\t],\n\t\"binds\":[\n\t\t[[id,sock],[id,method]]\n\t],\n\t\"methods\":{\n\t\t\"xxxx\":\"code .....\"\n\t},\n\t\"subwidgets\":[\n\t]\n}\n\"\"\"\n\nwidgettmpl=\"\"\"\n{% set init=False %}\n{% for b in bases %}\n{% if b.options %}\n{% set init=True %}\n{% endif %}\n{% endfor %}\n{% if subwidgets %}\n{% set init=True %}\n{% endif %}\n{% if binds %}\n{% set init=True %}\n{% endif %}\nclass {{widgettype}}({% for b in bases %}{{b.base}}{% if not loop.last %},{% endif %}{% endfor %}):\n{% if properties %}\n{% for name,value in properties.items() %}\n\t{{name}} = {{value}}\n{% endfor %}\n{% endif %}\n{% if init %}\n\tdef __init__(self,**kw):\n\t{% for b in bases %}\n\t\t{% if b.options %}\n\t\tkwargs = {}\n\t\tbopts={{json.dumps(b.options)}}\n\t\tkwargs.update(kw)\n\t\tkwargs.update(JDConvert(bopts))\n\t\t{{b.base}}.__init__(self,**kwargs)\n\t\t{% else %}\n\t\t{{b.base}}.__init__(self,**kw)\n\t\t{% endif %}\n\t{% endfor %}\n\t{% if subwidgets %}\n\t\tself.subwidgets()\n\t{% endif %}\n\t{% if binds %}\n\t\t{% for b in binds %}\n\t\tw1 = getWidgetById(self,'{{b[0][0]}}')\n\t\tw2 = getWidgetById(self,'{{b[1][0]}}')\n\t\tw1.bind({{b[0][1]}}=w2.{{b[1][1]}})\n\t\t{% endfor %}\n\t{% endif %}\n{% else %}\n\tpass\n{% endif %}\n{% if subwidgets %}\n\tdef subwidgets(self):\n\t\tif not hasattr(self,'ids'):\n\t\t\tself.ids = {}\n\t\tapp = App.get_running_app()\n\t\tif not hasattr(app,'ids'):\n\t\t\tapp.ids = {}\n\t\tjsonstr = '''{{json.dumps(subwidgets)}}'''\n\t\tsws = json.loads(jsonstr)\n\t\tfor o in sws:\n\t\t\tw = buildWidget(o,ancestor=self,parenturl='{{parenturl}}')\n\t\t\tself.add_widget(w)\n{% endif %}\n\n{% if methods %}\n{% for name,code in methods.items() %}\nm = {}\nexec('''{{code}}''',globals(),m)\nfor k,v in m.items():\n\t{{widgettype}}.{{name}} = v\n{% endfor %}\n{% endif %}\n\t\t\n\"\"\"\n\n\"\"\"\ninstance dic format\n{\n\t\"widgettype\":\"iconbutton\",\n\t\"options\":{\n\t},\n\t\"subwidgets\":[\n\t],\n\t\"binds\":[\n\t\t[[id,evt],[id,evt]]\n\t]\n}\n\"\"\"\n\ninstancetmpl = \"\"\"\n{% if options %}\njsonstr='''{{json.dumps(options)}}'''\noptions = json.loads(jsonstr)\noptions = JDConvert(options)\n{% if widgettype in ['urlwidget','filewidget'] %}\noptions['ancestor'] = ancestor\noptions['parenturl'] = '{{parenturl}}'\n__target__ = App.get_running_app().{{widgettype}}(**options)\n{% else %}\n__target__ = {{widgettype}}(**options)\n{% endif %}\n{% else %}\n__target__ = {{widgettype}}()\n{% endif %}\n{% if id %}\n{% if id[0] == '/' %}\napp = App.get_running_app()\nif not hasattr(app,'ids'):\n\tapp.ids = {}\napp.ids['{{id[1:]}}'] = __target__\n{% else %}\nif not hasattr(ancestor,'ids'):\n\tancestor.ids = {}\nancestor.ids['{{id}}'] = __target__\n{% endif %}\n{% endif %}\n{% if subwidgets %}\n{% for desc in subwidgets %}\njsonstr = '''{{json.dumps(desc)}}'''\ndesc = json.loads(jsonstr)\n# desc = JDConvert(desc)\nsw = buildWidget(desc,ancestor=__target__,parenturl='{{parenturl}}')\n__target__.add_widget(sw)\n{% endfor %}\n{% endif %}\n{% if binds %}\n{% for b in binds %}\nw1 = getWidgetById(__target__,'{{b[0][0]}}')\nw2 = getWidgetById(__target__,'{{b[1][0]}}')\nw1.bind({{b[0][1]}}=w2.{{b[1][1]}})\n{% endfor %}\n{% endif %}\n\"\"\"\n\ndef loadUserDefinedWidget():\n\tconfig = getConfig()\n\tif config.udws:\n\t\tfor udw in config.udws:\n\t\t\tudw = absurl(udw,'')\n\t\t\tudwdescs = App.get_running_app().hc.get(udw)\n\t\t\tfor desc in udwdescs:\n\t\t\t\tbuildClass(desc)\ndef buildClass(dic):\n\tte = MyTemplateEngine([])\n\ttry:\n\t\tcode = te.renders(widgettmpl,dic)\n\texcept Exception as e:\n\t\tprint(dic,e)\n\t\traise e\n\tprint(code)\n\tg = {}\n\ttry:\n\t\texec(code,globals(),g)\n\texcept Exception as e:\n\t\tprint(code,'-error-',e)\n\t\traise\n\tglobals().update(g)\n\tfor k,v in g.items():\n\t\treturn v\n\treturn None\n\ndef buildWidget(dic,ancestor=None,parenturl=''):\n\tif ancestor is None:\n\t\tancestor = App.get_running_app()\n\tte = MyTemplateEngine([])\n\tte.set('ancestor',ancestor)\n\tte.set('parenturl',parenturl)\n\ttry:\n\t\tcode = te.renders(instancetmpl,dic)\n\texcept Exception as e:\n\t\tprint(dic,e)\n\t\traise e\n\tg = {}\n\tg['ancestor'] = ancestor\n\t# print(code)\n\ttry:\n\t\texec(code,globals(),g)\n\texcept Exception as e:\n\t\tprint('CODE****',code,'****CODE','-error-',e,'-error-')\n\t\traise e\n\tfor k,v in g.items():\n\t\tif k=='__target__':\n\t\t\tif dic['widgettype'] in ['urlwidget', 'filewidget' ]:\n\t\t\t\treturn v\n\t\t\tif parenturl == '' and hasattr(ancestor,'parenturl'):\n\t\t\t\tparenturl = ancestor.parenturl\n\t\t\tv.parenturl = parenturl\n\t\t\treturn v\n\treturn None\n\nclass ArgumentError(Exception):\n\tdef __init__(self,p,msg):\n\t\tsuper().__init__()\n\t\tself.argument=p\n\t\tself.msg = msg\n\tdef __str__(self):\n\t\treturn \"Arguments(%s) error:%s\" % (self.argument, self.msg)\n\n\nif __name__ == '__main__':\n\tfrom kivy.app import App\n\tdic = {\n\t\t\"widgettype\":\"iconbutton\",\n\t\t\"bases\":[\n\t\t\t{\n\t\t\t\n\t\t\t\t\"base\":\"ButtonBehavior\"\n\t\t\t},\n\t\t\t{\n\t\t\t\t\"base\":\"Image\"\n\t\t\t}\n\t\t],\n\t\t\"methods\":[\n\t\t\t{\n\t\t\t\t\"name\":\"on_press\",\n\t\t\t\t\"code\":\"def on_press(self): print('pressed ')\"\n\t\t\t}\n\t\t]\n\t}\n\tclass MyApp(App):\n\t\tdef build(self):\n\t\t\tbuildClass(dic)\n\t\t\t\n\t\t\treturn buildWidget({'widgettype':'iconbutton',\n\t\t\t\t\t'options':{\n\t\t\t\t\t\t'source':\"/tmp/image.jpeg\"\n\t\t\t\t\t}\n\t\t\t})\n\tMyApp().run()\n","repo_name":"BadescuGabi/python-login-and-register-system","sub_path":"venv/Lib/site-packages/kivyblocks/derivedWidget.py","file_name":"derivedWidget.py","file_ext":"py","file_size_in_byte":5363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"13907557582","text":"\n\nimport numpy as np\nfrom PIL import Image\nfrom fastapi import UploadFile\nimport cv2\nimport numpy as np\nfrom PIL import Image\nimport os\nimport dlib\n\nasync def watermarking_async(_image:UploadFile,_logo:UploadFile):\n \n image = Image.open(_image.file)\n logo = Image.open(_logo.file)\n \n #image.resize((150,150))\n #logo.resize((150,150))\n image_logow = np.array(image.convert('RGB'))\n h_image, w_image, _ = image_logow.shape\n \n logo = np.array(logo.convert('RGB'))\n h_logo, w_logo, _ = logo.shape\n \n center_y = int(h_image / 2)\n center_x = int(w_image / 2)\n top_y = center_y - int(h_logo / 2)\n left_x = center_x - int(w_logo / 2)\n bottom_y = top_y + h_logo\n right_x = left_x + w_logo\n\n roi = image_logow[top_y: bottom_y, left_x: right_x]\n result = cv2.addWeighted(roi, 1, logo, 1, 0)\n #cv2.line(image_logow, (0, center_y), (left_x, center_y), (0, 0, 255), 1)\n #cv2.line(image_logow, (right_x, center_y), (w_image, center_y), (0, 0, 255), 1)\n #image_logow[top_y: bottom_y, left_x: right_x] = result\n #img = Image.fromarray(image_logow, 'RGB')\n res, im_png = cv2.imencode(\".png\", result)\n return im_png\n\n\nasync def textmark_async(_image:UploadFile,text:str) -> bytes:\n\n image = Image.open(_image.file)\n image_logow = np.array(image.convert('RGB'))\n h_image, w_image, _ = image_logow.shape\n \n #logo = Image.open(_logo.file)\n #logo = np.array(logo.convert('RGB'))\n \n cv2.putText(image_logow, text=text, org=(w_image - 295, h_image - 30), fontFace=cv2.FONT_HERSHEY_COMPLEX, fontScale=0.5,\n color=(0,0,255), thickness=2, lineType=cv2.LINE_4); \n res, im_png = cv2.imencode(\".png\", image_logow)\n return im_png\n #timg = Image.fromarray(image_logow, 'RGB') \n #return timg.tobytes()\n\nbase_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nshape_predictor_68_face_landmarks = base_dir+r'\\creative\\AI\\shape_predictor_68_face_landmarks.dat'\n\n\nasync def get_array_img(img):\n image = Image.open(img)\n image = image.resize((300,300))\n image_arr = np.array(image)\n return image_arr\n\nasync def extract_index_nparray(nparray):\n index = None\n for num in nparray[0]:\n index = num\n break\n return index\n\nasync def face_swap_async(face1:UploadFile,face2:UploadFile):\n source_upload = face1.file\n destination_upload = face2.file\n #destination_upload.resize((300,300))\n #source_upload.resize((300,300))\n \n img = await get_array_img(source_upload)\n img_gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n \n mask = np.zeros_like(img_gray)\n \n img2 = await get_array_img(destination_upload)\n img2_gray = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)\n \n detector = dlib.get_frontal_face_detector()\n predictor = dlib.shape_predictor(shape_predictor_68_face_landmarks)\n height, width, channels = img2.shape\n img2_new_face = np.zeros((height, width, channels), np.uint8)\t\n \n faces = detector(img_gray)\n for face in faces:\n landmarks = predictor(img_gray, face)\n landmarks_points = []\n for n in range(0, 68):\n x = landmarks.part(n).x\n y = landmarks.part(n).y\n landmarks_points.append((x, y))\t\n points = np.array(landmarks_points, np.int32)\n convexhull = cv2.convexHull(points)\n \n cv2.fillConvexPoly(mask, convexhull, 255)\t\n face_image_1 = cv2.bitwise_and(img, img, mask=mask)\t\n # Delaunay triangulation\n rect = cv2.boundingRect(convexhull)\n subdiv = cv2.Subdiv2D(rect)\n subdiv.insert(landmarks_points)\n triangles = subdiv.getTriangleList()\n triangles = np.array(triangles, dtype=np.int32)\t\n indexes_triangles = []\n for t in triangles:\n pt1 = (t[0], t[1])\n pt2 = (t[2], t[3])\n pt3 = (t[4], t[5])\t\n index_pt1 = np.where((points == pt1).all(axis=1))\n index_pt1 = await extract_index_nparray(index_pt1)\t\n index_pt2 = np.where((points == pt2).all(axis=1))\n index_pt2 = await extract_index_nparray(index_pt2)\t\n index_pt3 = np.where((points == pt3).all(axis=1))\n index_pt3 = await extract_index_nparray(index_pt3)\t\n if index_pt1 is not None and index_pt2 is not None and index_pt3 is not None:\n triangle = [index_pt1, index_pt2, index_pt3]\n indexes_triangles.append(triangle)\t\n # Face 2\n faces2 = detector(img2_gray)\n for face in faces2:\n landmarks = predictor(img2_gray, face)\n landmarks_points2 = []\n for n in range(0, 68):\n x = landmarks.part(n).x\n y = landmarks.part(n).y\n landmarks_points2.append((x, y))\n \n points2 = np.array(landmarks_points2, np.int32)\n convexhull2 = cv2.convexHull(points2)\n lines_space_mask = np.zeros_like(img_gray)\n lines_space_new_face = np.zeros_like(img2)\n for triangle_index in indexes_triangles:# Triangulation of both faces\n tr1_pt1 = landmarks_points[triangle_index[0]]# Triangulation of the first face\n tr1_pt2 = landmarks_points[triangle_index[1]]\n tr1_pt3 = landmarks_points[triangle_index[2]]\n triangle1 = np.array([tr1_pt1, tr1_pt2, tr1_pt3], np.int32)\n \n rect1 = cv2.boundingRect(triangle1)\n (x, y, w, h) = rect1\n cropped_triangle = img[y: y + h, x: x + w]\n cropped_tr1_mask = np.zeros((h, w), np.uint8)\n \n points = np.array([[tr1_pt1[0] - x, tr1_pt1[1] - y],\n [tr1_pt2[0] - x, tr1_pt2[1] - y],\n [tr1_pt3[0] - x, tr1_pt3[1] - y]], np.int32)\n \n cv2.fillConvexPoly(cropped_tr1_mask, points, 255)\n \n cv2.line(lines_space_mask, tr1_pt1, tr1_pt2, 255)# Lines space\n cv2.line(lines_space_mask, tr1_pt2, tr1_pt3, 255)\n cv2.line(lines_space_mask, tr1_pt1, tr1_pt3, 255)\n \n lines_space = cv2.bitwise_and(img, img, mask=lines_space_mask)\n \n tr2_pt1 = landmarks_points2[triangle_index[0]]# Triangulation of second face\n tr2_pt2 = landmarks_points2[triangle_index[1]]\n tr2_pt3 = landmarks_points2[triangle_index[2]]\n triangle2 = np.array([tr2_pt1, tr2_pt2, tr2_pt3], np.int32)\n \n rect2 = cv2.boundingRect(triangle2)\n (x, y, w, h) = rect2\n \n cropped_tr2_mask = np.zeros((h, w), np.uint8)\n \n points2 = np.array([[tr2_pt1[0] - x, tr2_pt1[1] - y],\n [tr2_pt2[0] - x, tr2_pt2[1] - y],\n [tr2_pt3[0] - x, tr2_pt3[1] - y]], np.int32)\n \n cv2.fillConvexPoly(cropped_tr2_mask, points2, 255)\n \n points = np.float32(points)# Warp triangles\n points2 = np.float32(points2)\n M = cv2.getAffineTransform(points, points2)\n warped_triangle = cv2.warpAffine(cropped_triangle, M, (w, h))\n warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=cropped_tr2_mask)\n \n img2_new_face_rect_area = img2_new_face[y: y + h, x: x + w]# Reconstructing destination face\n img2_new_face_rect_area_gray = cv2.cvtColor(img2_new_face_rect_area, cv2.COLOR_BGR2GRAY)\n _, mask_triangles_designed = cv2.threshold(img2_new_face_rect_area_gray, 1, 255, cv2.THRESH_BINARY_INV)\n warped_triangle = cv2.bitwise_and(warped_triangle, warped_triangle, mask=mask_triangles_designed)\n \n img2_new_face_rect_area = cv2.add(img2_new_face_rect_area, warped_triangle)\n img2_new_face[y: y + h, x: x + w] = img2_new_face_rect_area\n \n img2_face_mask = np.zeros_like(img2_gray)# Face swapped (putting 1st face into 2nd face)\n img2_head_mask = cv2.fillConvexPoly(img2_face_mask, convexhull2, 255)\n img2_face_mask = cv2.bitwise_not(img2_head_mask)\n \n img2_head_noface = cv2.bitwise_and(img2, img2, mask=img2_face_mask)\n result = cv2.add(img2_head_noface, img2_new_face)\n \n (x, y, w, h) = cv2.boundingRect(convexhull2)\n center_face2 = (int((x + x + w) / 2), int((y + y + h) / 2))\n \n seamlessclone = cv2.seamlessClone(result, img2, img2_head_mask, center_face2, cv2.NORMAL_CLONE)\n resultimg = Image.fromarray(seamlessclone, 'RGB')\n res, im_png = cv2.imencode(\".png\", np.array(resultimg))\n return im_png\n #\n #return resultimg.tobytes()","repo_name":"JesusColinV/apis_AI_fastapi","sub_path":"creative/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":8359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"39767799881","text":"from sys import argv\nimport decimal\nclass Solution(object):\n def gcube(self, nDim, start, end):\n vol = 1\n for i in range(start, end):\n vol *= nDim[i]\n vol = decimal.Decimal(vol)\n return vol**(decimal.Decimal(1.0/(end-start)))\n\nif __name__ == \"__main__\":\n try:\n filename = argv[1]\n try:\n f = open(filename)\n nf = open(filename[:-2]+\"out\", \"w\")\n \n sol = Solution()\n t = int(f.readline())\n for i in range(t):\n n, m = list(map(int, f.readline().split()))\n nDim = list(map(int, f.readline().split()))\n nf.write(\"Case #%d:\\n\" % (i+1))\n for j in range(m):\n start, end = list(map(int, f.readline().split()))\n res = sol.gcube(nDim, start, end+1)\n nf.write(\"%f\\n\" % (res))\n except IOError:\n print (\"No such file: %s\" % filename)\n except IndexError:\n print (\"Too few parameters!\")\n print (\"Usage: python gcube.py inputfile\")\n","repo_name":"Amanotoko/codejam","sub_path":"APAC2016/round1/gcube.py","file_name":"gcube.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"16336587162","text":"import serial\nfrom serial.tools.list_ports import comports as list_ports\nfrom serial.serialutil import SerialException\nfrom time import sleep\nimport threading\nfrom Document import *\n\ndef listDevices():\n ret=[]\n for p in list_ports():\n ret.append(p.device)\n return ret\n\nclass Device:\n def __init__(self, port):\n self.progress=0.0\n self.pause=False\n self.stop=False\n self.running=False\n try:\n self.ser = serial.Serial(port, baudrate=115200)\n sleep(1.0)\n self.readall()\n ret=self.command('M17')=='ok'\n self.connected = ret\n self.readall()\n self.park()\n except serial.serialutil.SerialException:\n self.connected = False\n\n def readall(self):\n while self.ser.inWaiting()>0:\n print(self.ser.readline().decode().strip())\n\n def startPlot(self, g, callback):\n t=threading.Thread( target = self.plot, args = (g, callback, ) )\n t.start()\n\n def plot(self, g, callback):\n self.progress=0.0\n self.pause=False\n self.stop=False\n self.running=True\n num=0\n self.ser.flushInput()\n self.command('M204 P900 T900 R900')\n for i in range(0,len(g)):\n if self.stop:\n self.command(gCodeMove(160,0,100, 1000))\n self.running=False\n callback()\n return\n if self.pause:\n cmd='G0'+g[max(i-1,0)][2:]\n self.command(cmd)\n while self.pause and not self.stop:\n sleep(0.5)\n out = self.command(g[i])\n if(out[3:6]=='E22'):\n print('Position is unreachable:\\n'+g[i].decode())\n print('Aborting...')\n out = self.command(gCodeMove(160,0,100, 1000))\n self.running=False\n callback()\n return\n num=num+1\n self.progress = float(num)/float(len(g))*100.0\n self.running=False\n self.stop=True\n self.park()\n callback()\n\n def isConnected(self):\n return self.connected\n\n def disconnect(self):\n self.park()\n self.ser.close()\n self.connected = False\n\n def engage(self):\n return self.command('M17')\n\n def disengage(self):\n return self.command('M2019')\n\n def getDeviceName(self):\n out = self.command('P2201')\n return out[3:]\n\n def getHWVersion(self):\n out = self.command('P2202')\n return out[3:]\n\n def getSWVersion(self):\n out = self.command('P2203')\n return out[3:]\n\n def getAPIVersion(self):\n out = self.command('P2204')\n return out[3:]\n\n def getMode(self):\n out = self.command('P2400')\n return int(out.decode()[4:5])\n\n def setMode(self, mode=1):\n if mode>=0 and mode<=3:\n self.command('M2400 S%d'%mode)\n else:\n print('Invalid mode '+str(mode))\n\n def command(self, cmd):\n self.ser.flushInput()\n self.ser.write((cmd+'\\n').encode())\n return self.ser.readline().decode().strip()\n\n def setServo(self, val):\n if val>=0 and val<=180:\n return self.command('G2202 N3 V%0.2f'%val)\n\n def park(self):\n self.command('G2201 S%0.2f R%0.2f H%0.2f F%0.2f'%(140,90.0,80,10000))\n\n\n","repo_name":"VladimirIvan/uArmUI","sub_path":"src/Device.py","file_name":"Device.py","file_ext":"py","file_size_in_byte":3389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"34173896391","text":"import logging\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(level=logging.DEBUG)\n\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n# FileHandler\nfile_handler = logging.FileHandler('result.log')\nfile_handler.setFormatter(formatter)\nlogger.addHandler(file_handler)\n\n# StreamHandler\nstream_handler = logging.StreamHandler()\nstream_handler.setFormatter(formatter)\nlogger.addHandler(stream_handler)\n\n# Log\nlogger.info('Start')\nlogger.warning('Something maybe fail.')\ntry:\n result = 10 / 0\nexcept Exception:\n logger.error('Faild to get result', exc_info=True)\nlogger.info('Finished')\n","repo_name":"Germey/LoggingTest","sub_path":"demo8.py","file_name":"demo8.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"} +{"seq_id":"21174979874","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 19 22:02:34 2020\n\n@author: Woody\n\"\"\"\nimport numpy as np\n\nimport paddle.fluid as fluid\nimport parl\nfrom parl import layers\n\nclass Agent(parl.Agent):\n def __init__(self, algorithm, obs_dim, act_dim):\n self.obs_dim = obs_dim\n self.act_dim = act_dim\n super(Agent, self).__init__(algorithm)\n\n def build_program(self):\n self.pred_program = fluid.Program()\n self.learn_program = fluid.Program()\n\n with fluid.program_guard(self.pred_program): # 搭建计算图用于 预测动作，定义输入输出变量\n obs = layers.data(\n name='obs', shape=[self.obs_dim], dtype='float32')\n self.act_prob = self.alg.predict(obs)\n\n with fluid.program_guard(\n self.learn_program): # 搭建计算图用于 更新policy网络，定义输入输出变量\n obs = layers.data(\n name='obs', shape=[self.obs_dim], dtype='float32')\n act = layers.data(name='act', shape=[1], dtype='int64')\n reward = layers.data(name='reward', shape=[], dtype='float32')\n self.cost = self.alg.learn(obs, act, reward)\n\n def sample(self, obs):\n obs = np.expand_dims(obs, axis=0) # 增加一维维度\n act_prob = self.fluid_executor.run(\n self.pred_program,\n feed={'obs': obs.astype('float32')},\n fetch_list=[self.act_prob])[0]\n act_prob = np.squeeze(act_prob, axis=0) # 减少一维维度\n act = np.random.choice(range(self.act_dim), p=act_prob) # 根据动作概率选取动作\n return act\n\n def predict(self, obs):\n obs = np.expand_dims(obs, axis=0)\n act_prob = self.fluid_executor.run(\n self.pred_program,\n feed={'obs': obs.astype('float32')},\n fetch_list=[self.act_prob])[0]\n act_prob = np.squeeze(act_prob, axis=0)\n act = np.argmax(act_prob) # 根据动作概率选择概率最高的动作\n return act\n\n def learn(self, obs, act, reward):\n act = np.expand_dims(act, axis=-1)\n feed = {\n 'obs': obs.astype('float32'),\n 'act': act.astype('int64'),\n 'reward': reward.astype('float32')\n }\n cost = self.fluid_executor.run(\n self.learn_program, feed=feed, fetch_list=[self.cost])[0]\n return cost","repo_name":"star2dust/parl-notes","sub_path":"results/pg/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"76"} +{"seq_id":"74779671284","text":"#!/usr/bin/env python\n\n# Slightly modified from the code in https://www.xltrail.com/blog/auto-export-vba-commit-hook, with thanks\n\nimport os\nimport shutil\nfrom oletools.olevba3 import VBA_Parser\n\nEXCEL_FILE_EXTENSIONS = ('xlsb', 'xls', 'xlsm', 'xla', 'xlt', 'xlam',)\nKEEP_NAME = False # Set this to True if you would like to keep \"Attribute VB_Name\"\n\ndef parse(workbook_path):\n vba_path = 'VBA Code'\n vba_parser = VBA_Parser(workbook_path)\n vba_modules = vba_parser.extract_all_macros() if vba_parser.detect_vba_macros() else []\n\n for _, _, filename, content in vba_modules:\n try:\n decoded_content = content.decode('latin-1')\n except:\n decoded_content = content\n \n lines = []\n if '\\r\\n' in decoded_content:\n lines = decoded_content.split('\\r\\n')\n else:\n lines = decoded_content.split('\\n')\n if lines:\n content = []\n for line in lines:\n if line.startswith('Attribute') and 'VB_' in line:\n if 'VB_Name' in line and KEEP_NAME:\n content.append(line)\n else:\n content.append(line)\n if content and content[-1] == '':\n content.pop(len(content)-1)\n non_empty_lines_of_code = len([c for c in content if c])\n if non_empty_lines_of_code > 0:\n if not os.path.exists(os.path.join(vba_path)):\n os.makedirs(vba_path)\n with open(os.path.join(vba_path, filename), 'w', encoding='utf-8') as f:\n f.write('\\n'.join(content))\n\n\nif __name__ == '__main__':\n # Delete the folder containing the VBA Code\n try:\n shutil.rmtree('./VBA Code')\n except:\n pass\n\n for root, dirs, files in os.walk('.'):\n for f in dirs:\n if f.endswith('.vba'):\n shutil.rmtree(os.path.join(root, f))\n\n for f in files:\n if f.endswith(EXCEL_FILE_EXTENSIONS):\n parse(os.path.join(root, f))","repo_name":"danguetta/xlkitlearn_old","sub_path":".github/workflows/extract_vba.py","file_name":"extract_vba.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"76"} +{"seq_id":"72050673526","text":"import sys\n\nn,m=map(int,sys.stdin.readline().split())\nlst=[]\nfor i in range(n):\n lst.append([1001]*(n))\n\nfor i in range(n-1):\n a, b, d=map(int,sys.stdin.readline().split())\n lst[a-1][b-1]=d\n lst[b - 1][a - 1] = d\n\nfor i in range(n):\n lst[i][i]=0\n\n\n\nfor i in range(n):\n premin = 0\n for j in range(n):\n min=1001\n min_index=0\n for m in range(n):\n if i!=m and lst[i][m]!=premin and lst[i][m]!=1001 and premin<=lst[i][m]lst[i][min_index]+lst[min_index][k]:\n lst[i][k]=lst[i][min_index]+lst[min_index][k]\n lst[k][i] = lst[i][min_index]+lst[min_index][k]\n\n\n\nfor i in range(m):\n x,y = map(int, sys.stdin.readline().split())\n print(lst[x-1][y-1])","repo_name":"algorithm-py/algorithm","sub_path":"sungkyu/distance of node.py","file_name":"distance of node.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"7138831564","text":"from django import template\nfrom django.utils.encoding import smart_str\nfrom django.core.urlresolvers import reverse, NoReverseMatch\nfrom django.db.models import get_model\n\n\nregister = template.Library()\n\n\nclass GroupURLNode(template.Node):\n def __init__(self, view_name, group, kwargs, asvar):\n self.view_name = view_name\n self.group = group\n self.kwargs = kwargs\n self.asvar = asvar\n \n def render(self, context):\n url = \"\"\n group = self.group.resolve(context)\n \n kwargs = {}\n for k, v in self.kwargs.items():\n kwargs[smart_str(k, \"ascii\")] = v.resolve(context)\n \n if group:\n bridge = group.content_bridge\n try:\n url = bridge.reverse(self.view_name, group, kwargs=kwargs)\n except NoReverseMatch:\n if self.asvar is None:\n raise\n else:\n try:\n url = reverse(self.view_name, kwargs=kwargs)\n except NoReverseMatch:\n if self.asvar is None:\n raise\n \n if self.asvar:\n context[self.asvar] = url\n return \"\"\n else:\n return url\n\n\nclass ContentObjectsNode(template.Node):\n def __init__(self, group_var, model_name_var, context_var):\n self.group_var = template.Variable(group_var)\n self.model_name_var = template.Variable(model_name_var)\n self.context_var = context_var\n \n def render(self, context):\n group = self.group_var.resolve(context)\n app_name, model_name = self.model_name_var.resolve(context).split(\".\")\n model = get_model(app_name, model_name)\n context[self.context_var] = group.content_objects(model)\n return \"\"\n\n\n@register.tag\ndef groupurl(parser, token):\n bits = token.contents.split()\n tag_name = bits[0]\n if len(bits) < 3:\n raise template.TemplateSyntaxError(\"'%s' takes at least two arguments\"\n \" (path to a view and a group)\" % tag_name)\n \n view_name = bits[1]\n group = parser.compile_filter(bits[2])\n args = []\n kwargs = {}\n asvar = None\n \n if len(bits) > 3:\n bits = iter(bits[3:])\n for bit in bits:\n if bit == \"as\":\n asvar = bits.next()\n break\n else:\n for arg in bit.split(\",\"):\n if \"=\" in arg:\n k, v = arg.split(\"=\", 1)\n k = k.strip()\n kwargs[k] = parser.compile_filter(v)\n elif arg:\n raise template.TemplateSyntaxError(\"'%s' does not support non-kwargs arguments.\" % tag_name)\n \n return GroupURLNode(view_name, group, kwargs, asvar)\n\n\n@register.tag\ndef content_objects(parser, token):\n \"\"\"\n {% content_objects group \"tasks.Task\" as tasks %}\n \"\"\"\n bits = token.split_contents()\n if len(bits) != 5:\n raise template.TemplateSyntaxError(\"'%s' requires five arguments.\" % bits[0])\n return ContentObjectsNode(bits[1], bits[2], bits[4])\n","repo_name":"skyl/mycogia.com","sub_path":"apps/pinax/apps/groups/templatetags/group_tags.py","file_name":"group_tags.py","file_ext":"py","file_size_in_byte":3119,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"76"} +{"seq_id":"13312739334","text":"import os\n\nfrom graphscope.framework.graph import Graph\nfrom graphscope.framework.loader import Loader\n\n\ndef load_modern_graph(sess, prefix, directed=True):\n \"\"\"Load modern graph.\n Modern graph consist 6 vertices and 6 edges, useful to test the basic\n functionalities.\n\n Args:\n sess (:class:`graphscope.Session`): Load graph within the session.\n prefix (str): Data directory.\n directed (bool, optional): Determine to load a directed or undirected graph.\n Defaults to True.\n\n Returns:\n :class:`graphscope.Graph`: A Graph object which graph type is ArrowProperty\n \"\"\"\n prefix = os.path.expandvars(prefix)\n graph = sess.g(directed=directed)\n graph = (\n graph.add_vertices(\n Loader(os.path.join(prefix, \"person.csv\"), delimiter=\"|\"),\n \"person\",\n [\"name\", (\"age\", \"int\")],\n \"id\",\n )\n .add_vertices(\n Loader(os.path.join(prefix, \"software.csv\"), delimiter=\"|\"),\n \"software\",\n [\"name\", \"lang\"],\n \"id\",\n )\n .add_edges(\n Loader(os.path.join(prefix, \"knows.csv\"), delimiter=\"|\"),\n \"knows\",\n [\"weight\"],\n src_label=\"person\",\n dst_label=\"person\",\n src_field=\"src_id\",\n dst_field=\"dst_id\",\n )\n .add_edges(\n Loader(os.path.join(prefix, \"created.csv\"), delimiter=\"|\"),\n \"created\",\n [\"weight\"],\n src_label=\"person\",\n dst_label=\"software\",\n src_field=\"src_id\",\n dst_field=\"dst_id\",\n )\n )\n return graph\n","repo_name":"Pengxiang-Huang/GraphScope","sub_path":"python/graphscope/dataset/modern_graph.py","file_name":"modern_graph.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"16259448724","text":"import os\nimport json\nimport boto3\nfrom botocore.exceptions import ClientError\nfrom ast import literal_eval\n\nfrom utils import sql_manager, aws, utils\nfrom alpha import api, table\n\n\ndef lambda_handler(event, context):\n \"\"\"Sample pure Lambda function\n\n Parameters\n ----------\n event: dict, required\n API Gateway Lambda Proxy Input Format\n\n Event doc: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html#api-gateway-simple-proxy-for-lambda-input-format\n\n context: object, required\n Lambda Context runtime methods and attributes\n\n Context doc: https://docs.aws.amazon.com/lambda/latest/dg/python-context-object.html\n\n Returns\n -------\n API Gateway Lambda Proxy Output Format: dict\n\n Return doc: https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html\n \"\"\"\n print(\"event\")\n print(event)\n print()\n\n # Example \n # http://127.0.0.1:3000/update-prices?symbols=AMZN,AAPL,MSFT\n # 'queryStringParameters': {'parallel': '0', 'symbols': 'AMZN,AAPL,MSFT'}\n\n # Inputs\n if 'queryStringParameters' in event:\n print(\"event['queryStringParameters']\")\n print(event[\"queryStringParameters\"])\n print()\n inputs = event[\"queryStringParameters\"]\n else:\n inputs = event\n\n # Gather parameters\n symbols = inputs[\"symbols\"].split(\",\") if \"symbols\" in inputs else []\n parallel = utils.str2bool(inputs[\"parallel\"]) if \"parallel\" in inputs else False\n print(f\"symbols = {symbols}\")\n print(f\"parallel = {parallel}\")\n\n # Decrypts secret using the associated KMS key.\n db_credentials = literal_eval(aws.get_secret(\"prod/awsportfolio/key\"))\n api_key = literal_eval(aws.get_secret(\"prod/AlphaApi/key\"))[\"ALPHAVANTAGE_API_KEY\"]\n\n alpha_scraper = api.AlphaScraper(api_key=api_key)\n accounting_keys = [\"symbol\", \"report_type\", \"report_date\", \"currency\", \"account_name\"]\n balance_accounts = ['totalAssets', 'commonStock', 'commonStockSharesOutstanding']\n alpha_balance = table.AlphaTableAccounting(\n \"balance_alpha\", \n \"BALANCE_SHEET\", \n accounting_keys, \n alpha_scraper, \n balance_accounts,\n sql_params=db_credentials\n )\n income_accounts = ['netIncome']\n alpha_income = table.AlphaTableAccounting(\n \"income_alpha\", \n \"INCOME_STATEMENT\", \n accounting_keys, \n alpha_scraper, \n income_accounts,\n sql_params=db_credentials\n )\n\n if symbols:\n\n print(\"Update balance sheet\")\n alpha_balance.update_list(symbols, parallel=parallel)\n\n print(\"Update income statement\")\n alpha_income.update_list(symbols, parallel=parallel)\n\n return {\n \"statusCode\": 200,\n \"body\": json.dumps({\n \"message\": f\"Accounting updated for symbols = {symbols}\",\n }),\n }\n","repo_name":"nico-corthorn/esg-portfolio","sub_path":"esgtools/update_accounting.py","file_name":"update_accounting.py","file_ext":"py","file_size_in_byte":2907,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"76"} +{"seq_id":"21342491621","text":"import common, cost, fetchresults\nimport logging, csv, math, sys\nimport numpy as np\n\n\nlogging.basicConfig(level=logging.INFO)\n#logging.basicConfig(level=logging.DEBUG)\n\n\nresults = fetchresults.get_last_24hrs()\ncost_24hrs = np.zeros((4,))\nnum_max_processes_all = 0\nfor result in results:\n if not result['duration_ix'] or result['duration_ix'] == 'NA':\n continue\n duration_ix = common.make_int_array(result['duration_ix'])\n duration_ecl2ix = common.make_int_array(result['duration_ecl2ix'])\n num_processes_ix = common.make_int_array(result['num_processes_ix'])\n process_seconds = np.sum(duration_ix * num_processes_ix + duration_ecl2ix)\n total_seconds = np.sum(duration_ix + duration_ecl2ix)\n num_max_processes = np.max(num_processes_ix)\n num_max_processes_all = max(num_max_processes, num_max_processes_all)\n cost_24hrs += cost.google_compute(total_seconds, process_seconds, num_max_processes)\n\nprint(cost_24hrs)\nprint(num_max_processes_all)\nsys.exit()\n\n\n\n\n\n\n\n\n\n\n","repo_name":"philippslang/tcstats","sub_path":"calctcregressioncost.py","file_name":"calctcregressioncost.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"41941161173","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport time\nimport numpy as np\nimport pynng\nimport argparse\nimport os\n\nipc_address = \"ipc:///tmp/abcde\"\ntcp_address = \"tcp://127.0.0.1:4321\"\n\n\ndef serve(address, np_convert):\n with pynng.Rep0(listen=address, recv_timeout=100, send_timeout=100) as rep:\n\n while True:\n try:\n # receive\n msg = rep.recv()\n # print(f\"got message from client, {msg.decode()}\")\n # print(f\"total received byte size, {len(msg)}\")\n if np_convert:\n input = np.frombuffer(msg, dtype=np.uint8)\n print(input.shape)\n # output\n output = np.zeros((1024), dtype=np.uint8).tobytes()\n\n # send\n rep.send(output)\n\n except pynng.Timeout:\n pass\n except KeyboardInterrupt:\n break\n except Exception:\n break\n # time.sleep(0.0001)\n\n\ndef main():\n # Parse Argument\n parser = argparse.ArgumentParser()\n parser.add_argument(\n \"--address\", help=\"ipc/tcp/ws address\", default=\"ipc:///tmp/abcde\", type=str\n )\n parser.add_argument(\n \"--np_convert\",\n help=\"whether convert input to np.array\",\n default=\"True\",\n type=eval,\n choices=[True, False],\n )\n\n args = parser.parse_args()\n\n # Start server\n serve(args.address, args.np_convert)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"liviaerxin/microservices-exmaples","sub_path":"ipc/nng-examples/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"40357474712","text":"import pbEngine\nfrom pathlib import Path\n\n\ndef main():\n cwd = Path()\n print(f'PROGRAM BEGIN, cwd={cwd.resolve()}')\n PBSlocation = cwd.joinpath('PBS')\n assert PBSlocation.is_dir(), f'{PBSlocation} directory not found !'\n\n import time \n start = time.time()\n engine = pbEngine.pbEngine( PBSlocation, demo=False )\n print(f'Loaded PBS in {round((time.time()-start)*1000)} ms.')\n print('END OF PROGRAM')\n\n\nif __name__ == '__main__':\n main()","repo_name":"DavidRodriguezSoaresCUI/PoGER","sub_path":"RMXP_research/PBSreader/PBSreader.py","file_name":"PBSreader.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"40513287652","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Mar 27 11:22:24 2015\n\n@author: Richard\n\"\"\"\n\ndef mass_spectrometry(input_file):\n '''\n (file) -> str\n Given a list L of n (n ≤ 100) of floats representing the prefix spectrum\n of a protein sequence, return a protein string of length n−1 whose\n prefix spectrum is equal to L (return any sequence if multiple exist)\n '''\n \n # mass table of each amino acid\n mass_table = {'A': 71.03711, 'C': 103.00919, 'D': 115.02694,\n 'E': 129.04259, 'F': 147.06841, 'G': 57.02146,\n 'H': 137.05891, 'I': 113.08406, 'K': 128.09496,\n 'L': 113.08406, 'M': 131.04049, 'N': 114.04293,\n 'P': 97.05276, 'Q': 128.05858, 'R': 156.10111,\n 'S': 87.03203, 'T': 101.04768, 'V': 99.06841, \n 'W': 186.07931, 'Y': 163.06333}\n \n # open file for reading\n infile = open(input_file, 'r')\n # create a list to hold the prefix spectrum\n weights = []\n for line in infile:\n line = line.rstrip()\n if line != '':\n weights.append(float(line))\n # close file\n infile.close()\n \n # The prefix spectrum of a protein is the collection of all\n # its prefix weights so the order of AA in the protein sequence can be\n # deduced from the weights of the prefices\n \n # check if the weights in prefix spectrum > weights of AA\n aa_weights = [weight for weight in mass_table.values()]\n heaviest_aa = max(aa_weights)\n \n # the prefix spectrum may contains weight that are very high, indicating\n # that the protein sequence is attached to something\n if heaviest_aa < weights[0]:\n additional_weight = True\n else:\n additional_weight = False\n \n # find the weights of the prefix\n peptide_weights = []\n if additional_weight:\n for i in range(1, len(weights)):\n peptide_weights.append(round(weights[i] - weights[i-1], 4))\n \n # weights of prefix reveal the order of the AA in the protein sequence:\n # weights[0] = weight_1st_aa + extra_weight\n # weights[1] = weight_1st_aa + weight_2nd_aa + extra_weight\n # weights[1] - weight[0] = weight_2nd_aa\n \n \n # initialize protein sequence\n protein = ''\n # initialize position of the aa in protein sequence\n i = 0\n # potential issue: identical weights fr some amino acid, take any AA\n # make a reverse dictionnary mass : aa\n mass_AA = {}\n for aa in mass_table:\n if round(mass_table[aa], 4) in mass_AA:\n mass_AA[round(mass_table[aa], 4)].append(aa)\n else:\n mass_AA[round(mass_table[aa], 4)] = [aa]\n \n for weight in peptide_weights:\n for mass in mass_AA:\n if weight == mass:\n protein += mass_AA[mass][0]\n return protein\n \n \n \n \n \n \n \n \n ","repo_name":"rjovelin/Rosalind","sub_path":"Stronghold/SPEC/mass_spectrometry.py","file_name":"mass_spectrometry.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"4091500029","text":"# https://leetcode.com/problems/smallest-subtree-with-all-the-deepest-nodes/\nclass Solution:\n def dfs(self, node, currDepth):\n ldepth = currDepth\n rdepth = currDepth\n l, r = node, node\n if node.left:\n l, ldepth = self.dfs(node.left, currDepth + 1)\n if node.right:\n r, rdepth = self.dfs(node.right, currDepth + 1)\n if ldepth == rdepth:\n return [node, ldepth]\n elif ldepth > rdepth:\n return [l, ldepth]\n else:\n return [r, rdepth]\n \n def subtreeWithAllDeepest(self, root: TreeNode) -> TreeNode:\n n, k = self.dfs(root, 0)\n return n","repo_name":"cosmicanant/algorithms","sub_path":"leetcode/dec/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"16947451260","text":"import os\nimport torch\nimport torchvision\nfrom models.fcn import FCN8s\nfrom torch.utils.data import DataLoader\nimport torchvision.transforms as transforms\nfrom data.pascal_voc import makecmap, VOCSegmentation\nfrom utils.metrics import pixel_accuracy, mean_pixel_accuarcy, mean_iou, iou, frequency_weighted_iou\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\ndef train(dataloader, model, loss_fn, optimizer):\n size = len(dataloader.dataset)\n batch = 0\n for X, y in dataloader:\n X, y = X.to(device), y.to(device)\n pred = model(X)[0]\n loss = loss_fn(pred, y)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n batch += 1\n if batch % 10 == 0:\n loss, current = loss.item(), batch * len(X)\n print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")\n\ndef test(dataloader, model, loss_fn):\n size = len(dataloader.dataset)\n num_batches = len(dataloader)\n model.eval()\n test_loss, correct = 0, 0\n with torch.no_grad():\n for X, y in dataloader:\n X, y = X.to(device), y.to(device)\n pred = model(X)[0]\n test_loss += loss_fn(pred, y).item()\n # correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n test_loss /= num_batches\n # correct /= size\n print(f\"Avg loss: {test_loss:>8f} \\n\")\n\n\n\ndef main():\n classes = ['background', 'aeroplane', 'bicycle', 'bird', 'boat',\n 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable',\n 'dog', 'horse', 'motorbike', 'person', 'potted plant',\n 'sheep', 'sofa', 'train', 'monitor']\n color_maps = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128],\n [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [192, 0, 0],\n [64, 128, 0], [192, 128, 0], [64, 0, 128], [192, 0, 128],\n [64, 128, 128], [192, 128, 128], [0, 64, 0], [128, 64, 0],\n [0, 192, 0], [128, 192, 0], [0, 64, 128]]\n\n\n base_path = r\"C:\\Users\\12112\\iPython\\Jupyter\\Deep Learning\\data\\Pascal VOC 2012\\train\"\n img_path = os.path.join(base_path, \"JPEGImages\")\n mask_path = os.path.join(base_path, \"SegmentationClass\")\n train_sets = os.path.join(base_path, \"ImageSets/Segmentation/train.txt\")\n val_sets = os.path.join(base_path, \"ImageSets/Segmentation/val.txt\")\n\n with open(train_sets,'r') as f:\n train_sets = f.read().split('\\n')\n with open(val_sets,'r') as f:\n val_sets = f.read().split('\\n')\n backbone = torchvision.models.vgg.vgg16(pretrained=True).features\n model = FCN8s(num_classes=len(classes), bakcbone=backbone)\n\n cm2lbl = makecmap(color_maps,size=(3, 256, 256))\n\n transform = transforms.Compose([transforms.ToTensor(),# PIL or ndarray\n transforms.CenterCrop((256, 256)), # tensor\n transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))\n ])\n\n train_data = VOCSegmentation(img_path, mask_path, train_sets, cm2lbl,transform)\n val_data = VOCSegmentation(img_path, mask_path, val_sets, cm2lbl,transform)\n\n train_dataloader = DataLoader(train_data,\n shuffle=True,\n batch_size=16)\n val_dataloader = DataLoader(val_data,\n shuffle=True,\n batch_size=16)\n i = 0\n for x, y in train_dataloader:\n pred = model(x)[0]\n print(mean_pixel_accuarcy(pred, y))\n print(mean_iou(pred, y))\n print(frequency_weighted_iou(pred, y))\n break\n # pix tensor(349.7634)\n # mean pix tensor(21.6136)\n # iou tensor(6.5387)\n # mean iou tensor(19.2851)\n # frequency weighted iou\n # tensor(83.2737)\n\n # loss_fn = nn.CrossEntropyLoss()\n # optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9, weight_decay=5e-4)\n # epochs = 2\n # for t in range(epochs):\n # print(f\"Epoch {t + 1}\\n-------------------------------\")\n # train(train_dataloader, model, loss_fn, optimizer)\n # test(val_dataloader, model, loss_fn)\n # print(\"Done!\")\n # torch.save(model.state_dict(), \"model.pth\")\n # print(\"Saved PyTorch Model State to model.pth\")\n\n\nif __name__ == '__main__':\n # m = torch.rand((7,7))\n # m[0, 0] += 1\n # print(m)\n # print(torch.sum(m[0,:]))\n # print(sum([1.9462, 0.0161, 0.5009, 0.3033, 0.5594, 0.6062, 0.5228]))\n main()","repo_name":"Nickiris/Segmentation","sub_path":"utils/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"1686693834","text":"#! /usr/bin/env python\n# coding: utf-8\n\nimport pandas as pd\nimport re\nimport subprocess\n\ndef output_log(id, res):\n log_file = \"zendesk_post.log\"\n log_message = \"{0}: {1}\\r\\n\".format(id, res.stdout)\n\n with open(log_file, mode='a') as f:\n f.write(log_message)\n\n return\n\ndef shave_2_html_tag(contents):\n ret = \"\"\n\n reg_obj = re.compile(r\"<[^>]*?>\")\n ret = reg_obj.sub(\"\", contents)\n \n return ret\n\ndef post_2_zendesk(title, contents, category, locale):\n \n domain = \"\"\n url = domain + \"/api/v2/help_center/{0}/sections/{1}/articles.json\".format(locale, category)\n mail = \"\"\n user_name = mail + \"/\" + \"token\"\n password = \"\"\n token = \"{0}:{1}\".format(user_name, password)\n headers = \"Content-Type: application/json\"\n\n payload = '''\\\n {{\n \"article\": {{\n \"title\": \"{title}\",\n \"body\": \"{contents}\",\n \"locale\": \"{locale}\",\n \"user_segment_id\": null,\n \"permission_group_id\": 4407131572367\n }},\n \"notify_subscribers\": false\n }}\n '''.format(title=title, contents=contents, locale=locale, category=category)\n\n cmd = 'curl -X POST {0} -u {1} -H \"{2}\" -d \\'{3}\\''.format(url, token, headers, payload)\n\n print(cmd)\n\n res = subprocess.run(cmd, shell=True, capture_output=True, text=True)\n print(res)\n\n return res\n\nexcel_file_name = \"\"\nexcel_sheet_name = \"\"\n\ndf = pd.read_excel(excel_file_name, sheet_name = excel_sheet_name)\n\ncnt = 0\nfor row in df.values:\n\n id = row[0]\n title = row[1]\n category = row[2]\n locale = row[3]\n contents_with_tag = row[4]\n status = row[5]\n contents = \"\"\n\n contents = shave_2_html_tag(contents_with_tag)\n res = post_2_zendesk(title, contents, category, locale)\n\n output_log(id, res)\n\n cnt = cnt+1\n \n if cnt >= 40:\n break\n\nprint(\"end\")","repo_name":"seiya-tsukada/hobby","sub_path":"python/zendesk/zendesk_api.py","file_name":"zendesk_api.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"6612742812","text":"import discord\n\nfrom discord.ext import commands, tasks\n\nfrom discord.voice_client import VoiceClient\n\nfrom discord.utils import get\n\nimport youtube_dl\n\nfrom random import choice\n# ---------------------------------------------------------------------------------------------------------------------\n\n\n\nyoutube_dl.utils.bug_reports_message = lambda: ''\n\nytdl_format_options = {\n 'format': 'bestaudio/best',\n 'outtmpl': '%(extractor)s-%(id)s-%(title)s.%(ext)s',\n 'restrictfilenames': True,\n 'noplaylist': True,\n 'nocheckcertificate': True,\n 'ignoreerrors': False,\n 'logtostderr': False,\n 'quiet': True,\n 'no_warnings': True,\n 'default_search': 'auto',\n 'source_address': '0.0.0.0'\n}\n\nffmpeg_options = {\n 'options': '-vn'\n}\n\nytdl = youtube_dl.YoutubeDL(ytdl_format_options)\n\nclass YTDLSource(discord.PCMVolumeTransformer):\n def __init__(self, source, *, data, volume=0.5):\n super().__init__(source, volume)\n\n self.data = data\n\n self.title = data.get('title')\n self.url = data.get('url')\n\n @classmethod\n async def from_url(cls, url, *, loop=None, stream=False):\n loop = loop or asyncio.get_event_loop()\n data = await loop.run_in_executor(None, lambda: ytdl.extract_info(url, download=not stream))\n\n if 'entries' in data:\n data = data['entries'][0]\n\n filename = data['url'] if stream else ytdl.prepare_filename(data)\n return cls(discord.FFmpegPCMAudio(filename, **ffmpeg_options), data=data)\n# ---------------------------------------------------------------------------------------------------------------------\n\n\n\n\nclient = commands.Bot(command_prefix=\"-\")\nclient.remove_command('help')\nqueue = []\n\n\n@client.event\nasync def on_ready():\n print(\"Bot is up and running!\")\n await client.change_presence(activity=discord.Activity(name= \"-help | Developed by Fares#0001\", type=0))\n\n@client.command(name='ping')\nasync def ping(ctx):\n await ctx.send(f'**Pong!**\\n`Latency: {round(client.latency * 1000)} ms`')\n\n@client.command(name='credits')\nasync def credits(ctx):\n await ctx.send(\"**Developed by** <@263409149147086850>\\n\\n\\n`note : bot is still under development`\")\n\n# ---------------------------------------------------------------------------------------------------------------------\n@client.command(aliases=['p', 'P','PLAY'],name='play')\nasync def play(ctx, url):\n if not ctx.message.author.voice:\n await ctx.send('You need to enter a voice channel first')\n return\n else:\n channel = ctx.message.author.voice.channel\n\n try:\n await channel.connect()\n server = ctx.message.guild\n voice_channel = server.voice_client\n\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=client.loop)\n voice_channel.play(player, after=lambda e: print('Player error: %s' %e) if e else None)\n\n await ctx.send(f'**Playing : **{player.title}')\n except Exception:\n try:\n server = ctx.message.guild\n voice_channel = server.voice_client\n\n async with ctx.typing():\n player = await YTDLSource.from_url(url, loop=client.loop)\n voice_channel.play(player, after=lambda e: print('Player error: %s' %e) if e else None)\n\n await ctx.send(f'**Playing : **{player.title}')\n except Exception:\n await ctx.send(\"A song is already playing, Make sure to skip it first!\")\n\n\n@client.command(name='skip',aliases = [\"s\",\"S\",'Skip','SKIP'])\nasync def skip(ctx):\n if not ctx.message.author.voice:\n await ctx.send('You are not in a voice channel with the bot!')\n return\n else:\n voice_client = ctx.message.guild.voice_client\n voice_client.stop()\n await ctx.send(\"Song skipped!\")\n\n@client.command(name='stop',aliases=[\"STOP\",\"Stop\",\"Disconnect\",'disconnect','DISCONNECT','Leave','leave',\"LEAVE\"])\nasync def stop(ctx):\n if not ctx.message.author.voice:\n await ctx.send(\"You are not in a voice channel with the bot!\")\n return\n else:\n voice_client = ctx.message.guild.voice_client\n await voice_client.disconnect()\n await ctx.send(\"Disconnected!\")\n# ---------------------------------------------------------------------------------------------------------------------\n\n\n# --- Error Handling ---\n@client.event\nasync def on_command_error(ctx,error):\n if isinstance(error,commands.MissingPermissions):\n \tpass\n elif isinstance(error,commands.MissingRequiredArgument):\n await ctx.send(\"There are missing required arguments!, -help if you can't use the command\")\n else:\n raise error\n# -----------------------\n\n\n\n# ----- Clear ------\n@client.command(aliases=['c','Clear',\"CLEAR\"])\n@commands.has_permissions(manage_messages = True)\nasync def clear(ctx,amount=99):\n if amount > 100 :\n await ctx.send(\"You can't clear more than 100 Messages\")\n \n elif amount <= 100 :\n await ctx.channel.purge(limit=amount+1)\n await ctx.send(\"Messages Deleted Successfully :white_check_mark:\", delete_after=2.5)\n\n@clear.error\nasync def clear_error(ctx,error):\n\tif isinstance(error, commands.MissingPermissions):\n\t\tawait ctx.send(\"You must have **Manage Messages** permission to perform this action!\")\n\telif isinstance(error,commands.errors.CommandInvokeError):\n\t\tawait ctx.send(\"I don't have **Manage Messages** permission to perform this action\")\n \n# -------------------\n\n# ----- Kick --------\n@client.command(aliases=['k', 'K','Kick'])\n@commands.has_permissions(kick_members = True)\nasync def kick(ctx , member : discord.Member ,*, reason= \"No Reason Provided\"): \n await member.kick(reason=reason)\n await ctx.send(member.mention + \" has been kicked successfully :white_check_mark:\")\n\n@kick.error\nasync def kick_error(ctx,error):\n\tif isinstance(error, commands.MissingPermissions):\n\t\tawait ctx.send(\"You must have **Kick Members** permission to perform this action!\")\n\telif isinstance(error,commands.errors.CommandInvokeError):\n\t\tawait ctx.send(\"I don't have **Kick Members** permission to perform this action\")\n\telse:\n\t\tif isinstance(error, commands.errors.MemberNotFound):\n\t\t\tawait ctx.send(\"Member was not found!\")\n\n# -------------------\n\n\n# ----- Ban ---------\n@client.command(aliases=['B', 'b','Ban'])\n@commands.has_permissions(ban_members = True)\nasync def ban(ctx,member : discord.Member,*,reason= \"No Reason Provided\"):\n await member.ban(delete_message_days=0,reason=reason)\n await ctx.send(member.mention + \" has been banned successfully :white_check_mark:\")\n\n@ban.error\nasync def ban_error(ctx,error):\n\tif isinstance(error, commands.MissingPermissions):\n\t\tawait ctx.send(\"You must have **Ban Members** permission to perform this action!\")\n\telif isinstance(error,commands.errors.CommandInvokeError):\n\t\tawait ctx.send(\"I don't have **Ban Members** permission to perform this action\")\n\telse:\n\t\tif isinstance(error, commands.errors.MemberNotFound):\n\t\t\tawait ctx.send(\"Member was not found!\")\n#---------------------\n\n# # ----- Unban ------\n@client.command(aliases=['Unb', 'UnB','UnBan'])\n@commands.has_permissions(ban_members = True)\nasync def unban(ctx,*,member):\n banned_users = await ctx.guild.bans()\n member_name, member_disc = member.split('#')\n\n for banned_entry in banned_users:\n user = banned_entry.user\n\n if(user.name, user.discriminator)==(member_name,member_disc):\n await ctx.guild.unban(user)\n await ctx.send(f'{user.name}#{user.discriminator} unbanned successfully :white_check_mark:')\n return\n await ctx.send(member+\" was not found!\") \n\n@unban.error\nasync def unban_error(ctx,error):\n\tif isinstance(error, commands.MissingPermissions):\n\t\tawait ctx.send(\"You must have **Ban Members** permission to perform this action!\")\n\telif isinstance(error,commands.errors.CommandInvokeError):\n\t\tawait ctx.send(\"I don't have **Ban Members** permission to perform this action\")\n\telse:\n\t\tif isinstance(error, commands.errors.MemberNotFound):\n\t\t\tawait ctx.send(\"Member was not found!\")\n#-------------------- \n\n# ----- Mute --------\n@client.command(aliases=['m', 'Mute','M'])\n@commands.has_permissions(manage_roles=True)\nasync def mute(ctx,member : discord.Member):\n role = discord.utils.get(ctx.guild.roles, name='Muted')\n if role in member.roles:\n await ctx.send(member.mention + \" is already Muted!\")\n else:\n await member.add_roles(role)\n await ctx.send(member.mention + \" has been muted successfully :white_check_mark:\")\n\n@mute.error\nasync def mute_error(ctx,error):\n\tif isinstance(error, commands.MissingPermissions):\n\t\tawait ctx.send(\"You must have **Manage Roles** permission to perform this action!\")\n\telif isinstance(error,commands.errors.CommandInvokeError):\n\t\tawait ctx.send(\"I don't have **Manage Roles** permission to perform this action or the role **'Muted'** is above my max role!\")\n\telse:\n\t\tif isinstance(error, commands.errors.MemberNotFound):\n\t\t\tawait ctx.send(\"Member was not found!\")\n# -------------------\n\n# ----- UnMute ------\n@client.command(aliases=['Unm', 'UnMute','UnM','unm','Unmute'])\n@commands.has_permissions(manage_messages=True)\nasync def unmute(ctx,member : discord.Member):\n role = discord.utils.get(ctx.guild.roles, name='Muted')\n \n if role in member.roles:\n await member.remove_roles(role)\n await ctx.send (member.mention + \" has been unmuted successfully :white_check_mark:\")\n else:\n await ctx.send(member.mention + \" is not Muted!\")\n\n@unmute.error\nasync def unmute_error(ctx,error):\n\tif isinstance(error, commands.MissingPermissions):\n\t\tawait ctx.send(\"You must have **Manage Roles** permission to perform this action!\")\n\telif isinstance(error,commands.errors.CommandInvokeError):\n\t\tawait ctx.send(\"I don't have **Manage Roles** permission to perform this action **or** the role **'Muted'** is above my max role!\")\n\telse:\n\t\tif isinstance(error, commands.errors.MemberNotFound):\n\t\t\tawait ctx.send(\"Member was not found!\")\n# -------------------\n\n# --- User Command ---\n@client.command(aliases=['whois','info','User','USER'])\nasync def user(ctx, member : discord.Member):\n embed = discord.Embed(title = \"User Info\" , color = discord.Colour(0x71368a))\n embed.add_field(name = \"Name :\", value = member.mention, inline = True)\n embed.add_field(name= \"Role :\", value = member.top_role)\n embed.add_field(name = \"ID :\", value = member.id, inline = True)\n embed.add_field(name = \"Joined Server :\", value = member.joined_at.strftime(\"`%d/%m/%Y`\"))\n embed.add_field(name = \"Account Created :\", value = member.created_at.strftime(\"`%d/%m/%Y`\"))\n embed.set_image(url = member.avatar_url)\n embed.set_footer(icon_url = ctx.author.avatar_url, text = f\"Requested by {ctx.author.name}\"+ '#' + member.discriminator)\n await ctx.send(embed=embed)\n# --------------------\n\n# --- Avatar ---\n@client.command(aliases=['AVATAR','av','pfp'])\nasync def avatar(ctx, member : discord.Member):\n t = discord.Embed(color = discord.Colour(0x71368a))\n t.set_author(name = member.name + \"#\" + member.discriminator,icon_url = member.avatar_url)\n t.set_image(url = member.avatar_url)\n t.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + member.discriminator)\n await ctx.send(embed=t)\n# --------------\n\n# help test \n\n@client.group(invoke_without_command=True)\nasync def help(ctx):\n em = discord.Embed(title=\"Help\", description=\"-help for more info about a certain command\", color = ctx.author.color)\n \n em.add_field(name=\"General Commands\", value = \"avatar , user , ping , credits\", inline = False)\n em.add_field(name=\"Moderation Commands\", value = \"ban , unban , kick , mute , unmute , clear\", inline = False)\n em.add_field(name='Music Commands', value = \"play , skip , stop\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def avatar(ctx):\n em = discord.Embed(title = \"Avatar\", description = \"Sends the user's avatar\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-avatar [member]\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n\n@help.command()\nasync def user(ctx):\n em = discord.Embed(title = \"User\", description = \"Sends info about the user\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-user [member]\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def ping(ctx):\n em = discord.Embed(title = \"Ping\", description = \"Sends the bot's latency\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-ping\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def credits(ctx):\n em = discord.Embed(title = \"Credits\", description = \"Credits to the developer\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-credits\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def ban(ctx):\n em = discord.Embed(title = \"Ban\", description = \"Bans the user from the server\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-ban [member]\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def unban(ctx):\n em = discord.Embed(title = \"Unban\", description = \"Unbans the user from the server\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-unban [user#0000] `make sure of capital letters` \")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def kick(ctx):\n em = discord.Embed(title = \"Kick\", description = \"Kicks the user from the server\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-kick [member]\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def mute(ctx):\n em = discord.Embed(title = \"Mute\", description = \"Adds the role \\\"Muted\\\" to the user\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-mute [member]\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def unmute(ctx):\n em = discord.Embed(title = \"Unmute\", description = \"Removes the role \\\"Muted\\\" from the user\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-unmute [member]\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def clear(ctx):\n em = discord.Embed(title = \"Clear\", description = \"Clears the amount of messages inserted\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-clear [Number of messages] `max 100` \")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def play(ctx):\n em = discord.Embed(title = \"Play\", description = \"Plays music to the channel you are connected to!\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-play \")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def stop(ctx):\n em = discord.Embed(title = \"Stop\", description = \"Stops the music and leaves the voice channel!\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-stop\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n\n@help.command()\nasync def skip(ctx):\n em = discord.Embed(title = \"Skip\", description = \"Skips the current song!\", color = ctx.author.color)\n em.add_field(name = \"**Syntax**\", value = \"-skip\")\n em.set_footer(icon_url = ctx.author.avatar_url, text = f'Requested by {ctx.author.name}'+ '#' + ctx.author.discriminator)\n await ctx.send(embed = em)\n","repo_name":"FaresF2/infinite-bot","sub_path":"infinite bot.py","file_name":"infinite bot.py","file_ext":"py","file_size_in_byte":16899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"35332506292","text":"#importing stuff and initialising pygame\r\nimport pygame\r\nfrom random import randint as rdt\r\nimport time\r\nfrom pygame.locals import * \r\n\r\npygame.init()\r\n\r\n# defining variables like x, y, position & velocity\r\nplayer = pygame.image.load(\"borb.jpg\")\r\nplayer_alt = pygame.image.load(\"seacat.jpg\")\r\n\r\ns_width = 800\r\ns_height = 600\r\n\r\nx = y = t = 0\r\n\r\npos = pygame.math.Vector2((0,0))\r\nvel = pygame.math.Vector2((0,0))\r\n\r\nclock = pygame.time.Clock()\r\nfps = 60\r\n\r\nscreen = pygame.display.set_mode((s_width,s_height))\r\n\r\n\r\n#gameloop\r\nrunning = True\r\nwhile running:\r\n keys = pygame.key.get_pressed()\r\n mKeys = pygame.mouse.get_pressed()\r\n \r\n #exiting the game(work in progress :) )\r\n for event in pygame.event.get():\r\n if event == pygame.KEYDOWN:\r\n if event.key == K_BACKSPACE:\r\n running = False\r\n \r\n move_speed = 1\r\n\r\n if keys[pygame.K_SPACE] and pos.y == 0:\r\n vel.y = 20\r\n\r\n #calculating player height\r\n vel += pygame.math.Vector2((0, -9.81)) * t\r\n print(f\"position = {pos}\")\r\n t += 1/fps\r\n pos += vel\r\n\r\n #bounding the player\r\n x_max = s_width - player.get_width()\r\n\r\n if pos.x <= 0: pos.x = 0\r\n elif pos.x >= x_max: pos.x = x_max\r\n screen.fill((102, 229, 255))\r\n if pos.y <= 0:\r\n pos.y = 0\r\n vel.y = 0\r\n t = 0\r\n\r\n screen.blit(player, (pos.x, s_height - pos.y - player.get_height()))\r\n \r\n else:\r\n screen.blit(player_alt, (pos.x, s_height - pos.y - player.get_height()))\r\n\r\n \"\"\"\r\n #if keys[K_s]:\r\n #y+=move_speed\r\n if keys[K_a]:\r\n x-=move_speed\r\n if keys[K_d]:\r\n x+=move_speed\r\n\r\n if x < 0: x = 0\r\n if y < 0: y = 0\r\n if x > s_width: x = s_width\r\n if y > s_height: x = s_height\r\n \r\n \r\n \r\n screen.blit(player.img, (x, y))\r\n\r\n pygame.display.flip()\r\n\r\n time.sleep(1/1000)\r\n \"\"\"\r\n \r\n\r\n pygame.display.flip()\r\n clock.tick(fps)\r\npygame.quit()","repo_name":"TimeWarp995/AdvancedCodingProject","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"40635930058","text":"import re \n\n\ndef get_new_dir(dir, r):\n if r == \"R\":\n if dir == (0, 1):\n return (1, 0)\n elif dir == (1, 0):\n return (0, -1)\n elif dir == (0, -1):\n return (-1, 0)\n elif dir == (-1, 0):\n return (0, 1)\n else:\n if dir == (0, 1):\n return (-1, 0)\n elif dir == (-1, 0):\n return (0, -1)\n elif dir == (0, -1):\n return (1, 0)\n elif dir == (1, 0):\n return (0, 1)\n\ndef get_sign(dir):\n if dir == (0, 1):\n return 0\n elif dir == (1, 0):\n return 1\n elif dir == (0, -1):\n return 2\n elif dir == (-1, 0):\n return 3\n\nwith open(\"input_22.txt\") as file:\n lines = file.read().splitlines()\n instructions = lines[-1]\n grove = []\n for line in lines[:-2]:\n grove.append(line)\n\n adj_grove = []\n width = max(map(len, grove))\n for line in grove:\n adj_grove.append(line.ljust(width))\n\nstart_column = adj_grove[0].find(\".\")\n\nloc = (0, start_column)\nd = (0, 1)\n\nmoves = re.findall(r\"(\\d+)([RL]?)\", instructions)\n\nfor x, dir in moves:\n length = int(x)\n for _ in range(length):\n new_loc = loc\n while True:\n prop_new_loc = tuple(map(sum, zip(new_loc, d)))\n new_loc = (\n prop_new_loc[0] % len(adj_grove),\n prop_new_loc[1] % width\n )\n if adj_grove[new_loc[0]][new_loc[1]] != \" \":\n break\n if adj_grove[new_loc[0]][new_loc[1]] == \"#\":\n break\n loc = new_loc\n \n if dir:\n d = get_new_dir(d, dir)\n\nsign = get_sign(d)\n\nprint(1000 * (loc[0] + 1) + 4 * (loc[1] + 1) + sign)","repo_name":"krzysiekprzekwas/advent-of-code","sub_path":"2022/22/solution_22.py","file_name":"solution_22.py","file_ext":"py","file_size_in_byte":1702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"35400213726","text":"f = open(\"A:\\\\Arima\\\\PROJECTS\\\\Outbox\\\\Anitha Akka\\\\Animals\\\\VGG19\\\\newhis.txt\", \"r\")\r\nnf = open(\"A:\\\\Arima\\\\PROJECTS\\\\Outbox\\\\Anitha Akka\\\\Animals\\\\VGG19\\\\acc.txt\", \"w+\")\r\ncontent = f.readlines()\r\n\r\ncontent_acc = [x.strip() for x in content[1::3]]\r\n\r\ncontent_acc = [x.split('-') for x in content_acc]\r\n\r\nfor j in content_acc:\r\n j.pop(0)\r\n j.pop(0) \r\n \r\n\r\n\r\nfor i in content_acc:\r\n for j in i:\r\n nf.write(j)\r\n nf.write(\"\\n\")\r\n nf.write(\"\\n\")\r\nnf.close()\r\n","repo_name":"Elmiar0642/ANITHA-AKKA-DISSERTATION","sub_path":"acc.py","file_name":"acc.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"10223962904","text":"# global\nimport sys\nimport numpy as np\n\n# local\nimport ivy\n\n# noinspection PyUnresolvedReferences\nuse = ivy.framework_handler.ContextManager(sys.modules[__name__])\n\nNativeArray = np.ndarray\nNativeVariable = np.ndarray\nDevice = str\nDtype = np.dtype\n\n# data types\nint8 = np.dtype('int8')\nint16 = np.dtype('int16')\nint32 = np.dtype('int32')\nint64 = np.dtype('int64')\nuint8 = np.dtype('uint8')\nuint16 = np.dtype('uint16')\nuint32 = np.dtype('uint32')\nuint64 = np.dtype('uint64')\nfloat16 = np.dtype('float16')\nfloat32 = np.dtype('float32')\nfloat64 = np.dtype('float64')\n# noinspection PyShadowingBuiltins\nbool = np.dtype('bool')\n\nall_dtypes = (int8, int16, int32, int64,\n uint8, uint16, uint32, uint64,\n float16, float32, float64)\nvalid_dtypes = all_dtypes\n\nall_dtype_strs = ('int8', 'int16', 'int32', 'int64',\n 'uint8', 'uint16', 'uint32', 'uint64',\n 'float16', 'float32', 'float64')\nvalid_dtype_strs = all_dtypes\ninvalid_dtype_strs = ('bfloat16',)\n\n\ndef closest_valid_dtype(type):\n if type is None:\n return ivy.default_dtype()\n type_str = dtype_to_str(type)\n if type_str in invalid_dtype_strs:\n return {'bfloat16': float16}[type_str]\n return type\n\n\nbackend = 'numpy'\n\n\n# local sub-modules\nfrom . import array_api\nfrom .array_api import *\nfrom . import array_builtins\nfrom .array_builtins import *\nfrom .core import *\nfrom . import nn\nfrom .nn import *\n","repo_name":"FerF8/ivy-old","sub_path":"ivy/functional/backends/numpy/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"11223940878","text":"import streamlit as st\nimport tensorflow as tf\nfrom PIL import Image\nimport numpy as np\n\n# Load the model from the trained session and set up classes/categories\n@st.cache\ndef fetch_model():\n return tf.keras.models.load_model('models/cifar_cnn1.h5')\n\nloaded_model = fetch_model()\n\nclass_names = ['airplane', 'car', 'bird', 'cat', 'deer',\n 'dog', 'frog', 'horse', 'ship', 'truck']\n\n# Define function to process an image and output a category in class_names\ndef pred_img(x):\n img1 = Image.open(x).convert(mode=\"RGB\")\n img1 = img1.resize((32,32))\n array1 = np.array(img1.getdata())\n img_np_array = np.reshape(array1, (32,32,3)) / 255.0\n return class_names[np.argmax(loaded_model.predict(np.expand_dims(img_np_array, axis=0)))]\n\n# Define the user interface\ndef app():\n st.title('CIFAR Image Classification')\n st.write('Use a CNN to classify images into general categories')\n st.markdown('----')\n\n col1, col2 = st.beta_columns([2,1])\n\n with col1:\n raw_image = st.file_uploader('Upload an Image')\n\n if raw_image:\n st.image(raw_image)\n\n with col2:\n st.write('Make a Prediction')\n if st.button('Run Model') and raw_image:\n st.write(f'`Prediction: ` {pred_img(raw_image)}')\n \n st.write('Output parameters per class:', pred_img(raw_image))\n\n \n\nif __name__ =='__main__':\n app()","repo_name":"Real-VeerSandhu/CIFAR-Image-Classification","sub_path":"predict_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"} +{"seq_id":"32456401403","text":"# -*- coding: utf-8 -*-\n\"\"\"A module for opsdroid to allow persist in mongo database.\"\"\"\nimport logging\nfrom contextlib import asynccontextmanager\nfrom motor.motor_asyncio import AsyncIOMotorClient\nfrom voluptuous import Any\n\nfrom opsdroid.database import Database\n\n_LOGGER = logging.getLogger(__name__)\nCONFIG_SCHEMA = {\n \"host\": str,\n \"port\": Any(int, str),\n \"database\": str,\n \"user\": str,\n \"password\": str,\n \"collection\": str,\n}\n\n\nclass DatabaseMongo(Database):\n \"\"\"A module for opsdroid to allow memory to persist in a mongo database.\"\"\"\n\n def __init__(self, config, opsdroid=None):\n \"\"\"Create the connection.\n\n Set some basic properties from the database config such as the name\n of this database.\n\n Args:\n config (dict): The config for this database specified in the\n `configuration.yaml` file.\n opsdroid (OpsDroid): An instance of opsdroid.core.\n\n \"\"\"\n super().__init__(config, opsdroid=opsdroid)\n _LOGGER.debug(\"Loaded mongo database connector.\")\n self.name = \"mongo\"\n self.config = config\n self.client = None\n self.database = None\n self.collection = config.get(\"collection\", \"opsdroid\")\n\n async def connect(self):\n \"\"\"Connect to the database.\"\"\"\n host = self.config.get(\"host\", \"localhost\")\n protocol = self.config.get(\"protocol\", \"mongodb\").replace(\"://\", \"\")\n port = self.config.get(\"port\", \"27017\")\n if port != \"27017\":\n host = f\"{host}:{port}\"\n database = self.config.get(\"database\", \"opsdroid\")\n user = self.config.get(\"user\")\n pwd = self.config.get(\"password\")\n if user and pwd:\n self.db_url = f\"{protocol}://{user}:{pwd}@{host}\"\n else:\n self.db_url = f\"{protocol}://{host}\"\n self.client = AsyncIOMotorClient(self.db_url)\n self.database = self.client[database]\n _LOGGER.info(\"Connected to MongoDB.\")\n\n async def put(self, key, data):\n \"\"\"Insert or replace an object into the database for a given key.\n\n Args:\n key (str): the key is the document lookup key.\n data (object): the data to be inserted or replaced\n\n \"\"\"\n _LOGGER.debug(\"Putting %s into MongoDB collection %s\", key, self.collection)\n\n if isinstance(data, str):\n data = {\"value\": data}\n if \"key\" not in data:\n data[\"key\"] = key\n\n return await self.database[self.collection].update_one(\n {\"key\": data[\"key\"]}, {\"$set\": data}, upsert=True\n )\n\n async def get(self, key):\n \"\"\"Get a document from the database (key).\n\n Args:\n key (str): the key is the document lookup key.\n\n \"\"\"\n _LOGGER.debug(\"Getting %s from MongoDB collection %s\", key, self.collection)\n\n response = await self.database[self.collection].find_one(\n {\"$query\": {\"key\": key}, \"$orderby\": {\"$natural\": -1}}\n )\n if response.keys() == {\"_id\", \"key\", \"value\"}:\n response = response[\"value\"]\n return response\n\n async def delete(self, key):\n \"\"\"Delete a document from the database (key).\n\n Args:\n key (str): the key is the document lookup key.\n\n \"\"\"\n _LOGGER.debug(\"Deleting %s from MongoDB collection %s.\", key, self.collection)\n\n return await self.database[self.collection].delete_one({\"key\": key})\n\n @asynccontextmanager\n async def memory_in_collection(self, collection):\n \"\"\"Use the specified collection rather than the default.\"\"\"\n db_copy = DatabaseMongo(self.config, self.opsdroid)\n try:\n await db_copy.connect()\n db_copy.collection = collection\n yield db_copy\n finally:\n if db_copy.client:\n db_copy.client.close()\n","repo_name":"opsdroid/opsdroid","sub_path":"opsdroid/database/mongo/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","stars":787,"dataset":"github-code","pt":"76"} +{"seq_id":"43878362051","text":"from sklearn.ensemble import RandomForestRegressor\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import QMainWindow\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics import mean_squared_error\nfrom math import sqrt\nfrom matplotlib import pyplot as plt\n\nclass Ui1_Dialog(object):\n\n def setupUi(self, Dialog):\n\n Dialog.setObjectName(\"Dialog\")\n Dialog.resize(802, 501)\n self.pushButton = QtWidgets.QPushButton(Dialog)\n self.pushButton.setGeometry(QtCore.QRect(30, 370, 281, 81))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.pushButton.setFont(font)\n self.pushButton.setObjectName(\"pushButton\")\n self.label = QtWidgets.QLabel(Dialog)\n self.label.setGeometry(QtCore.QRect(30, 40, 341, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label.setFont(font)\n self.label.setObjectName(\"label\")\n self.label_2 = QtWidgets.QLabel(Dialog)\n self.label_2.setGeometry(QtCore.QRect(30, 100, 341, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_2.setFont(font)\n self.label_2.setObjectName(\"label_2\")\n self.label_3 = QtWidgets.QLabel(Dialog)\n self.label_3.setGeometry(QtCore.QRect(30, 160, 341, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_3.setFont(font)\n self.label_3.setObjectName(\"label_3\")\n self.label_4 = QtWidgets.QLabel(Dialog)\n self.label_4.setGeometry(QtCore.QRect(30, 230, 341, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_4.setFont(font)\n self.label_4.setObjectName(\"label_4\")\n self.graphicsView = QtWidgets.QLabel(Dialog)\n self.graphicsView.setGeometry(QtCore.QRect(410, 40, 341, 291))\n self.graphicsView.setObjectName(\"graphicsView\")\n self.graphicsView.setPixmap(QtGui.QPixmap(\"images/run.png\"))\n self.label_5 = QtWidgets.QLabel(Dialog)\n self.label_5.setGeometry(QtCore.QRect(410, 330, 351, 41))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_5.setFont(font)\n self.label_5.setObjectName(\"label_5\")\n self.lineEdit = QtWidgets.QLineEdit(Dialog)\n self.lineEdit.setGeometry(QtCore.QRect(640, 380, 113, 31))\n self.lineEdit.setObjectName(\"lineEdit\")\n self.label_6 = QtWidgets.QLabel(Dialog)\n self.label_6.setGeometry(QtCore.QRect(520, 380, 121, 31))\n font = QtGui.QFont()\n font.setPointSize(10)\n self.label_6.setFont(font)\n self.label_6.setObjectName(\"label_6\")\n self.pushButton_2 = QtWidgets.QPushButton(Dialog)\n self.pushButton_2.setGeometry(QtCore.QRect(410, 380, 91, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.pushButton_2.setFont(font)\n self.pushButton_2.setObjectName(\"pushButton_2\")\n self.label_7 = QtWidgets.QLabel(Dialog)\n self.label_7.setGeometry(QtCore.QRect(410, 440, 351, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_7.setFont(font)\n self.label_7.setObjectName(\"label_7\")\n\n self.label_8 = QtWidgets.QLabel(Dialog)\n self.label_8.setGeometry(QtCore.QRect(30, 300, 341, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_8.setFont(font)\n self.label_8.setObjectName(\"label_8\")\n\n self.retranslateUi(Dialog)\n QtCore.QMetaObject.connectSlotsByName(Dialog)\n\n def retranslateUi(self, Dialog):\n\n train_rmse = (sqrt(mean_squared_error(m.predict(train), y_train)))\n validation_rmse = (sqrt(mean_squared_error(m.predict(validation), y_validation)))\n trainR2 = m.score(train, y_train)\n validationR2 = m.score(validation, y_validation)\n\n\n _translate = QtCore.QCoreApplication.translate\n Dialog.setWindowTitle(_translate(\"Dialog\", \"Dialog\"))\n self.pushButton.setText(_translate(\"Dialog\", \"Go back to model training\"))\n self.label.setText(_translate(\"Dialog\", \"RMSE of train set : %.5f\" % (train_rmse)))\n self.label_2.setText(_translate(\"Dialog\", \"RMSE of validation set : %.5f\" % (validation_rmse)))\n self.label_3.setText(_translate(\"Dialog\", \"R^2 of train set : %.5f\" % (trainR2)))\n self.label_4.setText(_translate(\"Dialog\", \"R^2 of validation set : %.5f\" % (validationR2)))\n self.label_5.setText(_translate(\"Dialog\", \"Select one data from test data set\"))\n self.label_6.setText(_translate(\"Dialog\", \"Index [0,27999] :\"))\n self.pushButton_2.setText(_translate(\"Dialog\", \"Test\"))\n self.label_7.setText(_translate(\"Dialog\", \"Estimation of the model [0,9] : - \"))\n\n if oob == True:\n\n self.label_8.setText(_translate(\"Dialog\", \"OOB Score : %.5f\" % m.oob_score_))\n\n self.pushButton.clicked.connect(self.css)\n\n self.test = pd.read_csv(\"test.csv\")\n\n self.pushButton_2.clicked.connect(self.testa)\n\n def css(self):\n\n ui.mainwin.hide()\n Dialog.hide()\n \n Dialog.show()\n\n def testa(self):\n\n self.x = None\n\n try:\n\n int(self.lineEdit.text())\n\n self.x = True\n\n except:\n\n self.x = False\n\n self.pushButton_2.setText(\"Hata !!!\")\n\n\n if self.x == True:\n\n if int(self.lineEdit.text()) >= 0 and int(self.lineEdit.text()) <= 27999: \n\n plt.figure(figsize=(3,3))\n row_index = int(self.lineEdit.text())\n grid_data = np.array(self.test.iloc[row_index]).reshape(28,28)\n plt.imshow(grid_data, interpolation = 'none', cmap= \"gray\")\n plt.savefig(\"images/a.png\")\n\n self.graphicsView.setPixmap(QtGui.QPixmap(\"images/a.png\"))\n\n self.a = []\n\n self.a.append(self.test.iloc[row_index])\n\n\n self.tah = m.predict(self.a)\n\n _translate = QtCore.QCoreApplication.translate\n\n self.label_7.setText(_translate(\"Dialog\", \"Estimation of the model [0,9] : {} \".format(round(self.tah[0]))))\n\n self.pushButton_2.setText(\"Test\")\n\n else:\n\n self.pushButton_2.setText(\"Hata !!!\")\n\n\n\nclass Ui_Dialog(object):\n\n def setupUi(self, Dialog):\n\n Dialog.setObjectName(\"Dialog\")\n Dialog.resize(802, 501)\n self.pushButton = QtWidgets.QPushButton(Dialog)\n self.pushButton.setGeometry(QtCore.QRect(340, 440, 121, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.pushButton.setFont(font)\n self.pushButton.setObjectName(\"pushButton\")\n self.label = QtWidgets.QLabel(Dialog)\n self.label.setGeometry(QtCore.QRect(40, 50, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label.setFont(font)\n self.label.setObjectName(\"label\")\n self.lineEdit = QtWidgets.QLineEdit(Dialog)\n self.lineEdit.setGeometry(QtCore.QRect(260, 150, 113, 31))\n self.lineEdit.setObjectName(\"lineEdit\")\n self.label_2 = QtWidgets.QLabel(Dialog)\n self.label_2.setGeometry(QtCore.QRect(40, 100, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_2.setFont(font)\n self.label_2.setObjectName(\"label_2\")\n self.label_3 = QtWidgets.QLabel(Dialog)\n self.label_3.setGeometry(QtCore.QRect(40, 250, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_3.setFont(font)\n self.label_3.setObjectName(\"label_3\")\n self.label_4 = QtWidgets.QLabel(Dialog)\n self.label_4.setGeometry(QtCore.QRect(40, 300, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_4.setFont(font)\n self.label_4.setObjectName(\"label_4\")\n self.label_5 = QtWidgets.QLabel(Dialog)\n self.label_5.setGeometry(QtCore.QRect(40, 350, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_5.setFont(font)\n self.label_5.setObjectName(\"label_5\")\n self.label_6 = QtWidgets.QLabel(Dialog)\n self.label_6.setGeometry(QtCore.QRect(40, 150, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_6.setFont(font)\n self.label_6.setObjectName(\"label_6\")\n self.label_7 = QtWidgets.QLabel(Dialog)\n self.label_7.setGeometry(QtCore.QRect(40, 200, 221, 31))\n font = QtGui.QFont()\n font.setPointSize(15)\n self.label_7.setFont(font)\n self.label_7.setObjectName(\"label_7\")\n self.lineEdit_2 = QtWidgets.QLineEdit(Dialog)\n self.lineEdit_2.setGeometry(QtCore.QRect(260, 50, 113, 31))\n self.lineEdit_2.setObjectName(\"lineEdit_2\")\n self.lineEdit_3 = QtWidgets.QLineEdit(Dialog)\n self.lineEdit_3.setGeometry(QtCore.QRect(260, 100, 113, 31))\n self.lineEdit_3.setObjectName(\"lineEdit_3\")\n self.lineEdit_4 = QtWidgets.QLineEdit(Dialog)\n self.lineEdit_4.setGeometry(QtCore.QRect(260, 200, 113, 31))\n self.lineEdit_4.setObjectName(\"lineEdit_4\")\n self.lineEdit_5 = QtWidgets.QLineEdit(Dialog)\n self.lineEdit_5.setGeometry(QtCore.QRect(260, 250, 113, 31))\n self.lineEdit_5.setObjectName(\"lineEdit_5\")\n self.lineEdit_6 = QtWidgets.QLineEdit(Dialog)\n self.lineEdit_6.setGeometry(QtCore.QRect(260, 350, 113, 31))\n self.lineEdit_6.setObjectName(\"lineEdit_6\")\n self.lineEdit_7 = QtWidgets.QLineEdit(Dialog)\n self.lineEdit_7.setGeometry(QtCore.QRect(260, 300, 113, 31))\n self.lineEdit_7.setObjectName(\"lineEdit_7\")\n self.graphicsView = QtWidgets.QLabel(Dialog)\n self.graphicsView.setGeometry(QtCore.QRect(400, 50, 361, 331))\n self.graphicsView.setObjectName(\"graphicsView\")\n self.graphicsView.setPixmap(QtGui.QPixmap(\"images/demir.png\"))\n\n self.retranslateUi(Dialog)\n QtCore.QMetaObject.connectSlotsByName(Dialog)\n\n def retranslateUi(self, Dialog):\n\n _translate = QtCore.QCoreApplication.translate\n Dialog.setWindowTitle(_translate(\"Dialog\", \"Dialog\"))\n self.pushButton.setText(_translate(\"Dialog\", \"Train\"))\n self.label.setText(_translate(\"Dialog\", \"n_estimators : \"))\n self.lineEdit.setText(_translate(\"Dialog\", \"1\"))\n self.label_2.setText(_translate(\"Dialog\", \"max_depth : \"))\n self.label_3.setText(_translate(\"Dialog\", \"bootstrap : \"))\n self.label_4.setText(_translate(\"Dialog\", \"oob_score : \"))\n self.label_5.setText(_translate(\"Dialog\", \"n_jobs : \"))\n self.label_6.setText(_translate(\"Dialog\", \"min_samples_leaf :\"))\n self.label_7.setText(_translate(\"Dialog\", \"max_features :\"))\n self.lineEdit_2.setText(_translate(\"Dialog\", \"100\"))\n self.lineEdit_3.setText(_translate(\"Dialog\", \"None\"))\n self.lineEdit_4.setText(_translate(\"Dialog\", \"auto\"))\n self.lineEdit_5.setText(_translate(\"Dialog\", \"True\"))\n self.lineEdit_6.setText(_translate(\"Dialog\", \"-1\"))\n self.lineEdit_7.setText(_translate(\"Dialog\", \"False\"))\n\n self.pushButton.clicked.connect(self.clicka)\n\n def clicka(self):\n\n self.train = pd.read_csv(\"train.csv\")\n self.y = self.train.label\n self.train.drop([\"label\"],axis=1,inplace = True)\n\n self.n_valid = 12000 # same as Kaggle's test set size\n self.n_train = len(self.train)-self.n_valid\n\n global train, validation, y_train, y_validation\n\n train, validation = self.split_train_val(self.train, self.n_train)\n y_train, y_validation = self.split_train_val(self.y, self.n_train)\n\n try:\n\n self.n_estimators = int(self.lineEdit_2.text())\n\n if self.lineEdit_3.text() == \"None\":\n\n self.max_depth = None\n\n else:\n\n self.max_depth = int(self.lineEdit_3.text())\n\n\n if type(eval(self.lineEdit.text())) == float:\n\n self.min_samples_leaf = float(self.lineEdit.text())\n\n else:\n\n self.min_samples_leaf = int(self.lineEdit.text())\n\n\n if self.lineEdit_4.text() == \"auto\":\n\n self.max_features = \"auto\"\n\n elif type(eval(self.lineEdit_4.text())) == float:\n\n self.max_features = float(self.lineEdit_4.text())\n\n else:\n\n self.max_features = int(self.lineEdit_4.text())\n\n if self.lineEdit_5.text() == \"False\":\n\n self.bootstrap = bool(0)\n\n else:\n\n self.bootstrap = bool(1)\n\n\n if self.lineEdit_7.text() == \"False\":\n\n self.oob_score = bool(0)\n\n else:\n\n self.oob_score = bool(1)\n\n global oob\n\n if self.oob_score == True:\n\n oob = True\n\n else:\n\n oob = False\n\n self.n_jobs = int(self.lineEdit.text())\n\n global m\n\n m = RandomForestRegressor(n_estimators=self.n_estimators, max_depth=self.max_depth, min_samples_leaf=self.min_samples_leaf, max_features=self.max_features, bootstrap=self.bootstrap, oob_score=self.oob_score, n_jobs=self.n_jobs)\n \n m.fit(self.train, self.y)\n\n self.pushButton.setText(\"Train\")\n\n\n except:\n\n self.pushButton.setText(\"Hata !!!\")\n\n if self.pushButton.text() != \"Hata !!!\":\n\n self.mainwin=QMainWindow()\n self.ui=Ui1_Dialog()\n self.ui.setupUi(self.mainwin)\n self.mainwin.setWindowTitle(\"RandomForestRegressor - Test\")\n self.mainwin.setFixedSize(802, 501)\n self.mainwin.move(300,100)\n self.mainwin.show()\n Dialog.hide()\n\n def split_train_val(self, df, n): \n \n return df[:n].copy(), df[n:].copy()\n\n\nif __name__ == \"__main__\":\n\n import sys\n app = QtWidgets.QApplication(sys.argv)\n Dialog = QtWidgets.QDialog()\n ui = Ui_Dialog()\n ui.setupUi(Dialog)\n Dialog.setWindowTitle(\"RandomForestRegressor - Train\")\n Dialog.setFixedSize(802, 501)\n Dialog.move(300,100)\n Dialog.show()\n Dialog.show()\n sys.exit(app.exec_())\n","repo_name":"AhmetFurkanDEMIR/Online-Istanbul-Applied-Data-Science-102-Bootcamp","sub_path":"Lesson1/My_own_project/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14244,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"76"} +{"seq_id":"31650003890","text":"from nesasm.tests import HexFileTestCase\nfrom unittest import SkipTest\nfrom os import remove, makedirs\nfrom os.path import abspath, basename, dirname, exists, splitext\nfrom glob import glob\n\nNESASM_C_BIN = abspath('tools/nesasm/bin/nesasm')\nNESASM_PY_BIN = abspath('bin/nesasm')\n\n\ndef call(*args):\n from subprocess import Popen, PIPE, STDOUT\n path = dirname(args[-1])\n p = Popen(args,\n stdin=PIPE, stdout=PIPE, stderr=PIPE,\n close_fds=True,\n cwd=path)\n exitcode = p.wait()\n if exitcode != 0:\n raise Exception('Exitcode %s: %s' % (exitcode, p.stderr.read()))\n\n\ndef nesasm_c(_input):\n call(NESASM_C_BIN, _input)\n\n\ndef nesasm_py(_input):\n call(NESASM_PY_BIN, 'asm', _input)\n\n\ndef prepare_scenario(_input, _output):\n from distutils.dir_util import copy_tree\n from shutil import rmtree\n if exists(_output):\n rmtree(_output)\n makedirs(_output)\n path = dirname(abspath(_input))\n filename = basename(_input)\n copy_tree(path, _output)\n\n for f in glob(_output + '/*.nes'):\n remove(f)\n\n return _output + '/' + filename\n\n\nclass NESAsmCiSuite(object):\n\n @classmethod\n def setUpClass(cls):\n if not exists(NESASM_C_BIN):\n return\n _input = 'fixtures/%s' % cls.fixture\n\n _filename = splitext(basename(_input))[0]\n\n from sys import version_info\n version = '_'.join([str(c) for c in version_info[:3]])\n c_out = '/tmp/nesasm/%s/%s/nesasm_c' % (version, _filename)\n cls._input_c = prepare_scenario(_input, c_out)\n py_out = '/tmp/nesasm/%s/%s/nesasm_py' % (version, _filename)\n cls._input_py = prepare_scenario(_input, py_out)\n\n def setUp(self):\n if not exists(NESASM_C_BIN):\n raise SkipTest('no NESASM bin')\n\n def nesasm_c(self, _input):\n nesfile = splitext(_input)[0] + '.nes'\n self.assertFalse(exists(nesfile), nesfile)\n nesasm_c(_input)\n self.assertTrue(exists(nesfile), nesfile)\n return nesfile\n\n def nesasm_py(self, _input):\n nesfile = splitext(_input)[0] + '.nes'\n self.assertFalse(exists(nesfile), nesfile)\n nesasm_py(_input)\n self.assertTrue(exists(nesfile), nesfile)\n return nesfile\n\n def test_binary_compare(self):\n _output_c = self.nesasm_c(self._input_c)\n _output_py = self.nesasm_py(self._input_py)\n\n self.assertHexFileEquals(_output_c, _output_py)\n\n def test_compare_labels(self):\n labels_file = splitext(self._input_c)[0] + '.fns'\n labels_c = {}\n with open(labels_file, 'r') as f:\n for line in f:\n if not line.startswith(';'):\n key, value = [d.strip() for d in line.split('=')]\n labels_c[key] = value\n\n from nesasm.compiler import lexical, syntax, get_labels\n\n with open(self._input_py) as f:\n source = f.read()\n\n ast = syntax(lexical(source))\n try:\n _items = get_labels(ast).iteritems\n except AttributeError:\n _items = get_labels(ast).items\n\n labels_py = {k: '${:02X}'.format(v) for k, v in _items()}\n\n self.assertEquals(labels_c, labels_py)\n\n\nclass MovingspriteTest(NESAsmCiSuite, HexFileTestCase):\n fixture = 'movingsprite/movingsprite.asm'\n\n\nclass BackgroundTest(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/background/background.asm'\n\n\nclass Background3Test(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/background/background3.asm'\n\n\nclass Scrolling1Test(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/scrolling/scrolling1.asm'\n\n\nclass Scrolling2Test(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/scrolling/scrolling2.asm'\n\n\nclass Scrolling3Test(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/scrolling/scrolling3.asm'\n\n\nclass Scrolling4Test(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/scrolling/scrolling4.asm'\n\n\nclass Scrolling5Test(NESAsmCiSuite, HexFileTestCase):\n fixture = 'nerdynights/scrolling/scrolling5.asm'\n","repo_name":"gutomaia/nesasm_py","sub_path":"nesasm/tests/nesasm_ci_test.py","file_name":"nesasm_ci_test.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"76"} +{"seq_id":"10005002282","text":"import plotly.graph_objs as go\n\ndef plot_mean_price_ca(df):\n # Get the dataframe and create a new one for plotly\n df_mean_price = df.groupby(\"comunidad\").mean()[\"precio\"].sort_values(ascending=False)\n # Create a horizontal bar graph\n fig = go.Figure(data=[go.Bar(x=df_mean_price.values,\n y=df_mean_price.index,\n orientation='h'\n )])\n # Set the color scale\n fig.update_layout(xaxis=dict(title='Precio medio del Kg por Comunidad Autónoma en Euros'),\n yaxis=dict(\n position=0,\n tickfont=dict(color='#292828'),\n side='left'),\n barmode='group',\n bargap=0.,\n bargroupgap=0.2,\n margin=dict(l=5,\n r=50,\n t=30,\n b=20),\n showlegend=False,\n )\n\n fig.update_traces (marker=dict (color=df_mean_price.tolist()))\n fig.update_traces (marker_colorscale='viridis')\n return fig\n\n\n'''\ndef plot_mean_price_pr(df):\n # aqui no van barras, va una tabla dataframe con colores si puede ser'''","repo_name":"dvegamar/farmer_prices_of_woody_crops","sub_path":"src/plots.py","file_name":"plots.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"10867873720","text":"#! -*- coding:utf-8 -*-\n\"\"\"\n@author: chengbo\n@software: PyCharm\n@file: test01.py\n@time: 2022/8/10 14:20\n\"\"\"\n\nimport time\nfrom threading import Thread\n\n\ndef task():\n print(\"开始做一个任务\")\n time.sleep(1)\n print(\"这个任务结束啦\")\n\n\ndef task1():\n print(\"开始做任务1啦\")\n time.sleep(1)\n print(\"任务1结束啦\")\n\n\ndef task2():\n print(\"开始做任务2啦\")\n for i in range(5):\n print(\"任务2-{}\".format(i))\n time.sleep(1)\n print(\"任务2结束啦\")\n\n\nclass NewThread(Thread):\n def __init__(self):\n Thread.__init__(self)\n\n def run(self):\n print(\"开始做一个任务啦\")\n time.sleep(1)\n print(\"这个任务结束啦\")\n\n\nif __name__ == \"__main__\":\n print(\"这里是主线程\")\n # t1 = Thread(target=task)\n # t1.start()\n # time.sleep(0.3)\n # t1 = NewThread()\n # t1.start()\n # time.sleep(0.3)\n\n # t1 = Thread(target=task1)\n # t2 = Thread(target=task2)\n # t2.setDaemon(True)\n #\n # t1.start()\n # t2.start()\n # time.sleep(0.3)\n\n t1 = Thread(target=task1)\n t1.start()\n t1.join()\n\n print(\"主线程结束了\")\n\n # print(\"主线程依然可以干别的事\")\n","repo_name":"chongzicbo/NLP-Learning","sub_path":"python_learning/thread_test/test01.py","file_name":"test01.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"76"} +{"seq_id":"28348892450","text":"import json\nfrom datetime import timedelta\nimport time\nimport key2token\nimport requests\nimport argparse\n\nfrom beaker.cache import CacheManager\nfrom beaker.util import parse_cache_config_options\n\ncache_opts = {\n 'cache.type': 'file',\n 'cache.data_dir': '/tmp/cache/data',\n 'cache.lock_dir': '/tmp/cache/lock'\n}\ncache = CacheManager(**parse_cache_config_options(cache_opts))\n\n@cache.cache('token_func', expire=3600)\n\ndef auth_header(): \n access_token, token_type, expires_in = key2token.get_token(\"./service_user.json\")\n return '{} {}'.format(token_type, access_token)\n\n\n## add clear-cache flag\nmy_parser = argparse.ArgumentParser(description='')\nmy_parser.add_argument('-orgid', metavar='orgid', type=str, help='your org id') # eg, 71641630146358541\nmy_parser.add_argument('-nc', '--no-cache', action='store_true', help='Don\\'t use cached tokens')\nmy_parser.add_argument('-d', '--dry-run', action='store_true', help='output request instead of sending')\nargs = my_parser.parse_args()\n\nurl = \"https://api.zitadel.ch/management/v1/projects/_search\"\ndata = {\n}\n\nheaders = {\n 'x-zitadel-orgid': args.orgid,\n 'Authorization': auth_header()\n}\n\nif args.dry_run: # Just construct the response, but don't send\n req = requests.Request('POST',url, headers, data=data)\n prepared = req.prepare()\n print('{}\\n{}\\r\\n{}\\r\\n\\r\\n{}'.format(\n '-----------REQUEST START-----------',\n req.method + ' ' + req.url,\n '\\r\\n'.join('{}: {}'.format(k, v) for k, v in req.headers.items()),\n req.data,\n ))\nelse:\n res = requests.post(url, data, headers=headers)\n print('Status: {}, Response: {}'.format(res.status_code, res.text))","repo_name":"zitadel/zitadel-examples","sub_path":"python3/service_user/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"76"} +{"seq_id":"72397622647","text":"#!/usr/bin/env python3\n\n# Optimizer calibration test for bitmap and brin indexes, also btree on AO tables\n#\n# This program runs a set of queries, varying several parameters:\n#\n# - Selectivity of predicates\n# - Plan type (plan chosen by the optimizer, various forced plans)\n# - Width of the selected columns\n#\n# The program then reports the result of explaining these queries and\n# letting the optimizer choose plans vs. forcing plans. Execution time\n# can be reported, computing a mean and standard deviation of several\n# query executions.\n#\n# The printed results are useful to copy and paste into a Google Sheet\n# (expand columns after pasting)\n#\n# Run this program with the -h or --help option to see argument syntax\n#\n# See comment \"How to add a test\" below in the program for how to\n# extend this program.\n\nimport argparse\nimport time\nimport re\nimport math\nimport os\nimport subprocess\nimport sys\n\ntry:\n from gppylib.db import dbconn\nexcept ImportError as e:\n sys.exit('ERROR: Cannot import modules. Please check that you have sourced greenplum_path.sh. Detail: ' + str(e))\n\n# constants\n# -----------------------------------------------------------------------------\n\n_help = \"\"\"\nRun optimizer bitmap calibration tests. Optionally create the tables before running, and drop them afterwards.\nThis explains and runs a series of queries and reports the estimated and actual costs.\nThe results can be copied and pasted into a spreadsheet for further processing.\n\"\"\"\n\nTABLE_NAME_PATTERN = r\"cal_txtest\"\nNDV_TABLE_NAME_PATTERN = r\"cal_ndvtest\"\nBFV_TABLE_NAME_PATTERN = r\"cal_bfvtest\"\nWIDE_TABLE_NAME_PATTERN = r\"cal_widetest\"\nBRIN_TABLE_NAME_PATTERN = r\"cal_brintest\"\n\nTABLE_SCAN = \"table_scan\"\nTABLE_SCAN_PATTERN = r\"Seq Scan\"\nTABLE_SCAN_PATTERN_V5 = r\"Table Scan\"\n\nINDEX_SCAN = \"index_scan\"\nINDEX_SCAN_PATTERN = r\"> Index Scan\"\nINDEX_SCAN_PATTERN_V5 = r\"> Index Scan\"\n\nINDEX_ONLY_SCAN = \"indexonly_scan\"\nINDEX_ONLY_SCAN_PATTERN = r\"> Index Only Scan\"\nINDEX_ONLY_SCAN_PATTERN_V5 = r\"> Index Only Scan\"\n\nBITMAP_SCAN = \"bitmap_scan\"\nBITMAP_SCAN_PATTERN = r\"Bitmap Heap Scan\"\nBITMAP_SCAN_PATTERN_V5 = r\"Bitmap Table Scan\"\n\nHASH_JOIN = \"hash_join\"\nHASH_JOIN_PATTERN = r\"Hash Join\"\nHASH_JOIN_PATTERN_V5 = r\"Hash Join\"\n\nNL_JOIN = \"nl_join\"\nNL_JOIN_PATTERN = r\"Nested Loop\"\nNL_JOIN_PATTERN_V5 = r\"Nested Loop\"\n\nFALLBACK_PLAN = \"fallback\"\nFALLBACK_PATTERN = \"Postgres query optimizer\"\nFALLBACK_PATTERN_V5 = \"legacy query optimizer\"\n\nOPTIMIZER_DEFAULT_PLAN = \"optimizer\"\n\n# global variables\n# -----------------------------------------------------------------------------\n\n# constants\n# only consider optimizer errors beyond x * sigma (standard deviation) as significant\nglob_sigma_diff = 3\nglob_log_file = None\nglob_exe_timeout = 40000\nglob_gpdb_major_version = 7\nglob_dim_table_rows = 10000\n\n# global variables that may be modified\nglob_verbose = False\nglob_rowcount = -1\nglob_appendonly = False\n\n# SQL statements, DDL and DML\n# -----------------------------------------------------------------------------\n\n_drop_tables = \"\"\"\nDROP TABLE IF EXISTS cal_txtest, cal_temp_ids, cal_dim, cal_bfvtest, cal_bfv_dim, cal_ndvtest, cal_widetest;\n\"\"\"\n\n# create the table. Parameters:\n# - WITH clause (optional), for append-only tables\n_create_cal_table = \"\"\"\nCREATE TABLE cal_txtest(id int,\n btreeunique int,\n btree10 int,\n btree100 int,\n btree1000 int,\n btree10000 int,\n bitmap10 int,\n bitmap100 int,\n bitmap1000 int,\n bitmap10000 int,\n txt text)\n%s\nDISTRIBUTED BY (id);\n\"\"\"\n\n_create_bfv_table = \"\"\"\nCREATE TABLE cal_bfvtest (col1 integer,\n wk_id int,\n id integer)\n%s\nDISTRIBUTED BY (col1);\n\"\"\"\n\n_create_ndv_table = \"\"\"\nCREATE TABLE cal_ndvtest (id int, val int)\n%s\nDISTRIBUTED BY (id);\n\"\"\"\n\n_create_brin_table = \"\"\"\nCREATE TABLE cal_brintest(id int,\n clust_10 int,\n clust_100 int,\n clust_1000 int,\n clust_10000 int,\n clust_uniq int,\n rand_10 int,\n rand_100 int,\n rand_1000 int,\n rand_10000 int,\n rand_uniq int,\n txt text)\n%s\nDISTRIBUTED BY (id);\n\"\"\"\n\n_with_appendonly = \"\"\"\nWITH (appendonly=true)\n\"\"\"\n\n_create_other_tables = [\"\"\"\nCREATE TABLE cal_temp_ids(f_id int, f_rand double precision) DISTRIBUTED BY (f_id);\n\"\"\",\n \"\"\"\nCREATE TABLE cal_dim(dim_id int,\n dim_id2 int,\n txt text)\nDISTRIBUTED BY (dim_id);\n\"\"\",\n \"\"\"\nCREATE TABLE cal_bfv_dim (id integer, col2 integer) DISTRIBUTED BY (id);\n\"\"\"]\n\n_create_indexonly_tables = [\n \"CREATE TABLE cal_widetest1(id int, sel int) %s\",\n \"CREATE TABLE cal_widetest2(id int, sel int, col1 text, col2 text, col3 text) %s\",\n \"CREATE TABLE cal_widetest3(id int, sel int, col1 text, col2 text, col3 text, col4 text, col5 text, col6 text) %s\"\n]\n\n# insert into temp table. Parameters:\n# - integer stop value (suggested value is 10,000,000)\n_insert_into_temp = \"\"\"\nINSERT INTO cal_temp_ids SELECT x, random() FROM (SELECT * FROM generate_series(1,%d)) T(x);\n\"\"\"\n\n_insert_into_table = \"\"\"\nINSERT INTO cal_txtest\nSELECT f_id,\n f_id,\n f_id%10 + 1,\n f_id%100 + 1,\n f_id%1000 + 1,\n f_id%10000 + 1,\n f_id%10 + 1,\n f_id%100 + 1,\n f_id%1000 + 1,\n f_id%10000 + 1,\n repeat('a', 960)\nFROM cal_temp_ids\norder by f_rand;\n\"\"\"\n\n# use a row_number() function to create column values that are strongly correlated\n# to the physical order of the rows on disk\n_insert_into_brin_table = \"\"\"\nINSERT INTO cal_brintest\nSELECT ordered_id,\n ceil(ordered_id*(10.0/{rows})),\n ceil(ordered_id*(100.0/{rows})),\n ceil(ordered_id*(1000.0/{rows})),\n ceil(ordered_id*(10000.0/{rows})),\n ordered_id,\n f_id%10 + 1,\n f_id%100 + 1,\n f_id%1000 + 1,\n f_id%10000 + 1,\n f_id,\n repeat('a', 956)\nFROM (select row_number() over(order by f_rand) as ordered_id, f_id, f_rand from cal_temp_ids) src\norder by f_rand;\n\"\"\"\n\n_insert_into_other_tables = \"\"\"\nINSERT INTO cal_dim SELECT x, x, repeat('d', 100) FROM (SELECT * FROM generate_series(%d,%d)) T(x);\n\"\"\"\n\n_insert_into_indexonly_tables = [\n\"\"\"\nINSERT INTO cal_widetest1 SELECT f_id, f_id%100 {} FROM (select row_number() over(order by f_rand) as ordered_id, f_id, f_rand from cal_temp_ids) src order by f_rand;\n\"\"\".format(','.join(\"repeat('a', 1024)\" for i in range(1, 1))),\n\"\"\"\nINSERT INTO cal_widetest2 SELECT f_id, f_id%100, {} FROM (select row_number() over(order by f_rand) as ordered_id, f_id, f_rand from cal_temp_ids) src order by f_rand;\n\"\"\".format(','.join(\"repeat('a', 1024)\" for i in range(1, 4))),\n\"\"\"\nINSERT INTO cal_widetest3 SELECT f_id, f_id%100, {} FROM (select row_number() over(order by f_rand) as ordered_id, f_id, f_rand from cal_temp_ids) src order by f_rand;\n\"\"\".format(','.join(\"repeat('a', 1024)\" for i in range(1, 7))),\n]\n\n_create_index_arr = [\"\"\"\nCREATE INDEX cal_txtest_i_bitmap_10 ON cal_txtest USING bitmap(bitmap10);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_bitmap_100 ON cal_txtest USING bitmap(bitmap100);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_bitmap_1000 ON cal_txtest USING bitmap(bitmap1000);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_bitmap_10000 ON cal_txtest USING bitmap(bitmap10000);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_widetext1_index ON cal_widetest1(sel);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_widetext4_index ON cal_widetest2(sel);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_widetext7_index ON cal_widetest3(sel);\n\"\"\",\n ]\n\n_create_bfv_index_arr = [\"\"\"\nCREATE INDEX idx_cal_bfvtest_bitmap ON cal_bfvtest USING bitmap(id);\n\"\"\",\n ]\n\n_create_ndv_index_arr = [\"\"\"\nCREATE INDEX cal_ndvtest_bitmap ON cal_ndvtest USING bitmap(val);\n\"\"\",\n ]\n\n_create_btree_indexes_arr = [\"\"\"\nCREATE INDEX cal_txtest_i_btree_unique ON cal_txtest USING btree(btreeunique);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_btree_10 ON cal_txtest USING btree(btree10);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_btree_100 ON cal_txtest USING btree(btree100);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_btree_1000 ON cal_txtest USING btree(btree1000);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_txtest_i_btree_10000 ON cal_txtest USING btree(btree10000);\n\"\"\",\n \"\"\"\nCREATE INDEX idx_cal_bfvtest_btree ON cal_bfvtest USING btree(id);\n\"\"\",\n \"\"\"\nCREATE INDEX cal_ndvtest_btree ON cal_ndvtest USING btree(val);\n\"\"\",\n ]\n\n_create_brin_index_arr = [\"\"\"\nCREATE INDEX cal_brintest_brin ON cal_brintest USING brin(\nid, clust_10, clust_100, clust_1000, clust_10000, clust_uniq, rand_10, rand_100, rand_1000, rand_10000, rand_uniq, txt)\nWITH(pages_per_range=4);\n\"\"\",\n ]\n\n_analyze_table = \"\"\"\nANALYZE cal_txtest;\nANALYZE cal_brintest;\n\"\"\"\n\n_allow_system_mods = \"\"\"\nSET allow_system_table_mods to on;\n\"\"\"\n\n_allow_system_mods_v5 = \"\"\"\nSET allow_system_table_mods to 'dml';\n\"\"\"\n\n# Make sure pg_statistics and pg_class have accurate statistics, so that the cardinality estimates we get are very precise\n\n_update_pg_class = \"\"\"\nUPDATE pg_class\n SET reltuples = %i\nWHERE relname = '%s';\n\"\"\"\n\n# add an MCV or histogram (stakind1 = 1 or 2) and a correlation (stakind2 = 3) value\n_update_pg_stats = \"\"\"\nUPDATE pg_statistic\n SET stadistinct = %f,\n stakind1 = %d,\n stanumbers1 = %s,\n\t stavalues1 = %s,\n\t stakind2 = 3,\n\t stanumbers2 = '{ %f }',\n\t stavalues2 = NULL,\n\t stakind3 = 0,\n\t stanumbers3 = NULL,\n\t stavalues3 = NULL,\n\t stakind4 = 0,\n\t stanumbers4 = NULL,\n\t stavalues4 = NULL\nWHERE starelid = '%s'::regclass AND staattnum = %i;\n\"\"\"\n\n# columns to fix, in the format (table name, column name, attnum, ndv, num rows, correlation)\n# use -1 as the NDV for unique columns and use -1 for the variable number of rows in the fact table\n_stats_cols_to_fix = [\n ('cal_txtest', 'id', 1, -1, -1, 0.0),\n ('cal_txtest', 'btreeunique', 2, -1, -1, 0.0),\n ('cal_txtest', 'btree10', 3, 10, -1, 0.0),\n ('cal_txtest', 'btree100', 4, 100, -1, 0.0),\n ('cal_txtest', 'btree1000', 5, 1000, -1, 0.0),\n ('cal_txtest', 'btree10000', 6, 10000, -1, 0.0),\n ('cal_txtest', 'bitmap10', 7, 10, -1, 0.0),\n ('cal_txtest', 'bitmap100', 8, 100, -1, 0.0),\n ('cal_txtest', 'bitmap1000', 9, 1000, -1, 0.0),\n ('cal_txtest', 'bitmap10000', 10, 10000, -1, 0.0),\n ('cal_dim', 'dim_id', 1, -1, glob_dim_table_rows, 0.0),\n ('cal_dim', 'dim_id2', 2, -1, glob_dim_table_rows, 0.0),\n ('cal_brintest','id', 1, -1, -1, 1.0),\n ('cal_brintest','clust_10', 2, 10, -1, 1.0),\n ('cal_brintest','clust_100', 3, 100, -1, 1.0),\n ('cal_brintest','clust_1000', 4, 1000, -1, 1.0),\n ('cal_brintest','clust_10000', 5, 10000, -1, 1.0),\n ('cal_brintest','clust_uniq', 6, -1, -1, 1.0),\n ('cal_brintest','rand_10', 7, 10, -1, 0.0),\n ('cal_brintest','rand_100', 8, 100, -1, 0.0),\n ('cal_brintest','rand_1000', 9, 1000, -1, 0.0),\n ('cal_brintest','rand_10000', 10, 10000, -1, 0.0),\n ('cal_brintest','rand_uniq', 11, -1, -1, 0.0)]\n\n# deal with command line arguments\n# -----------------------------------------------------------------------------\n\ndef parseargs():\n parser = argparse.ArgumentParser(description=_help)\n\n parser.add_argument(\"tests\", metavar=\"TEST\", choices=[[], \"all\", \"none\", \"bitmap_scan_tests\", \"btree_ao_scan_tests\",\n \"bitmap_ndv_scan_tests\", \"index_join_tests\", \"bfv_join_tests\",\n \"index_only_scan_tests\", \"brin_tests\"],\n nargs=\"*\",\n help=\"Run these tests (all, none, bitmap_scan_tests, btree_ao_scan_tests, bitmap_ndv_scan_tests, \"\n \"index_join_tests, bfv_join_tests, index_only_scan_tests, brin_tests), default is none\")\n parser.add_argument(\"--create\", action=\"store_true\",\n help=\"Create the tables to use in the test\")\n parser.add_argument(\"--execute\", type=int, default=\"0\",\n help=\"Number of times to execute queries, 0 (the default) means explain only\")\n parser.add_argument(\"--drop\", action=\"store_true\",\n help=\"Drop the tables used in the test when finished\")\n parser.add_argument(\"--verbose\", action=\"store_true\",\n help=\"Print more verbose output\")\n parser.add_argument(\"--logFile\", default=\"\",\n help=\"Log diagnostic output to a file\")\n parser.add_argument(\"--host\", default=\"\",\n help=\"Host to connect to (default is localhost or $PGHOST, if set).\")\n parser.add_argument(\"--port\", type=int, default=\"0\",\n help=\"Port on the host to connect to (default is 0 or $PGPORT, if set)\")\n parser.add_argument(\"--dbName\", default=\"\",\n help=\"Database name to connect to\")\n parser.add_argument(\"--appendOnly\", action=\"store_true\",\n help=\"Create an append-only table. Default is a heap table\")\n parser.add_argument(\"--numRows\", type=int, default=\"10000000\",\n help=\"Number of rows to INSERT INTO the table (default is 10 million)\")\n\n parser.set_defaults(verbose=False, filters=[], slice=(None, None))\n\n # Parse the command line arguments\n args = parser.parse_args()\n return args, parser\n\n\ndef log_output(str):\n if glob_verbose:\n print(str)\n if glob_log_file != None:\n glob_log_file.write(str + \"\\n\")\n\n\n# SQL related methods\n# -----------------------------------------------------------------------------\n\ndef connect(host, port_num, db_name):\n try:\n dburl = dbconn.DbURL(hostname=host, port=port_num, dbname=db_name)\n conn = dbconn.connect(dburl, encoding=\"UTF8\", unsetSearchPath=False)\n\n except Exception as e:\n print((\"Exception during connect: %s\" % e))\n quit()\n\n return conn\n\n\ndef select_version(conn):\n global glob_gpdb_major_version\n sqlStr = \"SELECT version()\"\n curs = dbconn.query(conn, sqlStr)\n\n rows = curs.fetchall()\n for row in rows:\n log_output(row[0])\n glob_gpdb_major_version = int(re.sub(\".*Greenplum Database ([0-9]*)\\..*\", \"\\\\1\", row[0]))\n log_output(\"GPDB major version is %d\" % glob_gpdb_major_version)\n\n log_output(\"Backend pid:\")\n sqlStr = \"SELECT pg_backend_pid()\"\n curs = dbconn.query(conn, sqlStr)\n\n rows = curs.fetchall()\n for row in rows:\n log_output(str(row[0]))\n\n\ndef execute_sql(conn, sqlStr, autocommit=True):\n try:\n log_output(\"\")\n log_output(\"Executing query: %s\" % sqlStr)\n dbconn.execSQL(conn, sqlStr, autocommit)\n except Exception as e:\n print(\"\")\n print((\"Error executing query: %s; Reason: %s\" % (sqlStr, e)))\n dbconn.execSQL(conn, \"abort\")\n\n\ndef select_first_int(conn, sqlStr):\n try:\n log_output(\"\")\n log_output(\"Executing query: %s\" % sqlStr)\n curs = dbconn.query(conn, sqlStr)\n rows = curs.fetchall()\n for row in rows:\n return int(row[0])\n\n except Exception as e:\n print(\"\")\n print((\"Error executing query: %s; Reason: %s\" % (sqlStr, e)))\n dbconn.execSQL(conn, \"abort\")\n\n\ndef execute_sql_arr(conn, sqlStrArr):\n for sqlStr in sqlStrArr:\n execute_sql(conn, sqlStr)\n\n\ndef execute_and_commit_sql(conn, sqlStr):\n execute_sql(conn, sqlStr)\n commit_db(conn)\n\n\ndef commit_db(conn):\n execute_sql(conn, \"commit\")\n\n\n# run an SQL statement and return the elapsed wallclock time, in seconds\ndef timed_execute_sql(conn, sqlStr):\n start = time.time()\n num_rows = select_first_int(conn, sqlStr)\n end = time.time()\n elapsed_time_in_msec = round((end - start) * 1000)\n log_output(\"Elapsed time (msec): %d, rows: %d\" % (elapsed_time_in_msec, num_rows))\n return elapsed_time_in_msec, num_rows\n\n\n# run an SQL statement n times, unless it takes longer than a timeout\n\ndef timed_execute_n_times(conn, sqlStr, exec_n_times):\n sum_exec_times = 0.0\n sum_square_exec_times = 0.0\n e = 0\n act_num_exes = exec_n_times\n num_rows = -1\n while e < act_num_exes:\n exec_time, local_num_rows = timed_execute_sql(conn, sqlStr)\n e = e + 1\n sum_exec_times += exec_time\n sum_square_exec_times += exec_time * exec_time\n if num_rows >= 0 and local_num_rows != num_rows:\n log_output(\"Inconsistent number of rows returned: %d and %d\" % (num_rows, local_num_rows))\n num_rows = local_num_rows\n if exec_time > glob_exe_timeout:\n # we exceeded the timeout, don't keep executing this long query\n act_num_exes = e\n log_output(\"Query %s exceeded the timeout of %d seconds\" % (sqlStr, glob_exe_timeout))\n\n # compute mean and standard deviation of the execution times\n mean = sum_exec_times / act_num_exes\n if exec_n_times == 1:\n # be safe, avoid any rounding errors\n variance = 0.0\n else:\n variance = sum_square_exec_times / act_num_exes - mean * mean\n return (round(mean, 3), round(math.sqrt(variance), 3), act_num_exes, num_rows)\n\n\n# Explain a query and find a table scan or index scan in an explain output\n# return the scan type and the corresponding cost.\n# Use this for scan-related tests.\n\ndef explain_index_scan(conn, sqlStr):\n cost = -1.0\n scan_type = \"\"\n try:\n log_output(\"\")\n log_output(\"Executing query: %s\" % (\"explain \" + sqlStr))\n exp_curs = dbconn.query(conn, \"explain \" + sqlStr)\n rows = exp_curs.fetchall()\n table_scan_pattern = TABLE_SCAN_PATTERN\n index_scan_pattern = INDEX_SCAN_PATTERN\n index_only_scan_pattern = INDEX_ONLY_SCAN_PATTERN\n bitmap_scan_pattern = BITMAP_SCAN_PATTERN\n fallback_pattern = FALLBACK_PATTERN\n if (glob_gpdb_major_version) <= 5:\n table_scan_pattern = TABLE_SCAN_PATTERN_V5\n index_scan_pattern = INDEX_SCAN_PATTERN_V5\n index_only_scan_pattern = INDEX_ONLY_SCAN_PATTERN_V5\n bitmap_scan_pattern = BITMAP_SCAN_PATTERN_V5\n fallback_pattern = FALLBACK_PATTERN_V5\n\n for row in rows:\n log_output(row[0])\n if (re.search(TABLE_NAME_PATTERN, row[0]) or re.search(NDV_TABLE_NAME_PATTERN, row[0]) or\n re.search(WIDE_TABLE_NAME_PATTERN, row[0]) or re.search(BRIN_TABLE_NAME_PATTERN, row[0])):\n if re.search(bitmap_scan_pattern, row[0]):\n scan_type = BITMAP_SCAN\n cost = cost_from_explain_line(row[0])\n elif re.search(index_scan_pattern, row[0]):\n scan_type = INDEX_SCAN\n cost = cost_from_explain_line(row[0])\n elif re.search(index_only_scan_pattern, row[0]):\n scan_type = INDEX_ONLY_SCAN\n cost = cost_from_explain_line(row[0])\n elif re.search(table_scan_pattern, row[0]):\n scan_type = TABLE_SCAN\n cost = cost_from_explain_line(row[0])\n elif re.search(fallback_pattern, row[0]):\n log_output(\"*** ERROR: Fallback\")\n scan_type = FALLBACK_PLAN\n\n except Exception as e:\n log_output(\"\\n*** ERROR explaining query:\\n%s;\\nReason: %s\" % (\"explain \" + sqlStr, e))\n\n return (scan_type, cost)\n\n\n# Explain a query and find a join in an explain output\n# return the scan type and the corresponding cost.\n# Use this for scan-related tests.\n\ndef explain_join_scan(conn, sqlStr):\n cost = -1.0\n scan_type = \"\"\n try:\n log_output(\"\")\n log_output(\"Executing query: %s\" % (\"explain \" + sqlStr))\n exp_curs = dbconn.query(conn, \"explain \" + sqlStr)\n rows = exp_curs.fetchall()\n hash_join_pattern = HASH_JOIN_PATTERN\n nl_join_pattern = NL_JOIN_PATTERN\n table_scan_pattern = TABLE_SCAN_PATTERN\n index_scan_pattern = INDEX_SCAN_PATTERN\n index_only_scan_pattern = INDEX_ONLY_SCAN_PATTERN\n bitmap_scan_pattern = BITMAP_SCAN_PATTERN\n fallback_pattern = FALLBACK_PATTERN\n if (glob_gpdb_major_version) <= 5:\n hash_join_pattern = HASH_JOIN_PATTERN_V5\n nl_join_pattern = NL_JOIN_PATTERN_V5\n table_scan_pattern = TABLE_SCAN_PATTERN_V5\n index_only_scan_pattern = INDEX_ONLY_SCAN_PATTERN_V5\n bitmap_scan_pattern = BITMAP_SCAN_PATTERN_V5\n fallback_pattern = FALLBACK_PATTERN_V5\n\n # save the cost of the join above the scan type\n for row in rows:\n log_output(row[0])\n if re.search(nl_join_pattern, row[0]):\n cost = cost_from_explain_line(row[0])\n elif re.search(hash_join_pattern, row[0]):\n cost = cost_from_explain_line(row[0])\n\n # mark the scan type used underneath the join\n if re.search(TABLE_NAME_PATTERN, row[0]) or re.search(BFV_TABLE_NAME_PATTERN, row[0]):\n if re.search(bitmap_scan_pattern, row[0]):\n scan_type = BITMAP_SCAN\n elif re.search(index_scan_pattern, row[0]):\n scan_type = INDEX_SCAN\n elif re.search(index_only_scan_pattern, row[0]):\n scan_type = INDEX_ONLY_SCAN\n elif re.search(table_scan_pattern, row[0]):\n scan_type = TABLE_SCAN\n elif re.search(fallback_pattern, row[0]):\n log_output(\"*** ERROR: Fallback\")\n scan_type = FALLBACK_PLAN\n\n except Exception as e:\n log_output(\"\\n*** ERROR explaining query:\\n%s;\\nReason: %s\" % (\"explain \" + sqlStr, e))\n\n return (scan_type, cost)\n\n\n# extract the cost c from the cost=x..c in an explain line\n\ndef cost_from_explain_line(line):\n return float(re.sub(r\".*\\.\\.([0-9.]+) .*\", r\"\\1\", line))\n\n\n# methods that run queries with varying parameters, recording results\n# and finding crossover points\n# -----------------------------------------------------------------------------\n\n\n# iterate over one parameterized query, using a range of parameter values, explaining and (optionally) executing the query\n\ndef find_crossover(conn, lowParamValue, highParamLimit, setup, parameterizeMethod, explain_method, reset_method,\n plan_ids, force_methods, execute_n_times):\n # expects the following:\n # - conn: A connection\n # - lowParamValue: The lowest (integer) value to try for the parameter\n # - highParamLimit: The highest (integer) value to try for the parameter + 1\n # - setup: A method that runs any sql needed for setup before a particular select run, given a parameterized query and a parameter value\n # - parameterizeMethod: A method to generate the actual query text, given a parameterized query and a parameter value\n # - explain_method: A method that takes a connection and an SQL string and returns a tuple (plan, cost)\n # - reset_method: A method to reset all gucs and similar switches, to get the default plan by the optimizer\n # the method takes one parameter, the connection\n # - plan_ids: A list with

plan ids returned by explain_method. Usually the number

is 2.\n # - force_methods: A list with

methods to force each plan id in the plan_ids array (these methods usually set gucs)\n # each methods takes one parameter, the connection\n # - execute_n_times: The number of times to execute the query (0 means don't execute, n>0 means execute n times)\n\n # returns the following:\n # - An explain dictionary, containing a mapping between a subset of the parameter values and result tuples, each result tuple consisting of\n #

+ 2 values:\n # - the plan id chosen by default by the optimizer\n # - the estimated cost for the optimal plan, chosen by the optimizer\n # - p values for the estimated cost when forcing plan i, 0 <= i < p\n # - An execution dictionary that, if execute_n_times is > 0, contains a mapping of a subset of the parameter values and plan ids\n # to execution times and standard deviations in execution times: (param_value, plan_id) -> (mean_exec_time, stddev_exec_time)\n # - mean_exec_time: average execution time (in seconds, rounded to milliseconds) for the plan\n # - stddev_exec_time: standard deviation of the different execution times for this parameter value and plan\n # - A list of error messages\n explainDict = {}\n execDict = {}\n errMessages = []\n timedOutDict = {}\n expCrossoverLow = lowParamValue - 1\n reset_method(conn)\n\n # determine the increment\n incParamValue = (highParamLimit - lowParamValue) // 10\n if incParamValue == 0:\n incParamValue = 1\n elif highParamLimit <= lowParamValue:\n errMessages.append(\n \"Low parameter value %d must be less than high parameter limit %d\" % (lowParamValue, highParamLimit))\n return (explainDict, execDict, errMessages)\n\n # first part, run through the parameter values and determine the plan and cost chosen by the optimizer\n for paramValue in range(lowParamValue, highParamLimit, incParamValue):\n\n # do any setup required\n setupString = setup(paramValue)\n execute_sql(conn, setupString)\n # explain the query and record which plan it chooses and what the cost is\n sqlString = parameterizeMethod(paramValue)\n (plan, cost) = explain_method(conn, sqlString)\n explainDict[paramValue] = (plan, cost)\n log_output(\"For param value %d the optimizer chose %s with a cost of %f\" % (paramValue, plan, cost))\n\n # execute the query, if requested\n if execute_n_times > 0:\n timed_execute_and_check_timeout(conn, sqlString, execute_n_times, paramValue, OPTIMIZER_DEFAULT_PLAN,\n execDict, timedOutDict, errMessages)\n\n # second part, force different plans and record the costs\n for plan_num in range(0, len(plan_ids)):\n plan_id = plan_ids[plan_num]\n reset_method(conn)\n log_output(\"----------- Now forcing a %s plan --------------\" % plan_id)\n force_methods[plan_num](conn)\n for paramValue in range(lowParamValue, highParamLimit, incParamValue):\n # do any setup required\n setupString = setup(paramValue)\n execute_sql(conn, setupString)\n # explain the query with the forced plan\n sqlString = parameterizeMethod(paramValue)\n (plan, cost) = explain_method(conn, sqlString)\n if plan_id != plan:\n errMessages.append(\"For parameter value %d we tried to force a %s plan but got a %s plan.\" % (\n paramValue, plan_id, plan))\n log_output(\"For parameter value %d we tried to force a %s plan but got a %s plan.\" % (\n paramValue, plan_id, plan))\n # update the result dictionary\n resultList = list(explainDict[paramValue])\n defaultPlanCost = resultList[1]\n # sanity check, the forced plan shouldn't have a cost that is lower than the default plan cost\n if defaultPlanCost > cost * 1.1:\n errMessages.append(\n \"For parameter value %d and forced %s plan we got a cost of %f that is lower than the default cost of %f for the default %s plan.\" % (\n paramValue, plan_id, cost, defaultPlanCost, resultList[0]))\n resultList.append(cost)\n explainDict[paramValue] = tuple(resultList)\n log_output(\"For param value %d we forced %s with a cost of %f\" % (paramValue, plan, cost))\n\n # execute the forced plan\n if execute_n_times > 0:\n # execute the query times and record the mean and stddev of the time in execDict\n timed_execute_and_check_timeout(conn, sqlString, execute_n_times, paramValue, plan_id, execDict,\n timedOutDict, errMessages)\n\n # cleanup at exit\n reset_method(conn)\n\n return (explainDict, execDict, errMessages)\n\n\n# Check for plans other than the optimizer-chosen plan that are significantly\n# better. Return the plan id and how many percent better that plan is or return (\"\", 0).\n\ndef checkForOptimizerErrors(paramValue, chosenPlan, plan_ids, execDict):\n # check whether a plan other that the optimizer's choice was better\n if chosenPlan in plan_ids:\n # take the best of the execution times (optimizer choice and the same plan forced)\n # and use the larger of the standard deviations\n defaultExeTime = 1E6\n defaultStdDev = 0.0\n if (paramValue, OPTIMIZER_DEFAULT_PLAN) in execDict:\n defaultExeTime, defaultStdDev, numRows = execDict[(paramValue, OPTIMIZER_DEFAULT_PLAN)]\n\n if (paramValue, chosenPlan) in execDict:\n forcedExeTime, forcedStdDev, numRows = execDict[(paramValue, chosenPlan)]\n if forcedExeTime < defaultExeTime:\n defaultExeTime = forcedExeTime\n defaultStdDev = forcedStdDev\n\n for pl in plan_ids:\n if (paramValue, pl) in execDict:\n altExeTime, altStdDev, numRows = execDict[(paramValue, pl)]\n\n # The execution times tend to be fairly unreliable. Try to avoid false positives by\n # requiring a significantly better alternative, measured in standard deviations.\n if altExeTime + glob_sigma_diff * max(defaultStdDev, altStdDev) < defaultExeTime:\n optimizerError = 100.0 * (defaultExeTime - altExeTime) / defaultExeTime\n # yes, plan pl is significantly better than the optimizer default choice\n return (pl, round(optimizerError, 1))\n elif chosenPlan == FALLBACK_PLAN:\n return (FALLBACK_PLAN, -1.0)\n\n # the optimizer chose the right plan (at least we have not enough evidence to the contrary)\n return (\"\", 0.0)\n\n\n# print the results of one test run\n\ndef print_results(testTitle, explainDict, execDict, errMessages, plan_ids, execute_n_times):\n # print out the title of the test\n print(\"\")\n print(testTitle)\n print(\"\")\n exeTimes = len(execDict) > 0\n\n # make a list of plan ids with the default plan ids as first entry\n plan_ids_with_default = [OPTIMIZER_DEFAULT_PLAN]\n plan_ids_with_default.extend(plan_ids)\n\n # print a header row\n headerList = [\"Parameter value\", \"Plan chosen by optimizer\", \"Cost\"]\n for p_id in plan_ids:\n headerList.append(\"Cost of forced %s plan\" % p_id)\n if exeTimes:\n headerList.append(\"Best execution plan\")\n headerList.append(\"Optimization error (pct)\")\n headerList.append(\"Execution time for default plan (ms)\")\n for p_id in plan_ids:\n headerList.append(\"Execution time for forced %s plan (ms)\" % p_id)\n if execute_n_times > 1:\n headerList.append(\"Std dev default\")\n for p_id in plan_ids:\n headerList.append(\"Std dev %s\" % p_id)\n headerList.append(\"Selectivity pct\")\n print((\", \".join(headerList)))\n\n # sort the keys of the dictionary by parameter value\n sorted_params = sorted(explainDict.keys())\n\n # for each parameter value, print one line with comma-separated values\n for p_val in sorted_params:\n # add the explain-related values\n vals = explainDict[p_val]\n resultList = [str(p_val)]\n for v in vals:\n resultList.append(str(v))\n # add the execution-related values, if applicable\n if exeTimes:\n # calculate the optimizer error\n bestPlan, optimizerError = checkForOptimizerErrors(p_val, vals[0], plan_ids, execDict)\n resultList.append(bestPlan)\n resultList.append(str(optimizerError))\n\n stddevList = []\n num_rows = -1\n # our execution times will be a list of 2* (p+1) + 1 items,\n # (default exe time, forced exe time plan 1 ... p, stddev for default time, stddevs for plans 1...p, selectivity)\n\n # now loop over the list of p+1 plan ids\n for plan_id in plan_ids_with_default:\n if (p_val, plan_id) in execDict:\n # we did execute the query for this, append the avg time\n # right away and save the standard deviation for later\n mean, stddev, local_num_rows = execDict[(p_val, plan_id)]\n resultList.append(str(mean))\n stddevList.append(str(stddev))\n if num_rows >= 0 and local_num_rows != num_rows:\n errMessages.append(\"Inconsistent number of rows for parameter value %d: %d and %d\" % (p_val, num_rows, local_num_rows))\n num_rows = local_num_rows\n else:\n # we didn't execute this query, add blank values\n resultList.append(\"\")\n stddevList.append(\"\")\n\n if execute_n_times > 1:\n # now add the standard deviations to the end of resultList\n resultList.extend(stddevList)\n # finally, the selectivity in percent\n resultList.append(str((100.0 * num_rows) / glob_rowcount))\n\n # print a comma-separated list of result values (CSV)\n print((\", \".join(resultList)))\n\n # if there are any errors, print them at the end, leaving an empty line between the result and the errors\n if (len(errMessages) > 0):\n print(\"\")\n print((\"%d diagnostic message(s):\" % len(errMessages)))\n for e in errMessages:\n print(e)\n\n\n# execute a query n times, with a guard against long-running queries,\n# and record the result in execDict and any errors in errMessages\n\ndef timed_execute_and_check_timeout(conn, sqlString, execute_n_times, paramValue, plan_id, execDict, timedOutDict,\n errMessages):\n # timedOutDict contains a record of queries that have previously timed out:\n # plan_id -> (lowest param value for timeout, highest value for timeout, direction)\n # right now we ignore low/high values and direction (whether the execution increases or decreases with\n # increased parameter values)\n if plan_id in timedOutDict:\n # this plan has timed out with at least one parameter value, decide what to do\n paramValLow, paramValHigh, direction = timedOutDict[plan_id]\n # for now, just return, once we time out for a plan we give up\n log_output(\"Not executing the %s plan for paramValue %d, due to previous timeout\" % (plan_id, paramValue))\n return\n\n # execute the query\n mean, stddev, num_execs, num_rows = timed_execute_n_times(conn, sqlString, execute_n_times)\n\n # record the execution stats\n execDict[(paramValue, plan_id)] = (mean, stddev, num_rows)\n\n # check for timeouts\n if num_execs < execute_n_times or mean > glob_exe_timeout:\n # record the timeout, without worrying about low/high values or directions for now\n timedOutDict[plan_id] = (paramValue, paramValue, \"unknown_direction\")\n errMessages.append(\n \"The %s plan for parameter value %d took more than the allowed timeout, it was executed only %d time(s)\" %\n (plan_id, paramValue, num_execs))\n\n\n# Definition of various test suites\n# -----------------------------------------------------------------------------\n\n# How to add a test:\n#\n# - Define some queries to run as text constants below. Use the tables\n# created by this program or add more tables to be created.\n# - Define methods that parameterize these test queries, given an integer\n# parameter value in a range that you can define later.\n# - Use the predefined types of plans (TABLE_SCAN, INDEX_SCAN, INDEX_ONLY_SCAN) or add your\n# own plan types above. Note that you will also need to change or implement\n# an explain method that takes a query, explains it, and returns the plan\n# type and the estimated cost.\n# - Define methods to force the desired plan types and also a method to reset\n# the connection so it doesn't force any of these plans.\n# - Now you are ready to add another test, using method run_bitmap_index_tests()\n# as an example.\n# - Add your test as a choice for the \"tests\" command line argument and add a\n# call to your test to the main program\n\n# SQL test queries\n# -----------------------------------------------------------------------------\n\n# ------------ SQL test queries - bitmap index scan --------------\n\n# GUC set statements\n\n_reset_index_scan_forces = [\"\"\"\nSELECT enable_xform('CXformImplementBitmapTableGet');\n\"\"\",\n \"\"\"\nSELECT enable_xform('CXformGet2TableScan');\n\"\"\",\n \"\"\"\nSELECT enable_xform('CXformIndexGet2IndexScan');\n\"\"\",\n \"\"\"\nSELECT enable_xform('CXformIndexGet2IndexOnlyScan');\n\"\"\" ]\n\n_force_sequential_scan = [\"\"\"\nSELECT disable_xform('CXformImplementBitmapTableGet');\n\"\"\",\n \"\"\"\nSELECT disable_xform('CXformIndexGet2IndexScan');\n\"\"\",\n \"\"\"\nSELECT disable_xform('CXformIndexGet2IndexOnlyScan');\n\"\"\"]\n\n_force_index_scan = [\"\"\"\nSELECT disable_xform('CXformGet2TableScan');\n\"\"\",\n \"\"\"\nSELECT disable_xform('CXformImplementBitmapTableGet');\n\"\"\",\n \"\"\"\nSELECT disable_xform('CXformIndexGet2IndexOnlyScan');\n\"\"\"]\n\n_force_bitmap_scan = [\"\"\"\nSELECT disable_xform('CXformGet2TableScan');\n\"\"\",\n \"\"\"\nSELECT disable_xform('CXformIndexGet2IndexOnlyScan');\n\"\"\"]\n\n_force_index_only_scan = [\"SELECT disable_xform('CXformGet2TableScan');\",\n \"SELECT disable_xform('CXformImplementBitmapTableGet');\",\n \"SELECT disable_xform('CXformIndexGet2IndexScan');\"]\n\n\n_reset_index_join_forces = [\"\"\"\nSELECT enable_xform('CXformPushGbBelowJoin');\n\"\"\",\n \"\"\"\nRESET optimizer_enable_indexjoin;\n\"\"\",\n \"\"\"\nRESET optimizer_enable_hashjoin;\n\"\"\"]\n\n_force_hash_join = [\"\"\"\nSELECT disable_xform('CXformPushGbBelowJoin');\n\"\"\",\n \"\"\"\nSET optimizer_enable_indexjoin to off;\n\"\"\"]\n\n_force_index_nlj = [\"\"\"\nSELECT disable_xform('CXformPushGbBelowJoin');\n\"\"\",\n \"\"\"\nSET optimizer_enable_hashjoin to off;\n\"\"\"]\n\n# setup statements\n\n_insert_into_bfv_tables = \"\"\"\nTRUNCATE cal_bfvtest;\nTRUNCATE cal_bfv_dim;\nINSERT INTO cal_bfvtest SELECT col1, col1, col1 FROM (SELECT generate_series(1,%d) col1)a;\nINSERT INTO cal_bfv_dim SELECT col1, col1 FROM (SELECT generate_series(1,%d,3) col1)a;\nANALYZE cal_bfvtest;\nANALYZE cal_bfv_dim;\n\"\"\"\n\n_insert_into_ndv_tables = \"\"\"\nTRUNCATE cal_ndvtest;\nINSERT INTO cal_ndvtest SELECT i, i %% %d FROM (SELECT generate_series(1,1000000) i)a;\nANALYZE cal_ndvtest;\n\"\"\"\n\n# query statements\n\n_bitmap_select = \"\"\"\nSELECT count(*) {sel}\nFROM cal_txtest\nWHERE {col} BETWEEN 0 AND {par};\n\"\"\"\n\n_bitmap_select_multi = \"\"\"\nSELECT count(*) {sel}\nFROM cal_txtest\nWHERE {col} = 0 OR {col} BETWEEN 2 AND {par}+1;\n\"\"\"\n\n_btree_select_unique_in = \"\"\"\nSELECT count(*) {sel}\nFROM cal_txtest\nWHERE {col} IN ( {inlist} );\n\"\"\"\n\n_bitmap_index_join = \"\"\"\nSELECT count(*) %s\nFROM cal_txtest f JOIN cal_dim d ON f.bitmap10000 = d.dim_id\nWHERE d.dim_id2 BETWEEN 0 AND %d;\n\"\"\"\n\n_btree_index_join = \"\"\"\nSELECT count(*) %s\nFROM cal_txtest f JOIN cal_dim d ON f.btree10000 = d.dim_id\nWHERE d.dim_id2 BETWEEN 0 AND %d;\n\"\"\"\n\n_bfv_join = \"\"\"\nSELECT count(*) \nFROM cal_bfvtest ft, cal_bfv_dim dt1\nWHERE ft.id = dt1.id;\n\"\"\"\n\n_bitmap_index_ndv = \"\"\"\nSELECT count(*)\nFROM cal_ndvtest\nWHERE val <= 1000000;\n\"\"\"\n\n_brin_select_range = \"\"\"\nSELECT count(*) {sel}\nFROM cal_brintest\nWHERE {col} BETWEEN 0 AND {par};\n\"\"\"\n\n_brin_select_multi = \"\"\"\nSELECT count(*) {sel}\nFROM cal_brintest\nWHERE {col} = 0 OR {col} BETWEEN 2 AND {par}+1;\n\"\"\"\n\n\n# Parameterize methods for the test queries above\n# -----------------------------------------------------------------------------\n\n\n# bitmap index scan with 0...100 % of values, for parameter values 0...10, in 10 % increments\ndef parameterize_bitmap_index_10_narrow(paramValue):\n return _bitmap_select.format(sel=\"\", col=\"bitmap10\", par=paramValue)\n\n\ndef parameterize_bitmap_index_10_wide(paramValue):\n return _bitmap_select.format(sel=\", max(txt)\", col=\"bitmap10\", par=paramValue)\n\n\n# bitmap index scan with 0...100 % of values, for parameter values 0...10,000, in .01 % increments\ndef parameterize_bitmap_index_10000_narrow(paramValue):\n return _bitmap_select.format(sel=\"\", col=\"bitmap10000\", par=paramValue)\n\n\ndef parameterize_bitmap_index_10000_wide(paramValue):\n return _bitmap_select.format(sel=\", max(txt)\", col=\"bitmap10000\", par=paramValue)\n\n\n# bitmap index scan with 0...100 % of values, for parameter values 0...10,000, in .01 % increments, multiple ranges\ndef parameterize_bitmap_index_10000_multi_narrow(paramValue):\n return _bitmap_select_multi.format(sel=\"\", col=\"bitmap10000\", par=paramValue)\n\n\ndef parameterize_bitmap_index_10000_multi_wide(paramValue):\n return _bitmap_select_multi.format(sel=\", max(txt)\", col=\"bitmap10000\", par=paramValue)\n\n\n# bitmap index scan on AO btree index with 0...100 % of values, for parameter values 0...10, in 10 % increments\ndef parameterize_btree_index_unique_narrow(paramValue):\n return _bitmap_select.format(sel=\"\", col=\"btreeunique\", par=paramValue)\n\n\ndef parameterize_btree_index_unique_wide(paramValue):\n return _bitmap_select.format(sel=\", max(txt)\", col=\"btreeunique\", par=paramValue)\n\n\ndef parameterize_btree_index_100_narrow(paramValue):\n return _bitmap_select.format(sel=\"\", col=\"btree100\", par=paramValue)\n\n\ndef parameterize_btree_index_100_wide(paramValue):\n return _bitmap_select.format(sel=\", max(txt)\", col=\"btree100\", par=paramValue)\n\n\n# bitmap index scan on AO btree index with 0...100 % of values, for parameter values 0...10,000, in .01 % increments\ndef parameterize_btree_index_10000_narrow(paramValue):\n return _bitmap_select.format(sel=\"\", col=\"btree10000\", par=paramValue)\n\n\ndef parameterize_btree_index_10000_wide(paramValue):\n return _bitmap_select.format(sel=\", max(txt)\", col=\"btree10000\", par=paramValue)\n\n\n# bitmap index scan on AO btree index with 0...100 % of values, for parameter values 0...10,000, in .01 % increments, multiple ranges\ndef parameterize_btree_index_10000_multi_narrow(paramValue):\n return _bitmap_select_multi.format(sel=\"\", col=\"btree10000\", par=paramValue)\n\n\ndef parameterize_btree_index_10000_multi_wide(paramValue):\n return _bitmap_select_multi.format(sel=\", max(txt)\", col=\"btree10000\", par=paramValue)\n\n\ndef parameterize_btree_unique_in_narrow(paramValue):\n inlist = \"0\"\n for p in range(1, paramValue+1):\n inlist += \", \" + str(5*p)\n return _btree_select_unique_in.format(sel=\"\", col=\"btreeunique\", inlist=inlist)\n\n\ndef parameterize_btree_unique_in_wide(paramValue):\n inlist = \"0\"\n for p in range(1, paramValue+1):\n inlist += \", \" + str(5*p)\n return _btree_select_unique_in.format(sel=\", max(txt)\", col=\"btreeunique\", inlist=inlist)\n\n\n# index join with 0...100 % of fact values, for parameter values 0...10,000, in .01 % increments\ndef parameterize_bitmap_join_narrow(paramValue):\n return _bitmap_index_join % (\"\", paramValue)\n\n\ndef parameterize_bitmap_join_wide(paramValue):\n return _bitmap_index_join % (\", max(f.txt)\", paramValue)\n\n\ndef parameterize_btree_join_narrow(paramValue):\n return _btree_index_join % (\"\", paramValue)\n\n\ndef parameterize_btree_join_wide(paramValue):\n return _btree_index_join % (\", max(f.txt)\", paramValue)\n\n\ndef parameterize_insert_join_bfv(paramValue):\n return _insert_into_bfv_tables % (paramValue, paramValue)\n\n\ndef parameterize_insert_ndv(paramValue):\n return _insert_into_ndv_tables % (paramValue)\n\n\ndef parameterize_bitmap_join_bfv(paramValue):\n return _bfv_join\n\n\ndef parameterize_bitmap_index_ndv(paramValue):\n return _bitmap_index_ndv\n\n# BRIN clustered scan with 0...100 % of values, for parameter values 0...10, in 10 % increments\ndef parameterize_brin_index_10c_narrow(paramValue):\n return _brin_select_range.format(sel=\"\", col=\"clust_10\", par=paramValue)\n\n\ndef parameterize_brin_index_10c_wide(paramValue):\n return _brin_select_range.format(sel=\", max(txt)\", col=\"clust_10\", par=paramValue)\n\n\n# BRIN clustered scan with 0...100 % of values, for parameter values 0...10,000, in .01 % increments\ndef parameterize_brin_index_10000c_narrow(paramValue):\n return _brin_select_range.format(sel=\"\", col=\"clust_10000\", par=paramValue)\n\n\ndef parameterize_brin_index_10000c_wide(paramValue):\n return _brin_select_range.format(sel=\", max(txt)\", col=\"clust_10000\", par=paramValue)\n\n\n# BRIN clustered scan with 0...100 % of values, for parameter values 0...10,000, in .01 % increments, multiple ranges\ndef parameterize_brin_index_10000c_multi_narrow(paramValue):\n return _brin_select_multi.format(sel=\"\", col=\"clust_10000\", par=paramValue)\n\n\ndef parameterize_brin_index_10000c_multi_wide(paramValue):\n return _brin_select_multi.format(sel=\", max(txt)\", col=\"clust_10000\", par=paramValue)\n\n\n# BRIN random scan with 0...100 % of values, for parameter values 0...10, in 10 % increments\ndef parameterize_brin_index_10r_narrow(paramValue):\n return _brin_select_range.format(sel=\"\", col=\"rand_10\", par=paramValue)\n\n\ndef parameterize_brin_index_10r_wide(paramValue):\n return _brin_select_range.format(sel=\", max(txt)\", col=\"rand_10\", par=paramValue)\n\n\n# BRIN random scan with 0...100 % of values, for parameter values 0...10,000, in .01 % increments\ndef parameterize_brin_index_10000r_narrow(paramValue):\n return _brin_select_range.format(sel=\"\", col=\"rand_10000\", par=paramValue)\n\n\ndef parameterize_brin_index_10000r_wide(paramValue):\n return _brin_select_range.format(sel=\", max(txt)\", col=\"rand_10000\", par=paramValue)\n\n\n# BRIN random scan with 0...100 % of values, for parameter values 0...10,000, in .01 % increments, multiple ranges\ndef parameterize_brin_index_10000r_multi_narrow(paramValue):\n return _brin_select_multi.format(sel=\"\", col=\"rand_10000\", par=paramValue)\n\n\ndef parameterize_brin_index_10000r_multi_wide(paramValue):\n return _brin_select_multi.format(sel=\", max(txt)\", col=\"rand_10000\", par=paramValue)\n\n\ndef noSetupRequired(paramValue):\n return \"SELECT 1;\"\n\n\ndef explain_bitmap_index(conn, sqlStr):\n return explain_index_scan(conn, sqlStr)\n\n\ndef reset_index_test(conn):\n execute_sql_arr(conn, _reset_index_scan_forces)\n\n\ndef force_table_scan(conn):\n execute_sql_arr(conn, _force_sequential_scan)\n\n\ndef force_bitmap_scan(conn):\n execute_sql_arr(conn, _force_bitmap_scan)\n\n\ndef force_index_scan(conn):\n execute_sql_arr(conn, _force_index_scan)\n\n\ndef force_index_only_scan(conn):\n execute_sql_arr(conn, _force_index_only_scan)\n\n\ndef reset_index_join(conn):\n execute_sql_arr(conn, _reset_index_join_forces)\n\n\ndef force_hash_join(conn):\n execute_sql_arr(conn, _force_hash_join)\n\n\ndef force_index_join(conn):\n execute_sql_arr(conn, _force_index_nlj)\n\n\n# Helper methods for running tests\n# -----------------------------------------------------------------------------\n\ndef run_one_bitmap_scan_test(conn, testTitle, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n execute_n_times):\n log_output(\"Running bitmap scan test \" + testTitle)\n plan_ids = [BITMAP_SCAN, TABLE_SCAN]\n force_methods = [force_bitmap_scan, force_table_scan]\n explainDict, execDict, errors = find_crossover(conn, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n explain_bitmap_index, reset_index_test, plan_ids, force_methods,\n execute_n_times)\n print_results(testTitle, explainDict, execDict, errors, plan_ids, execute_n_times)\n\n\ndef run_one_bitmap_join_test(conn, testTitle, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n execute_n_times):\n log_output(\"Running bitmap join test \" + testTitle)\n plan_ids = [BITMAP_SCAN, TABLE_SCAN]\n force_methods = [force_index_join, force_hash_join]\n explainDict, execDict, errors = find_crossover(conn, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n explain_join_scan, reset_index_join, plan_ids, force_methods,\n execute_n_times)\n print_results(testTitle, explainDict, execDict, errors, plan_ids, execute_n_times)\n\ndef run_one_index_scan_test(conn, testTitle, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n execute_n_times, plan_ids, force_methods):\n log_output(\"Running index scan test \" + testTitle)\n explainDict, execDict, errors = find_crossover(conn, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n explain_index_scan, reset_index_test, plan_ids, force_methods,\n execute_n_times)\n print_results(testTitle, explainDict, execDict, errors, plan_ids, execute_n_times)\n\ndef run_one_brin_scan_test(conn, testTitle, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n execute_n_times):\n log_output(\"Running BRIN scan test \" + testTitle)\n plan_ids = [BITMAP_SCAN, TABLE_SCAN]\n force_methods = [force_bitmap_scan, force_table_scan]\n explainDict, execDict, errors = find_crossover(conn, paramValueLow, paramValueHigh, setup, parameterizeMethod,\n explain_bitmap_index, reset_index_test, plan_ids, force_methods,\n execute_n_times)\n print_results(testTitle, explainDict, execDict, errors, plan_ids, execute_n_times)\n\n\ndef disable_bitmapscan(func):\n def inner(conn, *args, **kwargs):\n execute_sql(conn, \"set optimizer_enable_bitmapscan=off;\")\n func(conn, *args, **kwargs)\n execute_sql(conn, \"set optimizer_enable_bitmapscan=on;\")\n return inner\n\n# Main driver for the tests\n# -----------------------------------------------------------------------------\n\n@disable_bitmapscan\ndef run_index_only_scan_tests(conn, execute_n_times):\n plan_ids = [TABLE_SCAN, INDEX_ONLY_SCAN]\n force_methods = [force_table_scan, force_index_only_scan]\n\n for t in [\"cal_widetest1\", \"cal_widetest2\", \"cal_widetest3\"]:\n def parameterized_selectivity_method(paramValue):\n return \"\"\"\n SELECT count(sel)\n FROM \"\"\" + t + \"\"\"\n WHERE sel BETWEEN 0 AND {sel};\n \"\"\".format(sel=paramValue)\n run_one_index_scan_test(conn,\n \"Index Only Scan Test; \" + t + \" Varying Sel\",\n 1,\n 20,\n noSetupRequired,\n parameterized_selectivity_method,\n execute_n_times,\n plan_ids,\n force_methods)\n\n def parameterized_method(paramValue):\n return \"\"\"\n SELECT count(sel)\n FROM cal_widetest{column_width}\n WHERE sel=25;\n \"\"\".format(column_width=paramValue)\n\n run_one_index_scan_test(conn,\n \"Index Only Scan Test; Wide table; Narrow index\",\n 1,\n 4,\n noSetupRequired,\n parameterized_method,\n execute_n_times,\n plan_ids,\n force_methods)\n\n\ndef run_bitmap_index_scan_tests(conn, execute_n_times):\n\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; NDV=10; selectivity_pct=10*parameter_value; count(*)\",\n 0,\n 10,\n noSetupRequired,\n parameterize_bitmap_index_10_narrow,\n execute_n_times)\n\n # all full table scan, no crossover\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; NDV=10; selectivity_pct=10*parameter_value; max(txt)\",\n 0,\n 6,\n noSetupRequired,\n parameterize_bitmap_index_10_wide,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 600 if glob_appendonly else 20,\n noSetupRequired,\n parameterize_bitmap_index_10000_narrow,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300 if glob_appendonly else 20,\n noSetupRequired,\n parameterize_bitmap_index_10000_wide,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 600 if glob_appendonly else 20,\n noSetupRequired,\n parameterize_bitmap_index_10000_multi_narrow,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300 if glob_appendonly else 20,\n noSetupRequired,\n parameterize_bitmap_index_10000_multi_wide,\n execute_n_times)\n\n\ndef run_bitmap_ndv_scan_tests(conn, execute_n_times):\n run_one_bitmap_scan_test(conn,\n \"Bitmap Scan Test; ndv test; rows=1000000; parameter = insert statement modulo; count(*)\",\n 1,\n # modulo ex. would replace x in the following: SELECT i % x FROM generate_series(1,10000)i;\n 10000, # max here is 10000 (num of rows)\n parameterize_insert_ndv,\n parameterize_bitmap_index_ndv,\n execute_n_times)\n\n\ndef run_btree_ao_index_scan_tests(conn, execute_n_times):\n # use the unique btree index (no bitmap equivalent), 0 to 10,000 rows\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; unique; selectivity_pct=100*parameter_value/%d; count(*)\" % glob_rowcount,\n 0,\n glob_rowcount // 10, # 10% is the max allowed selectivity for a btree scan on an AO table\n noSetupRequired,\n parameterize_btree_index_unique_narrow,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; unique; selectivity_pct=100*parameter_value/%d; max(txt)\" % glob_rowcount,\n 0,\n glob_rowcount // 20,\n noSetupRequired,\n parameterize_btree_index_unique_wide,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; NDV=100; selectivity_pct=parameter_value; count(*)\",\n 0,\n 5,\n noSetupRequired,\n parameterize_btree_index_100_narrow,\n execute_n_times)\n\n # all full table scan, no crossover\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; NDV=100; selectivity_pct=parameter_value; max(txt)\",\n 0,\n 5,\n noSetupRequired,\n parameterize_btree_index_100_wide,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 500,\n noSetupRequired,\n parameterize_btree_index_10000_narrow,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 1000,\n noSetupRequired,\n parameterize_btree_index_10000_wide,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 1000,\n noSetupRequired,\n parameterize_btree_index_10000_multi_narrow,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 1000,\n noSetupRequired,\n parameterize_btree_index_10000_multi_wide,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; in-list; selectivity_pct=100*parameter_value/%d; count(*)\" % glob_rowcount,\n 0,\n 5000, # length of IN list\n noSetupRequired,\n parameterize_btree_unique_in_narrow,\n execute_n_times)\n\n run_one_bitmap_scan_test(conn,\n \"Btree Scan Test; in-list; selectivity_pct=100*parameter_value/%d; max(txt)\" % glob_rowcount,\n 0,\n 3000, # length of IN list\n noSetupRequired,\n parameterize_btree_unique_in_wide,\n execute_n_times)\n\n\ndef run_index_join_tests(conn, execute_n_times):\n run_one_bitmap_join_test(conn,\n \"Bitmap Join Test; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 400,\n noSetupRequired,\n parameterize_bitmap_join_narrow,\n execute_n_times)\n\n run_one_bitmap_join_test(conn,\n \"Bitmap Join Test; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300,\n noSetupRequired,\n parameterize_bitmap_join_wide,\n execute_n_times)\n\n run_one_bitmap_join_test(conn,\n \"Btree Join Test; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 500,\n noSetupRequired,\n parameterize_btree_join_narrow,\n execute_n_times)\n\n run_one_bitmap_join_test(conn,\n \"Btree Join Test; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 400,\n noSetupRequired,\n parameterize_btree_join_wide,\n execute_n_times)\n\n\ndef run_bfv_join_tests(conn, execute_n_times):\n run_one_bitmap_join_test(conn,\n \"Bitmap Join BFV Test; Large Data; parameter = num rows inserted\",\n 10000, # num of rows inserted\n 900000,\n parameterize_insert_join_bfv,\n parameterize_bitmap_join_bfv,\n execute_n_times)\n\n\ndef run_brin_tests(conn, execute_n_times):\n\n run_one_brin_scan_test(conn,\n \"BRIN clustered Scan Test; NDV=10; selectivity_pct=10*parameter_value; count(*)\",\n 0,\n 10,\n noSetupRequired,\n parameterize_brin_index_10c_narrow,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN clustered Scan Test; NDV=10; selectivity_pct=10*parameter_value; max(txt)\",\n 0,\n 6,\n noSetupRequired,\n parameterize_brin_index_10c_wide,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN clustered Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 600,\n noSetupRequired,\n parameterize_brin_index_10000c_narrow,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN clustered Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300,\n noSetupRequired,\n parameterize_brin_index_10000c_wide,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN clustered Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 600,\n noSetupRequired,\n parameterize_brin_index_10000c_multi_narrow,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN clustered Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300,\n noSetupRequired,\n parameterize_brin_index_10000c_multi_wide,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN random Scan Test; NDV=10; selectivity_pct=10*parameter_value; count(*)\",\n 0,\n 10,\n noSetupRequired,\n parameterize_brin_index_10r_narrow,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN random Scan Test; NDV=10; selectivity_pct=10*parameter_value; max(txt)\",\n 0,\n 6,\n noSetupRequired,\n parameterize_brin_index_10r_wide,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN random Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 600,\n noSetupRequired,\n parameterize_brin_index_10000r_narrow,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN random Scan Test; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300,\n noSetupRequired,\n parameterize_brin_index_10000r_wide,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN random Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; count(*)\",\n 0,\n 600,\n noSetupRequired,\n parameterize_brin_index_10000r_multi_narrow,\n execute_n_times)\n\n run_one_brin_scan_test(conn,\n \"BRIN random Scan Test; multi-range; NDV=10000; selectivity_pct=0.01*parameter_value; max(txt)\",\n 0,\n 300,\n noSetupRequired,\n parameterize_brin_index_10000r_multi_wide,\n execute_n_times)\n\n\n# common parts of all test suites, create tables, run tests, drop objects\n# -----------------------------------------------------------------------------\n\n# create the table(s), as regular or AO table, and insert num_rows into the main table\ndef createDB(conn, use_ao, num_rows, db_name):\n global glob_appendonly\n\n create_options = \"\"\n if use_ao:\n create_options = _with_appendonly\n glob_appendonly = True\n create_cal_table_stmt = _create_cal_table % create_options\n create_bfv_table = _create_bfv_table % create_options\n create_ndv_table = _create_ndv_table % create_options\n create_brin_table = _create_brin_table % create_options\n create_indexonly_tables = [table % create_options for table in _create_indexonly_tables]\n insert_into_temp_stmt = _insert_into_temp % num_rows\n insert_into_other_stmt = _insert_into_other_tables % (1, glob_dim_table_rows)\n insert_into_brin_table = _insert_into_brin_table.format(rows=num_rows)\n insert_into_indexonly_tables = _insert_into_indexonly_tables\n\n execute_sql(conn, _drop_tables)\n execute_sql(conn, create_cal_table_stmt)\n execute_sql(conn, create_bfv_table)\n execute_sql(conn, create_ndv_table)\n execute_sql(conn, create_brin_table)\n execute_sql_arr(conn, create_indexonly_tables)\n execute_sql_arr(conn, _create_other_tables)\n commit_db(conn)\n execute_and_commit_sql(conn, insert_into_temp_stmt)\n execute_and_commit_sql(conn, _insert_into_table)\n commit_db(conn)\n execute_and_commit_sql(conn, insert_into_brin_table)\n execute_sql_arr(conn, insert_into_indexonly_tables)\n execute_and_commit_sql(conn, insert_into_other_stmt)\n commit_db(conn)\n execute_sql_arr(conn, _create_index_arr)\n execute_sql_arr(conn, _create_bfv_index_arr)\n execute_sql_arr(conn, _create_ndv_index_arr)\n commit_db(conn)\n execute_sql_arr(conn, _create_btree_indexes_arr)\n execute_sql_arr(conn, _create_brin_index_arr)\n execute_sql(conn, _analyze_table)\n\n # workaround dbconn.execSQL transaction interface that prevent running VACUUM\n subprocess.run([\"vacuumdb\", \"-d\", db_name],\n stdout=subprocess.DEVNULL,\n stderr=subprocess.STDOUT)\n commit_db(conn)\n\n\ndef dropDB(conn):\n execute_sql(conn, _drop_tables)\n\n# smooth statistics for a single integer column uniformly distributed between 1 and row_count, with a given row count and NDV\n#\n# For NDVs of 100 or less, list all of them\n# For NDVs of more than 100, generate a histogram with 100 buckets\n# Set the correlation to 0 for all columns, since the data was shuffled randomly\ndef smoothStatisticsForOneCol(conn, table_name, attnum, row_count, ndv, corr):\n # calculate stadistinct value and ndv, if specified as -1\n if ndv == -1:\n stadistinct = -1\n ndv = row_count\n else:\n stadistinct = ndv\n\n # stakind: 1 is a list of most common values and frequencies, 2 is a histogram with range buckets\n stakind = 1\n # arrays for stanumbers and stavalues\n stanumbers = []\n stavalues = []\n stanumbers_txt = \"NULL\"\n num_values = min(ndv, 100)\n\n if ndv <= 100:\n # produce \"ndv\" MCVs, each with the same frequency\n for i in range(1,num_values+1):\n stanumbers.append(str(float(1)/ndv))\n stavalues.append(str(i))\n stanumbers_txt = \"'{ \" + \", \".join(stanumbers) + \" }'::float[]\"\n else:\n # produce a uniformly distributed histogram with 100 buckets (101 boundaries)\n stakind = 2\n stavalues.append(str(1))\n for j in range(1,num_values+1):\n stavalues.append(str((j*ndv) // num_values))\n\n stavalues_txt = \"'{ \" + \", \".join(stavalues) + \" }'::int[]\"\n execute_sql(conn, _update_pg_stats % (stadistinct, stakind, stanumbers_txt, stavalues_txt, corr, table_name, attnum))\n\n# ensure that we have perfect histogram statistics on the relevant columns\ndef smoothStatistics(conn, num_fact_table_rows):\n prev_table_name = \"\"\n if glob_gpdb_major_version > 5:\n execute_sql(conn, _allow_system_mods)\n else:\n execute_sql(conn, _allow_system_mods_v5)\n for tup in _stats_cols_to_fix:\n # note that col_name is just for human readability\n (table_name, col_name, attnum, ndv, table_rows, corr) = tup\n if table_rows == -1:\n table_rows = num_fact_table_rows\n smoothStatisticsForOneCol(conn, table_name, attnum, table_rows, ndv, corr)\n if prev_table_name != table_name:\n prev_table_name = table_name\n execute_sql(conn, _update_pg_class % (table_rows, table_name))\n commit_db(conn)\n\ndef inspectExistingTables(conn):\n global glob_rowcount\n global glob_appendonly\n\n sqlStr = \"SELECT count(*) from cal_txtest\"\n curs = dbconn.query(conn, sqlStr)\n\n rows = curs.fetchall()\n for row in rows:\n glob_rowcount = row[0]\n log_output(\"Row count of existing fact table is %d\" % glob_rowcount)\n\n if glob_gpdb_major_version < 7:\n sqlStr = \"SELECT lower(unnest(reloptions)) from pg_class where relname = 'cal_txtest'\"\n else:\n sqlStr = \"SELECT case when relam=3434 then 'appendonly' else 'appendonly,column' end from pg_class where relname = 'cal_txtest' and relam in (3434,3435)\"\n curs = dbconn.query(conn, sqlStr)\n\n rows = curs.fetchall()\n for row in rows:\n if re.search(\"appendonly\", row[0]):\n glob_appendonly = True\n\n if glob_appendonly:\n log_output(\"Existing fact table is append-only\")\n else:\n log_output(\"Existing fact table is not an append-only table\")\n\n\ndef main():\n global glob_verbose\n global glob_log_file\n global glob_rowcount\n\n args, parser = parseargs()\n if args.logFile != \"\":\n glob_log_file = open(args.logFile, \"wt\", 1)\n if args.verbose:\n glob_verbose = True\n log_output(\"Connecting to host %s on port %d, database %s\" % (args.host, args.port, args.dbName))\n conn = connect(args.host, args.port, args.dbName)\n select_version(conn)\n if args.create:\n glob_rowcount = args.numRows\n createDB(conn, args.appendOnly, args.numRows, args.dbName)\n smoothStatistics(conn, args.numRows)\n else:\n inspectExistingTables(conn)\n\n for test_unit in args.tests:\n if test_unit == \"all\":\n run_bitmap_index_scan_tests(conn, args.execute)\n if glob_appendonly:\n # the btree tests are for bitmap scans on AO tables using btree indexes\n run_btree_ao_index_scan_tests(conn, args.execute)\n run_index_join_tests(conn, args.execute)\n # skip the long-running bitmap_ndv_scan_tests and bfv_join_tests\n elif test_unit == \"index_only_scan_tests\":\n run_index_only_scan_tests(conn, args.execute)\n elif test_unit == \"bitmap_scan_tests\":\n run_bitmap_index_scan_tests(conn, args.execute)\n elif test_unit == \"bitmap_ndv_scan_tests\":\n run_bitmap_ndv_scan_tests(conn, args.execute)\n elif test_unit == \"btree_ao_scan_tests\":\n run_btree_ao_index_scan_tests(conn, args.execute)\n elif test_unit == \"index_join_tests\":\n run_index_join_tests(conn, args.execute)\n elif test_unit == \"bfv_join_tests\":\n run_bfv_join_tests(conn, args.execute)\n elif test_unit == \"brin_tests\":\n run_brin_tests(conn, args.execute)\n elif test_unit == \"none\":\n print(\"Skipping tests\")\n\n if args.drop:\n dropDB(conn)\n\n conn.close()\n if glob_log_file != None:\n glob_log_file.close()\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"greenplum-db/gpdb","sub_path":"src/backend/gporca/scripts/cal_bitmap_test.py","file_name":"cal_bitmap_test.py","file_ext":"py","file_size_in_byte":73654,"program_lang":"python","lang":"en","doc_type":"code","stars":6032,"dataset":"github-code","pt":"76"} +{"seq_id":"2946800189","text":"from datetime import timedelta\n\nfrom django.utils import timezone\nfrom rest_framework import status\nfrom rest_framework.generics import get_object_or_404\n\nfrom .models import Seat, History\n\nCURRENT_TIME = timezone.now()\nDEFAULT_TIME_BOOKING = CURRENT_TIME + timedelta(hours=1)\n\n\nclass SeatMessages:\n def __init__(self, instance):\n self.instance = instance\n\n def seat_busy_error(self):\n response = {\n \"data\": {\"error\": f\"Seat #{self.instance.id} is already taken by {self.instance.user.username}\"},\n \"status\": status.HTTP_403_FORBIDDEN\n }\n return response\n\n def seat_vacated_notification(self):\n response = {\n \"data\": {\"success\": f\"Seat #{self.instance.id} successfully vacated\"},\n \"status\": status.HTTP_204_NO_CONTENT\n }\n return response\n\n def seat_free_notification(self):\n response = {\n \"data\": {\"success\": f\"Seat #{self.instance.id} is free! You can take it\"},\n \"status\": status.HTTP_204_NO_CONTENT\n }\n return response\n\n def seat_delete_notification(self):\n response = {\n \"data\": {\"success\": f\"Seat successfully deleted\"},\n \"status\": status.HTTP_204_NO_CONTENT\n }\n return response\n\n\nclass SeatConfiguration(SeatMessages):\n def __init__(self, request, pk):\n self.request = request\n self.pk = pk\n self.instance = self.__get_instance()\n super().__init__(self.instance)\n\n def __get_instance(self):\n return get_object_or_404(Seat, pk=self.pk)\n\n def get_end_booking(self):\n end_booking = self.request.data.get('end_booking')\n if end_booking is None:\n end_booking = DEFAULT_TIME_BOOKING\n self.request.data['end_booking'] = end_booking\n return end_booking\n\n def increase_room_free_seats(self):\n room = self.instance.room\n room.free_seats += 1\n room.save()\n\n def reduce_room_free_seats(self):\n room = self.instance.room\n room.free_seats -= 1\n room.save()\n\n def __save(self):\n self.instance.save()\n\n def is_booked_time(self):\n return self.instance.end_booking > CURRENT_TIME\n\n def update_seat_instance(self, user_id=None, start_booking=None, end_booking=None):\n self.increase_room_free_seats()\n self.instance.user_id = user_id\n self.instance.start_booking = start_booking\n self.instance.end_booking = end_booking\n self.__save()\n\n def save_user_history(self, new_user):\n History.objects.create(user=new_user,\n room=self.instance.room,\n seat=self.instance,\n start_booking=CURRENT_TIME,\n end_booking=self.get_end_booking())\n","repo_name":"kdukdu/office-api-test-task","sub_path":"OfficeAPI/api/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"} +{"seq_id":"19094248797","text":"#!/usr/bin/env python3\nfrom collections import deque, Counter\nfrom heapq import heappop, heappush\nfrom bisect import bisect_right\n\n\nN, M = map(int, input().split())\nA = [None] * M\nB = [None] * M\nadj = [set() for _ in range(N)]\nfor i in range(M):\n A[i], B[i] = map(lambda x: int(x) - 1, input().split())\n adj[A[i]].add(B[i])\n adj[B[i]].add(A[i])\ndp = [N] * (1 << N)\nflag = [0] * (1 << N)\n\n\ndef f(S):\n # print(S)\n if flag[S]:\n return dp[S]\n else:\n flag[S] = 1\n tmp = N\n check = True\n # S内の任意の頂点が2点で結ばれるか調べる\n for i in range(N):\n for j in range(i + 1, N):\n if (1 << i & S) and (1 << j & S):\n if j not in adj[i]:\n check = False\n break\n if check == False:\n break\n if check:\n tmp = min(tmp, 1)\n\n T = (S - 1) & S\n # print(S, (S - 1) & S)\n while T > 0:\n tmp = min(tmp, f(T) + f(S ^ T))\n T = (T - 1) & S\n # print(T)\n dp[S] = tmp\n return dp[S]\n\n\nprint(f((1 << N) - 1))\n# print(dp)\n","repo_name":"ryu19-1/atcoder_python","sub_path":"abc187/f/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"24728157834","text":"from typing import Dict\nimport yagmail\nimport itertools\nimport pandas as pd\nimport os\nimport numpy as np\nfrom pathlib import Path\n\npath_of_excel=r\"C:/Users/Vipin Kumar Yadav/Desktop/email script/list.xlsx\"\ndf = pd.read_excel (path_of_excel)\n\ndf[\"Fname\"] = df[\"Name\"] +\" \"+ df[\"Lname\"]\n\nlist1 = list(df['Name']) \nlist2 = list(df['Lname'])\nlist3 = list(df['Fname'])\nlist4 = list(df['Email'])\nprint(list3)\n\n\nfor (i,j,k,l) in zip(list1,list2,list3,list4):\n # print('name is:'+i+\" \"+j+\" ,email is \"+l)\n\n\n receiver = l\n sub=\"Certificate of participation\"\n body = \"Mr./Ms.\"+' '+i+' '+j+',\\n '+\"Thanks for participating in SOlUTIONS 2k21. Hereby attached is your participation certificate.PFA.\"\n # filename = \"C:/Users/MSI/Desktop/cert gen/tunhi/Alan Price.pdf\"\n \n paths = Path('C:/Users/Vipin Kumar Yadav/Desktop/email script/tunhi/').glob(\"**/\"+k+\".pdf\")\n \n for path in paths:\n # because path is object not string\n path_in_str = str(path)\n # Do thing with the path\n # print(('C:/Users/MSI/Desktop/cert gen/tunhi/'+i+'.pdf'))\n if path_in_str == str(Path('C:/Users/Vipin Kumar Yadav/Desktop/email script/tunhi/'+k+'.pdf')):\n print(\"done and sent for \"+k)\n filename = 'C:/Users/Vipin Kumar Yadav/Desktop/email script/tunhi/'+k+'.pdf'\n # print(str(Path('C:/Users/MSI/Desktop/cert gen/tunhi/'+k+'.pdf')))\n\n\n yag = yagmail.SMTP('email','password')\n yag.send(\n to=receiver,\n subject=sub,\n contents=body, \n attachments=filename,\n )\n # print(body)\n\n\n","repo_name":"vipiny357/email_script","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"33980845851","text":"tempo = int(input())\n\n# 7400 -> 2 horas = 7200\n# fazer essa divisão, e vai pegando o que sobra\nhoras = int(tempo / 3600) # jogue fora a parte fracionaria\nhoras = tempo // 3600 # pega o inteiro\nresto = tempo % 3600 # resto da divisão\n# ao invés de usar uma outra variavel, posso usar a mesma variavel\ntempo = tempo % 3600\ntempo - horas * 3600 # não usamos muito, usamos mais o resto\n\n\"\"\"\n 7400 / 3600\n O resto da divisão, pode ser usado\n (3600 é uma hora)\n\"\"\"","repo_name":"cl1sman/saberesPython","sub_path":"Courses/Alg-Prog/Exercícios/Aula 01/24.py","file_name":"24.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"44500870947","text":"import time\nimport uuid\n\nclass Package:\n \"\"\"\n Package - пакет заданий для выполнения в системе\n \"\"\"\n def __init__(self, logger):\n self.__instructions = []\n self.__status = PackageStatus.New\n self.__current_time = 0\n self.__id = uuid.uuid4()\n self.__logger = logger\n self.__instructions_executed = 0\n self.__last_session_instructions_executed = 0\n self.__last_session_execution_time = 0\n\n def add_instruction(self, instruction):\n self.__instructions.append(instruction)\n\n def remove_instruction(self, instruction):\n self.__instructions.remove(instruction)\n\n @property\n def last_session_execution_time(self):\n return self.__last_session_execution_time\n\n @property\n def last_session_instructions_executed(self):\n return self.__last_session_instructions_executed\n\n @property\n def current_working_time(self):\n return self.__current_time\n\n @property\n def instructions_executed(self):\n return self.__instructions_executed\n\n @property\n def id(self):\n return self.__id\n\n @property\n def status(self):\n return self.__status\n\n @property\n def instructions(self):\n return self.__instructions\n\n\n def execute(self):\n if self.__status == PackageStatus.New:\n self.__start()\n elif self.__status == PackageStatus.Interrupted:\n self.__continue()\n else:\n message = f\"Невозможно выполнить пакет {self.__id} со статусом {self.__status}\"\n self.__logger.error(message)\n raise Exception(message)\n \n\n\n def __start(self):\n last_session_executed_tasks_count = 0\n self.__last_session_execution_time = 0\n self.__logger.info(f\"Начинает выполняться пакет {self.__id}\")\n self.__status = PackageStatus.Executing\n self.__current_time = 0\n timing = time.time()\n if self.__instructions:\n previous_instruction_type = self.__instructions[0].instructionType\n \n while self.__instructions:\n current_instruction = self.__instructions.pop(0)\n # если типы инструкций не совпадают, то необходимо прервать выполнение пакета и передать управление системе\n if previous_instruction_type != current_instruction.instructionType:\n # возвращаем инструкцию назад\n self.__instructions.insert(0, current_instruction)\n self.__status = PackageStatus.Interrupted\n self.__current_time = time.time() - timing\n self.__last_session_execution_time = self.__current_time\n self.__logger.info(f\"Пакет {self.__id} прервал выполнение\")\n self.__last_session_instructions_executed = last_session_executed_tasks_count\n return\n current_instruction.execute()\n last_session_executed_tasks_count += 1\n self.__instructions_executed += 1\n self.__current_time = time.time() - timing\n self.__last_session_execution_time = self.__current_time\n else:\n self.__logger.info(f\"Пакет {self.__id} не был выполнен, т.к. не было инструкций\")\n self.__last_session_instructions_executed = last_session_executed_tasks_count\n return\n self.__logger.info(f\"Пакет {self.__id} выполнен полностью\")\n self.__status = PackageStatus.Completed\n self.__last_session_instructions_executed = last_session_executed_tasks_count\n return\n\n def __continue(self):\n last_session_executed_tasks_count = 0\n self.__last_session_execution_time = 0\n self.__logger.info(f\"Продолжает выполняться пакет {self.__id}\")\n self.__status = PackageStatus.Executing\n timing = time.time()\n local_time = 0\n if self.__instructions:\n previous_instruction_type = self.__instructions[0].instructionType\n \n while self.__instructions:\n current_instruction = self.__instructions.pop(0)\n # если типы инструкций не совпадают, то необходимо прервать выполнение пакета и передать управление системе\n if previous_instruction_type != current_instruction.instructionType:\n # возвращаем инструкцию назад\n self.__instructions.insert(0, current_instruction)\n self.__status = PackageStatus.Interrupted\n local_time = time.time() - timing\n self.__last_session_execution_time = local_time\n self.__current_time += local_time\n self.__logger.info(f\"Пакет {self.__id} прервал выполнение\")\n self.__last_session_instructions_executed = last_session_executed_tasks_count\n return\n current_instruction.execute()\n last_session_executed_tasks_count += 1\n self.__instructions_executed += 1\n local_time = time.time() - timing\n else:\n self.__logger.info(f\"Пакет {self.__id} не продолжил выполняться, т.к. не было инструкций\")\n self.__last_session_instructions_executed = last_session_executed_tasks_count\n return\n self.__last_session_execution_time = local_time\n self.__current_time += local_time\n self.__logger.info(f\"Пакет {self.__id} выполнен полностью\")\n self.__status = PackageStatus.Completed\n self.__last_session_instructions_executed = last_session_executed_tasks_count\n return\n\n\n\n\nfrom enum import Enum\nclass PackageStatus(Enum):\n Executing = 1\n Interrupted = 2\n New = 3\n Completed = 4\n\n\n","repo_name":"DanilRukin/OperationalSystems","sub_path":"lr_1/src/package.py","file_name":"package.py","file_ext":"py","file_size_in_byte":6283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"37814574811","text":"from . import web\nfrom flask import request, render_template\nfrom libs.get_data import get_forecast_default, get_forecast_data\nfrom libs.format_time import format_time\nimport os\nimport json\nimport time\nfrom concurrent.futures import ProcessPoolExecutor\n\nexecutor = ProcessPoolExecutor(5)\n\n@web.route('/forecast')\ndef forecast():\n return render_template('forecast.html')\n\n\n@web.route('/forecast_pm25', methods=['POST', 'GET', 'PUT'])\ndef forecast_pm25():\n if request.method == 'GET':\n return get_forecast_default()\n elif request.method == 'POST':\n start_time = request.get_json()['start'].replace(\"T\", \" \")\n start_time = format_time(start_time)\n geopoint = request.get_json()['geopoint']\n forecast_day = request.get_json()['forecast_day']\n return get_forecast_data(start_time, forecast_day, geopoint)\n elif request.method == 'PUT':\n days = request.get_json()['days']\n geopoint = request.get_json()['geopoint']\n path = ('/home/AirNet/libs/forecast_pm25.py ' + geopoint + ' train ' + str(days))\n executor.submit(run_forecast(geopoint, days))\n return (json.dumps(\"后台任务已加入！\"))\n\n\ndef run_forecast(geopoint, days):\n path = ('~/test/forecast_pm25.py ' + geopoint + ' train ' + str(days))\n status = os.system(path)\n","repo_name":"Marticles/airnet-py","sub_path":"web/forecast.py","file_name":"forecast.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"76"} +{"seq_id":"4232100952","text":"import numpy as np\nfrom typing import Tuple\nfrom cutoff import Cutoff\n\n\ndef load_initial_density_profiles(filename: str) -> Tuple[float, int, float, np.ndarray, np.ndarray]:\n with np.load(filename) as loaded:\n dr = loaded[\"dr\"]\n num_bins = loaded[\"num_bins\"]\n bulk_density = loaded[\"bulk_density\"]\n rho_self = loaded[\"rho_self\"]\n rho_dist = loaded[\"rho_dist\"]\n cutoff = Cutoff(1e-70)\n rho_self = cutoff.cutoff(rho_self)\n rho_dist = cutoff.cutoff(rho_dist)\n return dr, num_bins, bulk_density, rho_self, rho_dist\n\n\ndef export(\n filename: str, dr: float, num_bins: int, bulk_density: float, rho_self: np.ndarray, rho_dist: np.ndarray\n) -> None:\n \"\"\"\n Save a compressed numpy file in the correct format for the loader to load\n :param filename: the name of the file (should include the .npz file extension)\n :param dr: the bin size of the discretised density profiles\n :param num_bins: the number of points of the discretised density profiles\n :param bulk_density: the mean density of the system\n :param rho_self: the density profile of the self particle\n :param rho_dist: the density profile of the distinct particles\n \"\"\"\n data = {\n \"dr\": dr,\n \"num_bins\": num_bins,\n \"bulk_density\": bulk_density,\n \"rho_self\": rho_self,\n \"rho_dist\": rho_dist\n }\n np.savez_compressed(filename, **data)\n","repo_name":"mithodin/ddft-spherical","sub_path":"initial.py","file_name":"initial.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"15939656073","text":"# coding=utf-8\n# @Time : 2022/12/20 11:55 AM\n# @Author : 王思哲\n# @File : getFakId.py\n# @Software: PyCharm\n\nimport requests\n\n\ndef get_fakid(headers, tok, query):\n '''\n\n :param headers:请求头\n :param tok: token\n :param query: 查询名称\n :return:\n '''\n url = 'https://mp.weixin.qq.com/cgi-bin/searchbiz'\n data = {\n 'action': 'search_biz',\n 'scene': 1, # 页数\n 'begin': 0,\n 'count': 10,\n 'query': query,\n 'token': tok,\n 'lang': 'zh_CN',\n 'f': 'json',\n 'ajax': '1',\n }\n # 发送请求\n r = requests.get(url, headers=headers, params=data)\n # 解析json\n dic = r.json()\n # 获取公众号名称、fakeid\n wpub_list = [\n {\n 'wpub_name': item['nickname'],\n 'wpub_fakid': item['fakeid']\n }\n for item in dic['list']\n ]\n\n return wpub_list\n","repo_name":"Zzzz0zzzZ/py-spider-for-wechat","sub_path":"utils/getFakId.py","file_name":"getFakId.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"76"} +{"seq_id":"30536685522","text":"import pandas as pd\nfrom mtranslate import translate\nfrom pymongo import MongoClient\n\n\n\n\n\ndef remove_noise(name):\n temp=0\n new_str=''\n noise=[\"*\",\"/\",\" \",\"!\"]\n for w in name:\n if(((w==noise[0] or w==noise[1] or w==noise[2] or w==noise[3]) and temp==1) or (w!=noise[0] and w!=noise[1] and w!=noise[2] and w!=noise[3])):\n if(temp==0):\n temp=1\n new_str=new_str+w\n return new_str\n\n\nclient = MongoClient('mongodb+srv://test:test@cluster0-12rwi.azure.mongodb.net/test?retryWrites=true&w=majority')\ndb = client.get_database('shop_list')\nitemlist = db.itemlist\n\ndata=pd.read_csv('items.csv')\n# data_english=pd.read_csv('new_item_data.csv')\nitem_name_russian_list=data[\"item_name\"].values\nitem_id_list=data[\"item_id\"].values\nitem_category_list=data[\"item_category_id\"].values\n\nprint(item_name_russian_list)\n\n\nfor i in range(0,len(item_name_russian_list)):\n itemlist.insert({\n \"item_id\":int(item_id_list[i]),\n \"item_name\":str(item_name_russian_list[i]),\n \"item_category\":int(item_category_list[i])\n })\n\n\n\n","repo_name":"Code-with-ease/Automated-grocery-list","sub_path":"src/Dataset upload code/item_atlas.py","file_name":"item_atlas.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"13420721310","text":"from waiting_phenomenon.mms import e_nbs, compute_p0, e_nbf, e_taf\n\ndef decimal_to_date(number: float, unit = 'minutes')-> str:\n if (unit == 'minutes'):\n minutes = int(number)\n seconds = (number*3600) % 60\n return f'{minutes} minutes, {seconds} seconds'\n elif (unit == 'hours'):\n hours = int(number)\n minutes = (number*60) % 60\n seconds = (number*3600) % 60\n return f'{hours} hours, {minutes} minutes, {seconds} seconds'\n\"\"\"\nIn some rare cases, the wording doesn't give mu or alpha, just psi.\nIn that case, we can compute alpha or mu if necessary.\n\"\"\"\nalpha = 2.7\nmu = 3\n\npsi = alpha/mu\nunit = 'minutes'\n\nclients = 'taxis'\n\nnb_stations = 1\n\np0_precision = 3\nnbf_precision = 2\n\n\np0 = compute_p0(nb_stations, psi)\np0 = round(p0, p0_precision)\n\nprint(f'p0 = {p0} = {p0*100}%')\n\nnbf = e_nbf(psi, p0, nb_stations)\nnbf = round(nbf, nbf_precision)\n\nprint(f'{nbf} {clients} en attente en moyenne.')\n\n\n\"\"\"\nMM/1\n\"\"\"\n\ndef etaf(psi, alpha, mu):\n a = psi ** 2\n b = alpha * (1-psi)\n return a/b\n\ntaf = etaf(psi, alpha, mu)\ntaf = round(taf, 3)\nprint(f\"{decimal_to_date(taf)} d'attente moyenne\")\n","repo_name":"mxmaxime/learn-random-numbers","sub_path":"cli_waiting.py","file_name":"cli_waiting.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"41266358597","text":"__author__ = \"Dmitry Dolzhenko\"\n__email__ = \"d.dolzhenko@gmail.com\"\n\n#-------------------------------------------------------------------------------\n\nimport os\n\nfrom jacis import utils, sync\n\n#-------------------------------------------------------------------------------\n\nclass BaseTestCase(utils.TestCase):\n\n def setUp(self):\n self.tmp = utils.temp_work_dir()\n self.tmp.__enter__()\n\n self.repos = {\n \"https://github.com/ddolzhenko/TestGit.git\" : dict(name=\"git-http\", hash=\"aaea772d08e46f700797a79615bb566b1254b48b\"),\n }\n\n def tearDown(self):\n self.tmp.__exit__()\n\n def cute(self, msg):\n return \"{}. CWD: '{}'\".format(msg, self.tmp)\n\n def test_full_repo(self):\n for url, data in self.repos.items():\n with self.subTest(url=url):\n repo = data[\"name\"]\n sync.git_clone(url, repo)\n self.assertPredicate(os.path.isdir, repo, self.cute(\"not a folder\"))\n with utils.work_dir(repo):\n self.assertEqual(utils.checksum('test'), data[\"hash\"], self.cute('folder checksum failed'))\n","repo_name":"ddolzhenko/jacis","sub_path":"jacis/plugins/tests/sync_test.py","file_name":"sync_test.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"76"} +{"seq_id":"33017137348","text":"\"\"\"\n\n\n\"\"\"\n\nimport cffi\nimport copy\nimport os\nimport re\nimport sys\nimport random\nfrom test_core import *\n\nalnum = \"abcdefghijklmnopqrstuvwxyz0123456789\"\n\ndlm = default_load_msgs = TranslationDict()\ndlm.set_msg(\"GenericErrorMsg\", \"fi\", dict(content=\"Koodin lataaminen kirjastona epäonnistui.\\nLisätietoja: {emsg}\"))\ndlm.set_msg(\"GenericErrorMsg\", \"en\", dict(content=\"Failed to load the code as a library.\\nInformation: {emsg}\"))\ndlm.set_msg(\"OSError\", \"fi\", dict(content=\"{emsg}\"))\ndlm.set_msg(\"OSError\", \"en\", dict(content=\"{esmg}\"))\ndlm.set_msg(\"NoStdIO\", \"fi\", dict(content=\"Koodista puuttui stdio-kirjaston include, jota tarvitaan tulostukseen ja syötteiden lukemiseen.\"))\ndlm.set_msg(\"NoStdIO\", \"en\", dict(content=\"Your code is missing an include for stdio which is needed for printing and reading input.\"))\ndlm.set_msg(\"LoadingLibrary\", \"fi\", dict(content=\"Ladataan kooditiedosto {name} kirjastona testattavaksi...\"))\ndlm.set_msg(\"LoadingLibrary\", \"en\", dict(content=\"Loading source code file {name} as a library for testing...\"))\ndlm.set_msg(\"CompileError\", \"fi\", dict(\n content=\"Kooditiedostoa ei voitu kääntää.\",\n hints=[\"Tarkista koneellasi, että koodi kääntyy.\", \"Tarkista, että koodissa on kaikki tehtävänannon tyyppimäärittelyt.\"]\n))\ndlm.set_msg(\"CompileError\", \"en\", dict(\n content=\"Unable to compile the source file.\",\n hints=[\"Make sure the code compiles on your computer.\", \"Make sure you have included all type definitions from the specification.\"]\n))\ndlm.set_msg(\"EncodingError\", \"fi\", dict(\n content=\"Kooditiedostoa ei voitu lukea UTF-8 koodauksella.\",\n hints=[\"Varmista, että tiedoston koodaus on UTF-8 tai poista sieltä kaikki ääkköset ja muut erikoismerkit - myös kommenteista.\"]\n))\ndlm.set_msg(\"EncodingError\", \"en\", dict(\n content=\"Unable to read the code file as UTF-8.\",\n hints=[\"Make sure your code file's encoding is UTF-8, or remove any non-ascii characters (even from comments).\"]\n))\ndlm.set_msg(\"InvalidPrototype\", \"fi\", dict(\n content=\"Yksi tai useampi funktion prototyyppi sisälsi virheitä. Tarkistinjärjestelmä asettaa prototyypeille joitain rajoituksia.\"\n))\ndlm.set_msg(\"InvalidPrototype\", \"en\", dict(\n content=\"One ore more prototypes contained errors. The checking system places certain additional limits for prototypes.\"\n))\n\n \n\nftme = function_test_msg_extension = TranslationDict()\nftme.set_msg(\"OutputEncodingError\", \"fi\", dict(content=\"Koodin tulostus sisälsä lukukelvottomia merkkejä.\"))\nftme.set_msg(\"OutputEncodingError\", \"en\", dict(content=\"Your code's output included garbage characters.\"))\n\ndefault_func_test_msgs.update(ftme)\n\n\n\nproto_pat = re.compile(\"(?:[A-Za-z0-9_]+\\s+)+[A-Za-z0-9_]+\\s*\\((?:[A-Za-z0-9_\\* ]+,?)*\\)\\s*;\")\ncdata_pat = re.compile(\"[A-Za-z0-9_ \\*]+)' owning (?P[0-9]+) bytes>\")\n\nffi = cffi.FFI()\n\ndef gen_random_binary(bits):\n \"\"\"\n gen_random_binary(bits) -> str\n \n This convenience function creates a randomized string with the given number\n of *bits*. Useful for testing functions that perform bitwise operations. \n \"\"\"\n \n i = random.randint(0, 2 ** bits - 1)\n return bin(i)[2:].rjust(bits, \"0\")\n\ndef find_prototypes(content):\n \"\"\"\n find_prototypes(content) -> list\n \n This function locates function prototypes from a .c file. These are needed \n for defining the functions within CFFI. It's a work in progress and at its \n present stage can sometimes fail to find prototypes even if they are \n there. The function is not used for .h files.\n \"\"\"\n \n \n protos = []\n comment = False\n for line in content:\n #if line.strip().endswith(\"{\") and not line.strip().startswith(\"struct\"):\n # break\n line = line.strip()\n \n if \"/*\" in line:\n if \"*/\" not in line:\n comment = True\n if comment and \"*/\" in line:\n comment = False\n \n if not comment:\n line = line.split(\"//\")[0].split(\"/*\")[0].strip()\n if line.endswith(\";\") and not line.startswith(\"return\"):\n if proto_pat.match(line):\n protos.append(line)\n return protos\n\n\ndef aggressive_rounding_validator(ref, res, out):\n \"\"\"\n This convenience validator performs more aggressive rounding validation for\n floats than the rounding validator in the core module. It rounds off all \n decimals. This can sometimes be useful if dealing with results with a lot \n of decimals. \n \"\"\" \n \n assert round(ref) == round(res)\n\ndef input_to_file(content):\n \"\"\"\n input_to_file(content) -> str\n \n This function is used internally to put inputs into a file - the method\n used by the core module (using StringIO) does not change where C code looks\n for its stdin. This function prepares a file that where we can redirect \n stdin for the C code.\n \"\"\"\n \n fn = \"\".join([random.choice(alnum) for i in range(16)])\n with open(fn, \"w\") as f:\n f.write(content + \"\\n\")\n return fn\n\n# https://stackoverflow.com/questions/20000332/repeated-redirection-of-low-level-stdin-in-python\ndef freopen(f, mode, stream):\n \"\"\"\n This function is used internally to redirect stdin and stdout to files. The\n method used by the core module is not sufficient for testing C code which \n is why we need to manipulate file descriptors through the os module instead. \n \"\"\"\n \n oldf = open(f, mode)\n oldfd = oldf.fileno()\n newfd = stream.fileno()\n os.close(newfd)\n os.dup2(oldfd, newfd)\n\ndef default_c_presenter(value):\n \"\"\"\n default_c_presenter(value) -> str\n \n .. deprecated:: 0.5\n \n This is the default presenter used by C function tests. Currently it is \n just a dummy as the presenter system is undergoing modifications. \n \"\"\"\n \n \n if isinstance(value, (list, tuple)):\n parts = []\n for val in value:\n if isinstance(val, ffi.CData):\n parts.append(str(val))\n #ctype = cdata_pat.search(str(val)).groupdict()[\"type\"]\n #if \"*\" in ctype:\n # parts.append(ctype + \"->\" + str(val[0]))\n #elif \"[\" in ctype:\n # pass #array printing\n \n else:\n parts.append(str(val))\n \n return \" \".join(parts) \n \n else:\n if isinstance(value, ffi.CData):\n return value\n #ctype = cdata_pat.search(str(value)).groupdict()[\"type\"]\n #if \"*\" in ctype:\n # return ctype + \"->\" + str(value[0])\n #elif \"[\" in ctype:\n # pass\n else:\n return value \n \ndef default_c_call_presenter(func_name, args):\n \"\"\"\n This function is used for showing the way the student function was called\n during a test. It forms a function call code line using the function name \n and its arguments. If the call would be long (over 80 characters), it is \n split to multiple lines. \n \"\"\"\n \n call = func_name + \"(\"\n if len(str(args)) > 80:\n call += \"\\n\"\n call += \",\\n\".join(\" \" + repr(arg) for arg in args)\n call += \"\\n)\"\n else:\n call += \", \".join(repr(arg) for arg in args)\n call += \");\"\n \n return \"{{{highlight=c\\n\" + call + \"\\n}}}\" \n \ndef default_c_value_presenter(value):\n return repr(value) \n\ndef load_with_verify(st_c_filename, lang=\"en\", custom_msgs={}, typedefs={}, req_stdio=False):\n lib_name, ext = os.path.splitext(st_c_filename)\n msgs = default_load_msgs.copy()\n msgs.update(custom_msgs)\n \n json_output.new_test(msgs.get_msg(\"LoadingLibrary\", lang)[\"content\"].format(name=st_c_filename))\n json_output.new_run() \n \n fd_o = sys.stderr.fileno()\n orig_stderr = os.fdopen(os.dup(fd_o), \"w\")\n \n save = sys.stderr\n \n if typedefs:\n for td in typedefs[lang]:\n ffi.cdef(td)\n \n if os.path.exists(lib_name + \".h\"):\n headers = lib_name + \".h\"\n else:\n headers = lib_name + \".c\"\n \n try:\n with open(headers, encoding=\"utf-8-sig\") as source:\n contents = source.readlines()\n protos = find_prototypes(contents)\n \n ffi.cdef(\"\\n\".join(protos))\n except UnicodeDecodeError:\n output(msgs.get_msg(\"EncodingError\", lang), ERROR)\n return None\n \n \n if req_stdio:\n ffi.cdef(\"FILE* stdout;\")\n ffi.cdef(\"void setbuf(FILE *stream, char *buf);\")\n\n freopen(\"err\", \"w\", sys.stderr)\n\n with open(st_c_filename) as source:\n try:\n st_lib = ffi.verify(source.read())\n except:\n os.dup2(orig_stderr.fileno(), sys.stderr.fileno())\n output(msgs.get_msg(\"CompileError\", lang), ERROR)\n with open(\"err\", \"r\") as f:\n print(f.read())\n return None\n \n os.dup2(orig_stderr.fileno(), sys.stderr.fileno())\n\n if req_stdio:\n try:\n st_lib.setbuf(st_lib.stdout, ffi.NULL)\n except AttributeError:\n output(msgs.get_msg(\"NoStdIO\", lang), ERROR)\n return None\n \n return st_lib\n \n \n \n \n\ndef load_library(st_c_filename, so_name, lang=\"en\", custom_msgs={}, typedefs={}, req_stdio=False):\n \"\"\"\n load_library(st_c_filename, so_name[, lang=\"en\"][, custom_msgs={}][, typedefs={}][, req_stdio=False]) -> CFFI dynamic library object\n \n This function loads the student code as a library so that we can later call\n its functions. The loading has two parts: initializing the CFFI dynamic \n library object, and defining the function headers. Both of these are \n hanled by `Link CFFI `_. \n In order to load the library, *so_name* must match the name given to the \n .so (or dll in Windows) when compiling. The argument is given as a \n dictionary with language codes as keys and corresponding so names as \n values. \n \n In the current implemenation struct definitions and similar are not parsed \n from .c files (but they are parsed from .h files). Instead, if students are \n epxected to use given structs, their definitios should be included in the \n *typedefs* argument. Note that this is only needed for types that need to \n be exposed to the checker - and usually in these situations you should \n already know what they are going to be. E.g. if you need to give pointers \n to structs in the test vector, then the definition of that struct needs to\n be in the *typedefs* dictionary. This dictionary has language codes as its\n keys and definition strings as values. All types should be in one string. \n \n If the student code is expected to print something that needs to be \n evaluated, then *req_stdio* must be set to True. There is a degree of \n mysticism involved in redirecting C stdio to files and setting the flag to\n True performs that particular sorcery. However, it fails if the student \n code does not include stdio. A message is shown in the output in this case.\n \"\"\"\n \n lib_name, ext = os.path.splitext(st_c_filename)\n so_name = so_name.get(lang, so_name[\"en\"])\n msgs = copy.deepcopy(default_load_msgs)\n msgs.update(custom_msgs)\n \n json_output.new_test(msgs.get_msg(\"LoadingLibrary\", lang)[\"content\"].format(name=st_c_filename))\n json_output.new_run() \n\n if \"/\" not in lib_name:\n lib_name = \"./\" + lib_name\n \n try:\n st_lib = ffi.dlopen(\"./\" + so_name + \".so\")\n except:\n etype, evalue, etrace = sys.exc_info()\n ename = evalue.__class__.__name__\n emsg = str(evalue)\n output(msgs.get_msg(ename, lang, default=\"GenericErrorMsg\"), ERROR, ename=ename, emsg=emsg)\n return None\n \n if typedefs:\n for td in typedefs[lang]:\n ffi.cdef(td)\n \n if os.path.exists(lib_name + \".h\"):\n headers = lib_name + \".h\"\n else:\n headers = lib_name + \".c\"\n\n try:\n with open(headers, encoding=\"utf-8-sig\") as source:\n contents = source.readlines()\n protos = find_prototypes(contents)\n \n ffi.cdef(\"\\n\".join(protos)) \n except UnicodeDecodeError:\n output(msgs.get_msg(\"EncodingError\", lang), ERROR)\n return None\n except cffi.api.CDefError:\n output(msgs.get_msg(\"InvalidPrototype\", lang), ERROR)\n return None\n \n \n # magic workaround; without this stdout redirects in the test_c_function function don't work.\n # the workaround sets the C stdout buffer to NULL which forces it to output everything \n # without buffering. \n \n if req_stdio:\n try:\n ffi.cdef(\"FILE* stdout;\")\n ffi.cdef(\"void setbuf(FILE *stream, char *buf);\")\n st_lib.setbuf(st_lib.stdout, ffi.NULL)\n except AttributeError:\n output(msgs.get_msg(\"NoStdIO\", lang), ERROR)\n return None\n \n return st_lib\n \ndefault_c_presenters = {\n \"arg\": default_c_value_presenter,\n \"input\": default_input_presenter, \n \"ref\": default_c_value_presenter,\n \"res\": default_c_value_presenter,\n \"parsed\": default_value_presenter ,\n \"call\": default_c_call_presenter\n}\n \n \ndef test_c_function(st_module, func_names, test_vector, ref_func, lang=\"en\", custom_msgs={}, inputs=[], hide_output=True, test_recurrence=True, ref_needs_inputs=False, error_refs=[], custom_tests=[], info_funcs=[], validator=result_validator, presenter=default_c_presenters, output_parser=default_parser, message_validator=None, result_object_extractor=None, argument_cloner=default_argument_cloner, repeat=1, new_test=default_new_test): \n \"\"\"\n test_c_function(st_module, func_names, test_vector, ref_func[, lang=\"en\"][, kwarg1][, ...])\n \n Tests a student's C function with a set of test vectors, against a reference \n function. The behavior of this function can be customized heavily by using \n callbacks and other optional keyword arguments. All arguments are listed and\n explained below. From the checker development perspective this function is \n almost identical with the core module's test_function. Important differences \n are highlighted. \n \n * *st_module* - a module object that contains the function that's being tested\n * *func_names* - a dictionary that has two character language codes as keys and\n corresponding function name in that language as values\n * *test_vector* - a list of argument vectors or a function that generates the \n the list. This vector must be sequences within a list, where each sequence \n is one test case. Each case vector is unpacked when reference and student \n functions are called. **Important**: all string must be bytestrings.\n * *ref_func* - reference function that gets called with the same arguments as\n the student function to obtain the reference result for each test case.\n * *lang* - language for messages and for finding the student function\n * *custom_msgs* - a TranslationDict object that includes additions/overrides \n to the default function test messages\n * *inputs* - input vectors to be given to the function; must have as many vectors \n as test_vector. Inputs are automatically put into separate lines in a file\n that is redirected to stdin on the operating system level.\n * *ref_needs_inputs* - if set to True, the reference function is given two \n lists instead of unpacking the argument vector for each case. In this case \n the reference function is always called with exactly two parameters: list of \n arguments and list of inputs. Default is False. This behavior is necessary if\n your reference function needs to change its result based on inputs. \n * *validator* - the function that performs the validation of the student function\n return value and/or parsed output against the reference. Validators must use \n assert. The assert's error message is used to retrieve a message from the \n dictionary to show in the output as the test result in case of failure.\n * *message_validator* - a function that validates the student function's raw \n output (as opposed to parsing values from it). This validation is done \n separately from the main validator function. Like the validator, it must use\n assert, and the assert's error message is used to retrieve a message to show. \n * *output_parser* - a function that retrieves data by parsing the student \n function's output. Values obtained by the parser are offered separately from \n the function's return values to the validator. Output parsers can abort the \n test case by raising OutputParseError.\n * *result_object_extractor* - a function that returns a result object that is \n to be used in validation instead of the student function's return value. The\n object can be selected from the argument vector, return value(s) or parsed \n values. If not set, this process will be skipped. Useful for testing functions\n that modify a mutable object. Works for anything that is passed with pointers.\n * *presenter* - a function or a dictionary with any or all of the following keys:\n arg, input, ref, res, parsed. Each key must be paired with a function that \n returns a string. These functions are used to make data structures cleaner in \n the output. See section :ref:`C Presenters ` for more \n information.\n * *error_refs* - a list of false reference functions that will be called if the\n student function output does not match the true reference. These are useful\n for exposing common implementation errors. See \n :ref:`Providing Debug Information ` for more about these \n functions. \n * *custom_tests* - a list of test functions that are called if the test is failed. \n These tests can examine any of the test parameters and raise AssertionError if \n problems are detected. See :ref:`Providing Debug Information ` \n for more about these functions. \n * *info_funcs* - a list of information functions that are called if the test fails.\n These are similar to custom tests, but instead of making asserts, they should \n return a value that is shown in the corresponding output message. See \n :ref:`Providing Debug Information ` for more about these \n functions. \n * *hide_output* - a flag to show/hide student function prints in the test \n output. By default student output is hidden. \n * *test_recurrence* - a flag to enable/disable a convenience test that checks\n if the student code repeatedly returns the same result regardless of \n arguments/inputs given to the function. Default is True. Should be disabled\n for functions that don't return anything to avoid confusing messages.\n * *argument_cloner* - a function that makes a copy of the argument vector for \n two purposes: calling the reference without contaminating a mutable object \n in the arguments; and being able to show the original state of the argument\n vector after the student function has been called. Usually needed for testing \n functions that modify mutable objects. \n * *repeat* - sets the number of times to call the student function before doing\n the evaluation. Default is 1. \n * *new_test* - a function that is called at the start of each test case. Can be\n used to reset the state of persistent objects within the checker. \n \n Test progression is divided into two steps: one-time preparations and actual \n test cases. One-time preparations proceed as follows.\n \n #. The file descriptor of the original sys.stdout is saved so that it can be \n restored later\n #. The messages dictionary is updated with messages received in the custom_msgs\n parameter\n #. Presenter functions are set for different categories\n #. If arguments and inputs are provided as functions, they are called\n #. Output is redirected to a file\n #. Test cases are prepared by obtaining the reference result for each test \n case - i.e. all reference results are obtained before running any tests\n before the student code has a chance to mess with things \n \n The number of test cases is determined from the length of the test vector. Even if \n the tested function takes no arguments, your test vector must contain an empty list\n for each test case! \n \n Each test case is processed as follows. During the test, sys.stdout is restored\n whenever a message is shown to the student.\n \n #. new_test callback is called\n #. Stored output file is cleared and output is redirected to it\n #. If there are inputs, an input file is formed and stdin is redirected to it\n #. A copy of arguments is made using argument_cloner\n #. The student function is called\n \n * If there is an error, the appropriate error message is retrieved from the \n dictionary. Arguments and inputs (if present) are also shown in the output.\n Testing proceeds to the next case.\n \n #. If hide_output is False, the student output is shown in the evaluation \n #. The student function output is parsed\n \n * If there is an error, OutputParseError message is shown along with \n OutputPatternInfo. Arguments and inputs (if present) are also shown in the \n evaluation output. Testing proceeds to the next case. \n \n #. If result_object_extractor has been, the student function's return value\n is replaced by the callback's return value. \n #. The validator is called \n \n * If succcessful, the CorrectResult message is shown in the output.\n * If unsuccessful, the following steps are taken to provide more information\n \n #. A message explaining the problem is shown, along with arguments, inputs \n (if present), the reference result and the student result.\n #. False reference functions are called and validated against the student \n result. A message corresponding to the function name is shown if \n the validation is a match. \n #. Custom test functions are called and appropriate messages are shown if \n they raise AssertionErrors. \n #. If test_recurrence is True, a message is printed if the student function\n returned the same result as the last test.\n #. Information functions are called and their corresponding messages are \n shown in the output, including the information function's return value.\n \n #. If test_messages is True, message_validator is called. \n \n * If successful, the CorrectMessage message is shown in the output.\n * If unsuccessful, a message explaining the problem is shown along with \n the MessageInfo message and the student function's raw output. If arguments\n and inputs have not been shown yet, they are also shown. \n \n #. The temporary input file is deleted\n \"\"\"\n\n\n # One time preparations\n fd_o = sys.stdout.fileno()\n fd_i = sys.stdin.fileno()\n orig_stdout = os.fdopen(os.dup(fd_o), \"w\")\n orig_stdin = os.fdopen(os.dup(fd_i), \"r\")\n\n save = sys.stdout\n msgs = default_func_test_msgs.copy()\n msgs.update(custom_msgs)\n\n # Set specific presenters to use generic presenter if not given\n if isinstance(presenter, dict):\n arg_presenter = presenter.get(\"arg\", default_c_value_presenter)\n input_presenter = presenter.get(\"input\", default_input_presenter)\n ref_presenter = presenter.get(\"ref\", default_c_value_presenter)\n res_presenter = presenter.get(\"res\", default_c_value_presenter) \n parsed_presenter = presenter.get(\"parsed\", default_value_presenter)\n call_presenter = presenter.get(\"call\", default_c_call_presenter)\n else: \n arg_presenter = presenter\n input_presenter = presenter\n ref_presenter = presenter\n res_presenter = presenter\n parsed_presenter = presenter\n call_presenter = presenter\n \n if inspect.isfunction(test_vector):\n test_vector = test_vector()\n \n if inspect.isfunction(inputs):\n inputs = inputs()\n \n \n json_output.new_test(msgs.get_msg(\"FunctionName\", lang)[\"content\"].format(name=func_names[lang]))\n \n tests = []\n if ref_needs_inputs:\n test_vector = zip(test_vector, inputs)\n for v, i in test_vector:\n tests.append((v, ref_func(argument_cloner(v), i)))\n else: \n for v in test_vector:\n tests.append((v, ref_func(*argument_cloner(v))))\n \n prev_res = None\n prev_out = None\n \n # Running tests\n for i, test in enumerate(tests): \n json_output.new_run()\n freopen(\"output\", \"w\", sys.stdout)\n \n # Test preparations\n args, ref = test\n new_test(args, inputs)\n \n try:\n inps = inputs[i] * repeat\n fn = input_to_file(\"\\n\".join([str(x) for x in inps]))\n freopen(fn, \"r\", sys.stdin)\n except IndexError:\n inps = []\n \n stored_args = argument_cloner(args)\n \n # Calling the student function\n try:\n st_func = getattr(st_module, func_names[lang])\n for i in range(repeat):\n res = st_func(*args)\n except:\n os.dup2(orig_stdout.fileno(), sys.stdout.fileno())\n etype, evalue, etrace = sys.exc_info()\n ename = evalue.__class__.__name__\n emsg = str(evalue)\n output(msgs.get_msg(ename, lang, default=\"GenericErrorMsg\"), ERROR, args=arg_presenter(stored_args), inputs=input_presenter(inps), emsg=emsg, ename=ename)\n output(msgs.get_msg(\"PrintTestVector\", lang), DEBUG, args=arg_presenter(stored_args), call=call_presenter(func_names[lang], stored_args), inputs=input_presenter(inps))\n if inputs:\n output(msgs.get_msg(\"PrintInputVector\", lang), DEBUG, inputs=input_presenter(inps))\n return\n \n # Validating function results\n os.dup2(orig_stdout.fileno(), sys.stdout.fileno())\n os.dup2(orig_stdin.fileno(), sys.stdin.fileno())\n try:\n with open(\"output\", \"r\") as f:\n out_content = f.read()\n except UnicodeDecodeError:\n output(msgs.get_msg(\"OutputEncodingError\", lang), ERROR)\n return\n\n values_printed = False\n \n if not hide_output:\n output(msgs.get_msg(\"PrintStudentOutput\", lang), DEBUG, output=out_content)\n\n try:\n st_out = output_parser(out_content)\n \n except OutputParseError as e:\n output(msgs.get_msg(\"OutputParseError\", lang), INCORRECT, args=arg_presenter(stored_args), inputs=input_presenter(inps), out=out_content, reason=e.msg)\n output(msgs.get_msg(\"PrintTestVector\", lang), DEBUG, args=arg_presenter(stored_args), call=call_presenter(func_names[lang], stored_args), inputs=input_presenter(inps))\n if inputs:\n output(msgs.get_msg(\"PrintInputVector\", lang), DEBUG, inputs=input_presenter(inps))\n output(msgs.get_msg(\"OutputPatternInfo\", lang), INFO)\n continue\n \n # The evaluated result must include an object that was changed during the function call\n if result_object_extractor:\n res = result_object_extractor(args, res, st_out)\n \n try: \n validator(ref, res, st_out)\n output(msgs.get_msg(\"CorrectResult\", lang), CORRECT)\n except AssertionError as e:\n output(msgs.get_msg(e, lang, \"IncorrectResult\"), INCORRECT)\n output(msgs.get_msg(\"PrintTestVector\", lang), DEBUG, args=arg_presenter(stored_args), call=call_presenter(func_names[lang], stored_args), inputs=input_presenter(inps))\n if inputs:\n output(msgs.get_msg(\"PrintInputVector\", lang), DEBUG, inputs=input_presenter(inps))\n output(msgs.get_msg(\"PrintStudentResult\", lang), DEBUG, res=res_presenter(res), parsed=parsed_presenter(st_out), output=out_content)\n output(msgs.get_msg(\"PrintReference\", lang), DEBUG, ref=ref_presenter(ref))\n values_printed = True\n if error_refs or custom_tests or test_recurrence:\n output(msgs.get_msg(\"AdditionalTests\", lang), INFO)\n for eref_func in error_refs:\n if ref_needs_inputs:\n eref = eref_func(argument_cloner(stored_args), inps)\n else:\n eref = eref_func(*argument_cloner(stored_args))\n try: \n validator(eref, res, st_out)\n output(msgs.get_msg(eref_func.__name__, lang), INFO)\n except AssertionError as e: \n pass\n for test in custom_tests: \n try: \n test(res, st_out, out_content, ref, stored_args, inps)\n except AssertionError as e:\n output(msgs.get_msg(e, lang, test.__name__), INFO)\n if test_recurrence and (res == prev_res or st_out and st_out == prev_out):\n output(msgs.get_msg(\"RepeatingResult\", lang), INFO)\n \n if info_funcs:\n output(msgs.get_msg(\"AdditionalInfo\", lang), INFO)\n for info_func in info_funcs:\n output(msgs.get_msg(info_func.__name__, lang), INFO, func_res=info_func(res, st_out, out_content, ref, stored_args, inps))\n else:\n output(msgs.get_msg(\"PrintTestVector\", lang), DEBUG, args=arg_presenter(stored_args), call=call_presenter(func_names[lang], stored_args), inputs=input_presenter(inps))\n if inputs:\n output(msgs.get_msg(\"PrintInputVector\", lang), DEBUG, inputs=input_presenter(inps))\n output(msgs.get_msg(\"PrintStudentResult\", lang), DEBUG, res=res_presenter(res), parsed=parsed_presenter(st_out), output=out_content) \n \n \n if message_validator:\n try: \n message_validator(out_content, stored_args, inps)\n output(msgs.get_msg(\"CorrectMessage\", lang), CORRECT)\n except AssertionError as e: \n output(msgs.get_msg(e, lang, \"IncorrectMessage\"), INCORRECT)\n output(msgs.get_msg(\"MessageInfo\", lang), INFO)\n output(msgs.get_msg(\"PrintStudentOutput\", lang), INFO, output=out_content)\n if not values_printed:\n output(msgs.get_msg(\"PrintTestVector\", lang), DEBUG, args=arg_presenter(stored_args), call=call_presenter(func_names[lang], stored_args), inputs=input_presenter(inps))\n \n \n prev_res = res\n prev_out = st_out\n if inps:\n os.remove(fn)\n\n \nif __name__ == \"__main__\":\n st_lib = load_library(\"testlib\")\n print(dir(st_lib))\n print(type(st_lib.simple_add))\n \n \n ","repo_name":"enkwolf/PySenpai","sub_path":"c_extension.py","file_name":"c_extension.py","file_ext":"py","file_size_in_byte":31267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"5848990083","text":"items=[\"Samosa\", \"Idli\", \"Maggie\", \"Dosa\", \"Tea\", \"Coffee\", \"Sandwich\", \"ColdDrink\"]\r\nprice=[15,30,50,70,10,20,35,35]\r\nfor i in range(len(items)):\r\n print((i+1), items[i], price[i])\r\narr=[]\r\nc=0\r\nt=0\r\nfor i in range(len(items)):\r\n arr.append(0)\r\nwhile True:\r\n try:\r\n i,n=map(int,input().split())\r\n arr[i-1]+=n\r\n c+=n*price[i-1]\r\n t+=n\r\n except ValueError:\r\n False\r\n for i in range(len(items)):\r\n if arr[i]>0:\r\n print(items[i],\",\", arr[i],\",\", \"Rs\", price[i]*arr[i])\r\n print('\\n')\r\n print(\"TOTAL\",\",\", t, \"items\",\",\", \"Rs\", c)","repo_name":"regular-life/Introduction-to-Programming-Assignments-and-Projects","sub_path":"2. Question 1.py","file_name":"2. Question 1.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"5333967988","text":"import requests\nfrom enum import StrEnum, auto\nfrom typing import Any\n\n\nfrom pydantic import BaseModel\nfrom dotenv import dotenv_values\n\n\nclass InvalidAPIKeyError(Exception):\n pass\n\n\nclass NoBedwarsStatsError(Exception):\n pass\n\n\nclass APIThrottleError(Exception):\n pass\n\n\nclass BedwarsMode(StrEnum):\n OVERALL = auto()\n EIGHT_ONE = auto()\n EIGHT_TWO = auto()\n FOURS_THREE = auto()\n FOURS_FOUR = auto()\n\n\nclass BedwarsStats(BaseModel):\n final_kills_bedwars: int\n final_deaths_bedwars: int\n kills_bedwars: int\n deaths_bedwars: int\n beds_broken_bedwars: int\n beds_lost_bedwars: int\n wins_bedwars: int\n losses_bedwars: int\n\n\nclass Bedwars:\n def __init__(self, api_key: str, player_name: str) -> None:\n self._key = api_key\n self.player_name = player_name\n self._response = self._handle_request()\n\n def _handle_request(self) -> requests.Response:\n response = requests.get(\n \"https://api.hypixel.net/player\",\n params={\"name\": self.player_name},\n headers={\"API-Key\": self._key}\n )\n \n if response.status_code == 403:\n raise InvalidAPIKeyError(\"Please provide a valid API key.\")\n elif response.status_code == 429:\n raise APIThrottleError(\n \"Please wait two minutes before calling the API with the same player name.\"\n )\n\n \n return response\n\n def stats(self, mode: BedwarsMode) -> BedwarsStats:\n json = self._response.json()\n stats = json.get(\"player\", {}).get(\"stats\", {}).get(\"Bedwars\")\n if stats is None:\n raise NoBedwarsStatsError(f\"Player '{self.player_name}' hasn't played bedwars.\")\n \n fields = BedwarsStats.__fields__.keys()\n \n if mode == BedwarsMode.OVERALL:\n return BedwarsStats(**{field: stats.get(field) for field in fields})\n \n true_stats = {\n field: stats.get(f\"{mode}_{field}\")\n for field in fields\n }\n\n return BedwarsStats(**true_stats)\n\n\nif __name__ == \"__main__\":\n env = dotenv_values(\".env\")\n key = env[\"API_KEY\"]\n if key is None:\n exit(-1)\n\n bw = Bedwars(key, \"Fuhts\")\n print(bw.stats(BedwarsMode.OVERALL))","repo_name":"abigfutz/bwstats","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"23299512348","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os, sys, re, time\nimport subprocess\nimport platform\n\nworkpath = sys.path[0]\nserverDir = '../server/src/proto'\nclientDir = '../client/Assets/Scripts/Game/Proto'\n\nproto_list = []\nsession_msg = {}\n\ndef id(name):\n\tn = 0\n\tfor c in 'proto.' + name:\n\t\ti = ord(c)\n\t\tn += (n << 5) + (i << 7) + i\n\tn &= 0xFFFF\n\treturn n\n\ndef checkProtoFiles():\n\tfor root, _, files in os.walk('./'):\n\t\tfor f in files:\n\t\t\tif not f.endswith('.proto'):\n\t\t\t\tcontinue\n\t\t\tproto_list.append(f)\n\t\ndef genGo():\n\tsysstr = platform.system()\n\tif sysstr == 'Windows':\n\t\tsuffix = '.exe'\n\telse:\n\t\tsuffix = ''\n\tsource = ' '.join(proto_list)\n\tcmd = 'protoc --plugin=protoc-gen-gogofaster=protoc-gen-gogofaster{} --gogofaster_out={} {}'.format(suffix, serverDir, source)\n\tprint(cmd)\n\tsubprocess.call(cmd.split(' '))\n\ndef genMsgInfo():\n\tremsg = re.compile(r\"(?P(//.*\\s)*)message\\s+(?P[a-zA-Z]+)\\s*\\{\")\n\tmsgs = []\n\tcoms = {}\n\tfor name in proto_list:\n\t\twith open(name, 'r', encoding='utf-8') as f:\n\t\t\tc = f.read()\n\t\t\tm = remsg.search(c)\n\t\t\tn = len(c)\n\t\t\twhile m != None:\n\t\t\t\ti = m.end(0)\n\t\t\t\tb = 1\n\t\t\t\twhile i < n:\n\t\t\t\t\tif c[i] == '{':\n\t\t\t\t\t\tb += 1\n\t\t\t\t\t\t#print('b=',b)\n\t\t\t\t\telif c[i] == '}':\n\t\t\t\t\t\tb -= 1\n\t\t\t\t\t\t#print('b=',b)\n\t\t\t\t\telif c[i] == 'u':\n\t\t\t\t\t\ts = 'uint32 session'\n\t\t\t\t\t\tif c[i:i+len(s)] == s:\n\t\t\t\t\t\t\tsession_msg[m.group('msg')] = True\n\t\t\t\t\ti += 1\n\t\t\t\t\tif b == 0:\n\t\t\t\t\t\tbreak\n\t\t\t\tif b != 0:\n\t\t\t\t\tprint('syntax error')\n\t\t\t\t\tbreak\n\n\t\t\t\tmsg = m.group('msg')\n\t\t\t\tcom = m.group('com')\n\t\t\t\t#print(\"com\", com, msg)\n\t\t\t\tmsgs.append(msg)\n\t\t\t\tif com :\n\t\t\t\t\tcoms[msg] = com#'\\n\t'.join(com.split('\\n'))\n\t\t\t\telse:\n\t\t\t\t\tcoms[msg] = ''\n\t\t\t\tm = remsg.search(c, i)\n\treturn msgs, coms\n\ndef genGoMsg(msgs, coms):\n\tTem = u'''// Generated by cat/proto/gen_proto.py\n// DO NOT EDIT!\n// Gen Time: {time}\n\npackage proto\n\nimport (\n\t\"github.com/davyxu/cellnet\"\n\t\"github.com/davyxu/cellnet/codec\"\n\t\"reflect\"\n)\n\nconst (\n{const}\n)\n\nfunc init() {{\n{reg}\n}}\n'''\n\tTem_const = u'''\t{com}Key{name} int = {msgid}'''\n\tTem_reg = u'''\t\tcellnet.RegisterMessageMeta(&cellnet.MessageMeta{{\n\t\t\tCodec: codec.MustGetCodec(\"gogopb\"),\n\t\t\tType: reflect.TypeOf((*{name})(nil)).Elem(),\n\t\t\tID: {msgid},\n\t\t}})'''\n\n\tw = '{}/proto.msg.go'.format(serverDir)\n\tprint('gen ' + w)\n\twith open(w, 'w', encoding='utf-8') as f:\n\t\treg = []\n\t\tconst = []\n\t\tfor m in msgs:\n\t\t\td = {'name':m, 'msgid':id(m), 'com':'\\n\t'.join(coms[m].strip().split('\\n'))}\n\t\t\tif d['com']:\n\t\t\t\td['com'] = d['com']+'\\n\\t'\n\t\t\treg.append(Tem_reg.format(**d))\n\t\t\tconst.append(Tem_const.format(**d))\n\t\t\t# print(d, Tem_const.format(**d))\n\t\tc = Tem.format(**{'time':time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()), \n\t\t\t'reg':'\\n'.join(reg), 'const':'\\n'.join(const)})\n\t\tf.write(c)\n\ndef genCsharpMsg(msgs, coms):\n\tTem = u'''// Generated by cat/proto/gen_proto.py\n// DO NOT EDIT!\n// Gen Time: {time}\n\nusing AM.Game;\n\nnamespace Proto {{\n\n\tpublic enum Keys {{\n{const}\n\t}}\n\n\tpublic static class ProtoMsg {{\n\t\tpublic static void init() {{\n{reg}\n\t\t}}\n\t}}\n\t\n\tpublic interface ISession {{\n\t\tuint Session {{ get; set; }}\n\t\tint Err {{ get; set; }}\n\t}}\n\n{interface}\n\n}}\n'''\n\tTem_const = u'''\t\t{com}\n\t\t{name} = {msgid},'''\n\tTem_reg = u'''\t\t\tMsgMetaSet.RegMsg({msgid}, typeof({name}), ()=>new {name}());'''\n\tTem_interface = u'''\tpublic partial class {name} : ISession {{}}'''\n\tw = '{}/Proto.msg.cs'.format(clientDir)\n\tprint('gen ' + w)\n\twith open(w, 'w', encoding='utf-8') as f:\n\t\treg = []\n\t\tconst = []\n\t\tinterface = []\n\t\t# print(session_msg)\n\t\tfor m in msgs:\n\t\t\td = {'name':m, 'msgid':id(m), 'com':'\\n\t\t'.join(coms[m].strip().split('\\n'))}\n\t\t\t# print(Tem_reg.format(**d))\n\t\t\treg.append(Tem_reg.format(**d))\n\t\t\t# d['com'] = ','.join(coms[m].strip().split('\\n//'))\n\t\t\tconst.append(Tem_const.format(**d))\n\t\t\t# print(Tem_const.format(**d))\n\t\t\tif (m in session_msg):\n\t\t\t\tinterface.append(Tem_interface.format(**d))\n\t\t\t\t# print(interface[len(interface)-1])\n\t\td = {'time':time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()), \n\t\t\t'reg':'\\n'.join(reg), 'const':'\\n'.join(const), 'interface':'\\n'.join(interface)}\n\t\tc = Tem.format(**d)\n\t\tf.write(c)\n\ndef genCsharp():\n\tsource = ' '.join(proto_list)\n\tcmd = 'protoc --csharp_out={} {}'.format(clientDir, source)\n\tprint(cmd)\n\tsubprocess.call(cmd.split(' '))\n\ndef main():\n\tif len(sys.argv) != 2:\n\t\tprint(\"arg error! uses: ./gen_proto.py client|server|all\")\n\t\treturn\n\topt = sys.argv[1]\n\tprint(workpath)\n\tos.chdir(workpath)\n\tcheckProtoFiles()\n\tmsgs, coms = genMsgInfo()\n\tif opt == 'client' or opt == 'all':\n\t\tif not os.path.exists(clientDir):\n\t\t\tos.makedirs(clientDir)\n\t\tgenCsharp()\n\t\tgenCsharpMsg(msgs, coms)\n\tif opt == 'server' or opt == 'all':\n\t\tif not os.path.exists(serverDir):\n\t\t\tos.makedirs(serverDir)\n\t\tgenGo()\n\t\tgenGoMsg(msgs, coms)\n\nif __name__ == '__main__':\n\ttry:\n\t\tmain()\n\texcept:\n\t\timport traceback\n\t\ttraceback.print_exc()\n\t\tinput('runtime error')\n","repo_name":"meamin9/cat","sub_path":"proto/gen_proto.py","file_name":"gen_proto.py","file_ext":"py","file_size_in_byte":4820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"73613228726","text":"from __future__ import absolute_import, print_function\n\nimport pytest\nfrom wtforms.validators import ValidationError\n\nfrom invenio_pages import InvenioPages, Page\nfrom invenio_pages.admin import same_page_choosen, template_exists\nfrom invenio_pages.views import blueprint\n\n\ndef test_template_exists(app):\n \"\"\"Test field validator.\"\"\"\n InvenioPages(app)\n app.register_blueprint(blueprint)\n\n class Field(object):\n def __init__(self, data):\n self.data = data\n\n with app.app_context():\n with pytest.raises(ValidationError):\n template_exists(None, Field('inexistent_template'))\n template_exists(None, Field('invenio_pages/base.html'))\n template_exists(None, Field('invenio_pages/default.html'))\n template_exists(None, Field('invenio_pages/edit.html'))\n\n\ndef test_same_page_choosen(app):\n \"\"\"Test same page choosen.\"\"\"\n def mock(attr, value):\n class Mock(object):\n pass\n setattr(Mock, attr, value)\n return Mock\n\n form = mock('_obj', mock('list_id', '1'))\n field = mock('data', mock('id', '1'))\n pytest.raises(ValidationError, same_page_choosen, form, field)\n\n\ndef test_pages_admin(admin_fixture):\n \"\"\"Test field validator.\"\"\"\n app = admin_fixture\n InvenioPages(app)\n app.register_blueprint(blueprint)\n\n with app.test_request_context():\n with app.test_client() as client:\n resp = client.get('/admin/page/')\n assert resp.status_code == 200\n for page in Page.query.all():\n assert page.url in str(resp.get_data())\n assert page.title in str(resp.get_data())\n resp = client.get('/admin/page/new/')\n assert resp.status_code == 200\n","repo_name":"invenio-toaster/invenio-pages","sub_path":"tests/test_admin.py","file_name":"test_admin.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"1405494747","text":"class Solution(object):\n \n # dynamic programming: O(Nk2) time, O(nk) space\n def minCostII(self, costs):\n \"\"\"\n :type costs: List[List[int]]\n :rtype: int\n \"\"\"\n if len(costs) == 0 or len(costs[0]) == 0:\n return 0\n n, k = len(costs), len(costs[0])\n # dp = [costs[0] for __ in xrange(n)] # BUG: each dp[i] will remain the same update \n # dp = [list(costs[0]) for __ in xrange(n)] # right way to copy without dependency \n\n dp = [[0 for _ in xrange(k)] for __ in xrange(n)] # smallest cost of painting each color so far\n dp[0] = costs[0] # base case\n for i in xrange(1, n):\n for j in xrange(k):\n dp[i][j] = min(dp[i - 1][:j] + dp[i - 1][j + 1:]) + costs[i][j]\n return min(dp[-1])\n\n\n # better dynamic programming: O(Nk) time, O(1) space\n def minCostII(self, costs):\n if len(costs) == 0 or len(costs[0]) == 0:\n return 0\n n, k = len(costs), len(costs[0])\n min1, min2 = (0, 0), (0, 1) # keep the smallest two paints\n \n for i in xrange(n):\n min1_i, min2_i = (float('inf'), -1), (float('inf'), -1) \n for j in xrange(k):\n curr = (min1[0] if min1[1] != j else min2[0]) + costs[i][j]\n \n if curr <= min1_i[0]:\n min2_i, min1_i = min1_i, (curr, j)\n elif curr <= min2_i[0]:\n min2_i = (curr, j)\n \n min1, min2 = min1_i, min2_i \n return min1[0]\n \n \n ","repo_name":"haomingchan0811/Leetcode","sub_path":"265. Paint House II.py","file_name":"265. Paint House II.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"42866539932","text":"from flask import flash, redirect, render_template, url_for, request, jsonify, Response, send_file\nfrom flask_login import current_user, login_required\nfrom dataclasses import dataclass\nimport os\nimport uuid\ntry:\n import app\nexcept:\n from ... import app\nimport random\nimport csv\nimport tempfile\nfrom io import StringIO, BytesIO\nimport zipfile\nimport json\n\nfrom . import api\nfrom .. import util\nfrom ..models import *\n\n@api.route('show_count', methods=['GET', 'POST'])\n@login_required\ndef show_count():\n try:\n x = request.form['name']\n x = globals()[x]\n count = db.session.query(x).count()\n except Exception:\n count = 2137\n\n return jsonify(count)\n\n\n@api.route('send_report', methods=['GET', 'POST'])\n@login_required\ndef send_report():\n x = request.form['type']\n y = request.form['desc']\n\n if x == 'bug' or x == 'info' or x == 'other':\n bug = Info(type=x, description=y, uid=current_user.id)\n\n try:\n db.session.add(bug)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n\n\n# verify is it really an image\n\n@api.route('send_profile_photo', methods=['GET', 'POST'])\n@login_required\ndef send_profile_photo():\n x = request.data\n file_ext = str(uuid.uuid4()) + '.jpg'\n open(('./app/static/user_content/profile_photo/' + file_ext), 'xb').write(x)\n\n n = Employee.query.get_or_404(current_user.get_id())\n n.profile_photo = file_ext\n\n util.LogEx('PPC', current_user.id, 'Zmieniono zdjęcie profilowe')\n\n try:\n db.session.add(n)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n\n\n@api.route('send_bg_photo', methods=['GET', 'POST'])\n@login_required\ndef send_bg_photo():\n util.check_admin()\n x = request.data\n file_ext = str(uuid.uuid4()) + '.jpg'\n open(('./app/static/user_content/background_photo/' + file_ext), 'xb').write(x)\n\n util.LogEx('BGC', current_user.id, 'Zmieniono zdjęcie tła')\n\n n = Setting.query.filter_by(name=\"bg_photo\").first()\n n.value = 'user_content/background_photo/' + file_ext\n\n try:\n db.session.add(n)\n db.session.commit()\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n try:\n s = Setting.query.filter_by(name=\"is_def_bg\").first()\n s.value = 0\n db.session.add(s)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n else:\n return 'BAD', 400\n\n\n@api.route('send_appname', methods=['GET', 'POST'])\n@login_required\ndef send_appname():\n util.check_admin()\n x = request.form['name']\n\n n = Setting.query.filter_by(name=\"app_name\").first()\n n.value = x\n\n util.LogEx('ANC', current_user.id, 'Zmieniono nazwę aplikacji')\n\n try:\n db.session.add(n)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n else:\n return 'BAD', 400\n\n\n@api.route('send_footer_photo', methods=['GET', 'POST'])\n@login_required\ndef send_footer_photo():\n util.check_admin()\n x = request.data\n file_ext = str(uuid.uuid4()) + '.jpg'\n open(('./app/static/user_content/footer_photo/' + file_ext), 'xb').write(x)\n\n n = Setting.query.filter_by(name=\"footer_photo\").first()\n n.value = 'user_content/footer_photo/' + file_ext\n\n util.LogEx('FPC', current_user.id, 'Zmieniono zdjęcie stopki')\n\n try:\n db.session.add(n)\n db.session.commit()\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n try:\n s = Setting.query.filter_by(name=\"is_def_footer\").first()\n s.value = 0\n db.session.add(s)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n else:\n return 'BAD', 400\n\n\n@api.route('send_login_photo', methods=['GET', 'POST'])\n@login_required\ndef send_login_photo():\n util.check_admin()\n x = request.data\n file_ext = str(uuid.uuid4()) + '.jpg'\n open(('./app/static/user_content/login_photo/' + file_ext), 'xb').write(x)\n\n n = Setting.query.filter_by(name=\"login_photo\").first()\n n.value = 'user_content/login_photo/' + file_ext\n\n util.LogEx('LPC', current_user.id, 'Zmieniono zdjęcie logowania')\n\n try:\n db.session.add(n)\n db.session.commit()\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n try:\n s = Setting.query.filter_by(name=\"is_def_login\").first()\n s.value = 0\n db.session.add(s)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n\n else:\n return 'BAD', 400\n\n\n@api.route('list_employees', methods=['GET', 'POST'])\n@login_required\ndef list_employees():\n employees = []\n util.check_admin()\n\n for e in Employee.query.all():\n xe = {}\n perm = PermissionUser.query.filter_by(userid=e.id).all()\n if e.is_admin:\n employees.append(e)\n else:\n pms = []\n for p in perm:\n dep = Department.query.filter_by(id=p.permissionid).first()\n if dep:\n pms.append({'id': dep.id, 'name': dep.name, 'p': p.relationid})\n else:\n pass\n if not pms:\n pms = e.is_admin\n\n xe['id'] = e.id\n xe['first_name'] = e.first_name\n xe['last_name'] = e.last_name\n xe['email'] = e.email\n xe['profile_photo'] = e.profile_photo\n xe['username'] = e.username\n xe['is_admin'] = pms\n employees.append(xe)\n\n return jsonify(employees)\n\n\n@api.route('list_departments', methods=['GET', 'POST'])\n@login_required\ndef list_departments():\n departments = Department.query.all()\n\n if current_user.is_admin:\n pass\n else:\n perm = PermissionUser.query.filter_by(userid=current_user.id).all()\n\n employees = Object.query.all()\n conn = RoleUser.query.all()\n role = Role.query.all()\n did = []\n departments = Department.query.all()\n\n if current_user.is_admin:\n for dp in departments:\n dd = [dp.id, dp.name]\n did.append(dd)\n else:\n for dp in departments:\n for p in perm:\n if dp.id == p.permissionid:\n dd = [dp.id, dp.name]\n did.append(dd)\n\n return jsonify(did)\n\n\n@api.route('list_roles', methods=['GET', 'POST'])\n@login_required\ndef list_roles():\n roles = Role.query.all()\n n = []\n\n for r in roles:\n owning = RoleUser.query.filter_by(roleid=r.id).count()\n m = {'id': r.id, 'name': r.name, 'description': r.description, 'value': r.value, 'multiple': r.multiple,\n 'parent_id': r.parent_id, 'owning': owning}\n n.append(m)\n\n return jsonify(n)\n\n\n@api.route('get_depart_by_id', methods=['GET', 'POST'])\n@login_required\ndef get_depart_by_id():\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'ERR', 400\n\n util.check_admin('Point Group List', request.form['id'])\n\n try:\n department = Department.query.get_or_404(id)\n except Exception:\n return 'ERR', 400\n\n return jsonify(department.name, department.description, department.master_name)\n\n@api.route('update_depart_by_id', methods=['GET', 'POST'])\n@login_required\ndef update_depart_by_id():\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'ERR', 400\n\n util.check_admin('Point Group List', request.form['id'])\n\n try:\n department = Department.query.get_or_404(id)\n except Exception:\n return 'ERR', 400\n\n try:\n if request.form['name']:\n department.name = request.form['name']\n except Exception:\n pass\n try:\n if request.form['description']:\n department.description = request.form['description']\n except Exception:\n pass\n try:\n if request.form['master_name']:\n department.master_name = request.form['master_name']\n except Exception:\n pass\n\n try:\n db.session.add(department)\n db.session.commit()\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n@api.route('list_role_parents', methods=['GET', 'POST'])\n@login_required\ndef list_role_parents():\n roles = RoleParent.query.all()\n\n return jsonify(roles)\n\n\n@api.route('list_reports', methods=['GET', 'POST'])\n@login_required\ndef list_reports():\n util.check_admin()\n\n try:\n if request.form['limit']:\n x = request.form['limit']\n reports = Info.query.all()[-x]\n except:\n reports = Info.query.all()\n\n return jsonify(reports)\n\n\n@api.route('remove_report', methods=['GET', 'POST'])\n@login_required\ndef remove_report():\n util.check_admin()\n\n try:\n if request.form['id']:\n x = request.form['id']\n conn = Info.query.get_or_404(x)\n db.session.delete(conn)\n db.session.commit()\n\n util.LogEx('RRC', current_user.id, 'Usunięto raport')\n\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'ERR', 400\n\n return jsonify(\"OK\")\n\n\n@api.route('create_student', methods=['GET', 'POST'])\n@login_required\ndef create_student():\n x = request.form['type']\n y = request.form['desc']\n\n if x == 'bug' or x == 'info' or x == 'other':\n bug = Info(type=x, description=y)\n\n try:\n db.session.add(bug)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n\n\n@api.route('create_depart', methods=['GET', 'POST'])\n@login_required\ndef create_depart():\n x = request.form['name']\n y = request.form['desc']\n\n dep = Department(name=x, description=y)\n\n try:\n db.session.add(dep)\n db.session.commit()\n v = PermissionUser(userid=current_user.id, permissionid=dep.id)\n db.session.add(v)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n\n\n@api.route('create_category', methods=['GET', 'POST'])\n@login_required\ndef create_category():\n util.check_admin()\n\n x = request.form['name']\n y = request.form['desc']\n z = request.form['value']\n aa = request.form['times']\n ab = request.form['parent']\n\n print(aa, ab)\n\n if aa == 'false':\n b = True\n else:\n b = False\n\n cat = Role(name=x, description=y, value=z, multiple=b, parent_id=ab)\n\n util.LogEx('CRC', current_user.id, 'Utworzono kategorię')\n\n try:\n db.session.add(cat)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n\n\n@api.route('create_category_parent', methods=['GET', 'POST'])\n@login_required\ndef create_category_parent():\n util.check_admin()\n\n x = request.form['name']\n y = request.form['color']\n\n cat = RoleParent(name=x, color=y)\n\n util.LogEx('CRCP', current_user.id, 'Utworzono kategorię nadrzędną')\n\n try:\n db.session.add(cat)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n\n\n@api.route('get_depart_objects', methods=['GET', 'POST'])\n@login_required\ndef get_depart_objects():\n objects = []\n\n if current_user.is_admin:\n try:\n if request.form['id']:\n x = request.form['id']\n o = Object.query.filter_by(department_id=x).all()\n objects.append(o)\n print(o)\n except Exception as e:\n util.handleException(e)\n return 'ERR', 400\n else:\n z = PermissionUser.query.filter_by(userid=current_user.id, permissionid=request.form['id']).first_or_404()\n if z:\n x = request.form['id']\n o = Object.query.filter_by(department_id=x).all()\n objects.append(o)\n\n return jsonify(objects)\n\n\n@api.route('add_depart_object', methods=['GET', 'POST'])\n@login_required\ndef add_depart_object():\n try:\n if request.form['id']:\n util.check_admin('Point Group List', request.form['id'])\n x = request.form['id']\n q = request.form['first_name']\n y = request.form['last_name']\n z = request.form['comment']\n cat = Object(first_name=q, last_name=y, comment=z, department_id=x)\n\n util.LogEx('AOC', current_user.id, 'Utworzono obiekt')\n\n try:\n db.session.add(cat)\n db.session.commit()\n return 'OK', 200\n except Exception as e:\n util.handleException(e)\n return 'BAD', 400\n else:\n return 'BAD', 400\n except Exception as e:\n util.handleException(e)\n return 'ERR', 400\n\n return 'ERR', 400\n\n\n@api.route('get_object_points_for_view', methods=['GET', 'POST'])\n@login_required\ndef get_object_points_for_view():\n points = []\n try:\n if request.form['id']:\n util.check_admin('Point Group List', request.form['id'])\n x = request.form['id']\n o = Object.query.filter_by(id=x).first()\n c = RoleUser.query.filter_by(userid=x).all()\n r = Role.query.all()\n for x in r:\n for y in c:\n if x.id == y.roleid:\n v = []\n v.append(x.name)\n v.append(x.value)\n points.append(v)\n except Exception as e:\n util.handleException(e)\n return 'ERR', 400\n\n return jsonify(points)\n\n\n@api.route('get_object_points_for_view2', methods=['GET', 'POST'])\n@login_required\ndef get_object_points_for_view2():\n points = []\n\n try:\n if request.form['id']:\n util.check_admin('Point Group List', request.form['id'])\n x = request.form['id']\n if not Role.query.count():\n return 'Dodaj najpiew jakieś role', 420\n r = Role.query.all()\n for t in r:\n m = RoleUser.query.filter_by(userid=x, roleid=t.id).first()\n if m:\n v = []\n v.append(t.id)\n v.append(m.value)\n v.append(t.value)\n points.append(v)\n print(v)\n else:\n v = []\n v.append(t.id)\n v.append(0)\n v.append(t.value)\n points.append(v)\n print(v)\n except Exception as e:\n util.handleException(e)\n return 'ERR', 400\n\n return jsonify(points)\n\n\n@api.route('get_logs', methods=['GET', 'POST'])\n@login_required\ndef get_logs():\n util.check_admin()\n points = []\n\n o = Log.query.all()\n\n util.LogEx('GLC', current_user.id, 'Wyświetlono logi')\n\n for x in o:\n points.append(x.comment)\n\n return jsonify(points)\n\n\n@api.route('remove_logs', methods=['GET', 'POST'])\n@login_required\ndef remove_logs():\n util.check_admin()\n\n util.LogEx('RLC', current_user.id, 'Usunięto logi')\n\n db.session.query(Log).delete()\n db.session.commit()\n\n return 'OK', 200\n\n\n@api.route('get_rapports', methods=['GET', 'POST'])\n@login_required\ndef get_rapports():\n util.check_admin()\n points = []\n\n util.LogEx('GRC', current_user.id, 'Wyświetlono raporty')\n\n o = Info.query.all()\n\n return jsonify(o)\n\n\n@api.route('remove_rapports', methods=['GET', 'POST'])\n@login_required\ndef remove_rapports():\n util.check_admin()\n\n util.LogEx('RRC', current_user.id, 'Usunięto raporty')\n\n db.session.query(Info).delete()\n db.session.commit()\n\n return 'OK', 200\n\n\n@api.route('remove_employee', methods=['GET', 'POST'])\n@login_required\ndef delete_user():\n util.check_admin()\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'ERR', 400\n\n util.LogEx('RUC', current_user.id, 'Usunięto użytkownika')\n\n if int(id) == int(current_user.id):\n return 'Nie można usuwać samego siebie', 400\n elif int(id) != int(current_user.id):\n try:\n employee = Employee.query.get_or_404(id)\n db.session.delete(employee)\n db.session.commit()\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('remove_category', methods=['GET', 'POST'])\n@login_required\ndef delete_category():\n util.check_admin()\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'Błędne zapytanie', 400\n\n util.LogEx('RCC', current_user.id, 'Usunięto kategorię')\n\n try:\n category = RoleParent.query.get_or_404(id)\n db.session.delete(category)\n db.session.commit()\n except Exception:\n return 'Wystąpił jakiś błąd po drodze', 400\n\n return 'OK', 200\n\n\n@api.route('remove_class', methods=['GET', 'POST'])\n@login_required\ndef delete_class():\n util.check_admin('Point Group List', request.form['id'])\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'Błędne zapytanie', 400\n\n util.LogEx('RDC', current_user.id, 'Usunięto klasę')\n\n objects_to_delete = Object.query.filter_by(department_id=id).all()\n for obj in objects_to_delete:\n db.session.delete(obj)\n\n db.session.commit()\n\n try:\n category = Department.query.get_or_404(id)\n db.session.delete(category)\n objects_to_delete = Object.query.filter_by(department_id=id).all()\n for obj in objects_to_delete:\n db.session.delete(obj)\n\n db.session.commit()\n\n except Exception:\n return 'Wystąpił jakiś błąd po drodze', 400\n\n return 'OK', 200\n\n\n@api.route('remove_student', methods=['GET', 'POST'])\n@login_required\ndef delete_student():\n util.check_admin('Point Group List', request.form['id'])\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'Błędne zapytanie', 400\n\n util.LogEx('RSC', current_user.id, 'Usunięto obiekt')\n\n try:\n category = Object.query.get_or_404(id)\n db.session.delete(category)\n db.session.commit()\n except Exception:\n return 'Wystąpił jakiś błąd po drodze', 400\n\n return 'OK', 200\n\n\n@api.route('remove_subcategory', methods=['GET', 'POST'])\n@login_required\ndef delete_subcategory():\n util.check_admin()\n\n if request.form['id']:\n id = request.form['id']\n else:\n return 'ERR', 400\n\n try:\n category = Role.query.get_or_404(id)\n db.session.delete(category)\n db.session.commit()\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('get_design', methods=['GET', 'POST'])\ndef get_design():\n try:\n v = Setting.query.filter_by(name='version').first()\n v.value = util.version()\n db.session.add(v)\n db.session.commit()\n except Exception:\n print('Cannot update version')\n try:\n name = Setting.query.filter_by(name=\"app_name\").first()\n author = Setting.query.filter_by(name=\"author\").first()\n version = Setting.query.filter_by(name=\"version\").first()\n url1 = Setting.query.filter_by(name=\"bg_photo\").first()\n url2 = Setting.query.filter_by(name=\"footer_photo\").first()\n url3 = Setting.query.filter_by(name=\"login_photo\").first()\n url1d = Setting.query.filter_by(name=\"is_def_bg\").first()\n url2d = Setting.query.filter_by(name=\"is_def_footer\").first()\n url3d = Setting.query.filter_by(name=\"is_def_login\").first()\n except Exception:\n return 'ERR', 400\n\n return jsonify(name.value, author.value, version.value, url1.value, url2.value, url3.value, url1d.value, url2d.value, url3d.value)\n\n\n@api.route('get_value', methods=['GET', 'POST'])\ndef get_value():\n #TODO: personals\n try:\n returned = []\n if request.form['type']:\n t = request.form['type'].split()\n else:\n return 'ERR', 400\n for i in t:\n name = Setting.query.filter_by(name=i).first()\n returned.append(name.value)\n except Exception:\n return 'ERR', 400\n\n return jsonify(returned)\n\n\n@api.route('add_point', methods=['GET', 'POST'])\n@login_required\ndef add_point():\n try:\n if request.form['value']:\n v = request.form['value']\n v = int(v)\n else:\n return 'ERR', 400\n if request.form['id']:\n i = request.form['id']\n else:\n return 'ERR', 400\n if request.form['user']:\n t = request.form['user']\n else:\n return 'ERR', 400\n except Exception:\n return 'ERR', 400\n try:\n p = RoleUser.query.filter_by(userid=t, roleid=i).first()\n u = Object.query.filter_by(id=t).first()\n x = util.check_admin('Point Group List', u.department_id)\n if p:\n r = Role.query.filter_by(id=i).first()\n if r.multiple:\n p.value = p.value + v\n p.addedby = current_user.id\n else:\n if v == 0 or v == 1:\n p.value = v\n p.addedby = current_user.id\n db.session.commit()\n else:\n r = Role.query.filter_by(id=i).first()\n if r.multiple:\n p = RoleUser(userid=t, roleid=i, value=v, addedby=current_user.id)\n else:\n if v == 0 or v == 1:\n p = RoleUser(userid=t, roleid=i, value=v, addedby=current_user.id)\n db.session.add(p)\n db.session.commit()\n except Exception as e:\n util.handleException(e)\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('set_value', methods=['GET', 'POST'])\n@login_required\ndef set_value():\n try:\n if util.check_admin():\n if request.form['type']:\n t = request.form['type']\n else:\n return 'ERR', 400\n if request.form['value']:\n v = request.form['value']\n else:\n return 'ERR', 400\n\n s = Setting.query.filter_by(name=t).first()\n s.value = v\n db.session.add(s)\n db.session.commit()\n else:\n #TODO: personals\n return 'ERR', 400\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('update_object', methods=['GET', 'POST'])\n@login_required\ndef update_object():\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['key']:\n u = request.form['key']\n else:\n return 'ERR', 400\n if request.form['value']:\n v = request.form['value']\n else:\n return 'ERR', 400\n\n s = Object.query.filter_by(id=t).first()\n util.check_admin('Point Group List', s.department_id)\n util.LogEx('UOC', current_user.id, 'Zmieniono obiekt')\n setattr(s, u, v)\n\n db.session.add(s)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('update_employee', methods=['GET', 'POST'])\n@login_required\ndef update_employee():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['key']:\n u = request.form['key']\n else:\n return 'ERR', 400\n if request.form['value']:\n v = request.form['value']\n else:\n return 'ERR', 400\n\n s = Employee.query.filter_by(id=t).first()\n setattr(s, u, v)\n\n util.LogEx('UEC', current_user.id, 'Zmieniono użytkownika')\n\n db.session.add(s)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('add_object_note', methods=['GET', 'POST'])\n@login_required\ndef add_object_note():\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['note']:\n u = request.form['note']\n else:\n return 'ERR', 400\n try:\n if request.form['value']:\n v = request.form['value']\n else:\n v = 0\n except Exception:\n v = 0\n\n n = Note()\n n.value = v\n n.body = u\n n.user_id = t\n\n o = Object.query.filter_by(id=t).first()\n util.check_admin('Point Group List', o.department_id)\n util.LogEx('AONC', current_user.id, 'Dodano notatkę')\n\n db.session.add(n)\n db.session.commit()\n return 'OK', 200\n except Exception:\n return 'ERR', 400\n\n\n@api.route('remove_object_note', methods=['GET', 'POST'])\n@login_required\ndef remove_object_note():\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n\n n = Note.query.filter_by(id=t).first()\n o = Object.query.filter_by(id=n.user_id).first()\n util.check_admin('Point Group List', o.department_id)\n util.LogEx('RONC', current_user.id, 'Usunięto notatkę')\n\n db.session.delete(n)\n db.session.commit()\n return 'OK', 200\n except Exception:\n return 'ERR', 400\n\n\n@api.route('get_object_notes', methods=['GET', 'POST'])\n@login_required\ndef get_object_notes():\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n\n n = Note.query.filter_by(user_id=t).all()\n o = Object.query.filter_by(id=t).first()\n util.check_admin('Point Group List', o.department_id)\n notes = []\n for x in n:\n note = []\n note.append(x.id)\n note.append(x.body)\n note.append(x.value)\n notes.append(note)\n\n return jsonify(notes)\n except Exception:\n return 'ERR', 400\n\n\n@api.route('update_category', methods=['GET', 'POST'])\n@login_required\ndef update_category():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['key']:\n u = request.form['key']\n else:\n return 'ERR', 400\n if request.form['value']:\n v = request.form['value']\n else:\n return 'ERR', 400\n\n s = RoleParent.query.filter_by(id=t).first()\n setattr(s, u, v)\n\n util.LogEx('UCC', current_user.id, 'Zmieniono kategorię')\n\n db.session.add(s)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('create_employee', methods=['POST'])\n@login_required\ndef create_employee():\n try:\n first_name = request.form.get('first_name', '')\n last_name = request.form.get('last_name', '')\n email = request.form.get('email', '')\n password = request.form.get('password', '')\n username = request.form.get('username', f'{first_name[:3]}{last_name[:3]}{random.randint(10, 90)}')\n is_admin = bool(request.form.get('is_admin', False))\n\n e = Employee(first_name=first_name, last_name=last_name, email=email, username=username, is_admin=is_admin)\n #TODO: hide it\n util.LogEx('CEC', current_user.id, f'Utworzono użytkownika {password}')\n e.password = password\n\n db.session.add(e)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n\n@api.route('get_my_data', methods=['GET', 'POST'])\n@login_required\ndef get_my_data():\n try:\n t = Employee.query.get_or_404(current_user.get_id())\n\n data = {'id': t.id, 'first_name': t.first_name, 'last_name': t.last_name, 'email': t.email, 'username': t.username, 'profile_photo': t.profile_photo, 'is_admin': t.is_admin}\n\n return jsonify(data)\n except Exception:\n return 'ERR', 400\n\n\n@api.route('update_subcategory', methods=['GET', 'POST'])\n@login_required\ndef update_subcategory():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['key']:\n u = request.form['key']\n else:\n return 'ERR', 400\n if request.form['value']:\n v = request.form['value']\n else:\n return 'ERR', 400\n\n if u == 'multiple':\n if v == 'true' or v == 1:\n v = True\n else:\n v = False\n\n s = Role.query.filter_by(id=t).first()\n setattr(s, u, v)\n\n db.session.add(s)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n@api.route('update_password', methods=['GET', 'POST'])\n@login_required\ndef update_password():\n try:\n t = Employee.query.get_or_404(current_user.get_id())\n\n if request.form['password']:\n u = request.form['password']\n else:\n return 'ERR', 400\n\n util.LogEx('UPC', current_user.id, 'Zmieniono hasło')\n\n t.password = u\n db.session.add(t)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n@api.route('move_student', methods=['GET', 'POST'])\n@login_required\ndef move_student():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['depart']:\n v = request.form['depart']\n else:\n return 'ERR', 400\n\n s = Object.query.filter_by(id=t).first()\n s.department_id = v\n\n db.session.add(s)\n db.session.commit()\n\n except Exception:\n return 'ERR', 400\n\n return 'OK', 200\n\n@api.route('download_logs')\n@login_required\ndef download_logs():\n util.check_admin()\n try:\n data = Log.query.all()\n\n # Create a CSV string using StringIO\n csv_output = StringIO()\n csv_writer = csv.writer(csv_output)\n\n # Write header\n header = [column.name for column in Log.__table__.columns]\n csv_writer.writerow(header)\n\n # Write data\n for row in data:\n csv_writer.writerow([getattr(row, column) for column in header])\n\n util.LogEx('DLC', current_user.id, 'Pobrano logi')\n\n # Create a Flask Response with CSV data\n response = Response(csv_output.getvalue(), mimetype='text/csv')\n response.headers['Content-Disposition'] = 'attachment; filename=data.csv'\n\n return response\n except Exception:\n return 'ERR', 400\n\n\n@api.route('list_with_category_in_class', methods=['GET', 'POST'])\n@login_required\ndef list_with_category_in_class():\n if request.form['class_id']:\n l = request.form['class_id']\n else:\n return 'ERR', 400\n\n util.check_admin('Point Group List', request.form['class_id'])\n\n if request.form['role_id']:\n t = request.form['role_id']\n else:\n return 'ERR', 400\n\n data = RoleUser.query.filter_by(roleid=t).all()\n objects = Object.query.filter_by(department_id=l).all()\n res_objects = []\n for x in objects:\n for y in data:\n if x.id == y.userid:\n res_objects.append([x.id, x.first_name, x.last_name, y.value])\n\n return jsonify(res_objects)\n\n@api.route('/export_data', methods=['POST'])\n@login_required\ndef export_data():\n util.check_admin()\n if request.form['type'] == 'zip':\n try:\n zip_buffer = tempfile.SpooledTemporaryFile(max_size=10 * 1024 * 1024) # Adjust the max_size as needed\n\n with zipfile.ZipFile(zip_buffer, 'a', zipfile.ZIP_DEFLATED, False) as zip_file:\n export_table_to_csv(zip_file, 'pracownicy.csv', Employee)\n export_table_to_csv(zip_file, 'klasy.csv', Department)\n export_table_to_csv(zip_file, 'uczniowie.csv', Object)\n export_table_to_csv(zip_file, 'kategorie.csv', RoleParent)\n export_table_to_csv(zip_file, 'podkategorie.csv', Role)\n export_table_to_csv(zip_file, 'przypisania.csv', RoleUser)\n export_table_to_csv(zip_file, 'przypisania_klas.csv', PermissionUser)\n export_table_to_csv(zip_file, 'notatki.csv', Note)\n export_table_to_csv(zip_file, 'raporty.csv', Info)\n export_table_to_csv(zip_file, 'logi.csv', Log)\n export_table_to_csv(zip_file, 'ustawienia.csv', Setting)\n export_table_to_csv(zip_file, 'ustawienia_personalne.csv', PersonalSettingOverride)\n\n zip_buffer.seek(0)\n return send_file(\n zip_buffer,\n mimetype='application/zip',\n as_attachment=True,\n download_name='exported_data.zip'\n )\n except Exception as e:\n print(f\"Error: {e}\")\n return \"Error occurred while exporting data\", 500\n elif request.form['type'] == 'sql':\n return send_file('../instance/main.db', as_attachment=True)\n else:\n return \"Invalid export type\", 400\n\ndef export_table_to_csv(zip_file, filename, model):\n with tempfile.NamedTemporaryFile(mode='w+', delete=False) as temp_csv:\n csv_writer = csv.writer(temp_csv)\n header = [column.name for column in model.__table__.columns]\n csv_writer.writerow(header)\n for row in model.query.all():\n csv_writer.writerow([getattr(row, column) for column in header])\n temp_csv.seek(0)\n zip_file.write(temp_csv.name, filename)\n\n\n@api.route('add_permission', methods=['GET', 'POST'])\n@login_required\ndef add_permission():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n if request.form['user']:\n u = request.form['user']\n else:\n return 'ERR', 400\n\n s = PermissionUser.query.filter_by(userid=u, permissionid=t).first()\n if not s:\n s = PermissionUser(userid=u, permissionid=t)\n db.session.add(s)\n db.session.commit()\n\n return 'OK', 200\n except Exception:\n return 'ERR', 400\n\n@api.route('remove_permission', methods=['GET', 'POST'])\n@login_required\ndef remove_permission():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n\n s = PermissionUser.query.filter_by(relationid=t).first()\n if s:\n db.session.delete(s)\n db.session.commit()\n\n return 'OK', 200\n except Exception:\n return 'ERR', 400\n\n\n@api.route('admin_switch', methods=['GET', 'POST'])\n@login_required\ndef admin_switch():\n util.check_admin()\n try:\n if request.form['id']:\n t = request.form['id']\n else:\n return 'ERR', 400\n\n util.LogEx('ASC', current_user.id, f'Zmieniono uprawnienia dla ID {id}')\n\n s = Employee.query.filter_by(id=t).first()\n if s:\n if s.is_admin:\n s.is_admin = False\n else:\n s.is_admin = True\n db.session.add(s)\n db.session.commit()\n\n return 'OK', 200\n except Exception:\n return 'ERR', 400\n\n","repo_name":"21warolkojtyla37/CalculatorZSE-public","sub_path":"app/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":36074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"44500873637","text":"import time\nfrom package import *\nimport datetime\nfrom operator import attrgetter\nfrom instruction import InstructionType\n\n\nclass System:\n \"\"\"\n System - класс, представляющий собой операционную систему, функционирующую в\n пакетном режиме.\n \"\"\"\n def __init__(self, logger):\n self.__tasks = []\n self.__current_time = 0\n self.__logger = logger\n\n @property\n def logger(self):\n return self.__logger\n\n @property\n def tasks(self):\n return self.__tasks\n \n @property\n def last_session_work_time(self):\n return self.__current_time\n \n def calculate_instructions_count_where(self, predicate):\n count = 0\n for task in self.__tasks:\n for inst in task.instructions:\n if predicate(inst) == True:\n count += 1\n return count\n \n\n def get_count_of_IO_instructions(self):\n return self.calculate_instructions_count_where(lambda inst: True if inst.instructionType == InstructionType.IO else False)\n \n def get_count_of_Process_instructions(self):\n return self.calculate_instructions_count_where(lambda inst: True if inst.instructionType == InstructionType.Process else False)\n \n\n def configure(self, config_function):\n config_function(self)\n\n def add_task(self, package):\n self.__tasks.append(package)\n\n def remove_task(self, package):\n self.__tasks.remove(package)\n\n def count_and_time_of_all_instructions_could_be_executed_by_system(self, max_execution_time):\n instructions = []\n for task in self.__tasks:\n instructions.extend(task.instructions)\n instructions.sort(key=lambda inst: inst.duration)\n count = 0\n common_time = 0\n for inst in instructions:\n if (common_time + inst.duration) > max_execution_time:\n break\n else:\n common_time += inst.duration\n count += 1\n return count, common_time\n\n def compare_instructions(instruction_a, instruction_b):\n return attrgetter('__duration')(instruction_a) - attrgetter('__duration')(instruction_b)\n\n def start(self, max_execution_time):\n self.__current_time = 0\n amount_of_executed_instructions = 0\n amount_of_executed_tasks = 0\n working_time_of_all_executed_instructions = 0\n execution_time = 0\n productivity = 0.0 # производительность\n working_time = 0 # оборотное время\n processor_downtime = 0 # время простоя процессора\n now = datetime.datetime.now()\n self.__logger.info(f\"Система запущена в {now.strftime('%d-%m-%d %H:%M:%S')}\")\n timing = time.time()\n # пока в очереди задач еще что-то есть\n while self.__tasks and self.__current_time < max_execution_time:\n current_task = self.__tasks.pop(0)\n current_task.execute()\n amount_of_executed_instructions += current_task.last_session_instructions_executed\n working_time_of_all_executed_instructions += current_task.last_session_execution_time\n # если задача отдала управление, но не закончилась\n if current_task.status == PackageStatus.Interrupted:\n self.__tasks.append(current_task)\n elif current_task.status == PackageStatus.Completed:\n amount_of_executed_tasks += 1\n execution_time += current_task.current_working_time\n self.__current_time = time.time() - timing\n now = datetime.datetime.now()\n self.__logger.info(f\"Система остановлена в {now.strftime('%d-%m-%d %H:%M:%S')}\")\n \n # время простоя процессора\n system_time = self.__current_time if self.__current_time > max_execution_time else max_execution_time \n processor_downtime = system_time - working_time_of_all_executed_instructions\n if processor_downtime < 0:\n processor_downtime = 0\n elif processor_downtime > system_time:\n processor_downtime = system_time\n\n # производительность \n productivity = amount_of_executed_instructions / system_time\n\n # оборотное время\n if amount_of_executed_tasks == 0:\n working_time = 0\n else:\n working_time = execution_time / amount_of_executed_tasks\n\n return productivity, working_time, processor_downtime\n","repo_name":"DanilRukin/OperationalSystems","sub_path":"lr_1/src/packet_system.py","file_name":"packet_system.py","file_ext":"py","file_size_in_byte":4691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"20004273042","text":"if __name__ == '__main__':\n\n year = int(input())\n\n def is_leap(year):\n leap = False\n # Write your logic here\n if year % 4 == 0 and year % 100 != 0:\n leap = True\n elif year % 4 == 0 and year % 100 == 0:\n if year % 400 == 0:\n leap = True\n\n return leap\n\n print(is_leap(year))\n\n","repo_name":"jeffthebrink/HackerRankPython","sub_path":"WriteAFunction.py","file_name":"WriteAFunction.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"76"} +{"seq_id":"74116328884","text":"__author__ = \"Tyler D. Hoffman cause@tdhoffman.com\"\n\n\"\"\"\nSets up simulation classes to be reused in experiments.\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport libpysal.weights as weights\nfrom scipy.special import expit\nfrom spopt.region import RandomRegion\n\n\nclass Simulator:\n def __init__(self, Nlat, D, sp_confound=None, interference=None):\n \"\"\"\n Template class for data simulation.\n Provides initialization for input parameters and prescribes base functionality.\n\n Nlat : int\n side length of the lattice\n D : int\n number of covariates to generate\n sp_confound : matrix, default None\n matrix specifying the mode of confounding between locations\n interference : matrix, string, or int, default None\n matrix specifying the mode of interference between locations\n string options:\n - \"general\": interference between all locations\n - \"partial\": interference only among locations in clusters\n generated using RandomRegion. Recommended to select the clusters\n yourself and manually input the adjacency matrix (block diagonal)\n as the RandomRegion call takes a while. If int, then generates\n that number of clusters.\n - \"network\": interference among adjacent locations using Queen weights\n - \"none\": no interference, same as passing None\n \"\"\"\n\n self.Nlat = Nlat\n self.N = Nlat ** 2\n self.D = D\n self.sp_confound = sp_confound\n\n # Parse interference options\n if type(interference) == str:\n interference = interference.lower()\n\n if interference is not None and interference != \"none\":\n if isinstance(interference, str) and interference == \"general\":\n interference = np.ones((self.N, self.N))\n elif isinstance(interference, str) and interference == \"network\":\n interference = weights.lat2W(Nlat, Nlat, rook=False).full()[0]\n elif isinstance(interference, int) or (\n isinstance(interference, str) and interference == \"partial\"\n ):\n W = weights.lat2W(Nlat, Nlat, rook=False)\n\n if type(interference) == int:\n nregs = interference\n else:\n nregs = np.random.randint(low=4, high=10)\n t1 = RandomRegion(\n W.id_order, num_regions=nregs, contiguity=W, compact=True\n )\n\n source = []\n dest = []\n for region in t1.regions:\n region = set(region)\n for node in region:\n source.append(node)\n dest += [i for i in region.difference({node})]\n adjlist = pd.DataFrame(\n columns=[\"source\", \"dest\"], data=np.dstack((source, dest))\n )\n\n interference = weights.W.from_adjlist(adjlist)\n elif isinstance(interference, weights.W):\n interference = interference.full()[0]\n elif isinstance(interference, np.ndarray):\n pass\n else:\n raise ValueError(\"Unknown kind of interference\")\n\n # Enforce row-standardization:\n interference /= interference.sum(1, keepdims=1)\n self.interference = interference\n\n def simulate(self, **kwargs):\n \"\"\"\n Simulate data based on some parameters.\n\n Subclass this method based on your desired functionality.\n \"\"\"\n\n return ValueError(\"Subclass this method\")\n\n def _create_Y(self, X, Z, treat, yconf, sp_yconf, interf, eps_sd, **kwargs):\n \"\"\"\n Generate Y based on parameters, confounders X, and treatment Z.\n \"\"\"\n\n return ValueError(\"Subclass this method\")\n\n def _create_Z(self, X, zconf, zcar_sd, sp_zconf, **kwargs):\n \"\"\"\n Generate Z based on parameters and confounders X.\n \"\"\"\n\n return ValueError(\"Subclass this method\")\n\n\nclass CARSimulator(Simulator):\n def simulate(\n self,\n treat=0.5,\n z_conf=0.25,\n y_conf=0.5,\n interf=0,\n x_sd=1,\n x_sp=0.9,\n ucar_sd=2,\n ucar_str=0.95,\n vcar_sd=2,\n vcar_str=0.95,\n balance=0.5,\n y_sd=0.1,\n **kwargs\n ):\n \"\"\"\n Simulate data based on some parameters.\n All the conf and interf parameters could be arrays of size D\n if different variables have different levels of confounding or interference.\n\n **Parameters**\n treat : float, default 0.5\n treatment effect of Z on Y\n z_conf : float, default 0.25\n effect of nonspatial confounding on Z\n y_conf : float, default 0.5\n effect of nonspatial confounding on Y\n interf : float, default 0\n effect of interference on Y\n x_sd : float, default 1\n standard deviation of confounders\n x_sp : float, default 0.9\n spatial autocorrelation parameter\n y_sd : float, default 0.1\n SD of nonspatial error term on Y\n ucar_sd : float, default 2\n SD of CAR term for confounding on Y\n vcar_sd : float, default 2\n SD of CAR term for confounding on Z\n ucar_str : float, default 0.95\n strength of spatial association for confounding on Y\n vcar_str : float, default 0.95\n strength of spatial association for confounding on Z\n balance : float, default 0.5\n balancing factor that parametrizes the shared\n spatial confounding between Y and Z\n\n **Returns**\n X : covariates (NxD)\n Y : outcomes (Nx1)\n Z : treatment (Nx1)\n \"\"\"\n\n if np.ndim(x_sd) == 0:\n x_sd = x_sd * np.ones((self.D, 1))\n if np.ndim(z_conf) == 0:\n z_conf = z_conf * np.ones((self.D, 1))\n if np.ndim(y_conf) == 0:\n y_conf = y_conf * np.ones((self.D, 1))\n\n # Confounders\n X = np.zeros((self.N, self.D))\n for d in range(self.D):\n X[:, d] = np.random.uniform(low=-x_sd[d], high=x_sd[d], size=(self.N,))\n\n # Create Queen weights and give X a little autocorrelation\n W = weights.lat2W(self.Nlat, self.Nlat, rook=False)\n W.transform = \"r\"\n W = W.full()[0]\n X = np.dot(np.linalg.inv(np.eye(self.N) - x_sp * W), X)\n\n # Set up CAR terms for spatial confounding on Y and Z\n if self.sp_confound is not None:\n self.sp_confound.transform = (\n \"r\" # row standardize so we don't need to use row sums\n )\n cov_u = (ucar_sd ** 2) * np.linalg.solve(\n np.eye(self.N) - ucar_str * self.sp_confound.sparse, np.eye(self.N)\n )\n cov_v = (vcar_sd ** 2) * np.linalg.solve(\n np.eye(self.N) - vcar_str * self.sp_confound.sparse, np.eye(self.N)\n )\n U = np.dot(cov_u, np.random.normal(size=(self.N, 1)))\n V = np.dot(cov_v, np.random.normal(size=(self.N, 1)))\n else:\n U = np.zeros((self.N, 1))\n V = np.zeros((self.N, 1))\n\n # Make propensity scores and generate Z\n prop_scores = self._create_Z(X, U, V, z_conf, balance)\n Z = np.random.binomial(1, prop_scores).reshape(-1, 1)\n\n # Make means and generate Y\n Ymeans = self._create_Y(X, Z, U, treat, y_conf, interf)\n Y = np.random.normal(Ymeans, scale=y_sd)\n\n # Compute treated percentage\n self.treated_pct = (Z == 1).sum() / self.N\n return X, Y, Z\n\n def _create_Z(self, X, U, V, z_conf, balance):\n return expit(np.dot(X, z_conf) + V + balance * U)\n\n def _create_Y(self, X, Z, U, treat, y_conf, interf):\n means = Z * treat + np.dot(X, y_conf) + U\n if self.interference is not None:\n means += interf * np.dot(self.interference, Z)\n return means\n\n\nif __name__ == \"__main__\":\n ## Imports\n import numpy as np\n import matplotlib.pyplot as plt\n from rundown import Simulator, FriedmanSimulator\n from libpysal import weights\n from spopt.region import RegionKMeansHeuristic\n\n Nlat = 40\n D = 2\n\n ## Nonspatial linear simulation (scenario 1)\n sim = Simulator(Nlat, D)\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Nonspatial nonlinear simulation (scenario 2)\n sim = FriedmanSimulator(Nlat, D)\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded linear simulation (scenario 3)\n sp_confound = weights.lat2W(Nlat, Nlat, rook=True).full()[0]\n sim = Simulator(Nlat, D, sp_confound=sp_confound)\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded nonlinear simulation (scenario 4)\n sim = FriedmanSimulator(Nlat, D, sp_confound=sp_confound)\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Linear partial spatial interference (scenario 5)\n data = np.vstack(\n (\n np.hstack(\n (np.ones((Nlat // 2, Nlat // 2)), 2 * np.ones((Nlat // 2, Nlat // 2)))\n ),\n np.hstack(\n (\n 3 * np.ones((Nlat // 2, Nlat // 2)),\n 4 * np.ones((Nlat // 2, Nlat // 2)),\n )\n ),\n )\n )\n interference = np.zeros((Nlat ** 2, Nlat ** 2))\n\n for p in range(Nlat ** 2):\n i1, j1 = np.unravel_index(p, (Nlat, Nlat))\n for q in range(Nlat ** 2):\n i2, j2 = np.unravel_index(q, (Nlat, Nlat))\n if data[i1, j1] == data[i2, j2]:\n interference[p, q] = 1\n\n # Plot regions\n _, ax = plt.subplots()\n plt.imshow(data)\n ax.set_xticks([])\n ax.set_yticks([])\n for x in np.arange(-0.5, 29, 1):\n plt.plot([x, x], [-0.5, 29.5], \"k\", linewidth=0.75)\n for y in np.arange(-0.5, 29, 1):\n plt.plot([-0.5, 29.5], [y, y], \"k\", linewidth=0.75)\n plt.title(\"Regions for weights construction\")\n plt.show()\n\n sim = Simulator(Nlat, D, interference=interference)\n X, Y, Z = sim.simulate(treat=0.2)\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Nonlinear partial spatial interference simulation (scenario 6)\n sim = FriedmanSimulator(Nlat, D, interference=interference)\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded partial spatial interference (scenario 7)\n sim = Simulator(Nlat, D, sp_confound=sp_confound, interference=interference)\n X, Y, Z = sim.simulate(treat=0.2)\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Nonlinear spatially confounded partial spatial interference simulation (scenario 8)\n sim = FriedmanSimulator(Nlat, D, sp_confound=sp_confound, interference=interference)\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Linear general spatial interference (scenario 9)\n sim = Simulator(Nlat, D, interference=\"general\")\n X, Y, Z = sim.simulate(treat=0.2)\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Nonlinear general spatial interference (scenario 10)\n sim = FriedmanSimulator(Nlat, D, interference=\"general\")\n X, Y, Z = sim.simulate(treat=0.5)\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded linear general spatial interference (scenario 11)\n sim = Simulator(Nlat, D, sp_confound=sp_confound, interference=\"general\")\n X, Y, Z = sim.simulate(treat=0.2)\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded nonlinear general spatial interference (scenario 12)\n sim = FriedmanSimulator(Nlat, D, sp_confound=sp_confound, interference=\"general\")\n X, Y, Z = sim.simulate(treat=0.2)\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Linear network spatial interference (scenario 13)\n sim = Simulator(Nlat, D, interference=\"network\")\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Nonlinear network spatial interference (scenario 14)\n sim = FriedmanSimulator(Nlat, D, interference=\"network\")\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded linear network spatial interference (scenario 15)\n sim = Simulator(Nlat, D, sp_confound=sp_confound, interference=\"network\")\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n ## Spatially confounded nonlinear network spatial interference (scenario 16)\n sim = FriedmanSimulator(Nlat, D, sp_confound=sp_confound, interference=\"network\")\n X, Y, Z = sim.simulate()\n\n _, axes = plt.subplots(ncols=3)\n axes[0].imshow(X[:, 0].reshape(Nlat, Nlat))\n axes[1].imshow(Y.reshape(Nlat, Nlat))\n axes[2].imshow(Z.reshape(Nlat, Nlat))\n plt.show()\n\n\n# Old confounder code:\n# means = np.random.choice(np.arange(-2 * self.D, 2 * self.D + 1, 1, dtype=int),\n# size=self.D, replace=False)\n# X = np.zeros((self.N, self.D))\n# for d in range(self.D):\n# X[:, d] = np.random.normal(loc=means[d], scale=x_sd[d], size=(self.N,))\n# X[:, d] = np.dot(np.linalg.inv(np.eye(self.N) - x_sp * W), X[:, d])\n","repo_name":"tdhoffman/spycause","sub_path":"spycause/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":15803,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"76"}