diff --git "a/4583.jsonl" "b/4583.jsonl" new file mode 100644--- /dev/null +++ "b/4583.jsonl" @@ -0,0 +1,758 @@ +{"seq_id":"304643351","text":"# sentiment.py\n\nimport argparse\nfrom models import *\nfrom model_LSTM import *\nfrom model_CNN import *\nfrom sentiment_data import *\n\ndef _parse_args():\n \"\"\"\n Command-line arguments to the system. --model switches between the main modes you'll need to use. The other arguments\n are provided for convenience.\n :return: the parsed args bundle\n \"\"\"\n parser = argparse.ArgumentParser(description='trainer.py')\n parser.add_argument('--model', type=str, default='CNN', help='model to run (FF or FANCY or CNN)')\n parser.add_argument('--word_vecs_path', type=str, default='data/glove.6B.50d-relativized.txt', help='path to word vectors file')\n parser.add_argument('--word_vecs_dim', type=int, default=300, help='dimention of word vector embeddings')\n parser.add_argument('--train_path', type=str, default='data/train.txt', help='path to train set (you should not need to modify)')\n parser.add_argument('--dev_path', type=str, default='data/dev.txt', help='path to dev set (you should not need to modify)')\n parser.add_argument('--blind_test_path', type=str, default='data/test-blind.txt', help='path to blind test set (you should not need to modify)')\n parser.add_argument('--test_output_path', type=str, default='test-blind.output.txt', help='output path for test predictions')\n parser.add_argument('--no_run_on_test', dest='run_on_test', default=True, action='store_false', help='skip printing output on the test set')\n args = parser.parse_args()\n return args\n\nif __name__ == '__main__':\n args = _parse_args()\n print(args)\n # Use either 50-dim or 300-dim vectors\n #word_vectors = read_word_embeddings(args.word_vecs_path)\n if args.word_vecs_dim == 50:\n word_vectors = read_word_embeddings('data/glove.6B.50d-relativized.txt')\n elif args.word_vecs_dim == 300:\n word_vectors = read_word_embeddings('data/glove.6B.300d-relativized.txt')\n\n # Load train, dev, and test exs\n train_exs = read_and_index_sentiment_examples(args.train_path, word_vectors.word_indexer)\n dev_exs = read_and_index_sentiment_examples(args.dev_path, word_vectors.word_indexer)\n test_exs = read_and_index_sentiment_examples(args.blind_test_path, word_vectors.word_indexer)\n print(repr(len(train_exs)) + \" / \" + repr(len(dev_exs)) + \" / \" + repr(len(test_exs)) + \" train/dev/test examples\")\n\n if args.model == \"FF\":\n test_exs_predicted = train_evaluate_ffnn(train_exs, dev_exs, test_exs, word_vectors, number_layer=1)\n write_sentiment_examples(test_exs_predicted, args.test_output_path, word_vectors.word_indexer)\n elif args.model == \"FANCY\":\n test_exs_predicted = train_evaluate_fancy(train_exs, dev_exs, test_exs, word_vectors)\n elif args.model == \"CNN\":\n test_exs_predicted = train_evaluate_CNN(train_exs, dev_exs, test_exs, word_vectors)\n else:\n raise Exception(\"Pass in either FF or FANCY to run the appropriate system\")","sub_path":"project_mini_2/sentiment.py","file_name":"sentiment.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"86066924","text":"# reference : https://codezup.com/socket-server-with-multiple-clients-model-multithreading-python/\nfrom socket import *\n\nclient = socket(AF_INET, SOCK_STREAM)\nclient.connect(('localhost', 8082))\n\nwhile True:\n msg = input('Input : ')\n client.send(msg.encode('utf-8'))\n ret_msg = client.recv(1024)\n print('받은 데이터 : {}'.format(ret_msg.decode(\"utf-8\")))\n","sub_path":"lec2_multiuser/client_multiuser.py","file_name":"client_multiuser.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"175757479","text":"st_N=input()\nN=int(st_N)\n\n\noutput=0\nfor i in range(1<<(len(st_N)-1)):\n f=st_N[0]\n for j in range(len(st_N)-1):\n\n if((i>>j) & 1)==1:\n f=f+'+'\n f=f+st_N[j+1]\n #print(f)\n output=output+sum(map(int,f.split('+')))\n\nprint(output)\n","sub_path":"Python_codes/p04001/s906919459.py","file_name":"s906919459.py","file_ext":"py","file_size_in_byte":261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"170925713","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nfrom Training_preprocessing_module.preprocessing_training import preprocessing_training_class\nimport os\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import RandomizedSearchCV\nfrom sklearn.metrics import r2_score\nimport pickle\nfrom Training_traintest_split_module.data_traintest_split import data_splitting\nimport shutil\nfrom Training_log_module.training_log import train_logging\nimport warnings\nwarnings.filterwarnings('ignore')\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error\n\nclass tuning_training:\n def __init__(self):\n self.split_obj = data_splitting()\n self.x_train, self.x_test, self.y_train, self.y_test = self.split_obj.standardization()\n\n self.log_filename = 'training_log.txt'\n self.log_obj = train_logging(self.log_filename)\n \n self.log_obj.true_log('Model Tuning and Model Training on various Algorithms operates here ******')\n \n def parameters(self):\n self.rf_parameter = {'n_estimators': [int(x) for x in np.linspace(start = 100, stop = 1000, num = 100)],\n 'max_features': ['auto', 'sqrt'],\n 'max_depth': [int(x) for x in np.linspace(10, 110, num = 11)],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 4],\n 'bootstrap': [True, False]\n }\n \n \n return self.rf_parameter\n \n def tune_train(self):\n self.rf_parameter = self.parameters()\n \n rf_reg=RandomForestRegressor()\n \n rf_random = RandomizedSearchCV( estimator = rf_reg, param_distributions = self.rf_parameter, n_iter = 10, cv = 5, verbose=2, random_state=42, n_jobs=1)\n rf_random.fit(self.x_train,self.y_train)\n\n n_estimators_rf = rf_random.best_params_['n_estimators']\n max_features_rf = rf_random.best_params_['max_features']\n max_depth_rf = rf_random.best_params_['max_depth']\n min_sample_split_rf = rf_random.best_params_['min_samples_split']\n min_samples_leaf_rf = rf_random.best_params_['min_samples_leaf']\n bootstrap_rf = rf_random.best_params_['bootstrap']\n \n rf_model=RandomForestRegressor(n_estimators=n_estimators_rf,max_features=max_features_rf,\n max_depth=max_depth_rf,min_samples_split=min_sample_split_rf,\n min_samples_leaf=min_samples_leaf_rf,bootstrap=bootstrap_rf)\n \n rf_model.fit(self.x_train,self.y_train)\n rf_predict = rf_model.predict(self.x_test)\n score_rf = r2_score(self.y_test,rf_predict)\n \n mse=mean_absolute_error(self.y_test,rf_predict)\n rmse=np.sqrt(mse)\n mae=mean_absolute_error(self.y_test,rf_predict)\n \n with open('randomforest.pkl','wb') as save_file:\n pickle.dump(rf_model,save_file)\n \n return f'r2_score:{round(100*score_rf,3)},RMSE:{round(rmse,3)} and MAE:{round(mae,3)}'\n\n","sub_path":"Training/Training_model_tunetrain_module/model_tuning_and_training.py","file_name":"model_tuning_and_training.py","file_ext":"py","file_size_in_byte":3212,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"155223353","text":"#!/user/bin/env python\nimport click\n\nfrom app import create_app, db, models, forms\nfrom tests import test_app\nfrom flask import jsonify\nfrom flask import request\nimport json\n\napp = create_app()\n\n\n# flask cli context setup\n@app.shell_context_processor\ndef get_context():\n\t\"\"\"Objects exposed here will be automatically available from the shell.\"\"\"\n\treturn dict(app=app, db=db, models=models, forms=forms)\n\n\n@app.cli.command()\ndef create_db():\n\t\"\"\"Create the configured database.\"\"\"\n\tdb.create_all()\n\n@app.route(\"/test\", methods=[\"POST\"])\ndef test():\n\tprint(request.get_json(force = True))\n\td = request.get_json(force = True)\n\tuser_request_dict = json.loads(d)\n\tuser_name = user_request_dict['User']\n\ttask_name = user_request_dict['Task']\n\t# print(user_name, task_name)\n\treturn jsonify(request.json)\n\n\n@app.cli.command()\n@click.confirmation_option(prompt='Drop all database tables?')\ndef drop_db():\n\t\"\"\"Drop the current database.\"\"\"\n\tdb.drop_all()\n\n\nif __name__ == '__main__':\n\tapp.run()\n","sub_path":"wsgi.py","file_name":"wsgi.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"411446041","text":"# python3 -m spacy download en\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport os, re, nltk, spacy, gensim\r\nfrom topicAyc.Fun_DTM import Fun_DTM\r\nfrom tqdm import tqdm\r\nfrom multiprocessing import Pool\r\n\r\n# Enable logging for gensim - optional\r\nimport logging\r\nlogging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\r\n\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\",category=DeprecationWarning)\r\n\r\nclass dtmAyc():\r\n @staticmethod\r\n def save_significance(rpath, fpathlist):\r\n for i in tqdm(range(0, len(fpathlist))):\r\n dynamicLDA = Fun_DTM(os.path.join(rpath,fpathlist[i]))\r\n dtmmod = gensim.models.wrappers.DtmModel.load( # get dynamic topic model\r\n 'D:\\\\3policyAyc\\\\_database\\\\_workshop\\\\dtmprovs_' + str(i + 1) + '29topics.model')\r\n\r\n df = dynamicLDA.getTopicsignificance(dtmmod)\r\n df.to_excel('D:\\\\2020止水禪心\\\\0.3新能源政策挖掘\\\\论文\\\\图表\\\\'+fpathlist[i].strip('.csv').strip('Wordlist_')+\r\n '_topicsignificance1.xlsx')\r\n\r\n\r\n @staticmethod\r\n def save_topics(fpathlist):\r\n writer = pd.ExcelWriter('D:\\\\2020止水禪心\\\\0.3新能源政策挖掘\\\\论文\\\\图表\\\\_topicTerms.xlsx')\r\n topn = 15\r\n for i in tqdm(range(len(fpathlist))):\r\n dtmmod = gensim.models.wrappers.DtmModel.load( # get dynamic topic model\r\n 'D:\\\\3policyAyc\\\\_database\\\\_workshop\\\\newdtmprovs_' + str(i + 1) + '29topics.model')\r\n topdict = {}\r\n for topicid in range(29):\r\n topics_over_time = dtmmod.show_topic(topicid=topicid, topn=topn, time=0)\r\n terms = [tup[1] for tup in topics_over_time]\r\n topdict.update({'Topic' + str(topicid + 1): terms})\r\n tdf = pd.DataFrame(topdict)\r\n tdf.to_excel(writer, sheet_name='Sheet_provs'+str(i+1))\r\n writer.save()\r\n\r\n @staticmethod\r\n def term_variations(rpath, fpathlist, tls, no):\r\n provdic = {1:'Inner Mongolia', 2:'Sichuan', 3:'Nationwide',4:'Shandong',5:'Guangdong', 6:'Sinkiang', 7:'Jiangsu'}\r\n dfls = []\r\n for i in tqdm(range(len(fpathlist))):\r\n # terms = ['新能源','风电','太阳能','低碳','减排']\r\n terms=tls\r\n termvaries = {}\r\n dynamicLDA = Fun_DTM(os.path.join(rpath,fpathlist[i]))\r\n dtmmod = gensim.models.wrappers.DtmModel.load( # get dynamic topic model\r\n 'D:\\\\3policyAyc\\\\_database\\\\_workshop\\\\dtmprovs_' + str(i + 1) + '29topics.model')\r\n doc_topic, topic_term, doc_lengths, term_frequency, vocab = \\\r\n dtmmod.dtm_vis(dynamicLDA.dcorpus,time=list(range(10)))# model results\r\n # save to one single excel\r\n for term in terms:\r\n try:\r\n term_ind = vocab.index(term) # locate topic for corresponding term\r\n except ValueError:\r\n print('The given term {} is invalid.'.format(term))\r\n continue\r\n termfreqs = topic_term[:,term_ind,:] # get term frequency\r\n fit_topicid = np.argmax(np.average(termfreqs,1)) # locate most significant topic\r\n termvaries.update({term:termfreqs[fit_topicid].tolist()})\r\n termvaries.update({'Provs': [provdic[i+1]]*10})\r\n termvaries.update({'Years': list(range(2010,2020))})\r\n termvarydf = pd.DataFrame(termvaries)\r\n dfls.append(termvarydf)\r\n dfall = pd.concat(dfls, ignore_index=True)\r\n savpath = 'D:\\\\2020止水禪心\\\\0.3新能源政策挖掘\\\\论文\\\\图表\\\\term_variations_part' + str(no) + '.xlsx'\r\n dfall.to_excel(savpath, sheet_name=provdic[i+1])\r\n\r\n\r\nif __name__ == '__main__':\r\n path = 'D:/3policyAyc/_database/_policytxt'\r\n fpathlist = ['Wordlist_内蒙古5.csv', 'Wordlist_四川5.csv', 'Wordlist_国家5.csv',\r\n 'Wordlist_山东5.csv', 'Wordlist_广东5.csv', 'Wordlist_新疆5.csv', 'Wordlist_江苏5.csv']\r\n # dyDTM = Fun_DTM(os.path.join(path, fpathlist[6]))\r\n # dtm = gensim.models.wrappers.DtmModel.load('D:\\\\3policyAyc\\\\_database\\\\_workshop\\\\dtmprovs_729topics.model')\r\n # for i in tqdm(range(len(fpathlist))):\r\n # dynamicLDA = Fun_DTM(os.path.join(path,fpathlist[i]))\r\n # model = dynamicLDA.getDTM(fpathlist[i].strip('5.csv').strip('Wordlist_')+'.model')\r\n termls = [[\"节能\", \"减排\", \"碳排放\", \"风电\", \"生物质\", \"新能源\", \"新兴产业\", \"煤炭\", \"新能源汽车\", \"环保\"],\r\n [\"节能\", \"低碳\", \"碳排放\", \"风能\", \"太阳能\", \"生物质能\", \"可再生能源\", \"装备\", \"煤炭\", \"电动汽车\", \"环保\"],\r\n [\"节能\", \"低碳\", \"风能\", \"太阳能\", \"生物质能\", \"新能源\", \"新兴产业\", \"新能源汽车\", \"环保\"],\r\n [\"节能\", \"减排\", \"碳排放\", \"风电\", \"生物质\", \"新能源\", \"新兴产业\", \"煤炭\", \"新能源汽车\", \"环保\", \"低碳\", \"风能\", \"太阳能\", \"生物质能\", \"可再生能源\",\r\n \"装备\", \"电动汽车\"]]\r\n for i,ter in enumerate(termls):\r\n dtmAyc.term_variations(path, fpathlist, ter, i+1)\r\n # dtmAyc.save_significance(path, fpathlist)\r\n # dtmAyc.save_topics(fpathlist)\r\n\r\n # train dtm models from 5 to 29 sep 2\r\n # provs = ['广东','新疆','江苏']\r\n # for provn in provs:\r\n # pool = Pool(4)\r\n # modelname = [provn + str(topicnum) + '_dtm.model' for topicnum in range(5, 30, 2)]\r\n # dyDTM = pool.map(Fun_DTM, modelname)\r\n\r\n\r\n # for i in range(10):\r\n # print('时间={}时的主题：'.format(i))`\r\n # for nums in range(29):\r\n # print(dtm.show_topic(topicid=nums,time=i, topn=5))\r\n\r\n\r\n\r\n\r\n# from gensim.models.coherencemodel import CoherenceModel\r\n# from topicAyc.Fun_DTM import Fun_DTM\r\n# import os\r\n# path = 'D:/3policyAyc/_database/_policytxt'\r\n# dynamicLDA = Fun_DTM(os.path.join(path, 'Wordlist_内蒙古5.csv'))\r\n# dtmmod = gensim.models.wrappers.DtmModel.load('D:\\\\3policyAyc\\\\_database\\\\_workshop\\\\dtmprovs_129topics.model')\r\n# cm = CoherenceModel(model=dtmmod, corpus=dynamicLDA.dcorpus, coherence='u_mass')\r\n# coherence = cm.get_coherence() # get coherence value\r\n#\r\n#\r\n# from topicAyc.Fun_staticLDA import Fun_staticLDA\r\n# c = Fun_staticLDA.compute_Cumass(1,2)\r\n# import pyLDAvis\r\n# from importlib import reload\r\n#\r\n# doc_topic, topic_term, doc_lengths, term_frequency, vocab = model.dtm_vis(time=0, corpus=corpus)\r\n# vis_wrapper = pyLDAvis.prepare(topic_term_dists=topic_term, doc_topic_dists=doc_topic, doc_lengths=doc_lengths, vocab=vocab, term_frequency=term_frequency)\r\n# pyLDAvis.display(vis_wrapper)\r\n# pyLDAvis.show(vis_wrapper)\r\n#\r\n# doc_topic, topic_term, doc_lengths, term_frequency, vocab = model.dtm_vis(time=1, corpus=corpus)\r\n# vis_wrapper = pyLDAvis.prepare(topic_term_dists=topic_term, doc_topic_dists=doc_topic, doc_lengths=doc_lengths, vocab=vocab, term_frequency=term_frequency)\r\n# pyLDAvis.display(vis_wrapper)\r\n# pyLDAvis.show(vis_wrapper)\r\n","sub_path":"topicAyc/dtmAyc.py","file_name":"dtmAyc.py","file_ext":"py","file_size_in_byte":7025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"159532078","text":"# ( input: data, project, tfs_method, request_method, image_format, image_coding; \n# output: all_dd_list with image which could be used by serving )\nimport numpy as np\nfrom PIL import Image\nimport base64, io, os, json\nimport cv2\n\ndef saiap_image_array(b64img):\n # b64img = base64.b64decode(b64img)\n f = io.BytesIO(b64img)\n img = Image.open(f)\n after_resize = img.resize((int(env_setting['image_input_width']), int(env_setting['image_input_height'])), Image.BILINEAR)\n # buffered = io.BytesIO()\n # after_resize.save(buffered, format=\"PNG\")\n # b64urlsafeimg = base64.urlsafe_b64encode(buffered.getvalue())\n npimg = np.array(after_resize, dtype=np.float32).tolist()\n return npimg\n\ndef oneai_image_array_postdata(readed_img):\n # img = cv2.imread(filename)\n b_readed_img = bytearray(readed_img)\n numpyarray = np.asarray(b_readed_img, dtype=np.uint8)\n bgrImage = cv2.imdecode(numpyarray, cv2.IMREAD_UNCHANGED)\n roi_img = cv2.cvtColor(bgrImage, cv2.COLOR_BGR2RGB)\n # img_height, img_width = model.layers[0].input_shape[1:3]\n pltimg = cv2.resize(roi_img, (int(env_setting['image_input_height']), int(env_setting['image_input_width'])))\n imlist = np.array(pltimg,dtype=np.float32)/255\n imlist = imlist.tolist()\n return imlist\n\ndef oneai_image_array(b64img):\n # img = cv2.imread(filename)\n imgString = base64.b64decode(b64img)\n nparr = np.fromstring(imgString,np.uint8) \n img = cv2.imdecode(nparr,cv2.IMREAD_COLOR)\n roi_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n # img_height, img_width = model.layers[0].input_shape[1:3]\n pltimg = cv2.resize(roi_img, (int(env_setting['image_input_height']), int(env_setting['image_input_width'])))\n imlist = np.array(pltimg,dtype=np.float32)/255\n imlist = imlist.tolist()\n return imlist\n\n\ndef image_b64_coding(b64_image):\n try:\n if env_setting['image_format'] == 'array':\n if env_setting['image_coding'] == 'oneai_image_array':\n image = oneai_image_array(b64_image)\n elif env_setting['image_coding'] == 'oneai_image_array_postdata':\n image = oneai_image_array_postdata(b64_image)\n elif env_setting['image_coding'] == 'saiap_image_array':\n image = saiap_image_array(b64_image)\n else:\n return f'ERROR: image_coding in env_setting not supported!'\n elif env_setting['image_format'] == 'b64':\n if env_setting['image_coding'] == 'saiap_coding':\n b64_image = base64.decodebytes(b64_image.encode()) # b64 string to image byte\n image = base64.urlsafe_b64encode(b64_image)\n if env_setting['tfs_method'] == 'rest':\n image = base64.b64encode(image).decode('utf-8')\n elif env_setting['tfs_method'] == 'grpc':\n image = image.decode('latin-1')\n elif env_setting['image_coding'] == 'b64_coding':\n if env_setting['tfs_method'] == 'rest':\n image = b64_image\n elif env_setting['tfs_method'] == 'grpc':\n image = base64.decodebytes(b64_image.encode()).decode('latin-1') # image byte to b64 string\n else:\n return f'ERROR: image_coding in env_setting not supported!'\n else:\n return f'ERROR: image_format in env_setting not supported!'\n return image\n except Exception as e:\n return f'ERROR: {e}'\n\ndef parse_image_filename(filename):\n try:\n symbols = {}\n symbols.update({'filename': filename}) # CN0RM5DRWS20007600PAA00_355_0DJ01_0001_TC9401_ElecCap_270_220_NA_0.png\n possible_comps = ['AluCap', 'ElecCap', 'acpi', 'Ins', 'SATA', 'L', 'BH', 'Jumper', 'PCI', 'Aud', 'Stud', 'NI', 'DimSoc', 'CONN', 'USB', 'VGA']\n possible_bool = False\n for c in possible_comps:\n if c in filename.split('_'):\n possible_bool = True\n symbols.update({'component': c})\n filename = os.path.splitext(filename)[0] # CN0RM5DRWS20007600PAA00_355_0DJ01_0001_TC9401_ElecCap_270_220_NA_0\n splited_filename = filename.split(f'_{c}_')\n back_filename = splited_filename[-1] # 270_220_NA_0\n front_filename = splited_filename[0] # CN0RM5DRWS20007600PAA00_355_0DJ01_0001_TC9401\n symbols.update({'SN': front_filename})\n symbols.update({'PanelNo': front_filename.split('_')[0]})\n front_filename = front_filename.replace(f'{symbols[\"PanelNo\"]}_', '') # 355_0DJ01_0001_TC9401\n symbols.update({'location': front_filename.split('_')[-1]})\n front_filename = front_filename.replace(f'_{symbols[\"location\"]}', '') # 355_0AH01_B002\n symbols.update({'eagle': front_filename.replace('_', '')})\n symbols.update({'degree': back_filename.split('_')[0]})\n symbols.update({'capacity': back_filename.split('_')[1]})\n symbols.update({'voltage': back_filename.split('_')[2]})\n symbols.update({'index': back_filename.split('_')[3]})\n if possible_bool == False:\n symbols = {\n 'filename': filename,\n 'component': 'Unknown',\n 'SN': 'SN',\n 'PanelNo': 'PanelNo',\n 'location': 'location',\n 'eagle': 'eagle',\n 'degree': 'degree',\n 'capacity': 'capacity',\n 'voltage': 'voltage',\n 'index': 'index',\n }\n return symbols\n except Exception as e:\n return e\n return symbols\n\ndef file_in_body_request(request):\n try:\n if (not request) or ('instances' not in request):\n return (False, 'instances not in request.json')\n data = request\n for idx, d in enumerate(data['instances']):\n data_dict = {\n 'logger' : 'predict', \n 'severity': 'debug', \n 'project' : str(env_setting['project_code']),\n }\n symbols = parse_image_filename(d['filename'])\n if symbols == {}:\n return (False, 'possible_comps does not exist in filename')\n elif type(symbols) == str:\n return (False, symbols)\n for key, value in symbols.items():\n data_dict.update({key: value})\n for index, (key, value) in enumerate(dict(d).items()):\n if key == 'eagle':\n data_dict.update({key: value.replace('_', '')})\n elif key == 'SN':\n data_dict.update({'PanelNo': value.split('_')[0]})\n data_dict.update({'location': value.split('_')[-1]})\n elif key == 'capvalue':\n data_dict.update({'capacity': value})\n elif key == 'image':\n data_dict.update({'saved_image': value['b64']})\n image = image_b64_coding(value['b64'])\n if (type(image) == str) and (image[0:5] == 'ERROR'):\n return (False, image)\n else:\n data_dict.update({'image': image})\n else:\n data_dict.update({key: value})\n if data_dict['component'] not in list(dict(model_setting).keys()):\n data_dict.update({'severity': 'info'})\n all_dd_list.append(data_dict)\n except Exception as e:\n return (False, e)\n return (True, all_dd_list)\n\nclass handler:\n @staticmethod\n def execute(input): # keys: request, env_setting, model_setting\n global model_setting, env_setting, all_dd_list\n all_dd_list = []\n # input = json.loads(input)\n env_setting = input['env_setting']\n model_setting = input['model_setting']\n (error_exists, parsed) = file_in_body_request(input['request'])\n if error_exists:\n input['request'] = parsed\n else:\n input['error'] = parsed\n return input","sub_path":"gateway/blueprints/applications/functions/pre_process/dip_parse_request/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":8067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"529047463","text":"import os,sys,datetime\nfrom PIL import Image\n\nif getattr(sys, 'frozen', False):\n mydir = os.path.dirname(sys.executable)\nelif __file__:\n mydir = os.path.dirname(os.path.abspath(__file__))\n\nprint(\"------------mydir------------\")\nprint(mydir)\nprint(\"------------------------\")\n\ncolor = input(\"цвет прозрачных пикселей (R G B A 255) через пробел по умолчанию '0 0 0 255' черный \")\ntry:\n color=color.split(\" \")\n print(color)\n if len(color)==1:#only alpha will changed\n _r=None\n _g=None\n _b=None\n _a=int(color[0]) if abs(int(color[0]))<256 else 255\n elif len(color)==2:#gray alpha will\n _r=abs(int(color[0])) if abs(int(color[0]))<256 else 255\n _g=abs(int(color[0])) if abs(int(color[0]))<256 else 255\n _b=abs(int(color[0])) if abs(int(color[0]))<256 else 255\n _a=int(color[1]) if abs(int(color[1]))<256 else 255\n elif len(color)==3:\n _r=abs(int(color[0])) if abs(int(color[0]))<256 else 255\n _g=abs(int(color[1])) if abs(int(color[1]))<256 else 255\n _b=abs(int(color[2])) if abs(int(color[2]))<256 else 255\n _a=None\n else:\n _r = abs(int(color[0])) if abs(int(color[0])) < 256 else 255\n _g = abs(int(color[1])) if abs(int(color[1])) < 256 else 255\n _b = abs(int(color[2])) if abs(int(color[2])) < 256 else 255\n _a = int(color[3]) if abs(int(color[2])) < 256 else 255\nexcept:\n print(\"badcolor\")\n print(sys.exc_info())\n _r,_g,_b,_a=0,0,0,255\n\n\nprint(\"цвет пикселей будет RGBA(\",_r,_g,_b,_a,\")\")\ntry:\n print(datetime.datetime.now().time())\n print(\"------------------ начало ----------------------\")\n fnames=[]\n for file in os.listdir(mydir):\n if file.endswith(\".png\"):\n fnames+=[file]\n print(fnames)\n for fname in fnames:\n\n adres=mydir+os.sep+fname\n print(adres)\n im = Image.open(adres).convert('RGBA')\n im.load()\n w,h=im.size\n w=range(w)\n h=range(h)\n\n R=list(im.getdata(0))\n G=list(im.getdata(1))\n B=list(im.getdata(2))\n alp=list(im.getdata(3)) #alpha tuple 0...255 кортеж прозрачности пикселей\n newalp=[]\n for i in range(len(alp)):\n r=_r if alp[i]==0 and _r is not None else R[i]\n g=_g if alp[i]==0 and _g is not None else G[i]\n b=_b if alp[i]==0 and _b is not None else B[i]\n a=_a if alp[i]==0 and _a is not None else alp[i]\n newalp.append((r,g,b,a))\n\n print(\"recount complete\")\n print(datetime.datetime.now().time())\n print(\"------------------ V ----------------------\")\n im.putdata(newalp)\n print(\"load image data complete\")\n print(datetime.datetime.now().time())\n print(\"------------------ V ----------------------\")\n im.save(\"_\"+fname)\n print(\"save image complete\")\n print(datetime.datetime.now().time())\n print(\"------------------ V ----------------------\")\n\nexcept:\n print(sys.exc_info())\ninput(\"done готово / enter чтобы закрыть\")\n","sub_path":"imagebatch/png/pngbatchA0tocolor/pngbatchA0tocolor.py","file_name":"pngbatchA0tocolor.py","file_ext":"py","file_size_in_byte":3157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"530578730","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport requests\nimport json\nfrom requests.auth import HTTPBasicAuth\nrequests.packages.urllib3.disable_warnings()\n\nif __name__ == \"__main__\":\n\n auth = HTTPBasicAuth('cisco', 'cisco')\n headers = {'Accept': 'application/vnd.yang.data+json'}\n url = 'http://10.10.10.6/restconf/api/config/native/ip/route?deep'\n response = requests.get(url, verify=False, headers=headers, auth=auth)\n rx_object = json.loads(response.text)\n static_routes = rx_object['ned:route']['ip-route-interface-forwarding-list']\n for route in static_routes:\n subnet = route['prefix']\n netmask = route['mask']\n next_hop = route['fwd-list'][0]['fwd']\n print('{}/{} via {}'.format(subnet, netmask, next_hop))\n # print(json.dumps(json.loads(response.text), indent=4))\n","sub_path":"NPDESI/CSR/CSR_RESTCONF_TRANSFORM_ROUTE.py","file_name":"CSR_RESTCONF_TRANSFORM_ROUTE.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"414778542","text":"from django.shortcuts import render, reverse, redirect\nfrom django.db.models import Q\nfrom django.views.generic import TemplateView, ListView, DetailView, View\nfrom django.views.generic.edit import FormMixin\n\nimport json\nfrom apps.accounts.models import User\nfrom .forms import SearchForm, MessageForm\nfrom .models import Message\n\n\nclass Home(TemplateView):\n template_name = 'matching/home.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n return context\n\n\nclass UserListView(ListView):\n model = User\n paginate_by = 20\n\n def favorite(self):\n if self.request.GET.get('fav'):\n if self.request.user.favorite is None:\n user = User.objects.get(id=self.request.user.id)\n favorite = [self.request.GET.get('fav')]\n user.favorite = json.dumps(favorite)\n user.save()\n else:\n user = User.objects.get(id=self.request.user.id)\n favorite = json.loads(user.favorite)\n if self.request.GET.get('fav') not in favorite:\n favorite += [self.request.GET.get('fav')]\n else:\n favorite.remove(self.request.GET.get('fav'))\n\n user.favorite = json.dumps(favorite)\n user.save()\n\n def get_queryset(self):\n self.favorite()\n\n user_type = 1 if self.request.user.user_type == 2 else 2\n search = self.request.GET.get('search')\n if search:\n return User.objects.filter(\n Q(username__contains=search)\n | Q(last_name__contains=search)\n | Q(first_name__contains=search)\n ).filter(user_type=user_type)\n else:\n return User.objects.filter(user_type=user_type)\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n search = self.request.GET.get('search')\n initial = {'search': search}\n context['searchform'] = SearchForm(initial=initial)\n\n user = User.objects.get(id=self.request.user.id)\n if user.favorite:\n favorite = json.loads(user.favorite)\n for user in context['object_list']:\n if str(user.id) in favorite:\n user.fav = True\n else:\n user.fav = False\n\n return context\n\n\nclass UserDetailView(FormMixin, DetailView):\n model = User\n form_class = MessageForm\n\n def get_success_url(self):\n return reverse('matching:userdetail', kwargs={'pk': self.kwargs['pk']})\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n context['object_list'] = Message.objects.filter(\n Q(message_from_id=self.request.user.id)\n & Q(message_to_id=self.kwargs['pk'])\n | Q(message_from_id=self.kwargs['pk'])\n & Q(message_to_id=self.request.user.id)\n ).order_by('-created_at')\n\n return context\n\n def post(self, request, *args, **kwargs):\n form = self.get_form()\n if form.is_valid():\n return self.form_valid(form)\n else:\n print('invalid')\n return self.form_invalid(form)\n\n def form_valid(self, form):\n message = form.save(commit=False)\n message.message_from_id = self.request.user.id\n message.message_to_id = self.kwargs['pk']\n message.save()\n return super().form_valid(form)\n\n\nclass FavoritListView(UserListView):\n model = User\n paginate_by = 20\n\n def get_queryset(self):\n self.favorite()\n user = User.objects.get(id=self.request.user.id)\n if user.favorite:\n favorite = json.loads(user.favorite)\n return User.objects.filter(id__in=favorite)\n else:\n return User.objects.filter(id=None)\n\n\nclass MessageListView(ListView):\n model = Message\n paginate_by = 20\n\n def get_queryset(self):\n return Message.objects.filter(message_from_id=self.request.user.id)\n","sub_path":"matching-system/apps/matching/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"180124291","text":"import csv\nimport os\n\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.db.models import Q\nfrom works_single_view.models import Song, Contributor\n\n\nFILE_DIR = os.path.dirname(__file__)\n\n\ndef get_data_from_file(file_name):\n file_path = os.path.join(FILE_DIR, file_name)\n songs_list = []\n contributors_set = set()\n\n with open(file_path, mode='r') as csv_file:\n reader = csv.DictReader(csv_file, delimiter=',')\n for r in reader:\n contributors = [c.strip() for c in r['contributors'].split('|')]\n songs_list.append({\n \"title\": r['title'],\n \"contributors\": contributors,\n \"iswc\": r['iswc'],\n })\n contributors_set.update(set(contributors))\n return songs_list, contributors_set\n\n\ndef add_all_contributors(contributors):\n for c in contributors:\n Contributor.objects.update_or_create(name=c, defaults={'name': c})\n\n\ndef add_contributor(song, contributors):\n new_contributors_ids = [c.id for c in Contributor.objects.filter(name__in=contributors)]\n song.contributors = [r for r in set(song.contributors + new_contributors_ids)]\n return song\n\n\ndef add_songs(songs):\n for s in songs:\n old_song = Song.objects.filter(Q(title=s['title']) | Q(iswc=s['iswc']))\n\n if not old_song:\n song = Song.objects.create(title=s['title'], iswc=s['iswc'], contributors=[])\n else:\n song = old_song[0]\n if s['title']:\n song.title = s['title']\n if s['iswc']:\n song.iswc = s['iswc']\n\n new_contributors_ids = [c.id for c in Contributor.objects.filter(name__in=s['contributors'])]\n song.contributors = [r for r in set(song.contributors + new_contributors_ids)]\n song.save()\n\n\nclass Command(BaseCommand):\n help = 'Add new song to db'\n\n def add_arguments(self, parser):\n parser.add_argument('--file', '-f', type=str)\n\n def handle(self, *args, **options):\n songs, authors = get_data_from_file(options['file'])\n add_all_contributors(authors)\n add_songs(songs)\n","sub_path":"works_single_view/management/commands/add_songs.py","file_name":"add_songs.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"615853022","text":"from fabricate import *\nimport sys, os\n\nimport glob\n\n\nCC='gcc'\n\nLINKFLAGS=['-lm']\n\nCFLAGS=['-std=gnu99','-Wall','-Werror']\n\ntest_sources = ['test']\nref_sources = ['reftest']\n\nprograms = [{'name':'test', 'sources':test_sources},\n {'name':'reftest', 'sources':ref_sources}]\n\ndef build():\n compile()\n link()\n\ndef compile():\n for prog in programs:\n for source in prog['sources']:\n run(CC,'-c','-o',source+'.o', CFLAGS, source+'.c')\n\ndef link():\n for prog in programs:\n objects = [s+'.o' for s in prog['sources']]\n run(CC,'-o', prog['name'], objects,LINKFLAGS)\n\ndef clean():\n autoclean()\n\n# send options so it won't crash on us\nmain()\n\n","sub_path":"VEC/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"455862580","text":"# Copyright 2015 Google Inc. All Rights Reserved.\n\"\"\"Command for abandoning instances managed by the instance group manager.\"\"\"\n\nfrom googlecloudsdk.calliope import arg_parsers\nfrom googlecloudsdk.compute.lib import base_classes\nfrom googlecloudsdk.compute.lib import utils\n\n\nclass AbandonInstances(base_classes.BaseAsyncMutator):\n \"\"\"Abandon instances managed by instance group manager.\"\"\"\n\n @staticmethod\n def Args(parser):\n parser.add_argument('instance_group_manager',\n help='Instance group manager name.')\n parser.add_argument(\n '--instances',\n type=arg_parsers.ArgList(min_length=1),\n action=arg_parsers.FloatingListValuesCatcher(),\n metavar='INSTANCE',\n required=True,\n help='Names of instances to abandon.')\n utils.AddZoneFlag(\n parser,\n resource_type='instance group manager',\n operation_type='abandon instances')\n\n def method(self):\n return 'AbandonInstances'\n\n @property\n def service(self):\n return self.compute.instanceGroupManagers\n\n @property\n def resource_type(self):\n return 'instanceGroupManagers'\n\n def CreateRequests(self, args):\n zone_ref = self.CreateZonalReference(args.instance_group_manager, args.zone)\n instance_refs = self.CreateZonalReferences(\n args.instances,\n zone_ref.zone,\n resource_type='instances')\n instances = [instance_ref.SelfLink() for instance_ref in instance_refs]\n return [(self.method(),\n self.messages.ComputeInstanceGroupManagersAbandonInstancesRequest(\n instanceGroupManager=zone_ref.Name(),\n instanceGroupManagersAbandonInstancesRequest=(\n self.messages.InstanceGroupManagersAbandonInstancesRequest(\n instances=instances,\n )\n ),\n project=self.project,\n zone=zone_ref.zone,\n ),),]\n\n\nAbandonInstances.detailed_help = {\n 'brief': 'Abandon instances managed by instance group manager.',\n 'DESCRIPTION': \"\"\"\n *{command}* abandons one or more instances from a managed instance\n group, thereby reducing the intendedSize of the group. Once the\n instances have been abandoned, the currentSize of the group is\n automatically reduced as well to reflect the changes.\n\n If you would like to delete the underlying virtual machines instead of\n only moving them out of the managed instance group, use the\n delete-instances command instead.\n \"\"\",\n}\n","sub_path":"smry/server-auth/ls/google-cloud-sdk/lib/googlecloudsdk/compute/subcommands/instance_groups/managed/abandon_instances.py","file_name":"abandon_instances.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"648187771","text":"from collections import OrderedDict\nimport theano\nimport sys\n\nclass TheanoFunction(object) :\n\t\"This class encapsulates a Theano function\"\n\n\tdef __init__(self, name, outputLayer, output_expressions, additional_input_expressions = {}, updates = [], **kwargs) :\n\t\tself.name = name\n\t\tself.outputLayer = outputLayer\n\t\t\n\t\tself.inputs = OrderedDict()\n\t\tself.tmpInputs = OrderedDict()\n\t\tfor inp in self.outputLayer.network.inputs.itervalues() :\n\t\t\tself.inputs[inp.name] = inp.outputs\n\t\tself.inputs.update(additional_input_expressions)\n\t\t\n\t\tfor i in self.inputs :\n\t\t\tself.tmpInputs[i] = None\n\t\t\n\t\tself.additional_input_expressions = additional_input_expressions\n\t\tself.outputs = output_expressions\n\t\tself.updates = updates\n\t\tself.theano_fct = theano.function(inputs = self.inputs.values(), outputs = self.outputs, updates = self.updates, **kwargs)\n\n\tdef printGraph(self) :\n\t\t\"\"\"Print the theano graph of the function\"\"\"\n\t\ttheano.printing.debugprint(self.theano_fct)\n\n\tdef run(self, **kwargs) :\n\t\tfor k in kwargs :\n\t\t\tself.tmpInputs[k] = kwargs[k]\n\n\t\ttry :\n\t\t\treturn self.theano_fct(*self.tmpInputs.values())\n\t\texcept Exception as e :\n\t\t\tsys.stderr.write(\"!!=> Error in function '%s' for layer '%s':\\n\" % (self.name, self.outputLayer.name))\n\t\t\tsys.stderr.write(\"\\t!!=> the arguments were:\\n %s\" % (kwargs))\n\t\t\traise e\n\n\tdef __call__(self, **kwargs) :\n\t\treturn self.run(**kwargs)\n\n\tdef __repr__(self) :\n\t\treturn \"\" % self.name\n\n\tdef __str__(self) :\n\t\treturn \"\" % (self.name, self.theano_fct)","sub_path":"Mariana/wrappers.py","file_name":"wrappers.py","file_ext":"py","file_size_in_byte":1534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"630530005","text":"# -*- coding:utf-8 -*-\n\"\"\"\n@contact: adonis_wu@outlook.com\n@time: 2019/4/2\n\"\"\"\n__author__ = '🍊 adowu 🍊'\nimport jieba\nimport os\nimport logging\nfrom gensim.models import Word2Vec\nfrom gensim.models.word2vec import PathLineSentences\nimport multiprocessing\nbase_url = \"\"\n# 多线程分词\n# jieba.enable_parallel()\n# 加载自定义词典\n# jieba.load_userdict(\"_word_chinese\")\n\n\n# 加载停用词\ndef getStopwords():\n stopwords = []\n with open(\"stop_words.txt\", \"r\", encoding='utf8') as f:\n lines = f.readlines()\n for line in lines:\n stopwords.append(line.strip())\n return stopwords\n\n\n# 分词\ndef segment(stopwords):\n file_nums = 0\n url = base_url + 'processed_data/demo/'\n fileNames = os.listdir(url)\n for file in fileNames:\n logging.info('starting ' + str(file_nums) + 'file word Segmentation')\n segment_file = open(url + file + '_segment', 'a', encoding='utf8')\n with open(url + file, encoding='utf8') as f:\n text = f.readlines()\n for sentence in text:\n sentence = list(jieba.cut(sentence))\n sentence_segment = []\n for word in sentence:\n if word not in stopwords:\n sentence_segment.append(word)\n segment_file.write(\" \".join(sentence_segment))\n del text\n f.close()\n segment_file.close()\n logging.info('finished ' + str(file_nums) + 'file word Segmentation')\n file_nums += 1\n\n\ndef train():\n input_dir = '../baike/segment'\n outp1 = 'model/baike.model'\n outp2 = 'model/word2vec_format'\n fileNames = os.listdir(input_dir)\n # 训练模型 输入语料目录 embedding size 256,共现窗口大小10,去除出现次数5以下的词,多线程运行,迭代10次\n model = Word2Vec(PathLineSentences(input_dir),\n size=256, window=10, min_count=5,\n workers=multiprocessing.cpu_count(), iter=10)\n model.save(outp1)\n model.wv.save_word2vec_format(outp2, binary=False)\n\n\ndef load(model_path, format_path):\n w2v = Word2Vec.load(model_path)\n # format_w2v = Word2Vec.load_word2vec_format(format_path, binary=False)\n topn4 = w2v.most_similar('一带一路', topn=9)\n print(topn4)\n\n\nload('/Users/wushaojun/Downloads/vectors/word2vec_model',None)\n","sub_path":"word2vec/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"119014164","text":"#checks to see if phone number given is valid\ndef isPhoneNumberWithRegex(text):\n\n\t\n #checking to ensure text given is 12 characters long\n\tif len(text) != 12:\n\t\treturn False\n\n\t#checks to ensure the first three characters are digits\n\tfor i in range(0, 3):\n\t\tif not text[i].isdecimal():\n\t\t\treturn False\n\t\t\n\t#ensures that the fourth character is a hyphen\t\n\tif text[3] != \"-\":\n\t\treturn False\n\t\n\t#checks to ensure the characters four to six are all digits\n\tfor i in range(4, 7):\n\t\tif not text[i].isdecimal():\n\t\t\treturn False\n\t\t\n\t#ensures that the eighth character is a hyphen\t\n\tif text[7] != \"-\":\n\t\treturn False\n\n\t#checks to ensure the last four characters are digits\n\tfor i in range(8, 12):\n\t\tif not text[i].isdecimal():\n\t\t\treturn False\n\t#if all other conditions are false & text is a valid phone number, return true\n\treturn True\n\n#test\nmessage = 'Call me at 415-555-1011 tomorrow. 415-555-9999 is my office.'\nfor i in range(len(message)):\n\tchunk = message[i:i+12]\n\tif isPhoneNumberWithRegex(chunk):\n\t\tprint(\"The phone number found is \" + chunk)\nprint(\"Done!\")","sub_path":"isPhoneNumberWithRegex.py","file_name":"isPhoneNumberWithRegex.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"470860978","text":"from __future__ import with_statement\nimport gocept.httpserverlayer.wsgi\nimport gocept.selenium\nimport mimetypes\nimport os.path\nimport pkg_resources\nimport zeit.cms.repository.interfaces\nimport zeit.cms.testing\nimport zeit.content.image.image\nimport zeit.content.image.imagegroup\nimport zeit.workflow.testing\nimport zope.component\n\n\nproduct_config = \"\"\"\n\n viewport-source file://{here}/tests/fixtures/viewports.xml\n display-type-source file://{here}/tests/fixtures/display-types.xml\n variant-source file://{here}/tests/fixtures/variants.xml\n copyright-company-source file://{here}/tests/fixtures/copyright-company.xml\n\n\"\"\".format(here=pkg_resources.resource_filename(__name__, '.'))\n\n\nZCML_LAYER = zeit.cms.testing.ZCMLLayer(\n 'ftesting.zcml', product_config=(\n product_config +\n zeit.cms.testing.cms_product_config +\n zeit.workflow.testing.product_config))\nWSGI_LAYER = zeit.cms.testing.WSGILayer(\n name='WSGILayer', bases=(ZCML_LAYER,))\nHTTP_LAYER = gocept.httpserverlayer.wsgi.Layer(\n name='HTTPLayer', bases=(WSGI_LAYER,))\nWD_LAYER = gocept.selenium.WebdriverLayer(\n name='WebdriverLayer', bases=(HTTP_LAYER,))\nWEBDRIVER_LAYER = gocept.selenium.WebdriverSeleneseLayer(\n name='WebdriverSeleneseLayer', bases=(WD_LAYER,))\n\n\ndef create_local_image(filename, path='browser/testdata/'):\n filetype = filename.rsplit('.', 1)[-1].lower()\n if filetype == 'jpg':\n image = zeit.content.image.image.LocalImage(mimeType='image/jpeg')\n else:\n image = zeit.content.image.image.LocalImage(\n mimeType=\"image/{}\".format(filetype))\n fh = image.open('w')\n file_name = pkg_resources.resource_filename(\n __name__, '%s%s' % (path, filename))\n fh.write(open(file_name, 'rb').read())\n fh.close()\n return image\n\n\ndef create_image_group():\n repository = zope.component.getUtility(\n zeit.cms.repository.interfaces.IRepository)\n repository['image-group'] = zeit.content.image.imagegroup.ImageGroup()\n group = repository['image-group']\n for filename in ('new-hampshire-450x200.jpg',\n 'new-hampshire-artikel.jpg',\n 'obama-clinton-120x120.jpg'):\n group[filename] = create_local_image(filename)\n return group\n\n\ndef create_image_group_with_master_image(file_name=None):\n repository = zope.component.getUtility(\n zeit.cms.repository.interfaces.IRepository)\n if file_name is None:\n file_name = 'DSC00109_2.JPG'\n fh = repository['2006'][file_name].open()\n else:\n try:\n fh = zeit.cms.interfaces.ICMSContent(file_name).open()\n except TypeError:\n fh = open(file_name)\n extension = os.path.splitext(file_name)[-1].lower()\n\n group = zeit.content.image.imagegroup.ImageGroup()\n group.master_images = (('desktop', u'master-image' + extension),)\n repository['group'] = group\n image = zeit.content.image.image.LocalImage()\n image.mimeType = mimetypes.types_map[extension]\n image.open('w').write(fh.read())\n repository['group'][group.master_image] = image\n return repository['group']\n","sub_path":"src/zeit/content/image/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":3159,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"504917512","text":"import numpy as np\nimport cv2\nfrom const import (FIRST_PLAYER, SECOND_PLAYER, EMPTY, COLOR_SCALE, GRAY_SCALE)\nfrom patternmatcher import PatternMatcher\n# 取得失敗時にframe移動できる限界\nFRAME_LIMIT_TO_FETCH_NEXT_PUYOS = 17\n# 落とした時のフレームは次の手を引き始めているので、少し後ろに戻させる\nDROP_PARAMETER = -4\n# 検知した時のフレームはまだネクスト欄が移動しきっていないので、少し前に進ませる\nDETECT_PARAMETER = 4\n\nclass KumipuyoFetcher:\n\n\n def __init__(self):\n self.game_data = None\n self.folder_path = None\n self.cross_detector = None\n\n def set_one_game_data(self, gmdt):\n self.game_data = gmdt\n\n def set_folder_path(self, fp):\n self.folder_path = fp\n\n def set_cross_detector(self, cd):\n self.cross_detector = cd\n\n # nextにあるぷよを取得\n # [next, double_next, frame]を返す\n # @param 取得したいフレーム\n # @param 取得したいplayer\n # @param Trueなら、取得失敗時前に進める、Falseなら後ろへ戻る\n def _fetch_next_kumipuyo(self, frame, player, increment_mode):\n \n frame_move_ct = 0\n while frame_move_ct <= FRAME_LIMIT_TO_FETCH_NEXT_PUYOS:\n img = cv2.imread(self.folder_path + \"%06d\" % frame + \".png\", COLOR_SCALE)\n next_img = None\n double_next_img = None\n\n # crossが検出された時は寿命が延長される\n self.cross_detector.set_and_clip_game_img(img)\n if self.cross_detector.detect_cross_img(player) == True:\n frame_move_ct = 0\n if increment_mode == True:\n frame += 1\n else:\n frame -= 1\n continue\n \n if player == FIRST_PLAYER:\n next_img = img[:self.game_data.neutral_upperwall_left_upper_coord.y,\\\n self.game_data.neutral_upperwall_left_upper_coord.x:\\\n self.game_data.center_x]\n double_next_img = next_img[:,\\\n len(next_img[0]) * 2 // 3 :]\n elif player == SECOND_PLAYER:\n next_img = img[:self.game_data.neutral_upperwall_left_upper_coord.y,\\\n self.game_data.center_x:\\\n self.game_data.left_upper_coord_field[SECOND_PLAYER].x]\n double_next_img = next_img[:,\\\n : len(next_img[0]) // 3]\n \n next_puyos = PatternMatcher.convert_next_image_to_puyos(next_img, False, player)\n double_next_puyos = PatternMatcher.convert_next_image_to_puyos(double_next_img, True, player)\n # 取得漏れしたぷよがなければ終了\n if next_puyos.count(EMPTY) == 0 and double_next_puyos.count(EMPTY) == 0:\n return [next_puyos, double_next_puyos, frame]\n frame_move_ct += 1\n if increment_mode == True:\n frame += 1\n else:\n frame -= 1\n\n return [[0, 0], [0, 0], 0]\n\n # [[now.parent, now.child], [next.parent, next.child], [double_next.parent, double_next.child]]\n # 取得に失敗した場合は全てにEMPTYをset\n # 取得したタイミングのframeは返さない\n # @pre folder_pathをセット済み\n # @pre game_dataをセット済み\n # @pre cross_detectorをセット済み\n def fetch_kumipuyos(self, drop_frame, detect_frame, player):\n # 最初に落とした盤面（設置する前の盤面）から手に入れる\n now_kumipuyo, next_puyos, frame = self._fetch_next_kumipuyo(drop_frame + DROP_PARAMETER, player, False)\n\n next_puyos_, double_next_puyos, frame_ = self._fetch_next_kumipuyo(detect_frame + DETECT_PARAMETER, player, True)\n # 同じはずのネクストが一致していない、又は空マスが存在する\n if next_puyos != next_puyos_ or now_kumipuyo.count(EMPTY) > 0 or\\\n next_puyos.count(EMPTY) > 0 or double_next_puyos.count(EMPTY):\n return [[0, 0], [0, 0], [0, 0]]\n return [now_kumipuyo, next_puyos, double_next_puyos]\n\n # 現在のネクスト欄のみ取得する。\n # 取得したタイミングのフレームも返す\n def fetch_only_current_next_kumipuyos_and_fetch_frame(self, detect_frame, player):\n next_puyos, double_next_puyos, frame = self._fetch_next_kumipuyo(detect_frame, player, False)\n return [next_puyos, double_next_puyos, frame]\n","sub_path":"kumipuyofetcher.py","file_name":"kumipuyofetcher.py","file_ext":"py","file_size_in_byte":4607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"496975290","text":"import selenium\nimport selenium.webdriver\nimport selenium.webdriver.common.keys\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium import webdriver\nimport time\n\n# 无界面模式\ndef ChromeDriverNOBrowser():\n chrome_options = Options()\n chrome_options.add_argument('--headless')\n chrome_options.add_argument('--disable-gpu')\n driverChrome = webdriver.Chrome(executable_path=\"./tools/chromedriver.exe\", chrome_options=chrome_options)\n return driverChrome\n\n\n# 有界面的就简单了\ndef ChromeDriverBrowser():\n driverChrome = webdriver.Chrome(\"./tools/chromedriver.exe\")\n return driverChrome\n\n\ndef get_baidu_search_res_page(word):\n driver = ChromeDriverBrowser()# 设置driver 模拟浏览器程序\n # driver.set_window_size(300,200) # ！注意，window_size遮住的按钮，是不可以实现模拟鼠标点击操作的。\n driver.get(\"http://www.baidu.com\") # 模拟浏览器访问url\n\n # 获取输入框\n input = driver.find_element_by_id('kw')\n input.clear() # 清空输入框\n input.send_keys(word) # 输入搜索内容\n input.send_keys(selenium.webdriver.common.keys.Keys.ENTER)\n time.sleep(3)\n\n #页面后退\n driver.back()\n time.sleep(3)\n #页面前进\n driver.forward()\n time.sleep(3)\n driver.close() # 关闭模拟器\n\n\nif __name__ == '__main__':\n get_baidu_search_res_page(\"Python\")","sub_path":"《尹成python爬虫教程》学习笔记/5，selenium模拟浏览器/12，页面的前进和后退.py","file_name":"12，页面的前进和后退.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"479036394","text":"n=int(input('digite um número de 4 dígitos:'))\na=int(input('digite o primeiro algarismo do seu número:'))\nb=int(input('digite o segundo algarismo do seu número:'))\nc=int(input('digite o terceiro algarismo do seu número:'))\nd=int(input('digite o quarto algarismo do seu número:'))\nn1=str(a)+str(b)\nn2=str(a)+str(c)\nn3=str(a)+str(d)\nn4=str(b)+str(a)\nn5=str(b)+str(c)\nn6=str(b)+str(d)\nn7=str(c)+str(a)\nn8=str(c)+str(b)\nn9=str(c)+str(d)\nn10=str(d)+str(a)\nn11=str(d)+str(b)\nn12=str(c)+str(d)\nif (n1*n9==n) or (n2*n6==n) or (n3*n5==n) or (n1*n12==n) or (n2*n11==n) or (n3*n8==n) or (n4*n9==n) or (n4*n12==n) or (n5*n10==n) or (n6*n7==n) or (n7*n11==n) or (n8*n10==n):\n print('é um número vampiro')\nelse:\n print('não é um número vampiro')","sub_path":"moodledata/vpl_data/59/usersdata/184/59258/submittedfiles/testes.py","file_name":"testes.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"396940497","text":"from django.shortcuts import render\nfrom qiniu import Auth\nimport base64\nimport json\nfrom django.views.generic import TemplateView\nfrom . import facetool\n\ndef index(request):\n host = request.META[\"HTTP_HOST\"]\n q = Auth(\"e2wP6U3ZwGVYi8Kj1wp_kyHIAPtwHsWJ7idlX-LQ\", \"ZZTAJyOcYbQH6jc-QFsiyVXAD24vBplqpjwuhdNE\")\n token = q.upload_token(\"face-age\", None, 7200, {\"returnUrl\": 'http://' + host + '/photodect', \"returnBody\": '{\"key\": $(key)}'})\n context = {\"token\": token}\n return render(request, 'index.html', context)\n\ndef photodect(request):\n qiniuDomain = 'http://7xixhk.com1.z0.glb.clouddn.com/'\n photoInfo = json.loads(base64.b64decode((request.GET['upload_ret'])).decode('utf-8'))\n photoKey = photoInfo['key']\n photoUrl = qiniuDomain + photoKey\n photoInfo = facetool.getImgInfo(photoUrl)\n img_width = photoInfo[\"img_width\"]\n img_height = photoInfo[\"img_height\"]\n for item in photoInfo[\"face\"]:\n item[\"position\"][\"left\"] = round((item[\"position\"][\"center\"][\"x\"] - item[\"position\"][\"width\"] / 2) / 100 * img_width, 2)\n item[\"position\"][\"top\"] = round((item[\"position\"][\"center\"][\"y\"] - item[\"position\"][\"height\"] / 2) / 100 * img_height, 2)\n item[\"position\"][\"width\"] = item[\"position\"][\"width\"] * img_width / 100\n item[\"position\"][\"height\"] = item[\"position\"][\"height\"] * img_height / 100\n context = {\"photoInfo\": photoInfo}\n return render(request, 'result.html', context)\n\nclass test(TemplateView):\n template_name = 'index.html'\n\n def get_context_data(self):\n return {\"test\": \"123\"}\n","sub_path":"mysite/face/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"528291884","text":"#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\n# In[ ]:\r\nimport tkinter as tk\r\nfrom tkinter import messagebox\r\nimport openpyxl\r\nfrom openpyxl import load_workbook\r\nfrom openpyxl.workbook import Workbook\r\nimport random\r\nimport sys\r\nimport logging \r\nimport traceback\r\nfrom datetime import datetime\r\nimport shutil\r\nimport os\r\nimport time\r\nimport json\r\nfrom ev_generator import generate_matrix\r\n\r\n\r\nlogging.basicConfig(level=logging.ERROR, filemode='simulation.log', format='%(levelname)s:%(message)s')\r\n\r\nprog_start = time.time()\r\n\r\n# Windows File System\r\nroot_sys = os.getcwd()\r\nroot_us = os.getcwd()\r\n\r\n# Mac File System\r\n\r\n# Debug\r\n# root_sys = r'C:\\\\Users\\\\james\\\\Desktop\\\\repository\\\\tandapay\\\\TandaPay v2\\\\TandaPay\\\\'\r\n# root_us = r'C:\\\\Users\\\\james\\\\Desktop\\\\repository\\\\tandapay\\\\TandaPay v2\\\\TandaPay\\\\'\r\n\r\n# Dev File System\r\n# root_sys = os.getcwd()\r\n# root_us = os.getcwd()\r\n\r\nsys_file = \"1 System Database.xlsx\"\r\n# if \"1 System Database.xlsx\" not in os.listdir(root_sys):\r\n# wb = openpyxl.Workbook()\r\n# wb.save(root_sys)\r\n# root_sys = r\"C:\\\\Tandapay\\\\\"\r\npath_system = root_sys + r'\\\\' + sys_file\r\nwb_system = load_workbook(path_system)\r\n# to identify the active sheet\r\nsh_system = wb_system.active\r\n\r\nus_file = \"2 User Database.xlsx\"\r\n# if \"2 User Database.xlsx\" not in os.listdir(root_us):\r\n# wb = openpyxl.Workbook()\r\n# wb.save(root_us)\r\n# root_us = r\"C:\\\\Tandapay\\\\\"\r\npath_user = root_us + r'\\\\' + us_file\r\nwb_user = load_workbook(path_user)\r\n# to identify the active sheet\r\nsh_user = wb_user.active\r\n\r\nmatrix_file = '3 Matrix Database.xlsx'\r\npath_matrix = root_us + r'\\\\' + matrix_file\r\nmatrix_wb = load_workbook(path_matrix)\r\nmatrix_var_sh = matrix_wb['Variable Map']\r\nmatrix_sys_log = matrix_wb['System Log']\r\n\r\n\r\nfor row in sh_user['A2:N200']:\r\n for cell in row:\r\n cell.value = None\r\n\r\nfor row in sh_system['C2:U37']:\r\n for cell in row:\r\n cell.value = None\r\n\r\nwb_user.save(path_user)\r\nwb_system.save(path_system)\r\n\r\nclass Simulator:\r\n def __init__(self, ui=True, _matrix=None, edge=False):\r\n self.ui = ui\r\n self._matrix = _matrix\r\n self.edge = edge # Used to test various edge case variables\r\n self.init_start = time.time()\r\n\r\n \t#Initialize Variables\r\n self.ev = []\r\n self.pv = []\r\n if self.ui == True:\r\n self.initVariable()\r\n\r\n # Build GUI\r\n self.app = tk.Tk()\r\n self.app.title(\"TandaPay\")\r\n self.app.geometry(\"600x700\")\r\n self.app.resizable(False, True)\r\n\r\n self.showLabels()\r\n self.showEntry()\r\n self.clearAction()\r\n self.showBtns()\r\n self.app.mainloop()\r\n self.startAction()\r\n elif self._matrix:\r\n prog_start = time.time()\r\n times = []\r\n failures = []\r\n for run, vector in enumerate(self._matrix):\r\n loop_start = time.time()\r\n for row in sh_user['A2:N200']:\r\n for cell in row:\r\n cell.value = None\r\n\r\n for row in sh_system['C2:U37']:\r\n for cell in row:\r\n cell.value = None\r\n\r\n wb_user.save(path_user)\r\n wb_system.save(path_system)\r\n self.run = run+1\r\n print(f'____ Run Number {self.run} of {len(self._matrix)}____')\r\n self.ev = vector[:9]\r\n self.ev.append(0)\r\n self.pv = vector[9:]\r\n self.start_ev = self.ev.copy()\r\n self.start_pv = self.pv.copy()\r\n try:\r\n self.startAction(vector=True, vector_array=vector)\r\n except Exception:\r\n print(traceback.format_exc())\r\n msg = {'vector':vector,'error msg':traceback.format_exc()}\r\n failures.append(msg)\r\n times.append(time.time()-loop_start)\r\n if run %20==0:\r\n avg_elapsed = sum(times)/len(times)\r\n runs_left = len(self._matrix) - self.run\r\n estimated_left = runs_left*avg_elapsed\r\n print(f'Elapsed: {time.time()-prog_start}')\r\n print(f'Estimated completion time: {estimated_left/60} minutes ({estimated_left/60/60} hours)')\r\n with open('failed_vectors.json', 'w') as f:\r\n f.write(json.dumps(failures,indent=4))\r\n\r\n def initVariable(self):\r\n for i in range(10):\r\n self.ev.append(0)\r\n for i in range(6):\r\n self.pv.append(0)\r\n\r\n def create_worksheet(self):\r\n if '2 User Database.xlsx' not in os.listdir():\r\n with open('2 User Database.xlsx') as f:\r\n f.write()\r\n \r\n def showBtns(self):\r\n startBtn = tk.Button(self.app, text=\"Start\", width=10, height=1, bg='green', fg='white', command=self.startAction)\r\n startBtn.grid(row=17, column=1, padx=30, sticky='w')\r\n \r\n stopBtn = tk.Button(self.app, text=\"Stop\", width=10, height=1, bg='red', fg='white', command=self.stopAction)\r\n stopBtn.grid(row=17, column=2, padx=30, sticky='w')\r\n \r\n clearBtn = tk.Button(self.app, text=\"Clear\", width=10, height=1, command=self.clearAction)\r\n clearBtn.grid(row=17, column=3, padx=30, sticky='w')\r\n \r\n closeBtn = tk.Button(self.app, text=\"Close\", width=10, height=1, command=self.closeAction)\r\n closeBtn.grid(row=17, column=4, padx=30, sticky='w')\r\n\r\n def keypress1(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n\r\n if event.char in '0123456789':\r\n self.ev[0] = self.ev[0] * 10 + int(event.char)\r\n\r\n if self.ev[0] > 0 and self.ev[0] < 4:\r\n messagebox.showinfo(\"Alert!\", \"Minimum member in the group is 4\")\r\n\r\n def keypress2(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n\r\n if event.char in '0123456789':\r\n self.ev[1] = self.ev[1] * 10 + int(event.char)\r\n \r\n def keypress3(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n\r\n if event.char in '0123456789':\r\n self.ev[2] = self.ev[2] * 10 + int(event.char)\r\n\r\n if self.ev[2] > 75 and self.ev[2] < 25:\r\n messagebox.showinfo(\"Alert!\", \"The value should be between 25 to 75\")\r\n\r\n def keypress4(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n\r\n if event.char in '0123456789':\r\n self.ev[3] = self.ev[3] * 10 + int(event.char)\r\n\r\n if self.ev[3] > 45 and self.ev[3] < 0:\r\n messagebox.showinfo(\"Alert!\", \"The value should be between 10 to 30\")\r\n \r\n def keypress5(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n\r\n if event.char in '0123456789':\r\n self.ev[4] = self.ev[4] * 10 + int(event.char)\r\n\r\n if self.ev[4] > 30 and self.ev[4] < 10:\r\n messagebox.showinfo(\"Alert!\", \"The value should be between 10 to 30\")\r\n \r\n def keypress6(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n\r\n if event.char in '0123456789':\r\n self.ev[5] = self.ev[5] * 10 + int(event.char)\r\n\r\n if self.ev[5] > 80 and self.ev[5] < 20:\r\n messagebox.showinfo(\"Alert!\", \"The value should be between 20 to 80\")\r\n \r\n def keypress7(self, event):\r\n # if event.char not in '234':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable. Try entering value 2, 3, 4\")\r\n # return\r\n\r\n if event.char in '234':\r\n self.ev[6] = self.ev[6] * 10 + int(event.char)\r\n\r\n if self.ev[6] > 4 and self.ev[6] < 2:\r\n messagebox.showinfo(\"Alert!\", \"Enter value 2, 3, 4 as per your requirement.\")\r\n \r\n def keypress8(self, event):\r\n # if event.char not in '0123':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable. Try entering value 0, 1, 2, 3\")\r\n # return\r\n\r\n if event.char in '0123':\r\n self.ev[7] = self.ev[7] * 10 + int(event.char)\r\n\r\n if self.ev[7] > 4 and self.ev[7] < 2:\r\n messagebox.showinfo(\"Alert!\", \"Enter value 0, 1, 2, 3 as per your requirement.\")\r\n \r\n def keypress9(self, event):\r\n print(event)\r\n\r\n def keypress10(self, event):\r\n print(event)\r\n\r\n def keypress1pv(self, event):\r\n \r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n if event.char in '0123456789':\r\n self.pv[0] = self.pv[0] * 10 + int(event.char)\r\n if self.pv[0] > 100 and self.ev[0] < 1:\r\n messagebox.showinfo(\"Alert!\", \"Enter value between 1-100\")\r\n\r\n def keypress2pv(self, event):\r\n # if event.char not in '0123456789':\r\n # messagebox.showinfo(\"Alert!\", \"Sorry. Only numbers are acceptable.\")\r\n # return\r\n if event.char in '0123456789':\r\n self.pv[1] = self.pv[1] * 10 + int(event.char)\r\n if self.pv[1] > 100 and self.ev[1] < 1:\r\n messagebox.showinfo(\"Alert!\", \"Enter value between 1-100\")\r\n\r\n def keypress3pv(self, event):\r\n return\r\n def keypress4pv(self, event):\r\n return\r\n def keypress5pv(self, event):\r\n return\r\n def keypress6pv(self, event):\r\n return\r\n def showLabels(self):\r\n # labels for EV\r\n for i in range(9):\r\n ev_lbl = tk.Label(self.app, text=\"EV \" + str(i + 1) + \":\")\r\n ev_lbl.grid(row=i, column=0, padx=10, pady=10, sticky='w') \r\n # labels for PV\r\n for i in range(6):\r\n ev_lbl = tk.Label(self.app, text=\"PV \" + str(i + 1) + \":\")\r\n ev_lbl.grid(row=i+10, column=0, padx=10, pady=10, sticky='w')\r\n\r\n def showEntry(self):\r\n self.entry1 = tk.Entry(self.app, width=20)\r\n self.entry1.bind(\"\", self.keypress1)\r\n self.entry1.grid(row=0, column=1, pady=10, sticky='w')\r\n\r\n self.entry2 = tk.Entry(self.app, width=20)\r\n self.entry2.bind(\"\", self.keypress2)\r\n self.entry2.grid(row=1, column=1, pady=10, sticky='w')\r\n\r\n self.entry3 = tk.Entry(self.app, width=20)\r\n self.entry3.bind(\"\", self.keypress3)\r\n self.entry3.grid(row=2, column=1, pady=10, sticky='w')\r\n\r\n self.entry4 = tk.Entry(self.app, width=20)\r\n self.entry4.bind(\"\", self.keypress4)\r\n self.entry4.grid(row=3, column=1, pady=10, sticky='w')\r\n\r\n self.entry5 = tk.Entry(self.app, width=20)\r\n self.entry5.bind(\"\", self.keypress5)\r\n self.entry5.grid(row=4, column=1, pady=10, sticky='w')\r\n\r\n self.entry6 = tk.Entry(self.app, width=20)\r\n self.entry6.bind(\"\", self.keypress6)\r\n self.entry6.grid(row=5, column=1, pady=10, sticky='w')\r\n\r\n self.entry7 = tk.Entry(self.app, width=20)\r\n self.entry7.bind(\"\", self.keypress7)\r\n self.entry7.grid(row=6, column=1, pady=10, sticky='w')\r\n\r\n self.entry8 = tk.Entry(self.app, width=20)\r\n self.entry8.bind(\"\", self.keypress8)\r\n self.entry8.insert(0, \"3\")\r\n self.entry8.grid(row=7, column=1, pady=10, sticky='w')\r\n\r\n self.entry9 = tk.Entry(self.app, width=20)\r\n self.entry9.bind(\"\", self.keypress9)\r\n self.entry9.insert(0, \"0.3333\")\r\n self.entry9.grid(row=8, column=1, pady=10, sticky='w')\r\n\r\n #entry values for PV varibles\r\n self.entry1pv = tk.Entry(self.app, width=20)\r\n self.entry1pv.bind(\"\", self.keypress1pv)\r\n self.entry1pv.grid(row=10, column=1, pady=10, sticky='w')\r\n\r\n self.entry2pv = tk.Entry(self.app, width=20)\r\n self.entry2pv.bind(\"\", self.keypress2pv)\r\n self.entry2pv.grid(row=11, column=1, pady=10, sticky='w')\r\n\r\n self.entry3pv = tk.Entry(self.app, width=20)\r\n self.entry3pv.bind(\"\", self.keypress3pv)\r\n self.entry3pv.grid(row=12, column=1, pady=10, sticky='w')\r\n\r\n self.entry4pv = tk.Entry(self.app, width=20)\r\n self.entry4pv.bind(\"\", self.keypress4pv)\r\n self.entry4pv.grid(row=13, column=1, pady=10, sticky='w')\r\n\r\n self.entry5pv = tk.Entry(self.app, width=20)\r\n self.entry5pv.bind(\"\", self.keypress5pv)\r\n self.entry5pv.grid(row=14, column=1, pady=10, sticky='w')\r\n\r\n self.entry6pv = tk.Entry(self.app, width=20)\r\n self.entry6pv.bind(\"\", self.keypress6pv)\r\n self.entry6pv.grid(row=15, column=1, pady=10, sticky='w')\r\n\r\n def matrix_run(self):\r\n pass\r\n\r\n #the enitre opertion starts here after the start button is clicked \r\n def startAction(self, vector=None, vector_array=None):\r\n\r\n def report(msg, _v):\r\n with open('checksum_failures.txt', 'a') as f:\r\n f.write('____________________\\n')\r\n f.write(msg + '\\n')\r\n f.write(', '.join(str(v) for v in _v))\r\n f.write('\\n')\r\n\r\n def _checksum(syfunc: int, period: int, line: int):\r\n c_count = 0\r\n c_value = 0\r\n last_checked = 0\r\n for i in range(self.ev1):\r\n c_UsRec3_val = sh_user.cell(i+2,4)\r\n c_UsRec8_val = sh_user.cell(i+2,9)\r\n if c_UsRec3_val.value == 0 or c_UsRec8_val.value == 'defected':\r\n continue\r\n c_UsRec4_val = sh_user.cell(i+2,5)\r\n if c_UsRec3_val.value != last_checked:\r\n for _i in range(self.ev1):\r\n c_ur3_sub = sh_user.cell(_i+2,4)\r\n c_ur8_sub = sh_user.cell(_i+2,9)\r\n if c_ur3_sub.value == 0 or c_ur8_sub.value == 'defected':\r\n continue\r\n c_ur4_sub = sh_user.cell(_i+2,5)\r\n if c_ur3_sub.value == c_UsRec3_val.value:\r\n c_count += 1\r\n c_value += c_ur4_sub.value\r\n if c_value%c_count != 0 or c_count != c_UsRec4_val.value:\r\n print('______________')\r\n print(f'Period {period}')\r\n print(f'-> Line {str(line)}')\r\n if self._matrix:\r\n run_log_index = self.run-1\r\n msg = f'Run {run_log_index}: SyFunc {syfunc} _checksum failed: c_value % c_count = {c_value%c_count} - supposed to be 0.\\nc_UsRec3_val:{c_UsRec3_val.value}'\r\n else:\r\n msg = f'SyFunc {syfunc} _checksum failed: c_value % c_count = {c_value%c_count} - supposed to be 0.\\nc_UsRec3_val:{c_UsRec3_val.value}'\r\n logging.error(msg)\r\n if vector_array:\r\n report(msg, vector_array)\r\n # time.sleep(5)\r\n # raise ValueError(f'SyFunc 3 Checksum failed: c_value % c_count = {c_value%c_count} - supposed to be 0.\\nc_UsRec3_val:{c_UsRec3_val.value}')\r\n last_checked = c_UsRec3_val.value\r\n c_count = 0\r\n c_value = 0\r\n \r\n def _checksum_sr1(_syRec1_val: int, syfunc: int, period: int, line: int):\r\n self.ev1 = self.ev[0]\r\n counter = 0\r\n for i in range(self.ev1):\r\n c_UsRec_3 = sh_user.cell(i+2, 4)\r\n if c_UsRec_3.value == 0:\r\n counter += 1\r\n if self.ev1 - _syRec1_val != counter:\r\n print('______________')\r\n print(f'Period {period}')\r\n print(f'-> Line {str(line)}')\r\n if self._matrix:\r\n run_log_index = self.run-1\r\n msg = f'Run {run_log_index}: SyFunc {syfunc} _checksum_sr1 failed: counter = {counter} - supposed to be {self.ev1 - _syRec1_val}'\r\n else:\r\n msg = f'SyFunc {syfunc} _checksum_sr1 failed: counter = {counter} - supposed to be {self.ev1 - _syRec1_val}'\r\n print(msg)\r\n logging.error(msg)\r\n if vector_array:\r\n report(msg, vector_array)\r\n \r\n\r\n start_iter=0\r\n counter = 0\r\n current_period_list=[]\r\n\r\n def get_valid_users() -> list:\r\n \"\"\"\r\n Returns list of user indexes (for Excel) where User Record 5\r\n is equal to 'valid'\r\n \"\"\"\r\n valid_users = []\r\n for i in range(self.ev1):\r\n UsRec5 = sh_user.cell(i+2,6)\r\n if UsRec5.value == 'valid':\r\n valid_users.append(i+2)\r\n return valid_users\r\n\r\n def get_select_users(_filter: str, u_rec: int) -> list:\r\n \"\"\"\r\n Returns list of user indexes (for Excel) where User Record 'u_rec'\r\n is equal to '_filter' argument\r\n \"\"\"\r\n select_users = []\r\n for i in range(self.ev1):\r\n UsRec = sh_user.cell(i+2,u_rec+1)\r\n if UsRec.value == _filter:\r\n select_users.append(i+2)\r\n return select_users\r\n \r\n def assign_variables():\r\n \"\"\"\r\n Assignes EV, PV, and System Record values based on current\r\n Period Data\r\n \"\"\"\r\n self.ev1 = self.ev[0]\r\n self.ev3 = self.ev[2]\r\n self.ev4 = self.ev[3]\r\n self.ev5 = self.ev[4]\r\n self.ev6 = self.ev[5]\r\n self.ev7 = self.ev[6]\r\n self.ev8 = self.ev[7]\r\n self.ev9 = self.ev[8]\r\n self.ev10 = self.ev[9]\r\n self.pv1 = self.pv[0]\r\n self.pv2 = self.pv[1]\r\n self.pv3 = self.pv[2]\r\n self.pv4 = self.pv[3]\r\n self.pv5 = self.pv[4]\r\n self.pv6 = self.pv[5]\r\n\r\n get_period = current_period_list.index(current_period_list[start_iter])\r\n self.row1=1\r\n self.row2=1\r\n self.row3=1\r\n if get_period==0:\r\n self.row1=2\r\n self.row2=3\r\n self.row3=4\r\n if get_period==1:\r\n self.row1=5\r\n self.row2=6\r\n self.row3=7\r\n if get_period==2:\r\n self.row1=8\r\n self.row2=9\r\n self.row3=10\r\n if get_period==3:\r\n self.row1=11\r\n self.row2=12\r\n self.row3=13\r\n if get_period==4:\r\n self.row1=14\r\n self.row2=15\r\n self.row3=16\r\n if get_period==5:\r\n self.row1=17\r\n self.row2=18\r\n self.row3=19\r\n if get_period==6:\r\n self.row1=20\r\n self.row2=21\r\n self.row3=22\r\n if get_period==7:\r\n self.row1=23\r\n self.row2=24\r\n self.row3=25\r\n if get_period==8:\r\n self.row1=26\r\n self.row2=27\r\n self.row3=28\r\n if get_period==9:\r\n self.row1=29\r\n self.row2=30\r\n self.row3=31\r\n\r\n self.SyRec1_p = sh_system.cell(self.row1,3)\r\n self.SyRec1_f = sh_system.cell(self.row2,3)\r\n self.SyRec1_r = sh_system.cell(self.row3,3) \r\n\r\n self.SyRec2_p = sh_system.cell(self.row1,4)\r\n self.SyRec2_f = sh_system.cell(self.row2,4)\r\n self.SyRec2_r = sh_system.cell(self.row3,4)\r\n\r\n self.SyRec3_p = sh_system.cell(self.row1,5)\r\n self.SyRec3_f = sh_system.cell(self.row2,5)\r\n self.SyRec3_r = sh_system.cell(self.row3,5)\r\n\r\n self.SyRec4_p = sh_system.cell(self.row1,6)\r\n self.SyRec4_f = sh_system.cell(self.row2,6)\r\n self.SyRec4_r = sh_system.cell(self.row3,6)\r\n\r\n self.SyRec5_p = sh_system.cell(self.row1,7)\r\n self.SyRec5_f = sh_system.cell(self.row2,7)\r\n self.SyRec5_r = sh_system.cell(self.row3,7)\r\n\r\n self.SyRec6_p = sh_system.cell(self.row1,8)\r\n self.SyRec6_f = sh_system.cell(self.row2,8)\r\n self.SyRec6_r = sh_system.cell(self.row3,8)\r\n\r\n self.SyRec7_p = sh_system.cell(self.row1,9)\r\n self.SyRec7_f = sh_system.cell(self.row2,9)\r\n self.SyRec7_r = sh_system.cell(self.row3,9)\r\n\r\n self.SyRec8_p = sh_system.cell(self.row1,10)\r\n self.SyRec8_f = sh_system.cell(self.row2,10)\r\n self.SyRec8_r = sh_system.cell(self.row3,10)\r\n\r\n self.SyRec9_p = sh_system.cell(self.row1,11)\r\n self.SyRec9_f = sh_system.cell(self.row2,11)\r\n self.SyRec9_r = sh_system.cell(self.row3,11)\r\n\r\n self.SyRec10_p = sh_system.cell(self.row1,12)\r\n self.SyRec10_f = sh_system.cell(self.row2,12)\r\n self.SyRec10_r = sh_system.cell(self.row3,12)\r\n\r\n self.SyRec11_p = sh_system.cell(self.row1,13)\r\n self.SyRec11_f = sh_system.cell(self.row2,13)\r\n self.SyRec11_r = sh_system.cell(self.row3,13)\r\n\r\n self.SyRec12_p = sh_system.cell(self.row1,14)\r\n self.SyRec12_f = sh_system.cell(self.row2,14)\r\n self.SyRec12_r = sh_system.cell(self.row3,14)\r\n\r\n self.SyRec13_p = sh_system.cell(self.row1,15)\r\n self.SyRec13_f = sh_system.cell(self.row2,15)\r\n self.SyRec13_r = sh_system.cell(self.row3,15)\r\n\r\n self.SyRec14_p = sh_system.cell(self.row1,16)\r\n self.SyRec14_f = sh_system.cell(self.row2,16)\r\n self.SyRec14_r = sh_system.cell(self.row3,16)\r\n\r\n self.SyRec15_p = sh_system.cell(self.row1,17)\r\n self.SyRec15_f = sh_system.cell(self.row2,17)\r\n self.SyRec15_r = sh_system.cell(self.row3,17)\r\n\r\n self.SyRec16_p = sh_system.cell(self.row1,18)\r\n self.SyRec16_f = sh_system.cell(self.row2,18)\r\n self.SyRec16_r = sh_system.cell(self.row3,18)\r\n\r\n self.SyRec17_p = sh_system.cell(self.row1,19)\r\n self.SyRec17_f = sh_system.cell(self.row2,19)\r\n self.SyRec17_r = sh_system.cell(self.row3,19)\r\n\r\n self.SyRec18_p = sh_system.cell(self.row1,20)\r\n self.SyRec18_f = sh_system.cell(self.row2,20)\r\n self.SyRec18_r = sh_system.cell(self.row3,20)\r\n\r\n self.SyRec19_p = sh_system.cell(self.row1,21)\r\n self.SyRec19_f = sh_system.cell(self.row2,21)\r\n self.SyRec19_r = sh_system.cell(self.row3,21)\r\n\r\n while start_iter<10:\r\n counter=counter+1\r\n current_period = 'Period Data {}'.format(counter)\r\n current_period_list.append(current_period)\r\n # print('The Current period is: {}'.format(current_period_list[start_iter]))\r\n\r\n if counter == 1:\r\n if not vector:\r\n self.ev[0] = int(self.entry1.get())\r\n self.ev1 = int(self.ev[0])\r\n print(self.ev1)\r\n \r\n self.ev[1] = float(self.entry2.get())\r\n self.ev2 = float(self.ev[1])\r\n print(self.ev2)\r\n \r\n self.ev[2] = float(self.entry3.get()) *.01\r\n self.ev3 = float(self.ev[2])\r\n print(self.ev3)\r\n \r\n self.ev[3] = float(self.entry4.get()) *.01\r\n self.ev4 = float(self.ev[3])\r\n print(self.ev4)\r\n \r\n self.ev[4] = float(self.entry5.get()) *.01\r\n self.ev5 = float(self.ev[4])\r\n print(self.ev5)\r\n \r\n self.ev[5] = float(self.entry6.get()) *.01\r\n self.ev6 = float(self.ev[5])\r\n print(self.ev6)\r\n \r\n self.ev[6] = float(self.entry7.get())\r\n self.ev7 = float(self.ev[6])\r\n print(self.ev7)\r\n \r\n self.ev[7] = float(self.entry8.get())\r\n self.ev8 = float(self.ev[7])\r\n print(self.ev8)\r\n \r\n self.ev[8] = float(self.entry9.get())\r\n self.ev9 = float(self.ev[8])\r\n print(self.ev9)\r\n\r\n self.ev[9] = float(self.ev2*0.025*self.ev1)\r\n self.ev10 = float(self.ev[9])\r\n print(self.ev10)\r\n\r\n self.pv[0] = float(self.entry1pv.get()) *.01\r\n self.pv1 = float(self.pv[0])\r\n print(self.pv1)\r\n \r\n self.pv[1] = float(self.entry2pv.get()) *.01\r\n self.pv2 = float(self.pv[1])\r\n print(self.pv2)\r\n \r\n self.pv[2] = float(self.entry3pv.get()) *.01\r\n self.pv3 = float(self.pv[2])\r\n print(self.pv3)\r\n \r\n self.pv[3] = float(self.entry4pv.get()) *.01\r\n self.pv4 = float(self.pv[3])\r\n print(self.pv4)\r\n \r\n self.pv[4] = float(self.entry5pv.get()) *.01\r\n self.pv5 = float(self.pv[4])\r\n print(self.pv5)\r\n \r\n self.pv[5] = float(self.entry6pv.get()) *.01\r\n self.pv6 = float(self.pv[5])\r\n print(self.pv6)\r\n\r\n elif vector:\r\n # Assigning vector values to EV and PV arrays\r\n\r\n if not self.edge:\r\n if self.ev[3]== 15:\r\n ev4_cell = 2\r\n if self.ev[3]== 18:\r\n ev4_cell = 3\r\n if self.ev[3]== 21:\r\n ev4_cell = 4\r\n if self.ev[3]== 24:\r\n ev4_cell = 5\r\n if self.ev[3]== 27:\r\n ev4_cell = 6\r\n \r\n if self.pv[3] == 8:\r\n pv4_cell = 7\r\n if self.pv[3] == 12:\r\n pv4_cell = 8\r\n if self.pv[3] == 16:\r\n pv4_cell = 9\r\n if self.pv[3] == 20:\r\n pv4_cell = 10\r\n\r\n if self.pv[4] == 77:\r\n pv5_cell = 11\r\n if self.pv[4] == 83:\r\n pv5_cell = 12\r\n if self.pv[4] == 89:\r\n pv5_cell = 13\r\n if self.pv[4] == 85:\r\n pv5_cell = 14\r\n\r\n if self.ev[2] == 40:\r\n ev3_cell = 15\r\n if self.ev[2] == 55:\r\n ev3_cell = 16\r\n if self.ev[2] == 70:\r\n ev3_cell = 17\r\n\r\n if self.ev[0] == 60:\r\n self.ev1_cell = 18\r\n if self.ev[0] == 68:\r\n self.ev1_cell = 19\r\n if self.ev[0] == 70:\r\n self.ev1_cell = 20\r\n if self.ev[0] == 85:\r\n self.ev1_cell = 21\r\n\r\n if self.ev[4] == 10:\r\n ev5_cell = 22\r\n if self.ev[4] == 20:\r\n ev5_cell = 23\r\n\r\n if self.ev[5] == 65:\r\n ev6_cell = 24\r\n if self.ev[5] == 85:\r\n ev6_cell = 25\r\n\r\n if self.ev[6] == 2:\r\n ev7_cell = 26\r\n if self.ev[6] == 3:\r\n ev7_cell = 27\r\n\r\n self.ev1 = int(self.ev[0])\r\n # print(self.ev1)\r\n \r\n self.ev2 = float(self.ev[1])\r\n # print(self.ev2)\r\n \r\n self.ev[2] = float(self.ev[2]) *.01\r\n self.ev3 = float(self.ev[2])\r\n # print(self.ev3)\r\n \r\n self.ev[3] = float(self.ev[3]) *.01\r\n self.ev4 = float(self.ev[3])\r\n # print(self.ev4)\r\n \r\n self.ev[4] = float(self.ev[4]) *.01\r\n self.ev5 = float(self.ev[4])\r\n # print(self.ev5)\r\n \r\n self.ev[5] = float(self.ev[5]) *.01\r\n self.ev6 = float(self.ev[5])\r\n # print(self.ev6)\r\n \r\n self.ev[6] = float(self.ev[6])\r\n self.ev7 = float(self.ev[6])\r\n # print(self.ev7)\r\n \r\n self.ev[7] = float(self.ev[7])\r\n self.ev8 = float(self.ev[7])\r\n # print(self.ev8)\r\n \r\n self.ev[8] = float(self.ev[8])\r\n self.ev9 = float(self.ev[8])\r\n # print(self.ev9)\r\n\r\n self.ev[9] = float(self.ev2*0.025*self.ev1)\r\n self.ev10 = float(self.ev[9])\r\n # print(self.ev10)\r\n\r\n self.pv[0] = float(self.pv[0]) *.01\r\n self.pv1 = float(self.pv[0])\r\n # print(self.pv1)\r\n \r\n self.pv[1] = float(self.pv[1]) *.01\r\n self.pv2 = float(self.pv[1])\r\n # print(self.pv2)\r\n \r\n self.pv[2] = float(self.pv[2]) *.01\r\n self.pv3 = float(self.pv[2])\r\n # print(self.pv3)\r\n \r\n self.pv[3] = float(self.pv[3]) *.01\r\n self.pv4 = float(self.pv[3])\r\n # print(self.pv4)\r\n \r\n self.pv[4] = float(self.pv[4]) *.01\r\n self.pv5 = float(self.pv[4])\r\n # print(self.pv5)\r\n \r\n self.pv[5] = float(self.pv[5]) *.01\r\n self.pv6 = float(self.pv[5])\r\n # print(self.pv6)\r\n\r\n \r\n # print(f'ev1: {self.ev1}')\r\n for i in range(self.ev1):\r\n val1 = sh_user.cell(i+2,1)\r\n val1.value = 'user{}'.format(i+1)\r\n #assigning UsRec9 into the database\r\n val2 = sh_user.cell(i+2,10)\r\n val2.value = 0\r\n #assigning UsRec10 into the database\r\n val3 = sh_user.cell(i+2,11)\r\n val3.value = 0\r\n #assigning UsRec11 into the database\r\n val4 = sh_user.cell(i+2,12)\r\n val4.value = self.ev1\r\n #assigning UsRec12 into the database\r\n val5 = sh_user.cell(i+2,13)\r\n val5.value = 'yes'\r\n #assigning UsRec13 into the database\r\n val6 = sh_user.cell(i+2,14)\r\n val6.value = 0\r\n wb_user.save(path_user)\r\n # print('Initial values for UsRec variables set!')\r\n\r\n # PAGE 8,9\r\n get_period = current_period_list.index(current_period_list[start_iter]) \r\n if current_period_list[get_period] == 'Period Data 1':\r\n for i in range(2):\r\n val = sh_system.cell(i+2,3)\r\n val.value = self.ev1\r\n #assigning SyRec2 into the database\r\n va2 = sh_system.cell(i+2,4)\r\n va2.value = self.ev10/self.ev1\r\n #assigning SyRec3 into the database\r\n va3 = sh_system.cell(i+2,5)\r\n va3.value = 0\r\n #assigning SyRec4 into the database\r\n va4 = sh_system.cell(i+2,6)\r\n va4.value = 0\r\n #assigning SyRec5 into the database\r\n va5 = sh_system.cell(i+2,7)\r\n va5.value = 0\r\n #assigning SyRec6 into the database\r\n va6 = sh_system.cell(i+2,8)\r\n va6.value = 0\r\n #assigning SyRec7 into the database\r\n va7 = sh_system.cell(i+2,9)\r\n va7.value = 0\r\n #assigning SyRec8 into the database\r\n va8 = sh_system.cell(i+2,10)\r\n va8.value = 0\r\n #assigning SyRec9 into the database\r\n val9 = sh_system.cell(i+2,11)\r\n val9.value = 0\r\n #assigning SyRec10 into the database\r\n val10 = sh_system.cell(i+2,12)\r\n val10.value = 0\r\n #assigning SyRec11 into the database\r\n val11 = sh_system.cell(i+2,13)\r\n val11.value = 0\r\n #assigning SyRec12 into the database\r\n val12 = sh_system.cell(i+2,14)\r\n val12.value = 0\r\n #assigning SyRec13 into the database\r\n val13 = sh_system.cell(i+2,15)\r\n val13.value = 0\r\n #assigning SyRec14 into the database\r\n val14 = sh_system.cell(i+2,16)\r\n val14.value = 0\r\n #assigning SyRec15 into the database\r\n val15 = sh_system.cell(i+2,17)\r\n val15.value = 0\r\n #assigning SyRec16 into the database\r\n val16 = sh_system.cell(i+2,18)\r\n val16.value = 'no'\r\n #assigning SyRec17 into the database\r\n val17 = sh_system.cell(i+2,19)\r\n val17.value = 0\r\n #assigning SyRec18 into the database\r\n val18 = sh_system.cell(i+2,20)\r\n val18.value = 0\r\n #assigning SyRec19 into the database\r\n val19 = sh_system.cell(i+2,21)\r\n val19.value = self.ev10/self.ev1\r\n\r\n for i in range(3,30):\r\n val = sh_system.cell(i+2,3)\r\n val.value = 0\r\n #assigning SyRec2 into the database\r\n va2 = sh_system.cell(i+2,4)\r\n va2.value = 0\r\n #assigning SyRec3 into the database\r\n va3 = sh_system.cell(i+2,5)\r\n va3.value = 0\r\n #assigning SyRec4 into the database\r\n va4 = sh_system.cell(i+2,6)\r\n va4.value = 0\r\n #assigning SyRec5 into the database\r\n va5 = sh_system.cell(i+2,7)\r\n va5.value = 0\r\n #assigning SyRec6 into the database\r\n va6 = sh_system.cell(i+2,8)\r\n va6.value = 0\r\n #assigning SyRec7 into the database\r\n va7 = sh_system.cell(i+2,9)\r\n va7.value = 0\r\n #assigning SyRec8 into the database\r\n va8 = sh_system.cell(i+2,10)\r\n va8.value = 0\r\n #assigning SyRec9 into the database\r\n val9 = sh_system.cell(i+2,11)\r\n val9.value = 0\r\n #assigning SyRec10 into the database\r\n val10 = sh_system.cell(i+2,12)\r\n val10.value = 0\r\n #assigning SyRec11 into the database\r\n val11 = sh_system.cell(i+2,13)\r\n val11.value = 0\r\n #assigning SyRec12 into the database\r\n val12 = sh_system.cell(i+2,14)\r\n val12.value = 0\r\n #assigning SyRec13 into the database\r\n val13 = sh_system.cell(i+2,15)\r\n val13.value = 0\r\n #assigning SyRec14 into the database\r\n val14 = sh_system.cell(i+2,16)\r\n val14.value = 0\r\n #assigning SyRec15 into the database\r\n val15 = sh_system.cell(i+2,17)\r\n val15.value = 0\r\n #assigning SyRec16 into the database\r\n val16 = sh_system.cell(i+2,18)\r\n val16.value = 0\r\n #assigning SyRec17 into the database\r\n val17 = sh_system.cell(i+2,19)\r\n val17.value = 0\r\n #assigning SyRec18 into the database\r\n val18 = sh_system.cell(i+2,20)\r\n val18.value = 0\r\n #assigning SyRec19 into the database\r\n val19 = sh_system.cell(i+2,21)\r\n val19.value = 0\r\n wb_system.save(path_system)\r\n # print('Initial values for SyRec variables set!')\r\n \r\n # Subgroup #FUNCTION FOR SUBGROUP EXECUTION\r\n self.ev1 = int(self.ev[0])\r\n step1_ev1 = self.ev1\r\n step2 = self.ev1/5\r\n step3 = round(step2/2.3333)\r\n step4 = step3*5\r\n step5 = step1_ev1 - step4\r\n step6 = step5/6\r\n step7 = round(step6/2)\r\n step8 = step7*6\r\n step9 = step5 - step8\r\n step10 = step9/7\r\n step11 = int(step10/2)\r\n step12 = step11*7\r\n step13 = step9 - step12\r\n step14 = int(step13/4)\r\n step15 = step13%4\r\n if step15 == 0:\r\n pass\r\n if step15 == 1:\r\n step3 = step3-1\r\n step7 = step7+1\r\n if step15 == 2:\r\n step3 = step3-1\r\n step11 = step11+1\r\n if step15 == 3:\r\n step3 = step3-1\r\n step14 = step14+2\r\n\r\n #subgroup division code END\r\n #now assigning number to the group\r\n #condition checking for group == 4 \r\n group_num = 1\r\n group_mem_count = 0\r\n temp_val_four = step14*4\r\n four_grp = []\r\n dep_num = 0\r\n for i in range(temp_val_four):\r\n d = sh_user.cell(i+2,3)\r\n d.value = 4\r\n a = sh_user.cell(i+2,5)\r\n a.value = 4\r\n label = sh_user.cell(i+2,2)\r\n # label.value = 'D'\r\n label.value = group_num\r\n four_grp.append(group_num)\r\n label_1 = sh_user.cell(i+2,4)\r\n # label_1.value = 'D'\r\n label_1.value = group_num\r\n group_mem_count += 1\r\n sh_user.cell(i+2,8).value = 'dependent'\r\n dep_num += 1\r\n if group_mem_count == 4:\r\n group_num += 1\r\n group_mem_count = 0\r\n \r\n # ('D group assigned!')\r\n # condition checking for group == 5 \r\n temp_val_five = step3*5\r\n for i in range(temp_val_five):\r\n d = sh_user.cell(i+temp_val_four+2,3)\r\n d.value = 5\r\n a = sh_user.cell(i+temp_val_four+2,5)\r\n a.value = 5\r\n label = sh_user.cell(i+temp_val_four+2,2)\r\n # label.value = 'A'\r\n label.value = group_num\r\n label_1 = sh_user.cell(i+temp_val_four+2,4)\r\n # label_1.value = 'A'\r\n label_1.value = group_num\r\n group_mem_count += 1\r\n if group_mem_count == 5:\r\n group_num += 1\r\n group_mem_count = 0\r\n \r\n # condition checking for group == 6\r\n temp_val_six = step7*6\r\n for i in range(temp_val_six):\r\n d = sh_user.cell(i+temp_val_four+temp_val_five+2,3)\r\n d.value = 6\r\n a = sh_user.cell(i+temp_val_four+temp_val_five+2,5)\r\n a.value = 6\r\n label = sh_user.cell(i+temp_val_four+temp_val_five+2,2)\r\n # label.value = 'B'\r\n label.value = group_num\r\n label_1 = sh_user.cell(i+temp_val_four+temp_val_five+2,4)\r\n # label_1.value = 'B'\r\n label_1.value = group_num\r\n group_mem_count += 1\r\n if group_mem_count == 6:\r\n group_num += 1\r\n group_mem_count = 0\r\n\r\n #condition checking for group == 7\r\n temp_val_seven = step11*7\r\n for i in range(temp_val_seven):\r\n d = sh_user.cell(i+temp_val_four+temp_val_five+temp_val_six+2,3)\r\n d.value = 7\r\n a = sh_user.cell(i+temp_val_four+temp_val_five+temp_val_six+2,5)\r\n a.value = 7\r\n label = sh_user.cell(i+temp_val_four+temp_val_five+temp_val_six+2,2)\r\n # label.value = 'C'\r\n label.value = group_num\r\n label_1 = sh_user.cell(i+temp_val_four+temp_val_five+temp_val_six+2,4)\r\n # label_1.value = 'C'\r\n label_1.value = group_num\r\n group_mem_count += 1\r\n if group_mem_count == 7:\r\n group_num += 1\r\n group_mem_count = 0\r\n wb_user.save(path_user)\r\n\r\n self.UsRec3_dict = {\"D\": temp_val_four, \"A\": temp_val_five, \"B\": temp_val_six , \"C\": temp_val_seven}\r\n checksum = temp_val_four + temp_val_five + temp_val_six + temp_val_seven\r\n if checksum != self.ev1:\r\n raise ValueError(f\"Initial group checksum failed: checksum:{checksum} != self.ev1:{self.ev1}\")\r\n # print(self.UsRec3_dict)\r\n\r\n #setting valid to UsRec5\r\n for i in range(self.ev1):\r\n valid_value = sh_user.cell(i+2,6)\r\n valid_value.value = 'valid'\r\n wb_user.save(path_user)\r\n # print ('group of four members: {}, group of five members: {}, group of six members: {}, group of seven members: {}, Total group: {})'.format(step14, step3, step7, step11, step14*4+step3*5+step7*6+step11*7))\r\n\r\n\r\n # RoleAssignment\r\n self.ev1 = self.ev[0]\r\n ev4 = self.ev[3]\r\n ev5 = self.ev[4]\r\n ev6 = self.ev[5]\r\n\r\n # Assign 'dependent' to equal EV6\r\n dependent_pct = dep_num/self.ev1\r\n remaining_pct = ev6 - dependent_pct\r\n if remaining_pct > 0:\r\n unassigned_dep = int(remaining_pct * self.ev1)\r\n\r\n rand_dep_user = sorted(random.sample(range(dep_num+1, self.ev1+1), unassigned_dep))\r\n \r\n #ROLE1 \r\n Role1_list = ['low-morale', 'unity-role']\r\n #EV 4 = Percentage of honest defectors\r\n role_ev4 = int(self.ev1*ev4)\r\n rand_defectors = sorted(random.sample(range(1, self.ev1), role_ev4))\r\n #EV 5 = Percentage of low-morale members\r\n role_ev5 = round(self.ev1*ev5)\r\n low_morale_list = []\r\n \r\n if ev5 > 0:\r\n while True:\r\n n = random.randint(1,self.ev1)\r\n if n not in rand_defectors and n not in low_morale_list:\r\n low_morale_list.append(n)\r\n if len(low_morale_list) == role_ev5 or len(low_morale_list) + len(rand_defectors) == self.ev1:\r\n # if len(low_morale_list) == role_ev5:\r\n break\r\n #Remaining members play a unity role\r\n unity_role = self.ev1 - (role_ev4 - role_ev5)\r\n #ROLE2\r\n #percentage of members unwilling to act alone\r\n role_ev6 = round(self.ev1*ev6)\r\n #Remaining members play a role of independent\r\n role_ev12 = self.ev1 - role_ev6\r\n #assigning UsRec6, ROLE1 values to excel\r\n assigned_dep = dep_num\r\n assigned_indep = 0\r\n for i in range(self.ev1):\r\n ur1 = sh_user.cell(i+2,2)\r\n UsRec6_init = sh_user.cell(i+2,7)\r\n if i+1 in rand_defectors:\r\n UsRec6_init.value = 'defector'\r\n elif i+1 in low_morale_list:\r\n UsRec6_init.value = 'low-morale'\r\n \r\n UsRec2_init = sh_user.cell(i+2,3)\r\n if UsRec2_init.value != 4 and i+1 in rand_dep_user:\r\n UsRec7_init = sh_user.cell(i+2,8)\r\n UsRec7_init.value = 'dependent'\r\n assigned_dep += 1\r\n elif UsRec2_init.value != 4 and i+1 not in rand_dep_user:\r\n UsRec7_init = sh_user.cell(i+2,8)\r\n UsRec7_init.value = 'independent'\r\n assigned_indep += 1\r\n wb_user.save(path_user)\r\n for i in range(self.ev1):\r\n UsRec6_init = sh_user.cell(i+2,7)\r\n if UsRec6_init.value != 'defector':\r\n if UsRec6_init.value != 'low-morale':\r\n UsRec6_init.value = 'unity-role'\r\n\r\n if assigned_dep + assigned_indep != self.ev1:\r\n print(f'Dependent/independent assignment error')\r\n \r\n wb_user.save(path_user)\r\n # print('Roles Assigned!')\r\n \r\n #################\r\n ## ___UsFunc1___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Pay Stage 1\r\n USER DEFECTION FUNCTION\r\n \"\"\"\r\n \r\n if current_period_list[start_iter] == 'Period Data 1':\r\n defector_count = 0\r\n current_group_num = 1\r\n \r\n # Setting defector values in each subgroup\r\n defected_cache = {}\r\n defected_subt = {}\r\n low_morale_cache = []\r\n lm_def = []\r\n for i in range(self.ev1):\r\n UsRec1 = sh_user.cell(i+2,2)\r\n UsRec13 = sh_user.cell(i+2,14)\r\n\r\n UsRec6 = sh_user.cell(i+2,7)\r\n UsRec7 = sh_user.cell(i+2,8)\r\n\r\n if UsRec6.value == 'defector' or i == self.ev1-1:\r\n if current_group_num not in defected_subt:\r\n defected_subt[current_group_num] = 0\r\n # PATH 1 for dependent\r\n if UsRec1.value == current_group_num or i == self.ev1-1:\r\n if UsRec6.value == 'defector':\r\n defector_count += 1\r\n UsRec13.value = defector_count\r\n if UsRec7.value == 'dependent':\r\n defected_subt[current_group_num] += 1\r\n if UsRec13.value >= self.ev7 or UsRec7.value == 'independent':\r\n # PATH 2 (Part 1 - assigning to cache for UsRec 6 & 13 incrementation in next for loop)\r\n # for independent and dependent\r\n defected_cache[current_group_num] = defector_count\r\n if UsRec1.value != current_group_num or i == self.ev1-1:\r\n if current_group_num in defected_cache:\r\n if defected_cache[current_group_num] < self.ev7:\r\n if defected_subt[current_group_num] != 0:\r\n defected_cache[current_group_num] = defector_count\r\n defected_cache[current_group_num] -= defected_subt[current_group_num]\r\n low_morale_cache.append(current_group_num)\r\n lm_def.append(current_group_num)\r\n \r\n if defector_count < self.ev7 and current_group_num not in defected_cache and defector_count != 0:\r\n # PATH 3 (Part 1 - assigning to cache for UsRec 6 & 7 values in next for loop)\r\n low_morale_cache.append(current_group_num)\r\n if i != self.ev1-1:\r\n defector_count = 1\r\n current_group_num = UsRec1.value\r\n if current_group_num not in defected_subt:\r\n defected_subt[current_group_num] = 0\r\n UsRec13.value = defector_count\r\n if UsRec7.value == 'independent':\r\n # PATH 2 (Part 1 - assigning to cache for UsRec 6 & 13 incrementation in next for loop)\r\n # for independent in next group\r\n defected_cache[current_group_num] = defector_count\r\n elif UsRec7.value == 'dependent':\r\n defected_subt[current_group_num] += 1\r\n wb_user.save(path_user)\r\n\r\n # PATH 2 & 3 (Part 2)\r\n for i in range(self.ev1):\r\n UsRec1 = sh_user.cell(i+2,2)\r\n UsRec3 = sh_user.cell(i+2,4)\r\n UsRec4 = sh_user.cell(i+2,5)\r\n UsRec5 = sh_user.cell(i+2,6)\r\n UsRec6 = sh_user.cell(i+2,7)\r\n UsRec7 = sh_user.cell(i+2,8)\r\n UsRec8 = sh_user.cell(i+2,9)\r\n UsRec12 = sh_user.cell(i+2,13)\r\n UsRec13 = sh_user.cell(i+2,14)\r\n\r\n if UsRec1.value in defected_cache:\r\n # PATH 2 (Part 2)\r\n UsRec13.value = defected_cache[UsRec1.value]\r\n group_mems = UsRec4.value - UsRec13.value\r\n if UsRec6.value == 'defector' and UsRec1.value not in lm_def:\r\n # Defectors >= ev7\r\n UsRec12.value = 'no'\r\n UsRec8.value = 'defected'\r\n self.SyRec1_p.value -= 1\r\n self.SyRec5_p.value += 1\r\n self.SyRec3_p.value += 1\r\n \r\n for _ in range(self.ev1):\r\n ur4 = sh_user.cell(_+2, 5)\r\n ur1 = sh_user.cell(_+2, 2)\r\n ur3 = sh_user.cell(_+2, 4)\r\n ur2 = sh_user.cell(_+2, 3)\r\n\r\n if ur4.value != 0:\r\n if UsRec3.value == ur3.value:\r\n ur4.value -= 1\r\n # ur4.value = group_mems\r\n if UsRec1.value == ur1.value:\r\n ur2.value -= 1\r\n UsRec3.value = 0\r\n UsRec4.value = 0\r\n UsRec5.value = 'NR'\r\n UsRec8.value = 'NR'\r\n UsRec12.value = 'NR'\r\n wb_user.save(path_user)\r\n\r\n if UsRec6.value == 'defector' and UsRec1.value in lm_def and UsRec7.value == 'independent':\r\n # Defectors < ev7 and independent defectors exist\r\n UsRec12.value = 'no'\r\n UsRec8.value = 'defected'\r\n self.SyRec1_p.value -= 1\r\n self.SyRec5_p.value += 1\r\n self.SyRec3_p.value += 1\r\n \r\n for _ in range(self.ev1):\r\n ur4 = sh_user.cell(_+2, 5)\r\n ur1 = sh_user.cell(_+2, 2)\r\n ur3 = sh_user.cell(_+2, 4)\r\n ur2 = sh_user.cell(_+2, 3)\r\n if ur4.value != 0:\r\n if UsRec3.value == ur3.value:\r\n ur4.value -= 1\r\n # ur4.value = group_mems\r\n if UsRec1.value == ur1.value:\r\n ur2.value -= 1\r\n UsRec3.value = 0\r\n UsRec4.value = 0\r\n UsRec5.value = 'NR'\r\n UsRec8.value = 'NR'\r\n UsRec12.value = 'NR'\r\n wb_user.save(path_user)\r\n\r\n if UsRec1.value in low_morale_cache and UsRec7.value == 'dependent':\r\n # PATH 3 (Part 2)\r\n if UsRec1.value in defected_cache:\r\n UsRec13.value = defected_cache[UsRec1.value]\r\n else:\r\n UsRec13.value = 0\r\n if UsRec6.value == 'defector':\r\n UsRec6.value = 'low-morale'\r\n wb_user.save(path_user)\r\n wb_system.save(path_system) \r\n\r\n _checksum(1, int(counter), 1080) \r\n \r\n #################\r\n # ___UsFunc2___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Pay Stage 2\r\n USER DEFECTION FUNCTION\r\n \"\"\"\r\n \r\n if current_period_list[start_iter] != 'Period Data 1':\r\n\r\n slope = (self.pv4 - self.pv2) / (self.pv3 - self.pv1)\r\n \r\n SyRec19_prev = sh_system.cell(self.row1-3,21)\r\n try:\r\n a = float(self.SyRec19_p.value)\r\n b = float(SyRec19_prev.value)\r\n Inc_premium = (a/b) - 1\r\n except Exception as e:\r\n print(e)\r\n print(f'SyRec19.value: {self.SyRec19_p.value}')\r\n print(f'SyRec19_prev.value: {SyRec19_prev.value}')\r\n print(f'row1: {self.row1}')\r\n print(f'row1-3: {self.row1-3}')\r\n\r\n valid_users = []\r\n for i in range(self.ev1):\r\n UsRec5 = sh_user.cell(i+2,6)\r\n if UsRec5.value == 'valid':\r\n valid_users.append(i+2)\r\n \r\n if Inc_premium >= self.pv1:\r\n # PATH1 \r\n skip_percent = (slope*Inc_premium - slope * self.pv1) + self.pv2\r\n\r\n skip_hash = round(self.SyRec1_p.value * skip_percent)\r\n skip_users = random.sample(valid_users, skip_hash)\r\n\r\n for i in range(self.ev1):\r\n index = i+2\r\n UsRec12 = sh_user.cell(index,13)\r\n if index in skip_users:\r\n UsRec12.value = 'no'\r\n\r\n if Inc_premium < self.pv1:\r\n try:\r\n num = (self.SyRec19_p.value/(float(self.ev10/self.ev1))-1)\r\n if num >= self.pv5:\r\n skip_hash = round(self.SyRec1_p.value * self.pv6)\r\n skip_users = random.sample(valid_users, skip_hash)\r\n\r\n # rand_skip_users = []\r\n # for _ in range(skip_hash):\r\n # n = random.randint(2,self.ev1)\r\n # while True:\r\n # if n in rand_skip_users:\r\n # n = random.randint(2,self.ev1)\r\n # elif n not in rand_skip_users:\r\n # rand_skip_users.append(n)\r\n # break\r\n for i in skip_users:\r\n UsRec12 = sh_user.cell(i,13)\r\n UsRec12.value = 'no'\r\n wb_user.save(path_user)\r\n if num < self.pv5:\r\n # PATH3\r\n if self.ev8 == 0:\r\n pass\r\n if self.ev8 == 1 or self.ev8 == 2 or self.ev8 == 3:\r\n self.ev[7] -= 1\r\n # valid_users = []\r\n # for i in range(self.ev1):\r\n # val = sh_user.cell(i+2,6)\r\n # if val.value == 'valid':\r\n # valid_users.append(i+2)\r\n rand_sel = random.choice(valid_users)\r\n rand_UsRec12 = sh_user.cell(rand_sel,13)\r\n rand_UsRec12.value = 'no'\r\n\r\n except ZeroDivisionError:\r\n pass\r\n\r\n wb_user.save(path_user)\r\n \r\n #################\r\n # ___SyFunc3___ #Validate premium function\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Pay Stage 3\r\n Validate premium function\r\n \"\"\"\r\n\r\n get_period = current_period_list.index(current_period_list[start_iter])\r\n valid_users = get_valid_users()\r\n\r\n path_1 = []\r\n path_2 = []\r\n for i in valid_users:\r\n \r\n UsRec1 = sh_user.cell(i, 2)\r\n UsRec2 = sh_user.cell(i, 3)\r\n UsRec3 = sh_user.cell(i, 4)\r\n UsRec4 = sh_user.cell(i, 5)\r\n UsRec5 = sh_user.cell(i, 6)\r\n UsRec8 = sh_user.cell(i, 9)\r\n UsRec12 = sh_user.cell(i, 13)\r\n \r\n if UsRec12.value == 'no':\r\n UsRec8.value = 'skipped'\r\n path_1.append(i)\r\n self.SyRec1_p.value -= 1\r\n self.SyRec5_p.value += 1 # potential incorrect copying or adding\r\n \r\n for _ in range(self.ev1):\r\n ur4 = sh_user.cell(_+2, 5)\r\n ur1 = sh_user.cell(_+2, 2)\r\n ur3 = sh_user.cell(_+2, 4)\r\n ur2 = sh_user.cell(_+2, 3)\r\n \r\n if ur4.value != 0:\r\n if UsRec3.value == ur3.value:\r\n ur4.value -= 1\r\n if UsRec1.value == ur1.value:\r\n ur2.value -= 1\r\n\r\n UsRec8.value = \"NR\"\r\n UsRec3.value = 0\r\n UsRec4.value = 0\r\n UsRec5.value = \"NR\"\r\n UsRec12.value = \"NR\"\r\n wb_user.save(path_user)\r\n\r\n elif UsRec12.value == 'yes':\r\n UsRec8.value = 'paid'\r\n \r\n wb_user.save(path_user)\r\n wb_system.save(path_system)\r\n\r\n _checksum(3, int(counter), 1265)\r\n _checksum_sr1(self.SyRec1_p.value, 3, int(counter), 1265)\r\n \r\n #################\r\n # ___SyFunc4___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Pay Stage 4\r\n Invalidate subgroup function\r\n \"\"\"\r\n\r\n get_period = current_period_list.index(current_period_list[start_iter])\r\n _path = 0\r\n\r\n for i in range(self.ev1): \r\n ur3 = sh_user.cell(i+2, 4)\r\n ur4 = sh_user.cell(i+2, 5)\r\n ur8 = sh_user.cell(i+2, 9)\r\n ur5 = sh_user.cell(i+2, 6)\r\n ur10 = sh_user.cell(i+2, 11)\r\n ur11 = sh_user.cell(i+2, 12)\r\n \r\n \r\n if ur4.value == 1 or ur4.value == 2 or ur4.value == 3:\r\n if ur8.value == 'paid':\r\n #UsRec8 = 'paid-invalid'\r\n ur8.value = 'paid-invalid'\r\n # UsRec5 = 'invalid'\r\n ur5.value = 'invalid'\r\n ur10.value = ur11.value\r\n \r\n self.SyRec6_p.value += 1 \r\n wb_system.save(path_system)\r\n wb_user.save(path_user)\r\n \r\n assign_variables()\r\n if current_period_list[get_period] == 'Period Data 1':\r\n self.SyRec1_f.value = self.SyRec1_p.value\r\n self.SyRec1_r.value = self.SyRec1_p.value\r\n \r\n # SyRec2 = sh_system.cell(2,4)\r\n # SyRec2_f = sh_system.cell(3,4)\r\n # SyRec2_f.value = SyRec2.value\r\n # SyRec2_r = sh_system.cell(4,4)\r\n # SyRec2_r.value = SyRec2.value\r\n self.SyRec2_f.value = self.SyRec2_p.value\r\n self.SyRec2_r.value = self.SyRec2_p.value\r\n \r\n self.SyRec3_f.value = self.SyRec3_p.value\r\n self.SyRec3_r.value = self.SyRec3_p.value\r\n \r\n self.SyRec4_f.value = self.SyRec4_p.value\r\n self.SyRec4_r.value = self.SyRec4_p.value\r\n \r\n self.SyRec5_f.value = self.SyRec5_p.value\r\n self.SyRec5_r.value = self.SyRec5_p.value\r\n \r\n self.SyRec6_f.value = self.SyRec6_p.value\r\n self.SyRec6_r.value = self.SyRec6_p.value\r\n \r\n self.SyRec7_f.value = self.SyRec7_p.value\r\n self.SyRec7_r.value = self.SyRec7_p.value\r\n \r\n self.SyRec8_f.value = self.SyRec8_p.value\r\n self.SyRec8_r.value = self.SyRec8_p.value\r\n \r\n self.SyRec9_f.value = self.SyRec9_p.value\r\n self.SyRec9_r.value = self.SyRec9_p.value\r\n \r\n self.SyRec10_f.value= self.SyRec10_p.value\r\n self.SyRec10_r.value = self.SyRec10_p.value\r\n \r\n self.SyRec11_f.value= self.SyRec11_p.value\r\n self.SyRec11_r.value = self.SyRec11_p.value\r\n \r\n self.SyRec12_f.value= self.SyRec12_p.value\r\n self.SyRec12_r.value = self.SyRec12_p.value\r\n \r\n self.SyRec13_f.value= self.SyRec13_p.value\r\n self.SyRec13_r.value = self.SyRec13_p.value\r\n \r\n self.SyRec14_f.value= self.SyRec14_p.value\r\n self.SyRec14_r.value = self.SyRec14_p.value\r\n \r\n self.SyRec15_f.value= self.SyRec15_p.value\r\n self.SyRec15_r.value = self.SyRec15_p.value\r\n \r\n self.SyRec16_f.value= self.SyRec16_p.value\r\n self.SyRec16_r.value = self.SyRec16_p.value\r\n \r\n self.SyRec17_f.value= self.SyRec17_p.value\r\n self.SyRec17_r.value = self.SyRec17_p.value\r\n \r\n self.SyRec18_f.value= self.SyRec18_p.value\r\n self.SyRec18_r.value = self.SyRec18_p.value\r\n \r\n self.SyRec19_f.value= self.SyRec19_p.value\r\n self.SyRec19_r.value = self.SyRec19_p.value\r\n\r\n #################\r\n # ___SyFunc5___\r\n #################\r\n assign_variables()\r\n self.SyRec9_f.value = self.SyRec3_f.value * self.SyRec19_f.value\r\n sh_system.cell(4,11).value = self.SyRec9_f.value\r\n wb_system.save(path_system)\r\n _checksum(4, int(counter), 1480)\r\n assign_variables()\r\n if current_period_list[get_period] != 'Period Data 1':\r\n\r\n self.SyRec1_r.value= self.SyRec1_p.value\r\n # elf.SyRec1 = sh_system.cell(row1,3)\r\n # self.SyRec1_r = sh_system.cell(row3,3)\r\n # self.SyRec1_r.value= self.SyRec1.value\r\n \r\n self.SyRec2_r.value= self.SyRec2_p.value\r\n \r\n self.SyRec3_r.value= self.SyRec3_p.value\r\n \r\n self.SyRec4_r.value= self.SyRec4_p.value\r\n \r\n self.SyRec5_r.value= self.SyRec5_p.value\r\n \r\n self.SyRec6_r.value= self.SyRec6_p.value\r\n \r\n self.SyRec7_r.value= self.SyRec7_p.value\r\n \r\n self.SyRec8_r.value= self.SyRec8_p.value\r\n \r\n self.SyRec9_r.value= self.SyRec9_p.value\r\n \r\n self.SyRec10_r.value= self.SyRec10_p.value\r\n \r\n self.SyRec11_r.value= self.SyRec11_p.value\r\n \r\n self.SyRec12_r.value= self.SyRec12_p.value\r\n \r\n self.SyRec13_r.value= self.SyRec13_p.value\r\n \r\n self.SyRec14_r.value= self.SyRec14_p.value\r\n \r\n self.SyRec15_r.value= self.SyRec15_p.value\r\n \r\n self.SyRec16_r.value= self.SyRec16_p.value\r\n \r\n self.SyRec17_r.value= self.SyRec17_p.value\r\n \r\n self.SyRec18_r.value= self.SyRec18_p.value\r\n \r\n self.SyRec19_r.value= self.SyRec19_p.value\r\n\r\n ################# \r\n # __SyFunc6__ #User quit function\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Reorg Stage 1\r\n \"\"\"\r\n\r\n low_morale_users = get_select_users('low-morale', 6)\r\n\r\n for i in range(self.ev1):\r\n _path = 0\r\n UsRec1 = sh_user.cell(i+2, 2)\r\n UsRec2 = sh_user.cell(i+2, 3)\r\n UsRec3 = sh_user.cell(i+2, 4)\r\n UsRec4 = sh_user.cell(i+2, 5)\r\n UsRec5 = sh_user.cell(i+2, 6)\r\n UsRec6 = sh_user.cell(i+2, 7)\r\n UsRec7 = sh_user.cell(i+2, 8)\r\n UsRec8 = sh_user.cell(i+2, 9)\r\n UsRec9 = sh_user.cell(i+2, 10)\r\n UsRec12 = sh_user.cell(i+2, 13)\r\n # 1 2 3 2 4\r\n if UsRec8.value == 'paid-invalid':\r\n if UsRec6.value == 'low-morale':\r\n # Path 1\r\n prob = random.uniform(0,1)\r\n if prob >= self.ev9:\r\n _path = 3\r\n elif prob < self.ev9:\r\n _path = 2\r\n\r\n if _path == 3:\r\n if UsRec7.value == 'independent' or UsRec2.value >= 2:\r\n #path4 \r\n self.SyRec8_r.value += 1\r\n else:\r\n _path = 2\r\n\r\n # wb_system.save(path_system) \r\n if _path == 2:\r\n UsRec8.value = 'quit'\r\n self.SyRec1_r.value -= 1\r\n self.SyRec7_r.value += 1\r\n for _i in range(self.ev1):\r\n ur4 = sh_user.cell(_i+2, 5)\r\n ur3 = sh_user.cell(_i+2, 4)\r\n ur2 = sh_user.cell(_i+2, 3)\r\n ur1 = sh_user.cell(_i+2, 2)\r\n\r\n if ur4.value != 0:\r\n if ur3.value == UsRec3.value:\r\n ur4.value -= 1\r\n if ur1.value == UsRec1.value:\r\n ur2.value -= 1\r\n UsRec8.value = \"NR\"\r\n UsRec3.value = 0\r\n UsRec4.value = 0\r\n UsRec5.value = \"NR\"\r\n UsRec12.value = \"NR\"\r\n wb_system.save(path_system)\r\n wb_user.save(path_user)\r\n\r\n for i in range(self.ev1):\r\n _path = 0\r\n UsRec1 = sh_user.cell(i+2, 2)\r\n UsRec2 = sh_user.cell(i+2, 3)\r\n UsRec3 = sh_user.cell(i+2, 4)\r\n UsRec4 = sh_user.cell(i+2, 5)\r\n UsRec5 = sh_user.cell(i+2, 6)\r\n UsRec6 = sh_user.cell(i+2, 7)\r\n UsRec7 = sh_user.cell(i+2, 8)\r\n UsRec8 = sh_user.cell(i+2, 9)\r\n UsRec9 = sh_user.cell(i+2, 10)\r\n UsRec12 = sh_user.cell(i+2, 13)\r\n # 1 2 3 2 4\r\n if UsRec8.value == 'paid-invalid':\r\n if UsRec6.value != 'low-morale':\r\n if UsRec7.value == 'independent' or UsRec2.value >= 2:\r\n #path4 \r\n self.SyRec8_r.value += 1\r\n else:\r\n _path = 2\r\n\r\n # wb_system.save(path_system) \r\n if _path == 2:\r\n UsRec8.value = 'quit'\r\n self.SyRec1_r.value -= 1\r\n self.SyRec7_r.value += 1\r\n for _i in range(self.ev1):\r\n ur4 = sh_user.cell(_i+2, 5)\r\n ur3 = sh_user.cell(_i+2, 4)\r\n ur2 = sh_user.cell(_i+2, 3)\r\n ur1 = sh_user.cell(_i+2, 2)\r\n\r\n if ur4.value != 0:\r\n if ur3.value == UsRec3.value:\r\n ur4.value -= 1\r\n if ur1.value == UsRec1.value:\r\n ur2.value -= 1\r\n UsRec8.value = \"NR\"\r\n UsRec3.value = 0\r\n UsRec4.value = 0\r\n UsRec5.value = \"NR\"\r\n UsRec12.value = \"NR\"\r\n wb_system.save(path_system)\r\n wb_user.save(path_user)\r\n\r\n _checksum(6, int(counter), 1520)\r\n _checksum_sr1(self.SyRec1_r.value, 6, int(counter), 1520)\r\n\r\n ################# \r\n # ___SyFunc7___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Reorg Stage 2\r\n \"\"\"\r\n\r\n _path = 0\r\n loop_reset = False\r\n invalid_loop = 0 # UsRec4 for group absorbing invalid member not reassigned twice\r\n found_subgrp = 0\r\n pass_over = [\"defected\",\"skipped\",\"quit\",\"NR\"]\r\n for i in range(self.ev1):\r\n ur8 = sh_user.cell(i+2, 9) \r\n ur4 = sh_user.cell(i+2, 5)\r\n if ur8.value =='paid-invalid':\r\n\r\n if ur4.value == 1:\r\n ur1 = sh_user.cell(i+2, 2)\r\n base_ur4 = ur4.value\r\n invalid_loop += 1\r\n\r\n # Chnage ur4 values of group absorbing invalid member\r\n if invalid_loop == 1:\r\n old_ur4 = 0\r\n new_ur4 = 0 # Used to set subgroup so not override values in edge cases\r\n for _i in range(self.ev1):\r\n ur4_sub = sh_user.cell(_i+2, 5)\r\n ur3_sub = sh_user.cell(_i+2, 4)\r\n ur5_sub = sh_user.cell(_i+2, 6)\r\n ur8_sub = sh_user.cell(_i+2, 9)\r\n if new_ur4 == 0:\r\n if ur4_sub.value == 6 and ur8_sub.value not in pass_over and ur5_sub.value == 'valid':\r\n found_subgrp = ur3_sub.value\r\n ur4_sub.value = 7\r\n old_ur4 = 6\r\n new_ur4 = 7\r\n _path = 1\r\n elif ur4_sub.value == old_ur4 and ur8_sub.value not in pass_over and ur5_sub.value == 'valid':\r\n if ur3_sub.value == found_subgrp:\r\n ur4_sub.value = new_ur4\r\n\r\n if _path != 1:\r\n for _i in range(self.ev1):\r\n ur4_sub = sh_user.cell(_i+2, 5)\r\n ur3_sub = sh_user.cell(_i+2, 4)\r\n ur5_sub = sh_user.cell(_i+2, 6)\r\n ur8_sub = sh_user.cell(_i+2, 9)\r\n if new_ur4 == 0:\r\n if ur4_sub.value == 5 and ur8_sub.value not in pass_over and ur5_sub.value == 'valid':\r\n found_subgrp = ur3_sub.value\r\n ur4_sub.value = 6\r\n old_ur4 = 5\r\n new_ur4 = 6\r\n elif ur4_sub.value == old_ur4 and ur8_sub.value not in pass_over and ur5_sub.value == 'valid':\r\n if ur3_sub.value == found_subgrp:\r\n ur4_sub.value = new_ur4\r\n\r\n if invalid_loop == base_ur4:\r\n loop_reset = True\r\n ur4.value = new_ur4\r\n ur3 = sh_user.cell(i+2, 4)\r\n ur3.value = found_subgrp #!!! not referenced\r\n ur5= sh_user.cell(i+2, 6)\r\n ur5.value = 'valid'\r\n ur8.value = 'reorg'\r\n ur9= sh_user.cell(i+2, 10)\r\n ur9.value = ur9.value + 1\r\n\r\n if loop_reset:\r\n invalid_loop = 0\r\n loop_reset = False\r\n wb_user.save(path_user)\r\n _checksum(7, int(counter), 1761)\r\n \r\n loop_reset = False\r\n invalid_loop = 0 # UsRec4 for group absorbing invalid member not reassigned twice\r\n _path = 0\r\n found_subgrp = 0\r\n reorg_cache = {}\r\n for i in range(self.ev1):\r\n ur8 = sh_user.cell(i+2, 9)\r\n ur3 = sh_user.cell(i+2, 4)\r\n ur4 = sh_user.cell(i+2, 5)\r\n if ur8.value =='paid-invalid':\r\n ur1 = sh_user.cell(i+2, 2)\r\n\r\n if ur4.value == 2:\r\n base_ur4 = ur4.value\r\n invalid_loop += 1\r\n\r\n # Change ur4 values of group absorbing invalid member\r\n if invalid_loop == 1 and ur3.value not in reorg_cache:\r\n old_ur4 = 0\r\n new_ur4 = 0 # Used to set subgroup so not override values in edge cases\r\n for _i in range(self.ev1):\r\n ur4_sub = sh_user.cell(_i+2, 5)\r\n ur3_sub = sh_user.cell(_i+2, 4)\r\n ur5_sub = sh_user.cell(_i+2, 6)\r\n ur8_sub = sh_user.cell(_i+2, 9)\r\n if new_ur4 == 0:\r\n if ur4_sub.value == 5 and ur8_sub.value not in pass_over:# and ur5_sub.value == 'valid':\r\n found_subgrp = ur3_sub.value\r\n ur4_sub.value = 7\r\n old_ur4 = 5\r\n new_ur4 = 7\r\n elif ur4_sub.value == 4 and ur8_sub.value not in pass_over:# and ur5_sub.value == 'valid':\r\n found_subgrp = ur3_sub.value\r\n ur4_sub.value = 6\r\n old_ur4 = 4\r\n new_ur4 = 6\r\n elif ur4_sub.value == old_ur4 and ur8_sub.value not in pass_over:# and ur5_sub.value == 'valid':\r\n if ur3_sub.value == found_subgrp:\r\n ur4_sub.value = new_ur4\r\n\r\n reorg_cache.update({ur3.value:found_subgrp})\r\n \r\n if invalid_loop == base_ur4:\r\n loop_reset = True\r\n if ur3.value in reorg_cache:\r\n found_subgrp = reorg_cache[ur3.value]\r\n loop_reset = True\r\n ur4.value = new_ur4\r\n ur3.value = found_subgrp\r\n ur5= sh_user.cell(i+2, 6)\r\n ur5.value = 'valid'\r\n ur8.value = 'reorg'\r\n ur9= sh_user.cell(i+2, 10)\r\n ur9.value = ur9.value + 1\r\n \r\n if loop_reset:\r\n invalid_loop = 0\r\n loop_reset = False\r\n wb_user.save(path_user)\r\n _checksum(7, int(counter), 1761) \r\n \r\n loop_reset = False\r\n invalid_loop = 0 # UsRec4 for group absorbing invalid member not reassigned twice\r\n _path = 0\r\n found_subgrp = 0\r\n reorg_cache = {}\r\n for i in range(self.ev1):\r\n ur8 = sh_user.cell(i+2, 9)\r\n ur4 = sh_user.cell(i+2, 5)\r\n \r\n if ur8.value =='paid-invalid':\r\n ur1 = sh_user.cell(i+2, 2)\r\n ur3 = sh_user.cell(i+2, 4)\r\n\r\n if ur4.value == 3:\r\n base_ur4 = ur4.value\r\n invalid_loop += 1\r\n\r\n # Change ur4 values of group absorbing invalid member\r\n if invalid_loop == 1 and ur3.value not in reorg_cache:\r\n grp_found = 0\r\n old_ur4 = 0\r\n new_ur4 = 0 # Used to set subgroup so not override values in edge cases\r\n for _i in range(self.ev1):\r\n \r\n ur4_sub = sh_user.cell(_i+2, 5)\r\n ur3_sub = sh_user.cell(_i+2, 4)\r\n ur5_sub = sh_user.cell(_i+2, 6)\r\n ur8_sub = sh_user.cell(_i+2, 9)\r\n if new_ur4 == 0:\r\n if ur4_sub.value == 3 and ur3_sub.value != ur3.value and ur8_sub.value not in pass_over:# and ur5_sub.value == 'valid':\r\n found_subgrp = ur3_sub.value\r\n ur4_sub.value = 6\r\n old_ur4 = 3\r\n new_ur4 = 6\r\n _path = 2\r\n ur5_sub.value = 'valid'\r\n ur8_sub.value = 'reorg'\r\n ur9_sub = sh_user.cell(_i+2, 10)\r\n ur9_sub.value = ur9_sub.value + 1\r\n grp_found = ur3_sub.value\r\n elif ur4_sub.value == old_ur4 and ur8_sub.value not in pass_over: #and ur5_sub.value == 'valid':\r\n if ur3_sub.value == found_subgrp:\r\n ur4_sub.value = new_ur4\r\n ur5_sub.value = 'valid'\r\n ur8_sub.value = 'reorg'\r\n ur9_sub = sh_user.cell(_i+2, 10)\r\n ur9_sub.value = ur9_sub.value + 1\r\n\r\n if grp_found != 0:\r\n reorg_cache.update({ur3.value:found_subgrp})\r\n\r\n if grp_found == 0:\r\n for _i in range(self.ev1):\r\n \r\n ur4_sub = sh_user.cell(_i+2, 5)\r\n ur3_sub = sh_user.cell(_i+2, 4)\r\n ur5_sub = sh_user.cell(_i+2, 6)\r\n ur8_sub = sh_user.cell(_i+2, 9)\r\n if new_ur4 == 0:\r\n if ur4_sub.value == 4 and ur8_sub.value not in pass_over: #and ur5_sub.value == 'valid':\r\n found_subgrp = ur3_sub.value\r\n old_subgroup = ur3.value\r\n ur4_sub.value = 7\r\n old_ur4 = 4\r\n new_ur4 = 7\r\n _path = 1\r\n \r\n elif ur4_sub.value == old_ur4 and ur8_sub.value not in pass_over:# and ur5_sub.value == 'valid':\r\n if ur3_sub.value == found_subgrp:\r\n ur4_sub.value = new_ur4\r\n reorg_cache.update({ur3.value:found_subgrp})\r\n\r\n if invalid_loop == base_ur4:\r\n loop_reset = True\r\n\r\n if ur3.value in reorg_cache:\r\n found_subgrp = reorg_cache[ur3.value]\r\n loop_reset = True\r\n\r\n if _path == 2:\r\n ur4.value = new_ur4\r\n ur3.value = found_subgrp\r\n ur5= sh_user.cell(i+2, 6)\r\n ur5.value = 'valid'\r\n ur8.value = 'reorg'\r\n ur9= sh_user.cell(i+2, 10)\r\n ur9.value = ur9.value + 1\r\n\r\n if _path == 1:\r\n ur4.value = new_ur4\r\n ur3 = sh_user.cell(i+2, 4)\r\n ur3.value = found_subgrp\r\n ur5= sh_user.cell(i+2, 6)\r\n ur5.value = 'valid'\r\n ur8.value = 'reorg'\r\n ur9= sh_user.cell(i+2, 10)\r\n ur9.value = ur9.value + 1\r\n \r\n if loop_reset:\r\n invalid_loop = 0\r\n loop_reset = False\r\n\r\n wb_user.save(path_user)\r\n\r\n _checksum(7, int(counter), 1761)\r\n\r\n #################\r\n # ___SyFunc8___ Claims / refunds function\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Reorg Stage 4\r\n \"\"\"\r\n \r\n prob = round(random.uniform(0,1),2)\r\n if self.ev3 > prob:\r\n self.SyRec16_r.value = 'yes'\r\n elif self.ev3 < prob:\r\n self.SyRec16_r.value = \"no\"\r\n self.SyRec17_r.value = self.SyRec2_r.value\r\n wb_system.save(path_system)\r\n \r\n #################\r\n # ___SyFunc8.5___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Reorg Stage 4.5\r\n \"\"\"\r\n self.SyRec11_r.value = self.SyRec5_r.value * self.SyRec19_r.value\r\n self.SyRec13_r.value = self.SyRec6_r.value * self.SyRec19_r.value\r\n wb_system.save(path_system) \r\n\r\n ################# \r\n # ___SyFunc9___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Reorg Stage 5\r\n \"\"\"\r\n \r\n try:\r\n self.SyRec2_r.value = float(self.ev10)/self.SyRec1_r.value\r\n except ZeroDivisionError:\r\n pass\r\n\r\n try:\r\n self.SyRec14_r.value = self.SyRec9_r.value + self.SyRec11_r.value + self.SyRec13_r.value\r\n except ZeroDivisionError:\r\n pass\r\n\r\n try:\r\n self.SyRec15_r.value = self.SyRec14_r.value / self.SyRec1_r.value\r\n except ZeroDivisionError:\r\n pass\r\n \r\n for i in range(self.ev1):\r\n us10 = sh_user.cell(i+2, 11)\r\n us11 = sh_user.cell(i+2, 12)\r\n if us10.value != 0:\r\n us11.value = self.SyRec2_r.value + self.SyRec15_r.value - us10.value\r\n us10.value = 0\r\n else:\r\n us11.value = self.SyRec2_r.value + self.SyRec15_r.value - self.SyRec18_r.value\r\n self.SyRec19_r.value = self.SyRec2_r.value + self.SyRec15_r.value\r\n wb_system.save(path_system)\r\n wb_user.save(path_user) \r\n\r\n #################\r\n # ___SyFunc11___\r\n #################\r\n assign_variables()\r\n \"\"\"\"\r\n Reorg Stage 7\r\n \"\"\"\r\n _path = 0\r\n get_period = current_period_list.index(current_period_list[start_iter]) \r\n\r\n if current_period_list[get_period] != 'Period Data 10':\r\n total = self.SyRec3_r.value+self.SyRec5_r.value+self.SyRec7_r.value\r\n\r\n if total > 0:\r\n _path = 1\r\n elif total == 0:\r\n _path = 2\r\n\r\n if _path == 1:\r\n \r\n if get_period==0:\r\n pay_row=2\r\n reorg_row=4\r\n new_pay_row = 5\r\n if get_period==1:\r\n pay_row=5\r\n reorg_row=7\r\n new_pay_row = 8\r\n if get_period==2:\r\n pay_row=8\r\n reorg_row=10\r\n new_pay_row = 11\r\n if get_period==3:\r\n pay_row=11\r\n reorg_row=13\r\n new_pay_row = 14\r\n if get_period==4:\r\n pay_row=14\r\n reorg_row=16\r\n new_pay_row = 17\r\n if get_period==5:\r\n pay_row=17\r\n reorg_row=19\r\n new_pay_row = 20\r\n if get_period==6:\r\n pay_row=20\r\n reorg_row=22\r\n new_pay_row = 23\r\n if get_period==7:\r\n pay_row=23\r\n reorg_row=25\r\n new_pay_row = 26\r\n if get_period==8:\r\n pay_row=26\r\n reorg_row=28\r\n new_pay_row = 29\r\n if get_period==9:\r\n pay_row=29\r\n reorg_row=31\r\n\r\n #copying values of previous to current\r\n SyRec1_r = sh_system.cell(reorg_row,3)\r\n SyRec1_new_p = sh_system.cell(new_pay_row,3)\r\n # SyRec1_r.value = SyRec1_p.value\r\n SyRec1_new_p.value = SyRec1_r.value\r\n\r\n SyRec2_r = sh_system.cell(reorg_row,4)\r\n SyRec2_new_p = sh_system.cell(new_pay_row,4)\r\n # SyRec2_r.value = SyRec2_p.value\r\n SyRec2_new_p.value = SyRec2_r.value\r\n\r\n SyRec3_r = sh_system.cell(reorg_row,5)\r\n SyRec3_new_p = sh_system.cell(new_pay_row,5)\r\n # SyRec3_r.value = SyRec3_p.value\r\n SyRec3_new_p.value = SyRec3_r.value\r\n \r\n SyRec4_r = sh_system.cell(reorg_row,6)\r\n SyRec4_new_p = sh_system.cell(new_pay_row,6)\r\n # SyRec4_r.value = SyRec4_p.value\r\n SyRec4_new_p.value = SyRec4_r.value\r\n \r\n SyRec5_r = sh_system.cell(reorg_row,7)\r\n SyRec5_new_p = sh_system.cell(new_pay_row,7)\r\n # SyRec5_r.value = SyRec5_p.value\r\n SyRec5_new_p.value = SyRec5_r.value\r\n\r\n SyRec6_r = sh_system.cell(reorg_row,8)\r\n SyRec6_new_p = sh_system.cell(new_pay_row,8)\r\n # SyRec6_r.value = SyRec6_p.value\r\n SyRec6_new_p.value = SyRec6_r.value\r\n \r\n SyRec7_r = sh_system.cell(reorg_row,9)\r\n SyRec7_new_p = sh_system.cell(new_pay_row,9)\r\n # SyRec7_r.value = SyRec7_p.value\r\n SyRec7_new_p.value = SyRec7_r.value\r\n \r\n SyRec8_r = sh_system.cell(reorg_row,10)\r\n SyRec8_new_p = sh_system.cell(new_pay_row,10)\r\n # SyRec8_r.value = SyRec8_p.value\r\n SyRec8_new_p.value = SyRec8_r.value\r\n \r\n SyRec9_r = sh_system.cell(reorg_row,11)\r\n SyRec9_new_p = sh_system.cell(new_pay_row,11)\r\n # SyRec9_r.value = SyRec9_p.value\r\n SyRec9_new_p.value = SyRec9_r.value\r\n \r\n SyRec10_r = sh_system.cell(reorg_row,12)\r\n SyRec10_new_p = sh_system.cell(new_pay_row,12)\r\n # SyRec10_r.value = SyRec10_p.value\r\n SyRec10_new_p.value = SyRec10_r.value\r\n \r\n SyRec11_r = sh_system.cell(reorg_row,13)\r\n SyRec11_new_p = sh_system.cell(new_pay_row,13)\r\n # SyRec11_r.value = SyRec11_p.value\r\n SyRec11_new_p.value = SyRec11_r.value\r\n \r\n SyRec12_r = sh_system.cell(reorg_row,14)\r\n SyRec12_new_p = sh_system.cell(new_pay_row,14)\r\n # SyRec12_r.value = SyRec12_p.value\r\n SyRec12_new_p.value = SyRec12_r.value\r\n \r\n SyRec13_r = sh_system.cell(reorg_row,15)\r\n SyRec13_new_p = sh_system.cell(new_pay_row,15)\r\n # SyRec13_r.value = SyRec13_p.value\r\n SyRec13_new_p.value = SyRec13_r.value\r\n \r\n SyRec14_r = sh_system.cell(reorg_row,16)\r\n SyRec14_new_p = sh_system.cell(new_pay_row,16)\r\n # SyRec14_r.value = SyRec14_p.value\r\n SyRec14_new_p.value = SyRec14_r.value\r\n \r\n SyRec15_r = sh_system.cell(reorg_row,17)\r\n SyRec15_new_p = sh_system.cell(new_pay_row,17)\r\n # SyRec15_r.value = SyRec15_p.value\r\n SyRec15_new_p.value = SyRec15_r.value\r\n \r\n SyRec16_r = sh_system.cell(reorg_row,18)\r\n SyRec16_new_p = sh_system.cell(new_pay_row,18)\r\n # SyRec16_r.value = SyRec16_p.value\r\n SyRec16_new_p.value = SyRec16_r.value\r\n \r\n SyRec17_r = sh_system.cell(reorg_row,19)\r\n SyRec17_new_p = sh_system.cell(new_pay_row,19)\r\n # SyRec17_r.value = SyRec17_p.value\r\n SyRec17_new_p.value = SyRec17_r.value\r\n \r\n SyRec18_r = sh_system.cell(reorg_row,20)\r\n SyRec18_new_p = sh_system.cell(new_pay_row,20)\r\n # SyRec18_r.value = SyRec18_p.value\r\n SyRec18_new_p.value = SyRec18_r.value\r\n \r\n SyRec19_r = sh_system.cell(reorg_row,21)\r\n SyRec19_new_p = sh_system.cell(new_pay_row,21)\r\n # SyRec19_r.value = SyRec19_p.value\r\n SyRec19_new_p.value = SyRec19_r.value\r\n \r\n wb_system.save(path_system)\r\n # Overwriting values in new row\r\n\r\n SyRec18_new_p.value = SyRec17_new_p.value\r\n wb_system.save(path_system)\r\n SyRec11_new_p.value = 0\r\n SyRec13_new_p.value = 0\r\n SyRec14_new_p.value = 0\r\n SyRec15_new_p.value = 0\r\n SyRec9_new_p.value = 0\r\n SyRec10_new_p.value = 0\r\n SyRec17_new_p.value = 0 \r\n SyRec3_new_p.value = 0\r\n SyRec5_new_p.value = 0 \r\n SyRec6_new_p.value = 0\r\n wb_system.save(path_system)\r\n\r\n _checksum_sr1(SyRec1_new_p.value, 11, int(counter), 2089)\r\n _checksum(11,int(counter),2088)\r\n \r\n \r\n #logging to log file \r\n if current_period_list[get_period] == 'Period Data 10' or _path == 2:\r\n # print(f'Run complete, logging simulation results')\r\n try:\r\n if not self._matrix:\r\n x = datetime.now()\r\n file1_path = os.path.join(root_sys, 'LOGS', 'myfile.txt')\r\n \r\n file1 = open(file1_path, 'w')\r\n # file1 = open(\"C:\\\\Tandapay\\\\LOGS\\\\myfile.txt\", 'w')\r\n log1 = f'{self.ev1} is the number of members at the start of the simulation\\n'\r\n log2 = f'{self.SyRec1_r.value} is the number of valid members remaining at the end of the simulation\\n'\r\n log3 = f'{round(((self.ev1 - self.SyRec1_r.value) / self.ev1)*100,2)}% of policyholders left the group by end of simulation\\n'\r\n log4 = f'{round(sh_system.cell(2,21).value)} was the initial premium members were asked to pay.\\n'\r\n log5 = f'{self.SyRec19_f.value} is the final premium members were asked to pay.\\n'\r\n log6 = f'Premiums increased by {round((self.SyRec19_f.value / sh_system.cell(2,21).value)*100,2)}% by end of simulation\\n'\r\n log7 = f'self.SyRec 3 (period 0 finalize) = {sh_system.cell(3,5).value}\\n'\r\n log8 = f'{self.ev4*100}% of policyholders who were assigned to defect\\n'\r\n log9 = (sh_system.cell(3,5).value/self.ev1)*100\r\n log9 = f'{round(log9,2)}% of policyholders who actually defected\\n'\r\n log10 = f'{(self.pv5)*100}% was the initial collapse threshold set for PV 5\\n'\r\n \r\n L=[log1, log2, log3, log4, log5, log6, log7, log8, log9, log10]\r\n for _log in L:\r\n print(_log)\r\n file1.writelines(L)\r\n file1.close()\r\n\r\n #copying excel file to another location\r\n \r\n original_sys = os.path.join(root_sys, '1 System Database.xlsx')\r\n original_us = os.path.join(root_sys, '2 User Database.xlsx')\r\n\r\n original_sys = os.path.join(root_sys, '1 System Database.xlsx')\r\n original_us = os.path.join(root_sys, '2 User Database.xlsx')\r\n edge_sys = os.path.join(root_sys, '1 System Database.xlsx')\r\n edge_us = os.path.join(root_sys, '2 User Database.xlsx')\r\n\r\n target = os.path.join(root_sys, 'LOGS', '1 System Database temp copy.xlsx')\r\n print(original_sys + ' -> ' + target)\r\n # original = r\"C:\\Tandapay\\1 System Database.xlsx\"\r\n # target = r\"C:\\Tandapay\\LOGS\\1 System Database temp copy.xlsx\"\r\n shutil.copyfile(original_sys, target)\r\n\r\n if self.edge:\r\n shutil.copyfile(original_sys, edge_sys)\r\n shutil.copyfile(original_us, edge_us)\r\n\r\n elif self._matrix:\r\n log1 = self.ev1\r\n log2 = self.SyRec1_r.value\r\n log3 = round(((self.ev1 - self.SyRec1_p.value) / self.ev1)*100,2)\r\n log4 = round(sh_system.cell(2,21).value)\r\n log5 = self.SyRec19_f.value\r\n log6 = round((self.SyRec19_f.value / sh_system.cell(2,21).value)*100,2)\r\n log7 = sh_system.cell(3,5).value\r\n log8 = self.ev4*100\r\n log9 = (sh_system.cell(3,5).value/self.ev1)*100\r\n log9 = round(log9,2)\r\n log10 = (self.pv5)*100\r\n \r\n L=[log1, log2, log3, log4, log5, log6, log7, log8, log9, log10]\r\n\r\n # self.SyRec1_p = sh_system.cell(self.row3,3)\r\n \r\n if self.SyRec1_f.value < self.ev1/2:\r\n collapse = 1\r\n else:\r\n collapse = 0\r\n \r\n row = self.run + 1\r\n\r\n if not self.edge:\r\n matrix_var_sh.cell(row, 1).value = self.run\r\n matrix_sys_log.cell(row, 1).value = self.run\r\n\r\n matrix_var_sh_cols = [ev4_cell,pv4_cell,pv5_cell,ev3_cell,self.ev1_cell,ev5_cell,ev6_cell,ev7_cell]\r\n\r\n for col in matrix_var_sh_cols:\r\n matrix_var_sh.cell(row, col).value = collapse\r\n\r\n for index, var in enumerate(self.start_ev):\r\n log_index = index+2\r\n matrix_sys_log.cell(row, log_index).value = var\r\n if log_index == 8:\r\n break\r\n for index, var in enumerate(self.start_pv):\r\n log2_index = log_index + index + 1\r\n matrix_sys_log.cell(row, log2_index).value = var\r\n\r\n for index, var in enumerate(L):\r\n log3_index = log2_index + index + 1\r\n matrix_sys_log.cell(row, log3_index).value = var\r\n\r\n matrix_wb.save(path_matrix)\r\n return\r\n elif self.edge:\r\n file1_path = os.path.join(root_sys, 'LOGS', str(self.run) +'_'+'log.txt')\r\n \r\n file1 = open(file1_path, 'w')\r\n # file1 = open(\"C:\\\\Tandapay\\\\LOGS\\\\myfile.txt\", 'w')\r\n log1 = f'{self.ev1} is the number of members at the start of the simulation\\n'\r\n log2 = f'{self.SyRec1_r.value} is the number of valid members remaining at the end of the simulation\\n'\r\n log3 = f'{round(((self.ev1 - self.SyRec1_f.value) / self.ev1)*100,2)}% of policyholders left the group by end of simulation\\n'\r\n log4 = f'{round(sh_system.cell(2,21).value)} was the initial premium members were asked to pay.\\n'\r\n log5 = f'{self.SyRec19_f.value} is the final premium members were asked to pay.\\n'\r\n log6 = f'Premiums increased by {round((self.SyRec19_f.value / sh_system.cell(2,21).value)*100,2)}% by end of simulation\\n'\r\n log7 = f'self.SyRec 3 (period 0 finalize) = {sh_system.cell(3,5).value}\\n'\r\n log8 = f'{self.ev4*100}% of policyholders who were assigned to defect\\n'\r\n log9 = (sh_system.cell(3,5).value/self.ev1)*100\r\n log9 = f'{round(log9,2)}% of policyholders who actually defected\\n'\r\n log10 = f'{(self.pv5)*100}% was the initial collapse threshold set for PV 5\\n'\r\n \r\n L=[log1, log2, log3, log4, log5, log6, log7, log8, log9, log10]\r\n for _log in L:\r\n print(_log)\r\n file1.writelines(L)\r\n file1.close()\r\n\r\n original_sys = os.path.join(root_sys, '1 System Database.xlsx')\r\n original_us = os.path.join(root_sys, '2 User Database.xlsx')\r\n edge_sys = os.path.join(root_sys, str(self.run) + '_' + '1 System Database.xlsx')\r\n edge_us = os.path.join(root_sys, str(self.run) + '_' + '2 User Database.xlsx')\r\n\r\n shutil.copyfile(original_sys, edge_sys)\r\n shutil.copyfile(original_us, edge_us)\r\n return\r\n \r\n except Exception as e:\r\n print(e)\r\n\r\n start_iter = start_iter+1\r\n if start_iter == 11:\r\n # if start_iter == 1:\r\n print(f'Iteration {start_iter} times complete! Please run the entire application again.')\r\n return\r\n\r\n def stopAction(self):\r\n sys.exit()\r\n print('Stopped')\r\n \r\n def clearAction(self):\r\n #clearing EV Variables\r\n self.entry1.delete(0,100)\r\n self.entry2.delete(0,100)\r\n self.entry3.delete(0,100)\r\n self.entry4.delete(0,100)\r\n self.entry5.delete(0,100)\r\n self.entry6.delete(0,100)\r\n self.entry7.delete(0,100)\r\n # clearing PV Variables\r\n self.entry1pv.delete(0,100)\r\n self.entry2pv.delete(0,100)\r\n self.entry3pv.delete(0,100)\r\n self.entry4pv.delete(0,100)\r\n self.entry5pv.delete(0,100)\r\n self.entry6pv.delete(0,100)\r\n\r\n def closeAction(self):\r\n self.app.destroy()\r\n \r\ndef main(matrix=False):\r\n if not matrix:\r\n simulator = Simulator(ui=True)\r\n elif matrix:\r\n generate_matrix()\r\n with open('matrix.json', 'r') as f:\r\n content = json.loads(f.read())\r\n matrix = content['matrix']\r\n simulator = Simulator(ui=False, _matrix=matrix)\r\n\r\nif __name__ == '__main__':\r\n arg = sys.argv[-1]\r\n main_start = time.time()\r\n if sys.argv[-1] == '-m':\r\n main(matrix=True)\r\n else:\r\n main()\r\n print(f'Total program runtime: {(time.time()-main_start)/60} minutes')\r\n\r\n\r\n# In[ ]:\r\n\r\n\r\n\r\n\r\n","sub_path":"Tandapay.py","file_name":"Tandapay.py","file_ext":"py","file_size_in_byte":105426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"630661723","text":"from .LoadTpl import *\n\n\nclass Player:\n _playername: str = ''\n _raiting: int = 0\n _id: int = -1\n\n def __init__(self):\n pass\n\n def show(self, q):\n print(loadTpl('show_player').format(\n q['student'].value,\n self._id,\n self._playername,\n self._raiting\n ))\n\n def show_edit(self, q, url):\n print(loadTpl('save_player').format(\n url,\n q['student'].value,\n q['player_id'].value,\n self._playername,\n self._raiting\n ))\n\n def set_parameters_from_q(self, q):\n self._id = q['player_id'].value\n self._playername = q['playername'].value\n self._raiting = q['raiting'].value\n\n def set_parameters_from_db(self, data):\n self._id = data['id']\n self._playername = data['playername']\n self._raiting = data['raiting']\n\n def insert_into_db(self, cursor):\n cursor.execute('INSERT into Container (playername, raiting, reaction, is_goalkeeper) values (?, ?, NULL, 0)',\n (self._playername, self._raiting))\n\n def update_db(self, connection):\n connection.execute('UPDATE Container set playername = ?, raiting = ? WHERE id =?',\n (self._playername, self._raiting, self._id))","sub_path":"cgi-bin/st25/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"584293268","text":"\"\"\"Create seasons clubs table\n\nRevision ID: 000004\nRevises: 000003\nCreate Date: 2019-08-02 19:06:54.263674\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '000004'\ndown_revision = '000003'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.create_table(\n 'seasons_clubs',\n sa.Column('id', sa.Integer, primary_key=True),\n sa.Column('season_id', sa.Integer, sa.ForeignKey('seasons.id'), nullable=False),\n sa.Column('club_id', sa.Integer, sa.ForeignKey('clubs.id'), nullable=False),\n )\n op.create_foreign_key(\n 'fk_clubs_on_seasons_clubs',\n 'seasons_clubs',\n 'clubs',\n ['club_id'],\n ['id'],\n ondelete='CASCADE',\n )\n op.create_foreign_key(\n 'fk_seasons_on_seasons_clubs',\n 'seasons_clubs',\n 'seasons',\n ['season_id'],\n ['id'],\n ondelete='CASCADE',\n )\n\n\ndef downgrade():\n op.drop_table('seasons_clubs')\n","sub_path":"db/versions/000004_create_seasons_clubs_table.py","file_name":"000004_create_seasons_clubs_table.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"626664996","text":"import os\nimport argparse\nimport numpy as np\nfrom data.make_dataset import load_mitbih, load_ptbdb, upsample\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix, classification_report, roc_auc_score, auc, precision_recall_curve\nfrom xgboost import XGBClassifier\nimport pickle\nimport yaml\n\ndef main():\n ### load config ###\n parser = argparse.ArgumentParser()\n parser.add_argument('-c', '--config', help='Specify path to yaml config file.')\n args = parser.parse_args()\n try:\n with open(args.config,'r') as file:\n config = yaml.safe_load(file)\n except Exception as e:\n print('Error reading the config file')\n\n dirName = './results/'+config['experiment_name']\n if not os.path.exists(dirName):\n os.mkdir(dirName)\n print(\"Directory \" , dirName , \" Created \")\n else:\n print(\"Directory \" , dirName , \" already exists\")\n\n ### load & preprocess data ###\n print(\"Load data ...\")\n if config['dataset'] == 'mitbih':\n train, test, y_train, y_test = load_mitbih()\n train, test = train[:,:,0], test[:,:,0]\n objective = 'multi:softmax'\n eval_metric = ['merror', 'mlogloss']\n elif config['dataset'] == 'ptbdb':\n train, test, y_train, y_test = load_ptbdb()\n train, test = train[:,:,0], test[:,:,0]\n objective = 'binary:logistic'\n eval_metric = ['logloss', 'aucpr', 'auc']\n N_CLASSES = len(np.unique(y_train))\n train, val, y_train, y_val = train_test_split(train, y_train,\n test_size=config['val_split_size'],\n stratify=y_train)\n\n ### define model ###\n\n xgb_model = XGBClassifier(max_depth=10,\n n_estimators=256,\n objective=objective,\n eval_metric=eval_metric,\n learning_rate = 0.1,\n nthread=4,\n random_state=42)\n\n print('Start training ...')\n xgb_model.fit(train,y_train,\n eval_set=[(val,y_val)],\n early_stopping_rounds=3,\n verbose=True)\n print('Stopped Training.')\n model_path = f\"{dirName}/{config['experiment_name']}-xgb.json\"\n xgb_model.save_model(model_path)\n\n ### eval ###\n pred_test = xgb_model.predict(test)\n pred_val = xgb_model.predict(val)\n pred_train = xgb_model.predict(train)\n try:\n pickle.dump(pred_test,open(f\"{dirName}/{config['experiment_name']}-preds.pkl\",'wb'))\n except Exception as e:\n print(e)\n\n print(classification_report(y_test, pred_test))\n print(confusion_matrix(y_test,pred_test))\n report_dict = {}\n if config['dataset'] == 'ptbdb':\n AUROC = roc_auc_score(y_test,pred_test)\n print(f\"AUROC: {AUROC}\")\n precision, recall, _ = precision_recall_curve(y_test, pred_test)\n AUPRC = auc(recall, precision)\n print(f\"AUPRC: {AUPRC}\")\n report_dict = {'AUROC':AUROC.item(), 'AUPRC':AUPRC.item()}\n\n report_dict = {**report_dict,\n 'test-data':classification_report(y_test,pred_test,output_dict=True),\n 'val-data':classification_report(y_val,pred_val,output_dict=True),\n 'train-data':classification_report(y_train,pred_train,output_dict=True)}\n confmat_dict = {'confusion_matrix':confusion_matrix(y_test, pred_test).tolist()}\n res_dict = {**report_dict, **confmat_dict}\n with open(f'{dirName}/eval.yaml', 'w') as file:\n documents = yaml.dump(res_dict, file)\n\n return 0\n\nif __name__ == '__main__':\n main()\n","sub_path":"ECG-time-series/train_xgb.py","file_name":"train_xgb.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"4295060","text":"from Training import Training\nt = Training(input_shape=[32,16,1])\nt.use_batchnorm=False\nt.winit='normal'\nt.lr = 0.00025\nt.wreg = 0.0\nt.conv(32,3)\nt.conv(32,3)\nt.maxpool()\nt.conv(64,3)\nt.conv(64,3)\nt.maxpool()\nt.conv(128,3)\nt.conv(128,3)\nt.maxpool()\nt.dense(256)\nt.dense(256)\nt.binary_classifier()\noptions={'max_blur':2.5, 'max_rotation':15, 'min_size': 0.5, 'min_color_delta': 8, 'min_noise':4, 'max_noise':8}\noptions['num_epochs'] = 200\nt.train_segmentation_generator(options=options)\n","sub_path":"train-segmentation.py","file_name":"train-segmentation.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"533079632","text":"#!/usr/bin/env/python\n\nfrom datetime import datetime\nfrom my_devices import cisco3,arista1,arista2,srx2\nfrom my_functions import ssh_command2\nfrom concurrent.futures import ThreadPoolExecutor, wait\n\ndevice_list = [cisco3,arista1,arista2,srx2]\n\nstart_time = datetime.now()\n\npool = ThreadPoolExecutor(4)\nfuture_list = []\n\nfor device in device_list:\n future = pool.submit(ssh_command2, device, \"show version\")\n future_list.append(future)\n\nwait(future_list)\n\nfor future in future_list:\n print (future.result())\n\n\nprint(datetime.now() - start_time)\n","sub_path":"Class_10/task3a.py","file_name":"task3a.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"407409343","text":"\"\"\"\nApi key queries\n\"\"\"\n\nfrom typing import Optional\n\nfrom typeguard import typechecked\n\nfrom ...helpers import Compatible, format_result, fragment_builder\nfrom .queries import gql_api_keys, GQL_API_KEYS_COUNT\nfrom ...types import ApiKey as ApiKeyType\n\n\nclass QueriesApiKey:\n \"\"\"\n Set of ApiKey queries\n \"\"\"\n # pylint: disable=too-many-arguments,too-many-locals\n\n def __init__(self, auth):\n \"\"\"\n Initializes the subclass\n\n Parameters\n ----------\n - auth : KiliAuth object\n \"\"\"\n self.auth = auth\n\n # pylint: disable=dangerous-default-value\n @Compatible(['v2'])\n @typechecked\n def api_keys(self, api_key_id: Optional[str] = None, user_id: Optional[str] = None,\n api_key: Optional[str] = None, skip: int = 0,\n fields: list = ['id', 'name', 'createdAt', 'revoked'],\n first: Optional[int] = 100):\n # pylint: disable=line-too-long\n \"\"\"\n Get an array of api keys respecting a set of constraints\n\n Parameters\n ----------\n - api_key_id : str, optional (default = None)\n The unique id of the api key to retrieve.\n - user_id : str\n Identifier of the user (you can only query your own api keys).\n - api_key : str\n Value of the api key (you can only query your own api keys).\n - skip : int, optional (default = None)\n Number of assets to skip (they are ordered by their date of creation, first to last).\n - fields : list of string, optional (default = ['id', 'name', 'createdAt', 'revoked'])\n All the fields to request among the possible fields for the assets.\n See [the documentation](https://cloud.kili-technology.com/docs/python-graphql-api/graphql-api/#apikey) for all possible fields.\n - first : int, optional (default = None)\n Maximum number of assets to return. Can only be between 0 and 100.\n\n Returns\n -------\n - a result object which contains the query if it was successful, or an error message else.\n\n Examples\n -------\n >>> kili.api_keys(user_id=user_id)\n >>> kili.api_keys(api_key=api_key)\n \"\"\"\n variables = {\n 'where': {\n 'user': {\n 'id': user_id,\n 'apiKey': api_key\n },\n 'id': api_key_id,\n },\n 'skip': skip,\n 'first': first,\n }\n _gql_issues = gql_api_keys(fragment_builder(fields, ApiKeyType))\n result = self.auth.client.execute(_gql_issues, variables)\n return format_result('data', result)\n\n @Compatible(['v2'])\n @typechecked\n def count_api_keys(self, api_key_id: Optional[str] = None, user_id: Optional[str] = None,\n api_key: Optional[str] = None):\n \"\"\"\n Count and return the number of api keys with the given constraints\n\n Parameters\n ----------\n - api_key_id : str, optional (default = None)\n The unique id of the api key to retrieve.\n - user_id : str\n Identifier of the user (you can only query your own api keys).\n - api_key : str\n Value of the api key (you can only query your own api keys).\n\n Returns\n -------\n - a result object which contains the query if it was successful, or an error message else.\n\n Examples\n -------\n >>> kili.count_api_keys(user_id=user_id)\n 3\n >>> kili.count_api_keys(api_key=api_key)\n 1\n \"\"\"\n variables = {\n 'where': {\n 'user': {\n 'id': user_id,\n 'apiKey': api_key\n },\n 'id': api_key_id,\n },\n }\n result = self.auth.client.execute(GQL_API_KEYS_COUNT, variables)\n count = format_result('data', result)\n return count\n","sub_path":"kili/queries/api_key/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3968,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"646604576","text":"'''\n@author Daniel Becker\n@since 1.0\n\nFile with identifyer for input\n'''\n\nimport sys\nimport t9_readFile as tr\n\n#input map\nnumberToChracter = {\n '0':['0'],\n '1':['.' , ',' , '1'],\n '2':['a' , 'A' , 'b' , 'B' , 'c' , 'C' , '2'],\n '3':['d' , 'D' , 'e' , 'E' , 'f' , 'F' , '3'],\n '4':['g' , 'G' , 'h' , 'H' , 'i' , 'I' , '4'],\n '5':['j' , 'J' , 'k' , 'K' , 'l' , 'L' , '5'],\n '6':['m' , 'M' , 'n' , 'N' , 'o' , 'O' , '6'],\n '7':['p' , 'P' , 'q' , 'Q' , 'r' , 'R' , '7'],\n '8':['s' , 'S' , 't' , 'T' , 'u' , 'U' , '8'],\n '9':['v' , 'V' , 'w' , 'W' , 'x' , 'X' , 'y' , 'Y', 'z' , 'Z' , '9'],\n '*':[],\n '#':[' ']\n}\n\ncharacterToNumber = {\n '.' : ['1'],\n ',' : ['1'],\n 'a' : ['2'],\n 'A' : ['2'],\n 'b' : ['2'],\n 'B' : ['2'],\n 'c' : ['2'],\n 'C' : ['2'],\n 'd' : ['3'],\n 'D' : ['3'],\n 'e' : ['3'],\n 'E' : ['3'],\n 'f' : ['3'],\n 'F' : ['3'],\n 'g' : ['4'],\n 'G' : ['4'],\n 'h' : ['4'],\n 'H' : ['4'],\n 'i' : ['4'],\n 'I' : ['4'],\n 'j' : ['5'],\n 'J' : ['5'],\n 'k' : ['5'],\n 'K' : ['5'],\n 'l' : ['5'],\n 'L' : ['5'],\n 'm' : ['6'],\n 'M' : ['6'],\n 'n' : ['6'],\n 'N' : ['6'],\n 'o' : ['6'],\n 'O' : ['6'],\n 'p' : ['7'],\n 'P' : ['7'],\n 'q' : ['7'],\n 'Q' : ['7'],\n 'r' : ['7'],\n 'R' : ['7'],\n 's' : ['8'],\n 'S' : ['8'],\n 't' : ['8'],\n 'T' : ['8'],\n 'u' : ['8'],\n 'U' : ['8'],\n 'v' : ['9'],\n 'V' : ['9'],\n 'w' : ['9'],\n 'W' : ['9'],\n 'x' : ['9'],\n 'X' : ['9'],\n 'y' : ['9'],\n 'Y' : ['9'],\n 'z' : ['9'],\n 'Z' : ['9'],\n '0' : ['0'],\n '1' : ['1'],\n '2' : ['2'],\n '3' : ['3'],\n '4' : ['4'],\n '5' : ['5'],\n '6' : ['6'],\n '7' : ['7'],\n '8' : ['8'],\n '9' : ['9'],\n '' : ['*'],\n ' ' : ['#']\n}\n\ndef printRadnomNumberToCharacter():\n \"\"\"a random outout of map elements\"\"\"\n print(numberToChracter)\n\ndef printNumberToCharakter():\n \"\"\"(pretty) print of map\"\"\"\n print(\"Identifyer will be mapped in following schema\")\n for i in range(0, 9):\n print(str(i), ': ' , numberToChracter[str(i)])\n print('*' , ': ', numberToChracter['*'])\n print('#' , ': ', numberToChracter['#'])\n\n# e.g. 1 is True, \"e\" is False\ndef validateInput(input):\n \"\"\"validates if a given number is in numberToChracter-list\"\"\"\n if input not in numberToChracter:\n print(\"Validation failed\")\n return False\n else:\n return True\n\n\ndef getCharacter(identifier , image):\n \"\"\"get a specific image from a identifiier\"\"\"\n length = imageSize(identifier)\n if validateInput(identifier):\n if(image <= length):\n return numberToChracter[identifier][image]\n else:\n print('Out of bounds')\n return False\n else:\n print(\"Number not in predefined list\")\n return False\n\ndef imageSize(row):\n \"\"\"get size/length of a specific row\"\"\"\n return len(numberToChracter[row][0:])\n\n\ndef getIdentifier(identifier):\n \"\"\"get the original identifyer for the row\"\"\"\n if validateInput(identifier):\n if not identifier:\n #empty string means '*'\n return '*'\n return numberToChracter[identifier][-1]\n #should work for '#' too #TODO test\n else:\n #print(\"Number not in predefined list\")\n return False\n\ndef getImages(identifier):\n \"\"\"get the row for specific identifyer\"\"\"\n if validateInput(identifier):\n return numberToChracter[identifier][0:]\n else:\n print(\"Number not in predefined list\")\n return False\n\n#Test get_ operations\n#print(get_element(2 ,1)) #should return A\n#print(get_identifyer(2)) #should return 2\n#print(get_row(2)) #should return ['a' , 'A' , 'b' , 'B' , 'c' , 'C' , '2']\n\n\ndef mapCharacter(character):\n \"\"\"return the number fitting to the digit\"\"\"\n #print(token)\n if character not in characterToNumber:\n return False\n return characterToNumber[character][0]\n\n\n#print(mapChracter('c'))\n#print(mapChracter('z'))\n#print(mapChracter(' '))\n\n\ndef buildRanking():\n \"\"\"Returns a dict like numbertoCharakter but this one is ordered by most common letters \"\"\"\n id = \"\"\n valuedList = {}\n rank = tr.countLetter()\n for letter in rank:\n id = mapCharacter(str(letter[0]))\n if id == False:\n print(\"Unknown letter \", letter[0])\n # Does not work. Don't even know if we have to delte this token\n #del(rank[str(letter[0])])\n else:\n #build a dict. first should stand the number aka id. then the tupel with the most common calls\n valuedList.setdefault(id, []).append(letter[0])\n\n\n return valuedList\n\n#print(buildRanking())\n\n\ndef buildByNumber():\n \"\"\"BUG!! SEE TODO !Returns a dict like numbertoCharakter but this one has the calle time of value fitting to the letter. Main difference to countLetter() ist that no unknown elements are in returned list \"\"\"\n id = \"\"\n valuedList = {}\n rank = tr.countLetter()\n for letter in rank:\n id = mapCharacter(str(letter[0]))\n if id == False:\n #todo does not seem to work\n rank.remove(letter)\n return valuedList\n\nprint(buildByNumber())","sub_path":"ASP/src/Deprecated/t9_dictionary.py","file_name":"t9_dictionary.py","file_ext":"py","file_size_in_byte":5155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"230614100","text":"import os\n\nimport torch\nimport torchvision as tv\nfrom torch.nn.functional import sigmoid\n\n_use_cuda = torch.cuda.is_available()\nDEVICE = torch.device('cuda' if _use_cuda else 'cpu')\n\nlr = 1e-2\nn_epochs = 100\nbatch_size = 32\n\nsave_dir = './heim/'\nos.makedirs(save_dir, exist_ok=True)\n\ntrain_iter = torch.utils.data.DataLoader(\n dataset=tv.datasets.MNIST(\n root='../../Datasets/MNIST/',\n transform=tv.transforms.ToTensor(),\n train=True,\n download=True\n ),\n batch_size=batch_size,\n shuffle=True,\n drop_last=True,\n num_workers=2,\n)\n\ntest_iter = torch.utils.data.DataLoader(\n dataset=tv.datasets.MNIST(\n root='../../Datasets/MNIST/',\n transform=tv.transforms.ToTensor(),\n train=False,\n download=True\n ),\n batch_size=64,\n shuffle=True,\n drop_last=True,\n num_workers=2,\n)\n\n# net arch\nh0_dim = 64\nh1_dim = 256\nv_dim = 784\n\n# net weight init\nB_g = torch.zeros(h0_dim, device=DEVICE)\nW_g = torch.zeros(h0_dim, h1_dim, device=DEVICE)\nV_g = torch.zeros(h1_dim, v_dim, device=DEVICE)\nW_r = torch.zeros(h1_dim, h0_dim, device=DEVICE)\nV_r = torch.zeros(v_dim, h1_dim, device=DEVICE)\n\nfor e in range(n_epochs):\n lr = lr * 0.9\n for x, _ in train_iter:\n x = x.view(-1, 28 * 28).to(DEVICE)\n x = (x > 0.5).float()\n # wake phase\n h1 = torch.bernoulli(sigmoid(x @ V_r))\n h0 = torch.bernoulli(sigmoid(h1 @ W_r))\n\n ksi = sigmoid(B_g)\n psi = sigmoid(h0 @ W_g)\n delta = sigmoid(h1 @ V_g)\n\n B_g += lr * torch.mean(h0 - ksi, 0)\n W_g += lr * h0.t() @ (h1 - psi) / batch_size\n V_g += lr * h1.t() @ (x - delta) / batch_size\n\n # sleep phase\n h0 = torch.bernoulli(sigmoid(B_g).repeat(batch_size, 1))\n h1 = torch.bernoulli(sigmoid(h0 @ W_g))\n v = torch.bernoulli(sigmoid(h1 @ V_g))\n\n psi = sigmoid(v @ V_r)\n ksi = sigmoid(h1 @ W_r)\n\n V_r += lr * v.t() @ (h1 - psi) / batch_size\n W_r += lr * h1.t() @ (h0 - ksi) / batch_size\n\n print('[%d/%d]' % (e + 1, n_epochs))\n\n # top-down 随机采样\n h0 = torch.bernoulli(sigmoid(B_g).repeat(64, 1))\n h1 = torch.bernoulli(sigmoid(h0 @ W_g))\n v = torch.bernoulli(sigmoid(h1 @ V_g))\n tv.utils.save_image(v.view(-1, 1, 28, 28), save_dir + 'r{}.png'.format(e + 1))\n\n # 重构\n x = next(iter(test_iter))[0]\n x = x.view(-1, 28 * 28).to(DEVICE)\n x = (x > 0.5).float()\n\n h1 = torch.bernoulli(sigmoid(x @ V_r))\n h0 = torch.bernoulli(sigmoid(h1 @ W_r))\n # tv.utils.save_image(h0.view(-1, 1, 8, 8), save_dir + 'h0_{}.png'.format(e + 1))\n h1 = torch.bernoulli(sigmoid(h0 @ W_g))\n v = torch.bernoulli(sigmoid(h1 @ V_g))\n tv.utils.save_image(x.view(-1, 1, 28, 28), save_dir + 'x{}.png'.format(e + 1))\n tv.utils.save_image(v.view(-1, 1, 28, 28), save_dir + 'v{}.png'.format(e + 1))\n\n # 显示filter\n tv.utils.save_image(V_r.t()[:64].view(-1, 1, 28, 28), save_dir + 'V_r{}.png'.format(e + 1))\n tv.utils.save_image(V_g.t()[:64].view(-1, 1, 16, 16), save_dir + 'V_g{}.png'.format(e + 1))\n tv.utils.save_image(W_r.t()[:64].view(-1, 1, 16, 16), save_dir + 'W_r{}.png'.format(e + 1))\n tv.utils.save_image(W_g.t()[:64].view(-1, 1, 8, 8), save_dir + 'W_g{}.png'.format(e + 1))\n","sub_path":"BNs/helmholtz.py","file_name":"helmholtz.py","file_ext":"py","file_size_in_byte":3256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"581909568","text":"class UsuarioFileReader:\r\n #Crea una nueva instancia del estudiante\r\n def __init__(self, inpsrc):\r\n self._inpsrc=inpsrc\r\n self._inputfile =None\r\n\r\n def open(self):\r\n self._inputfile = open(self._inpsrc, \"r\")\r\n\r\n def close(self):\r\n self._inputfile.close()\r\n self._inputfile = None\r\n\r\n def buscaTod(self):\r\n listaUsu=list()\r\n usuario = self.buscaReg()\r\n while usuario != None:\r\n listaUsu.append(usuario)\r\n usuario = self.buscaReg()\r\n return listaUsu\r\n\r\n def buscaReg(self):\r\n linea = self._inputfile.readline()\r\n if linea == \"\" :\r\n return None\r\n usuario = Usuario()\r\n usuario.id = int(linea)\r\n usuario.name = self._inputfile.readline().strip()\r\n usuario.fecha=self._inputfile.readline().strip()\r\n return usuario\r\n\r\nclass Usuario:\r\n def __init__(self):\r\n self.id =0\r\n self.name = None\r\n self.fecha = None\r\n\r\n\r\n\r\n","sub_path":"usuariofile.py","file_name":"usuariofile.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"78234553","text":"import tkinter as tk\nfrom autoclicker.commons import clicker\n\n\nclass MainApplication(tk.Frame):\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent, *args, **kwargs)\n self.parent = parent\n\n self.click_key = tk.StringVar()\n self.click_key.trace(\"w\", self.key_trace)\n\n #All elements\n self.frame1 = tk.Frame(\n self.parent\n )\n\n self.frame2 = tk.Frame(\n self.parent\n )\n\n self.label = tk.Label(\n self.frame1,\n text=\"Activation key-binding:\"\n )\n\n self.click_entry = tk.Entry(\n self.frame1,\n textvariable=self.click_key,\n borderwidth=1,\n background=self.parent.cget('bg'),\n font=\"Calibiri 12 \",\n width=1\n )\n\n self.start_button = tk.Button(\n self.frame2,\n text=\"Start\",\n command=self.start\n )\n\n self.stop_button = tk.Button(\n self.frame2,\n text=\"Stop\",\n command=self.stop\n )\n\n self.left_option = tk.Checkbutton(\n self.parent,\n text=\"Mouse1\",\n command=self.set_left,\n state=tk.DISABLED\n )\n\n self.right_option = tk.Checkbutton(\n self.parent,\n text=\"Mouse2\",\n command=self.set_right,\n state=tk.DISABLED\n )\n\n self.msg_label = tk.Label(\n self.parent,\n text=\"Press 'esc' button for quick and easy stoppage of program.\",\n height=6,\n wraplength=100\n )\n\n #Element decoration\n self.start_button.config(height=5,width=10)\n self.stop_button.config(height=5, width=10)\n\n #Element placements\n self.label.grid(row=0, column=0)\n self.click_entry.grid(row=0,column=1)\n self.frame1.pack()\n\n self.start_button.grid(row=0,column=0,padx=10,pady=2)\n self.stop_button.grid(row=1,column=0,padx=10)\n self.frame2.pack(fill=\"both\",side=\"left\")\n\n self.left_option.pack(pady=2)\n self.right_option.pack()\n self.msg_label.pack(fill=tk.BOTH)\n\n def key_trace(self, *args):\n value = self.click_key.get()\n if len(value) > 1: #set charlimit to 1\n self.click_key.set(value[:1])\n\n def start(self): #start threads\n self.start_button.config(state=tk.DISABLED)\n\n #threads\n self.mouse_thread = clicker.Mouse(0.1, daemon=True)\n self.key_listener = clicker.keyboard.Listener(on_press=self.on_press, daemon=True)\n\n self.mouse_thread.start()\n self.key_listener.start()\n\n self.left_option.config(state=tk.NORMAL)\n self.right_option.config(state=tk.NORMAL)\n\n def stop(self): #stop threads\n self.start_button.config(state=tk.NORMAL)\n\n self.mouse_thread.stop()\n self.mouse_thread.join()\n\n self.key_listener.stop()\n self.key_listener.join()\n\n self.left_option.deselect()\n self.right_option.deselect()\n self.left_option.config(state=tk.DISABLED)\n self.right_option.config(state=tk.DISABLED)\n\n def on_press(self,key):\n print(key)\n if key == clicker.keyboard.Key.esc:\n self.stop()\n\n elif key == clicker.keyboard.KeyCode(char=self.click_key.get()):\n self.mouse_thread.toggle()\n\n def set_left(self):\n self.mouse_thread.mouse_change(\"mouse1\")\n self.right_option.deselect()\n\n def set_right(self):\n self.mouse_thread.mouse_change(\"mouse2\")\n self.left_option.deselect()","sub_path":"autoclicker/commons/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":3622,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"167415501","text":"# uisng my_module.py file here\n\n# any name we can give instead of mm\nimport my_module as mm\ncourses = [\"History\",\"Chem\",\"Math\",\"Science\"]\n\nindex = mm.find_index(courses,\"Math\")\nprint(index)\n\n# using the test string from my_module\nprint(mm.test)\n\n\n","sub_path":"python Basics/LearnImport1.py","file_name":"LearnImport1.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"460059066","text":"import torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nfrom torch.optim import lr_scheduler\r\n\r\nimport torchvision\r\nfrom torchvision import datasets, models, transforms\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport time\r\nimport os\r\nimport copy\r\n\r\n#########################\r\n\r\n# GLOBAL VARIABLES\r\n# directory with images folder\r\nDATA_DIR = 'data'\r\n# path to the model, in order to both saving and loading it\r\nMODEL_PATH = 'model_1.pth'\r\n\r\n# percantage of data to be validated\r\nVAL_SIZE = 0.4\r\n\r\n# decide if the files are to be moved to subfolders\r\nMOVE_FILES = False # SET IT TO TRUE ONLY WITH THE FIRST RUN OF CODE!\r\n# decide if the model is to be trained and saved or just loaded\r\nTRAIN = False\r\n# decide if the parameters of model are to be frozen or not\r\n# in other words, decide if you want train the last layer or fine tune all layers\r\nFINE_TUNE_ALL = False\r\n\r\n#########################\r\n\r\ntrain_labels = pd.read_csv(os.path.join(DATA_DIR,'train.csv'))\r\ntrain_size = int((1-VAL_SIZE) * len(train_labels))\r\nval_size = len(train_labels) - train_size\r\n\r\nclass_names = train_labels.columns[1:].values\r\nclass_num = len(class_names)\r\nprint(class_names)\r\n\r\n# auxiliary function used for creating class folders and moving files into them\r\ndef moveFiles(labels): \r\n \r\n # create folders for train, validation and test\r\n for x in ['train','val','test']:\r\n if os.path.isdir(os.path.join(DATA_DIR,x)) == False:\r\n os.mkdir(os.path.join(DATA_DIR,x))\r\n \r\n # move all test files to test folder\r\n for filename in os.listdir(os.path.join(DATA_DIR, 'images')):\r\n if filename.startswith('Test'):\r\n os.rename(os.path.join(os.getcwd(),DATA_DIR,'images',filename),\r\n os.path.join(os.getcwd(),DATA_DIR,'test',filename))\r\n \r\n # create folders for every class in train and validation folders\r\n for x in ['train','val']:\r\n for class_name in class_names:\r\n if os.path.isdir(os.path.join(DATA_DIR,x,class_name)) == False:\r\n os.mkdir(os.path.join(DATA_DIR,x,class_name))\r\n \r\n # move every file into its class folder\r\n # train\r\n for index, row in labels[:train_size].iterrows():\r\n file_class = row[row.isin([1])].index.values[0]\r\n file_id = row['image_id'] + '.jpg'\r\n os.rename(os.path.join(os.getcwd(),DATA_DIR,'images',file_id),\r\n os.path.join(os.getcwd(),DATA_DIR,'train',file_class,file_id))\r\n \r\n # val\r\n for index, row in labels[train_size:].iterrows():\r\n file_class = row[row.isin([1])].index.values[0]\r\n file_id = row['image_id'] + '.jpg'\r\n os.rename(os.path.join(os.getcwd(),DATA_DIR,'images',file_id),\r\n os.path.join(os.getcwd(),DATA_DIR,'val',file_class,file_id))\r\n \r\n \r\n# do it only once!!! \r\nif MOVE_FILES:\r\n moveFiles(train_labels)\r\n\r\n\r\n# data augmentation and normalization for training\r\n# just normalization for validation\r\ndata_transforms = {\r\n 'train': transforms.Compose([\r\n transforms.RandomResizedCrop(224),\r\n transforms.RandomHorizontalFlip(),\r\n transforms.ToTensor(),\r\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\r\n ]),\r\n 'val': transforms.Compose([\r\n transforms.Resize(256),\r\n transforms.CenterCrop(224),\r\n transforms.ToTensor(),\r\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\r\n ]),\r\n}\r\n\r\nimage_datasets = {x: datasets.ImageFolder(os.path.join(DATA_DIR,x),\r\n data_transforms[x]) \r\n for x in ['train','val']}\r\n\r\ndataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\r\n shuffle=True, num_workers=0)\r\n for x in ['train','val']}\r\n\r\ndataset_sizes = {x: len(image_datasets[x]) for x in ['train','val']}\r\nprint(dataset_sizes)\r\n\r\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\r\n\r\n# visualize a few images in order to understand data augmentation\r\ndef imshow(inp, title=None):\r\n \"\"\"Imshow for Tensor.\"\"\"\r\n inp = inp.numpy().transpose((1, 2, 0))\r\n mean = np.array([0.485, 0.456, 0.406])\r\n std = np.array([0.229, 0.224, 0.225])\r\n inp = std * inp + mean\r\n inp = np.clip(inp, 0, 1)\r\n plt.imshow(inp)\r\n if title is not None:\r\n plt.title(title)\r\n #plt.pause(0.001) # pause a bit so that plots are updated\r\n\r\n\r\n# Get a batch of training data\r\ninputs, classes = next(iter(dataloaders['train']))\r\n\r\n# Make a grid from batch\r\nout = torchvision.utils.make_grid(inputs)\r\n\r\nimshow(out, title=[class_names[x] for x in classes])\r\n\r\n\r\n# general function to train model\r\n# scheduling the learning rate\r\n# saving the best model\r\n\r\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=50):\r\n since = time.time()\r\n \r\n # initial values\r\n best_model_wts = copy.deepcopy(model.state_dict())\r\n best_acc = 0.0 \r\n\r\n for epoch in range(num_epochs):\r\n print('Epoch {}/{}'.format(epoch+1, num_epochs))\r\n print('-' * 20)\r\n # Each epoch has a training and validation phase\r\n for phase in ['train', 'val']:\r\n if phase == 'train':\r\n model.train() # Set model to training mode\r\n else:\r\n model.eval() # Set model to evaluate mode\r\n \r\n running_loss = 0.0\r\n running_corrects = 0\r\n\r\n # iterate over data.\r\n for inputs, labels in dataloaders[phase]:\r\n inputs = inputs.to(device)\r\n labels = labels.to(device)\r\n \r\n # zero the parameter gradients\r\n optimizer.zero_grad()\r\n \r\n # forward\r\n # track history only in train\r\n with torch.set_grad_enabled(phase == 'train'):\r\n outputs = model(inputs)\r\n _, preds = torch.max(outputs, 1)\r\n loss = criterion(outputs, labels) \r\n \r\n # backward + optimize only if in training phase\r\n if phase == 'train':\r\n loss.backward()\r\n optimizer.step()\r\n \r\n # statistics\r\n running_loss += loss.item() * inputs.size(0)\r\n running_corrects += torch.sum(preds == labels.data)\r\n\r\n\r\n if phase == 'train':\r\n scheduler.step() \r\n \r\n epoch_loss = running_loss / dataset_sizes[phase]\r\n epoch_acc = running_corrects.double() / dataset_sizes[phase]\r\n\r\n print('{} Loss: {:.4f} Acc: {:.4f}'.format(\r\n phase, epoch_loss, epoch_acc))\r\n\r\n # deep copy the model\r\n if phase == 'val' and epoch_acc > best_acc:\r\n best_acc = epoch_acc\r\n best_model_wts = copy.deepcopy(model.state_dict()) \r\n \r\n time_elapsed = time.time() - since\r\n print('Training complete in {:.0f}m {:.0f}s'.format(\r\n time_elapsed // 60, time_elapsed % 60))\r\n print('Best val Acc: {:4f}'.format(best_acc))\r\n\r\n # load best model weights\r\n model.load_state_dict(best_model_wts)\r\n return model\r\n\r\n\r\ndef visualizeModel(model, num_images=6):\r\n \r\n was_training = model.training\r\n model.eval()\r\n images_so_far = 0\r\n fig = plt.figure()\r\n \r\n with torch.no_grad():\r\n for i, (inputs, labels) in enumerate(dataloaders['val']):\r\n inputs = inputs.to(device)\r\n labels = labels.to(device)\r\n \r\n outputs = model(inputs)\r\n _, preds = torch.max(outputs, 1)\r\n \r\n for j in range(inputs.size()[0]):\r\n images_so_far += 1\r\n ax = plt.subplot(num_images//2, 2, images_so_far)\r\n ax.axis('off')\r\n ax.set_title('predicted {}'.format(class_names[preds[j]]))\r\n imshow(inputs.cpu().data[j])\r\n \r\n if images_so_far == num_images:\r\n model.train(mode=was_training)\r\n return\r\n \r\n model.train(mode=was_training) \r\n \r\n# use pretrained version of resnet50\r\nmodel = models.resnet50(pretrained=True)\r\n\r\n# freeze model parameters\r\nfor param in model.parameters():\r\n param.requires_grad = FINE_TUNE_ALL\r\n\r\n# number of input features in the fully connected layer\r\nnum_ftrs = model.fc.in_features\r\n\r\n# replace the last layer with linear unit with 4 outputs\r\nmodel.fc = nn.Linear(num_ftrs, class_num)\r\n\r\n# calculate on cuda if available\r\nmodel.to(device)\r\n\r\n# because it is multi-class problem, therefore we use cross entropy loss\r\ncriterion = nn.CrossEntropyLoss()\r\n\r\nif FINE_TUNE_ALL:\r\n # all parameters are being optimized\r\n optimizer = optim.Adam(model.parameters(), lr=0.001)\r\nelse:\r\n # only parameters of final layer are being optimized \r\n optimizer = optim.Adam(model.fc.parameters(), lr=0.001)\r\n\r\n# Decay LR by a factor of 0.1 every 10 epochs\r\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)\r\n\r\nif TRAIN:\r\n model = train_model(model, criterion, optimizer, exp_lr_scheduler, num_epochs=20)\r\n torch.save(model, MODEL_PATH)\r\nelse:\r\n model = torch.load(MODEL_PATH)\r\n model.eval()\r\n\r\n\r\nvisualizeModel(model)\r\n\r\n\r\n","sub_path":"data_gen.py","file_name":"data_gen.py","file_ext":"py","file_size_in_byte":9497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"459970742","text":"from datetime import datetime\nimport pytest\n\nfrom mikeio import eum\nfrom fmskill.model import ModelResult\nfrom fmskill.model.abstract import ModelResultInterface\nfrom fmskill.model import DataFramePointModelResult, DataFramePointModelResultItem\nfrom fmskill.observation import PointObservation\n\n\n@pytest.fixture\ndef klagshamn():\n fn = \"tests/testdata/smhi_2095_klagshamn.dfs0\"\n return PointObservation(fn, item=0, x=366844, y=6154291, name=\"Klagshamn\")\n\n\n@pytest.fixture\ndef drogden():\n\n # >>> from pyproj import Transformer\n # >>> t = Transformer.from_crs(4326,32633, always_xy=True)\n # >>> t.transform(12.7113,55.5364)\n # (355568.6130331255, 6156863.0187071245)\n\n fn = \"tests/testdata/dmi_30357_Drogden_Fyr.dfs0\"\n return PointObservation(fn, item=0, x=355568.0, y=6156863.0)\n\n\n@pytest.fixture\ndef hd_oresund_2d():\n return \"tests/testdata/Oresund2D.dfsu\"\n\n\n@pytest.fixture\ndef Hm0_HKNA():\n fn = \"tests/testdata/SW/HKNA_Hm0.dfs0\"\n return PointObservation(fn, item=0, x=4.2420, y=52.6887, name=\"HKNA\")\n\n\n@pytest.fixture\ndef wind_HKNA():\n fn = \"tests/testdata/SW/HKNA_wind.dfs0\"\n return PointObservation(fn, item=0, x=4.2420, y=52.6887, name=\"HKNA\")\n\n\n@pytest.fixture\ndef Hm0_EPL():\n fn = \"tests/testdata/SW/eur_Hm0.dfs0\"\n return PointObservation(fn, item=0, x=3.2760, y=51.9990, name=\"EPL\")\n\n\n@pytest.fixture\ndef sw_dutch_coast():\n return \"tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu\"\n\n\n@pytest.fixture\ndef sw_total_windsea():\n return \"tests/testdata/SW/SW_Tot_Wind_Swell.dfsu\"\n\n\ndef test_repr(hd_oresund_2d):\n mr = ModelResult(hd_oresund_2d)\n txt = repr(mr)\n assert \"Oresund2D.dfsu\" in txt\n\n\ndef test_dfs_object(hd_oresund_2d):\n mr = ModelResult(hd_oresund_2d)\n\n assert mr.dfs.is_2d\n\n\ndef test_ModelResultType(sw_dutch_coast):\n mr = ModelResult(sw_dutch_coast)\n\n assert mr.is_dfsu\n\n\ndef test_ModelResultType0():\n mr = ModelResult(\"tests/testdata/TS.dfs0\")\n\n assert mr.is_dfs0\n\n\n# def test_extract_observation(sw_dutch_coast, Hm0_HKNA):\n# mr = ModelResult(sw_dutch_coast)\n# c = mr.extract_observation(Hm0_HKNA) # infer item by EUM\n# assert c.n_points == 386\n\n\ndef test_extract_observation_no_matching_item(sw_total_windsea, wind_HKNA):\n mr = ModelResult(sw_total_windsea) # No wind speed here !\n\n with pytest.raises(Exception): # More specific error?\n _ = mr.extract_observation(wind_HKNA)\n\n\ndef test_extract_observation_total_windsea_swell_not_possible(\n sw_total_windsea, Hm0_HKNA\n):\n mr = ModelResult(sw_total_windsea)\n \"\"\"\n Items:\n 0: Sign. Wave Height (meter)\n 1: Sign. Wave Height, W (meter)\n 2: Sign. Wave Height, S (meter)\n \"\"\"\n\n # with pytest.raises(Exception):\n # c = mr.extract_observation(Hm0_HKNA) # infer item by EUM is ambigous\n\n # Specify Swell item explicitely\n c = mr[\"Sign. Wave Height, S\"].extract_observation(Hm0_HKNA)\n assert c.n_points > 0\n\n\ndef test_extract_observation_validation(hd_oresund_2d, klagshamn):\n mr = ModelResult(hd_oresund_2d)\n with pytest.raises(Exception):\n c = mr[0].extract_observation(klagshamn, validate=True)\n\n c = mr[0].extract_observation(klagshamn, validate=False)\n assert c.n_points > 0\n\n\ndef test_extract_observation_outside(hd_oresund_2d, klagshamn):\n mr = ModelResult(hd_oresund_2d)\n # correct eum, but outside domain\n klagshamn.itemInfo = eum.ItemInfo(eum.EUMType.Surface_Elevation)\n klagshamn.y = -10\n with pytest.raises(ValueError):\n _ = mr[0].extract_observation(klagshamn, validate=True)\n\n\nfrom fmskill.model import DfsModelResultItem, DfsModelResult # , ModelResultFactory\n\n\ndef test_dfs_model_result(hd_oresund_2d):\n mr = DfsModelResult(hd_oresund_2d, \"Oresund\")\n assert mr.n_items == 7\n assert isinstance(mr, DfsModelResult)\n\n mr0 = mr[0]\n assert isinstance(mr0, DfsModelResultItem)\n assert mr.item_names[0] == mr0.item_name\n\n mr1 = mr[\"Surface elevation\"]\n assert mr.item_names[0] == mr1.item_name\n assert mr.filename == mr1.filename\n assert mr.name == mr1.name\n\n\ndef test_factory(hd_oresund_2d):\n mr = ModelResult(hd_oresund_2d, name=\"myname\")\n assert isinstance(mr, DfsModelResult)\n assert mr.name == \"myname\"\n assert mr.n_items == 7\n\n mri = ModelResult(hd_oresund_2d, item=\"Surface elevation\")\n assert isinstance(mri, DfsModelResultItem)\n assert mri.item_name == \"Surface elevation\"\n","sub_path":"tests/test_modelresult.py","file_name":"test_modelresult.py","file_ext":"py","file_size_in_byte":4488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"428883835","text":"import re\nimport random\nimport time\nimport copy\n\n\nclass Create(object):\n MOULD = [\n ['1', '2', '3', '4', '5', '6', '7', '8', '9'],\n ['4', '5', '6', '7', '8', '9', '1', '2', '3'],\n ['7', '8', '9', '1', '2', '3', '4', '5', '6'],\n ['2', '3', '1', '5', '6', '4', '8', '9', '7'],\n ['5', '6', '4', '8', '9', '7', '2', '3', '1'],\n ['8', '9', '7', '2', '3', '1', '5', '6', '4'],\n ['3', '1', '2', '6', '4', '5', '9', '7', '8'],\n ['6', '4', '5', '9', '7', '8', '3', '1', '2'],\n ['9', '7', '8', '3', '1', '2', '6', '4', '5']\n ]\n\n def __init__(self):\n self.object = copy.deepcopy(Create.MOULD)\n self.target = []\n self.data_generate = []\n self.data_solve = []\n self.select = None\n\n def __rand(self, h, l, n):\n source = self.object[h][l]\n if source == n:\n return\n self.object[h][l] = n\n # print(h,l,'--->',n)\n for i in range(9):\n if (i != h) and (self.object[i][l] == n):\n self.__rand(i, l, source)\n if (i != l) and (self.object[h][i] == n):\n self.__rand(h, i, source)\n if (i != (h % 3)*3+(l % 3)) and (self.object[(h//3)*3+(i//3)][(l//3)*3+(i % 3)] == n):\n self.__rand((h//3)*3+(i//3), (l//3)*3+(i % 3), source)\n\n def generate(self, level_difficult=40, level_random=1.0):\n self.object = copy.deepcopy(Create.MOULD)\n for h in range(9):\n for l in range(9):\n if random.randint(1, int(1 / level_random)) == 1:\n self.__rand(h, l, str(random.randint(1, 9)))\n remove_list = random.sample(range(81), level_difficult)\n for remove in remove_list:\n self.object[remove // 9][remove % 9] = '?'\n self.data_generate = self.object\n return self.data_generate\n\n def solve(self, data_file=None, data_list=None):\n if data_file is not None:\n self.target = self.__read_file(data_file)\n elif data_list is not None:\n self.target = data_list\n else:\n self.target = self.data_generate\n self.object = copy.deepcopy(Create.MOULD)\n\n count = 0\n mark = False\n while True:\n for h in range(9):\n for l in range(9):\n if self.target[h][l] != '?' and self.target[h][l] != self.object[h][l]:\n mark = True\n if random.randint(1, 2) == 1:\n continue\n # print(h,l,'-->',create_target[h][l])\n self.__rand(h, l, self.target[h][l])\n if not mark:\n break\n mark = False\n count += 1\n # 程序中，更新采用随机机制，如果进入死胡同，就从来吧\n if count > 50:\n self.object = copy.deepcopy(Create.MOULD)\n count = 0\n self.data_solve = self.object\n return self.data_solve\n\n @staticmethod\n def __read_file(path):\n create = []\n with open(path) as f:\n # 按行读取\n for line in f:\n # +号开头的行内无数据，抛弃\n if line.startswith('+'):\n continue\n # re的分割更好用\n tmp_line = re.split('[ |]', line)\n # 分割后，头尾元素无用\n # print(tmp_line)\n del tmp_line[0]\n del tmp_line[9] # 第二次元素少了１个\n create.append(tmp_line)\n return create\n\n @staticmethod\n def __make_str(data_list):\n if not data_list:\n return '请先调用方法生成数据'\n\n s = '+-----+-----+-----+'\n data = s + '\\r\\n'\n for i, line in enumerate(data_list):\n for j, x in enumerate(line):\n if j % 3 == 0:\n data += '|'\n else:\n data += ' '\n data += str(x)\n data += '|'\n data += '\\r\\n'\n if i % 3 == 2:\n data += s + '\\r\\n'\n return data\n\n @property\n def str_generate(self):\n return self.__make_str(self.data_generate)\n\n @property\n def str_solve(self):\n return self.__make_str(self.data_solve)\n\nif __name__ == '__main__':\n a = Create()\n for iii in range(9):\n # print(a.str_generate)\n print(time.time())\n a.solve(data_list=a.generate())\n print(time.time())\n print(a.str_generate)\n print(a.str_solve)\n # print(a)\n","sub_path":"create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":4636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"560439240","text":"import codecademylib\nimport pandas as pd\n\nad_clicks = pd.read_csv('ad_clicks.csv')\n\n# print(ad_clicks.head(10))\n\n# utm_count = ad_clicks.groupby('utm_source').user_id.count().reset_index()\n# print(utm_count)\n\nad_clicks['is_click'] = ~ad_clicks.ad_click_timestamp.isnull()\n# print(ad_clicks.head())\n\nclicks_by_source = ad_clicks.groupby(['utm_source','is_click']).user_id.count().reset_index()\n\nclicks_pivot = clicks_by_source.pivot(\ncolumns = 'is_click',\nindex = 'utm_source',\nvalues = 'user_id').reset_index()\n\n# print(clicks_pivot)\nclicks_pivot['percent_clicked'] = (clicks_pivot[True] / (clicks_pivot[True]+clicks_pivot[False]))\n# print(clicks_pivot)\n\n# print(ad_clicks.head(10))\nexp_count = ad_clicks.groupby('experimental_group').user_id.count().reset_index()\nprint(exp_count)\n\nab_click = ad_clicks.groupby(['experimental_group','is_click']).user_id.count().reset_index().pivot(\n columns = 'is_click',\n index = 'experimental_group',\n values = 'user_id').reset_index()\nprint(ab_click)\n\na_clicks = ad_clicks[ad_clicks.experimental_group == 'A']\nb_clicks = ad_clicks[ad_clicks.experimental_group == 'B']\n\n# for A\na_clicks_pivot = a_clicks.groupby(['is_click','day']).user_id.count().reset_index().pivot(\ncolumns = 'is_click',\nindex = 'day',\nvalues = 'user_id').reset_index()\n\na_clicks_pivot['percent_clicked'] = a_clicks_pivot[True] / (a_clicks_pivot[True]+a_clicks_pivot[False])\n\nprint(a_clicks_pivot)\n\n# for B\nb_clicks_pivot = b_clicks.groupby(['is_click','day']).user_id.count().reset_index().pivot(\ncolumns = 'is_click',\nindex = 'day',\nvalues = 'user_id').reset_index()\n\nb_clicks_pivot['percent_clicked'] = b_clicks_pivot[True] / (b_clicks_pivot[True]+b_clicks_pivot[False])\n\nprint(b_clicks_pivot)\n\n# A is better except Tuesday\n","sub_path":"Data Analysis with Pandas/A-B Testing for ShoeFly.com.py","file_name":"A-B Testing for ShoeFly.com.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"648555726","text":"#!/usr/local/bin/python3\n\n# start your camera using photobooth for a preview and to warm up the camera before running this script\n\nimport numpy as np\nimport cv2\nimport boto3\nimport os\nfrom sys import argv\nfrom botocore.exceptions import BotoCoreError, ClientError\nimport json\nimport pyaudio\nimport inflect\nfrom time import sleep\n\nimport contextlib\nimport sys\n\nclass bcolors:\n HEADER = '\\033[95m'\n BLUE = '\\033[94m'\n GREEN = '\\033[92m'\n YELLOW = '\\033[93m'\n RED = '\\033[91m'\n ENDC = '\\033[0m'\n\n@contextlib.contextmanager\ndef ignore_stderr():\n devnull = os.open(os.devnull, os.O_WRONLY)\n old_stderr = os.dup(2)\n sys.stderr.flush()\n os.dup2(devnull, 2)\n os.close(devnull)\n try:\n yield\n finally:\n os.dup2(old_stderr, 2)\n os.close(old_stderr)\n\nregion = 'eu-west-1' # change this to switch to another AWS region\ncolors = [ ['green', 0,255,0], ['blue', 255,0,0], ['red', 0,0,255], ['purple', 255,0,255], ['silver', 192,192,192], ['cyan', 0,255,255], ['orange', 255,99,71], ['white', 255,255,255], ['black', 0,0,0] ]\n\npolly = boto3.client(\"polly\", region_name=region)\nreko = boto3.client('rekognition', region_name=region)\np = inflect.engine()\npya = pyaudio.PyAudio()\n\n# Take a photo with USB webcam\n# Set save to True if you want to save the image (in the current working directory)\n# and open Preview to see the image\ndef take_photo(save=False):\n with ignore_stderr():\n speak(\"Please point your external camera at the subject\")\n sleep(1)\n with ignore_stderr():\n speak(\"Taking a photo\")\n #vidcap=cv2.VideoCapture()\n # change the number of the camera that you open to cycle through different options if you have multiple connected cameras\n cam = cv2.VideoCapture(0)\n #sleep(2)\n\n cv2.namedWindow(\"Preview\", cv2.WINDOW_NORMAL)\n\n img_counter = 0\n flag = True\n while flag:\n ret, frame = cam.read()\n cv2.imshow(\"Preview\", frame)\n if not ret:\n break\n k = cv2.waitKey(1)\n\n if k%256 == 27:\n # ESC pressed\n print(\"Escape hit, closing...\")\n break\n elif k%256 == 32:\n # SPACE pressed\n #img_name = \"opencv_frame_{}.png\".format(img_counter)\n retval, image = cam.retrieve()\n #cv2.imwrite(img_name, frame)\n #print(\"{} written!\".format(img_name))\n flag = False\n cam.release()\n cv2.destroyAllWindows()\n small = cv2.resize(image, (0,0), fx=0.60, fy=0.60)\n if save:\n cv2.imwrite('image.png', small)\n # os.system('open -a Preview image.png')\n retval, encoded_image = cv2.imencode('.png',small)\n encoded_image_bytes = encoded_image.tobytes()\n return encoded_image_bytes\n\n# Read image from file\ndef read_image(filename):\n try:\n fin = open(filename, 'r')\n encoded_image_bytes = fin.read()\n fin.close()\n return encoded_image_bytes\n except IOError as e:\n print (\"I/O error({0}): {1}\".format(e.errno, e.strerror))\n exit(-1)\n\n# Provide a string and an optional voice attribute and play the streamed audio response\n# Defaults to the Salli voice\ndef speak(text_string, voice=\"Joanna\"):\n try:\n # Request speech synthesis\n response = polly.synthesize_speech(Text=text_string,\n TextType=\"text\", OutputFormat=\"pcm\", VoiceId=voice)\n except (BotoCoreError, ClientError) as error:\n # The service returned an error, exit gracefully\n print(error)\n exit(-1)\n # Access the audio stream from the response\n if \"AudioStream\" in response:\n stream = pya.open(format=pya.get_format_from_width(width=2), channels=1, rate=16000, output=True)\n stream.write(response['AudioStream'].read())\n sleep(1)\n stream.stop_stream()\n stream.close()\n else:\n # The response didn't contain audio data, return False\n print(\"Could not stream audio\")\n return(False)\n\n# Amazon Rekognition label detection\ndef reko_detect_labels(image_bytes):\n print (\"Calling Amazon Rekognition: detect_labels\")\n# speak(\"Detecting labels with Amazon Recognition\")\n response = reko.detect_labels(\n Image={\n 'Bytes': image_bytes\n },\n MaxLabels=8,\n MinConfidence=60\n )\n return response\n\n# rekognition facial detection\ndef reko_detect_faces(image_bytes):\n print (\"Calling Amazon Rekognition: detect_faces\")\n response = reko.detect_faces(\n Image={\n 'Bytes': image_bytes\n },\n Attributes=['ALL']\n )\n print (json.dumps(response, sort_keys=True, indent=4))\n return response\n\n# create verbal response describing the detected lables in the response from Rekognition\n# there needs to be more than one lable right now, otherwise you'll get a leading 'and'\ndef create_verbal_response_labels(reko_response):\n mystring = \"I detected the following labels: \"\n humans = False\n labels = len(reko_response['Labels'])\n if labels == 0:\n mystring = \"I cannot detect anything.\"\n else:\n i = 0\n for mydict in reko_response['Labels']:\n i += 1\n if mydict['Name'] == 'People':\n humans = True\n continue\n print (\"%s\\t(%.2f)\" % (mydict['Name'], mydict['Confidence']))\n if i < labels:\n newstring = \"%s, \" % (mydict['Name'].lower())\n mystring = mystring + newstring\n else:\n newstring = \"and %s. \" % (mydict['Name'].lower())\n mystring = mystring + newstring\n if ('Human' in mydict.values()) or ('Person' in mydict.values()) :\n humans = True\n return humans, mystring\n\ndef create_verbal_response_face(reko_response):\n mystring = \"\"\n\n persons = len(reko_response['FaceDetails'])\n print (\"number of persons = \", persons)\n\n if persons == 1:\n mystring = \"I can see one face. \"\n else:\n mystring = \"I can see %d faces. \" % (persons)\n i = 0\n for mydict in reko_response['FaceDetails']:\n # Boolean True|False values for these facial features\n beard = mydict['Beard']['Value']\n eyeglasses = mydict['Eyeglasses']['Value']\n sunglasses = mydict['Sunglasses']['Value']\n mustache = mydict['Mustache']['Value']\n smile = mydict['Smile']['Value']\n if mydict['Gender']['Confidence'] > 60:\n if persons == 1:\n mystring = mystring + \"The person is %s. \" % (mydict['Gender']['Value'].lower())\n else:\n mystring = mystring + \"The %s person is %s. \" % (p.number_to_words(p.ordinal(str([i+1]))), mydict['Gender']['Value'].lower())\n if mydict['Gender']['Value'] == 'Male':\n he_she = 'he'\n else:\n he_she = 'she'\n else:\n he_she = 'he'\n if persons == 1:\n mystring = mystring + \"This is a person. \"\n else:\n mystring = mystring + \"The %s person is a human. \" % p.number_to_words(p.ordinal(str([i+1])))\n print (\"Person %d (%s):\" % (i+1, colors[i][0]))\n print (\"\\tGender: %s\\t(%.2f)\" % (mydict['Gender']['Value'], mydict['Gender']['Confidence']))\n print (\"\\tEyeglasses: %s\\t(%.2f)\" % (eyeglasses, mydict['Eyeglasses']['Confidence']))\n print (\"\\tSunglasses: %s\\t(%.2f)\" % (sunglasses, mydict['Sunglasses']['Confidence']))\n print (\"\\tSmile: %s\\t(%.2f)\" % (smile, mydict['Smile']['Confidence']))\n if eyeglasses == True and sunglasses == True:\n mystring = mystring + \"%s is wearing glasses. \" % (he_she.capitalize(), )\n elif eyeglasses == True and sunglasses == False:\n mystring = mystring + \"%s is wearing spectacles. \" % (he_she.capitalize(), )\n elif eyeglasses == False and sunglasses == True:\n mystring = mystring + \"%s is wearing sunglasses. \" % (he_she.capitalize(), )\n if smile:\n true_false = 'is'\n else:\n true_false = 'is not'\n mystring = mystring + \"%s %s smiling. \" % (he_she.capitalize(), true_false)\n print (\"\\tEmotions:\")\n j = 0\n selected_emotion = ''\n\n mydict['Emotions'].sort(key = my_sort, reverse=True)\n\n for emotion in mydict['Emotions']:\n print (\"\\t\\t%s\\t(%.2f)\" % (emotion['Type'], emotion['Confidence']))\n if j == 0 or selected_emotion == '':\n if emotion['Type'].lower() != 'disgusted':\n mystring = mystring + \"%s looks %s. \" % (he_she.capitalize(), emotion['Type'].lower())\n selected_emotion = emotion['Type'].lower()\n j += 1\n # Find bounding box for this face\n height = mydict['BoundingBox']['Height']\n left = mydict['BoundingBox']['Left']\n top = mydict['BoundingBox']['Top']\n width = mydict['BoundingBox']['Width']\n i += 1\n\n if i > 2:\n break\n\n return mystring\n\ndef my_sort(e):\n return e['Confidence']\n\ndef save_image_with_bounding_boxes(encoded_image, reko_response):\n encoded_image=np.fromstring(encoded_image,np.uint8);\n image = cv2.imdecode(encoded_image, cv2.IMREAD_COLOR)\n image_height, image_width = image.shape[:2]\n i = 0\n for mydict in reko_response['FaceDetails']:\n # Find bounding box for this face\n height = mydict['BoundingBox']['Height']\n left = mydict['BoundingBox']['Left']\n top = mydict['BoundingBox']['Top']\n width = mydict['BoundingBox']['Width']\n # draw this bounding box\n image = draw_bounding_box(image, image_width, image_height, width, height, top, left, colors[i], i)\n i += 1\n if i > 2:\n break\n # write the image to a file\n cv2.imwrite('face_bounding_boxes.jpg', image)\n os.system('open -a Preview face_bounding_boxes.jpg')\n\n\n# draw bounding boxe around one face\ndef draw_bounding_box(cv_img, cv_img_width, cv_img_height, width, height, top, left, color, i):\n # calculate bounding box coordinates top-left - x,y, bottom-right - x,y\n width_pixels = int(width * cv_img_width)\n height_pixels = int(height * cv_img_height)\n left_pixel = int(left * cv_img_width)\n top_pixel = int(top * cv_img_height)\n cv2.rectangle(cv_img,(left_pixel, top_pixel),(left_pixel+width_pixels, top_pixel+height_pixels),(color[1],color[2],color[3]),2)\n font = cv2.FONT_HERSHEY_SIMPLEX\n cv2.putText(cv_img, str(i + 1), (left_pixel + 5, top_pixel + height_pixels + 25), font, 0.8, (color[1],color[2],color[3]), 2)\n return cv_img\n\n\n## START MAIN\n\n# if no arguments take a photo\n# if one argument open the image file and decode it\n# if more than on argument exit gracefully and print usage guidance\nif len(argv) == 1:\n encoded_image = take_photo(save=True)\nelif len(argv) == 2:\n print (\"opening image in file: \", argv[1])\n encoded_image=read_image(argv[1])\nelse:\n print (\"Use with no arguments to take a photo with the camera, or one argument to use a saved image\")\n exit(-1)\n\ntranslate = 'ru'\nlabels=reko_detect_labels(encoded_image)\nhumans, labels_response_string = create_verbal_response_labels(labels)\nprint (bcolors.GREEN + labels_response_string + bcolors.ENDC)\n\nwith ignore_stderr():\n speak(labels_response_string)\n\nif humans:\n print (\"Detected Human: \", humans, \"\\n\")\n reko_response = reko_detect_faces(encoded_image)\n faces_response_string = create_verbal_response_face(reko_response)\n save_image_with_bounding_boxes(encoded_image, reko_response)\n print (bcolors.GREEN + faces_response_string + bcolors.ENDC)\n sleep(1)\n with ignore_stderr():\n speak(faces_response_string)\n\n if translate:\n command = \"aws translate translate-text --text '%s' --source-language-code en --target-language-code %s > tmp\" % (faces_response_string, translate)\n translated = os.system(command)\n output = open('tmp', 'r')\n translation = json.load(output)\n print (bcolors.RED + '\\n\\n\\nTranslated to %s' % translate)\n print (translation['TranslatedText'])\n speak (json.dumps(translation['TranslatedText'], ensure_ascii=False).encode('utf8'), \"Lucia\")\nelse:\n print (\"No humans detected. Skipping facial recognition\")\n","sub_path":"capture_new.py","file_name":"capture_new.py","file_ext":"py","file_size_in_byte":12227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"346505671","text":"from getNw import *\nfrom numpy import *\ndef radarFields_3d(nx1,nx2,ny1,ny2,qs,qg,qh,qr,qc,qv,T,prs,ncs,ncg,nch,ncr,rho,wm,z,dm_z,vt_R,vt_S,pyHB2,nz,freqs):\n nfreq=8\n z3d=zeros((nx2-nx1,ny2-ny1,nz,3),float)\n v3d=zeros((nx2-nx1,ny2-ny1,nz,3),float)\n kext3d=zeros((nx2-nx1,ny2-ny1,nz,3),float)\n salb3d=zeros((nx2-nx1,ny2-ny1,nz,3),float)\n asym3d=zeros((nx2-nx1,ny2-ny1,nz,3),float)\n dns=zeros((nx2-nx1,ny2-ny1,nz),float)\n dnr=zeros((nx2-nx1,ny2-ny1,nz),float)\n dng=zeros((nx2-nx1,ny2-ny1,nz),float)\n qag=qh.copy()*0.\n ncag=nch.copy()*0.\n freqsT=[10.000000,19.000000,22.000000,37.000000,85.000000,165, 186.31,190.31]\n snowDBL=[]\n rainDBL=[]\n snowDBL=[]\n for j in range(ny1,ny2):\n print(j)\n for i in range(nx1,nx2):\n zKu_1d=zeros((nz),float)-99\n zKa_1d=zeros((nz),float)-99\n zW_1d=zeros((nz),float)-99\n vW_1d=zeros((nz),float)-99\n vKa_1d=zeros((nz),float)-99\n vKu_1d=zeros((nz),float)-99\n piaKu=0.\n piaKa=0.\n piaW=0.\n for k in range(nz-1,-1,-1):\n iwc=(qs[k,j,i]+qg[k,j,i]+qh[k,j,i]+qag[k,j,i])*rho[k,j,i]*1e3\n pwc=qr[k,j,i]*rho[k,j,i]*1e3\n if k==nz-1:\n dr=(z[nz-1]-z[nz-2])/1.\n else:\n dr=(z[k+1]-z[k])/1.\n if iwc>1e-4:\n if qs[k,j,i]>0.001e-4:\n lams,nws=getn0s(qs[k,j,i],ncs[k,j,i])\n nws=nws*rho[k,j,i]*1e-8\n else:\n nws=0.08\n if qg[k,j,i]>0.00001e-4:\n lamg,nwg=getn0g(qg[k,j,i],ncg[k,j,i])\n nwg=nwg*rho[k,j,i]*1e-8\n else:\n nwg=0.08\n if qh[k,j,i]>0.00001e-4:\n lamh,nwh=getn0g(qh[k,j,i],nch[k,j,i])\n nwh=nwh*rho[k,j,i]*1e-8\n else:\n nwh=0.08\n if qag[k,j,i]>0.00001e-4:\n lamag,nwag=getn0s(qag[k,j,i],ncag[k,j,i])\n nwag=nwag*rho[k,j,i]*1e-8\n else:\n nwag=0.08\n dns[i-nx1,j-ny1,k]=log10(nws/0.08)\n dng[i-nx1,j-ny1,k]=log10(nwg/0.08)\n iwc1=(qs[k,j,i])*rho[k,j,i]*1e3\n iwc2=(qg[k,j,i])*rho[k,j,i]*1e3\n iwc3=(qh[k,j,i])*rho[k,j,i]*1e3\n iwc4=(qag[k,j,i])*rho[k,j,i]*1e3\n kextS1,salbS1,asymS1,dpiaKu_S1,dpiaKa_S1,zKu_S1,zKa_S1,zW_S1,dmS1 = pyHB2.getsnowp2(log10(nws/0.08),(iwc1),nfreq)\n kextS2,salbS2,asymS2,dpiaKu_S2,dpiaKa_S2,zKu_S2,zKa_S2,zW_S2,dmS2 = pyHB2.getsnowp2(log10(nwg/0.08),(iwc2),nfreq)\n kextS3,salbS3,asymS3,dpiaKu_S3,dpiaKa_S3,zKu_S3,zKa_S3,zW_S3,dmS3 = pyHB2.getsnowp2(log10(nwh/0.08),(iwc3),nfreq)\n kextS4,salbS4,asymS4,dpiaKu_S4,dpiaKa_S4,zKu_S4,zKa_S4,zW_S4,dmS4 = pyHB2.getsnowp2(log10(nwag/0.08),(iwc4),nfreq)\n zW_S1=getZ_W(dmS1,zKu_S1,nws,dm_z,zW_S1)\n zW_S2=getZ_W(dmS2,zKu_S2,nwg,dm_z,zW_S2)\n zW_S3=getZ_W(dmS3,zKu_S3,nwh,dm_z,zW_S3)\n zW_S4=getZ_W(dmS4,zKu_S4,nwag,dm_z,zW_S4)\n zKu_S=log10(10.**(0.1*zKu_S1)+10.**(0.1*zKu_S2)+10.**(0.1*zKu_S3)+10.**(0.1*zKu_S4))*10\n zKa_S=log10(10.**(0.1*zKa_S1)+10.**(0.1*zKa_S2)+10.**(0.1*zKa_S3)+10.**(0.1*zKa_S4))*10\n zWS=log10(10.**(0.1*zW_S1)+10.**(0.1*zW_S2)+10.**(0.1*zW_S3)+10.**(0.1*zW_S4))*10\n if iwc1>0.01:\n snowDBL.append([dpiaKu_S1+dpiaKu_S2,dpiaKa_S1+dpiaKa_S2,\n zKu_S,zKa_S,iwc1+iwc2])\n i0s1=int((zKu_S1-10*log10(nws/0.08)+12.)/0.5)\n i0s1=max(i0s1,0)\n i0s1=min(114,i0s1)\n i0s2=int((zKu_S2-10*log10(nwg/0.08)+12.)/0.5)\n i0s2=max(i0s2,0)\n i0s2=min(114,i0s2)\n i0s3=int((zKu_S3-10*log10(nwh/0.08)+12.)/0.5)\n i0s3=max(i0s3,0)\n i0s3=min(114,i0s3)\n i0s4=int((zKu_S4-10*log10(nwag/0.08)+12.)/0.5)\n i0s4=max(i0s4,0)\n i0s4=min(114,i0s4)\n vs1=vt_S[i0s1,1:]\n vs2=vt_S[i0s2,1:]\n vs3=vt_S[i0s3,1:]\n vs4=vt_S[i0s4,1:]\n z_S1=array([zKu_S1,zKa_S1,zW_S1])\n z_S2=array([zKu_S2,zKa_S2,zW_S2])\n z_S3=array([zKu_S3,zKa_S3,zW_S3])\n z_S4=array([zKu_S4,zKa_S4,zW_S4])\n vS=(10**(0.1*z_S1)*vs1+10**(0.1*z_S2)*vs2+10**(0.1*z_S3)*vs3+10**(0.1*z_S4)*vs4)/\\\n (10**(0.1*z_S1)+10**(0.1*z_S2)+10**(0.1*z_S3)+10**(0.1*z_S4))\n \n dpiaW_S1=kextS1[4]*4.343\n dpiaW_S2=kextS2[4]*4.343\n dpiaW_S3=kextS3[4]*4.343\n dpiaW_S4=kextS4[4]*4.343\n else:\n zKu_S=-99\n zKa_S=-99\n zWS=-99\n vS=array([0.,0.,0.])\n dpiaKu_S1=0\n dpiaKu_S2=0\n dpiaKu_S3=0\n dpiaKu_S4=0\n dpiaKa_S1=0\n dpiaKa_S2=0\n dpiaKa_S3=0\n dpiaKa_S4=0\n dpiaW_S1=0\n dpiaW_S2=0\n dpiaW_S3=0\n dpiaW_S4=0\n kextS1=zeros((nfreq),float)\n kextS2=zeros((nfreq),float)\n kextS3=zeros((nfreq),float)\n kextS4=zeros((nfreq),float)\n asymS1=zeros((nfreq),float)\n asymS2=zeros((nfreq),float)\n asymS3=zeros((nfreq),float)\n asymS4=zeros((nfreq),float)\n salbS1=zeros((nfreq),float)\n salbS2=zeros((nfreq),float)\n salbS3=zeros((nfreq),float)\n salbS4=zeros((nfreq),float)\n n0w=0.\n if pwc>1e-9:\n lamr,nwr=getn0w(qr[k,j,i],ncr[k,j,i])\n nwr=nwr*rho[k,j,i]*1e-8\n dnr[i-nx1,j-ny1,k]=log10(nwr/0.08)\n \n kextR,salbR,asymR,dpiaKu_R,dpiaKa_R,zKu_R,zKa_R,zWR, dmR = pyHB2.getrainp2(log10(nwr/0.08),(pwc),nfreq)\n x=[dpiaKu_R,dpiaKa_R,zKu_R,zKa_R,pwc]\n rainDBL.append(x)\n i0dm=int((dmR-0.2)/0.02)\n if i0dm>0 and i0dm<190:\n dZ=zKu_R-10*log10(nwr/0.08)-dm_z[i0dm,1]\n zWR=dm_z[i0dm,3]+dZ+10*log10(nwr/0.08)\n i0r=int((zKu_R-10*log10(nwr/0.08)+12)/0.5)\n i0r=max(i0r,0)\n i0r=min(124,i0r)\n vR=vt_R[i0r,1:]\n if zKu_R>300:\n stop\n dpiaW_R=kextR[4]*4.343\n else:\n zKu_R=-99\n zKa_R=-99\n zWR=-99\n dpiaKu_R=0\n dpiaKa_R=0.\n dpiaW_R=0.\n dmR=0\n vR=array([0,0.,0])\n kextR=zeros((nfreq),float)\n salbR=zeros((nfreq),float)\n asymR=zeros((nfreq),float)\n a=2.65\n b=0.098\n dpiaL=[]\n ireturn=0\n asymg=(asymS1*salbS1*kextS1+asymS2*salbS2*kextS2+asymS3*salbS3*kextS3+asymS4*salbS4*kextS4+asymR*salbR*kextR)/\\\n (salbS1*kextS1+salbS2*kextS2+salbS3*kextS3+salbS4*kextS4+salbR*kextR+1e-5)\n salbg=(salbS1*kextS1+salbS2*kextS2+salbS3*kextS3+salbS4*kextS4+salbR*kextR)/\\\n (kextS1+kextS2+kextS3+kextS4+kextR+1e-5)\n #if zKu_R<0 and zKu_S>20:\n #print(asymg)\n #print(salbg)\n #print(salbS1)\n #print(zKu_S1,zKa_S1,zW_S1)\n #print(salbS2)\n #print(zKu_S2,zKa_S2,zW_S2)\n #print(salbS3)\n #print(zKu_S3,zKa_S3,zW_S3)\n #print(salbS4)\n #stop\n asymRadFreq=interp(freqs,freqsT,asymg)\n salbRadFreq=interp(freqs,freqsT,salbg)\n kextL=[]\n for freq in freqs:\n cldw=qc[k,j,i]*1e3*rho[k,j,i]\n z_clw = pyHB2.gcloud(freq,T[k,j,i],cldw)\n absair,abswv = pyHB2.gasabsr98(freq,T[k,j,i],qv[k,j,i],prs[k,j,i],ireturn)\n dpiaL.append((z_clw+absair+abswv)*4.343)\n kextL.append((z_clw+absair+abswv))\n zKu_1d[k]=log10(10.**(0.1*zKu_S)+10.**(0.1*zKu_R))*10-piaKu-(dpiaKu_S1+dpiaKu_S2+dpiaKu_S3+dpiaKu_S4+dpiaKu_R+dpiaL[0])*dr\n zKa_1d[k]=log10(10.**(0.1*zKa_S)+10.**(0.1*zKa_R))*10-piaKa-(dpiaKa_S1+dpiaKa_S2+dpiaKa_S3+dpiaKa_S4+dpiaKa_R+dpiaL[1])*dr\n zW_1d[k]=log10(10.**(0.1*zWS)+10.**(0.1*zWR))*10.-piaW-(dpiaW_S1+dpiaW_S2+dpiaW_S3+dpiaW_S4+dpiaW_R+dpiaL[2])*dr\n piaKu+=2*(dpiaKu_S1+dpiaKu_S2+dpiaKu_R+dpiaL[0])*dr # attenuation due to cloud and water vapor\n piaKa+=2*(dpiaKa_S1+dpiaKa_S2+dpiaKa_R+dpiaL[1])*dr # not included yet\n piaW+=2*(dpiaW_S1+dpiaW_S2+dpiaW_R+dpiaL[2])*dr\n z3d[i-nx1,j-ny1,k,0]=log10(10.**(0.1*zKu_S)+10.**(0.1*zKu_R))*10\n z3d[i-nx1,j-ny1,k,1]=log10(10.**(0.1*zKa_S)+10.**(0.1*zKa_R))*10\n z3d[i-nx1,j-ny1,k,2]=log10(10.**(0.1*zWS)+10.**(0.1*zWR))*10\n \n kext3d[i-nx1,j-ny1,k,0]=(dpiaKu_S1+dpiaKu_S2+dpiaKu_S3+dpiaKu_S4+\\\n dpiaKu_R+dpiaL[0])/4.343\n kext3d[i-nx1,j-ny1,k,1]=(dpiaKa_S1+dpiaKa_S2+dpiaKa_S3+dpiaKa_S4+\\\n dpiaKa_R+dpiaL[1])/4.343\n kext3d[i-nx1,j-ny1,k,2]=(dpiaW_S1+dpiaW_S2+dpiaW_S3+dpiaW_S4+\\\n dpiaW_R+dpiaL[2])/4.343\n if piaKu<0:\n stop\n vT=(10**(0.1*zKu_S)*vS+10**(0.1*zKu_R)*vR)/\\\n (10**(0.1*zKu_S)+10**(0.1*zKu_R))\n vW_1d[k]=vT[2]-wm[k,j,i]\n vKu_1d[k]=vT[0]-wm[k,j,i]\n vKa_1d[k]=vT[1]-wm[k,j,i]\n v3d[i-nx1,j-ny1,k,:]=vT-wm[k,j,i]\n asym3d[i-nx1,j-ny1,k,:]=asymRadFreq\n salb3d[i-nx1,j-ny1,k,:]=salbRadFreq\n #print(piaKu,piaKa,piaW)\n return z3d,kext3d,salb3d,asym3d,v3d, dnr,dns,dng, rainDBL,snowDBL\n\n","sub_path":"forwardModel/fields3D_CM1.py","file_name":"fields3D_CM1.py","file_ext":"py","file_size_in_byte":10965,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"180060064","text":"# -*- coding: utf-8 -*-\n# tim.lansen@gmail.com\n\n# Non-blocking std pipes for Windows\n# Thanks to https://stackoverflow.com/questions/34504970/non-blocking-read-on-os-pipe-on-windows\n\nimport os\nif os.name == 'nt':\n import msvcrt\n from ctypes import windll, byref, wintypes, GetLastError, WinError\n from ctypes.wintypes import HANDLE, DWORD, LPDWORD, BOOL\n PIPE_NOWAIT = wintypes.DWORD(0x00000001)\n ERROR_NO_DATA = 232\nelse:\n import fcntl\n\n\ndef pipe_nowait(pipe):\n if os.name == 'nt':\n method = windll.kernel32.SetNamedPipeHandleState\n method.argtypes = [HANDLE, LPDWORD, LPDWORD, LPDWORD]\n method.restype = BOOL\n h = msvcrt.get_osfhandle(pipe.fileno())\n res = method(h, byref(PIPE_NOWAIT), None, None)\n if res == 0:\n print(WinError())\n return False\n return True\n flags = fcntl.fcntl(pipe, fcntl.F_GETFL)\n fcntl.fcntl(pipe, fcntl.F_SETFL, flags | os.O_NONBLOCK)\n return True\n\n\nif __name__ == '__main__':\n # CreatePipe\n r, w = os.pipe()\n\n pipe_nowait(r)\n\n print(os.write(w, b'xxx'))\n print(os.read(r, 1024))\n try:\n print(os.write(w, b'yyy'))\n print(os.read(r, 1024))\n print(os.read(r, 1024))\n except OSError as e:\n print('{} {} {}'.format(dir(e), e.errno, GetLastError()))\n print(WinError())\n if GetLastError() != ERROR_NO_DATA:\n raise\n","sub_path":"backend/modules/utils/pipe_nowait.py","file_name":"pipe_nowait.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"141748718","text":"import sys\nimport os\n\nfrom pyglet.gl import *\n\nfrom cocos.scene import Scene\nfrom cocos.layer import Layer, ColorLayer\nfrom cocos.sprite import Sprite\nfrom cocos.director import director\n\nfrom cocos.menu import Menu, ImageMenuItem, LEFT, CENTER, \\\n zoom_in, zoom_out\n\nfrom veronica_logic import CommonTower, HardTower\n\nfrom utils import get_cell_from_point\nfrom settings import GRID_CELL, GRID_SIZE\n\n# FIXME silly images until we get some graphics:\nimages_for_sprites = {\n CommonTower: 'common_tower_head.png',\n HardTower: 'hard_tower_head.png',\n }\n\n\nclass HudLayer(Menu):\n def __init__(self, level):\n super(HudLayer, self).__init__()\n self.level = level\n \n # configure the menu:\n self.menu_halign = LEFT\n self.menu_valign = CENTER\n self.selected = None\n self.mouse_x, self.mouse_y = None, None\n \n items = []\n towers = [CommonTower, HardTower]\n for tower in towers:\n item = ImageMenuItem(images_for_sprites[tower], \n getattr(self, 'on_tower_callback'),\n tower)\n items.append(item)\n \n self.create_menu(items, selected_effect=zoom_in(),\n unselected_effect=zoom_out())\n \n def on_quit(self):\n pass\n \n def on_tower_callback(self, tower_class):\n # check if the operation can be done\n if not self.level.resources.can_be_done(\"add tower\"):\n return\n \n # mark this tower as selected and drag a sprite:\n if self.selected == tower_class or self.mouse_x is None:\n return\n self.drag_object = TowerCreationLayer(tower_class,\n self,\n self.mouse_x,\n self.mouse_y)\n \n self.get_ancestor(Scene).add(self.drag_object, z=10)\n self.selected = tower_class \n \n def on_mouse_motion(self, x, y, dx, dy):\n Menu.on_mouse_motion(self, x, y, dx, dy)\n self.mouse_x, self.mouse_y = director.get_virtual_coordinates(x,y)\n \n def end_drag(self):\n self.get_ancestor(Scene).remove(self.drag_object)\n self.selected = None\n\nclass TowerCreationLayer(Layer):\n is_event_handler = True\n \n def __init__(self, tower_class, menu, x=0, y=0):\n super(TowerCreationLayer, self).__init__()\n self.tower_class = tower_class\n self.menu = menu\n self.draging = (x,y)\n\n # a rect to indicate the object's possible position:\n self.rect_size_w = self.tower_class.size[0] * GRID_CELL\n self.rect_size_h = self.tower_class.size[1] * GRID_CELL\n self.rect_layer = ColorLayer(0,250,0,55,\n self.rect_size_w, self.rect_size_h)\n \n grid_pos = get_cell_from_point(self.draging[0], self.draging[1])\n self.rect_layer.position = (grid_pos[0]*GRID_CELL,\n grid_pos[1]*GRID_CELL)\n self.add(self.rect_layer)\n \n # dragging sprite of the map object:\n self.sprite = Sprite(images_for_sprites[tower_class])\n self.sprite.scale = 0.5\n self.sprite.position = self.draging\n self.add(self.sprite)\n \n def on_mouse_motion(self, x, y, dx, dy):\n self.draging = director.get_virtual_coordinates(x,y)\n grid_pos = get_cell_from_point(self.draging[0], self.draging[1])\n \n # TODO this call is ugly, so loong:\n is_out = self.menu.level.world.grid.is_out_at(self.tower_class, grid_pos)\n if is_out:\n self.rect_layer.visible = False\n else:\n self.rect_layer.visible = True\n \n # TODO this call is ugly, so loong:\n can_fit = self.menu.level.world.grid.can_fit_at(self.tower_class, grid_pos)\n self.rect_layer.position = (grid_pos[0]*GRID_CELL,\n grid_pos[1]*GRID_CELL)\n if can_fit:\n self.rect_layer.color = 0, 255, 0\n else:\n self.rect_layer.color = 255, 0, 0\n \n self.sprite.position = self.draging\n \n def on_mouse_press (self, x, y, buttons, modifiers):\n if buttons == pyglet.window.mouse.LEFT:\n grid_pos = get_cell_from_point(self.draging[0], self.draging[1])\n \n is_out = self.menu.level.world.grid.is_out_at(self.tower_class,\n grid_pos)\n \n can_fit = self.menu.level.world.grid.can_fit_at(self.tower_class,\n grid_pos)\n if not is_out and can_fit:\n self.menu.level.add_tower(self.tower_class, grid_pos)\n self.menu.end_drag()\n\n\nif __name__ == \"__main__\":\n import pyglet\n pyglet.resource.path.append(\"images\")\n pyglet.resource.reindex()\n \n import settings\n from logic import World\n \n class DummyWorldLayer(Layer):\n def __init__(self, world):\n super(DummyWorldLayer, self).__init__()\n hud_layer = HudLayer(world)\n self.add(hud_layer)\n\n world = World(grid_size=settings.GRID_SIZE)\n \n director.init( resizable=False)\n bg = ColorLayer(255,255,255,255)\n world_layer = DummyWorldLayer(world)\n director.run(Scene(bg, world_layer))\n","sub_path":"hud_layer.py","file_name":"hud_layer.py","file_ext":"py","file_size_in_byte":5427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"331715380","text":"from django.shortcuts import render, get_object_or_404\nfrom django.http import HttpResponse, HttpResponseRedirect, StreamingHttpResponse\nfrom django.http import HttpRequest\n\nimport urllib\nimport urllib.parse\nimport urllib.request\n\nfrom .models import EnvioPost, Gustos, GustoCliente\nfrom .forms import PostForm, GustosForm\n\nimport datetime\nimport json\nimport csv\n\n\n# Create your views here.\n\n\n\ndef envioPost_wellcome(request, id=None):\n objeto=EnvioPost.objects.get(id=id)\n datosRespuesta=json.loads(objeto.respuesta[2:len(objeto.respuesta)-1])\n\n form =GustosForm(request.POST or None)\n if form.is_valid():\n instance = form.save(commit=False)\n instance.idCliente=objeto\n instance.save()\n return HttpResponseRedirect('/home/thanks/' + str(objeto.id))\n context = {\n \"objeto\": objeto,\n \"respuesta\": objeto.respuesta[1:],\n \"datosRespuesta\":datosRespuesta,\n \"form\":form\n\n }\n return render(request, \"wellcome.html\", context)\n\ndef envioPost_home(request):\n form = PostForm(request.POST or None)\n if request.method == \"POST\":\n print(request.POST)\n\n if form.is_valid():\n instance = form.save(commit=False)\n\n print(instance.id)\n\n url = 'https://pre-mrz.leanhotelsystem.com'\n values = {'MRZ': instance.MRZ,\n 'hotel': 'test',\n 'app': 'TEST'}\n\n data = urllib.parse.urlencode(values).encode(\"utf-8\")\n req = urllib.request.Request(url)\n with urllib.request.urlopen(req, data=data) as f:\n resp = f.read()\n instance.respuesta=resp\n\n # response = urllib.request.urlopen(req)\n # the_page = response.read()\n # print(response)\n # print(the_page)\n print(str(instance.fechaRespuesta))\n instance.save()\n return HttpResponseRedirect('wellcome/'+str(instance.id))\n else:\n print(\"INVALIDO\")\n\n context = {\n \"form\":form\n\n }\n\n return render(request, \"index.html\", context)\n\n\n\n\n\ndef envioPost_data(request, id=None):\n #instance= EnvioPost.objects.get(id=1)\n\n objetoCliente = get_object_or_404(EnvioPost.objects.filter(id=id))\n objetoGustos = Gustos.objects.all().values()\n\n objetoClienteGustos = GustoCliente.objects.filter(idCliente=objetoCliente)\n listaGustos=[]\n listaGustos.append(objetoClienteGustos[0].interes1)\n listaGustos.append(objetoClienteGustos[0].interes2)\n listaGustos.append(objetoClienteGustos[0].interes3)\n listaGustos.append(objetoClienteGustos[0].interes4)\n listaGustos.append(objetoClienteGustos[0].interes5)\n listaGustos.append(objetoClienteGustos[0].interes6)\n empresa = objetoClienteGustos[0].empresa\n\n if request.method == \"POST\":\n output = []\n\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"DatosCliente'+id+'.csv\"'\n writer = csv.writer(response, delimiter =';')\n\n\n\n # CSV Data\n writer.writerow(['ID Cliente', 'Hotel', 'App', 'Respuesta','MRZ','Fecha Checking','empresa',objetoGustos[0][\"nombreGusto\"],objetoGustos[1][\"nombreGusto\"],objetoGustos[2][\"nombreGusto\"],objetoGustos[3][\"nombreGusto\"],objetoGustos[4][\"nombreGusto\"],objetoGustos[5][\"nombreGusto\"]])\n writer.writerow([objetoCliente.id, objetoCliente.hotel, objetoCliente.app , objetoCliente.respuesta, objetoCliente.MRZ, objetoCliente.fechaRespuesta, empresa,objetoClienteGustos[0].interes1, objetoClienteGustos[0].interes2, objetoClienteGustos[0].interes3, objetoClienteGustos[0].interes4, objetoClienteGustos[0].interes5, objetoClienteGustos[0].interes6])\n output.append([objetoClienteGustos[0].interes1])\n writer.writerows(output)\n return response\n\n context = {\n\n \"obj\" : objetoCliente,\n \"gustosCliente\" : objetoClienteGustos,\n \"gustos\" : objetoGustos,\n \"nombreGustos\" : listaGustos,\n \"empresa\" : empresa\n\n\n }\n return render(request, \"data.html\", context)\n\ndef envioPost_thanks(request, id=None):\n # instance= EnvioPost.objects.get(id=1)\n objeto = get_object_or_404(EnvioPost.objects.filter(id=id))\n context = {\n \"RMZ\": \"admin\",\n \"id\": objeto.id,\n\n }\n return render(request, \"thanks.html\", context)","sub_path":"chapp/EnvioPost/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"432916179","text":"from fixture.changes import ChangeFields\n\nclass ContactHelper(ChangeFields):\n\n def __init__(self, app):\n self.app = app\n\n def create_new(self, contact):\n wd = self.app.wd\n # create new contact\n wd.find_element_by_link_text(\"nowy wpis\").click()\n self.fill_contact_form(contact)\n # accept new contact\n wd.find_element_by_xpath(\"//div[@id='content']/form/input[21]\").click()\n self.app.open_home_page()\n\n def fill_contact_form(self, contact):\n wd = self.app.wd\n self.change_fields(\"firstname\", contact.firstname)\n self.change_fields(\"lastname\", contact.lastname)\n self.change_fields(\"address\", contact.address)\n self.change_fields(\"fax\", contact.fax)\n\n def modify_first_contact(self, new_contact_data):\n wd = self.app.wd\n self.app.open_home_page()\n # click edit selected\n wd.find_element_by_xpath(\"//table[@id='maintable']/tbody/tr[2]/td[8]/a/img\").click()\n self.fill_contact_form(new_contact_data)\n # update\n wd.find_element_by_name(\"update\").click()\n # return to home page\n self.app.open_home_page()\n\n def delete_first_contact(self):\n wd = self.app.wd\n self.app.open_home_page()\n # select first element\n wd.find_element_by_xpath(\"//table[@id='maintable']/tbody/tr[2]/td/input\").click()\n # click delete\n wd.find_element_by_xpath(\"//input[@value='Usuń']\").click()\n # confirm deleting popup\n wd.switch_to_alert().accept()\n\n def count_of_contact(self):\n wd = self.app.wd\n return len(wd.find_elements_by_name(\"selected[]\"))\n\n\n\n\n\n\n\n","sub_path":"fixture/contact.py","file_name":"contact.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"106643992","text":"from app import app\r\nfrom flask import render_template\r\n\r\n@app.route('/')\r\n@app.route('/index')\r\ndef index():\r\n\ttitle = 'Mi Microblog'\r\n\tuser = {'username' : 'nroy'}\r\n\tposts = [\r\n\t\t{\r\n\t\t\t'author': {'username' : 'Mariola'},\r\n\t\t\t'body': 'que lugar absolutamente bello'\r\n\t\t},\r\n\t\t{\r\n\t\t\t'author': {'username': 'Carlos'},\r\n\t\t\t'body': 'Volvere aqui de nuevo - Hermosa Espana'\r\n\t\t}\r\n\t]\r\n\treturn render_template('index.html', user=user['username'], title=title, posts=posts)\r\n","sub_path":"Flask_Udemy/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"492440711","text":"import pymel.core as pc\nfrom maya import cmds\nimport pyblish.api\nimport openpype.hosts.maya.api.action\nfrom openpype.hosts.maya.api.lib import maintained_selection\nfrom openpype.pipeline.publish import (\n RepairAction,\n ValidateMeshOrder,\n)\n\n\nclass ValidateMeshArnoldAttributes(pyblish.api.InstancePlugin):\n \"\"\"Validate the mesh has default Arnold attributes.\n\n It compares all Arnold attributes from a default mesh. This is to ensure\n later published looks can discover non-default Arnold attributes.\n \"\"\"\n\n order = ValidateMeshOrder\n hosts = [\"maya\"]\n families = [\"model\"]\n category = \"geometry\"\n label = \"Mesh Arnold Attributes\"\n actions = [\n openpype.hosts.maya.api.action.SelectInvalidAction,\n RepairAction\n ]\n optional = True\n if cmds.getAttr(\n \"defaultRenderGlobals.currentRenderer\").lower() == \"arnold\":\n active = True\n else:\n active = False\n\n @classmethod\n def get_invalid_attributes(cls, instance, compute=False):\n invalid = []\n\n if compute:\n # Get default arnold attributes.\n temp_transform = pc.polyCube()[0]\n\n for shape in pc.ls(instance, type=\"mesh\"):\n for attr in temp_transform.getShape().listAttr():\n if not attr.attrName().startswith(\"ai\"):\n continue\n\n target_attr = pc.PyNode(\n \"{}.{}\".format(shape.name(), attr.attrName())\n )\n if attr.get() != target_attr.get():\n invalid.append(target_attr)\n\n pc.delete(temp_transform)\n\n instance.data[\"nondefault_arnold_attributes\"] = invalid\n else:\n invalid.extend(instance.data[\"nondefault_arnold_attributes\"])\n\n return invalid\n\n @classmethod\n def get_invalid(cls, instance):\n invalid = []\n\n for attr in cls.get_invalid_attributes(instance, compute=False):\n invalid.append(attr.node().name())\n\n return invalid\n\n @classmethod\n def repair(cls, instance):\n with maintained_selection():\n with pc.UndoChunk():\n temp_transform = pc.polyCube()[0]\n\n attributes = cls.get_invalid_attributes(\n instance, compute=False\n )\n for attr in attributes:\n source = pc.PyNode(\n \"{}.{}\".format(\n temp_transform.getShape(), attr.attrName()\n )\n )\n attr.set(source.get())\n\n pc.delete(temp_transform)\n\n def process(self, instance):\n\n invalid = self.get_invalid_attributes(instance, compute=True)\n if invalid:\n raise RuntimeError(\n \"Non-default Arnold attributes found in instance:\"\n \" {0}\".format(invalid)\n )\n","sub_path":"openpype/hosts/maya/plugins/publish/validate_mesh_arnold_attributes.py","file_name":"validate_mesh_arnold_attributes.py","file_ext":"py","file_size_in_byte":2938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"329045458","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 5 07:40:18 2020\n\n@author: jingqiu\n\"\"\"\nimport matplotlib.pyplot as plt\n\ndef fig1plot(lamda, parameters, correlations1,correlations2,correlations3):\n naive,=plt.semilogx(parameters,correlations1,'r-*',label='naive',linewidth=2.0)\n truncate,= plt.plot(parameters,correlations2,'g-^',label='truncate')\n backtracking,=plt.plot(parameters,correlations3,'b-D',label='NBW')\n #saw,=plt.plot(parameters,correlations4,'-ro',label='SAW')\n worst,=plt.plot(parameters, [1-1/lamda**2 for parameter in parameters],label='worst',color='grey')\n #worst,=plt.plot(lamdas,[1-1/lamda**2 for lamda in lamdas],label='worst',color='grey')\n plt.legend(handles=[naive,truncate,backtracking,worst],loc='best',frameon=False);\n plt.xlabel(r'signal strength $\\lambda$')\n plt.xlabel(r'SNR $\\lambda$')\n plt.xlabel(r'parameter $d$')\n plt.ylabel('Squared correlation')\n #plt.savefig('MixedExperiment200parameters2.eps')\n plt.savefig('MinusERExperiment400lamda11.eps')\n plt.close()","sub_path":"fig1plot.py","file_name":"fig1plot.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"220786863","text":"\"\"\"Lists -> they are mutable\"\"\"\n\ncourses = [\"History\", \"Math\", \"physics\", \"Compsci\"]\ncourses2 = [\"CT\", \"eg\"]\nnum=[1,2,3]\n\n\"\"\"mutable\"\"\"\nlist1 = [\"hi\",\"hello\",\"bye\"]\nlist2 = list1 # list1 and list2 are pointing to same memory\nprint(\"{}\".format(\"list1= \"),list1, \"{}\".format(\"list2= \"),list2)\nlist1[0] = \"Art\"\nprint(\"{}\".format(\"list1= \"),list1, \"{}\".format(\"list2= \"),list2)\nlist3 = list1[:] # list3 and list 1 are pointing to different memory\nprint(\"{}\".format(\"list3= \"),list3,\"{}\".format(\"list1= \"),list1)\nlist1[0] = \"Arts\"\nprint(\"{}\".format(\"list3= \"),list3,\"{}\".format(\"list1= \"),list1)\n\n\"\"\" Methods with list\"\"\"\ncourses.append(\"art\")# add \"art\" to courses at end\ncourses.insert(1,\"electronics\")# add electronics to specific index\ncourses.extend(courses2)#extend the courses with courses2\ncourses.remove(\"Math\")\ncourses.pop()# remove the last item in the list and returns the value that is removed.\ncourses.reverse()# last item to 1st item\ncourses.sort()# sort in alphabetical/ ascending order.\ncourses.sort(reverse = True) # sort in descending order.\nprint(courses.index(\"physics\")) # prints the index number of physics. if value is not present we will get a value error.\nprint(\"Art\" in courses) # returns true if art in courses.\n\n\"\"\"functions with list\"\"\"\nsorted(courses) # sorts the list, but list is unchanged.\nsorted(courses, reverse = True) # sorts the list in reverse, but list is unchanged.\nprint(min(courses))# min value of list.\nprint(max(courses))\nsum(num)\n\n\"\"\" enumerate function\"\"\"\n\nfor index, item in enumerate(courses):\n print(index,item)# prints the index and the item.\n\n\"\"\" convert list into string \"\"\"\ncourse_str = \",\".join(courses) # History, Math, Physics it will be printed like that.\n\n\n\"\"\"Tuple => they are immutable\"\"\"\ntuple1 = (\"history\", \"Math\", \"Art\")\ntuple2 = tuple1\n\n\"\"\"functions with tuple\"\"\"\nsorted(tuple1) # sorts the tuple, but tuple is unchanged.\n\n\"\"\"sets are unordered and no duplicates\"\"\"\nset1 = {1,2,3,4,5}\nset1.remove(5) #will remove if element is present or it will throw keyerror.\nset1.discard(6) #will remove if element is present or it wont remove\nprint(set1)\ncourseSet = {\"History\", \"Math\", \"Physics\", \"Compsci\", \"History\"}\ncourseArt = {\"History\", \"Math\", \"Art\", \"Design\"}\ncourseArt.update({\"civics\", \"geography\"}) #add elements to set\nprint(courseArt)\ncourseArt.remove(\"civics\")#to remove elements.\nprint(courseArt)\nprint(courseSet.intersection(courseArt)) #prints common items in both\nprint(courseSet.difference(courseArt)) #prints the unique items in courSet\nprint(courseSet.symmetric_difference(courseArt)) # prints unique items in both sets.\nprint(courseSet.union(courseArt))# prints all items in both\n\n\n ","sub_path":"_3ListSetTuple.py","file_name":"_3ListSetTuple.py","file_ext":"py","file_size_in_byte":2663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"473090274","text":"import ConfigParser\nimport io\nfrom reportlab.pdfgen import canvas \nfrom db import find_data\nimport datetime as dt\nclass MakeReport(object):\n def __init__(self, frequency, reason):\n self.frequency = frequency\n self.reason = reason\n f = open('./report_' + str(frequency[0]) + '.conf')\n file_string = f.read()\n self.config = ConfigParser.RawConfigParser(allow_no_value=True)\n self.config.readfp(io.BytesIO(file_string))\n self.pdf = canvas.Canvas('example.pdf')\n self.pdf.setFont('Helvetica', 12)\n self.signature_row = 100\n\n def write_title(self):\n self.pdf.setFont('Helvetica', 20)\n self.pdf.drawString(50,750,self.config.get('title','VENTAS'))\n self.pdf.line(50,740,600,740) # Horizontal Line\n\n def write_message(self):\n self.pdf.setFont('Helvetica', 12)\n padding = 0\n counter = 0\n for message in self.config.options('headers'):\n to_write = self.config.get('headers', message)\n if counter == 0:\n to_write += ' ' + self.reason\n elif counter == 1: \n to_write += ' ' + self.config.get('frequency', 'FREQUENCY')\n self.pdf.drawString(50,710 - padding,to_write)\n padding += 16\n counter += 1\n\n def write_signature(self):\n self.pdf.setFont('Helvetica', 14)\n self.pdf.line(50,self.signature_row,600,100) # Horizontal Line\n self.pdf.drawString(400,self.signature_row - 20,self.config.get('greetings','MSG1'))\n\n def save_it(self):\n self.pdf.save()\n\n def get_data(self):\n start_row = 600\n self.pdf.setFont('Helvetica', 12)\n padding = 14\n days_back = 0\n if 'd' in self.frequency:\n days_back = 1\n elif 'w' in self.frequency:\n days_back = 7\n else: #monthly\n days_back = 30\n date_today = dt.datetime.now()\n delta = dt.timedelta(days_back,0,0)\n date_back = date_today - delta\n query_find = {'date':{'$gt':date_back, '$lte':date_today} }\n query_group = {'$group':{'_id':'$license_plate', 'total':{'$sum':'$amount'}}}\n query_match = {'$match':query_find}\n data = find_data.find_data(query_find, self.reason)\n pipeline = [query_match, query_group]\n aggr_data = find_data.aggregate_generic(self.reason, pipeline)\n for element in data:\n start_row -= padding\n self.pdf.drawString(50,start_row, \"Patente: \" + element['license_plate'] + \n ' Motivo: ' + element['name'] + \n ' Total: ' + str(element['amount']))\n for element in aggr_data:\n start_row -= padding\n self.pdf.drawString(50,start_row, \"Patente: \" + element['_id'] + \n ' ' + self.reason + ' Total: ' + str(element['total']))\n","sub_path":"reporting/reporting.py","file_name":"reporting.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"209734984","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom datetime import datetime\n\n# Create your views here.\n\n\ndef task_1(request):\n\ttext = \"Hello!!! If you want to get some information go to task_2/\"\n\treturn HttpResponse(text)\n\t\t\n\n\ndef task_2(request):\n\ttext = \"In task_3/ you can find out the date today. In task_4/ you can see the result of the program.In task_5/ you can see even numbers and odd numbers\"\n\treturn HttpResponse(text)\n\n\ndef task_3(request):\n\ttime = datetime.now()\n\tdate = f\"Today is {time.day}/{time.month}/{time.year}. The time now is {time.hour}:{time.minute}:{time.second}\"\n\n\treturn HttpResponse(date)\n\n\ndef task_4(request):\n\tdict_ = {}\n\t\n\tfor i in range(1,16):\n\t\tdict_.update({i:i**2})\n\t\n\ttext = \"The result of the program:\" + str(dict_)\n\n\treturn HttpResponse(text)\n\n\ndef task_5(request):\n\todd_list = []\n\teven_list = []\n\tfor i in range(1,16):\n\t\tif i % 2 == 0:\n\t\t\teven_list.append(i)\n\t\telse:\n\t\t\todd_list.append(i)\n\n\ttext = f\"Odd numbers: {str(odd_list)}\\n Even numbers: {str(even_list)}\"\n\treturn HttpResponse(text)\n\n","sub_path":"Homework_1/home_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"27468689","text":"import os\nfrom celery.schedules import crontab\n\nfrom celery import Celery\n\n# set the default Django settings module for the 'celery' program.\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'stock_analysis.settings')\n\napp = Celery('stock_analysis')\n\n# Using a string here means the worker doesn't have to serialize\n# the configuration object to child processes.\n# - namespace='CELERY' means all celery-related configuration keys\n# should have a `CELERY_` prefix.\napp.config_from_object('django.conf:settings', namespace='CELERY')\napp.conf.broker_transport_options = {'visibility_timeout': 3600} # 1 hour.\napp.conf.broker_url = 'redis://127.0.0.1:6379/0'\napp.conf.result_backend = 'redis://127.0.0.1:6379/0'\napp.conf.task_serializer = 'json'\napp.conf.timezone = 'Asia/Chongqing'\n\napp.conf.imports = ['tasks.celery_jobs', 'tasks.update_holdernumber',\n 'tasks.update_stock_exchange', 'utils.stock_markets']\n\napp.conf.update(\n task_routes = {\n 'tasks.celery_jobs.*': {'queue': 'celery_jobs'},\n 'tasks.update_holdernumber.*': {'queue': 'celery_jobs'},\n 'tasks.update_stock_exchange.*': {'queue': 'celery_jobs'},\n 'utils.stock_markets.get_stock_exchange_data': {'queue': 'celery_jobs'},\n },\n)\n\napp.conf.beat_schedule = {\n 'add-every-30-seconds': {\n 'task': 'tasks.celery_jobs.add',\n 'schedule': 3600.0,\n 'args': (16, 16),\n 'kwargs': {'test': 100}\n },\n 'run-update-holder_job': {\n 'task': 'tasks.update_holdernumber.all_holder_tasks',\n 'schedule': crontab(hour=3, minute=59),\n 'args': ()\n },\n 'run-update_stock_job': {\n 'task': 'tasks.update_stock_exchange.all_stock_tasks',\n 'schedule': crontab(hour=1, minute=30),\n 'args': (),\n 'kwargs': {'days': 2}\n },\n}\n\n# Load task modules from all registered Django app configs.\napp.autodiscover_tasks()\n","sub_path":"stock_analysis/celery.py","file_name":"celery.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"630834474","text":"from flask import Blueprint\nfrom flask import request\nfrom .response import success, fail\nfrom processors import ScheduleManager\n\n\nservice = Blueprint('schedules', __name__, url_prefix='/schedules')\n\n\n@service.route('', methods=['GET'])\ndef get_all():\n manager = ScheduleManager()\n schedules = manager.list()\n schedules = [s.serialize() for s in schedules]\n return success(schedules=schedules)\n\n\n@service.route('', methods=['POST'])\ndef add():\n request_info = request.get_json()\n\n if request_info is None:\n return fail(schedule=None, errors=['You need pass more parameters.'])\n\n name = request_info.get('name')\n time = request_info.get('time')\n method = request_info.get('method')\n uri = request_info.get('uri')\n parameters = request_info.get('parameters')\n comment = request_info.get('comment')\n\n manager = ScheduleManager()\n try:\n schedule = manager.add(name, time, method, uri, parameters, comment)\n return success(schedule=schedule.serialize())\n except ValueError as ex:\n return fail(schedule=None, errors=ex.args)\n\n\n@service.route('/')\ndef get(schedule_id):\n manager = ScheduleManager()\n schedule = manager.get(schedule_id)\n return success(schedule=schedule.serialize())\n\n\n@service.route('/name/')\ndef get_by_name(schedule_name):\n schedules = ScheduleManager().get_by_name(schedule_name)\n schedules = [s.serialize() for s in schedules]\n return success(schedules=schedules)\n\n\n@service.route('/', methods=['DELETE'])\ndef delete(schedule_id):\n manager = ScheduleManager()\n result = manager.delete(schedule_id)\n if result:\n return success()\n return fail()\n\n\n@service.route('/name/', methods=['DELETE'])\ndef delete_by_name(schedule_name):\n result = ScheduleManager().delete_by_name(schedule_name)\n return success() if result else fail()\n\n\n@service.route('/', methods=['PUT'])\ndef update(schedule_id):\n request_info = request.get_json()\n\n name = request_info.get('name')\n time = request_info.get('time')\n method = request_info.get('method')\n uri = request_info.get('uri')\n parameters = request_info.get('parameters')\n comment = request_info.get('comment')\n\n manager = ScheduleManager()\n schedule = manager.update(schedule_id, name, time, method, uri, parameters, comment)\n\n return success(schedule=schedule.serialize())\n\n\n@service.route('/name/', methods=['PUT'])\ndef update_by_name(schedule_name):\n request_info = request.get_json()\n\n name = request_info.get('name')\n time = request_info.get('time')\n method = request_info.get('method')\n uri = request_info.get('uri')\n parameters = request_info.get('parameters')\n comment = request_info.get('comment')\n\n manager = ScheduleManager()\n schedules = manager.update_by_name(schedule_name, name, time, method, uri, parameters, comment)\n schedules = [s.serialize() for s in schedules]\n\n return success(schedules=schedules)\n","sub_path":"src/apis/schedules.py","file_name":"schedules.py","file_ext":"py","file_size_in_byte":3061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"354828714","text":"from typing import (\n Tuple,\n Union,\n)\n\nfrom eth_typing import (\n Hash32,\n)\n\nfrom eth_utils import (\n ValidationError,\n encode_hex,\n)\nfrom eth_utils.toolz import (\n sliding_window,\n)\n\nfrom eth2.beacon.types.blocks import BaseBeaconBlock\nfrom eth2.beacon.typing import (\n Slot,\n)\n\nfrom trinity.protocol.common.validators import BaseValidator\n\nfrom trinity.protocol.bcc.commands import (\n RequestMessage,\n ResponseMessage,\n)\n\n\nclass BeaconBlocksValidator(BaseValidator[Tuple[BaseBeaconBlock, ...]]):\n\n def __init__(self, block_slot_or_hash: Union[Slot, Hash32], max_blocks: int) -> None:\n self.block_slot_or_hash = block_slot_or_hash\n self.max_blocks = max_blocks\n\n def validate_result(self, result: Tuple[BaseBeaconBlock, ...]) -> None:\n self._validate_first_block(result)\n self._validate_number(result)\n self._validate_sequence(result)\n\n @property\n def _is_numbered(self) -> bool:\n return isinstance(self.block_slot_or_hash, int)\n\n def _validate_first_block(self, blocks: Tuple[BaseBeaconBlock, ...]) -> None:\n \"\"\"Validate that the first returned block (if any) is the one that we requested.\"\"\"\n try:\n first_block = blocks[0]\n except IndexError:\n return\n\n if self._is_numbered:\n if first_block.slot != self.block_slot_or_hash:\n raise ValidationError(\n f\"Requested blocks starting with slot #{self.block_slot_or_hash} but first \"\n f\"returned block is from slot #{first_block.slot}\"\n )\n else:\n if first_block.hash != self.block_slot_or_hash:\n raise ValidationError(\n f\"Requested blocks starting with hash {encode_hex(self.block_slot_or_hash)} \"\n f\"but first returned block has hash {encode_hex(first_block.hash)}\"\n )\n\n def _validate_number(self, blocks: Tuple[BaseBeaconBlock, ...]) -> None:\n \"\"\"Validate that no more than the maximum requested number of blocks is returned.\"\"\"\n if len(blocks) > self.max_blocks:\n raise ValidationError(\n f\"Requested up to {self.max_blocks} blocks but received {len(blocks)}\"\n )\n\n def _validate_sequence(self, blocks: Tuple[BaseBeaconBlock, ...]) -> None:\n # workaround for https://github.com/pytoolz/cytoolz/issues/123#issuecomment-432905716\n if not blocks:\n return\n\n for parent, child in sliding_window(2, blocks):\n # check that the received blocks form a sequence of descendents connected by parent\n # hashes, starting with the oldest ancestor\n if child.parent_root != parent.hash:\n raise ValidationError(\n \"Returned blocks are not a connected branch\"\n )\n\n\ndef match_payload_request_id(request: RequestMessage, response: ResponseMessage) -> None:\n if request['request_id'] != response['request_id']:\n raise ValidationError(\"Request `id` does not match\")\n","sub_path":"trinity/protocol/bcc/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"236965965","text":"# name:\tAbbyBot\n# Username:\tAbby124Bot\n# t.me/Abby124Bot\n# token = \"474898864:AAGroqV6gFJP5d8oPJhu_lDwSq92N_Atg3k\"\n#524642824:AAHUUM5rq-P39D2eMyPDRtnkIZc1lOwlr1M\n\n\n#Walker's ID: 240552054\n#Anindya's ID: 223753440\n\nimport telegram\nimport logging, random\nfrom telegram.ext import Updater, CommandHandler, MessageHandler, Filters\nfrom league import Summoner\nimport strings\nimport fortnite as fnite\n\nTOKEN = \"524642824:AAHUUM5rq-P39D2eMyPDRtnkIZc1lOwlr1M\"\n\n\n\ndef start(bot, update):\n\tbot.send_message(chat_id=update.message.chat_id, text=\"Beep Beep Boop! I am a bot!\")\n\ndef echo(bot, update):\n\tbot.send_message(chat_id=update.message.chat_id, text=update.message.text)\n\ndef help(bot, update):\n\tbot.send_message(chat_id=update.message.chat_id, text=\"I am nigger and I cannot help :(\")\n\ndef stats(bot, update, args):\n\tsummoner_name = \"\"\n\tmsg_ID = update.message.message_id\n\n\tfor i in args:\n\t\tsummoner_name = summoner_name + i + \" \"\n\n\tprint(args)\n\tprint(summoner_name)\n\ttry:\n\t\tsummoner = Summoner(summoner_name)\n\t\trank_info = summoner.division + \" \" + summoner.rank + \", \" + str(summoner.lp) + \" LP \"\n\t\treply = rank_info\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=reply)\n\t#If an IndexError occurs, then the summoner hasn't finished their placement matches\n\texcept IndexError:\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=\"Play ranked you idiot\")\n\t#If a KeyError occurs, then the summoner name doesn't exist\n\texcept KeyError:\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=\"That account doesn't exist dumbshit\")\n\ndef league(bot, update):\n\tmsg_ID = update.message.message_id\n\tquestion = \"@SaveTheBeeees @DankMemesCanMeltSteelBeams @hotterthanahotdog @bleachonmytshirt @Insolent_child league?\"\n\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=question)\n\ndef fortnite(bot, update, args):\n\tmsg_ID = update.message.message_id\n\tif not args:\n\t\tquestion = \"@TheBoneDoctor @prankpatrol @Insolent_child @bleachonmytshirt @AtraWolf @hotterthanahotdog @SaveTheBeeees fortnite?\"\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=question)\n\n\telse:\n\t\tname = \"\"\n\t\tfor word in args:\n\t\t\tname = name + word + \" \"\n\n\t\tresp = fnite.getStats(name)\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=resp)\n\ndef overwatch(bot, update):\t\t\n\tmsg_ID = update.message.message_id\n\tquestion = \"@SaveTheBeeees @DankMemesCanMeltSteelBeams @hotterthanahotdog @bleachonmytshirt @prankpatrol @AtraWolf Overwatch?\"\n\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=question)\n\ndef forest(bot, update):\n\tmsg_ID = update.message.message_id\n\tquestion = \"@prankpatrol @Insolent_child @AtraWolf @SaveTheBeeees @DankMemesCanMeltSteelBeams forest?\"\n\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=question)\n\ndef dauntless(bot, update):\n\tmsg_ID = update.message.message_id\n\tquestion = \"@prankpatrol @Insolent_child @AtraWolf @SaveTheBeeees @DankMemesCanMeltSteelBeams dauntless?\"\n\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=question)\n\n#Function that reads the chat, and if a user says certain words it will interrupt.\ndef interjection(bot, update):\n\tfrom_user = update.message.from_user.first_name\n\tmsg_ID = update.message.message_id\n\tmsg_text = update.message.text.lower()\n\tmsg_lst = msg_text.split()\n\n\t#bot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=from_user)\n\t#If Kalada says the word yi, then reply\n\tif(from_user == \"Kalada\" and \"yi\" in msg_lst):\n\t\ti = random.randrange(len(strings.master_yi))\n\t\treply = strings.master_yi[i]\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=reply)\n\n\tif(from_user == \"Hardeep\" and \"for honor\" in msg_lst):\n\t\treply = \"Dead Game\"\n\t\tbot.send_message(chat_id=update.message.chat_id, reply_to_message_id=msg_ID, text=reply)\n\n\ndef unknown(bot, update):\n\tbot.send_message(chat_id=update.message.chat_id, text=\"Sorry I didn't understand that command. :(\")\n\ndef caps(bot, update, args):\n\ttext_caps = ' '.join(args).upper()\n\tbot.send_message(chat_id=update.message.chat_id, text=text_caps)\n\n\ndef main():\n\tbot = telegram.Bot(token=TOKEN)\n\tupdater = Updater(token=TOKEN)\n\tdispatcher = updater.dispatcher\n\tlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO)\n\n\t#echo_handler = MessageHandler(Filters.text, echo)\n\tstart_handler = CommandHandler('start', start)\n\thelp_handler = CommandHandler('help', help)\n\tcaps_handler = CommandHandler('caps', caps, pass_args=True)\n\tstats_handler = CommandHandler('stats', stats, pass_args=True)\n\tleague_handler = CommandHandler('league', league)\n\tfortnite_handler = CommandHandler('fortnite', fortnite, pass_args=True)\n\toverwatch_handler = CommandHandler('overwatch', overwatch)\n\tforest_handler = CommandHandler('forest', forest)\n\tdauntless_handler = CommandHandler('dauntless', dauntless)\n\tinterjection_handler = MessageHandler(Filters.all, interjection)\n\n\tunknown_handler = MessageHandler(Filters.command, unknown)\n\n\n\t#dispatcher.add_handler(echo_handler)\n\tdispatcher.add_handler(start_handler)\n\tdispatcher.add_handler(help_handler)\n\tdispatcher.add_handler(caps_handler)\n\tdispatcher.add_handler(stats_handler)\n\tdispatcher.add_handler(league_handler)\n\tdispatcher.add_handler(fortnite_handler)\n\tdispatcher.add_handler(overwatch_handler)\n\tdispatcher.add_handler(forest_handler)\n\tdispatcher.add_handler(dauntless_handler)\n\n\tdispatcher.add_handler(interjection_handler)\t\n\tdispatcher.add_handler(unknown_handler)\n\n\tupdater.start_polling()\n\nmain()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"456019040","text":"# Resolve the problem!!\n\nPALINDROMES = [\n 'Acaso hubo buhos aca',\n 'A la catalana banal atacala',\n 'Amar da drama',\n]\n\nNOT_PALINDROMES = [\n 'Hola como estas',\n 'Platzi'\n 'Oscar',\n]\n\n\ndef is_palindrome(palindrome):\n # Start coding here\n word_treatment = palindrome.casefold().replace(' ','')\n forwards = list(word_treatment) \n backwards = list(reversed(forwards))\n\n if forwards == backwards:\n answer = True\n\n else:\n answer = False \n\n return answer\n\ndef validate():\n for palindrome in PALINDROMES:\n if not is_palindrome(palindrome):\n return False\n\n for not_palindrome in NOT_PALINDROMES:\n if is_palindrome(not_palindrome):\n return False\n return True\n\n\ndef run():\n if validate():\n print('Completaste el test')\n else:\n print('No completaste el test')\n\n\nif __name__ == '__main__':\n run()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"510394775","text":"#\n# Part of `python-smartcash`\n#\n# Copyright 2018 dustinface\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\nimport re\nimport subprocess\nimport json\nimport logging\nimport re\nimport copy\nimport base64\ntry:\n import http.client as http\nexcept ImportError:\n import httplib as http\n\ntry:\n import urllib.parse as urlparse\nexcept ImportError:\n import urlparse\n\nlogger = logging.getLogger(\"smartcash.rpc\")\n\nclass RPCException(Exception):\n def __init__(self, code = None, message = None):\n super(RPCException, self).__init__()\n self.error = RPCError(code,message)\n\n def __str__(self):\n return str(self.error)\n\nclass RPCError(object):\n def __init__(self, code = None, message = None):\n self.code = code if code else \"?\"\n self.message = message if message else \"?\"\n\n def __str__(self):\n return '{} => {}'.format(self.code, self.message)\n\nclass RPCResponse(object):\n def __init__(self, data = None, error = None):\n self.data = data\n self.error = error\n\n def __contains__(self, key):\n return False if not self.data else key in self.data\n\n def __getitem__(self, key):\n return self.data[key]\n\nclass RPCConfig(object):\n def __init__(self, user, password, url = \"http://127.0.0.1\", port = 9679, timeout = 20):\n\n self.port = port\n\n self.url = urlparse.urlparse(url)\n\n try:\n self.user = user.encode('utf8')\n except:\n self.user = user\n\n try:\n self.password = password.encode('utf8')\n except:\n self.password = password\n\n self.authHeader = b'Basic ' + base64.b64encode(self.user + b':' + self.password)\n self.timeout = timeout\n\nclass SmartCashRPC(object):\n\n def __init__(self, config):\n\n self.config = copy.deepcopy(config)\n self.connection = None\n\n\n def request(self, method, args = None):\n\n self.connection = http.HTTPConnection(self.config.url.hostname, self.config.port,\n timeout=self.config.timeout)\n\n post = json.dumps({'version': '1.1',\n 'method': method,\n 'params': args})\n\n try:\n self.connection.request('POST', self.config.url.path, post,\n {'Host': self.config.url.hostname,\n 'Authorization': self.config.authHeader,\n 'Content-type': 'application/json'})\n\n self.connection.sock.settimeout(self.config.timeout)\n\n response = self.connection.getresponse()\n\n except Exception as e:\n raise RPCException(10,'Request error - {}'.format(e))\n else:\n\n if response is None:\n raise RPCException(11,'No response from server')\n\n if response.getheader('Content-Type') != 'application/json':\n raise RPCException(12, 'Non JSON response: {}, {}'.format(response.status, response.reason))\n\n try:\n data = response.read().decode('utf8')\n response = json.loads(data)\n except:\n response = None\n\n if not response:\n raise RPCException(13, 'JSON response parse error')\n\n error = response['error'] if 'error' in response else None\n result = response['result'] if 'result' in response else None\n\n if error:\n raise RPCException(response['error']['code'],response['error']['message'])\n\n if not result:\n raise RPCException(14,' RPC result missing')\n\n return result\n\n def raw(self, method, args):\n\n response = RPCResponse()\n\n try:\n response.data = self.request(method, args)\n except RPCException as e:\n response.error = e.error\n logging.debug(method, exc_info=e)\n\n return response\n\n\n def validateAddress(self, address):\n\n cleanAddress = re.sub('[^A-Za-z0-9]+', '', address)\n\n response = RPCResponse()\n\n try:\n response.data = self.request('validateaddress', [address])\n except RPCException as e:\n response.error = e.error\n logging.debug('validateaddress', exc_info=e)\n\n return response\n\n def getInfo(self):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('getinfo')\n except RPCException as e:\n response.error = e.error\n logging.debug('getInfo', exc_info=e)\n\n return response\n\n def getBlockByHash(self, blockHash):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('getblock', [blockHash])\n except RPCException as e:\n response.error = e.error\n logging.debug('getBlockByHash', exc_info=e)\n\n return response\n\n def getBlockByNumber(self, number):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('getblockhash', [number])\n except RPCException as e:\n response.error = e.error\n logging.debug('getBlockByNumber', exc_info=e)\n else:\n\n if response.data:\n return self.getBlockByHash(response.data)\n\n return response\n\n def getRawTransaction(self, txhash):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('getrawtransaction', [txhash, 1])\n except RPCException as e:\n response.error = e.error\n logging.debug('getRawTransaction', exc_info=e)\n\n return response\n\n def getSyncStatus(self):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('snsync', ['status'])\n except RPCException as e:\n response.error = e.error\n logging.debug('snsync', exc_info=e)\n else:\n\n # {\n # \"AssetID\": 999,\n # \"AssetName\": \"SMARTNODE_SYNC_FINISHED\",\n # \"Attempt\": 0,\n # \"IsBlockchainSynced\": true,\n # \"IsMasternodeListSynced\": true,\n # \"IsWinnersListSynced\": true,\n # \"IsSynced\": true,\n # \"IsFailed\": false\n # }\n if not response.data:\n logging.debug('getSyncStatus no status')\n elif not 'IsBlockchainSynced' in response.data:\n err = 'getSyncStatus no IsBlockchainSynced'\n response.data = None\n response.error = RPCError(16,err)\n logging.debug(err)\n elif not 'IsSmartnodeListSynced' in response.data:\n err = 'getSyncStatus no IsSmartnodeListSynced'\n response.data = None\n response.error = RPCError(16,err)\n logging.debug(err)\n elif not 'IsWinnersListSynced' in response.data:\n err = 'getSyncStatus no IsWinnersListSynced'\n response.data = None\n response.error = RPCError(16,err)\n logging.debug(err)\n\n return response\n\n def getSmartNodeList(self, mode):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('smartnode', ['list', mode ])\n except RPCException as e:\n response.error = e.error\n logging.debug('snsync', exc_info=e)\n\n return response\n\n def unlockWallet(self, password, timeout = 200):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('walletpassphrase', [password, timeout ])\n except RPCException as e:\n\n # Missing RPC result is expected when unlocking\n if e.error.code != 14:\n response.error = e.error\n logging.debug('walletpassphrase', exc_info=e)\n else:\n response.error = None\n response.data = True\n\n return response\n\n def lockWallet(self):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('walletlock')\n except RPCException as e:\n\n # Missing RPC result is expected when locking\n if e.error.code != 14:\n response.error = e.error\n logging.debug('walletlock', exc_info=e)\n else:\n response.error = None\n response.data = True\n\n return response\n\n def getAccounts(self):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('listaccounts')\n except RPCException as e:\n response.error = e.error\n logging.debug('listaccounts', exc_info=e)\n\n return response\n\n def getAddressGroupings(self):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('listaddressgroupings')\n except RPCException as e:\n response.error = e.error\n logging.debug('listaddressgroupings', exc_info=e)\n\n return response\n\n\n def signMessage(self, address, message):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('signmessage', [address, message])\n except RPCException as e:\n response.error = e.error\n logging.debug('signmessage', exc_info=e)\n\n return response\n\n\n def verifyMessage(self, address, message, signature):\n\n response = RPCResponse()\n\n try:\n response.data = self.request('verifymessage', [address, signature, message])\n except RPCException as e:\n response.error = e.error\n logging.debug('verifymessage', exc_info=e)\n\n return response\n","sub_path":"smartcash/rpc.py","file_name":"rpc.py","file_ext":"py","file_size_in_byte":10675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"333480712","text":"case_size = int(input())\nfor case in range(1, case_size + 1):\n cards = input()\n stack = []\n is_error = False\n deck = {'S': 13, 'D': 13, 'H': 13, 'C': 13}\n\n for i in range(0, len(cards) - 2, 3):\n card = (cards[i], cards[i+1:i+3])\n if card in stack:\n is_error = True\n break\n else:\n stack.append(card)\n \n if not is_error:\n for i in range(len(stack)):\n deck[stack.pop()[0]] -= 1\n print(f'#{case} {deck[\"S\"]} {deck[\"D\"]} {deck[\"H\"]} {deck[\"C\"]}')\n else:\n print(f'#{case} ERROR')\n ","sub_path":"Python/SWEA/D3/4047_영준이의 카드 카운팅.py","file_name":"4047_영준이의 카드 카운팅.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"542175506","text":"# we will use shell commands to train the model and perform actions\n# the necessary commands will be mentioned wherever reqd\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array\nfrom tensorflow.keras.applications import MobileNetV2\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\nfrom tensorflow.keras.utils import to_categorical\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\nfrom imutils import paths\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport os\n\n# creating the argument parser and parsing the shell commands\nap = argparse.ArgumentParser()\nap.add_argument('-d', '--dataset', required=True, help='path to the input dataset')\nap.add_argument('-p', '--plot', type=str, default='plot.png', help='path to output loss/accuracy plot')\nap.add_argument('-m', '--model', type=str, help='path to output model')\nargs = vars(ap.parse_args())\n\n# initializing the global variables\nINIT_LR = 1e-4\nEPOCHS = 20\nBS = 32\n\n# run the program using python3 train_detector.py -d /path/to/dataset_dir\n# (not necessary to run, I have already run it and the mask_detector.model file is already created.)\n\n# initialise the input images in their directory\nprint('loading images...')\nimage_paths = list(paths.list_images(args['dataset']))\ndata = []\nlabels = []\n\n# looping over the image paths\nfor image_path in image_paths:\n # extract the class label from the filename, but it must follow the current directory structure\n label = image_path.split(os.path.sep)[-2]\n\n # load and prerocess the image\n image = load_img(image_path, target_size=(224,224))\n image = img_to_array(image)\n image = preprocess_input(image)\n\n # add the image data and labels to lists\n data.append(image)\n labels.append(label)\n\n# update the data and labels list to numpy arrays\ndata = np.array(data, dtype='float32')\nlabels = np.array(labels)\n\n# perform OHE on the labels\nlb = LabelBinarizer()\nlabels = lb.fit_transform(labels)\nlabels = to_categorical(labels)\n\n# split the data into training and testing sets\ntrainX, testX, trainY, testY = train_test_split(data, labels, test_size=0.25, stratify=labels)\n\n# construct a data generator for image augmentation\naug = ImageDataGenerator(\n rotation_range=20,\n zoom_range=0.15,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.15,\n horizontal_flip=True,\n fill_mode='nearest'\n)\n\n# load the MobileNetV2() model in the headless mode\nbase_model = MobileNetV2(weights='imagenet', include_top=False, \n input_tensor=keras.layers.Input(shape=(224,224,3)))\n\n# constructing the head of the model\nhead_model = base_model.output\nhead_model = keras.layers.AveragePooling2D(pool_size=(7,7))(head_model)\nhead_model = keras.layers.Flatten(name='flatten')(head_model)\nhead_model = keras.layers.Dense(128, activation='relu')(head_model)\nhead_model = keras.layers.Dropout(0.5)(head_model)\nhead_model = keras.layers.Dense(2, activation='softmax')(head_model)\n\n# creating the final trainable model \nmodel = Model(inputs=base_model.input, outputs=head_model)\n\n# making the layers of the base_model untrainable\nfor layer in base_model.layers:\n layer.trainable = False\n\n# compiling our model\nprint('compiling model...')\nopt = keras.optimizers.Adam(lr=INIT_LR, decay=INIT_LR/EPOCHS)\nmodel.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n\n# train the head of the network\nprint('training model...')\nhistory = model.fit(\n aug.flow(trainX, trainY, batch_size=BS),\n steps_per_epoch=len(trainX)//BS,\n validation_data=(testX, testY),\n validation_steps=len(testX)//BS,\n epochs=EPOCHS\n)\n\n# make predictions on the testing network\nprint('evaluating model...')\npred_idxs = model.predict(testX, batch_size=BS)\n\n# we need to find out the predicted values for each column\npred_idxs = np.argmax(pred_idxs, axis=1)\n\n# displaying the classification report\nprint(classification_report(testY.argmax(axis=1), pred_idxs, target_names=lb.classes_))\n\n# serialize the model to disk\nprint('saving model...')\nmodel.save(args['model'], save_format='h5')\n\n# plot the training and testing loss and accuracy\nn = EPOCHS\nplt.style.use('ggplot')\nplt.figure()\nplt.plot(np.arange(0,n), history.history['loss'], label='Training Loss')\nplt.plot(np.arange(0,n), history.history['val_loss'], label='Validation Loss')\nplt.plot(np.arange(0,n), history.history['accuracy'], label='Training Accuracy')\nplt.plot(np.arange(0,n), history.history['val_accuracy'], label='Validation Accuracy')\nplt.title('Losses and Accuracy')\nplt.xlabel('Epoch #')\nplt.ylabel('Loss/Accuracy')\nplt.legend(loc='lower left')\nplt.savefig(args['plot'])\n","sub_path":"train_detector.py","file_name":"train_detector.py","file_ext":"py","file_size_in_byte":4901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"114098661","text":"from Bio import SeqIO\nfrom Bio import AlignIO\n\nfilename = \"Seq_Aligned.txt\"\nfilehandle = open (filename, \"r\")\noutfilename =\"Seq_Align_Out.txt\"\noutfilehandle = open (outfilename, \"w\")\n\nalignments = AlignIO.read (filehandle, \"fasta\")\n# records = list (SeqIO.parse(filehandle, \"fasta\"))\nfilehandle.close()\n\nfor alignment in alignments:\n\toutfilehandle.write(\">\"+str(alignment.description)+\"\\t\"+str(alignment.seq+\"\\n\"))\n\n#for record in records:\n#\tprint record.description\n#\tprint record.seq\n\noutfilehandle.close()\n","sub_path":"DPP4/V0.2/NameFormatting-Step1.py","file_name":"NameFormatting-Step1.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"46978223","text":"\nfrom logzero import logger\nimport logzero\nimport logging\nimport glob\nimport pandas as pd\n\n# Scrapy packages\nimport scrapy\nimport requests\nfrom scrapy.selector import Selector\nfrom TA_scrapy.items import ReviewRestoItem, RestoItem, UserItem\nfrom TA_scrapy.spiders import get_info\n\n# Chromedriver package and options\nfrom selenium import webdriver\nfrom webdriver_manager.chrome import ChromeDriverManager\nchrome_options = webdriver.ChromeOptions()\nchrome_options.add_argument('--headless')\nchrome_options.add_argument('--no-sandbox')\nchrome_options.add_argument('--disable-dev-shm-usage')\n\nclass BokanSpider(scrapy.Spider):\n name = \"BokanSpider\"\n\n def __init__(self, directory='./scraped_data/', \n root_url='https://www.tripadvisor.co.uk/Restaurant_Review-g186338-d12156905-Reviews-Bokan_37_Restaurant-London_England.html', \n debug=0, scrap_user=1, scrap_website_menu=0, id_resto=0,\n *args, **kwargs):\n \n super(BokanSpider, self).__init__(*args, **kwargs)\n\n # Set logging level\n logzero.loglevel(int(debug))\n if int(debug) == 0 :\n logging.disable(logging.DEBUG)\n\n\n # # Setting the list of already scraped restaurants\n # existing_jsons = glob.glob(\"./scraped_data/restaurants/*.json\")\n # logger.warn(f' > FINDING EXISTING JSONS {existing_jsons}')\n # self.already_scraped_restaurants = []\n self.next_file_id = 1\n # for json in existing_jsons:\n # json_df = pd.read_json(json, lines=True)\n # restaurants = json_df['resto_TA_url'].to_list()\n # restaurants = [resto.split(\"https://www.tripadvisor.co.uk\")[1] for resto in restaurants]\n # self.already_scraped_restaurants += restaurants\n\n # User defined parameters\n self.directory = directory\n self.root_url = root_url\n self.id_resto = int(id_resto)\n # self.maxpage_reviews = int(maxpage_reviews)\n self.scrap_user = int(scrap_user)\n self.scrap_website_menu = int(scrap_website_menu)\n # self.nb_resto = int(nb_resto)\n # self.only_bokan = only_bokan\n\n # To track the evolution of scrapping\n # self.resto_offset = len(self.already_scraped_restaurants)\n # self.review_offset = self.get_review_offset()\n self.main_nb = 0\n self.resto_nb = 0\n self.review_nb = 0\n self.restaurants_ids = []\n\n # logger.warn(f\"FINDING {self.resto_offset} EXISTING RESTAURANTS\")\n # logger.warn(f\"FINDING {self.review_offset} EXISTING REVIEWS\")\n # \n\n def start_requests(self):\n \"\"\" Give the urls to follow to scrapy\n - function automatically called when using \"scrapy crawl my_spider\"\n \"\"\"\n\n # Basic restaurant page on TripAdvisor GreaterLondon\n yield scrapy.Request(url=self.root_url, callback=self.parse_review_page)\n \n \n\n def parse_review_page(self, response):\n \"\"\" SECOND PARSING : Given a review page, gets each review url and get to parse it\n - Usually there are 10 reviews per page\n \"\"\"\n\n\n # Parse the restaurant if it has not been parsed yet\n if self.id_resto not in self.restaurants_ids:\n yield self.parse_resto(response)\n self.restaurants_ids.append(self.id_resto)\n\n # Get the list of reviews on the page\n urls_review = get_info.get_urls_reviews_in_review_page(response)\n\n # For each review open the link and parse it into the parse_review method\n for url_review in urls_review:\n yield response.follow(url=url_review, callback=self.parse_review)\n\n # Get next page information\n next_page, next_page_number = get_info.get_urls_next_list_of_reviews(response)\n \n # Follow the page if we decide to\n # if get_info.go_to_next_page(next_page, next_page_number, max_page=self.maxpage_reviews):\n yield response.follow(next_page, callback=self.parse_review_page)\n\n def parse_resto(self, response):\n \"\"\" Create Restaurant Item saved in specific JSON file \"\"\"\n\n logger.info(' > PARSING NEW RESTO ({})'.format(self.id_resto))\n \n xpath_name = '//h1[@class=\"_3a1XQ88S\"]/text()'\n xpath_nb_reviews = '//div[@class=\"_1ud-0ITN\"]/span/a/span/text()'\n xpath_price_cuisine = '//span[@class=\"_13OzAOXO _34GKdBMV\"]//a/text()'\n xpath_phone_number = '//div[@class=\"_1ud-0ITN\"]/span/span/span/a/text()'\n xpath_website = '//a[@class=\"_2wKz--mA _15QfMZ2L\"]/@data-encoded-url'\n xpath_ranking = '//*[@id=\"component_44\"]/div/div[2]/span[2]/a/span/b/span/text()'\n xpath_ranking_out_of = '//span[@class=\"_13OzAOXO _2VxaSjVD\"]/a/span/text()'\n xpath_rating = '//div[@class=\"_1ud-0ITN\"]/span/a/svg/@title'\n xpath_address = '//span[@class=\"_13OzAOXO _2VxaSjVD\"]/span[1]/a/text()'\n \n resto_item = RestoItem()\n resto_item['restaurant_id'] = self.id_resto\n resto_item['name'] = response.xpath(xpath_name).get()\n resto_item['resto_TA_url'] = response.url\n resto_item['nb_reviews'] = response.xpath(xpath_nb_reviews).get()\n price_cuisine = response.xpath(xpath_price_cuisine).getall()\n\n # Retrieve price in the right format\n raw_price = price_cuisine[0]\n try:\n min_price, max_price = raw_price.split(' - ')\n except ValueError:\n min_price, max_price = raw_price, raw_price\n\n resto_item['min_price'] = len(min_price)\n resto_item['max_price'] = len(max_price)\n resto_item['cuisine'] = price_cuisine[1:]\n resto_item['address'] = response.xpath(xpath_address).get()\n resto_item['phone_number'] = response.xpath(xpath_phone_number).get()\n\n # Scrap websites and menus depending on user input\n if self.scrap_website_menu:\n driver = webdriver.Chrome(ChromeDriverManager().install(), chrome_options=chrome_options)\n driver.get(response.url)\n\n # Catch website (use Selenium as URL is generated by JS)\n website = driver.find_element_by_class_name('_2wKz--mA')\n website_url = website.get_attribute('href') \n if website_url is None:\n resto_item['website'] = 'Website not found'\n else:\n resto_item['website'] = website_url\n\n # Catch menu\n menu = driver.find_elements_by_xpath('//span[@class=\"_13OzAOXO _2VxaSjVD ly1Ix1xT\"]/a')\n try:\n resto_item['menu'] = menu[0].get_attribute('href')\n except IndexError:\n resto_item['menu'] = 'Menu not found'\n else:\n resto_item['website'] = 'Website not scraped'\n resto_item['menu'] = 'Menu not scraped'\n \n if response.xpath(xpath_ranking).get() is not None and response.xpath(xpath_ranking_out_of).get() is not None:\n resto_item['ranking'] = response.xpath(xpath_ranking).get() + response.xpath(xpath_ranking_out_of).get()\n else:\n resto_item['ranking'] = 'Ranking not found'\n\n resto_item['rating'] = response.xpath(xpath_rating).get().split()[0]\n\n return resto_item\n\n\n def parse_review(self, response):\n \"\"\" FINAL PARSING : Open a specific page with review and client opinion\n - Read these data and store them\n - Get all the data you can find and that you believe interesting\n \"\"\"\n \n logger.debug(' > PARSING NEW REVIEW ({})'.format(self.review_nb))\n if self.review_nb % 100 == 0:\n logger.info(' > PARSING NEW REVIEW ({})'.format(self.review_nb))\n self.review_nb += 1\n\n xpath_username = '//div[@class=\"username mo\"]/span/text()'\n xpath_date_of_visit = '//div[@class=\"prw_rup prw_reviews_stay_date_hsx\"]/text()'\n xpath_date_of_review = '//span[@class=\"ratingDate relativeDate\"]/@title'\n xpath_rating = '//div[@class=\"rating reviewItemInline\"]/span[1]/@class'\n xpath_title = '//div[@class=\"quote\"]/a/span/text()'\n xpath_comment = '(//p[@class=\"partial_entry\"])[1]/text()'\n\n date_of_review = response.xpath(xpath_date_of_review).get()\n if date_of_review is None:\n xpath_date_of_review = '//span[@class=\"ratingDate\"]/@title'\n date_of_review = response.xpath(xpath_date_of_review).get()\n \n \n review_item = ReviewRestoItem()\n review_item['review_id'] = self.review_nb\n review_item['restaurant_id'] = self.id_resto\n username = response.xpath(xpath_username).get()\n review_item['username'] = username\n review_item['date_of_visit'] = response.xpath(xpath_date_of_visit).get()\n review_item['rating'] = response.xpath(xpath_rating).get()[-2]\n review_item['title'] = response.xpath(xpath_title).get()\n review_item['comment'] = ' '.join(response.xpath(xpath_comment).getall())\n review_item['date_of_review'] = date_of_review\n \n yield review_item\n\n # Scrap user if wanted and username in correct format (no spaces)\n if (self.scrap_user != 0) and (\" \" not in username):\n yield response.follow(url=\"https://www.tripadvisor.co.uk/Profile/\" + username, \n callback=self.parse_user, cb_kwargs=dict(username=username))\n\n\n def parse_user(self, response, username):\n \"\"\" Create User Item saved in specific JSON file \"\"\"\n \n xpath_fullname = '//span[@class=\"_2wpJPTNc _345JQp5A\"]/text()'\n xpath_date_joined = '//span[@class=\"_1CdMKu4t\"]/text()'\n xpath_all = '//a[@class=\"_1q4H5LOk\"]/text()'\n xpath_nb_followers = '//div[@class=\"_1aVEDY08\"][2]/span[@class=\"iX3IT_XP\"]/text()'\n xpath_nb_following = '//div[@class=\"_1aVEDY08\"][3]/span[@class=\"iX3IT_XP\"]/text()'\n xpath_location = '//span[@class=\"_2VknwlEe _3J15flPT default\"]/text()'\n\n user_item = UserItem()\n user_item['username'] = username\n user_item['fullname'] = response.xpath(xpath_fullname).get()\n user_item['date_joined'] = response.xpath(xpath_date_joined).get()\n user_item['location'] = response.xpath(xpath_location).get()\n\n # Retrieve info about nb of contributions, nb of followers and nb of following\n all_infos = response.xpath(xpath_all).getall()\n \n # Assign info to correct field\n if len(all_infos) == 3:\n user_item['nb_contributions'] = int(all_infos[0].replace(',',''))\n user_item['nb_followers'] = int(all_infos[1].replace(',',''))\n user_item['nb_following'] = int(all_infos[2].replace(',',''))\n elif len(all_infos) == 2:\n user_item['nb_contributions'] = int(all_infos[0].replace(',',''))\n nb_followers = response.xpath(xpath_nb_followers).get()\n if nb_followers is None:\n user_item['nb_followers'] = int(all_infos[1].replace(',',''))\n user_item['nb_following'] = 0\n else:\n user_item['nb_followers'] = 0\n user_item['nb_following'] = int(all_infos[1].replace(',',''))\n elif len(all_infos) == 1:\n user_item['nb_contributions'] = int(all_infos[0].replace(',',''))\n user_item['nb_followers'] = 0\n user_item['nb_following'] = 0\n\n yield user_item\n\n","sub_path":"scraper/scraper_rooftops/TA_scrapy/spiders/bokanSpider.py","file_name":"bokanSpider.py","file_ext":"py","file_size_in_byte":11342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"389878508","text":"import time\n\nstart = time.time()\n\ntot = 0\nisPand = [False] * 100000\nuh = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n\nfor i in range(10000, 999, -1):\n for j in range(1, 1001):\n if i % j == 0:\n k = int(i / j)\n else:\n continue\n if sorted(str(i) + str(j) + str(k)) == uh:\n if isPand[i]:\n continue\n else:\n isPand[i] = True\n print(i)\n tot += i\n # this took: 2.0912818908691406\n\n\"\"\"for i in range(1, 10000):\n for j in range(1, 1001):\n k = i*j\n if sorted(str(i)+str(j)+str(k)) == uh:\n if isPand[k] == True:\n continue\n else:\n isPand[k] = True\n print(k)\n tot += k\n #That takes 58 entire seconds\n\"\"\"\n\nprint(tot)\n\nprint(\"this took:\", time.time() - start)\n","sub_path":"Solved 1-50/32. Pandigital Products.py","file_name":"32. Pandigital Products.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"145688008","text":"from pulumi_snowflake import Client\r\n\r\nfrom ..baseprovider import BaseDynamicProvider\r\nfrom ..provider import Provider\r\n\r\n\r\nclass DatabaseProvider(BaseDynamicProvider):\r\n \"\"\"\r\n Dynamic provider for Snowflake Database resources.\r\n \"\"\"\r\n\r\n def __init__(self, provider_params: Provider, connection_provider: Client):\r\n super().__init__(provider_params, connection_provider, resource_type=\"Database\")\r\n\r\n def generate_sql_create_statement(self, name, inputs, environment):\r\n template = environment.from_string(\r\n\"\"\"CREATE{% if transient %} TRANSIENT{% endif %} {{ resource_type | upper }} {{ full_name }}\r\n{% if share %}FROM SHARE {{ share | sql_identifier }}\r\n{% endif %}\r\n{%- if data_retention_time_in_days %}DATA_RETENTION_TIME_IN_DAYS = {{ data_retention_time_in_days | sql }}\r\n{% endif %}\r\n{%- if comment %}COMMENT = {{ comment | sql }}\r\n{% endif %}\r\n\"\"\")\r\n\r\n sql = template.render({\r\n **inputs,\r\n \"full_name\": self._get_full_object_name(inputs, name),\r\n \"resource_type\": self.resource_type\r\n })\r\n\r\n return sql\r\n\r\n def generate_sql_drop_statement(self, name, inputs, environment):\r\n template = environment.from_string(\"DROP {{ resource_type | upper }} {{ full_name }}\")\r\n sql = template.render({\r\n \"full_name\": self._get_full_object_name(inputs, name),\r\n \"resource_type\": self.resource_type\r\n })\r\n return sql\r\n","sub_path":"pulumi_snowflake/database/database_provider.py","file_name":"database_provider.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"583454871","text":"#!/usr/bin/env python3\nimport sys\nsys.path.append('/home/baothach/dvrk_shape_servo/src/dvrk_env/dvrk_gazebo_control/src/shape_servo')\nfrom ShapeServo import *\nimport os\nimport pickle\nimport open3d\nfrom utils import open3d_ros_helper as orh\nfrom sklearn.decomposition import PCA\nimport numpy as np\n\n\n\nrospy.init_node('isaac_grasp_client')\nVFH_135s = []\nVFH_135s_flatten = []\nVFH_30s = []\npositions = []\n# with open('/home/baothach/shape_servo_data/batch_1_shuffled', 'rb') as handle:\n# data = pickle.load(handle)\n\n\ndata_recording_path = \"/home/baothach/shape_servo_data/VFH/batch1/data\"\ndata_processed_path = \"/home/baothach/shape_servo_data/VFH/batch1/processed\"\n# i = 6000\n\nfor i in range(0, 3000): \n\n file_name = os.path.join(data_recording_path, \"sample \" + str(i) + \".pickle\")\n with open(file_name, 'rb') as handle:\n data = pickle.load(handle) \n\n temp = []\n for points in data[\"point clouds\"]:\n pcd = open3d.geometry.PointCloud()\n pcd.points = open3d.utility.Vector3dVector(np.array(points)) \n ros_cloud = orh.o3dpc_to_rospc(pcd)\n feature_vector_135 = np.array(VFH_client(ros_cloud))\n \n VFH_135s_flatten.append(feature_vector_135)\n \n temp.append(feature_vector_135)\n\n positions.append(data[\"positions\"])\n VFH_135s.append(temp)\n \n\n\n\npca = PCA(n_components=30)\n# pca.fit(VFH_135s) \npca.fit(VFH_135s_flatten)\n\nfor i, feature_vector_135 in enumerate(VFH_135s):\n feature_vector_30_current = pca.transform(feature_vector_135[0].reshape(1, -1))\n feature_vector_30_goal = pca.transform(feature_vector_135[1].reshape(1, -1))\n VFH_30 = (feature_vector_30_current, feature_vector_30_goal)\n # VFH_30s.append(feature_vector_30_goal - feature_vector_30_current)\n if i % 50 == 0:\n print(\"count: \", i)\n\n \n processed_data = {\"positions\": positions[i], \"VFH_30\": VFH_30, \"VFH_135\": feature_vector_135}\n with open(os.path.join(data_processed_path, \"processed sample \" + str(i) + \".pickle\"), 'wb') as handle:\n pickle.dump(processed_data, handle, protocol=pickle.HIGHEST_PROTOCOL) \n\n\n\n\n\n\n\n\n \n\n \n\n\n\n\n\n\n\n\n\n# for two_pc in data[\"point clouds\"]:\n# temp = []\n# for points in two_pc:\n# pcd = open3d.geometry.PointCloud()\n# pcd.points = open3d.utility.Vector3dVector(np.array(points)) \n# ros_cloud = orh.o3dpc_to_rospc(pcd)\n# feature_vector_135 = np.array(VFH_client(ros_cloud))\n \n# VFH_135s_flatten.append(feature_vector_135)\n \n# temp.append(feature_vector_135)\n# VFH_135s.append(temp)\n\n# data = {\"point clouds\": data[\"point clouds\"], \"target\": data[\"positions\"], \"input\": VFH_30s}\n\n# with open('/home/baothach/shape_servo_data/batch_1_shuffled_VFH_30', 'wb') as handle:\n# pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n\n\n# data = VFH_135s_flatten\n\n# with open('/home/baothach/shape_servo_data/feature_vectors_135', 'wb') as handle:\n# pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL) ","sub_path":"shape_servo_control/src/utils/prepare_VFH_data.py","file_name":"prepare_VFH_data.py","file_ext":"py","file_size_in_byte":3028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"585975510","text":"\"\"\"\n-------------------------------------------------\n File Name： BaseDriver\n Description :\n Author : zws\n date： 2018/3/5\n-------------------------------------------------\n Change Activity:\n 2018/3/5:\n-------------------------------------------------\n\"\"\"\n__author__ = 'zws'\n\nimport os\nfrom openpyxl import load_workbook\nfrom appium import webdriver\n\nclass BaseDriver():\n\n def init_driver(self):\n # chuizi_jianguo\n chuizi_jianguo = {}\n chuizi_jianguo['platformName'] = 'Android'\n chuizi_jianguo['platformVersion'] = '5.1.1'\n chuizi_jianguo['automationName'] = 'uiautomator2'\n chuizi_jianguo['deviceName'] = 'c8a135a3'\n #chuizi_jianguo['app'] = '/Users/zws/longbeach/appium/jiemo.apk'\n chuizi_jianguo['appPackage'] = 'com.jiemoapp'\n chuizi_jianguo['appActivity'] = 'com.jiemoapp.activity.SplashActivity'\n chuizi_jianguo['noReset'] = \"False\"\n\n # oppo_x9007\n oppo_x9007 = {}\n oppo_x9007['platformName'] = 'Android'\n oppo_x9007['platformVersion'] = '4.4.2'\n oppo_x9007['deviceName'] = '5b18cef9'\n oppo_x9007['app'] = 'http://apk.s.diandian.com/jiemoApp/dev/jiemo_jiemo_1.4.31_10430_%E5%86%85%E7%BD%91_debug.apk'\n oppo_x9007['noReset'] = \"False\"\n oppo_x9007[\"unicodeKeyboard\"] = \"True\"\n oppo_x9007[\"resetKeyboard\"] = \"True\"\n\n # htc_d820u\n htc_d820u = {}\n htc_d820u['platformName'] = 'Android'\n htc_d820u['platformVersion'] = '4.4.4'\n htc_d820u['deviceName'] = 'HC4BSYC03884 '\n htc_d820u[\n 'app'] = 'http://apk.s.diandian.com/jiemoApp/dev/jiemo_jiemo_1.4.31_10430_%E5%86%85%E7%BD%91_debug.apk'\n htc_d820u['noReset'] = \"False\"\n htc_d820u[\"unicodeKeyboard\"] = \"True\"\n htc_d820u[\"resetKeyboard\"] = \"True\"\n\n # simulator\n simulator = {}\n simulator['platformName'] = 'Android'\n simulator['platformVersion'] = '4.4'\n #simulator['automationName'] = 'uiautomator2'\n simulator['deviceName'] = 'Android Emulator'\n # simulator['app'] = '/Users/zws/longbeach/appium/jiemo.apk'\n simulator['noReset'] = \"False\"\n simulator['appPackage'] = 'com.jiemoapp'\n simulator['appActivity'] = 'com.jiemoapp.activity.SplashActivity'\n simulator[\"unicodeKeyboard\"] = \"True\"\n simulator[\"resetKeyboard\"] = \"True\"\n\n # mate_10\n mate_10 = {}\n mate_10['platformName'] = 'Android'\n mate_10['platformVersion'] = '8.0'\n mate_10['automationName'] = 'uiautomator2'\n mate_10['deviceName'] = 'D3H0217A17005776'\n mate_10['app'] = '/Users/zws/longbeach/appium/jiemo.apk'\n mate_10['noReset'] = \"False\"\n mate_10[\"unicodeKeyboard\"] = \"True\"\n mate_10[\"resetKeyboard\"] = \"True\"\n\n # driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub', mate_10)\n #driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub', simulator)\n driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub', chuizi_jianguo)\n # driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub',oppo_x9007)\n # driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub',htc_d820u)\n driver.implicitly_wait(10)\n\n return driver\n\n\n\n","sub_path":"Common/BaseDriver.py","file_name":"BaseDriver.py","file_ext":"py","file_size_in_byte":3292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"547647706","text":"# 从给定目录下查找包含有hello的py文件\nimport os\n\nfile_list = []\n\n\n# 递归函数，该函数中所有的文件路径全部采用绝对路径，parent_dir：文件所在父目录的绝对路径，file_name表示当前你要处理的文件或者目录\ndef find_hello(parent_dir, file_name):\n file_abspath = os.path.join(parent_dir, file_name) # 当前正在处理的文件或者目录的绝对路径\n if os.path.isdir(file_abspath): # 判断该绝对路径是目录\n for f in os.listdir(file_abspath): # 进入该绝对路径并列出该目录下的所有文件和目录列表\n find_hello(file_abspath, f) # 递归调用本身函数直到目录下不再有目录\n else:\n if file_abspath.endswith(\".py\"): # 如果是文件判断该文件是以.py结尾的py文件\n if find_and_exist_hello(file_abspath): # 如果是py文件调用hello的查找方法\n file_list.append(file_abspath) # 将包含hello的文件保存到列表中\n\n\ndef find_and_exist_hello(py_file):\n flag = False # 定义一个是否包含有hello的标记变量，默认文件中不包含hello为False\n f = open(py_file, \"r\", encoding=\"UTF-8\") # 因为文件名字可能包含中文所有打开文件指定文件编码为UTF-8\n while True: # 由于是一行一行的读取文件，所以是死循环\n line = f.readline() # 读取其中一行\n if line == \"\": # 文件读取到最后一行，终止循环\n break\n elif \"hello\" in line:\n flag = True # 该行中包含有hello则给标记变量flag赋值True并终止循环\n break\n f.close()\n return flag # 文件中查找到hello返回True，没查找到则返回False\n\n\nfind_hello(r\"D:\\MyProjects\", \"python\")\nprint(file_list)\n","sub_path":"python/exer/递归求目录.py","file_name":"递归求目录.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"559730485","text":"from utils.constants import SMUR, AM, P, VOR\n\nclass VersionPattern:\n \"\"\"\n Regex patterns that are either indicative of or never found on a certain manuscript version\n \"\"\"\n def __init__(self, pattern, indicative_of=None, not_found_on=None, error_ratio=0.1):\n \"\"\"\n :param pattern: The regex pattern\n :param indicative_of: list of manuscript versions we could expect to find pattern on\n :param never_found_on: list of manuscript versions we would not normally expect to find pattern on\n :param error_ratio: the tolerance for fuzzy matching of pattern\n \"\"\"\n self.pattern = pattern,\n self.indicative_of = indicative_of\n self.not_found_on = not_found_on\n self.error_ratio = error_ratio\n if self.indicative_of and not self.not_found_on:\n # https://stackoverflow.com/a/4211228/11999227\n self.not_found_on = [x for x in [SMUR, AM, P, VOR] if x not in self.indicative_of]\n elif self.not_found_on and not self.indicative_of:\n self.indicative_of = [x for x in [SMUR, AM, P, VOR] if x not in self.not_found_on]\n\n\nVERSION_PATTERNS = [\n VersionPattern(\"bioRxiv preprint first posted online\", indicative_of=[SMUR]),\n VersionPattern(\"The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has \"\n \"granted bioRxiv a license to display the preprint in perpetuity\", indicative_of=[SMUR]),\n VersionPattern(\"Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation\",\n indicative_of=[SMUR, AM]),\n VersionPattern(\"This article has been accepted for publication and undergone full peer review but has not been \"\n \"through the copyediting, typesetting, pagination and proofreading process, which may lead to \"\n \"differences between this version and the Version of Record\", indicative_of=[AM]),\n VersionPattern(\"UNCORRECTED PROOF\", indicative_of=[P]),\n VersionPattern(\"Available online xxx\", indicative_of=[P]),\n\n]\n\n# modified from: https://www.crossref.org/blog/dois-and-matching-regular-expressions/\nDOI_PATTERN = '10\\.\\d{4,9}/[-._;()/:a-zA-Z0-9]+'\n\nCC0 = {'short name': 'CC0',\n 'long name': 'Public domain',\n 'url': 'https://creativecommons.org/publicdomain/zero/1.0/'}\n\nCC_BY = {'short name': 'CC BY',\n 'long name': 'Creative Commons Attribution',\n 'url': 'https://creativecommons.org/licenses/by/4.0/'}\n\nCC_BY_NC = {'short name': 'CC BY-NC',\n 'long name': 'Creative Commons Attribution-NonCommercial',\n 'url': 'https://creativecommons.org/licenses/by-nc/4.0/'}\n\nCC_BY_ND = {'short name': 'CC BY-ND',\n 'long name': 'Creative Commons Attribution-NoDerivatives',\n 'url': 'https://creativecommons.org/licenses/by-nd/4.0/'}\n\nCC_BY_SA = {'short name': 'CC BY-SA',\n 'long name': 'Creative Commons Attribution-ShareAlike',\n 'url': 'https://creativecommons.org/licenses/by-sa/4.0/'}\n\nCC_BY_NC_ND = {'short name': 'CC BY-NC-ND',\n 'long name': 'Creative Commons Attribution-NonCommercial-NoDerivatives',\n 'url': 'https://creativecommons.org/licenses/by-nc-nd/4.0/'}\n\nCC_BY_NC_SA = {'short name': 'CC BY-NC-SA',\n 'long name': 'Creative Commons Attribution-NonCommercial-ShareAlike',\n 'url': 'https://creativecommons.org/licenses/by-nc-sa/4.0/'}\n\nALL_CC_LICENCES = [CC0, CC_BY, CC_BY_NC, CC_BY_ND, CC_BY_SA, CC_BY_NC_ND, CC_BY_NC_SA]\n\n# not currently used anywhere:\nADDITIONAL_CC_PATTERNS = [\n {'pattern': \"This is an open access article under the terms of the CC BY 4.0 license\", 'error ratio': 0.1},\n {'pattern': \"This is an open access article under the CC BY license\", 'error ratio': 0.1},\n {'pattern': \"http://creativecommons.org/licenses/by/4.0/\", 'error ratio': 0.1},\n {'pattern': \"This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 \"\n \"Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build \"\n \"upon this work for any purpose, provided the original work is properly cited, a link to the licence \"\n \"is given, and indication of whether changes were made. \"\n \"See: https://creativecommons.org/licenses/by/4.0/\"}\n]\n\nRIGHTS_RESERVED_PATTERNS = [\n {'pattern': \"All rights reserved.\", 'error ratio': 0.1},\n]\n","sub_path":"utils/patterns.py","file_name":"patterns.py","file_ext":"py","file_size_in_byte":4482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"601318904","text":"# Author: Sachin Kumar\n# Github: https://github.com/skumar24\n\n# Day 1: Interquartile Range\n# Problem Url: https://www.hackerrank.com/challenges/s10-interquartile-range/problem\n# Score: 30\n\ndef get_median(nums):\n # Returns the median and index point where it was found\n l = len(nums)\n nums.sort()\n if l % 2 == 1: return (nums[l//2], l//2)\n else: return ((nums[l//2-1] + nums[l//2]) / 2, (l//2-0.5))\n\nn = int(input())\nX = list(map(int, input().strip().split(\" \")))\nF = list(map(int, input().strip().split(\" \")))\nnumbers = []\nfor idx, f in enumerate(F):\n for i in range(f):\n numbers.append(X[idx])\nq2, mi = get_median(numbers)\nq1 = get_median([x for idx, x in enumerate(numbers) if idx < mi])\nq3 = get_median([x for idx, x in enumerate(numbers) if idx > mi])\nprint('{0:.1f}'.format(q3[0]-q1[0]))","sub_path":"10 Days of Statistics/Day-1-Interquartile-Range.py","file_name":"Day-1-Interquartile-Range.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"221816533","text":"import tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport numpy as np\n\n#生成整数型属性\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n#生成字符串型属性\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\nmnist = input_data.read_data_sets(\"/path/to/mnist_data\", dtype=tf.uint8,one_hot=True)\nimages = mnist.validation.images\nlabels = mnist.validation.labels\n\n#pixels = images.shape[1]\nnum_examples = mnist.validation.num_examples\n\n#输出TFRecord的文件地址\nfilename = \"/path/to/tfrecord/output_validation.tfrecords\"\n#创建一个writer来写TFRecord文件\nwriter = tf.python_io.TFRecordWriter(filename)\nfor index in range(num_examples):\n # 将图像矩阵转化为一个字符串\n image_raw = images[index].tostring()\n height = 28\n width = 28\n #将一个样例专户为Example protocol Buffer 并将所有信息写入该数据结构\n example = tf.train.Example(features=tf.train.Features(feature={\n 'image': _bytes_feature(image_raw),\n 'label': _int64_feature(np.argmax(labels[index])),\n 'height': _int64_feature(height),\n 'width': _int64_feature(width),\n 'channel': _int64_feature(index % 100)\n }))\n\n #将一个Example写入TFRecord\n writer.write(example.SerializeToString())\nwriter.close()\n\n\n","sub_path":"Scripts/TF/dataset/To_TfRecord_test.py","file_name":"To_TfRecord_test.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"632366474","text":"from datetime import datetime\n\nfrom flask import Blueprint, request, render_template, flash, g, session, redirect, url_for, jsonify\nfrom app import app, db, utils\nfrom app.models import User, Media, Genre, StoryType, MediaType, Country\n\nmod_news = Blueprint('news', __name__, url_prefix='/news')\n\n@mod_news.route('/all')\ndef all():\n selected_genre = 1 # skateboarding by default\n if(request.args.get('genre')):\n selected_genre = request.args.get('genre')\n\n total = utils.get_total_news_count(selected_genre)\n page, per_page, offset = utils.get_page_args(page_parameter='page', per_page_parameter='per_page')\n news = db.session.query(\n Media.media_type.in_((4,5,))\n ).join(Genre\n ).join(MediaType\n ).join(StoryType\n ).join(Country\n ).add_columns(\n Media.media_id,\n (MediaType.type_name).label(\"mediatype_name\"),\n (StoryType.type_name).label(\"storytype_name\"),\n (Country.country_code).label(\"country_code\"),\n Media.hidden,\n Media.media_topic,\n Media.create_time,\n Media.owner\n ).filter(\n Media.media_genre==selected_genre\n ).filter(\n Media.story_type==utils.get_story_type('news')\n ).filter(\n Media.hidden==0\n ).order_by(\n Media.create_time.desc()\n ).offset(offset).limit(per_page)\n\n pagination = utils.get_pagination(page=page, per_page=per_page, total=total, record_name=' news', format_total=True, format_number=True,)\n\n return render_template(\"news/news.html\", news=news, pagination=pagination, selected_genre=selected_genre)\n\n@mod_news.route('/item/')\ndef item(media_id):\n news_item = Media.query.filter_by(media_id=media_id).first()\n return render_template('news/news_item.html', news_item=news_item)\n","sub_path":"app/mod_news/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":1862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"132792324","text":"from cinp import client\n\nfrom django.conf import settings\n\n\nCONTRACTOR_API_VERSION = '0.9'\n\n\ndef getContractor():\n return Contractor( settings.CONTRACTOR_HOST, settings.CONTRACTOR_USERNAME, settings.CONTRACTOR_PASSWORD, settings.CONTRACTOR_PROXY )\n\n\nclass Contractor():\n def __init__( self, host, username, password, proxy=None ):\n super().__init__()\n self.cinp = client.CInP( host, '/api/v1/', proxy )\n root = self.cinp.describe( '/api/v1/' )\n if root[ 'api-version' ] != CONTRACTOR_API_VERSION:\n raise Exception( 'Expected API version \"{0}\" found \"{1}\"'.format( CONTRACTOR_API_VERSION, root[ 'api-version' ] ) )\n\n token = self.cinp.call( '/api/v1/Auth/User(login)', { 'username': username, 'password': password } )\n self.cinp.setAuth( username, token )\n\n # Site functions\n def getSiteList( self ):\n return self.cinp.getFilteredURIs( '/api/v1/Site/Site' )\n\n def createSite( self, name, **value_map ):\n data = { 'name': name }\n try:\n value_map[ 'parent' ] = '/api/v1/Site/Site:{0}:'.format( value_map[ 'parent' ] )\n except KeyError:\n pass\n\n for name in ( 'description', 'config_values', 'parent' ):\n try:\n data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n self.cinp.create( '/api/v1/Site/Site', data )\n\n def getSite( self, id ):\n result = self.cinp.get( '/api/v1/Site/Site:{0}:'.format( id ) )\n if result[ 'parent' ] is not None:\n result[ 'parent' ] = result[ 'parent' ].split( ':' )[1]\n\n return result\n\n def updateSite( self, id, **value_map ):\n try:\n value_map[ 'parent' ] = '/api/v1/Site/Site:{0}:'.format( value_map[ 'parent' ] )\n except KeyError:\n pass\n\n data = {}\n for name in ( 'description', 'config_values', 'parent' ): # do we really want to allow switching parent?\n try:\n data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n if data:\n self.cinp.update( '/api/v1/Site/Site:{0}:'.format( id ), data )\n\n def deleteSite( self, id ):\n self.cinp.delete( '/api/v1/Site/Site:{0}:'.format( id ) )\n\n # AddressBlock functions\n def getAddressBlockMap( self, site_id ):\n result = {}\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Utilities/AddressBlock', 'site', { 'site': '/api/v1/Site/Site:{0}:'.format( site_id ) } ):\n item[ 'reserved_offset_list' ] = list( self.getAddressBlockReserved( uri ).keys() )\n item[ 'dynamic_offset_list' ] = list( self.getAddressBlockDynamic( uri ).keys() )\n item[ 'id' ] = uri.split( ':', 2 )[ 1 ]\n result[ item[ 'name' ] ] = item\n\n return result\n\n def createAddressBlock( self, site_id, name, **value_map ):\n data = { 'site': '/api/v1/Site/Site:{0}:'.format( site_id ), 'name': name }\n for name in ( 'subnet', 'prefix', 'gateway_offset' ):\n try:\n data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n # data[ 'reserved_offset_list' ] = value_map[ 'reserved_offset_list' ] # for now we will let the up next update set the reserved_offset_list\n # data[ 'dynamic_offset_list' ] = value_map[ 'dynamic_offset_list' ] # for now we will let the up next update set the dynamic_offset_list\n\n self.cinp.create( '/api/v1/Utilities/AddressBlock', data )\n\n def updateAddressBlock( self, id, **value_map ):\n uri = '/api/v1/Utilities/AddressBlock:{0}:'.format( id )\n data = {}\n for name in ( 'subnet', 'prefix', 'gateway_offset' ):\n try:\n data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n if data:\n self.cinp.update( uri, data )\n\n reserved_offset_list = value_map.get( 'reserved_offset_list', None )\n dynamic_offset_list = value_map.get( 'dynamic_offset_list', None )\n\n if reserved_offset_list is not None:\n reserved_offset_list = set( reserved_offset_list )\n current_map = self.getAddressBlockReserved( uri )\n current = set( current_map.keys() )\n\n for offset in reserved_offset_list - current:\n data = { 'address_block': uri, 'offset': offset, 'reason': 'Architect Reserved' }\n self.cinp.create( '/api/v1/Utilities/ReservedAddress', data )\n\n for offset in current - reserved_offset_list:\n self.cinp.delete( current_map[ offset ] )\n\n if dynamic_offset_list is not None:\n dynamic_offset_list = set( dynamic_offset_list )\n current_map = self.getAddressBlockDynamic( uri )\n current = set( current_map.keys() )\n\n for offset in dynamic_offset_list - current:\n data = { 'address_block': uri, 'offset': offset }\n self.cinp.create( '/api/v1/Utilities/DynamicAddress', data )\n\n for offset in current - dynamic_offset_list:\n self.cinp.delete( current_map[ offset ] )\n\n def deleteAddressBlock( self, id ):\n return self.cinp.delete( '/api/v1/Utilities/AddressBlock:{0}:'.format( id ) )\n\n def getAddressBlockReserved( self, uri ):\n result = {}\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Utilities/ReservedAddress', 'address_block', { 'address_block': uri } ):\n result[ int( item[ 'offset' ] ) ] = uri\n\n return result\n\n def getAddressBlockDynamic( self, uri ):\n result = {}\n for _, item in self.cinp.getFilteredObjects( '/api/v1/Utilities/DynamicAddress', 'address_block', { 'address_block': uri } ):\n result[ int( item[ 'offset' ] ) ] = uri\n\n return result\n\n def getAddressBlock( self, site, name ):\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Utilities/AddressBlock', 'site', { 'site': site } ):\n if item[ 'name' ] == name:\n return uri\n\n raise ValueError( 'AddressBlock \"{0}\" in site \"{1}\", not found'.format( name, site ) )\n\n # Structure Functions - for these the Foundation is a part of the Structure as far as Architect is concerened\n # for now we are going to assume these instances are created atomically with both foundation and structure, so pull the structures, that is what we are really after anyway\n def getStructureMap( self, site_id ):\n site = '/api/v1/Site/Site:{0}:'.format( site_id )\n address_block_cache = {}\n\n def addressLookup( address_block ):\n try:\n return address_block_cache[ address_block ]\n except KeyError:\n tmp = self.cinp.get( address_block )\n address_block_cache[ address_block ] = tmp[ 'name' ]\n return address_block_cache[ address_block ]\n\n result = {}\n foundation_map = {}\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Building/Foundation', 'site', { 'site': site } ):\n foundation_map[ uri ] = item\n\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Building/Structure', 'site', { 'site': site } ):\n address_list = list( self.cinp.getFilteredObjects( '/api/v1/Utilities/Address', 'structure', { 'structure': uri } ) )\n foundation = foundation_map[ item[ 'foundation' ] ]\n tmp = {}\n tmp[ 'type' ] = foundation[ 'type' ]\n tmp[ 'blueprint' ] = item[ 'blueprint' ].split( ':' )[1]\n tmp[ 'address_list' ] = [ { 'address_block': addressLookup( i[1][ 'address_block' ] ), 'offset': i[1][ 'offset' ] } for i in address_list ]\n tmp[ 'config_values' ] = item[ 'config_values' ]\n result[ item[ 'hostname' ] ] = tmp\n\n return result\n\n def createStructure( self, site_id, name, **value_map ):\n site = '/api/v1/Site/Site:{0}:'.format( site_id )\n address_list = value_map[ 'address_list' ]\n data = {}\n data[ 'site' ] = site\n data[ 'locator' ] = name\n if value_map[ 'type' ] == 'Manual':\n data[ 'blueprint' ] = '/api/v1/BluePrint/FoundationBluePrint:manual-foundation-base:'\n foundation = self.cinp.create( '/api/v1/Manual/ManualFoundation', data )[0]\n\n elif value_map[ 'type' ] == 'VCenter':\n data[ 'blueprint' ] = '/api/v1/BluePrint/FoundationBluePrint:vcenter-vm-base:'\n data[ 'vcenter_complex' ] = '/api/v1/VCenter/VCenterComplex:{0}:'.format( value_map[ 'complex' ] )\n foundation = self.cinp.create( '/api/v1/VCenter/VCenterFoundation', data )[0]\n\n else:\n raise ValueError( 'Unknown foundation type \"{0}\"'.format( value_map[ 'type' ] ) )\n\n foundation_id = self.cinp.uri.extractIds( foundation )[0]\n\n data = {}\n data[ 'site' ] = site\n data[ 'foundation' ] = '/api/v1/Building/Foundation:{0}:'.format( foundation_id )\n data[ 'hostname' ] = name\n data[ 'blueprint' ] = '/api/v1/BluePrint/StructureBluePrint:{0}:'.format( value_map[ 'blueprint' ] )\n # data[ 'config_values' ] = value_map[ 'config_values' ]\n structure = self.cinp.create( '/api/v1/Building/Structure', data )[0]\n structure_id = self.cinp.uri.extractIds( structure )[0]\n\n data = {}\n data[ 'foundation' ] = '/api/v1/Building/Foundation:{0}:'.format( foundation_id )\n data[ 'name' ] = 'eth0'\n data[ 'physical_location' ] = 'eth0'\n data[ 'is_provisioning' ] = True\n self.cinp.create( '/api/v1/Utilities/RealNetworkInterface', data )\n\n address_id_list = []\n is_primary = True\n for address in address_list:\n data = {}\n data[ 'networked' ] = '/api/v1/Utilities/Networked:{0}:'.format( structure_id )\n data[ 'address_block' ] = self.getAddressBlock( site, address[ 'address_block' ] )\n data[ 'offset' ] = address[ 'offset' ]\n data[ 'interface_name' ] = 'eth0'\n data[ 'vlan' ] = 0\n data[ 'is_primary' ] = is_primary\n address_id_list.append( self.cinp.create( '/api/v1/Utilities/Address', data )[0] )\n is_primary = False\n\n self.cinp.call( '/api/v1/Building/Foundation:{0}:(setLocated)'.format( foundation_id ), {} )\n self.cinp.call( '/api/v1/Building/Foundation:{0}:(doCreate)'.format( foundation_id ), {} )\n self.cinp.call( '/api/v1/Building/Structure:{0}:(doCreate)'.format( structure_id ), {} )\n\n print( '************************ created \"{0}\" and \"{1}({2})\"'.format( foundation, structure, address_id_list ) )\n\n def updateStructure( self, id, **value_map ):\n if list( value_map.keys() ) != [ 'config_values' ]:\n raise ValueError( 'Only config_values of Instance are update-able' )\n\n structure_data = {}\n for name in ( 'config_values', ):\n try:\n structure_data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n if structure_data:\n foundation = self.cinp.get( '/api/v1/Building/Foundation:{0}:'.format( id ) )\n\n self.cinp.update( foundation[ 'structure' ], structure_data )\n\n def deleteStructure( self, id ):\n foundation_uri = '/api/v1/Building/Foundation:{0}:'.format( id )\n foundation = self.cinp.get( foundation_uri )\n structure_uri = foundation[ 'structure' ]\n structure = self.cinp.get( structure_uri )\n\n if structure[ 'state' ] == 'planned': # for now we will have to go over this twice, first to destroy and second to delete\n self.cinp.delete( structure_uri )\n self.cinp.delete( foundation_uri )\n return\n\n structure_job = self.cinp.call( '/api/v1/Foreman/StructureJob(getStructureJob)', { 'structure': structure_uri } )\n\n if structure_job is None: # assuming the structure is following the foundation, so we don't end up creating a job for a foundation that just finished destroying\n self.cinp.call( '{0}(doDestroy)'.format( structure_uri ), {} )\n self.cinp.call( '{0}(doDestroy)'.format( foundation_uri ), {} )\n\n # Complex functions\n #\n # def getComplex( self, id ):\n # complex = self.cinp.get( '/api/v1/Building/Complex:{0}:'.format( id ) )\n # complex[ 'site' ] = self.cinp.uri.extractIds( complex[ 'site' ] )[0]\n # return complex\n\n def getComplexMap( self, site_id ):\n result = {}\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Building/Complex', 'site', { 'site': '/api/v1/Site/Site:{0}:'.format( site_id ) } ):\n item[ 'member_list' ] = self.cinp.uri.extractIds( list( i[1][ 'foundation' ] for i in self.cinp.getFilteredObjects( '/api/v1/Building/Structure', 'complex', { 'complex': uri } ) ) )\n result[ item[ 'name' ] ] = item\n\n return result\n\n def createComplex( self, site_id, name, **value_map ):\n data = { 'name': name, 'site': '/api/v1/Site/Site:{0}:'.format( site_id ) }\n for name in ( 'description', 'built_percentage' ):\n try:\n data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n # data[ 'member_list' ] = value_map[ 'member_list' ] # for now we will let the up comming update set the member_list\n\n if value_map[ 'type' ] == 'Manual':\n self.cinp.create( '/api/v1/Manual/ManualComplex', data )\n\n elif value_map[ 'type' ] == 'VCenter':\n data[ 'vcenter_host' ] = value_map[ 'host' ]\n data[ 'vcenter_datacenter' ] = value_map[ 'datacenter' ]\n data[ 'vcenter_cluster' ] = value_map[ 'cluster' ]\n data[ 'vcenter_username' ] = value_map[ 'username' ]\n data[ 'vcenter_password' ] = value_map[ 'password' ]\n self.cinp.create( '/api/v1/VCenter/VCenterComplex', data )\n else:\n raise ValueError( 'Unknown foundation type \"{0}\"'.format( value_map[ 'type' ] ) )\n\n def updateComplex( self, id, **value_map ):\n uri = '/api/v1/Building/Complex:{0}:'.format( id )\n data = {}\n for name in ( 'description', 'built_percentage' ):\n try:\n data[ name ] = value_map[ name ]\n except KeyError:\n pass\n\n if data:\n self.cinp.update( uri, data )\n\n member_list = value_map.get( 'member_list', None )\n\n if member_list is not None:\n member_list = set( member_list )\n current_map = self.getComplexMembers( uri )\n current = set( current_map.keys() )\n\n for member in member_list - current:\n # This is a bit of a hack to find the structure by looking up the foundation by the name, which *SHOULD* match the locator, there should be a better way\n foundation = self.cinp.get( '/api/v1/Building/Foundation:{0}:'.format( member ) )\n structure = foundation[ 'structure' ]\n data = { 'complex': uri, 'structure': structure }\n self.cinp.create( '/api/v1/Building/ComplexStructure', data )\n\n for member in current - member_list:\n self.cinp.delete( current_map[ member ] )\n\n def getComplexMembers( self, uri ):\n result = {}\n for uri, item in self.cinp.getFilteredObjects( '/api/v1/Building/ComplexStructure', 'complex', { 'complex': uri } ):\n result[ item[ 'structure' ].split( ':', 2 )[ 1 ] ] = uri\n\n return result\n\n # functions used by contractor sync\n def getBluePrints( self ):\n return self.cinp.getFilteredURIs( '/api/v1/BluePrint/StructureBluePrint' )\n\n # functions for the dynamic building from Plans\n def createComplexFoundation( self, complex, blueprint, hostname ):\n foundation = self.cinp.call( '/api/v1/Building/Complex:{0}:(createFoundation)'.format( complex ), { 'hostname': hostname } )\n print( '************************ created \"{0}\"'.format( foundation ) )\n\n foundation_id = self.cinp.uri.extractIds( foundation )[0]\n return foundation_id\n\n def buildFoundation( self, id ):\n job_id = self.cinp.call( '/api/v1/Building/Foundation:{0}:(doCreate)'.format( id ), {} )\n print( '------------------------- create Job foundation \"{0}\"'.format( job_id ) )\n\n def destroyFoundation( self, id ):\n self.cinp.call( '/api/v1/Building/Foundation:{0}:(doDestroy)'.format( id ), {} )\n\n def createComplexStructure( self, site_id, foundation_id, blueprint, hostname, config_values, address_block_id ):\n site = '/api/v1/Site/Site:{0}:'.format( site_id )\n\n data = {}\n data[ 'site' ] = site\n data[ 'foundation' ] = '/api/v1/Building/Foundation:{0}:'.format( foundation_id )\n data[ 'hostname' ] = hostname\n data[ 'blueprint' ] = '/api/v1/BluePrint/StructureBluePrint:{0}:'.format( blueprint )\n data[ 'config_values' ] = config_values\n structure = self.cinp.create( '/api/v1/Building/Structure', data )[0]\n\n data = {}\n data[ 'structure' ] = structure\n data[ 'interface_name' ] = 'eth0'\n data[ 'is_primary' ] = True\n address = self.cinp.call( '{0}(nextAddress)'.format( self.getAddressBlock( site, address_block_id ) ), data )\n print( '************************ created \"{0}({1})\"'.format( structure, address ) )\n\n structure_id = self.cinp.uri.extractIds( structure )[0]\n return structure_id\n\n def buildStructure( self, id ):\n job_id = self.cinp.call( '/api/v1/Building/Structure:{0}:(doCreate)'.format( id ), {} )\n print( '------------------------- create Job structure \"{0}\"'.format( job_id ) )\n\n def destroyStructure( self, id ):\n self.cinp.call( '/api/v1/Building/Structure:{0}:(doDestroy)'.format( id ), {} )\n\n def registerWebHook( self, target, job_id, target_id, token ):\n data = {}\n data[ target ] = '/api/v1/Building/{0}:{1}:'.format( target.title(), target_id )\n data[ 'one_shot' ] = True\n data[ 'extra_data' ] = { 'token': token, 'target': target }\n data[ 'type' ] = 'call'\n data[ 'url' ] = 'http://127.0.0.1:8880/api/v1/Builder/Job:{0}:(jobNotify)'.format( job_id )\n if target == 'foundation':\n self.cinp.create( '/api/v1/PostOffice/FoundationBox', data )\n else:\n self.cinp.create( '/api/v1/PostOffice/StructureBox', data )\n","sub_path":"architect/Contractor/Contractor.py","file_name":"Contractor.py","file_ext":"py","file_size_in_byte":16960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"48403665","text":"import datetime\nimport sqlite3\n\n\ndef create_headlines_table(db, table):\n \"\"\"Creates sqlite3 table in database for storing BBC news headlines if one does not exist\"\"\"\n connection = sqlite3.connect(db)\n c = connection.cursor()\n\n c.execute('CREATE TABLE IF NOT EXISTS {t} (date_added date, headline text)'.format(t=table))\n\n connection.commit()\n connection.close()\n\n\ndef add_headlines_to_table(headlines, db, table):\n \"\"\"Inserts list of headlines into table in db with date of insertion\"\"\"\n connection = sqlite3.connect(db)\n c = connection.cursor()\n\n today = str(datetime.date.today())\n\n for h in headlines:\n c.execute('INSERT INTO {t} VALUES (?,?)'.format(t=table), (today, h))\n\n connection.commit()\n connection.close()\n\n\ndef get_headlines_from_table(db, table):\n \"\"\"Return headlines from table\"\"\"\n connection = sqlite3.connect(db)\n c = connection.cursor()\n\n c.execute('SELECT * FROM {t}'.format(t=table))\n\n headline_list = c.fetchall()\n\n connection.commit()\n connection.close()\n\n return headline_list\n\n\ndef drop_headlines_table(db, table):\n \"\"\"Drops sqlite3 table in database for storing BBC news headlines if one does not exist\"\"\"\n connection = sqlite3.connect(db)\n c = connection.cursor()\n\n c.execute('DROP TABLE IF EXISTS {t}'.format(t=table))\n\n connection.commit()\n connection.close()\n","sub_path":"headlines_database.py","file_name":"headlines_database.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"522277684","text":"# Copyright 2018 Red Hat, Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n\nimport mock\nimport sys\n\nfrom osc_lib.tests import utils\nfrom tripleoclient.v1 import tripleo_validator\n\nVALIDATIONS_LIST = [{\n 'description': 'My Validation One Description',\n 'groups': ['prep', 'pre-deployment'],\n 'id': 'my_val1',\n 'name': 'My Validition One Name',\n 'parameters': {}\n}, {\n 'description': 'My Validation Two Description',\n 'groups': ['prep', 'pre-introspection'],\n 'id': 'my_val2',\n 'name': 'My Validition Two Name',\n 'parameters': {}\n}]\n\n\nclass TestValidatorList(utils.TestCommand):\n\n def setUp(self):\n super(TestValidatorList, self).setUp()\n\n # Get the command object to test\n self.cmd = tripleo_validator.TripleOValidatorList(self.app, None)\n\n @mock.patch('tripleoclient.utils.parse_all_validations_on_disk',\n return_value=VALIDATIONS_LIST)\n def test_validation_list_noargs(self, mock_validations):\n arglist = []\n verifylist = []\n\n parsed_args = self.check_parser(self.cmd, arglist, verifylist)\n\n self.cmd.take_action(parsed_args)\n\n\nclass TestValidatorRun(utils.TestCommand):\n\n def setUp(self):\n super(TestValidatorRun, self).setUp()\n\n # Get the command object to test\n self.cmd = tripleo_validator.TripleOValidatorRun(self.app, None)\n\n @mock.patch('sys.exit')\n @mock.patch('logging.getLogger')\n @mock.patch('pwd.getpwuid')\n @mock.patch('os.getuid')\n @mock.patch('tripleoclient.utils.get_tripleo_ansible_inventory',\n return_value='/home/stack/inventory.yaml')\n @mock.patch('tripleoclient.utils.run_ansible_playbook',\n autospec=True)\n def test_validation_run_with_ansible(self, plan_mock, mock_inventory,\n mock_getuid, mock_getpwuid,\n mock_logger, mock_sysexit):\n mock_pwuid = mock.Mock()\n mock_pwuid.pw_dir = '/home/stack'\n mock_getpwuid.return_value = mock_pwuid\n\n mock_log = mock.Mock()\n mock_logger.return_value = mock_log\n\n playbooks_dir = '/usr/share/openstack-tripleo-validations/playbooks'\n arglist = [\n '--validation-name',\n 'check-ftype'\n ]\n verifylist = []\n\n parsed_args = self.check_parser(self.cmd, arglist, verifylist)\n self.cmd.take_action(parsed_args)\n\n plan_mock.assert_called_once_with(\n logger=mock_log,\n plan='overcloud',\n inventory='/home/stack/inventory.yaml',\n workdir=playbooks_dir,\n log_path_dir='/home/stack',\n playbook='check-ftype.yaml',\n retries=False,\n output_callback='validation_output',\n extra_vars={},\n python_interpreter='/usr/bin/python{}'.format(sys.version_info[0]),\n gathering_policy='explicit'\n )\n\n assert mock_sysexit.called\n","sub_path":"tripleoclient/tests/v1/tripleo/test_tripleo_validator.py","file_name":"test_tripleo_validator.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"252106058","text":"#coding=utf-8\r\nimport urllib.request\r\nimport http.cookiejar\r\nfrom PIL import Image\r\nfrom bs4 import BeautifulSoup\r\nfrom pytesseract import image_to_string\r\nimport datetime\r\nimport os\r\nimport time\r\nfilename=\"cookies.txt\"\r\ntessdata_dir_config = '--tessdata-dir \"C:\\\\Program Files (x86)\\\\Tesseract-OCR\\\\tessdata\"'\r\nplane_code=\"\"\r\nall_info=[]\r\ndate=\"\"\r\ncookies = http.cookiejar.MozillaCookieJar(filename)\r\n\r\n#遍历两个时间段内的每一天\r\ndef getEveryDay(begin_date,end_date):\r\n date_list = []\r\n begin_date = datetime.datetime.strptime(begin_date, \"%Y-%m-%d\")\r\n end_date = datetime.datetime.strptime(end_date,\"%Y-%m-%d\")\r\n while begin_date <= end_date:\r\n date_str = begin_date.strftime(\"%Y%m%d\")\r\n date_list.append(date_str)\r\n begin_date += datetime.timedelta(days=1)\r\n return date_list\r\n\r\n#设置cookie\r\ndef openner_cookie():\r\n # cookies=http.cookiejar.MozillaCookieJar(filename)\r\n handle=urllib.request.HTTPCookieProcessor(cookies)\r\n openner=urllib.request.build_opener(handle)\r\n return openner\r\n\r\n#获取html中包含需要信息的航班\r\ndef get_simpleplane(url_link):\r\n html=openner_cookie().open(url_link)\r\n obj=BeautifulSoup(html.read(),\"html.parser\")\r\n plane_info=obj.find_all(\"\",{\"class\":\"fly_list\"})\r\n return plane_info\r\n#获取详细的航班信息，包括起终点，到达时间，以及是否准点\r\ndef get_detailplane(url_link):\r\n simplane_info=get_simpleplane(url_link)\r\n result_0=simplane_info[-1].find_all(\"\",{\"class\":\"li_com\"})\r\n result_1=result_0[-1].get_text().splitlines()\r\n # result_2=result_1.readlines()\r\n url_list=image_url(url_link)\r\n\r\n down_image(url_list)\r\n number_info=distinguish()\r\n begin_area=result_1[13]\r\n begin_time=result_1[9]\r\n end_area=result_1[19]\r\n end_time=result_1[15]\r\n v_stime=number_info[0]\r\n v_etime=number_info[1]\r\n v_percent=number_info[2]\r\n detailinfo=[plane_code,begin_area,begin_time,end_area,end_time,v_stime,v_etime,v_percent]\r\n all_info.append(detailinfo)\r\n return all_info\r\n#解析并生产图片地址，此图片地址有三个，分别为起飞和到达的实际时间以及准点率\r\ndef image_url(url_link):\r\n plane_info=get_simpleplane(url_link)\r\n content=plane_info[-1].find_all(name=\"img\")\r\n url_list=[]\r\n x=0\r\n for con in content:\r\n con2=con.get(\"src\")\r\n if \"http\" not in con2:\r\n url_end=\"http://www.variflight.com\"+con2\r\n url_list.append(url_end)\r\n x=x+1\r\n return url_list\r\n#识别图像中的数字\r\ndef distinguish():\r\n number_info = []\r\n for m in range(3):\r\n number_info.append(image_to_string(Image.open(\"D:\\\\plane_test\\\\%s\\\\%s\\\\%s.jpg\"% (plane_code,date, m)),config=tessdata_dir_config))\r\n return number_info\r\n# def save_path():\r\n# return (\"D:\\\\plane_test\\\\%s\")\r\n#将图片下载到本地\r\ndef down_image(urllist):\r\n x=0\r\n for image_url in urllist:\r\n fp=\"D:\\\\plane_test\\\\%s\\\\%s\\\\%s.jpg\"% (plane_code,date,x)\r\n with open(fp, \"wb\") as f:\r\n response = openner_cookie().open(image_url)\r\n f.write(response.read())\r\n x+=1\r\ndef generate_url(plane_code,date):\r\n url_link=\"http://www.variflight.com/flight/fnum/%s.html?AE71649A58c77&fdate=%s\"% (plane_code,date)\r\n return url_link\r\n#创建保存目录\r\ndef mk_planedirs(plane_code,date):\r\n os.makedirs(\"D:\\\\plane_test\\\\%s\\\\%s\"% (plane_code,date))\r\n\r\n#保存文件内容\r\ndef save_txt(txt_content):\r\n with open(\"D:\\\\plane_test\\\\%s\\\\%s.txt\"% (plane_code,plane_code),\"w\") as f:\r\n f.write(txt_content)\r\n\r\nif __name__ == '__main__':\r\n start=time.clock()\r\n plane_code=\"AF116\"\r\n date_list=getEveryDay('2018-03-11', '2018-03-13')\r\n # plane_code=input(\"Please input the code of plane:\\n\")\r\n # start_date=input(\"please input the start date:\\n\")\r\n # end_date=input(\"please input the end date:\\n\")\r\n for dateday in date_list:\r\n date=dateday\r\n mk_planedirs(plane_code,date)\r\n url_link=generate_url(plane_code,date)\r\n detal_info=get_detailplane(url_link)\r\n print(all_info)\r\n save_txt(str(all_info))\r\n end=time.clock()\r\n print(\"Total time is %s\" %(end-start))","sub_path":"plane.py","file_name":"plane.py","file_ext":"py","file_size_in_byte":4187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"532261554","text":"first_name = input(\"Enter a name: \")\nsecond_name = input(\"Enter your second name: \")\nthird_name = input(\"Enter your family name: \")\nbirth_year = input(\"Enter your year of birth: \")\nbirth_year = int(birth_year)\n\nage = 2015 - birth_year\n\ninfo ={\"name\" : first_name, \"second name\" : second_name,\n \"family name\" : third_name, \"birth year\" : birth_year, \"age\" : age}\n\nfor i in info:\n value = info[i]\n print(i, value)\n","sub_path":"Programming0-1/week4/1-Person/01_program.py","file_name":"01_program.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"510033332","text":"import json\nimport os\nimport sys\n\nDUMMY_SUBJ = \"DUMMYSUBJ\"\nDUMMY_REL = \"DUMMYREL\"\nDUMMY_VAL = \"DUMMYVAL\"\n\nADD_DUMMY = False\n\n\ndef normalize_kb(d, eric=False):\n \"\"\"\n takes kb dictionary and decides which type of info to extract\n\n this is unnecessarily done in separate functions\n because I initially believed the database info\n was different for different tasks\n \"\"\"\n intent = d[\"task\"][\"intent\"]\n if d[\"kb\"][\"items\"] is None:\n return []\n\n return eval(\"normalize_{}(d, eric={})\".format(intent, eric))\n\n\ndef normalize_weather(d, eric=False):\n \"\"\"\n takes a kb with task intent \"weather\" and items with\n locations and normalizes all items into triples\n of the form: \n\n (subject , relation , value) #generic\n (location, weekday , info ) #weather specific\n (\"san mateo\", \"monday\", \"dry, low of 80F, high of 90F\") #example \n \"\"\"\n normed_kb = [] #to add (subj, rel, val) triples\n assert d[\"task\"][\"intent\"]==\"weather\"\n locations = d[\"kb\"][\"items\"]\n location_keys_length = set()\n for location in locations:\n location_keys_length |= {len(location.keys())}\n\n subject = location[\"location\"]\n today = location[\"today\"]\n\n for weekday in location.keys():\n\n if weekday == \"location\": # add (\"ohio\", \"location\", \"ohio\")\n\n normed_kb.append((subject+\" \"+location[weekday], weekday, location[weekday]))\n\n elif weekday != \"today\": # add (\"san francisco\", \"monday weather\", \"rain\")\n\n weather_info = location[weekday].split(\",\")\n weather_attribute, temp_low, temp_high = weather_info\n\n temp_low = temp_low.split()[-1] # just degree f\n temp_high = temp_high.split()[-1] # just degree f\n\n normed_kb.append((subject+\" \"+weekday.split()[-1],weekday+\" date\",weekday.split()[-1])) # 'monday' or 'today'\n normed_kb.append((subject+\" \"+weather_attribute,weekday+\" weather\",weather_attribute))\n normed_kb.append((subject+\" \"+temp_low,weekday+\" temperature low\",temp_low))\n normed_kb.append((subject+\" \"+temp_high,weekday+\" temperature high\",temp_high))\n\n assert len(location_keys_length) == 1, f\" {location_keys_length}; all KB items (locations) should have same number of keys (weekdays)\"\n # add how many dummy entries using num of entries from first kb example\n if ADD_DUMMY:\n\n normed_kb_with_dummy_entries = [(DUMMY_SUBJ, entry[1], DUMMY_VAL) for entry in normed_kb if\n entry[0]==normed_kb[0][0]]\n normed_kb_with_dummy_entries += normed_kb\n return normed_kb_with_dummy_entries\n else:\n return normed_kb\n\ndef normalize_navigate(d, eric=False):\n \"\"\"\n takes a kb with task intent \"navigate\" \n and normalizes all items into triples\n of the form: \n\n (subject , relation , value) #generic\n (poi, dist/traff/type/addr , info ) #weather specific\n (\"Pizza Hut\", \"address\", \"704 El Camino Real\") #example\n\n \"\"\"\n normed_kb = [] #to add (subj, rel, val) triples\n assert d[\"task\"][\"intent\"]==\"navigate\"\n blimps = d[\"kb\"][\"items\"] # blimps as in map marker blips\n blimp_keys_length = set()\n for blimp in blimps:\n blimp_keys_length |= {len(blimp.keys())}\n\n poi_type = blimp[\"poi_type\"]\n poi = blimp[\"poi\"]\n\n if eric:\n subject = poi\n else:\n if poi_type != poi:\n subject = poi_type + \" \" + poi\n else:\n # avoid \"home home\"; instead put \"home\"\n assert poi_type.lower() == \"home\"\n subject = poi_type\n\n for relation in blimp.keys():\n normed_kb.append((subject+\" \"+blimp[relation],relation,blimp[relation]))\n\n assert len(blimp_keys_length) == 1, f\"{blimp_keys_length}; all KB items ) should have same number of keys (weekdays)\"\n # add how many dummy entries using num of entries from first kb example\n if ADD_DUMMY:\n\n normed_kb_with_dummy_entries = [(DUMMY_SUBJ, entry[1], DUMMY_VAL) for entry in normed_kb if\n entry[0]==normed_kb[0][0]]\n normed_kb_with_dummy_entries += normed_kb\n\n return normed_kb_with_dummy_entries\n else:\n return normed_kb\n\ndef normalize_schedule(d, eric=False):\n \"\"\"\n eric isnt used here :/\n takes a kb with task intent \"schedule\"\n and normalizes all items into triples\n of the form: \n\n (subject , relation , value) #generic\n (event, room/agenda/time/date/party, info) #weather specific\n (\"lab appointment\", \"date\", \"wednesday\") #example\n\n \"\"\"\n normed_kb = [] #to add (subj, rel, val) triples\n assert d[\"task\"][\"intent\"]==\"schedule\"\n appointments = d[\"kb\"][\"items\"]\n appointment_keys_lengths = set()\n for appointment in appointments:\n appointment_keys_lengths |= {len(appointment.keys())}\n\n event = appointment[\"event\"]\n for relation in appointment.keys():\n # also check appointment[relation] != \"-\"\n # to filter out unassigned rooms/agendas/..\n # if appointment[relation] != \"-\":\n normed_kb.append((event+\" \"+appointment[relation],relation,appointment[relation]))\n\n assert len(appointment_keys_lengths) == 1, f\"{appointment_keys_lengths}; all KB items should have same number of keys\"\n # add how many dummy entries using num of entries from first kb example\n if ADD_DUMMY:\n\n normed_kb_with_dummy_entries = [(DUMMY_SUBJ, entry[1], DUMMY_VAL) for entry in normed_kb if\n entry[0]==normed_kb[0][0]]\n normed_kb_with_dummy_entries += normed_kb\n return normed_kb_with_dummy_entries\n else:\n return normed_kb\n\n\n\ndef main(args):\n\n EXT = \"FINAL\"\n directory = \"../kvr/\"\n\n if args==0: #use defaults\n splitpart = \"dev\"\n else:\n splitpart = args[0]\n if len(args) > 1:\n EXT = args[1]\n\n eric = True # TODO add to args\n\n # filename = splitpart+\".json\" # NOTE used to be called this dont call this!\n filename = \"scenarios_\"+splitpart+\"_\"+EXT+\".json\"\n with open(directory+filename, \"r\") as scenarios:\n settings = json.load(scenarios)\n\n normed_kbs = [normalize_kb(kb, eric=eric) for kb in settings]\n lens = [str(len(kb))+\"\\n\" for kb in normed_kbs]\n\n normed_kbs_inner = [triple for scenario in normed_kbs for triple in scenario]\n kb_list = [\"::\".join(t)+\"\\n\" for t in normed_kbs_inner]\n\n #line formatted normalized kb\n filestamm = splitpart \n ext = \"kb\"+EXT\n save_as = filestamm+\".\"+ext\n with open(directory+save_as, \"w\") as o:\n o.writelines(kb_list)\n\n # line formatted kb length lookup file\n # * dev.len\n # saves number of lines to put in kb batch in \n # * dev.kb\n # from start of current line from last example\n # according to \n # * dev.lkp \n\n lengths_ext = \"len\"+EXT\n save_lengths = filestamm + \".\" + lengths_ext\n with open(directory+save_lengths, \"w\") as l:\n # assert False, ((\"lens: \", lens[117:124], directory+save_lengths))\n l.writelines(lens)\n\n return 0\n\nif __name__ == \"__main__\":\n if len(sys.argv) >1:\n sys.exit(main(sys.argv[1:]))\n else:\n sys.exit(main(0))\n","sub_path":"data/scripts_kvr/normalize_scenarios.py","file_name":"normalize_scenarios.py","file_ext":"py","file_size_in_byte":7268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"376208528","text":"from pathlib import Path\n\nfrom cwltool.main import main\n\nfrom .util import needs_docker\n\nscript = \"\"\"\n#!/usr/bin/env cwl-runner\ncwlVersion: v1.0\nclass: CommandLineTool\ninputs:\n - id: input\n type: File\n inputBinding:\n position: 0\noutputs:\n - id: output\n type: File\n outputBinding:\n glob: test.txt\nstdout: test.txt\nbaseCommand: [cat]\n\"\"\"\n\n\n@needs_docker\ndef test_spaces_in_input_files(tmp_path: Path) -> None:\n script_name = tmp_path / \"script\"\n spaces = tmp_path / \"test with spaces\"\n spaces.touch()\n with script_name.open(mode=\"w\") as script_file:\n script_file.write(script)\n\n params = [\n \"--debug\",\n \"--outdir\",\n str(tmp_path / \"outdir\"),\n str(script_name),\n \"--input\",\n str(spaces),\n ]\n assert main(params) == 1\n assert main([\"--relax-path-checks\"] + params) == 0\n","sub_path":"tests/test_relax_path_checks.py","file_name":"test_relax_path_checks.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"176628464","text":"from unidecode import unidecode\nimport pandas as pd\nfrom urllib.parse import urlparse, parse_qs\nimport requests\nfrom bs4 import BeautifulSoup \n\nclass Advfn:\n \n __dc__ = {'domain': 'https://br.advfn.com/bolsa-de-valores/bovespa/',\n 'home': '/cotacao',\n 'getYields': '/dividendos/historico-de-proventos',\n 'getPrices': '/historico/mais-dados-historicos'}\n \n def __init__(self, codigo):\n self.__link__ = self.__dc__['domain'] + codigo\n \n url = self.__link__ + self.__dc__['home']\n soup = self.__getSoup__(url)\n df = self.__tableSoupToDf__(soup.find('div', {'class': 'TableElement'}))\n self.__dfToAttrs__(df)\n \n def __dfToAttrs__(self, df):\n for column in df.columns:\n name = unidecode(column.strip().replace(' ', '_')).lower()\n value = df[column].iloc[0]\n setattr(self, name, value)\n \n def __getSoup__(self, url):\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36'}\n response = requests.get(url, headers=headers)\n return BeautifulSoup(response.text, 'html.parser')\n \n def __tableSoupToDf__(self, soup, dic = ''):\n table = soup.find('table', dic).findAll('tr')\n \n header = [[th.text for th in tr.find_all('th')] for tr in table if [th.text for th in tr.find_all('th')] != []]\n data = [[td.text for td in tr.find_all('td')] for tr in table if [td.text for td in tr.find_all('td')] != []]\n \n if len(data[0]) == len(header[0]):\n return pd.DataFrame(data, columns=header[0])\n else:\n return pd.DataFrame()\n \n \n def getYields(self):\n url = self.__link__ + self.__dc__['getYields']\n\n soup = self.__getSoup__(url)\n df = self.__tableSoupToDf__(soup, {'class': 'dividends'}) \n \n return df \n \n #Date format dd/mm/yyyy\n def getPrices(self, date_start, date_end):\n url = self.__link__ + self.__dc__['getPrices'] + '?&Date1=' + date_start + '&Date2=' + date_end\n\n soup = self.__getSoup__(url)\n \n #Getting number of pages for the iteration.\n try:\n t = soup.find('a', {'class': 'date-control'})['href']\n latest_page = int(parse_qs(urlparse(t).query)['current'][0])\n except:\n latest_page = 0\n \n df = self.__tableSoupToDf__(soup, {'class': 'histo-results'})\n li = [df]\n for page in range(1, latest_page + 1):\n url = self.__link__ + self.__dc__['getPrices'] + '?&Date1=' + date_start + '&Date2=' + date_end + '¤t=' + str(page)\n soup = self.__getSoup__(url)\n \n df = self.__tableSoupToDf__(soup, {'class': 'histo-results'}) \n \n li.append(df)\n \n df = pd.concat(li, axis=0, ignore_index=True)\n \n return df\n","sub_path":"Advn.py","file_name":"Advn.py","file_ext":"py","file_size_in_byte":3002,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"170487269","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 2 22:26:54 2019\n\n@author: innerm\n\"\"\"\nimport json\nimport pandas as pd\nimport re\n\ndef clear_title(data):\n data = re.sub('\\S*@\\S*\\s?',' ', data)\n data = re.sub('\\s+', ' ', data)\n data = re.sub(\"\\'\", \" \", data)\n return data\n\nfile_en='stage41.csv'\nfile_ru='stage42.csv'\n\ndf=pd.DataFrame()\ndf1=pd.read_csv(file_en)\ndf2=pd.read_csv(file_ru)\ndf=df.append(df1)\ndf=df.append(df1)\ndel df1,df2\n\ndf=df[df.real_news==1]\ncategories=[(1,'society'),(2,'economy'),(3,'technology'),(4,'entertainment'),(5,'science'),(6,'sport'),(7,'others')]\n\ndf=df.sort_values(by=['rate_thread'],ascending=False)\ndf=df[df['rate_thread']>3]\nth=df.thread.tolist()\ndt = {i:th.count(i) for i in th}\n\n\nfor item in dt.keys():\n \n dd=df[df['thread']==item]\n dd=dd.sort_values(by=['prob'],ascending=False)\n tt=dd.title.tolist()[0]\n tt=clear_title(tt)\n files=dd.files.tolist()\n edict={'articles':files}\n exp2='thread:'+tt\n n={exp2:edict}\n print(json.dumps(n, sort_keys=True,indent=3))\n\nfor item in categories:\n df1=df[df.theme==item[0]]\n df1=df1.sort_values(by=['rate_thread'],ascending=False)\n df1=df1[df1['rate_thread']>3]\n th=df1.thread.tolist()\n dt = {i:th.count(i) for i in th}\n exp='category:'+item[1]\n for item2 in dt.keys():\n dd=df1[df1['thread']==item2]\n dd=dd.sort_values(by=['prob'],ascending=False)\n tt=dd.title.tolist()[0]\n tt=clear_title(tt)\n files=dd.files.tolist()\n edict={'articles':files}\n exp2='thread:'+tt\n n={exp2:edict}\n m={exp:n}\n print(json.dumps(m, sort_keys=True,indent=4))\n","sub_path":"top.py","file_name":"top.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"393639750","text":"# from import \nfrom models import Link, Robot\nfrom math import pi\n\ndef main():\n links = getLinks()\n robot = Robot(links)\n robot.setSensor(\"color\",1)\n robot.setGearRatio([3,3.25,3])\n\n q1 = robot.fkine([0,0])\n q2 = robot.fkine([0,-45])\n \n print(q1[3,4])\n print(q2[3,4])\n\n \n \ndef getLinks():\n #-------------- DH PARAMETERS--------------\n a = [-9,0,0]\n alpha = [-pi/2, -pi/2, pi/2]\n d = [0, 0,-24]\n theta = [None, None, None]\n offset = [0,pi/2,pi/2]\n limits = [[-90,90],[-90,0],[0,90]] \n # [PWM(+), PWM(-)]\n # for gravity compensation\n PWMs = [[30,-30],[10,-30],[50,-50]]\n #------------------------------------------\n links = []\n #for i in range(0,len(d)):\n for i in range(0,len(d)):\n link = Link(d[i],theta[i],a[i],alpha[i],\\\n offset[i],limits[i],PWMs[i])\n links.append(link)\n\n return links\n\n \n\n# start point\nq0 = [0,0,0]\n\n# first operation piotn\nq1up = [-90, -45, 0]\nq1down = [-90, -5, 0]\n\n# second operation point\nq2up = [-45, -45, 0]\nq2down = [-45, -5, 0]\n\n# third operation point\nq3up = [45, -45, 0]\nq3down = [45, -5, 0]\n\n# assembly point\napup = [90, -45, 0]\napdown = [90, -5, 0]\n\n# operation points\nops = [[q1up, q1down],[q2up, q2down],[q3up, q3down]]\n#assembly point\nap = [apup, apdown]\n#colors \ncolors = [\"red\", \"black\", \"red\"]\n\n\nif __name__==\"__main__\":\n main()","sub_path":"src/fkine_test.py","file_name":"fkine_test.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"548005046","text":"# Copyright (C) 2016 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 \n\nimport names\nimport random\nimport string\nimport datetime\n\nfrom ggrc import db\nfrom ggrc import models\nfrom ggrc.app import app\nfrom ggrc.services import common\nfrom ggrc_basic_permissions import models as permissions_models\nfrom integration.ggrc import api_helper\n\n\nclass Generator():\n \"\"\"Generator base class.\"\"\"\n\n def __init__(self):\n self.api = api_helper.Api()\n self.resource = common.Resource()\n\n def random_str(self, length=8,\n chars=string.ascii_uppercase + string.digits + \" _.-\"):\n return ''.join(random.choice(chars) for _ in range(length))\n\n def random_date(self, start=datetime.date.today(), end=None):\n if not end or start > end:\n end = start + datetime.timedelta(days=7)\n return start + datetime.timedelta(\n seconds=random.randint(0, int((end - start).total_seconds())))\n\n def generate(self, obj_class, obj_name=None, data=None):\n if obj_name is None:\n obj_name = obj_class._inflector.table_plural\n if data is None:\n data = {}\n response = self.api.post(obj_class, data)\n response_obj = None\n if response.json:\n try:\n response_obj = obj_class.query.get(response.json[obj_name]['id'])\n except TypeError:\n raise Exception(\"Invalid response.\\nResponse: {}\\nError: {}\".format(\n response,\n response.data\n ))\n return response, response_obj\n\n def modify(self, obj, obj_name, data):\n obj_class = obj.__class__\n response = self.api.put(obj, data)\n response_obj = None\n if response.json:\n response_obj = obj_class.query.get(response.json[obj_name]['id'])\n return response, response_obj\n\n def obj_to_dict(self, obj, model_name=None):\n with app.app_context():\n return self.resource.object_for_json(obj, model_name)\n\n\nclass ObjectGenerator(Generator):\n \"\"\"Main object generator class.\n\n This class is used as a helper for generating ggrc objects via the API. This\n is used for writing integration tests on thigs that attach on api callbacs,\n such as model_posted, model_put and model_deleted.\n \"\"\"\n\n def create_stub(self, obj):\n return {\n \"id\": obj.id,\n \"href\": \"/api/{}/{}\".format(obj._inflector.table_name, obj.id),\n \"type\": obj.type,\n }\n\n def generate_object(self, obj_class, data=None):\n if data is None:\n data = {}\n obj_name = obj_class._inflector.table_singular\n obj = obj_class()\n obj_dict = self.obj_to_dict(obj, obj_name)\n obj_dict[obj_name].update({\n \"owners\": [{\n \"id\": 1,\n \"href\": \"/api/people/1\",\n \"type\": \"Person\"\n }],\n \"title\": self.random_str(),\n })\n obj_dict[obj_name].update(data)\n return self.generate(obj_class, obj_name, obj_dict)\n\n def generate_relationship(self, source, destination, context=None, **kwargs):\n \"\"\"Create relationship between two objects.\n\n Args:\n source (db.Model): source model of the relationship.\n destination (db.Model): destination model of the relationship.\n context (Context): context of the relationship. Usually a context of one\n of the related objects.\n kwargs (dict): various arguments for the given relationship, such as\n relationship attributes.\n\n Returns:\n response object and the actual relationship that was created.\n \"\"\"\n if context:\n context = self.create_stub(context)\n data = {\n \"source\": self.create_stub(source),\n \"destination\": self.create_stub(destination),\n \"context\": context,\n }\n data.update(kwargs)\n return self.generate_object(models.Relationship, data=data)\n\n def generate_comment(self, commentable, assignee_type, description,\n **kwargs):\n \"\"\"Create a comment on a commentable object.\n\n This function creates a comment for a given object and generates the\n correct relationship to that object. The result of generating the\n relationship is discarded and the user will only see if a comment is\n created.\n\n Args:\n commentable (db.Model): Model that is commentable such as Request or\n Assessment.\n assignee_type (string): Assignee type of the person creating the comment.\n description (string): Comment content.\n kwargs (dict): Any additional data added to the comments.\n\n Returns:\n Server response and the generated comment.\n \"\"\"\n data = {\n \"description\": description,\n \"assignee_type\": assignee_type,\n \"context\": self.create_stub(commentable),\n }\n data.update(kwargs)\n response, comment_ = self.generate_object(models.Comment, data=data)\n # Refresh the object after an API call.\n commentable = commentable.__class__.query.get(commentable.id)\n self.generate_relationship(commentable, comment_, commentable.context)\n return response, comment_\n\n def generate_user_role(self, person, role):\n data = {\n \"user_role\": {\n \"context\": None,\n \"person\": self.create_stub(person),\n \"role\": self.create_stub(role),\n }\n }\n return self.generate(permissions_models.UserRole, \"user_role\", data)\n\n def generate_person(self, data={}, user_role=None):\n obj_name = 'person'\n name = names.get_full_name()\n default = {\n obj_name: {\n \"context\": None,\n \"name\": name,\n \"email\": \"%s@test.com\" % name.replace(\" \", \".\").lower(),\n }\n }\n default[obj_name].update(data)\n response, person = self.generate(models.Person, obj_name, default)\n\n if person and user_role:\n role = db.session.query(permissions_models.Role).filter(\n permissions_models.Role.name == user_role).first()\n self.generate_user_role(person, role)\n\n return response, person\n\n def generate_random_objects(self, count=5):\n random_objects = []\n classes = [\n models.Control,\n models.Objective,\n models.Standard,\n models.System,\n models.OrgGroup,\n ]\n for _ in range(count):\n obj_class = random.choice(classes)\n response, obj = self.generate_object(obj_class)\n random_objects.append(obj)\n return random_objects\n\n def generate_random_people(self, count=5, **kwargs):\n random_people = []\n for _ in range(count):\n _, person = self.generate_person(**kwargs)\n if person:\n random_people.append(person)\n return random_people\n\n def generate_notification_setting(self, user_id, notif_type, enable_flag):\n obj_name = \"notification_config\"\n data = {\n obj_name: {\n \"person_id\": user_id,\n \"notif_type\": notif_type,\n \"enable_flag\": enable_flag,\n \"context\": None,\n \"type\": \"NotificationConfig\",\n }\n }\n return self.generate(models.NotificationConfig, obj_name, data)\n\n def generate_custom_attribute(self, definition_type, **kwargs):\n obj_name = \"custom_attribute_definition\"\n data = {\n obj_name: {\n \"title\": kwargs.get(\"title\", self.random_str()),\n \"custom_attribute_definitions\": [],\n \"custom_attributes\": {},\n \"definition_type\": definition_type,\n \"modal_title\": kwargs.get(\"modal_title\", self.random_str()),\n \"attribute_type\": kwargs.get(\"attribute_type\", \"Text\"),\n \"mandatory\": kwargs.get(\"mandatory\", False),\n \"helptext\": kwargs.get(\"helptext\", False),\n \"placeholder\": kwargs.get(\"placeholder\", False),\n \"context\": {\"id\": None},\n \"multi_choice_options\": kwargs.get(\"options\", False),\n }\n }\n data[obj_name].update(kwargs)\n self.generate(models.CustomAttributeDefinition, obj_name, data)\n","sub_path":"test/integration/ggrc/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":7751,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"407005353","text":"import random\nfrom pomoc import euclid\n\ndef ksrednie(dane, k, metryka=euclid, prog = pow(10, -1)):\n\t#dane w formie tablicy tablic\n\t#zakresy - tablica n tupli, z minimalna i maksymalna wartoscia kazdego wymiaru, gdzie n to liczba wymiarow\n\tn = len(dane[0])\n\tm = len(dane)\n\tzakresy = [ ( min( [wiersz[i] for wiersz in dane ] ), max( [wiersz[i] for wiersz in dane ] )) for i in range(n) ] \n\tsrodki = [ [zakresy[i][0] + random.random()*( zakresy[i][1]-zakresy[i][0] ) for i in range(n)] for i in range(k) ] \n\n\tprzypisane = {}\n\tfor j in range(k):\n\t\tprzypisane[j] = []\n\tfor i in range(m):\n\t\tnajmOd = metryka( dane[i], srodki[0] )\n\t\tsrodek = 0\n\t\tfor j in range(1, k):\n\t\t\td = metryka( dane[i], srodki[j] )\n\t\t\tif d < najmOd:\n\t\t\t\tnajmOd = d\n\t\t\t\tsrodek = j\n\t\tprzypisane[srodek].append(dane[i])\n\n\tkoszt = 1/n*sum( [sum( [euclid(przypisane[i][j], srodki[i]) for j in range( len(przypisane[i])) ] ) for i in range(k)] ) \n\tprint( koszt )\n\tnowe = {}\n\tfor j in range(k):\n\t\tnowe[j] = []\n\tdk = prog + 1\n\twhile dk > prog:\n\t\t\n\t\tfor i in range(k):\n\t\t\tl = len(przypisane[i])\n\t\t\tfor j in range(n):\n\t\t\t\t\n\t\t\t\tsrodki[i][j] = 1/l*sum( [przypisane[i][c][j] for c in range(l) ] )\n\n\t\tfor j in range(k):\n\t\t\tprzypisane[j] = []\n\t\tfor i in range(m):\n\t\t\tnajmOd = metryka( dane[i], srodki[0] )\n\t\t\tsrodek = 0\n\t\t\tfor j in range(1, k):\n\t\t\t\td = metryka( dane[i], srodki[j] )\n\t\t\t\tif d < najmOd:\n\t\t\t\t\tnajmOd = d\n\t\t\t\t\tsrodek = j\n\t\t\tprzypisane[srodek].append(dane[i])\n\n\t\tnkoszt = 1/n*sum( [sum( [euclid(przypisane[i][j], srodki[i]) for j in range( len(przypisane[i])) ] ) for i in range(k)] ) \n\t\tdk = abs(nkoszt - koszt)\n\t\tprint( dk)\n\t\tkoszt = nkoszt\n\t\t\n\t\t\n\treturn przypisane\n\t\t\t\n","sub_path":"ksrednie.py","file_name":"ksrednie.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"4523574","text":"from flask import abort, flash, redirect, render_template, url_for, request, make_response\nfrom flask_login import current_user, login_required\nfrom flask_rq import get_queue\n\nfrom .forms import (ChangeAccountTypeForm, ChangeUserEmailForm, InviteUserForm,\n NewUserForm, NewCandidateForm, DemographicForm,\n EditParticipantForm, NewTermForm, EditTermForm, EditStatusForm,\n InviteAcceptedCandidatesForm, StatsSelectTermForm)\nfrom . import admin\nfrom .. import db\nfrom ..decorators import admin_required\nfrom ..email import send_email\nfrom ..models import Role, User, Candidate, Demographic, Donor, EditableHTML, Status, DonorStatus, Term\n\n\n\n@admin.route('/')\n@login_required\n@admin_required\ndef index():\n \"\"\"Admin dashboard page.\"\"\"\n return render_template('admin/index.html')\n\n\n@admin.route('/new-user', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef new_user():\n \"\"\"Create a new user.\"\"\"\n form = NewUserForm()\n if form.validate_on_submit():\n user = User(\n role=form.role.data,\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n email=form.email.data,\n password=form.password.data)\n db.session.add(user)\n db.session.commit()\n flash('User {} successfully created'.format(user.full_name()),\n 'form-success')\n return render_template('admin/new_user.html', form=form)\n\n@admin.route('/term-management', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef term_management():\n \"\"\"Manage terms\"\"\"\n form = NewCandidateForm()\n terms = Term.query.all()\n return render_template('admin/term_management.html', Status=Status, terms=terms, form=form)\n\n\n@admin.route('/new-term', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef new_term():\n \"\"\"Create a new term.\"\"\"\n form = NewTermForm()\n if form.validate_on_submit():\n term = Term(\n name=form.name.data,\n in_progress=True,\n start_date=form.start_date.data,\n end_date=form.end_date.data,\n candidates= [],\n )\n db.session.add(term)\n db.session.commit()\n flash('Term {} successfully created'.format(term.name),\n 'form-success')\n return render_template('admin/new_term.html', form=form)\n\n@admin.route('/edit-term/', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef edit_term(term_id):\n \"\"\"Edit a term.\"\"\"\n term = Term.query.filter_by(id=term_id).first();\n if term is None:\n abort(404)\n form = EditTermForm(obj=term_id)\n\n if request.method == 'GET':\n form.name.data = term.name\n form.start_date.data = term.start_date\n form.end_date.data = term.end_date\n\n if form.validate_on_submit():\n term.name = form.name.data\n term.start_date = form.start_date.data\n term.end_date = form.end_date.data\n\n db.session.add(term)\n db.session.commit()\n flash('Term {} successfully updated'.format(term.name),\n 'form-success')\n return render_template('admin/edit_term.html', form=form, term=term)\n\n@admin.route('/terms//_delete')\n@login_required\n@admin_required\ndef delete_term(term_id):\n \"\"\"Delete a term.\"\"\"\n term = Term.query.filter_by(id=term_id).first()\n db.session.delete(term)\n db.session.commit()\n\n candidates = Candidate.query.filter_by(term_id=term_id)\n for candidate in candidates:\n candidate.term_id = null\n candidate.term = null\n db.session.add(candidate)\n db.session.commit()\n\n flash('Successfully deleted term %s.' % term.name, 'success')\n return redirect(url_for('admin.term_management'))\n\n@admin.route('/participants', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef participants():\n \"\"\"Manage participants\"\"\"\n participants = Candidate.query.all()\n\n status_forms = { p.id: EditStatusForm(participant=p.id, status=p.status.name, term=p.term) for p in participants }\n for f in status_forms:\n form = status_forms[f]\n if form.submit_status.data and form.validate():\n user = Candidate.query.filter_by(id=form.participant.data).first()\n user.status = form.status.data\n user.term = form.term.data\n db.session.add(user)\n db.session.commit()\n flash('Status for user {} successfully changed to {}.'\n .format(user.first_name, user.status), 'form-success')\n return redirect(url_for('admin.participants'))\n\n # Populate statistics with latest term\n stats = {}\n stat_term = Term.query.order_by(Term.start_date.desc()).first()\n stat_form = StatsSelectTermForm()\n if stat_form.submit_term.data and stat_form.validate():\n stat_term = stat_form.term.data\n\n if stat_term is not None:\n stats['Race Statistics'] = Candidate.race_stats(stat_term.id)\n stats['Class Statistics'] = Candidate.class_stats(stat_term.id)\n stats['Gender Statistics'] = Candidate.gender_stats(stat_term.id)\n stats['Sexual Orientation Statistics'] = Candidate.sexual_orientation_stats(stat_term.id)\n # TODO - Probably move this to its own page\n stats['Cohort Statistics'] = Candidate.cohort_stats(stat_term.id)\n\n return render_template('admin/participant_management.html',\n Status=Status,\n participants=participants, demographics=Demographic.demographics_dict(),\n terms=Term.query.order_by(Term.start_date.desc()).all(),\n status_forms=status_forms,\n stats=stats,\n stat_term=stat_term,\n stat_form=stat_form)\n\n@admin.route('/participants/demographic_graphs//')\n@login_required\ndef make_graph(name, stats):\n import numpy as np\n import matplotlib\n matplotlib.use('TkAgg')\n from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n from matplotlib.figure import Figure\n import ast\n import math\n from textwrap import wrap\n from io import StringIO, BytesIO\n\n # Setup data\n stats_obj=ast.literal_eval(stats)\n objects = [ stat[0] for stat in stats_obj.items() ]\n objects = [ o.title().replace('_', ' ') for o in objects]\n objects = [ '\\n'.join(wrap(o, 11)) for o in objects ]\n amt = [ int(stat[1]) for stat in stats_obj.items() ]\n y_ticks = np.arange(len(amt))\n\n # Setup graph\n fig=Figure()\n ax=fig.add_subplot(111)\n ax.bar(y_ticks, amt, align='center', alpha=0.5)\n ax.set_yticks(y_ticks, amt)\n ax.set_ylabel('Number of candidates')\n ax.set_xticks(np.arange(len(objects)))\n ax.set_xticklabels(objects, rotation = 0, ha='center')\n ax.set_title('Graph for {}'.format(name))\n\n # Add labels to inside of bars\n for i, v in enumerate(amt):\n if v != 0:\n ax.text(i, v * 0.8, str(v), color='gray')\n\n # Convert to png\n canvas=FigureCanvas(fig)\n png_output=BytesIO()\n canvas.print_png(png_output)\n response=make_response(png_output.getvalue())\n response.headers['Content-Type'] = 'image/png'\n\n return response\n\n@admin.route('/new-candidate', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef new_candidate():\n \"\"\"Create a new candiate.\"\"\"\n form = NewCandidateForm()\n if form.validate_on_submit():\n demographic = Demographic(\n race=form.demographic.race.data,\n gender=form.demographic.gender.data,\n age=form.demographic.age.data,\n sexual_orientation=form.demographic.sexual_orientation.data,\n soc_class=form.demographic.soc_class.data\n )\n candidate = Candidate(\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n email=form.email.data,\n phone_number=form.phone_number.data,\n term=form.term.data,\n source=form.source.data,\n staff_contact=form.staff_contact.data,\n notes=form.notes.data,\n demographic=demographic,\n demographic_id=demographic.id,\n status=Status.PENDING,\n amount_donated=0\n )\n db.session.add(demographic)\n db.session.add(candidate)\n db.session.commit()\n\n admins = []\n for u in User.query.all():\n if u.is_admin():\n admins.append(u)\n\n # Notify admins via email\n for a in admins:\n get_queue().enqueue(\n send_email,\n recipient=a.email,\n subject='New Giving Project Candidate',\n template='admin/email/new_candidate',\n user=a,\n candidate=candidate,\n add_method='Added by {}'.format(current_user.full_name()))\n\n flash('Candidate {} successfully created'.format(candidate.first_name),\n 'form-success')\n return render_template('admin/new_candidate.html', form=form)\n\n\n@admin.route('/edit-participant/', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef edit_participant(part_id):\n \"\"\"Edit a participant.\"\"\"\n part = Candidate.query.filter_by(id=part_id).first();\n if part is None:\n abort(404)\n form = EditParticipantForm(obj=part_id)\n demographic = part.demographic\n\n if request.method == 'GET':\n form.first_name.data = part.first_name\n form.last_name.data = part.last_name\n form.email.data = part.email\n form.phone_number.data = part.phone_number\n form.source.data = part.source\n form.staff_contact.data = part.staff_contact\n form.notes.data = part.notes\n form.status.data = part.status\n form.assigned_term.data = part.term\n form.amount_donated.data = part.amount_donated\n form.applied.data = part.applied\n\n form.demographic.race.data = part.demographic.race\n form.demographic.gender.data = part.demographic.gender\n form.demographic.age.data = part.demographic.age\n form.demographic.sexual_orientation.data = part.demographic.sexual_orientation\n form.demographic.soc_class.data = part.demographic.soc_class\n\n if form.validate_on_submit():\n part.first_name = form.first_name.data\n part.last_name = form.last_name.data\n part.email = form.email.data\n part.phone_number = form.phone_number.data\n part.source = form.source.data\n part.staff_contact = form.staff_contact.data\n part.notes = form.notes.data\n part.status = form.status.data\n part.term = form.assigned_term.data\n part.amount_donated = form.amount_donated.data\n part.applied = form.applied\n\n demographic = part.demographic\n demographic.race = form.demographic.race.data\n demographic.gender = form.demographic.gender.data\n demographic.age = form.demographic.age.data\n demographic.sexual_orientation = form.demographic.sexual_orientation.data\n demographic.soc_class = form.demographic.soc_class.data\n\n db.session.add(demographic)\n db.session.add(part)\n db.session.commit()\n flash('Participant {} successfully saved'.format(part.first_name),\n 'form-success')\n return render_template('admin/edit_participant.html', form=form)\n\n@admin.route('/all-donors')\n@login_required\n@admin_required\ndef all_donors():\n \"\"\"View and manage all donors\"\"\"\n donors = Donor.query.all()\n return render_template('admin/all_donors.html', DonorStatus=DonorStatus, donors=donors, demographics=Demographic.demographics_dict())\n\n@admin.route('/received-donation/')\n@login_required\n@admin_required\ndef received_donation(donor_id):\n \"\"\" Mark a donation as received for this donor. \"\"\"\n donor = Donor.query.filter_by(id=donor_id).first()\n # TODO: receive a donation\n return redirect(url_for('admin.all_donors'))\n\n\n@admin.route('/invite-user', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef invite_user():\n \"\"\"Invites a new user to create an account and set their own password.\"\"\"\n form = InviteUserForm()\n if form.validate_on_submit():\n user = User(\n role=form.role.data,\n first_name=form.first_name.data,\n last_name=form.last_name.data,\n email=form.email.data)\n db.session.add(user)\n db.session.commit()\n token = user.generate_confirmation_token()\n invite_link = url_for(\n 'account.join_from_invite',\n user_id=user.id,\n token=token,\n _external=True)\n get_queue().enqueue(\n send_email,\n recipient=user.email,\n subject='You Are Invited To Join',\n template='account/email/invite',\n user=user,\n invite_link=invite_link, )\n flash('User {} successfully invited'.format(user.full_name()),\n 'form-success')\n return render_template('admin/new_user.html', form=form)\n\n@admin.route('/invite-accepted-candidates', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef invite_accepted_candidates():\n \"\"\"Invites accepted candidates to create an account and set their own password.\"\"\"\n form = InviteAcceptedCandidatesForm()\n if form.validate_on_submit():\n selected = [ Candidate.query.filter_by(id=c).first() for c in form.selected_candidates.data.split(',') ]\n user_role = Role.query.filter_by(name='User').first()\n # for each selected candidate create a new user account\n for candidate in selected:\n user = User.query.filter_by(email=candidate.email).first()\n if user is None:\n user = User(\n role=user_role,\n first_name=candidate.first_name,\n last_name=candidate.last_name,\n email=candidate.email,\n candidate=candidate)\n db.session.add(user)\n db.session.commit()\n token = user.generate_confirmation_token()\n invite_link = url_for(\n 'account.join_from_invite',\n user_id=user.id,\n token=token,\n _external=True)\n get_queue().enqueue(\n send_email,\n recipient=user.email,\n subject='You Are Invited To Join',\n template='account/email/invite',\n user=user,\n invite_link=invite_link)\n str = ''\n for c in selected:\n str += c.first_name + ' ' + c.last_name + ', '\n str = str[:-2]\n\n flash('Candidates {} successfully invited'.format(str),\n 'form-success')\n return render_template('admin/invite_accepted_candidates.html', form=form, all_terms=Term.query.order_by(Term.end_date.desc()).all(), accepted_candidates=Candidate.query.filter_by(status=Status.ASSIGNED).all())\n\n\n@admin.route('/users')\n@login_required\n@admin_required\ndef registered_users():\n \"\"\"View all registered users.\"\"\"\n users = User.query.all()\n roles = Role.query.all()\n return render_template(\n 'admin/registered_users.html', users=users, roles=roles)\n\n\n@admin.route('/user/')\n@admin.route('/user//info')\n@login_required\n@admin_required\ndef user_info(user_id):\n \"\"\"View a user's profile.\"\"\"\n user = User.query.filter_by(id=user_id).first()\n if user is None:\n abort(404)\n return render_template('admin/manage_user.html', user=user)\n\n\n@admin.route('/user//change-email', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef change_user_email(user_id):\n \"\"\"Change a user's email.\"\"\"\n user = User.query.filter_by(id=user_id).first()\n if user is None:\n abort(404)\n form = ChangeUserEmailForm()\n if form.validate_on_submit():\n user.email = form.email.data\n db.session.add(user)\n db.session.commit()\n flash('Email for user {} successfully changed to {}.'\n .format(user.full_name(), user.email), 'form-success')\n return render_template('admin/manage_user.html', user=user, form=form)\n\n\n@admin.route(\n '/user//change-account-type', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef change_account_type(user_id):\n \"\"\"Change a user's account type.\"\"\"\n if current_user.id == user_id:\n flash('You cannot change the type of your own account. Please ask '\n 'another administrator to do this.', 'error')\n return redirect(url_for('admin.user_info', user_id=user_id))\n\n user = User.query.get(user_id)\n if user is None:\n abort(404)\n form = ChangeAccountTypeForm()\n if form.validate_on_submit():\n user.role = form.role.data\n db.session.add(user)\n db.session.commit()\n flash('Role for user {} successfully changed to {}.'\n .format(user.full_name(), user.role.name), 'form-success')\n return render_template('admin/manage_user.html', user=user, form=form)\n\n\n@admin.route('/user//delete')\n@login_required\n@admin_required\ndef delete_user_request(user_id):\n \"\"\"Request deletion of a user's account.\"\"\"\n user = User.query.filter_by(id=user_id).first()\n if user is None:\n abort(404)\n return render_template('admin/manage_user.html', user=user)\n\n\n@admin.route('/user//_delete')\n@login_required\n@admin_required\ndef delete_user(user_id):\n \"\"\"Delete a user's account.\"\"\"\n if current_user.id == user_id:\n flash('You cannot delete your own account. Please ask another '\n 'administrator to do this.', 'error')\n else:\n user = User.query.filter_by(id=user_id).first()\n db.session.delete(user)\n db.session.commit()\n flash('Successfully deleted user %s.' % user.full_name(), 'success')\n return redirect(url_for('admin.registered_users'))\n\n\n@admin.route('/participant//_delete')\n@login_required\n@admin_required\ndef delete_participant(participant_id):\n \"\"\"Delete a participant.\"\"\"\n p = Candidate.query.filter_by(id=participant_id).first()\n db.session.delete(p)\n db.session.commit()\n flash('Successfully deleted participant %s.' % p.first_name, 'success')\n return redirect(url_for('admin.participants'))\n\n\n@admin.route('/_update_editor_contents', methods=['POST'])\n@login_required\n@admin_required\ndef update_editor_contents():\n \"\"\"Update the contents of an editor.\"\"\"\n\n edit_data = request.form.get('edit_data')\n editor_name = request.form.get('editor_name')\n\n editor_contents = EditableHTML.query.filter_by(\n editor_name=editor_name).first()\n if editor_contents is None:\n editor_contents = EditableHTML(editor_name=editor_name)\n editor_contents.value = edit_data\n\n db.session.add(editor_contents)\n db.session.commit()\n\n return 'OK', 200\n\n@admin.route('/download/participants', methods=['GET'])\n@login_required\ndef download_participants():\n # format string or list of strings to be csv-friendly\n def csv_friendly(str):\n if str == \"\":\n return \"NOT SPECIFIED\"\n return '\\\"{}\\\"'.format(str.replace('\\\"', '\\\"\\\"')) if str else ''\n\n # write headers\n csv = 'First Name,Last Name,Term,Email,Phone,Source,Staff Contact,Notes,Status,Amount Donated,Applied,Age,Race,Class,Gender,Sexual Orientation\\n'\n\n # write each candidate\n candidates = Candidate.query.all()\n for candidate in candidates:\n demographics = candidate.demographic.demographic_strings()\n csv += ','.join([\n csv_friendly(candidate.first_name),\n csv_friendly(candidate.last_name),\n csv_friendly(candidate.term.name if candidate.term else \"\"),\n csv_friendly(candidate.email),\n csv_friendly(candidate.phone_number),\n csv_friendly(candidate.source),\n csv_friendly(candidate.staff_contact),\n csv_friendly(candidate.notes),\n csv_friendly(candidate.status_name()),\n csv_friendly(str(candidate.amount_donated)),\n csv_friendly(str(candidate.applied)),\n csv_friendly(str(candidate.demographic.age)),\n csv_friendly(demographics['Race']),\n csv_friendly(demographics['Class']),\n csv_friendly(demographics['Gender']),\n csv_friendly(demographics['SexualOrientation']),\n ])\n csv += '\\n'\n\n # send csv response\n response = make_response(csv)\n response.headers['Content-Disposition'] = 'attachment; filename=participants.csv'\n response.mimetype = 'text/csv'\n print(response)\n return response\n\n\n@admin.route('/download/donors', methods=['GET'])\n@login_required\ndef download_donors():\n # format string or list of strings to be csv-friendly\n def csv_friendly(str):\n if str == \"\":\n return \"NOT SPECIFIED\"\n return '\\\"{}\\\"'.format(str.replace('\\\"', '\\\"\\\"')) if str else ''\n\n # write headers\n # TODO put remaining headers\n csv = 'First Name,Last Name,Email,Phone,Street,City,State,Zip,Status,Contact Data,Amount Asking,Amount Pledged,Amount Received,Data Received,Race,Class,Gender,Sexual Orientation,Age,Interested in Future GP,Want to learn,Interested in Volunteering,Notes\\n'\n\n # write each resource\n donors = Donor.query.all()\n for donor in donors:\n demographics = donor.demographic.demographic_strings()\n csv += ','.join([\n csv_friendly(donor.first_name),\n csv_friendly(donor.last_name),\n csv_friendly(donor.email),\n csv_friendly(donor.phone_number),\n csv_friendly(donor.street_address),\n csv_friendly(donor.city),\n csv_friendly(donor.state),\n csv_friendly(donor.zipcode),\n csv_friendly(donor.get_status()),\n csv_friendly(str(donor.contact_date)),\n csv_friendly(str(donor.amount_asking_for)),\n csv_friendly(str(donor.amount_pledged)),\n csv_friendly(str(donor.amount_received)),\n csv_friendly(str(donor.date_received)),\n csv_friendly(demographics['Race']),\n csv_friendly(demographics['Class']),\n csv_friendly(demographics['Gender']),\n csv_friendly(demographics['SexualOrientation']),\n csv_friendly(str(donor.demographic.age)),\n csv_friendly(str(donor.interested_in_future_gp)),\n csv_friendly(str(donor.want_to_learn_about_brf_guarantees)),\n csv_friendly(str(donor.interested_in_volunteering)),\n csv_friendly(str(donor.notes)),\n ])\n csv += '\\n'\n\n # send csv response\n response = make_response(csv)\n response.headers['Content-Disposition'] = 'attachment; filename=donors.csv'\n response.mimetype = 'text/csv'\n print(response)\n return response\n","sub_path":"app/admin/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":22906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"508464952","text":"import db\nimport tornado.web\n\nfrom collections import Counter, defaultdict\nfrom filterednotetree import NONE_STRING\n\n\nclass Nav(tornado.web.UIModule):\n\n def render(self, compose_tags):\n notes = db.read_all_notes(query_archive=False)\n people_counts, people_none_count = self._get_people_counts(notes)\n project_counts, project_none_count = self._get_project_counts(notes)\n return self.render_string(\n \"nav.html\",\n compose_tags=compose_tags,\n people_counts=people_counts,\n people_none_count=people_none_count,\n project_counts=project_counts,\n project_none_count=project_none_count,\n none_string=NONE_STRING,\n )\n\n def _get_people_counts(self, notes):\n people_counts = Counter()\n none_count = 0\n for note in notes:\n people = note[\"people\"]\n if people:\n people_counts.update(people)\n else:\n none_count += 1\n return sorted(people_counts.items()), none_count\n\n def _get_project_counts(self, notes):\n project_counts = defaultdict(int)\n none_count = 0\n for note in notes:\n project = note.get(\"project\")\n if project:\n project_counts[project] += 1\n else:\n none_count += 1\n return sorted(project_counts.items()), none_count\n\n\nclass Note(tornado.web.UIModule):\n\n def render(self, note):\n return self.render_string(\"note.html\", note=note)\n","sub_path":"uimodules.py","file_name":"uimodules.py","file_ext":"py","file_size_in_byte":1537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"393565954","text":"# -*- encoding: utf-8 -*-\n\n__author__ = 'kotaimen'\n__date__ = '11/01/2017'\n\nimport click\n\nfrom ...config import load_stack_config\nfrom ...cli import template\nfrom ..utils import boto3_exception_handler, load_template_body\n\n\n@template.command()\n@click.argument('config_file', type=click.Path(exists=True))\n@click.pass_context\n@boto3_exception_handler\ndef validate(ctx, config_file):\n \"\"\"Validate template specified in the stack configuration file.\n\n AWS CloudFormation first checks if the template is valid JSON. If it isn't,\n AWS CloudFormation checks if the template is valid YAML. If both these\n checks fail, AWS CloudFormation returns a template validation error.\n\n CONFIG_FILE Stack configuration file.\n \"\"\"\n session = ctx.obj['session']\n\n click.echo('Validating template...')\n stack_config = load_stack_config(config_file)\n load_template_body(session, stack_config)\n\n region = stack_config.pop('Region')\n client = session.client('cloudformation', region_name=region)\n\n parameters = dict()\n parameter_keys = ['TemplateBody', 'TemplateURL']\n for key in parameter_keys:\n if key in stack_config:\n parameters[key] = stack_config.get(key)\n\n client.validate_template(**parameters)\n\n click.echo('Template validation completed.')\n","sub_path":"awscfncli/commands/template/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":1303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"136441927","text":"import fileinput\nimport sys\nimport re\n\ndef avg(li):\n print(li)\n result = 0;\n for val in li:\n result += val\n result /= len(li)\n return result\n\nfileName = sys.argv[1]\n\nvalues = []\n\nf = open(fileName + \"_processed\", \"w\")\niterCount = 0\n\nfor line in fileinput.input(fileName):\n start = re.search(r\"(?P[0-9]+\\.[0-9]+): start\", line)\n if start is not None:\n startTime = float(start.group(\"name\"))\n\n end = re.search(r\"(?P[0-9]+\\.[0-9]+): end\", line)\n if end is not None:\n endTime = float(end.group(\"name\"))\n runTime = endTime - startTime\n values.append(runTime)\n\n timer = re.search(r\"improve: [0-9]+ (?P[0-9]+) [0-9]+\", line)\n if timer is not None:\n interval = int(timer.group(\"name\"))\n f.write(\"time:\" + str(runTime) + \" interval:\" + str(interval) + \"\\n\")\n\n# Mean of last 5 vals\nf.write(\"mean:\" + str(avg(values)));\nprint(str(avg(values)))\n","sub_path":"experiment/timer_eval/parser_100.py","file_name":"parser_100.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"541155039","text":"abc_ru = [chr(i) for i in range(ord('а'), ord('я') + 1)]\r\nABC_RU = [chr(i) for i in range(ord('А'), ord('Я') + 1)]\r\nabc_en = [chr(i) for i in range(ord('a'), ord('z') + 1)]\r\nABC_EN = [chr(i) for i in range(ord('A'), ord('Z') + 1)]\r\n\r\ndef check_text(txt, lng):\r\n for i in txt:\r\n if lng == '1' and i.isalpha() and i not in abc_en and i not in ABC_EN:\r\n return False\r\n elif lng == '2' and i.isalpha() and i not in abc_ru and i not in ABC_RU:\r\n return False\r\n else:\r\n return True\r\n\r\ndef CeasarCode() :\r\n print('Добро пожаловать в программу \"Шифр Цезаря\"!\\nВыберете нужный Вам режим:', '1 - шифрование', '2 - дешифрование', '3 - взлом шифра без пароля', sep='\\n')\r\n while True:\r\n task = input()\r\n if task != '1' and task != '2' and task != '3':\r\n print('Пожалуйста, введите цифру \"1\", \"2\" или \"3\" для выбора режима:\\n1 - шифрование\\n2 - дешифрование\\n3 - взлом шифра без пароля\\n')\r\n else:\r\n break\r\n if task == '1':\r\n print('Режим \"шифрования\" активирован!')\r\n elif task == '2':\r\n print('Режим \"дешифрования\" активирован!')\r\n else:\r\n print('Режим взлома активирован!')\r\n print('Выберите алфавит кодировки для работы:', '1 - латиница', '2 - кириллица', sep='\\n')\r\n while True:\r\n language = input()\r\n if language != '1' and language != '2':\r\n print('Пожалуйста, введите корректные данные:\\n')\r\n else:\r\n break\r\n print('Введите текст:')\r\n while True:\r\n text = input()\r\n if check_text(text, language):\r\n break\r\n else:\r\n print('Текст не соответствует выбранному языку ввода. Попробуйте еще раз!')\r\n if task != '3':\r\n print('Введите пароль - шаг сдвига вправо')\r\n while True:\r\n shift = input()\r\n if not shift.isdigit():\r\n print('Пожалуйста, введите целое положительное число:\\n')\r\n else:\r\n shift = int(shift)\r\n break \r\n elif task == '3':\r\n print('Выберите подходящий вариант из представленных ниже расшифровок:')\r\n shift = None\r\n new_text = ''\r\n\r\n def main(task, language, text, shift, new_text) :\r\n if language == '1':\r\n borders = ['a', 'A', 'z', 'Z']\r\n total = 26\r\n abc_low = abc_en\r\n abc_high = ABC_EN\r\n elif language == '2':\r\n borders = ['а', 'А', 'я', 'Я']\r\n total = 32\r\n abc_low = abc_ru\r\n abc_high = ABC_RU\r\n if task == '1':\r\n for i in text:\r\n if i.isalpha():\r\n if i in abc_low and ord(i) + shift <= ord(borders[2]):\r\n new_text += chr(ord(i) + shift)\r\n elif i in abc_high and ord(i) + shift <= ord(borders[3]):\r\n new_text += chr(ord(i) + shift)\r\n else:\r\n new_text += chr(ord(i) + shift - total)\r\n else:\r\n new_text += i\r\n elif task == '2':\r\n for i in text:\r\n if i.isalpha():\r\n if i in abc_low and ord(i) - shift >= ord(borders[0]):\r\n new_text += chr(ord(i) - shift)\r\n elif i in abc_high and ord(i) - shift >= ord(borders[1]):\r\n new_text += chr(ord(i) - shift)\r\n else:\r\n new_text += chr(ord(i) - shift + total)\r\n else:\r\n new_text += i\r\n elif task == '3':\r\n for j in range(1, len(abc_low) + 1):\r\n new_text = ''\r\n for i in text:\r\n if i.isalpha():\r\n if i in abc_low and ord(i) - j >= ord(borders[0]):\r\n new_text += chr(ord(i) - j)\r\n elif i in abc_high and ord(i) - j >= ord(borders[1]):\r\n new_text += chr(ord(i) - j)\r\n else:\r\n new_text += chr(ord(i) - j + total)\r\n else:\r\n new_text += i\r\n print('Шаг сдвига = ', j, '||', 'зашифрованный текст - ', new_text) \r\n if task != '3':\r\n print('----------------------------\\nРезультат обработки текста: = ', new_text,'\\n----------------------------')\r\n\r\n main(task, language, text, shift, new_text)\r\n\r\nCeasarCode()\r\nwhile True:\r\n print()\r\n q = input('Хотите продолжить?\\nВведите 1 для повторного запуска программы\\nВведите 2 для выхода из программы\\n')\r\n if q not in '1, 2':\r\n print('Пожалуйста, введите цифру \"1\" для повтора или цифру \"2\" для выхода!\\n')\r\n elif q == '1':\r\n CeasarCode()\r\n else:\r\n break\r\n","sub_path":"Шифр Цезаря.py","file_name":"Шифр Цезаря.py","file_ext":"py","file_size_in_byte":5523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"348277656","text":"import json\n\nimport pandas as pd\nfrom ekstep_data_pipelines.common.dao.constants import (\n GET_UNIQUE_ID,\n IS_EXIST,\n COMMAND_WITH_LICENSE,\n COMMAND_WITHOUT_LICENSE,\n LICENSE,\n)\nfrom ekstep_data_pipelines.common.utils import get_logger\n\nLOGGER = get_logger(\"CatalogueDao\")\n\n\nclass CatalogueDao:\n def __init__(self, postgres_client):\n self.postgres_client = postgres_client\n\n def get_utterances(self, audio_id):\n parm_dict = {\"audio_id\": audio_id}\n utterances = self.postgres_client.execute_query(\n \"select utterances_files_list from media_metadata_staging where audio_id = :audio_id\",\n **parm_dict,\n )\n return json.loads(utterances[0][0]) if len(utterances) > 0 else []\n\n def get_valid_utterances_for_audio_id_and_stt(self, audio_id, stt_api, data_type):\n parm_dict = {\"audio_id\": audio_id, \"stt_api\": stt_api, \"data_type\": data_type}\n utterances = self.postgres_client.execute_query(\n \"select array_to_json(array_agg(r)) from (SELECT clipped_utterance_file_name as name,clipped_utterance_duration as duration,snr as snr_value,status as status from media_speaker_mapping where audio_id = :audio_id and staged_for_transcription = true and :stt_api != ALL(stt_api_used) \"\n \"and (COALESCE(data_type,'') = :data_type or data_type = :data_type))r\",\n **parm_dict,\n )[0][0]\n return utterances if utterances is not None else []\n\n def get_utterances_by_source(self, source, language, status, data_set):\n parm_dict = {\"source\": source, \"language\": language, \"status\": status, \"data_set\": data_set}\n data = self.postgres_client.execute_query(\n \"select speaker_id, clipped_utterance_file_name, clipped_utterance_duration, \"\n \"audio_id, snr \"\n \"from media_speaker_mapping \"\n \"where audio_id \"\n 'in (select audio_id from media_metadata_staging where \"source\" = :source and \"language\" = :language and (data_set_used_for IS NULL or data_set_used_for = :data_set)) '\n \"and status = :status \"\n \"and staged_for_transcription = false \"\n \"and clipped_utterance_duration >= 0.5 and clipped_utterance_duration <= 15\",\n **parm_dict,\n )\n return data\n\n def update_utterances(self, audio_id, utterances):\n update_query = (\n \"update media_metadata_staging \"\n \"set utterances_files_list = :utterances where audio_id = :audio_id\"\n )\n utterances_json_str = json.dumps(utterances)\n LOGGER.info(\"utterances_json_str:%s\", utterances_json_str)\n LOGGER.info(\"utterances:%s\", str(utterances))\n parm_dict = {\"utterances\": utterances_json_str, \"audio_id\": audio_id}\n self.postgres_client.execute_update(update_query, **parm_dict)\n return True\n\n def find_utterance_by_name(self, utterances, name):\n filtered_utterances = list(filter(lambda d: d[\"name\"] == name, utterances))\n if len(filtered_utterances) > 0:\n return filtered_utterances[0]\n else:\n return None\n\n def update_utterance_status(self, audio_id, utterance):\n update_query = (\n \"update media_speaker_mapping set status = :status, \"\n \"fail_reason = :reason,is_transcribed = (SELECT case when is_transcribed = TRUE then true else :is_transcribed end as e from unnest(ARRAY[is_transcribed])),stt_api_used =(select array_agg(distinct e) from unnest(stt_api_used || ARRAY[:stt_api_used]) e) where audio_id = :audio_id \"\n \"and clipped_utterance_file_name = :name\"\n )\n name = utterance[\"name\"]\n reason = utterance[\"reason\"]\n status = utterance[\"status\"]\n is_transcribed = utterance[\"is_transcribed\"]\n stt_api_used = utterance[\"stt_api\"]\n param_dict = {\n \"status\": status,\n \"is_transcribed\": is_transcribed,\n \"stt_api_used\": stt_api_used,\n \"reason\": reason,\n \"audio_id\": audio_id,\n \"name\": name,\n }\n self.postgres_client.execute_update(update_query, **param_dict)\n return True\n\n def update_audio_ids_with_data_type(self, source, language, audio_ids, data_set):\n if len(audio_ids) <= 0:\n return True\n\n update_query = (\n \"update media_metadata_staging set data_set_used_for = :data_set \"\n 'where \"source\" = :source and \"language\" = :language and audio_id in '\n )\n audio_ids = list(map(str, audio_ids))\n update_query = update_query + \"(\" + \",\".join(audio_ids) + \")\"\n param_dict = {\"source\": source, \"language\": language, \"data_set\": data_set}\n self.postgres_client.execute_update(update_query, **param_dict)\n return True\n\n def update_utterances_staged_for_transcription(self, utterances, source, language, data_set):\n if len(utterances) <= 0:\n return True\n\n update_query = (\n \"update media_speaker_mapping set staged_for_transcription = true,\"\n \"data_type = :data_set \"\n \"where audio_id in (select audio_id from media_metadata_staging \"\n 'where \"source\" = :source and \"language\" = :language) and clipped_utterance_file_name in '\n )\n utterance_names = list(map(lambda u: f\"'{u[1]}'\", utterances))\n update_query = update_query + \"(\" + \",\".join(utterance_names) + \")\"\n param_dict = {\"source\": source, \"language\": language, \"data_set\": data_set}\n self.postgres_client.execute_update(update_query, **param_dict)\n return True\n\n def get_unique_id(self):\n return self.postgres_client.execute_query(GET_UNIQUE_ID)[0][0]\n\n def check_file_exist_in_db(self, file_name, hash_code):\n\n param_dict = {\"file_name\": file_name, \"hash_code\": hash_code}\n return self.postgres_client.execute_query(IS_EXIST, **param_dict)[0][0]\n\n def upload_file(self, meta_data_path):\n \"\"\"\n Uploading the meta data file from local to\n \"\"\"\n db = self.postgres_client.db\n\n with open(meta_data_path, \"r\") as file:\n dataframe = pd.read_csv(meta_data_path)\n columns = dataframe.columns\n cmd = COMMAND_WITHOUT_LICENSE\n if LICENSE in columns:\n cmd = COMMAND_WITH_LICENSE\n conn = db.raw_connection()\n cursor = conn.cursor()\n cursor.copy_expert(cmd, file)\n conn.commit()\n\n def upload_file_to_downloaded_source(self, file_path):\n\n db_conn = self.postgres_client.db\n\n LOGGER.info(\"uploading data to source_metadata\")\n with open(file_path, \"r\") as file:\n conn = db_conn.raw_connection()\n cursor = conn.cursor()\n cmd = (\n \"COPY source_metadata_downloaded(source,num_speaker,total_duration,num_of_audio)\"\n \" FROM STDIN WITH (FORMAT CSV, HEADER)\"\n )\n cursor.copy_expert(cmd, file)\n conn.commit()\n\n def insert_speaker(self, source, speaker_name):\n param_dict = {\"speaker_name\": speaker_name, \"source\": source}\n insert_query = (\n \"insert into speaker (source, speaker_name) values (:source, :speaker_name)\"\n )\n self.postgres_client.execute_update(insert_query, **param_dict)\n return True\n\n def update_utterance_speaker(\n self, utterance_file_names, speaker_name, was_noise=False\n ):\n update_query = (\n \"update media_speaker_mapping \"\n \"set speaker_id=(select speaker_id from speaker where speaker_name=:speaker_name \"\n \"limit 1) \"\n \", was_noise=:was_noise \"\n \"where clipped_utterance_file_name in \"\n )\n utterance_names = list(map(lambda u: f\"'{u}'\", utterance_file_names))\n update_query = update_query + \"(\" + \",\".join(utterance_names) + \")\"\n param_dict = {\"speaker_name\": speaker_name, \"was_noise\": was_noise}\n self.postgres_client.execute_update(update_query, **param_dict)\n return True\n\n def select_speaker(self, speaker_name, source):\n param_dict = {\"speaker_name\": speaker_name, \"source\": source}\n sql = \"select speaker_id from speaker where speaker_name=:speaker_name and source=:source\"\n result = self.postgres_client.execute_query(sql, **param_dict)\n return result[0][0] if len(result) > 0 else -1\n\n def update_utterance_speaker_gender(self, utterance_file_names, speaker_gender):\n update_query = (\n \"update media_speaker_mapping \"\n \"set speaker_gender=:speaker_gender \"\n \" where clipped_utterance_file_name in \"\n )\n\n utterance_names = list(map(lambda u: f\"'{u}'\", utterance_file_names))\n update_query = update_query + \"(\" + \",\".join(utterance_names) + \")\"\n\n param_dict = {\"speaker_gender\": speaker_gender}\n\n self.postgres_client.execute_update(update_query, **param_dict)\n return True\n\n def get_utterance_details_by_source(self, source, language, count, is_transcribed, is_labelled, include_rejected):\n\n is_transcribed_check = \"msp.is_transcribed = :is_transcribed\" if is_transcribed \\\n else \"(msp.is_transcribed = :is_transcribed or msp.is_transcribed is null)\"\n\n is_labelled_artifact_check = \"msp.labelled_artifact_name is null\" if is_labelled \\\n else \"msp.unlabelled_artifact_name is null\"\n\n status = \"('Clean','Rejected')\" if include_rejected else \"('Clean')\"\n\n parm_dict = {\"source\": source, \"status\": status, \"language\": language, \"count\": count,\n \"is_transcribed\": is_transcribed}\n\n query = f\"\"\"\n select msp.clipped_utterance_file_name as audio_file_name, \n msp.clipped_utterance_duration as duration, msp.snr , s.speaker_name, \n mms.source_url as collection_source , mms.source_website as main_source, msp.speaker_gender as gender,\n msp.audio_id, msp.status\n from media_speaker_mapping msp \n inner join media_metadata_staging mms \n on msp.audio_id = mms.audio_id\n left outer join speaker s \n on s.speaker_id = msp.speaker_id \n where mms.source = :source and mms.language=:language and msp.status in {status} and msp.staged_for_transcription = true\n and {is_transcribed_check} and {is_labelled_artifact_check}\n limit :count\n \"\"\"\n print(\"query:\", query)\n data = self.postgres_client.execute_query(query, **parm_dict)\n return data\n\n def update_utterance_artifact(self, utterance_file_names, artifact_name, is_labelled, audio_id):\n set_artifact = \"labelled_artifact_name=:artifact_name\" if is_labelled \\\n else \"unlabelled_artifact_name=:artifact_name\"\n update_query = (\n f\"\"\"update media_speaker_mapping\n set {set_artifact}\n where audio_id=:audio_id and clipped_utterance_file_name in \"\"\"\n )\n\n utterance_names = list(map(lambda u: f\"'{u}'\", utterance_file_names))\n update_query = update_query + \"(\" + \",\".join(utterance_names) + \")\"\n print(\"query:\", update_query)\n\n param_dict = {\"artifact_name\": artifact_name, \"audio_id\": audio_id}\n\n self.postgres_client.execute_update(update_query, **param_dict)\n return True\n","sub_path":"packages/ekstep_data_pipelines/common/dao/catalogue_dao.py","file_name":"catalogue_dao.py","file_ext":"py","file_size_in_byte":11412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"12634408","text":"import json\nimport unittest\nfrom unittest import mock\nfrom django.test import RequestFactory\nfrom django.conf import settings\nfrom api import views, middleware\n\nsettings.configure()\n\n\nclass TestStringMethods(unittest.TestCase):\n\n def setUp(self):\n self.factory = RequestFactory()\n self.middleware = middleware.ViewExceptionMiddleware()\n\n def process_request(self, request):\n \"\"\"\n helper method for making POST request to /avtale\n as well as running exceptions through middleware\n \"\"\"\n try:\n response = views.avtale(request)\n response_json = json.loads(response.content.decode())\n return response, response_json\n except Exception as e:\n response = self.middleware.process_exception(e)\n try:\n response_json = json.loads(response.content.decode())\n return response, response_json\n except:\n return response, None\n\n @mock.patch('api.services.fagsystem.opprett_kunde')\n @mock.patch('api.services.fagsystem.opprett_avtale')\n @mock.patch('api.services.fagsystem.oppdater_avtale')\n @mock.patch('api.services.send_email')\n def test_ok_response(self, send_email, oppdater_avtale, opprett_avtale, opprett_kunde):\n # mock\n request = self.factory.post(path='/avtale')\n opprett_kunde.return_value = ('2222', 'mail@mail.com')\n opprett_avtale.return_value = '3333'\n oppdater_avtale.return_value = True\n send_email.return_value = 'avtale sendt'\n\n # make request\n response, response_json = self.process_request(request)\n\n\n # assert 200 OK\n self.assertEquals(response.status_code, 200, 'OK')\n\n # assert json body\n self.assertAlmostEquals(response_json.get('avtalenummer'), '3333', 'avtale nummer er riktig')\n self.assertAlmostEquals(response_json.get('status'), 'avtale sendt', 'status er riktig')\n\n # assert mail was sendt\n send_email.assert_called_with('mail@mail.com', '3333')\n\n def test_503_if_service_unavailable(self):\n # mock, but let fagsystem attempt request to nowhere\n request = self.factory.post(path='/avtale')\n\n # make request\n response, response_json = self.process_request(request)\n self.assertEquals(response.status_code, 503)\n\n @mock.patch('api.services.fagsystem.opprett_kunde')\n @mock.patch('api.services.fagsystem.opprett_avtale')\n @mock.patch('api.services.fagsystem.put')\n @mock.patch('api.services.send_email')\n def test_could_not_update_avtale(self, send_email, http_put, opprett_avtale, opprett_kunde):\n request = self.factory.post(path='/avtale')\n opprett_kunde.return_value = ('2222', 'mail@mail.com')\n opprett_avtale.return_value = '3333'\n send_email.return_value = 'avtale sendt'\n\n # Mock http PUT to return with HTTP STATUS 400, thereby failing\n http_put.return_value = (True, 400)\n\n # make request\n response, response_json = self.process_request(request)\n\n self.assertAlmostEqual(response_json.get('status'), 'Avtale sendt, men vi klarte ikke oppdatere databasen vår. Kontakt support')\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"523683692","text":"import pandas as pd\ndef read_data(path,input_col=None,**kwargs):\n \"\"\"\n \n This funtion takes in a file path - CSV, excel or parquet and reads the data\n based on the input columns specified \n \n Returns:\n dataset to be used for training\n \n \"\"\"\n \n if path.endswith('.csv'):\n data = pd.read_csv(path, usecols = input_col)\n print('CSV file read sucessfully')\n data = data.reindex(columns = input_col)\n return data\n \n elif path.endswith('.parquet'):\n data = pd.read_parquet(path, engine = 'pyarrow', columns = input_col)\n print('Parquet file read sucessfully')\n data.columns = data.columns.astype(str)\n data = data.reindex(columns = input_col)\n return data\n \n elif path.endswith('.xls'):\n data = pd.read_excel(path, usecols = input_col)\n print('Excel file read success')\n data = data.reindex(columns = input_col)\n return data\n \n else:\n return ('No CSV file or Parquet file or Excel file was passed')\n ","sub_path":"Catboost-local/src/features/raw.py","file_name":"raw.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"622351923","text":"class RingBuffer:\n\tdef __init__(self, capacity):\n\t\tself.capacity = capacity\n\t\tself.current = 0\n\t\tself.storage = [None]*capacity\n\n\tdef append(self, item):\n\t\tfor i in range(0, len(self.storage)):\n\t\t\tif self.storage[i] is None:\n\t\t\t\t# print('in if')\n\t\t\t\tself.storage[i] = item\n\t\t\t\treturn\n\n\t\tif not None in self.storage:\n\t\t\tself.storage[self.current] = item\n\t\t\n\t\tself.current += 1\n\n\t\tif self.current is self.capacity:\n\t\t\tself.current = 0\n\n\tdef get(self):\n\t\tnew_list = [item for item in self.storage if item is not None]\n\t\t\n\t\treturn new_list\n\n\n# r = RingBuffer(5)\n\n# r.append('a')\n# r.append('b')\n# r.append('c')\n# r.append('d')\n# r.append('e')\n# # Past Capacity - should remove oldest (A)\n# r.append('f') # get should return ['f', 'b', 'c', 'd', 'e']\n# r.append('g') # get should return ['f', 'g', 'c', 'd', 'e']\n# r.append('h') # get should return ['f', 'g', 'h', 'd', 'e']\n\n# print(r.storage)\n# print(r.get())","sub_path":"ring_buffer/ring_buffer.py","file_name":"ring_buffer.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"278373483","text":"#!/usr/bin/python3\n\"\"\"\nlibraryfs - Library-like filesystems for Python-LLFUSE\n\nCopyright © Christof Hanke \n\nPermission is hereby granted, free of charge, to any person obtaining a copy of\nthis software and associated documentation files (the \"Software\"), to deal in\nthe Software without restriction, including without limitation the rights to\nuse, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\nthe Software, and to permit persons to whom the Software is furnished to do so.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\nFOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\nCOPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\nIN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\nCONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\"\"\"\n\nfrom argparse import ArgumentParser\nimport llfuse\nimport logging\nimport os\nimport yaml\n\nfrom Libfs.misc import get_available_plugins\nfrom Libfs.business_logic import BusinessLogic\nfrom Libfs.operations import Operations\nimport faulthandler\n\nfaulthandler.enable()\n\nLOGGER = logging.getLogger(__name__)\n\ndef main():\n \"\"\"\n main (only) script to use libfs\n \"\"\"\n parser = ArgumentParser()\n subparsers = parser.add_subparsers(dest='subparser_name', help='sub-command help')\n parser_mount = subparsers.add_parser('mount', help='mount a libfs')\n parser_update = subparsers.add_parser('update', help='update a library')\n #\n # options for mount subcommand\n #\n parser_mount.add_argument('library', type=str,\n help='Library file for the views')\n parser_mount.add_argument('--debug_fuse', action='store_true',\n help='debug fuse')\n parser_mount.add_argument('mountpoint', type=str,\n help='Where to mount the file system')\n #\n # options for update subcommand\n #\n\n parser_update.add_argument('source', type=str,\n help='Data directory to scan')\n parser_update.add_argument('library', type=str,\n help='Library file for the views')\n parser_update.add_argument(\"--type\", type=str, required=True,\n choices=get_available_plugins(),\n help=\"type of library fos scanning.\")\n parser_update.add_argument(\"--remove_obsolete\", action='store_true',\n help=\"remove entries from db which are not found under source\")\n #\n # common options\n #\n parser.add_argument('--logconf', type=str,\n help='path to a YAML logging configuration file')\n parser.add_argument('--view', type=str,\n help='name of the view (virtual directory structure) to use.')\n\n options = parser.parse_args()\n\n if options.logconf:\n with open(options.logconf, \"r\") as f:\n logging_dict = yaml.load(f)\n logging.config.dictConfig(logging_dict)\n\n #\n # mount libfs\n #\n\n if options.subparser_name == 'mount':\n LOGGER.debug('Mounting...')\n fuse_options = set(llfuse.default_options)\n fuse_options.add('fsname=libraryfs')\n fuse_options.add('default_permissions')\n if options.debug_fuse:\n fuse_options.add('debug')\n\n operations = Operations(options.library, options.mountpoint, options.view)\n llfuse.init(operations, options.mountpoint, fuse_options)\n try:\n LOGGER.debug('Entering main loop..')\n llfuse.main(workers=1)\n except:\n llfuse.close(unmount=False)\n raise\n LOGGER.debug('Umounting..')\n llfuse.close()\n elif options.subparser_name == 'update':\n\n #\n # update library\n #\n from importlib import import_module\n\n plugin = import_module(\"Libfs.plugins.%s\" % options.type)\n magix = {}\n magix[\"valid_keys\"] = plugin.get_valid_keys()\n magix[\"default_view\"] = plugin.get_default_view()\n magix[\"plugin\"] = options.type\n bl = BusinessLogic(options.library, magix=magix)\n for root, dirs, files in os.walk(options.source):\n for f in files:\n full_path = os.path.abspath(\"%s/%s\" % (root, f))\n try:\n metadata = plugin.read_metadata(full_path)\n except Exception as excep:\n LOGGER.warning(\"cannot read metadata of file: %s. Exception=%s\", full_path, excep)\n continue\n bl.add_entry(full_path, metadata)\n # remove obsolete entries, if desired\n # useful for updating\n if options.remove_obsolete:\n for src_name in bl.get_all_src_names():\n if not os.path.exists(src_name):\n bl.remove_entry(src_name)\n else: # should never arrive here\n parser.error(\"No command given\")\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"scripts/libfs.py","file_name":"libfs.py","file_ext":"py","file_size_in_byte":5124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"36557654","text":"import os\nimport pickle\nimport hashlib\nimport numpy as np\nimport cv2\n\ndef get_camera_calibration_params(filenames, chessboard_shape=(9,6)):\n \"\"\"Given a list of calibration images, extract calibration parameters.\n Inspired to https://github.com/udacity/CarND-Camera-Calibration/blob/master/camera_calibration.ipynb\n \"\"\"\n # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)\n objp = np.zeros((np.product(chessboard_shape), 3), np.float32)\n objp[:,:2] = np.mgrid[0:chessboard_shape[0], 0:chessboard_shape[1]].T.reshape(-1,2)\n\n # Arrays to store object points and image points from all the images.\n objpoints = [] # 3d points in real world space\n imgpoints = [] # 2d points in image plane.\n\n # Step through the list and search for chessboard corners\n for fname in filenames:\n img = cv2.imread(fname)\n gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n img_size = gray.shape[::-1]\n\n # Find the chessboard corners\n ret, corners = cv2.findChessboardCorners(gray, chessboard_shape, None)\n\n # If found, add object points, image points\n if ret == True:\n objpoints.append(objp)\n imgpoints.append(corners)\n\n # Do camera calibration given object points and image points\n ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)\n return {'dist': dist, 'mtx': mtx}\n\ndef undistort(image, params):\n return cv2.undistort(image, params['mtx'], params['dist'], None, params['mtx'])\n\nclass Calibration(object):\n \"\"\"Class for computing, caching and applying calibration parameters.\"\"\"\n def __init__(self, filenames, chessboard_shape=(9,6), cache_dir=os.path.curdir):\n hash = hashlib.md5(str(sorted(filenames)).encode()).hexdigest()\n self.cache = os.path.join(cache_dir, \"calibration-%s.P\" % hash)\n try:\n with open(self.cache, 'rb') as f:\n self.params = pickle.load(f)\n except:\n self.params = None\n\n if not self.params:\n self.params = get_camera_calibration_params(filenames, chessboard_shape)\n with open(self.cache, 'wb') as f:\n pickle.dump(self.params, f)\n\n def __call__(self, image):\n return undistort(image, self.params)\n\nclass PerspectiveTransformation(object):\n def __init__(self, corners, dest_corners=None, input_shape=None, output_shape=None):\n src = np.array(corners, dtype='float32')\n if dest_corners is not None:\n dst = np.array(dest_corners, dtype='float32')\n else:\n # define destination points as the bounding box\n dst = np.array([(src[...,0].min(), src[...,1].max()),\n (src[...,0].min(), 0),\n (src[...,0].max(), 0),\n (src[...,0].max(), src[...,1].max())],\n dtype='float32')\n # get the transform matrix\n self.M = cv2.getPerspectiveTransform(src, dst)\n self.source_corners = src\n self.dest_corners = dst\n self._inverse = None\n self.input_shape = input_shape\n self.output_shape = output_shape\n\n @property\n def inverse(self):\n if self._inverse is None:\n self._inverse = PerspectiveTransformation(self.dest_corners, self.source_corners,\n self.output_shape, self.input_shape)\n self._inverse._inverse = self\n return self._inverse\n\n def __call__(self, img, y=None):\n \"\"\"Apply perspective transformation to an image or to points.\n If y is None, warp the image img. Otherwise, apply transformation to\n points with coordinates img,y.\n \"\"\"\n if y is None:\n return cv2.warpPerspective(img, self.M, (self.output_shape or img.shape)[1::-1])\n else:\n x, y, w = np.broadcast_arrays(img, y, 1)\n x, y, w = np.dot(self.M, np.stack((x, y, w)))\n return x/w, y/w\n","sub_path":"camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":4017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"164649821","text":"from getinput import get_input\nfrom wizard import *\nimport heapq\nimport itertools\nimport copy\n\n\ndef get_min_mana(hard_mode):\n attempts = [Fight(hard_mode)]\n while attempts[0].enemy_hp > 0:\n fight = heapq.heappop(attempts)\n for next_spell in PlayerAction:\n new_fight = copy.copy(fight) # type: Fight\n try:\n new_fight.do_turn(next_spell)\n heapq.heappush(attempts, new_fight)\n except BadAction:\n continue\n return attempts[0].spent_mana\n\n\ndef part_1():\n return get_min_mana(hard_mode=False)\n\n\ndef part_2():\n return get_min_mana(hard_mode=True)\n\n\nif __name__ == \"__main__\":\n print('Part 1:', part_1())\n print('Part 2:', part_2())\n","sub_path":"2015/day22.py","file_name":"day22.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"442211103","text":"# c11ex06.py\n# Algorithm to shuffle a list (simulates random.shuffle)\n\nfrom random import randrange\n\ndef shuffle(lst):\n for i in range(len(lst)):\n j = randrange(i,len(lst))\n lst[i], lst[j] = lst[j], lst[i]\n\ndef main():\n lst1 = list(range(30))\n shuffle(lst1)\n print(\"This should be in random order.\")\n print(lst1)\n \nif __name__ == '__main__':\n main()\n","sub_path":"code/chapter11/c11ex06.py","file_name":"c11ex06.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"57201566","text":"from tkinter import *\nfrom tkinter.messagebox import showinfo,showwarning,showerror\nfrom functools import partial as ptl\n\nCRIT='Crit'\nWARN='Warn'\nINFO='Info'\n\nSIGHS={\n 'do not enter':CRIT,\n 'roading crossing':INFO,\n '55/n speed limit':WARN,\n 'wrong way':CRIT,\n 'merging traffic':INFO,\n 'one way':WARN\n}\n\nroot=Tk()\nroot.title(\"traffic informantion\")\n\ncritCB=lambda : showerror('Error','Error Button Pressed')\nwarnCB=lambda : showwarning('Warn','Warning Button Pressed')\ninfoCB=lambda : showinfo('Info','Information Button Pressed')\n\n\nquitButton=Button(root,text='QUIT',command=root.quit,fg='green',bg='red')\nquitButton.pack()\n\nparentButton=ptl(Button,root)\nCritButton=ptl(parentButton,command=critCB,fg='red',bg='white')\nInfoButton=ptl(parentButton,command=warnCB,fg='black',bg='white')\nWarnButton=ptl(parentButton,command=infoCB,fg='black',bg='goldenrod')\n\nfor sign in SIGHS:\n sign_value=SIGHS[sign]\n cmd='%sButton(text=%r%s).pack(fill=X,expand=YES)'%(sign_value,sign,'.upper()' if sign_value==CRIT else '.title()')\n print(cmd)\n eval(cmd)\n\nroot.mainloop()\n","sub_path":"tkinter/pfaGUI3.py","file_name":"pfaGUI3.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"460451861","text":"#!/usr/bin/env python3\n\n# this script will copy the project's resources (pngs, ttfs, svgs etc) to the correct place\n# depending on the value of PLUG_SHARED_RESOURCES in config.h\n# resources can either be copied into the plug-in bundle or into a shared path\n# since the shared path should be accesible from the mac app sandbox,\n# the path used is ~/Music/SHARED_RESOURCES_SUBPATH\n\nimport os, sys, shutil\n\nscriptpath = os.path.dirname(os.path.realpath(__file__))\nprojectpath = os.path.abspath(os.path.join(scriptpath, os.pardir))\n\nIPLUG2_ROOT = \"../../iPlug2\"\n\nsys.path.insert(0, os.path.join(os.getcwd(), IPLUG2_ROOT + '/Scripts'))\n\nfrom parse_config import parse_config\n\ndef main():\n config = parse_config(projectpath)\n\n print(\"Copying resources ...\")\n\n if config['PLUG_SHARED_RESOURCES']:\n dst = os.path.expanduser(\"~\") + \"/Music/\" + config['SHARED_RESOURCES_SUBPATH'] + \"/Resources\"\n else:\n dst = os.environ[\"TARGET_BUILD_DIR\"] + os.environ[\"UNLOCALIZED_RESOURCES_FOLDER_PATH\"]\n\n if os.path.exists(dst) == False:\n os.makedirs(dst + \"/\", 0o0755 )\n\n if os.path.exists(projectpath + \"/resources/img/\"):\n imgs = os.listdir(projectpath + \"/resources/img/\")\n for img in imgs:\n print(\"copying \" + img + \" to \" + dst)\n shutil.copy(projectpath + \"/resources/img/\" + img, dst)\n\n if os.path.exists(projectpath + \"/resources/fonts/\"):\n fonts = os.listdir(projectpath + \"/resources/fonts/\")\n for font in fonts:\n print(\"copying \" + font + \" to \" + dst)\n shutil.copy(projectpath + \"/resources/fonts/\" + font, dst)\n\nif __name__ == '__main__':\n main()\n","sub_path":"PDSynth/scripts/prepare_resources-mac.py","file_name":"prepare_resources-mac.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"410883630","text":"import torch\nfrom torch.utils.data import Dataset\nimport utils\nimport numpy as np\nimport NNClass as nnClass\nimport cPickle as pickle\nimport NNTrainer\n\n\ndef get_train_dataset(tagger_type):\n sentences = pickle.load(open(tagger_type + \"TrainSentences.p\", \"rb\"))\n data, tag = utils.window_data_and_tag(sentences)\n x, y = np.asarray(data, np.float32), np.asarray(tag, np.int32)\n x, y = torch.from_numpy(x), torch.from_numpy(y)\n x, y = x.type(torch.LongTensor), y.type(torch.LongTensor)\n return torch.utils.data.TensorDataset(x, y)\n\n\ndef get_dev_dataset(tagger_type):\n sentences = pickle.load(open(tagger_type + \"DevSentences.p\", \"rb\"))\n data, tag = utils.window_data_and_tag(sentences)\n x, y = np.asarray(data, np.float32), np.asarray(tag, np.int32)\n x, y = torch.from_numpy(x), torch.from_numpy(y)\n x, y = x.type(torch.LongTensor), y.type(torch.LongTensor)\n return torch.utils.data.TensorDataset(x, y)\n\n\ndef get_test_dataset(tagger_type):\n sentences = pickle.load(open(tagger_type + \"TestSentences.p\", \"rb\"))\n data = utils.window_data(sentences)\n x = np.asarray(data, np.float32)\n x = torch.from_numpy(x)\n x = x.type(torch.LongTensor)\n return x\n\n\ndef init_sets(tagger_type):\n tag_set = pickle.load(open(tagger_type + \"TrainTagSet.p\", \"rb\"))\n word_set = pickle.load(open(tagger_type + \"TrainWordSet.p\", \"rb\"))\n utils.init_words_and_tags(word_set, tag_set)\n\n\nif __name__ == '__main__':\n # train NER\n init_sets(\"NER\")\n train_data = get_train_dataset(\"NER\")\n dev_data = get_dev_dataset(\"NER\")\n test_data = get_test_dataset(\"NER\")\n\n model = nnClass.NET()\n trainer = NNTrainer.ModelTrainer(model, train_data, dev_data, test_data, \"NER\")\n trainer.run()\n\n # train NER\n init_sets(\"POS\")\n train_data = get_train_dataset(\"POS\")\n dev_data = get_dev_dataset(\"POS\")\n test_data = get_test_dataset(\"POS\")\n\n model = nnClass.NET()\n trainer = NNTrainer.ModelTrainer(model, train_data, dev_data, test_data, \"POS\")\n trainer.run()\n\n print(\"finish tagger1\")\n","sub_path":"NLP/ass2submit/tagger1.py","file_name":"tagger1.py","file_ext":"py","file_size_in_byte":2045,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"279886705","text":"from collections import defaultdict\nfrom fifo_cache import *\nimport math\nimport bisect\nimport random\nimport operator\nimport sys\n\nclass objPosVirtual:\n def __init__(self, grid_s):\n self.cache = defaultdict(lambda : defaultdict(lambda : defaultdict(list)))\n self.virtual_to_real = defaultdict(lambda : defaultdict(lambda : defaultdict()))\n self.grid = grid_s\n\n def checkIfInGrid(self, v):\n if v[0] >= 0 and v[0] <= self.grid[0] and v[1] >= 0 and v[1] <= self.grid[1]:\n return True\n else:\n return False\n\n def findOriginalPoint(self, v, mapped_x, mapped_y):\n x = 0\n y = 0\n\n if v[0] >= self.grid[0] and mapped_x == True:\n x = round(v[0] - self.grid[0],3)\n elif v[0] <= 0 and mapped_x == True:\n x = round(v[0] + self.grid[0],3)\n else :\n x = round(v[0],3)\n\n if v[1] >= self.grid[1] and mapped_y == True:\n y = round(v[1] - self.grid[1],3)\n elif v[1] <= 0 and mapped_y == True:\n y = round(v[1] + self.grid[1],3)\n else :\n y = round(v[1],3)\n\n return np.array([x,y,v[2]])\n \n def insert(self, point, score, real_object):\n\n def check(test,array):\n return any(np.array_equal([x[0], x[1], x[2]], test) for x in array)\n\n\n if self.checkIfInGrid(point) == True:\n\n if check(point, self.cache[int(point[0])][int(point[1])][int(point[2])]) == True:\n return [point, False]\n else:\n self.virtual_to_real[point[0]][point[1]][point[2]] = real_object\n for s in score:\n point = np.append(point, s)\n\n if point[0] < 0: \n print(\"Inserting a negative point 1\")\n\n self.cache[int(point[0])][int(point[1])][int(point[2])].append(point) \n\n return [point, True]\n else :\n new_point = self.findOriginalPoint(point, True, True)\n\n if check(new_point, self.cache[int(new_point[0])][int(new_point[1])][int(new_point[2])]) == True:\n return [point, False]\n else :\n self.virtual_to_real[new_point[0]][new_point[1]][new_point[2]] = real_object\n\n\n if new_point[0] < 0:\n print(\"Inserting a negative point 2\")\n ### Need to check what this means - there are three scores\n for s in [-2, -1]:\n new_point = np.append(new_point, point[s])\n\n\n if len(new_point) < 5:\n print(\"Inserting inappropriate point 2\")\n\n self.cache[int(new_point[0])][int(new_point[1])][int(new_point[2])].append(new_point) \n\n return [point, True]\n\n\n def delete(self, point, mapped_x, mapped_y, peak = False):\n\n if mapped_x == False and mapped_y == False:\n real_obj = self.virtual_to_real[point[0]][point[1]][point[2]]\n\n ## Clear from the map\n if peak == False:\n try:\n del self.virtual_to_real[point[0]][point[1]][point[2]]\n except :\n print(\"Real object : \", real_obj)\n \n \n score = [x[3:] for x in list(self.cache[int(point[0])][int(point[1])][int(point[2])]) if x[0] == point[0] and x[1] == point[1] and x[2] == point[2]]\n\n if peak == False :\n self.cache[int(point[0])][int(point[1])][int(point[2])] = [x for x in list(self.cache[int(point[0])][int(point[1])][int(point[2])]) if x[0] != point[0] or x[1] != point[1] or x[2] != point[2]]\n\n return [real_obj, score, point] \n else:\n new_point = self.findOriginalPoint(point, mapped_x, mapped_y)\n #print(\"Do i ever delete from here ? \",new_point, mapped_x, mapped_y, point, self.virtual_to_real[int(new_point[0])][int(new_point[1])], self.virtual_to_real[int(point[0])][int(point[1])])\n \n real_obj = self.virtual_to_real[new_point[0]][new_point[1]][new_point[2]]\n\n if peak == False:\n try:\n del self.virtual_to_real[new_point[0]][new_point[1]][new_point[2]]\n except:\n print(\"Real object : \", real_obj)\n \n\n score = [x[3:] for x in list(self.cache[int(new_point[0])][int(new_point[1])][int(new_point[2])]) if x[0] == new_point[0] and x[1] == new_point[1] and x[2] == new_point[2]]\n\n if peak == False:\n self.cache[int(new_point[0])][int(new_point[1])][int(new_point[2])] = [x for x in list(self.cache[int(new_point[0])][int(new_point[1])][int(new_point[2])]) if x[0] != new_point[0] or x[1] != new_point[1] or x[2] != new_point[2]]\n\n return [real_obj, score, new_point] \n\n\n def resetScore(self, curr):\n curr += 3\n for a in self.cache:\n for b in self.cache[a]:\n for c in self.cache[a][b]: \n for i in range(len(self.cache[a][b][c])):\n self.cache[a][b][c][i][curr] = 0\n\n# for p in self.cache[a][b]:\n# p[curr] = 0\n\n\n def getRealObject(self, point):\n return self.virtual_to_real[point[0]][point[1]][point[2]]\n\n\n def printCacheContents(self, policy, curr, f):\n no_objects = 0\n for a in self.cache:\n for b in self.cache[a]:\n for c in self.cache[a][b]:\n for o in self.cache[a][b][c]:\n #print(o)\n no_objects += 1\n o = self.getRealObject(o)\n f.write(str(o[0]) + \" \" + str(o[1]) + \" \" + str(o[2]))\n f.write(\"\\n\")\n f.flush()\n\n\n f.close()\n return no_objects\n\nclass CacheGridReal():\n\n def __init__(self, capacity, dim, learning_rate, integral=False, grid_s=[313,313,313], policy=\"lfu\"):\n self.cache_size = 0\n self.curr_rotation = 0\n self.grid = grid_s\n self.obj_count = 0\n\n if policy == \"dual\" or policy == \"fifo\":\n self.freq_map = [defaultdict(list), defaultdict(list)]\n self.freq_sorted_list = [[], []]\n self.fifo = FIFO(2)\n else :\n self.freq_map = defaultdict(list)\n self.freq_sorted_list = [] \n \n\n def getUsefulness(self, policy, curr): \n if policy == \"lru\":\n sum_score = sum(self.freq_sorted_list)\n else:\n sum_score = sum(self.freq_sorted_list[curr])\n \n return sum_score\n\n def getCurrent(self):\n return self.curr_rotation\n\n def initializeIterativeSearch(self, dim):\n self.obj_pos = objPosVirtual(self.grid)\n\n def insertInit(self, obj, iter, policy):\n if policy == \"lfu\" :\n\n [point, success] = self.obj_pos.insert(obj, [0], obj)\n\n if success == True:\n if 0 not in self.freq_map:\n self.freq_map[0] = []\n \n self.obj_count += 1\n\n self.freq_map[0].append(obj)\n bisect.insort(self.freq_sorted_list, 0)\n\n elif policy == \"lru\" :\n\n [point, success] = self.obj_pos.insert(obj, [iter], obj)\n\n if success == True:\n \n self.obj_count += 1\n\n if iter not in self.freq_map:\n self.freq_map[iter] = []\n\n self.freq_map[iter].append(obj)\n bisect.insort(self.freq_sorted_list, iter)\n\n elif policy == \"dual\" or policy == \"fifo\":\n\n [point, success] = self.obj_pos.insert(obj, [0,0], obj)\n\n if success == True:\n self.obj_count += 1\n\n for iter in [0,1]:\n self.freq_map[iter][0].append(obj)\n bisect.insort(self.freq_sorted_list[iter], 0)\n\n \n def get_mapped_points(self, v):\n #print(\"Length(v) : \" ,len(v))\n if v[0] <= float(self.grid[0]/2) and v[0] >= 0:\n\n if v[1] <= float(self.grid[1]/2) and v[1] >= 0:\n\n first_point = np.array([round(v[0] + self.grid[0], 3), round(v[1],3), v[2]])\n second_point = np.array([round(v[0], 3), round(v[1] + self.grid[1],3), v[2]])\n third_point = np.array([round(v[0] + self.grid[0],3), round(v[1] + self.grid[1],3), v[2]])\n else:\n if v[1] > float(self.grid[1]/2) and v[1] <= self.grid[1]:\n first_point = np.array([round(v[0] + self.grid[0],3), round(v[1],3), v[2]])\n second_point = np.array([round(v[0],3), round(v[1] - self.grid[1],3), v[2]])\n third_point = np.array([round(v[0] + self.grid[0],3), round(v[1] - self.grid[1],3), v[2]])\n else :\n if v[0] > float(self.grid[0]/2) and v[0] <= self.grid[0]:\n if v[1] <= float(self.grid[1]/2) and v[1] >= 0:\n first_point = np.array([round(v[0] - self.grid[0],3), round(v[1],3), v[2]])\n second_point = np.array([round(v[0],3) , round(v[1] + self.grid[1],3), v[2]])\n third_point = np.array([round(v[0] - self.grid[0],3), round(v[1] + self.grid[1],3), v[2]])\n else: \n if v[1] > float(self.grid [1]/2) and v[1] <= self.grid[1]:\n first_point = np.array([round(v[0] - self.grid[0],3), round(v[1],3), v[2]])\n second_point = np.array([round(v[0],3), round(v[1] - self.grid[1],3), v[2]])\n third_point = np.array([round(v[0] - self.grid[0],3), round(v[1] - self.grid[1],3), v[2]])\n\n\n first_point = np.append(first_point, v[3:])\n second_point = np.append(second_point, v[3:])\n third_point = np.append(third_point, v[3:])\n\n return np.array([first_point, second_point, third_point])\n\n\n def resetScore(self):\n i = self.curr_rotation\n self.freq_sorted_list[i] = [0 for x in self.freq_sorted_list[i]]\n\n to_delete = []\n\n objects_to_append = []\n\n for s in self.freq_map[i]:\n if s == 0:\n continue\n\n objects_to_append.extend(self.freq_map[i][s])\n to_delete.append(s)\n \n for o in objects_to_append:\n self.freq_map[i][0].append(o)\n\n for s in to_delete:\n if s!= 0:\n del self.freq_map[i][s]\n \n self.obj_pos.resetScore(self.curr_rotation)\n\n\n def getObjCount(self):\n return self.obj_count\n\n def updateVirtualObjectAndFreq(self, nearest_obj, new_obj_loc, mapped_x, mapped_y, iter, policy, gain, reset_interval, req_obj, threshold): \n [real_obj, score, point_grid] = self.obj_pos.delete(nearest_obj, mapped_x, mapped_y, True) \n\n score = score[0]\n if policy == \"dual\" or policy == \"fifo\":\n score1 = score[0] \n score2 = score[1]\n else:\n score = score[0]\n\n if iter%reset_interval == 0:\n self.resetScore()\n self.curr_rotation = (self.curr_rotation + 1)%2\n\n if policy == \"lfu\":\n score_new = score + 1\n elif policy == \"lru\" :\n score_new = iter\n else:\n score_new_1 = score1 + gain\n score_new_2 = score2 + gain\n score_new = [score_new_1, score_new_2] \n\n if policy != \"dual\" and policy != \"fifo\":\n [obj_loc, success] = self.obj_pos.insert(new_obj_loc, [score_new], real_obj)\n else :\n [obj_loc, success] = self.obj_pos.insert(new_obj_loc, score_new, real_obj)\n\n if success == True: \n \n if policy != \"dual\" and policy != \"fifo\":\n\n self.freq_map[score] = [x for x in self.freq_map[score] if (x[0] != point_grid[0] or x[1] != point_grid[1] or x[2] != point_grid[2])]\n \n index = bisect.bisect(self.freq_sorted_list, score)\n \n self.freq_sorted_list.pop(index - 1)\n \n self.freq_map[score_new].append(obj_loc)\n \n bisect.insort(self.freq_sorted_list, score_new) \n \n [real_obj, score, point_grid] = self.obj_pos.delete(nearest_obj, mapped_x, mapped_y) \n\n else :\n self.fifo.updateScore(req_obj, threshold)\n\n for iter in [0,1]:\n \n self.freq_map[iter][score[iter]] = [x for x in self.freq_map[iter][score[iter]] if (x[0] != point_grid[0] or x[1] != point_grid[1] or x[2] != point_grid[2])]\n \n index = bisect.bisect(self.freq_sorted_list[iter], score[iter])\n \n self.freq_sorted_list[iter].pop(index - 1)\n \n\n if obj_loc[0] < 0 or obj_loc[1] < 0:\n print(\"inserting a negative object : \", obj_loc)\n\n self.freq_map[iter][score_new[iter]].append(obj_loc)\n \n bisect.insort(self.freq_sorted_list[iter], score_new[iter]) \n \n [real_obj, score, point_grid] = self.obj_pos.delete(nearest_obj, mapped_x, mapped_y) \n\n \n\n def getReal(self, point):\n return self.obj_pos.getRealObject(point)\n \n def insert(self, obj, iter, policy):\n if policy == \"lfu\" :\n [point, success] = self.obj_pos.insert(obj, [0], obj)\n elif policy == \"lru\" :\n [point, success] = self.obj_pos.insert(obj, [iter], obj)\n elif policy == \"dual\":\n [point, success] = self.obj_pos.insert(obj, [0,0], obj)\n elif policy == \"fifo\":\n evicted_obj = self.fifo.insert(obj)\n\n if evicted_obj == None:\n [point, success] = [0, False]\n else :\n obj = [evicted_obj[0], evicted_obj[1], evicted_obj[2]]\n score = evicted_obj[3]\n\n least_score = self.leastUseful(policy)\n \n if least_score < score:\n [point, success] = self.obj_pos.insert(obj, [score, score], obj) \n else :\n [point, success] = [0, False]\n\n if success == True:\n\n self.evict(policy)\n\n if policy == \"lfu\":\n self.freq_map[0].append(obj)\n bisect.insort(self.freq_sorted_list, 0)\n\n elif policy == \"lru\" :\n self.freq_map[iter].append(obj)\n bisect.insort(self.freq_sorted_list, iter)\n\n elif policy == \"dual\":\n for i in [0,1]:\n self.freq_map[i][0].append(obj)\n bisect.insort(self.freq_sorted_list[i], 0)\n\n elif policy == \"fifo\":\n for i in [0,1]:\n self.freq_map[i][score].append(obj)\n bisect.insort(self.freq_sorted_list[i], score)\n\n\n \n def updateRealObject(self, obj, iter, policy, virtual_object, orig_real_obj):\n self.obj_pos.virtual_to_real[virtual_object[0]][virtual_object[1]][virtual_object[2]] = obj\n\n def leastUseful(self, policy):\n if policy == \"lru\" or policy == \"lfu\":\n return self.freq_sorted_list[0]\n else :\n i = self.curr_rotation\n return self.freq_sorted_list[i][0]\n \n def evict(self, policy):\n if policy == \"lru\" or policy == \"lfu\":\n least_freq = self.freq_sorted_list[0]\n\n obj_to_evict = self.freq_map[least_freq][0] \n \n score = least_freq\n \n self.freq_map[score] = [x for x in self.freq_map[score] if x[0] != obj_to_evict[0] or x[1] != obj_to_evict[1] or x[2] != obj_to_evict[2]]\n\n if len(self.freq_map) == 0:\n del self.freq_map[score]\n\n self.obj_pos.delete(obj_to_evict, False, False)\n \n index = bisect.bisect(self.freq_sorted_list, least_freq)\n self.freq_sorted_list.pop(index - 1)\n\n else:\n i = self.curr_rotation\n j = 0\n\n if i == 0:\n j = 1\n \n least_freq = self.freq_sorted_list[i][0]\n\n obj_to_evict = self.freq_map[i][least_freq][0] \n \n score = least_freq\n\n index = bisect.bisect(self.freq_sorted_list[i], score)\n\n self.freq_sorted_list[i].pop(index - 1)\n \n self.freq_map[i][score] = [x for x in self.freq_map[i][score] if x[0] != obj_to_evict[0] or x[1] != obj_to_evict[1] or x[2] != obj_to_evict[2]]\n\n if len(self.freq_map[i][score]) == 0:\n del self.freq_map[i][score]\n\n #print(\"object to evict : \", obj_to_evict)\n\n [real_obj, score, point_grid] = self.obj_pos.delete(obj_to_evict, False, False) \n\n score = score[0][j]\n\n index = bisect.bisect(self.freq_sorted_list[j], score)\n\n self.freq_sorted_list[j].pop(index - 1)\n\n self.freq_map[j][score] = [x for x in self.freq_map[j][score] if x[0] != obj_to_evict[0] or x[1] != obj_to_evict[1] or x[2] != obj_to_evict[2]]\n\n if len(self.freq_map[j][score]) == 0:\n del self.freq_map[j][score]\n\n\n def findNearestVirtual(self, vec, policy=\"dual\"):\n i = 0\n min_point = [0,0]\n found = False\n candidates = [] \n break_i = self.grid[0]\n first = True\n\n while i <= break_i:\n\n if i >= 8:\n break\n\n x1 = (int(vec[0])-i)%self.grid[0]\n x2 = (int(vec[0])+i)%self.grid[0]\n\n y1 = (int(vec[1])-i)%self.grid[1]\n y2 = (int(vec[1])+i)%self.grid[1]\n\n #z1 = max(0, (int(vec[2])-i))\n #z2 = min((int(vec[2])+i),self.grid[2])\n\n z1 = int(vec[2])\n z2 = int(vec[2])\n\n a = i\n for x in range(int(vec[0])-i, int(vec[0]) + i + 1):\n x = x%self.grid[0]\n \n if first == True or (first == False and abs(a) + i <= break_i):\n for zz in range(z1, z2 + 1):\n candidates.extend(self.obj_pos.cache[x][y1][zz])\n candidates.extend(self.obj_pos.cache[x][y2][zz])\n \n if first == True:\n if len(self.obj_pos.cache[x][y1][zz]) > 0:\n if found == False:\n found = True\n \n if len(self.obj_pos.cache[x][y2][zz]) > 0:\n if found == False:\n found = True\n a=a-1\n \n a = i\n for y in range(int(vec[1])-i, int(vec[1]) + i + 1):\n y = y%self.grid[1]\n \n if first == True or (first == False and abs(a) + i <= break_i):\n for zz in range(z1, z2 + 1):\n candidates.extend(self.obj_pos.cache[x1][y][zz])\n candidates.extend(self.obj_pos.cache[x2][y][zz]) \n\n if first == True:\n if len(self.obj_pos.cache[x1][y][zz]) > 0:\n if found == False:\n found = True\n \n if len(self.obj_pos.cache[x2][y][zz]) > 0:\n if found == False:\n found = True \n\n a=a-1\n \n if found == True and first == True:\n break_i = math.ceil(i * 2) + 1\n first = False\n\n i += 1\n\n def dist(c, v, break_i):\n\n if policy == \"lru\":\n first = np.linalg.norm((c[:-1]-v), ord=1)\n else:\n first = np.linalg.norm((c[:-2]-v), ord=1)\n \n if first > 4 * break_i:\n mapped_points = self.get_mapped_points(c)\n mapped = [(c, (np.linalg.norm((c[:2]-v[:2]), ord=1) + 1 * abs(c[2] - v[2]))) for c in mapped_points]\n best = min(mapped, key=operator.itemgetter(1))\n\n if best[0][0] == mapped_points[0][0] and best[0][1] == mapped_points[0][1]:\n return [best[0], best[1], True, False]\n elif best[0][0] == mapped_points[1][0] and best[0][1] == mapped_points[1][1]:\n return [best[0], best[1], False, True]\n else : \n return [best[0], best[1], True, True]\n\n else:\n return [c , np.linalg.norm((c[:2]-v[:2]), ord=1) + 1 * abs(c[2] - v[2]), False, False]\n \n try:\n\n candidates = [dist(c, vec, break_i) for c in candidates]\n if len(candidates) == 0:\n return [\"Not found\"] * 4\n \n random.shuffle(candidates)\n best_candidate = min(candidates, key=operator.itemgetter(1))\n\n return [best_candidate[0], best_candidate[1], best_candidate[2], best_candidate[3]]\n\n except Exception as e:\n no_objects = self.obj_pos.printCacheContents()\n print(e)\n print(no_objects)\n sys.exit(0)\n\n\n def findNearestReal(self, vec):\n i = 0\n min_dist = 10000\n min_point = [0,0]\n found = False\n candidates = [] \n break_i = self.grid[0]\n first = True\n\n while i <= break_i:\n\n if i>=8:\n break\n\n x1 = (int(vec[0])-i)%self.grid[0]\n x2 = (int(vec[0])+i)%self.grid[0]\n\n y1 = (int(vec[1])-i)%self.grid[1]\n y2 = (int(vec[1])+i)%self.grid[1]\n\n #z1 = max(0, (int(vec[2])-i))\n #z2 = min((int(vec[2])+i),self.grid[2])\n\n z1 = int(vec[2])\n z2 = int(vec[2])\n\n a = i\n for x in range(int(vec[0])-i, int(vec[0]) + i + 1):\n\n x = x%self.grid[0]\n \n if first == True or (first == False and abs(a) + i <= break_i):\n\n for zz in range(z1, z2 + 1):\n candidates.extend([self.obj_pos.virtual_to_real[req_point[0]][req_point[1]][req_point[2]] for req_point in self.obj_pos.cache[x][y1][zz]])\n candidates.extend([self.obj_pos.virtual_to_real[req_point[0]][req_point[1]][req_point[2]] for req_point in self.obj_pos.cache[x][y2][zz]])\n \n if first == True:\n if len(self.obj_pos.cache[x][y1][zz]) > 0:\n if found == False:\n found = True\n \n if len(self.obj_pos.cache[x][y2][zz]) > 0:\n if found == False:\n found = True\n a=a-1\n \n a = i\n for y in range(int(vec[1])-i, int(vec[1]) + i + 1):\n y = y%self.grid[1]\n \n if first == True or (first == False and abs(a) + i <= break_i):\n for zz in range(z1, z2 + 1):\n candidates.extend([self.obj_pos.virtual_to_real[req_point[0]][req_point[1]][req_point[2]] for req_point in self.obj_pos.cache[x1][y][zz]])\n candidates.extend([self.obj_pos.virtual_to_real[req_point[0]][req_point[1]][req_point[2]] for req_point in self.obj_pos.cache[x2][y][zz]]) \n\n if first == True:\n if len(self.obj_pos.cache[x1][y][zz]) > 0:\n if found == False:\n found = True\n \n if len(self.obj_pos.cache[x2][y][zz]) > 0:\n if found == False:\n found = True \n\n a=a-1\n \n if found == True and first == True:\n break_i = math.ceil(i * 2) + 1\n first = False\n\n i += 1\n\n def dist(c, v, break_i):\n first = np.linalg.norm((np.array(c[:2])-np.array(v[:2])), ord=1)\n if first > 4 * break_i:\n mapped_points = self.get_mapped_points(c)\n mapped = [(c, np.linalg.norm((np.array(c[:2])-np.array(v[:2])), ord=1) + 1 * abs(v[2] - c[2])) for c in mapped_points]\n best = min(mapped, key=operator.itemgetter(1)) \n if best[0][0] == mapped_points[0][0] and best[0][1] == mapped_points[0][1] and best[0][2] == mapped_points[0][2]:\n return [best[0], best[1], True, False]\n elif best[0][0] == mapped_points[1][0] and best[0][1] == mapped_points[1][1] and best[0][2] == mapped_points[1][2]:\n return [best[0], best[1], False, True]\n else :\n return [best[0], best[1], True, True]\n\n else:\n return [c , first, False, False]\n\n\n candidates = [x for x in candidates if x[2] == vec[2]]\n candidates = [dist(c, vec, break_i) for c in candidates]\n\n if len(candidates) == 0:\n return [\"Not found\"] * 4\n\n random.shuffle(candidates)\n best_candidate = min(candidates, key=operator.itemgetter(1))\n return [best_candidate[0], best_candidate[1], best_candidate[2], best_candidate[3]]\n\n\n\n\n","sub_path":"similarity_caching_3d/cacheGrid3d.py","file_name":"cacheGrid3d.py","file_ext":"py","file_size_in_byte":25871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"428546743","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\nimport sys\nimport re\n\n# incorrect output\n\nwith open(sys.argv[1]) as f:\n for line in f:\n if 'miles_start' in line:\n continue\n try:\n start, stop, gals, car = line.rstrip().split(',')\n mpg = (float(stop) - float(start)) / float(gals)\n print(\"{0:>10} {1:05.2f}\".format(car, mpg))\n except:\n continue","sub_path":"Down_With_MPG.py","file_name":"Down_With_MPG.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"340618286","text":"import os\n\nfrom dvc.lock import LockError\nfrom dvc.main import main\nfrom dvc.lock import Lock\n\nfrom tests.basic_env import TestDvc\n\n\nclass TestLock(TestDvc):\n def test_with(self):\n lockfile = os.path.join(self.dvc.dvc_dir, \"lock\")\n lock = Lock(lockfile)\n with lock:\n with self.assertRaises(LockError):\n lock2 = Lock(lockfile)\n with lock2:\n self.assertTrue(False)\n\n def test_cli(self):\n lockfile = os.path.join(self.dvc.dvc_dir, \"lock\")\n lock = Lock(lockfile)\n with lock:\n ret = main([\"add\", self.FOO])\n self.assertEqual(ret, 1)\n","sub_path":"tests/func/test_lock.py","file_name":"test_lock.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"178011321","text":"#!/usr/bin/env python3\n# Copyright 2022-2023 Robert Krawitz/Red Hat\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom .analyze_spreadsheet_generic import SpreadsheetAnalysis\n\n\nclass fio_analysis(SpreadsheetAnalysis):\n \"\"\"\n Analyze fio data\n \"\"\"\n\n def __init__(self, workload: str, data: dict, metadata: dict):\n dimensions = ['By Pod Count', 'By Engine', 'By I/O Depth', '-By Fdatasync', '-By Direct', 'By Operation', 'By Blocksize']\n variables = [\n {\n 'var': 'throughput',\n 'name': 'Throughput',\n 'unit': ' (MB/sec)',\n 'multiplier': .000001\n },\n {\n 'var': 'iops',\n 'name': 'IO/sec',\n 'base': 0,\n 'detail': False\n }\n ]\n filters = {\n 'By Direct': self.__filter_direct\n }\n super().__init__(workload, data, metadata, dimensions, variables, filters=filters)\n\n def __filter_direct(self, dimension, value):\n return value != 0\n\n def _retrieve_datum(self, var: str, value: dict):\n return value['total'].get(var, 0)\n","sub_path":"lib/clusterbuster/reporting/analysis/spreadsheet/fio_analysis.py","file_name":"fio_analysis.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"322134788","text":"import tornado.ioloop\r\nimport tornado.web\r\nimport time\r\nimport asyncio\r\n\r\nclass MainHandler(tornado.web.RequestHandler):\r\n async def get(self):\r\n # print('get a request', time.time())\r\n await asyncio.sleep(2)\r\n self.write('asd')\r\n # print('end the request', time.time())\r\n\r\nif __name__ == '__main__':\r\n app = tornado.web.Application([\r\n (r'/', MainHandler),\r\n ], debug=True)\r\n app.listen(8088)\r\n tornado.ioloop.IOLoop.current().start()\r\n","sub_path":"server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"260527739","text":"\"\"\"VRGAN metrics\r\n\r\nFile that contain functions for calculating the normalized cross-correlation \r\nbetween two images and a class Metrics for storing losses and metrics during \r\nmini batch inferences, so that you can get an epoch summary after the epoch \r\nis complete.\r\n\"\"\"\r\n\r\nimport collections\r\nimport torch\r\nimport synth_dataset\r\n\r\ndef normalized_cross_correlation(a,v): \r\n a = a.squeeze(1)\r\n v = v.squeeze(1) \r\n norm_std = torch.std(a.view([a.size(0),-1]), dim = 1)*torch.std(v.view([v.size(0),-1]), dim = 1)\r\n step1a = (a - torch.mean(a.view([a.size(0),-1]), dim = 1).unsqueeze(1).unsqueeze(2))\r\n step1v = (v - torch.mean(v.view([v.size(0),-1]), dim = 1).unsqueeze(1).unsqueeze(2))\r\n step2 = torch.sum((step1a*step1v).view([a.size(0),-1]), dim = 1)\r\n step3 = step2/norm_std\r\n step3 = step3/torch.prod(torch.tensor(a.size()[-2:]))\r\n return step3\r\n \r\ndef get_groundtruth_toy(pft_desired, pft_true):\r\n I0 = torch.zeros([pft_desired.size(0),1, 224,224])\r\n I1 = torch.zeros([pft_desired.size(0),1, 224,224])\r\n for i in range(pft_desired.size(0)):\r\n I0[i,0,:,:] = torch.tensor(synth_dataset.get_clean_square(pft_true[i], 224))\r\n I1[i,0,:,:] = torch.tensor(synth_dataset.get_clean_square(pft_desired[i], 224))\r\n im_diff = ((I1-I0))\r\n return im_diff.cuda().float()\r\n\r\nclass Metrics():\r\n def __init__(self):\r\n self.values = collections.defaultdict(list)\r\n \r\n def add_ncc(self, pft_desired, pft_true, delta_x):\r\n pft_desired = pft_desired.detach()\r\n pft_true = pft_true.detach()\r\n delta_x = delta_x.detach()\r\n gt_toy = get_groundtruth_toy(pft_desired, pft_true)\r\n self.add_list('ncc', normalized_cross_correlation(gt_toy, delta_x))\r\n \r\n def add_list(self, key, value):\r\n value = value.detach().cpu().tolist()\r\n self.values[key] += value\r\n \r\n def add_value(self, key, value):\r\n value = value.detach().cpu()\r\n self.values[key].append( value)\r\n \r\n def calculate_average(self):\r\n self.average = {}\r\n for key, element in self.values.items():\r\n n_values = len(element)\r\n sum_values = sum(element)\r\n self.average[key] = sum_values/float(n_values)\r\n self.values = collections.defaultdict(list)\r\n \r\n def get_average(self):\r\n self.calculate_average()\r\n return self.average\r\n \r\n def get_keys(self):\r\n return self.values.keys()\r\n \r\n def get_last_added_value(self,key):\r\n return self.values[key][-1]","sub_path":"metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"167518161","text":"import numpy as np\n\n\nclass SumTree:\n \"\"\"\n A sum-tree data structure for prioritized replay buffer.\n \"\"\"\n\n def __init__(self, capacity):\n \"\"\"\n Initialize sum-tree with given capacity.\n \"\"\"\n\n ## self.data is used to store data\n self.data = np.zeros(capacity, dtype=object)\n ## self.tree is used to record weights\n self.tree = np.zeros(2*capacity-1)\n self.capacity = capacity\n\n ## self.position is used to record data index\n self.position = 0\n \n def _propogate(self, idx, change):\n \"\"\"\n Update the weights recursively from bottom up.\n \"\"\"\n\n parent = (idx - 1) // 2\n\n self.tree[parent] += change\n\n if parent != 0:\n self._propogate(parent, change)\n\n def _retrieve(self, idx, s):\n \"\"\"\n Traverse the tree recursively from top down.\n Return the leaf index in the tree.\n \"\"\"\n left = 2*idx + 1\n right = 2*idx + 2\n\n ## return idx if it's already leaf\n if left >= len(self.tree):\n return idx\n \n ## traverse recursively\n if s <= self.tree[left]:\n return self._retrieve(left, s)\n else:\n return self._retrieve(right, s-self.tree[left])\n\n def push(self, data, p):\n \"\"\"\n Add a new node into the tree.\n \"\"\"\n idx = self.position + self.capacity - 1\n\n self.data[self.position] = data\n self.update(idx, p)\n\n self.position += 1\n if self.position >= self.capacity:\n self.position = 0\n\n def update(self, idx, p):\n change = p - self.tree[idx]\n\n self.tree[idx] = p\n self._propogate(idx, change)\n\n def get(self, s):\n idx = self._retrieve(0, s)\n dataIdx = idx - (self.capacity - 1)\n\n return idx, self.tree[idx], self.data[dataIdx]\n \n @property\n def total(self):\n return self.tree[0]","sub_path":"Project/infrastructure/sumtree.py","file_name":"sumtree.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"186071362","text":"import threading\nimport numpy as np\nimport cv2\nimport pyaudio\nimport wave\nimport os\nfrom utils import *\nfrom shutil import copyfile\nfrom datetime import datetime\nclass MotionSensorThread(threading.Thread):\n def __init__(self,Bot,cap,message):\n threading.Thread.__init__(self)\n self.Bot = Bot\n self.cap = cap\n self.message = message\n def run(self):\n past_frame = np.array([])\n past_fast_motion_value = -1\n while self.Bot.settings.motionSensor:\n ret,curr_frame = self.cap.read()\n if not past_frame.size == 0:\n fast_motion_value = np.sum((curr_frame - past_frame) ** 2)\n motionValue = fast_motion_value / past_fast_motion_value\n past_fast_motion_value = fast_motion_value\n if motionValue >= self.Bot.settings.motionThresh:\n frameBytes = cv2.imencode('.png', curr_frame)\n self.Bot.bot.send_photo(self.message.chat.id, frameBytes,\n caption=\"Движение c значением {}, порог: {} \".format(round(motionValue,3),round(self.Bot.settings.motionThresh,3)))\n src_write = \"out/photo/\"\n if self.Bot.settings.saveMotionImages:\n if not os.path.exists(src_write):\n os.makedirs(src_write)\n files = os.listdir(src_write)\n if (len(files) == self.Bot.settings.saveMotionLimit):\n deleteOldestFile(src_write, files)\n cv2.imwrite(\"{}{}_motion.png\".format(src_write, datetime.now().strftime('%Y-%m-%d %H-%M-%S-%f')),curr_frame)\n\n\n\n\n past_frame = curr_frame\n\nclass AudioRecordThread(threading.Thread):\n def __init__(self,Bot,message,rec_sec):\n threading.Thread.__init__(self)\n self.message = message\n self.Bot = Bot\n self.rec_sec = rec_sec\n def run(self):\n FORMAT = pyaudio.paInt16\n CHANNELS = 2\n RATE = 44100\n CHUNK = 1024\n audio = pyaudio.PyAudio()\n print(\"record audio...\")\n stream = audio.open(format=FORMAT, channels=CHANNELS,\n rate=RATE, input=True,\n frames_per_buffer=CHUNK)\n frames = []\n for i in range(0, int(RATE / CHUNK * self.rec_sec)):\n data = stream.read(CHUNK)\n frames.append(data)\n stream.stop_stream()\n stream.close()\n audio.terminate()\n if not os.path.exists(\"tmp/\"):\n os.makedirs(\"tmp/\")\n waveFile = wave.open(\"tmp/tmp.wav\", 'wb')\n waveFile.setnchannels(CHANNELS)\n waveFile.setsampwidth(audio.get_sample_size(FORMAT))\n waveFile.setframerate(RATE)\n waveFile.writeframes(b''.join(frames))\n waveFile.close()\n src_write = \"out/audio/\"\n src_tmp = \"tmp/tmp.wav\"\n if self.Bot.settings.saveRecordedSounds:\n if not os.path.exists(src_write):\n os.makedirs(src_write)\n files = os.listdir(src_write)\n if (len(files)==self.Bot.settings.saveSoundsLimit):\n deleteOldestFile(src_write,files)\n copyfile(src_tmp, \"{}{}({} сек.).wav\".format(src_write,datetime.now().strftime('%Y-%m-%d %H-%M-%S-%f'),self.rec_sec))\n\n waveFile_to_send = open(src_tmp,'rb')\n try:\n self.Bot.bot.send_audio(self.message.chat.id, waveFile_to_send, caption=\"Запись закончена\",title=\"Ваша запись\",\n timeout=max(self.Bot.settings.recordAudioVariants))\n except Exception as e:\n\n self.Bot.bot.send_message(self.message.chat.id,\"Ошибка записи аудиофайла\")\n print (e)\n waveFile_to_send.close()\n os.remove(src_tmp)\n print(\"finished recording\")\n\n\n","sub_path":"BotGuardThreads.py","file_name":"BotGuardThreads.py","file_ext":"py","file_size_in_byte":3925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"19307873","text":"import bst\n\n\nclass AVLNode(bst.BSTNode):\n\n\n def __init__(self, value):\n super().__init__(value)\n # Augmented tree!\n self.height = 1\n\n def adjust_height(self):\n \"\"\"recalculate node height based on children\"\"\"\n if (not self.left) and self.right:\n self.height = self.right.height + 1\n elif (not self.right) and self.left:\n self.height = self.left.height + 1\n elif (not self.right) and (not self.left): \n self.height = 1\n else:\n self.height = max(self.left.height, self.right.height) + 1\n\n @property\n def balance(self):\n \"\"\"returns right - left balance\"\"\"\n if (not self.left) and self.right:\n return self.right.height\n elif (not self.right) and self.left:\n return 0 - self.left.height\n elif (not self.left) and (not self.right):\n return 0\n else:\n return self.right.height - self.left.height\n\n\nclass AVL(bst.BST):\n\n\n def __init__(self, data):\n self._visited = []\n super().__init__(data)\n\n @property\n def height(self):\n if self.root:\n return self.root.height\n else:\n return 0\n\n def insert(self, value, method='iterative'):\n \"\"\"insert value into tree. Enforces type consistency. Supports \n iterative and recursive methods\"\"\"\n\n # have to overwrite bst insert because need AVLNode\n new_node = AVLNode(value)\n\n if not self.root:\n self.root = new_node\n elif self.root and method == 'iterative':\n self._visited = self._iterative_insert(self.root, new_node)\n elif self.root and method == 'recursive':\n self._recursive_insert(self.root, new_node)\n\n self._iterative_rebalance(value) # will clear _visited\n\n def _iterative_insert(self, node, new_node):\n \"\"\"iterative helper for insert\"\"\"\n visited = []\n while True:\n visited.append(node)\n if new_node.value <= node.value and node.left:\n node = node.left\n elif new_node.value <= node.value and not node.left:\n node.left = new_node\n break\n elif new_node.value > node.value and node.right:\n node = node.right\n elif new_node.value > node.value and not node.right:\n node.right = new_node\n break\n\n return visited\n\n def _recursive_insert(self, node, new_node):\n \"\"\"recursive helper for insert\"\"\"\n self._visited.append(node)\n if new_node.value <= node.value:\n if not node.left:\n node.left = new_node\n else:\n self._recursive_insert(node.left, new_node)\n elif new_node.value > node.value:\n if not node.right:\n node.right = new_node\n else:\n self._recursive_insert(node.right, new_node)\n\n def _iterative_rebalance(self, value):\n \"\"\"correct heights, rotate if necessary\"\"\"\n while self._visited:\n node = self._visited.pop()\n node.adjust_height()\n parent = self._visited[-1] if self._visited else None\n\n # balance is right minus left\n if node.balance > 1: # right heavy\n if value <= node.right.value:\n self._right_rotate(node.right, node)\n self._left_rotate(node, parent)\n else:\n self._left_rotate(node, parent)\n elif node.balance < -1: # left heavy\n if value <= node.left.value:\n self._right_rotate(node, parent)\n else:\n self._left_rotate(node.left, node)\n self._right_rotate(node, parent)\n\n def _recursive_rebalance():\n pass\n\n def _right_rotate(self, node, parent):\n new_root = node.left\n node.left = new_root.right\n new_root.right = node\n\n new_root.adjust_height()\n node.adjust_height()\n\n # reset parent link to the altered subtree root\n if parent is None:\n self.root = new_root\n elif parent.right is node:\n parent.right = new_root\n else:\n parent.left = new_root\n\n def _left_rotate(self, node, parent):\n new_root = node.right\n node.right = new_root.left\n new_root.left = node\n\n node.adjust_height()\n new_root.adjust_height()\n\n # reset parent link to the altered subtree root\n if parent is None:\n self.root = new_root\n elif parent.right is node:\n parent.right = new_root\n else:\n parent.left is new_root","sub_path":"Trees/py/avl.py","file_name":"avl.py","file_ext":"py","file_size_in_byte":4744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"420837943","text":"# class Tree:\n# def __init__(self, val, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nclass Solution:\n def solve(self, root):\n if root is None:\n return []\n\n q = [root]\n temp = []\n aux = []\n\n ans = [root.val]\n as_stack = True\n\n while len(q) > 0:\n while len(q) > 0:\n curr = q.pop(0)\n\n if not curr.left is None:\n temp.append(curr.left)\n aux.append(curr.left)\n\n if not curr.right is None:\n temp.append(curr.right)\n aux.append(curr.right)\n\n while len(aux) > 0:\n if as_stack:\n ans.append(aux.pop().val)\n else:\n ans.append(aux.pop(0).val)\n\n q = temp\n temp = []\n as_stack = not as_stack\n\n return ans","sub_path":"top-questions/amazon/trees/level-order-alternating.py","file_name":"level-order-alternating.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"492063528","text":"# Copyright (C) 2019, NGINX, Inc.\n# Sphinx extension to support advanced markup capabilities in .rst files.\n#\n# Usage:\n#\n# conf.py:\n#\n# extensions += ['nxt']\n#\n# 1. Enables tooltips for terms in text and code-blocks.\n#\n# .rst file:\n#\n# .. markup:: (including literal blocks)\n#\n# :nxt_term:`term ` and more text\n#\n# .html file:\n#\n# term and more text\n#\n# 2. Enables adaptive CSS-based tabbing on pages.\n#\n# .rst file:\n#\n# .. tabs::\n#\n# .. tab:: Plain Text Foo\n#\n# Foo bar foo bar foo bar:\n#\n# .. markup::\n#\n# Foo bar\n#\n# .. tab:: Plain Text Bar\n#\n# Foo bar foo bar foo bar:\n#\n# .. markup::\n#\n# Foo bar\n\nfrom docutils import nodes\nfrom docutils.parsers.rst import Directive, roles\nfrom sphinx.writers.html import HTMLTranslator\nimport re\n\n\n# writer-related classes and functions\n\nclass nxt_tabs(nodes.container): pass\nclass nxt_tab_head(nodes.Text): pass\nclass nxt_tab_body(nodes.container): pass\nclass nxt_term(nodes.container): pass\n# dummy classes, required for docutils dispatcher's Visitor pattern\n\nnxt_term_regex = r'`({0}*[^\\s])\\s*<({0}+)>`'.format(r'[\\w\\s\\.\\,\\?\\!\\-\\/\\:#_]')\n# matches `text w/punctuation ` in ':nxt_term:' directives\n\n\ndef nxt_term_role_fn(name, rawtext, text, lineno, inliner,\n options={}, content=[]):\n# ':nxt_term:' role handler for inline text outside literal blocks\n\n node = nxt_term()\n groups = re.search(nxt_term_regex, \\\n rawtext.replace('\\n', ' ').replace('\\r', ''))\n\n try:\n node.term, node.tip = groups.group(1), groups.group(2)\n except:\n msg = inliner.reporter.error(\n 'Inline term \"%s\" is invalid.' % text, line=lineno)\n prb = inliner.problematic(rawtext, rawtext, msg)\n return [prb], [msg]\n\n return [node], []\n\n\nclass nxt_highlighter(object):\n# extends default highlighter to handle ':nxt_term:' inside literal blocks\n\n def __init__(self, highlighter):\n self.highlighter = highlighter\n\n def highlight_block(self, *args, **kwargs):\n groups = re.findall(nxt_term_regex, args[0])\n\n rawsource = args[0]\n\n for c, g in enumerate(groups):\n rawsource = re.sub(':nxt_term:' + nxt_term_regex, \\\n 'nxt_term_{0}'.format(c), rawsource, count=1)\n\n highlighted = self.highlighter.highlight_block(rawsource, *args[1:], \\\n **kwargs)\n\n for c, g in enumerate(groups):\n highlighted = re.sub('nxt_term_{0}'.format(c), \\\n '{1}'.\\\n format(g[1], g[0]), highlighted, count=1)\n\n return highlighted\n\nclass nxt_translator(HTMLTranslator):\n# adds dispatcher methods to handle 'nxt_tabs' and 'nxt_tab' doctree nodes\n# replaces default highlighter to enable ':nxt_term:' inside literal blocks\n\n def __init__(self, builder, *args, **kwargs):\n HTMLTranslator.__init__(self, builder, *args, **kwargs)\n self.highlighter = nxt_highlighter(builder.highlighter)\n\n def visit_nxt_term(self, node):\n self.body.append('{0}'.\\\n format(node.term, node.tip))\n return nodes.SkipNode\n\n def depart_nxt_term(self, node):\n return nodes.SkipNode\n\n def visit_nxt_tabs(self, node):\n HTMLTranslator.visit_container(self,node)\n\n def depart_nxt_tabs(self, node):\n HTMLTranslator.depart_container(self,node)\n\n def visit_nxt_tab_head(self, node):\n self.body.append('''\n \n '''.format(node.tabs_id, node.tab_id, node.tab_checked))\n self.body.append('''\n ')\n HTMLTranslator.depart_Text(self,node)\n\n def visit_nxt_tab_body(self, node):\n HTMLTranslator.visit_container(self,node)\n\n def depart_nxt_tab_body(self, node):\n HTMLTranslator.depart_container(self,node)\n\n\n# doctree-related classes\n\nclass TabsDirective(Directive):\n# handles the '.. tabs::' directive, adding an 'nxt_tabs' container node\n\n has_content = True\n\n def run(self):\n self.assert_has_content()\n env = self.state.document.settings.env\n\n node = nxt_tabs()\n node['classes'] = ['nxt_tabs']\n\n # tab groups numbering logic\n if 'tabs_id' not in env.temp_data:\n env.temp_data['tabs_id'] = 0\n\n # individual tab numbering logic\n env.temp_data['tabs_id'] += 1\n env.temp_data['tab_id'] = 0\n env.temp_data['tab_checked'] = 'checked'\n\n self.state.nested_parse(self.content, self.content_offset, node)\n\n return [node]\n\n\nclass TabDirective(Directive):\n# handles the '.. tab::' directive, adding an 'nxt_tab' container node\n\n has_content = True\n\n def run(self):\n self.assert_has_content()\n env = self.state.document.settings.env\n\n tab_head = nxt_tab_head(self.content[0])\n\n tab_head.tabs_id = 'nxt_tabs{0}'.format(env.temp_data['tabs_id'])\n tab_head.tab_id = 'nxt_tab{0}_{1}'.\\\n format(env.temp_data['tabs_id'], env.temp_data['tab_id'])\n tab_head.tab_checked = env.temp_data['tab_checked']\n # each tab stores tab ID and tab group ID\n\n env.temp_data['tab_id'] += 1\n if env.temp_data['tab_checked']:\n env.temp_data['tab_checked'] = ''\n # first tab in a group is checked by default, others are unchecked\n\n text = '\\n'.join(self.content)\n tab_body = nxt_tab_body(text)\n tab_body['classes'] = ['nxt_tab']\n self.state.nested_parse(self.content[2:], self.content_offset, \\\n tab_body)\n\n return [tab_head, tab_body]\n\n\ndef setup(app):\n\n app.add_directive('tabs', TabsDirective)\n app.add_directive('tab', TabDirective)\n\n app.set_translator('dirhtml', nxt_translator)\n\n roles.register_canonical_role('nxt_term', nxt_term_role_fn)\n","sub_path":"source/exts/nxt.py","file_name":"nxt.py","file_ext":"py","file_size_in_byte":6128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"82854174","text":"class Solution:\n # @param n, an integer\n # @return an integer\n def reverseBits0(self, n):\n binary = bin(n)[2:].zfill(32)\n reverse = str(binary)[::-1]\n return int(reverse, 2)\n\n def reverseBits(self, n):\n binary = self.intToBinary(n)\n binary32 = \"0\"*(32-len(binary))+binary\n return self.binToInt(binary32[::-1])\n\n\n def intToBinary(self, n):\n b = \"\"\n while n > 0:\n bit = n % 2\n b = str(bit) + b\n n = n / 2\n return b\n\n def binToInt(self, binStr):\n res = 0\n power = 0\n for i in binStr[::-1]:\n res += int(i) * (2 ** power)\n power += 1\n return res\n\n def reverseBits1(self, n):\n ans = 0\n for i in range(32):\n ans <<= 1\n ans |= n & 1\n n >>= 1\n return ans\n\nprint(Solution().reverseBits1(11))","sub_path":"algorithm/reverse-bits.py","file_name":"reverse-bits.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"423215563","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 13 18:39:50 2020\n\n@author: elijahsheridan\n\"\"\"\n\nimport opt_helper as opt\nimport numpy as np\n\n# Don't think I ever actually needed pta/maa optimization histos?\n\n\ndef pta_maa_norm_one_histos():\n file_names = ['./pta_maa_optimization/'\n + 'loose_analysis_pta_maa_2.6_mjj_1250/Output/Histos/'\n + 'MadAnalysis5job_0/selection_' + str(i) + '.py' for\n i in range(16)]\n out_file_names = ['selection_'+str(i) for i in range(16)]\n labels = [r'$pp \\; \\to \\; jj + ax \\; (\\to \\; \\gamma\\gamma)$',\n r'$pp \\; \\to \\; jj + \\gamma\\gamma \\; (QCD = 0)$',\n r'$pp \\; \\to \\; jj + \\gamma\\gamma$']\n ylabel = r'Arbitrary Units (Normalized to Unity)'\n xlabels = [r'$p_T^{j_1}$ (GeV)', r'$\\eta^{j_1}$', r'$\\phi^{j_1}$',\n r'$p_T^{j_2}$ (GeV)', r'$\\eta^{j_2}$', r'$\\phi^{j_2}$',\n r'$\\Delta R^{jj}$', r'$m^{jj}$ (GeV)',\n r'$\\Delta \\eta^{jj}$', r'$m^{\\gamma\\gamma}$ (GeV)',\n r'$p_T^{\\gamma_1}$ (GeV)', r'$p_T^{\\gamma_2}$ (GeV)',\n r'$THT$ (GeV)', r'$MET$ (GeV)', r'$TET$ (GeV)']\n\n for file_name, out_file_name, xlabel in zip(file_names, out_file_names,\n xlabels):\n divisions = [0, 1, 9] # for some reason gen_norm_one_histo overwrites?\n opt.gen_histo(file_name, divisions,\n out_file_name=out_file_name, labels=labels,\n ylabel=ylabel, save=True, norm_one=False, xlabel=xlabel)\n","sub_path":"optimization/legacy.py","file_name":"legacy.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"256964723","text":"from django.shortcuts import render\nfrom project2app.forms import PipelineForm, ContactInfoForm, PersonInfoForm, UserForm, UserRegistrationForm\nfrom project2app.models import PersonInfo, ContactInfo\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.db import connection # sql raw queries\nfrom django.contrib import messages # import message output\n\n\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\n\n\ndef index(request):\n return render(request,'project2app_template/index.html')\n\n@login_required\ndef user_logout(request):\n logout(request)\n return HttpResponseRedirect(reverse('index'))\n\n@login_required\ndef new_record(request):\n if request.method == 'POST':\n form_person_info = PersonInfoForm(data=request.POST)\n form_contact_info = ContactInfoForm(data=request.POST)\n\n if form_person_info.is_valid() and form_contact_info.is_valid():\n person_info = form_person_info.save(commit=True)\n contact_info = form_contact_info.save(commit=False)\n contact_info.id = person_info\n contact_info.save()\n\n messages.add_message(request, messages.SUCCESS , 'New record successfully added!')\n form_person_info = PersonInfoForm()\n form_contact_info = ContactInfoForm()\n\n else:\n messages.add_message(request, messages.ERROR, 'Error! please provide correct information!')\n\n else:\n form_person_info = PersonInfoForm()\n form_contact_info = ContactInfoForm()\n\n return render(request, 'project2app_template/new_record.html',{'form_person_info':form_person_info,'form_contact_info':form_contact_info,})\n\n@login_required\ndef pipeline_calculation(request):\n form = PipelineForm()\n\n if request.method == 'POST':\n\n form = PipelineForm(request.POST)\n\n if form.is_valid():\n form.save(commit=True)\n #add message output\n messages.add_message(request, messages.SUCCESS, 'Pipeline Saved!')\n form = PipelineForm()#clear form after submit\n #return HttpResponseRedirect('pipeline_calculation')\n else:\n return HttpResponse(\"Error in request\")\n\n\n return render(request, 'project2app_template/pipeline_calculation.html',{'form':form})\n\ndef user_login(request):\n\n if request.method == 'POST':\n username = request.POST.get('username')\n password = request.POST.get('password')\n\n user = authenticate(username=username, password=password)\n\n if user:\n if user.is_active:\n login(request,user)\n return HttpResponseRedirect(reverse('index'))\n else:\n messages.add_message(request, messages.WARNING, 'Account Not Active')\n else:\n messages.add_message(request, messages.ERROR, 'Invalid Account!')\n\n return render(request, 'project2app_template/user_login.html')\n\ndef user_registration(request):\n\n if request.method == 'POST':\n user_reg_form = UserForm(data=request.POST)\n user_profile_form = UserRegistrationForm(data=request.POST)\n if user_reg_form.is_valid() and user_profile_form.is_valid():\n user = user_reg_form.save()\n user.set_password(user.password)\n user.save()\n\n profile = user_profile_form.save(commit=False)\n profile.user = user\n\n if 'profile_pic' in request.FILES:\n profile.profile_pic = request.FILES['profile_pic']\n\n profile.save()\n messages.add_message(request, messages.SUCCESS, 'You have successfully registered!')\n\n user_reg_form = UserForm()\n user_profile_form = UserRegistrationForm()\n\n else:\n messages.add_message(request, messages.ERROR, 'Error: Validate the form')\n else:\n user_reg_form = UserForm()\n user_profile_form = UserRegistrationForm()\n\n return render(request, 'project2app_template/user_registration.html',{'user_reg_form':user_reg_form,'user_profile_form':user_profile_form})\n\n@login_required\ndef view_records(request):\n person_list = PersonInfo.objects.all()\n context_dictionary = {'join_list':person_list}\n return render(request, 'project2app_template/view_records.html',context=context_dictionary)\n","sub_path":"project2/project2app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"568507878","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# vi: ts=4 sw=4\n\n\n\n\n################################################################################\n# Short-term settings (specific to a particular user/experiment) can\n# be placed in this file. You may instead wish to make a copy of this file in\n# the user's data directory, and use that as a working copy.\n################################################################################\n\n\n#logbooks_default = ['User Experiments']\n#tags_default = ['CFN Soft-Bio']\n\n\nconfig_load()\n\n\nif False:\n # The following shortcuts can be used for unit conversions. For instance,\n # for a motor operating in 'mm' units, one could instead do:\n # sam.xr( 10*um )\n # To move it by 10 micrometers. HOWEVER, one must be careful if using\n # these conversion parameters, since they make implicit assumptions.\n # For instance, they assume linear axes are all using 'mm' units. Conversely,\n # you will not receive an error if you try to use 'um' for a rotation axis!\n m = 1e3\n cm = 10.0\n mm = 1.0\n um = 1e-3\n nm = 1e-6\n \n inch = 25.4\n pixel = 0.172 # Pilatus\n \n deg = 1.0\n rad = np.degrees(1.0)\n mrad = np.degrees(1e-3)\n urad = np.degrees(1e-6)\n \n \n\n\ndef get_default_stage():\n return stg\n\n\nclass SampleTSAXS(SampleTSAXS_Generic):\n \n def __init__(self, name, base=None, **md):\n super().__init__(name=name, base=base, **md)\n self.naming_scheme = ['name', 'extra', 'exposure_time']\n\nclass SampleGISAXS(SampleGISAXS_Generic):\n \n def __init__(self, name, base=None, **md):\n super().__init__(name=name, base=base, **md)\n self.naming_scheme = ['name', 'extra', 'th', 'exposure_time']\n\nclass SampleCDSAXS(SampleCDSAXS_Generic):\n \n def __init__(self, name, base=None, **md):\n super().__init__(name=name, base=base, **md)\n self.naming_scheme = ['name', 'extra', 'phi', 'exposure_time']\n\n\nclass Sample(SampleTSAXS):\n \n def _measureTimeSeries(self, exposure_time=None, num_frames=10, wait_time=None, extra=None, measure_type='measureTimeSeries', verbosity=3, **md):\n \n self.naming_scheme_hold = self.naming_scheme\n self.naming_scheme = ['name', 'extra', 'clock', 'exposure_time']\n super().measureTimeSeries(exposure_time=exposure_time, num_frames=num_frames, wait_time=wait_time, extra=extra, measure_type=measure_type, verbosity=verbosity, **md)\n self.naming_scheme = self.naming_scheme_hold\n \n def goto(self, label, verbosity=3, **additional):\n super().goto(label, verbosity=verbosity, **additional)\n # You can add customized 'goto' behavior here\n \n \n\n\n#cms.SAXS.setCalibration([247.5, 528.0], 2.395, [0, 27.52]) # 2017-01-30, 17 keV\n#cms.SAXS.setCalibration([263.5, 552.0], 5.038, [0.00, 35.00]) # 2017-02-08, 13.5 keV\ncms.SAXS.setCalibration([379.0, 552.0], 5.038, [20.00, 35.00]) # 2017-02-08, 13.5 keV\n\nprint('\\n\\n\\nReminders:')\nprint(' Define your detectors using, e.g.: detselect(pilatus2M)')\nprint(' Reload your user-specific script, e.g.: %run -i /nsls2/xf11bm/data/2017_2/user_group/user.py')\nprint('\\n')\n \nif False:\n # For testing (and as examples...)\n # %run -i /opt/ipython_profiles/profile_collection/startup/98-user.py\n \n hol = CapillaryHolder(base=stg)\n hol.addSampleSlot( Sample('test_sample_01'), 1.0 )\n hol.addSampleSlot( Sample('test_sample_02'), 3.0 )\n hol.addSampleSlot( Sample('test_sample_03'), 5.0 )\n \n sam = hol.getSample(1) \n \n \n \n","sub_path":"startup/97-user.py","file_name":"97-user.py","file_ext":"py","file_size_in_byte":3530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"61079766","text":"import sqlite3\n\ntasks = []\n\n# DB connection and creation of the table\ndb = sqlite3.connect(\"tasks.db\")\nc = db.cursor()\nc.execute(\"DROP TABLE IF EXISTS tasks\")\nc.execute(\"CREATE TABLE IF NOT EXISTS tasks(id INTEGER PRIMARY KEY AUTOINCREMENT, todo VARCHAR(256), urgent INTEGER)\")\n\n# Reading tasks\ntry:\n with open(\"task_list.txt\", \"r+\") as file:\n for line in file:\n tasks.append({\"description\": line.split(\";\")[0], \"urgent\": line.split(\";\")[1].strip(\"\\n\")})\nexcept FileNotFoundError:\n print(\"FileNotFoundError\")\n\nfor task in tasks:\n c.execute(\"INSERT INTO tasks(todo, urgent) VALUES(?,?)\", (task[\"description\"], task[\"urgent\"],))\n\nprint(c.execute(\"SELECT * FROM tasks\").fetchall())\n\ndb.commit()\ndb.close()\n","sub_path":"AmI-Labs/lab_7/generate_db.py","file_name":"generate_db.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"369129067","text":"# -*- coding: utf-8 -*-\nfrom Qt.QtWidgets import *\nfrom Qt.QtCore import *\nfrom Qt.QtGui import *\nfrom miraFramework.Filter import Filter\nimport pipeGlobal\nfrom miraLibs.pyLibs import join_path\n\n\nclass ReplaceAssetUI(QDialog):\n def __init__(self, parent=None):\n super(ReplaceAssetUI, self).__init__(parent)\n self.resize(1000, 600)\n self.setObjectName(\"Replace Asset\")\n self.setup_ui()\n self.setWindowFlags(Qt.Window)\n\n def setup_ui(self):\n main_layout = QVBoxLayout(self)\n main_layout.setContentsMargins(0, 2, 0, 0)\n top_layout = QHBoxLayout()\n\n convert_layout = QHBoxLayout()\n convert_label = QLabel(\"Convert\")\n self.src_cbox = QComboBox()\n to_label = QLabel(\"=========>\")\n self.dst_cbox = QComboBox()\n convert_layout.addWidget(convert_label)\n convert_layout.addWidget(self.src_cbox)\n convert_layout.addWidget(to_label)\n convert_layout.addWidget(self.dst_cbox)\n\n self.select_check = QCheckBox(\"Select in Maya\")\n self.select_check.setChecked(True)\n\n self.filter_le = Filter()\n self.filter_le.setPlaceholderText(\"Search...\")\n self.update_btn = QToolButton()\n icon_path = join_path.join_path2(pipeGlobal.icons_dir, \"update.png\")\n self.update_btn.setIcon(QIcon(icon_path))\n self.update_btn.setStyleSheet(\"QToolButton{background:transparent;border: 0px;}\"\n \"QToolButton::hover{background:#AAAAAA;}\")\n\n top_layout.addLayout(convert_layout)\n top_layout.addWidget(self.select_check)\n top_layout.addStretch()\n top_layout.addWidget(self.filter_le)\n top_layout.addWidget(self.update_btn)\n\n self.tree_view = QTreeView()\n\n btn_layout = QHBoxLayout()\n self.replace_btn = QPushButton(\"Replace\")\n self.replace_btn.setFixedHeight(30)\n btn_layout.addWidget(self.replace_btn)\n\n main_layout.addLayout(top_layout)\n main_layout.addWidget(self.tree_view)\n main_layout.addLayout(btn_layout)\n\n\nif __name__ == \"__main__\":\n import sys\n app = QApplication(sys.argv)\n rau = ReplaceAssetUI()\n rau.show()\n app.exec_()\n\n\n\n\n","sub_path":"miraPipeline/maya/replace_asset/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"194519755","text":"from django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('', views.signin, name='home'),\n path('account/', views.signin, name='account'),\n path('account/login/', views.signin, name='signin'),\n path('account/logout/', views.signout, name='signout'),\n path('account/register/', views.signup, name='signup'),\n path('account/success-register/', views.success_register, name='success_register'),\n]\n","sub_path":"project/apps/account/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"36865399","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/local/lib/python2.7/site-packages/pytestipdb/ptipdb.py\n# Compiled at: 2014-09-02 16:56:16\n\"\"\" Interactive debugging with ipdb, the IPython Debugger. \"\"\"\nimport inspect, sys, py, pytest\n\ndef pytest_addoption(parser):\n group = parser.getgroup('general')\n group._addoption('--ipdb', action='store_true', dest='use_ipdb', default=False, help='Starts the interactive IPython debugger on errors.')\n\n\ndef patch_ipdb(config):\n \"\"\"patch ipdb.set_trace to first disable stdout capturing\"\"\"\n try:\n original_trace = py.std.ipdb.set_trace\n except AttributeError:\n return\n\n def cleanup():\n py.std.ipdb.set_trace = original_trace\n\n def set_trace(frame=None):\n if frame is None:\n frame = sys._getframe().f_back\n capman = config.pluginmanager.getplugin('capturemanager')\n if capman:\n out, err = capman.suspendcapture()\n original_trace(frame)\n return\n\n py.std.ipdb.set_trace = set_trace\n config._cleanup.append(cleanup)\n return\n\n\ndef pytest_configure(config):\n patch_ipdb(config)\n if config.getvalue('use_ipdb'):\n config.pluginmanager.register(IpdbInvoker(), 'ipdbinvoker')\n pytest.set_trace = PytestIpdb().set_trace\n\n\nclass PytestIpdb:\n \"\"\" Pseudo ipdb that defers to the real ipdb. \"\"\"\n item = None\n collector = None\n\n def set_trace(self):\n \"\"\" invoke ipdb set_trace debugging, dropping any IO capturing. \"\"\"\n frame = sys._getframe().f_back\n item = self.item or self.collector\n if item is not None:\n capman = item.config.pluginmanager.getplugin('capturemanager')\n out, err = capman.suspendcapture()\n if hasattr(item, 'outerr'):\n item.outerr = (\n item.outerr[0] + out, item.outerr[1] + err)\n tw = py.io.TerminalWriter()\n tw.line()\n tw.sep('>', 'PDB set_trace (IO-capturing turned off)')\n import ipdb\n ipdb.set_trace(frame)\n return\n\n\ndef ipdbitem(item):\n PytestIpdb.item = item\n\n\npytest_runtest_setup = pytest_runtest_call = pytest_runtest_teardown = ipdbitem\n\n@pytest.mark.tryfirst\ndef pytest_make_collect_report(__multicall__, collector):\n try:\n PytestIpdb.collector = collector\n return __multicall__.execute()\n finally:\n PytestIpdb.collector = None\n\n return\n\n\ndef pytest_runtest_makereport():\n PytestIpdb.item = None\n return\n\n\nclass IpdbInvoker:\n\n @pytest.mark.tryfirst\n def pytest_runtest_makereport(self, item, call, __multicall__):\n rep = __multicall__.execute()\n if not call.excinfo or call.excinfo.errisinstance(pytest.skip.Exception) or call.excinfo.errisinstance(py.std.bdb.BdbQuit):\n return rep\n if hasattr(rep, 'wasxfail'):\n return rep\n tw = item.config.pluginmanager.getplugin('terminalreporter')._tw\n tw.line()\n tw.sep('>', 'traceback')\n rep.toterminal(tw)\n tw.sep('>', 'entering PDB')\n if isinstance(call.excinfo.value, py.std.doctest.UnexpectedException):\n tb = call.excinfo.value.exc_info[2]\n else:\n tb = call.excinfo._excinfo[2]\n post_mortem(tb)\n rep._ipdbshown = True\n return rep\n\n\ndef post_mortem(tb):\n import IPython\n stdout = sys.stdout\n try:\n sys.stdout = sys.__stdout__\n if hasattr(IPython, 'InteractiveShell'):\n if hasattr(IPython.InteractiveShell, 'instance'):\n shell = IPython.InteractiveShell.instance()\n p = IPython.core.debugger.Pdb(shell.colors)\n else:\n shell = IPython.InteractiveShell()\n ip = IPython.core.ipapi.get()\n p = IPython.core.debugger.Pdb(ip.colors)\n else:\n shell = IPython.Shell.IPShell(argv=[''])\n ip = IPython.ipapi.get()\n p = IPython.Debugger.Pdb(ip.options.colors)\n p.reset()\n frame, filename, line, func_name, ctx, idx = inspect.getinnerframes(tb)[(-1)]\n p.interaction(frame, tb)\n finally:\n sys.stdout = stdout","sub_path":"pycfiles/pytest-ipdb-0.1-prerelease2.macosx-10.9-x86_64.tar/ptipdb.py","file_name":"ptipdb.py","file_ext":"py","file_size_in_byte":4262,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"208990168","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom apscheduler.schedulers.blocking import BlockingScheduler\n#from datetime import datetime\n#sudo pip3 install apscheduler\nfrom os import getcwd\nimport glob\nimport tensorflow as tf\nfrom scipy import misc\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport argparse\nimport facenet\nimport detect_face\nimport os\nfrom os.path import join as pjoin\nimport sys\nimport time\nimport copy\nimport math\nimport pickle\nfrom sklearn.svm import SVC\nfrom sklearn.externals import joblib\nimport serial\nimport time\nimport threading\nimport tkinter\nfrom tkinter import Tk, Label, Entry, Radiobutton, IntVar\n#import datetime,re\nimport re\nfrom datetime import datetime, timedelta\nfrom multiprocessing import Process, Pipe\n#import queue\n\nimport chardet\n#from ftplib import FTP\nfrom ftplib import FTP_TLS\n#from PIL import Image, ImageTk\n#update release 2019/12/23 v2.0 更新gui為同一份code,整合成兩個threading\n#update release 2019/12/24 v2.1更新清除登入門禁人物資訊\n#update release 2020/02/07 v2.2新增整理每月彙整表及上傳資料\n#update release 2020/02/10 v2.2修改資料夾位置models/day ==>放每天檔案 ./data==>放整理後每月的資料 ftp: /home/AccessFace/day==>放每天 /home/AccessFace/month==>放每個月\n#update release 2020/02/27 v2.3修改禮拜一到五啟動六日不運作\nreading='stranger'\npredictionMax=0.67\n#predictionMax=0.73 #default\npredictionMin=0.50\n#predictionMin=0.60#default\nsetFailLimit=0.60\n#setFailLimit=0.67 #default\nsuccessNum=1\nfailNum=2\n#failNum=5#default\nstrangerNum=5\npassRate=1\n\nstartime=1 #設定1開啟定時模式週一到週五 6:30 啟動,設定0則不運作\nstart_hour=6\nstart_min=30\nupload_hour=20\nupload_min=30\n\ntrain_hour=11\ntrain_min=19\n\n\nser = serial.Serial('/dev/ttyS3', 115200) \nser.write( 'set_0'.encode('utf-8') + str(successNum).encode('utf-8')+'_0'.encode('utf-8')+str(strangerNum).encode('utf-8')+'_0'.encode('utf-8')+str(failNum).encode('utf-8')+'_'.encode('utf-8')+str(int(predictionMax*1000)).encode('utf-8')+'_'.encode('utf-8')+str(int(setFailLimit*1000)).encode('utf-8')+'_'.encode('utf-8')+str(passRate*100).encode('utf-8')+'\\r\\n'.encode('utf-8'))\nprint('[Set System] (Success Limit): %s (Stranger Limit): %s (Fail Limit): %s (Prediction Max): %s (Prediction Min): %s (Set Fail Limit): %s (Pass Rate): %s \\n'%(str(successNum) ,str(strangerNum) ,str(failNum) ,str(predictionMax) ,str(predictionMin) ,str(setFailLimit) ,str(passRate) ) )\n#set_success筆數_stranger筆數_fail筆數_辨識度上限_辨識度下限_打折率\\r\\n\n\n#q = queue.Queue(maxsize = 300)\n\ndef frameflesh(start_hour,start_min):\n\n with tf.Graph().as_default():\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)\n sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))\n with sess.as_default():\n \n pnet, rnet, onet = detect_face.create_mtcnn(sess, '.')\n \n minsize = 80 # minimum size of face\n threshold = [0.6, 0.7, 0.7] # three steps's threshold\n factor = 0.709 # scale factor\n #factor = 0.709 # scale factor\n margin = 44\n #margin = 44\n frame_interval = 3\n batch_size = 1000\n image_size = 182\n input_image_size = 170\n \n #train human name\n HumanNames =read_train_object()\n print (str(HumanNames))\n \n print('Loading feature extraction model')\n \n modeldir = '/home/vincent/facenet/models/20180402-114759/20180402-114759.pb'\n #modeldir = '/home/vincent/facenet/models/20180408-102900/20180408-102900.pb'\n facenet.load_model(modeldir)\n \n images_placeholder = tf.get_default_graph().get_tensor_by_name(\"input:0\")\n embeddings = tf.get_default_graph().get_tensor_by_name(\"embeddings:0\")\n \n phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(\"phase_train:0\")\n embedding_size = embeddings.get_shape()[1]\n \n classifier_filename = '/home/vincent/facenet/models/my_classifier.pkl'\n classifier_filename_exp = os.path.expanduser(classifier_filename)\n with open(classifier_filename_exp, 'rb') as infile:\n (model, class_names) = pickle.load(infile)\n print('load classifier file-> %s' % classifier_filename_exp)\n \n video_capture = cv2.VideoCapture(0)\n video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\n video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n c = 0\n \n # #video writer\n # fourcc = cv2.VideoWriter_fourcc(*'DIVX')\n # out = cv2.VideoWriter('3F_0726.avi', fourcc, fps=30, frameSize=(640,480))\n setting4_fix(start_hour,start_min)\n print('Start Recognition!')\n prevTime = 0\n #ser.open()\n \n #while not q.empty():\n #reading=q.get()\n #updategui(reading)\n \n savereset=3 \n while True:\n \n #reading=q.get()\n #updategui(reading) \n #data =recv(ser) \n \n ret, frame = video_capture.read()\n saveframe = frame.copy()\n # frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5) #resize frame (optional)\n \n curTime = time.time() # calc fps\n timeF = frame_interval # = 3\n \n if (c % timeF == 0):\n find_results = []\n \n # if 2dim img then cvt to rgb\n if frame.ndim == 2:\n frame = facenet.to_rgb(frame)\n frame = frame[:, :, 0:3]\n \n bounding_boxes, _ = detect_face.detect_face(frame, minsize, pnet, rnet, onet, threshold, factor)\n \n nrof_faces = bounding_boxes.shape[0]\n \n print('Number of Faces are detected: {}'.format(nrof_faces))\n if nrof_faces==0:\n #print (ser.portstr)\n ser.write('ready\\r\\n'.encode('utf-8'))\n #self.textCtrl1.Value = ser.read(5)\n if nrof_faces > 0:\n det = bounding_boxes[:, 0:4]\n \n img_size = np.asarray(frame.shape)[0:2]\n \n cropped = []\n scaled = []\n scaled_reshape = []\n bb = np.zeros((nrof_faces,4), dtype=np.int32)\n \n for i in range(nrof_faces):\n emb_array = np.zeros((1, embedding_size))\n \n bb[i][0] = det[i][0]\n bb[i][1] = det[i][1]\n bb[i][2] = det[i][2]\n bb[i][3] = det[i][3]\n \n # inner exception\n if bb[i][0] <= 0 or bb[i][1] <= 0 or bb[i][2] >= len(frame[0]) or bb[i][3] >= len(frame):\n print('face is inner of range!')\n continue\n \n cropped.append(frame[bb[i][1]:bb[i][3], bb[i][0]:bb[i][2], :])\n try:\n cropped[i] = facenet.flip(cropped[i], False)\n scaled.append(misc.imresize(cropped[i], (image_size, image_size), interp='bilinear'))\n \n scaled[i] = cv2.resize(scaled[i], (input_image_size,input_image_size),\n interpolation=cv2.INTER_CUBIC)\n scaled[i] = facenet.prewhiten(scaled[i])\n scaled_reshape.append(scaled[i].reshape(-1,input_image_size,input_image_size,3))\n feed_dict = {images_placeholder: scaled_reshape[i], phase_train_placeholder: False}\n \n emb_array[0, :] = sess.run(embeddings, feed_dict=feed_dict)\n \n predictions = model.predict_proba(emb_array)\n best_class_indices = np.argmax(predictions, axis=1)\n best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]\n \n \n #plot result idx under box\n text_x = bb[i][0]\n text_y = bb[i][3] + 20\n \n \n if np.max(predictions[0]) > predictionMax :\n put_text = '{name} {confidence: 3.2f}'.format(name = HumanNames[best_class_indices[0]], confidence = (np.max(predictions[0]).tolist())*100)\n #gonumber=int((np.max(predictions[0]).tolist())*1000)\n #print(gonumber)\n ser.write( 'success_'.encode('utf-8')+str(int((np.max(predictions[0]).tolist())*1000)).encode('utf-8')+'_'.encode('utf-8')+HumanNames[best_class_indices[0]].encode('utf-8')+'\\r\\n'.encode('utf-8') )\n reading= 'success_'.encode('utf-8')+str(int((np.max(predictions[0]).tolist())*1000)).encode('utf-8')+'_'.encode('utf-8')+HumanNames[best_class_indices[0]].encode('utf-8')+'\\r\\n'.encode('utf-8')\n cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (225, 225, 0), 2) #boxing face\n cv2.putText(frame, put_text, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1, (225, 225, 0), thickness=2, lineType=2)\n #confidence111=' {confidence: 3.2f}'.format(confidence = (np.max(predictions[0]).tolist())*100)\n print('Identification pass by name: %s'%(HumanNames[best_class_indices[0]])+' confidence:' + str(np.max(predictions[0]).tolist()) ) \n #x = datetime.datetime.now()\n #print(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")) \n historyFull(HumanNames[best_class_indices[0]] ,int((np.max(predictions[0]).tolist())*100) )\n print('HumanNames[best_class_indices',HumanNames[best_class_indices[0]])\n keyname=HumanNames[best_class_indices[0]].split('_')\n #reading=HumanNames[best_class_indices[0]] ,int((np.max(predictions[0]).tolist())*100) \n #reading(ser)\n #updategui(reading)\n xtime=datetime.now().strftime(\"%Y-%m-%d_%H%M%S\")\n nowtime=datetime.now()\n #print('savereset',savereset)\n #print('nowtime',nowtime)\n \n if savereset==3 :\n \n cv2.imwrite('../datasets/historyImage/'+keyname[0]+'_'+keyname[1]+'/' +keyname[0]+'_'+keyname[1]+'_'+xtime+'_'+ str(bb[i][0])+'_'+ str(bb[i][1])+'_'+ str(bb[i][2])+'_'+ str(bb[i][3]) +'.jpg',saveframe,[int(cv2.IMWRITE_JPEG_QUALITY), 100])\n savereset=1\n savetime=nowtime\n #print('savetime',savetime)\n #q.put('../datasets/historyImage/'+keyname[0]+'_'+keyname[1]+'/' +keyname[0]+'_'+keyname[1]+'_'+xtime+'_'+ str(bb[i][0])+'_'+ str(bb[i][1])+'_'+ str(bb[i][2])+'_'+ str(bb[i][3]) +'.jpg')\n \n if savereset==1 and nowtime-savetime > timedelta(seconds=7) :\n \n cv2.imwrite('../datasets/historyImage/'+keyname[0]+'_'+keyname[1]+'/' +keyname[0]+'_'+keyname[1]+'_'+xtime+'_'+ str(bb[i][0])+'_'+ str(bb[i][1])+'_'+ str(bb[i][2])+'_'+ str(bb[i][3]) +'.jpg',saveframe,[int(cv2.IMWRITE_JPEG_QUALITY), 100])\n savetime=nowtime\n print('hello============')\n #print('savetime',savetime)\n #q.put('../datasets/historyImage/'+keyname[0]+'_'+keyname[1]+'/' +keyname[0]+'_'+keyname[1]+'_'+xtime+'_'+ str(bb[i][0])+'_'+ str(bb[i][1])+'_'+ str(bb[i][2])+'_'+ str(bb[i][3]) +'.jpg')\n \n #存JPG寫法\n #cv2.imwrite('../datasets/historyImage/'+keyname[0]+'_'+keyname[1]+'/' +keyname[0]+'_'+keyname[1]+'_'+xtime+'_'+ str(bb[i][0])+'_'+ str(bb[i][1])+'_'+ str(bb[i][2])+'_'+ str(bb[i][3]) +'.jpg',saveframe,[int(cv2.IMWRITE_JPEG_QUALITY), 100])\n #存PNG寫法\n #cv2.imwrite('../models/historyImage/'+keyname[0]+'_'+keyname[1]+'/' +keyname[0]+'_'+keyname[1]+'_'+xtime+'_'+ str(bb[i][0])+'_'+ str(bb[i][1])+'_'+ str(bb[i][2])+'_'+ str(bb[i][3]) +'.png',saveframe,[int(cv2.IMWRITE_PNG_COMPRESSION), 8])\n \n \n elif np.max(predictions[0]) < predictionMax and np.max(predictions[0]) > predictionMin and HumanNames[best_class_indices[0]]:\n put_text = '{name} {confidence: 3.2f}'.format(name = HumanNames[best_class_indices[0]], confidence = (np.max(predictions[0]).tolist())*100)\n #gonumber1=int((np.max(predictions[0]).tolist())*1000)\n if np.max(predictions[0]) > setFailLimit :\n cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (225, 0, 255), 2) #boxing face\n cv2.putText(frame, put_text, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1, (225, 0, 255), thickness=2, lineType=2) \n else:\n cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 0, 255), 2) #boxing face\n cv2.putText(frame, put_text, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1, (0, 0, 255), thickness=2, lineType=2) \n \n ser.write( 'fail_'.encode('utf-8')+str(int((np.max(predictions[0]).tolist())*1000)).encode('utf-8')+'_'.encode('utf-8')+HumanNames[best_class_indices[0]].encode('utf-8')+'\\r\\n'.encode('utf-8') )\n #reading= 'fail_'.encode('utf-8')+str(int((np.max(predictions[0]).tolist())*1000)).encode('utf-8')+'_'.encode('utf-8')+HumanNames[best_class_indices[0]].encode('utf-8')+'\\r\\n'.encode('utf-8')\n historyFull(HumanNames[best_class_indices[0]] ,int((np.max(predictions[0]).tolist())*100) )\n #reading(ser)\n #updategui(reading)\n else:\n put_text = 'Stranger'\n cv2.rectangle(frame, (bb[i][0], bb[i][1]), (bb[i][2], bb[i][3]), (0, 225, 255), 2) #boxing face\n cv2.putText(frame, put_text, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,\n 1, (0, 225, 255), thickness=2, lineType=2) \n historyFull(put_text ,put_text )\n ser.write('stranger\\r\\n'.encode('utf-8'))\n reading= 'stranger\\r\\n'.encode('utf-8')\n #updategui(reading)\n #cv2.putText(frame, put_text, (text_x, text_y), cv2.FONT_HERSHEY_COMPLEX_SMALL,\n #1, (25, i*125, 25), thickness=2, lineType=2) \n #reading(ser)\n except IndexError :\n print(\"Oh No! IndexError : list index out of range for multi-faces\") \n \n \n else:\n print('Unable to align')\n \n \n sec = curTime - prevTime\n prevTime = curTime\n fps = 1 / (sec)\n str1 = 'FPS: %2.3f' % fps\n text_fps_x = len(frame[0]) - 150\n text_fps_y = 20\n cv2.putText(frame, str1, (text_fps_x, text_fps_y),\n cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 0), thickness=1, lineType=2)\n # c+=1\n cv2.imshow('Video', frame)\n \n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n \n video_capture.release()\n # #video writer\n # out.release()\n cv2.destroyAllWindows()\n\n\ndef recordfile(dataframe):\n hhq=open(dataframe, 'r') \n lines = hhq.readlines() \n last_line = lines[-1] \n return last_line\n\n\n\n#def read_from_port():\n #while True:\n #full = ser.readline()\n ##print('full: ',full)\n #historyFull_setting(str(full))\n #q.put(full)\n \n#def read_from_port1():\n ##while True:\n #full = ser.readline()\n ##print('full: ',full)\n ##historyFull_setting(str(full))\n\n\n\ndef month_and_day():\n x = datetime.now()\n \n # x.month=10\n if x.month<10 and x.month>=1:\n month='0'+str(x.month)\n #print (month)\n else :\n month=str(x.month)\n if x.day<10 and x.day>=1:\n day='0'+str(x.day)\n #print (day)\n else:\n day=str(x.day) \n year=str(x.year)\n return year+month+day\n\n \n\n\ndef historyIdentification(name,confidence):\n date=month_and_day()\n xtime=datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n chineseNow = open('/home/vincent/facenet/models/'+date+'-Identification','a')\n \n chineseNow.write(name+'@'+confidence+'@'+xtime+'\\n')\n chineseNow.close \n\ndef historyFull_setting(setting):\n date=month_and_day()\n xtime=datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n \n chineseNow = open('/home/vincent/facenet/models/day/'+date+'-Full','a')\n \n chineseNow.write(setting+'\\n')\n \n chineseNow.close \n\ndef historyFull(name,confidence):\n date=month_and_day()\n xtime=datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n \n chineseNow = open('/home/vincent/facenet/models/day/'+date+'-Full','a')\n \n chineseNow.write(name+'@'+str(confidence)+'@'+xtime+'\\n')\n chineseNow.close \n\ndef read_name():\n try:\n detect_path = '/home/vincent/facenet/models/name.txt'\n detectfile1=open(detect_path,'r')\n lines = detectfile1.readlines() #讀取所有行\n if 0==len(lines):\n write_name('unknow')\n lines='unknow'\n lines1=lines[-1].strip('\\n')\n detectfile1.close\n return lines1\n except:\n print('error: please mk file \"facenet/models/name.txt\". ') \n\n\ndef read_train_object():\n train_name = open('/home/vincent/facenet/models/name.txt','r') \n \n lines = train_name.readlines()\n count=0\n for a in lines:\n b=a.split('\\n')\n lines[count]=b[0]\n count += 1\n train_name.close\n return lines\n\ndef alert_time():\n timefile=open('/home/vincent/facenet/models/time.txt','r')\n lines = timefile.readlines()\n count=0\n for a in lines:\n b=a.split('\\n')\n lines[count]=b[0]\n count += 1\n timefile.close\n return lines\n\n\ndef settime2(hours_t,min_t):\n scheduler = BackgroundScheduler()\n scheduler.add_job(refleshDay, 'cron', hour=hours_t, minute=min_t)\n scheduler.start()\n\ndef setting3_main(start_hour,start_min):\n #btime=datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n #print(btime)\n # BlockingScheduler\n scheduler = BackgroundScheduler()\n scheduler.add_job(frameflesh,'cron', args=(start_hour,start_min), day_of_week='mon-fri', hour=start_hour, minute=start_min)\n scheduler.start()\n \ndef setting4_fix(start_hour,start_min):\n #print(btime)\n # BlockingScheduler\n scheduler = BackgroundScheduler()\n scheduler.add_job(fixgui, 'cron', day_of_week='mon-fri', hour=start_hour, minute=start_min+1)\n scheduler.start() \n \ndef setting5_retrain(start_hour,start_min):\n #print(btime)\n # BlockingScheduler\n scheduler = BackgroundScheduler()\n scheduler.add_job(retraining, 'cron', day_of_week='mon-fri', hour=start_hour, minute=start_min)\n scheduler.start()\n \n \ndef setting6_saveimage():\n #print(btime)\n # BlockingScheduler\n scheduler = BackgroundScheduler()\n scheduler.add_job(saveimage, 'interval', seconds=3)\n scheduler.dae\n scheduler.start()\n \n \ndef saveimage():\n print('===========flesh==========')\n reflesh()\n\ndef retraining():\n os.system(\"sudo chown -R vincent:vincent ../datasets/historyImage/\")\n \n\ndef fixgui():\n os.system(\"wmctrl -r Video -e 0,630,10,645,485\")\n print('setting wmctrl Video seccess')\ndef refleshDay():\n \n \n date=month_and_day()\n mdate=date[0:6]\n #date=date[:3]+'-'+date[3:]\n print('mdate',mdate)\n #將輸入的日期格式轉換ex. 2020/1 ==> 202001\n\n month_file=glob.glob(r'../models/day/'+mdate+'*-Full')\n print('month_file',month_file)\n \n month_file.sort()\n \n \n folder = 'data/'+mdate+'-face.csv'\n command = 'rm -r %s'%(folder)\n result = os.system(command)\n if result == 0:\n print ('delete ==> '+mdate+'.cav')\n else:\n print ('==> '+mdate+'-face.csv is not exist') \n \n for dday in month_file:\n \n print('dday',dday)\n day_used = np.loadtxt(dday,dtype=np.str,delimiter=' ',usecols=(0))\n day_used=day_used.flatten()\n \n \n #將日期切割成單純的日期時間2019-12-11 18:07:33\n lined=0\n for findtime in day_used :\n \n timemark=findtime.split('@')\n #print('in',np.argwhere(findtime))\n day_used[lined]=timemark[-1]\n lined=lined+1\n \n \n #找出有open及open前一列的日期時間 \n index_open=[]\n line_open=0 \n for findopen in day_used:\n if \"Open\" in findopen :\n index_open.append(line_open-1)\n index_open.append(line_open)\n line_open=line_open+1\n \n #利用index再次找出只有open及時間的陣列到day_used\n day_used=day_used[index_open ]\n \n \n #np.append(day_used,[['eeeeeeeeeeeeeeeee','111']],0)\n #print ('index_open',index_open)\n \n a1=np.array(dday)\n np.insert(day_used,0,values=a1,axis=0)\n print (\"AFTER\" ,day_used)\n #https://www.twblogs.net/a/5be2440f2b717720b51d2722\n \n index_hash=[]\n line_hash=0 \n hash1=mdate[:4]+'-'+mdate[4:] #將202001變成2020-01\n #搜尋在202001＊裡面有2020-01＊開頭的時間將該行跟下一行抓取出來，以避免used id重複\n for day_check in day_used:\n if hash1 in day_check :\n index_hash.append(line_hash)\n index_hash.append(line_hash+1)\n line_hash=line_hash+1\n day_used=day_used[index_hash ]\n \n #轉換為open,030704,Vincent,754,2019-07-02,09:21:07格式\n finalid=[]\n index_ting=[]\n line_ting=0 \n for day_check1 in day_used:\n if hash1 in day_check1 :\n #print('ddddddddd',day_used[line_ting+1])\n #print('line_ting',line_ting)\n groupid=day_used[line_ting+1].split(',')\n realid=groupid[1].split(':')\n #print('realid',realid[1])\n dayandtime=day_check1.split(' ')\n try :\n newid='open,'+realid[1]+','+persenID[realid[1]] +','+ pdID[realid[1]] +','+dayandtime[0]+','+dayandtime[1]\n #print('newid',newid)\n finalid.append(newid)\n except:\n print('worning:'+realid[1]+'已經消失了')\n line_ting=line_ting+1\n \n #print('finalid',finalid)\n \n #存檔\n with open('data/'+mdate+'-face.csv','a') as f: \n #for i in range(5): \n #newresult = np.random.rand(2, 3) \n \n np.savetxt(f, finalid,fmt='%s', delimiter=\",\") \n \n #ftp參考 \n #https://www.itread01.com/content/1549578987.html\n #ftp = FTP()\n #timeout = 30\n #port = 21\n ftp=FTP_TLS('dsm2.tul.com.tw')\n #ftp.connect('192.168.91.158',port,timeout) # 連線FTP伺服器\n ftp.login('TulAccessControl','@Tul760acc') # 登入\n ftp.encoding='utf-8'\n print (ftp.getwelcome()) # 獲得歡迎資訊 \n #d=ftp.cwd('home/AccessFace/') # 設定FTP路徑\n name=mdate+'-face.csv'\n path = 'data/' # 檔案儲存路徑\n name1=date+'-Full'\n path1 = '../models/day/' # 檔案儲存路徑 \n try:\n #d=ftp.cwd('home/AccessFace/')\n ftp.storbinary('STOR '+'home/AccessFace/month/'+name , open(path+name, 'rb')) # 上傳FTP檔案\n print(\"succes upload: \" +'home/AccessFace/month/'+name)\n ftp.storbinary('STOR '+'home/AccessFace/day/'+name1 , open(path1+name1, 'rb')) # 上傳FTP檔案\n print(\"succes upload: \" +'home/AccessFace/month /'+name)\n except:\n print(\"upload failed. check.......................\")\n \n ftp.quit() # 退出FTP伺服器 \n\ndef settime(hours_t,min_t):\n scheduler = BackgroundScheduler()\n scheduler.add_job(restart, 'cron', hour=hours_t, minute=min_t)\n scheduler.start()\n\ndef restart():\n os.system(\"sudo reboot\")\n \n \ndef reflesh():\n\n time.sleep(3)\n firstLabel = Label(mainWin, text='辨識中..' ,font=('Arial',40) ) \n successLabel = Label(mainWin, text=\"���禁限制\",font=('Arial',40),fg=\"#DC143C\" )\n \n resultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',40))\n personLabel = Label(mainWin, text=' ',font=('Arial',40),fg=\"#9400D3\" )\n \n pdLabel = Label(mainWin, text=\"部門為\",font=('Arial',40))\n pdresultLabel = Label(mainWin, text=' ',font=('Arial',40),fg=\"#9400D3\" ) \n firstLabel.grid(row=1,column=0, sticky='w')\n successLabel.grid(row=1,column=1, sticky='w')\n resultLabel.grid(row=2,column=0, sticky='w')\n personLabel.grid(row=2,column=1, sticky='w')\n pdLabel.grid(row=3,column=0, sticky='w')\n pdresultLabel.grid(row=3,column=1, sticky='w') \n mainWin.update() \n\n\n\n#os.system(\"sudo ln -sf /dev/ttyACM0 /dev/ttyS3\")\nprint ('set : train and timer')\n#time.sleep(1)\nset_time=alert_time()\nhours_t=int(set_time[0])\nmin_t=int(set_time[1])\nprint('initialization set timer: '+str(hours_t)+':'+str(min_t) )\n#time.sleep(1)\nprint ('start system....') \nsettime(hours_t,min_t)\nsettime2(upload_hour,upload_min)\nif startime==1:\n setting3_main(start_hour,start_min)\nelse:\n #建立子程序\n ts=threading.Thread(target=frameflesh,args=(start_hour,start_min))\n ts.start()\n \nsetting5_retrain(train_hour,train_min) \n\n \n \nprint('Creating networks and loading parameters')\nhistoryFull_setting('[Set System] (Success Limit): '+ str(successNum) + ' (Stranger Limit): '+ str(strangerNum)+ ' (Fail Limit): '+ str(failNum) + ' (Prediction Max): '+ str(predictionMax) + ' (Prediction Min): '+ str(predictionMin) + ' (Set Fail Limit): '+ str(setFailLimit) + ' (Pass Rate): '+ str(passRate))\n\n#建立子程序\n#ts=threading.Thread(target=frameflesh,args=())\n#ts.start()\n\ntoday=month_and_day()\nprint(today)\ndataframe = '/home/vincent/facenet/models/day/'+today+'-Full'\nprint(dataframe)\n# 步驟二：建立主視窗。\nmainWin = Tk()\n#var = IntVar()\noperation = [ '+', '-', '*', '/']\n\n# 視窗標題\nmainWin.title(\"face-gui\")\n# 視窗大小\nmainWin.geometry(\"550x500\")\n\n# 步驟三：建立視窗控制項元件。\n# 建立標籤\n\n#var1 = tkinter.StringVar()\n#a1='辨識中..'\n#var1.set(a1)\n\n\n\n\nallname=read_train_object()\nprint(allname)\nname123=[]\nnumber123=[]\npd123=[]\nfor a in allname:\n print(a)\n all_23=a.split(\"_\")\n print(all_23)\n number15=all_23[0]\n name15=all_23[1]\n pd15=all_23[2]\n number123.append(number15)\n name123.append(name15)\n pd123.append(pd15)\n \nprint(name123)\nprint(number123)\nprint(pd123)\n\npersenID = dict(zip(number123, name123))\npdID = dict(zip(number123, pd123))\nprint(persenID)\nprint(pdID)\n\n\n\n#建立資料夾\n#建立資料夾\nif not os.path.isdir('../datasets/'):\n os.mkdir('../datasets/') \nelse :\n print ('data file exist')\n\nif not os.path.isdir('../datasets/historyImage/'):\n os.mkdir('../datasets/historyImage/') \nelse :\n print ('data file exist') \n \n#建立每個人照片資料夾\nfor numberid in number123:\n if not os.path.isdir('../datasets/historyImage/'+numberid+'_'+persenID[numberid]):\n os.mkdir('../datasets/historyImage/'+numberid+'_'+persenID[numberid]) \n else :\n print ('../datasets/historyImage/'+numberid+'_'+persenID[numberid]+' file exist') \n\n\n\nfirstLabel = Label(mainWin, text='辨識中..' ,font=('Arial',40) ) \nsuccessLabel = Label(mainWin, text=\"門禁限制\",font=('Arial',40),fg=\"#DC143C\" )\n\nresultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',40))\npersonLabel = Label(mainWin, text=' ',font=('Arial',40),fg=\"#9400D3\" )\n\npdLabel = Label(mainWin, text=\"部門為\",font=('Arial',40))\npdresultLabel = Label(mainWin, text=' ',font=('Arial',40),fg=\"#9400D3\" ) \n \nfirstLabel.grid(row=1,column=0, sticky='w')\nsuccessLabel.grid(row=1,column=1, sticky='w')\nresultLabel.grid(row=2,column=0, sticky='w')\npersonLabel.grid(row=2,column=1, sticky='w')\npdLabel.grid(row=3,column=0, sticky='w')\npdresultLabel.grid(row=3,column=1, sticky='w') \nmainWin.update() \nshowreset=3\nwhile True:\n\n\n reading = ser.readline()\n historyFull_setting(str(reading)) \n encode_type = chardet.detect(reading) \n reading = reading.decode(encode_type['encoding']) \n print('reading : ',reading)\n #https://blog.csdn.net/jieli_/article/details/70166244\n #mainWin.after(20) \n \n \n if re.findall(\"Open\", reading) or re.findall(\"Pass\", reading) :\n \n\n \n print(reading)\n strangercount=re.findall(\"Stranger:+[0-9]+[0-9]\", reading)\n strangercount1=strangercount[0].split(\":\")\n print(strangercount1)\n \n if re.findall(\"Open\", reading):\n \n \n \n print(re.findall(\"ID:+[0-9]+[0-9]\", reading) )\n idd=re.findall(\"ID:+[0-9]+[0-9]\", reading)\n print(idd)\n iddd=idd[0].split(\":\")\n realID=iddd[1]\n\n\n \n \n \n \n nowtime=datetime.now()\n \n \n \n if showreset==3 :\n \n \n person = tkinter.StringVar()\n person.set(persenID[realID]+' ')\n \n pd = tkinter.StringVar()\n pd.set('DP '+pdID[realID]+' ') \n \n firstLabel = Label(mainWin, text='辨識中..',font=('Arial',40) ) \n successLabel = Label(mainWin,text=\"辨識成功\",font=('Arial',40),fg=\"#40E0D0\" )\n \n resultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',40))\n personLabel = Label(mainWin, textvariable=person,font=('Arial',40),fg=\"#9400D3\" )\n \n pdLabel = Label(mainWin, text=\"部門為\",font=('Arial',40))\n pdresultLabel = Label(mainWin, textvariable=pd,font=('Arial',40),fg=\"#9400D3\" )\n #reading=q.get()\n #print('q.get: ',reading) \n \n \n #photosucs = tkinter.PhotoImage(file=reading) #file：t图片路径\n #imgLabelsucs = tkinter.Label(mainWin,image=photosucs)#把图片整合到标签类中\n #imgLabelsucs.grid(column=0, row=4, sticky='w')\n \n \n firstLabel.grid(row=1,column=0, sticky='w')\n successLabel.grid(row=1,column=1, sticky='w')\n resultLabel.grid(row=2,column=0, sticky='w')\n personLabel.grid(row=2,column=1, sticky='w')\n pdLabel.grid(row=3,column=0, sticky='w')\n \n pdresultLabel.grid(row=3,column=1, sticky='w') \n #imgLabelsucs.grid(column=0, row=4, sticky='w')\n mainWin.update() \n \n \n \n reflesh()\n savetime=nowtime\n showreset=1\n saveID=realID\n #t1= threading.Timer(5,function=reflesh)\n #t1.start\n \n elif showreset==1 and saveID==realID and nowtime-savetime > timedelta(seconds=6) :\n \n \n \n person = tkinter.StringVar()\n person.set(persenID[realID]+' ')\n \n pd = tkinter.StringVar()\n pd.set('DP '+pdID[realID]+' ') \n \n firstLabel = Label(mainWin, text='辨識中..',font=('Arial',40) ) \n successLabel = Label(mainWin,text=\"辨識成功\",font=('Arial',40),fg=\"#40E0D0\" )\n \n resultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',40))\n personLabel = Label(mainWin, textvariable=person,font=('Arial',40),fg=\"#9400D3\" )\n \n pdLabel = Label(mainWin, text=\"部門為\",font=('Arial',40))\n pdresultLabel = Label(mainWin, textvariable=pd,font=('Arial',40),fg=\"#9400D3\" )\n #reading=q.get()\n #print('q.get: ',reading) \n \n \n #photosucs = tkinter.PhotoImage(file=reading) #file：t图片路径\n #imgLabelsucs = tkinter.Label(mainWin,image=photosucs)#把图片整合到标签类中\n #imgLabelsucs.grid(column=0, row=4, sticky='w')\n \n \n firstLabel.grid(row=1,column=0, sticky='w')\n successLabel.grid(row=1,column=1, sticky='w')\n resultLabel.grid(row=2,column=0, sticky='w')\n personLabel.grid(row=2,column=1, sticky='w')\n pdLabel.grid(row=3,column=0, sticky='w')\n \n pdresultLabel.grid(row=3,column=1, sticky='w') \n #imgLabelsucs.grid(column=0, row=4, sticky='w')\n mainWin.update() \n \n \n savetime=nowtime\n reflesh()\n #setting6_saveimage()\n #t1= threading.Timer(5,function=reflesh)\n #t1.start \n \n elif showreset==1 and saveID != realID :\n \n \n person = tkinter.StringVar()\n person.set(persenID[realID]+' ')\n \n pd = tkinter.StringVar()\n pd.set('DP '+pdID[realID]+' ') \n \n firstLabel = Label(mainWin, text='辨識中..',font=('Arial',40) ) \n successLabel = Label(mainWin,text=\"辨識成功\",font=('Arial',40),fg=\"#40E0D0\" )\n \n resultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',40))\n personLabel = Label(mainWin, textvariable=person,font=('Arial',40),fg=\"#9400D3\" )\n \n pdLabel = Label(mainWin, text=\"部門為\",font=('Arial',40))\n pdresultLabel = Label(mainWin, textvariable=pd,font=('Arial',40),fg=\"#9400D3\" )\n #reading=q.get()\n print('q.get: ',reading) \n \n \n #photosucs = tkinter.PhotoImage(file=reading) #file：t图片路径\n #imgLabelsucs = tkinter.Label(mainWin,image=photosucs)#把图片整合到标签类中\n #imgLabelsucs.grid(column=0, row=4, sticky='w')\n \n \n firstLabel.grid(row=1,column=0, sticky='w')\n successLabel.grid(row=1,column=1, sticky='w')\n resultLabel.grid(row=2,column=0, sticky='w')\n personLabel.grid(row=2,column=1, sticky='w')\n pdLabel.grid(row=3,column=0, sticky='w')\n \n pdresultLabel.grid(row=3,column=1, sticky='w') \n #imgLabelsucs.grid(column=0, row=4, sticky='w')\n mainWin.update() \n \n \n\n savetime=nowtime\n saveID=realID\n reflesh()\n #setting6_saveimage()\n #t1= threading.Timer(5,function=reflesh)\n #t1.start \n \n \n \n\n #elif re.findall(\"Pass\", autosave) and int(strangercount1[1])<=8:\n #print(re.findall(\"ID:+[0-9]+[0-9]\", reading) )\n #idd=re.findall(\"ID:+[0-9]+[0-9]\", reading)\n #print(idd)\n #iddd=idd[0].split(\":\")\n #realID=iddd[1]\n\n #person = tkinter.StringVar()\n #person.set(persenID[realID]+' ')\n\n #pd = tkinter.StringVar()\n #pd.set('DP '+pdID[realID]+' ') \n\n #firstLabel = Label(mainWin, text='辨識中..' ,font=('Arial',50) ) \n #successLabel = Label(mainWin, text=\"辨識失敗\",font=('Arial',50),fg=\"#DC143C\" )\n\n #resultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',50))\n #personLabel = Label(mainWin, text=\" \",font=('Arial',50),fg=\"#9400D3\" )\n\n #pdLabel = Label(mainWin, text=\"提醒 \",font=('Arial',50),fg=\"#DC143C\")\n #pdresultLabel = Label(mainWin, text=\"請勿兩人同時辨識\",font=('Arial',28),fg=\"#9400D3\" )\n \n elif re.findall(\"Pass\", reading) and int(strangercount1[1])>=8 :\n firstLabel = Label(mainWin, text='辨識中..' ,font=('Arial',40) ) \n successLabel = Label(mainWin, text=\"門禁限制\",font=('Arial',40),fg=\"#DC143C\" )\n\n resultLabel = Label(mainWin, text=\"辨識身份\",font=('Arial',40))\n personLabel = Label(mainWin, text=\"陌生人 \",font=('Arial',40),fg=\"#9400D3\" )\n\n pdLabel = Label(mainWin, text=\"提醒 \",font=('Arial',40),fg=\"#DC143C\")\n pdresultLabel = Label(mainWin, text=\"請看鏡頭重新辨識\",font=('Arial',28),fg=\"#9400D3\" )\n firstLabel.grid(row=1,column=0, sticky='w')\n successLabel.grid(row=1,column=1, sticky='w')\n resultLabel.grid(row=2,column=0, sticky='w')\n personLabel.grid(row=2,column=1, sticky='w')\n pdLabel.grid(row=3,column=0, sticky='w')\n pdresultLabel.grid(row=3,column=1, sticky='w') \n mainWin.update() \n\n\n\n\n\n","sub_path":"conbine2-0313.py","file_name":"conbine2-0313.py","file_ext":"py","file_size_in_byte":40324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"653619046","text":"r\"\"\"Tests for drivers sub-package.\"\"\"\nfrom yggdrasil import drivers\nfrom yggdrasil.tests import scripts\n\n\ndef test_create_driver():\n r\"\"\"Test driver creation w/ and w/o args.\"\"\"\n drivers.create_driver('Driver', 'test_io_driver')\n drivers.create_driver('ExecutableModelDriver', 'test_model_driver',\n args=scripts['python'])\n\n\n__all__ = []\n","sub_path":"yggdrasil/drivers/tests/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"512065684","text":"from random import randint\nimport math\n# Taking input from users\nlower_range = int(input(\"Enter Lower range => \"))\n# upper range\nupper_range = int(input(\"Enter Upper range => \"))\n\n# creating A random number between lower and upper range\nrandom_number = randint(lower_range,upper_range)\nprint(random_number)\n\n# Calculating minimum Guessing Number limit\nguessing_limit = round(math.log2(upper_range - lower_range + 1))\n\nprint(\"You Have Only \",guessing_limit,\"chance to guess the number\")\ncount = 0\n# to show remaining guessing chance\nchance_used = 0\nwhile count < guessing_limit:\n \n guess = int(input(\"Guess the Number => \"))\n count += 1\n chance_used += 1\n # checking the guessed number and random number\n if random_number == guess:\n print(\"Congratulation !!, You Guessed the right number\")\n break\n elif random_number > guess:\n print(\"You Guessed too Low\")\n print(\"Guessing chance left =>\",guessing_limit - chance_used)\n elif random_number < guess:\n print(\"You Guessed too High \")\n print(\"Guessing chance left =>\",guessing_limit - chance_used)\n \n # putting condition if count exceed guessing limit\n if count >= guessing_limit:\n print(\"Sorry !!,You exceeded the guessing Limit\")\n","sub_path":"code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"627969031","text":"import sys\nimport random\n\n###########################################################\n#http://www.practicepython.org/exercise/2014/03/12/06-string-lists.html\nword= input('Enter a word to check if its a palindrome:\\n')\nprint(word)\nnewword=''\n\nfor letter in range(1,len(word)+1):\n\tnewword+=word[-letter]\n\nprint(newword)\n\nif newword==word:\n\tprint('The word is palindrome indeed.')\nelse:\n\tprint('not a palindrome, try again later.')\n#The following solution was offered by:\n#https://gist.github.com/anonymous/3299da3707670a919d4d\n#I had not seen this: name[::-1]\n\"\"\"Ask the user for a string and print out whether this string is a palindrome or not.\n (A palindrome is a string that reads the same forwards and backwards.)\"\"\"\n\nname = input(\"Enter a string \")\nname_reversed = name[::-1]\n\nif name == name_reversed:\n print(\"A palindrome string\")\nelse:\n\tprint(\"Not a palindrom string\")\n\n###########################################################\nprint('This program has finished. \\n Press Enter to exit')\ninput()\nsys.exit()","sub_path":"exercise6stringlists.py","file_name":"exercise6stringlists.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"410205961","text":"from label import Label\nfrom button import IconButton\n\nclass IncrementLabel:\n step = 10\n\n def __init__(self, context, text, parameter, x, y):\n ''' A visual display for displaying and incrementing parametric.Parameter\n values in fixed steps.\n '''\n x *= context.xscale\n y *= context.yscale\n self.parameter = parameter\n fontsize = 80\n Label(context, text, int(x), int(y), fontsize, context.black, centered=False)\n context.add_widget(IconButton(context, \"./icons/remove-circle.png\", x+120, y+18, self.minus_x, scale=1.25))\n self.value = Label(context, '+200.0', x+160, y, fontsize, context.black, centered=False)\n context.add_widget(IconButton(context, \"./icons/add-circle.png\", x+370, y+18, self.plus_x, scale=1.25))\n self.update(self.parameter.value)\n\n def minus_x(self):\n self.update(str(float(self.value.text)-self.step))\n\n def plus_x(self):\n self.update(str(float(self.value.text)+self.step))\n\n def update(self, text):\n text = '{:>+{w}.{p}f}'.format(float(text), w=5, p=1)\n self.parameter(float(text))\n index=-1\n self.value.update(text)\n print(self.parameter)\n","sub_path":"gui/parameter_widget.py","file_name":"parameter_widget.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"307852081","text":"__author__ = 'Eric'\nimport math\ndef prime(n):\n if n % 2 == 0 and n > 2:\n return False\n return all(n % i for i in range(3, int(math.sqrt(n)) + 1, 2))\ncount =0\nnumber = 2\nwhile count != 6:\n if prime(number):\n count += 1\n number += number\n else:\n number += number\n\nprint(number)\n","sub_path":"Euler7.py","file_name":"Euler7.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"331924355","text":"statesDone = False\nstatesTraveled = []\n\nwhile statesDone == False:\n state = str(input(\"Have you traveled to any other states? \"))\n if state.lower() == \"no\":\n print (statesTraveled)\n print (\"You have been to \" + str(len(statesTraveled)) + \" states.\")\n break;\n else:\n statesTraveled.append(state)\n\n \n","sub_path":"Python/Basics7/Capitalization.py","file_name":"Capitalization.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"608878894","text":"# Proprietary: Benten Technologies, Inc.\n# Author: Pranav H. Deo\n# Copyright Content\n\n# Code Description:\n# Appending columns: OpenFace_PSPI, SUM_AU, AU43_c and Labels: OPR, AFF, SEN, VAS\n\nimport pandas as pd\nimport numpy as np\nimport os\n\n\ndef Sliding_Window_AU43(x_data, num_steps):\n # Create empty list of the size of AU_45c\n new = pd.DataFrame(np.zeros((len(x_data), 1)), columns=['AU43_c'])\n # Loop over AU_45c and identify longer lists of eye closure detections\n for i in range(x_data.shape[0]):\n # compute a new (sliding window) index\n end_ix = i + num_steps\n # if index is larger than the size of the list, end loop\n if end_ix >= x_data.shape[0]:\n break\n # Compute product of the elements and check for 1 (Eye closure) over the num_steps\n score = x_data[i:end_ix]\n mult = np.prod(score)\n if mult == 1.0:\n new['AU43_c'][i:end_ix] = 1.0\n return new\n\n\ninpath = '/Users/pranavdeo/Desktop/UNBC/'\noutpath = '/Users/pranavdeo/Desktop/UNBC_Out/'\nsummary_file = pd.read_csv('/Users/pranavdeo/Desktop/UNBC_Labels.csv')\n\ndir_list = os.listdir(inpath)\n\nfor d in dir_list:\n if d != '.DS_Store':\n ip = inpath + d\n op = outpath + d\n os.mkdir(op)\n files = os.listdir(ip)\n csv_files = [fl for fl in files if fl.endswith('.csv')]\n\n for f in csv_files:\n videofile = os.path.splitext(f)[0]\n df = pd.read_csv(ip + '/' + f)\n Final_DF = pd.DataFrame()\n Final_DF['frames'] = df['frame']\n Final_DF['timestamp'] = df['timestamp']\n Final_DF['AU_01r'] = df['AU01_r']\n Final_DF['AU_02r'] = df['AU02_r']\n Final_DF['AU_04r'] = df['AU04_r']\n Final_DF['AU_05r'] = df['AU05_r']\n Final_DF['AU_06r'] = df['AU06_r']\n Final_DF['AU_07r'] = df['AU07_r']\n Final_DF['AU_09r'] = df['AU09_r']\n Final_DF['AU_10r'] = df['AU10_r']\n Final_DF['AU_12r'] = df['AU12_r']\n Final_DF['AU_14r'] = df['AU14_r']\n Final_DF['AU_15r'] = df['AU15_r']\n Final_DF['AU_17r'] = df['AU17_r']\n Final_DF['AU_20r'] = df['AU20_r']\n Final_DF['AU_23r'] = df['AU23_r']\n Final_DF['AU_25r'] = df['AU25_r']\n Final_DF['AU_26r'] = df['AU26_r']\n Final_DF['AU_45r'] = df['AU45_r']\n\n Final_DF['AU_01c'] = df['AU01_c']\n Final_DF['AU_02c'] = df['AU02_c']\n Final_DF['AU_04c'] = df['AU04_c']\n Final_DF['AU_05c'] = df['AU05_c']\n Final_DF['AU_06c'] = df['AU06_c']\n Final_DF['AU_07c'] = df['AU07_c']\n Final_DF['AU_09c'] = df['AU09_c']\n Final_DF['AU_10c'] = df['AU10_c']\n Final_DF['AU_12c'] = df['AU12_c']\n Final_DF['AU_14c'] = df['AU14_c']\n Final_DF['AU_15c'] = df['AU15_c']\n Final_DF['AU_17c'] = df['AU17_c']\n Final_DF['AU_20c'] = df['AU20_c']\n Final_DF['AU_23c'] = df['AU23_c']\n Final_DF['AU_25c'] = df['AU25_c']\n Final_DF['AU_26c'] = df['AU26_c']\n Final_DF['AU_45c'] = df['AU45_c']\n\n num_steps = 8\n new = Sliding_Window_AU43(Final_DF['AU_45c'], num_steps + 5)\n\n df['AU43_c'] = new['AU43_c']\n Final_DF['AU_43c'] = new['AU43_c']\n\n row_count = df.shape[0]\n PSPI = [0.0] * row_count\n indx = 0\n for index, row in df.iterrows():\n PSPI[indx] = PSPI[indx] + row['AU04_r'] + max(row['AU06_r'], row['AU07_r']) + \\\n max(row['AU09_r'], row['AU10_r']) + row['AU43_c']\n indx = indx + 1\n\n sum_AU_r = df['AU01_r'] + df['AU02_r'] + df['AU04_r'] + df['AU05_r'] + df['AU06_r'] + df['AU07_r'] \\\n + df['AU09_r'] + df['AU10_r'] + df['AU12_r'] + df['AU14_r'] + df['AU15_r'] + df['AU17_r'] \\\n + df['AU20_r'] + df['AU23_r'] + df['AU25_r'] + df['AU26_r'] + df['AU43_c']\n\n Final_DF['OpenFace_PSPI'] = PSPI\n Final_DF['sum_AU_r'] = sum_AU_r\n\n for indx, rw in summary_file.iterrows():\n if videofile == rw['Video_name']:\n Final_DF['OPR'] = rw['OPR']\n Final_DF['AFF'] = rw['AFF']\n Final_DF['SEN'] = rw['SEN']\n Final_DF['VAS'] = rw['VAS']\n\n Final_DF.to_csv(op + '/' + f, index=False)\n print('>File Done')\n\nprint('\\nProcess Complete')\n","sub_path":"Back_End/Facial Pain/Archive/Adding_Cols_sumAU_OFpspi.py","file_name":"Adding_Cols_sumAU_OFpspi.py","file_ext":"py","file_size_in_byte":4535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"504238641","text":"class BITree(object):\n def __init__(self, matrix):\n \"\"\"\n initialize your data structure here.\n :type matrix: List[List[int]]\n \"\"\"\n self.matrix = matrix\n m, n = len(matrix), len(matrix[0]) if matrix else 0 \n self.sum = [[0] * (n + 1) for _ in xrange(m + 1)]\n [operator.setitem(self.sum[row], col, self.sum[row][col] + self.matrix[i-1][j-1]) \\\n for row in xrange(1, m + 1)\\\n for col in xrange(1, n + 1)\\\n for i in xrange(row + 1 - (row & -row), row + 1)\n for j in xrange(col + 1 - (col & -col), col + 1)]\n \n \n\n def update(self, row, col, val):\n \"\"\"\n update the element at matrix[row,col] to val.\n :type row: int\n :type col: int\n :type val: int\n :rtype: void\n \"\"\"\n i = row + 1\n while i < len(self.sum):\n j = col + 1\n while j < len(self.sum[0]):\n self.sum[i][j] += val - self.matrix[row][col]\n j += j & -j\n i += i & -i\n self.matrix[row][col] = val\n def sumRange(self, row, col):\n rst, i = 0, row\n while i > 0:\n j = col\n while j > 0:\n rst += self.sum[i][j]\n j -= j & -j\n i -= i&-i\n return rst\nclass NumMatrix(object):\n def __init__(self, matrix):\n self.tree = BITree(matrix)\n def update(self, row, col, val):\n self.tree.update(row, col, val)\n\n def sumRegion(self, row1, col1, row2, col2):\n \"\"\"\n sum of elements matrix[(row1,col1)..(row2,col2)], inclusive.\n :type row1: int\n :type col1: int\n :type row2: int\n :type col2: int\n :rtype: int\n \"\"\"\n return self.tree.sumRange(row2 + 1, col2 + 1) - self.tree.sumRange(row1, col2 + 1) - self.tree.sumRange(row2 + 1, col1) + self.tree.sumRange(row1, col1)\n \n \n \n\n# Your NumMatrix object will be instantiated and called as such:\n# numMatrix = NumMatrix(matrix)\n# numMatrix.sumRegion(0, 1, 2, 3)\n# numMatrix.update(1, 1, 10)\n# numMatrix.sumRegion(1, 2, 3, 4)","sub_path":"308-Range-Sum-Query-2D---Mutable/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"652627486","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jan 25 13:24:00 2017\r\n\r\n@author: rda4abt\r\n\"\"\"\r\n\r\nimport sys\r\nfrom PyQt4 import QtGui, QtCore\r\nimport numpy as np\r\nfrom datetime import datetime\r\nimport time\r\nsys.path.append('../')\r\n\r\nclass ModuleGraphicsBase(QtGui.QFrame):\r\n\r\n def __init__(self, keyword, logo, text):\r\n super(ModuleGraphicsBase, self).__init__()\r\n self.setGeometry(0, 0, 80, 90)\r\n self.setMaximumSize(80,90)\r\n self.keyword = keyword\r\n self.logo = logo\r\n\r\n self.font = QtGui.QFont()\r\n self.font.setBold(True)\r\n\r\n self.box_layout = QtGui.QVBoxLayout()\r\n self.setFrameStyle(QtGui.QFrame.Panel | QtGui.QFrame.Raised)\r\n self.setCursor(QtGui.QCursor(QtCore.Qt.OpenHandCursor))\r\n self.setStatusTip('Drag File Loader and drop on to an empty tile.')\r\n self.setLayout(self.box_layout)\r\n\r\n def define_box(self):\r\n icon = QtGui.QLabel()\r\n icon.setPixmap(QtGui.QPixmap('asat\\\\Icons\\\\' + self.icon))\r\n self.box_layout.addWidget(icon)\r\n text = QtGui.QLabel()\r\n text.setText(self.display_text)\r\n text.setFont(self.font)\r\n self.box_layout.addWidget(text)\r\n\r\n def mousePressEvent(self, QMouseEvent):\r\n self.setFrameStyle(QtGui.QFrame.Panel | QtGui.QFrame.Sunken)\r\n self.setCursor(QtGui.QCursor(QtCore.Qt.ClosedHandCursor))\r\n password_new_tile = 'qwerty1234'\r\n if QMouseEvent.button() == QtCore.Qt.LeftButton:\r\n mime_data = QtCore.QMimeData()\r\n mime_data.setData('text/plain', (password_new_tile + ':' + self.keyword))\r\n drag_icon = QtGui.QPixmap('asat\\\\Icons\\\\' + self.logo)\r\n drag = QtGui.QDrag(self)\r\n drag.setMimeData(mime_data)\r\n drag.setPixmap(drag_icon)\r\n drag.setDragCursor(QtGui.QPixmap('asat\\\\Icons\\\\addmodule.png'), QtCore.Qt.MoveAction)\r\n drag.exec_()\r\n self.mouseReleaseEvent(QMouseEvent)\r\n\r\n def mouseReleaseEvent(self, QMouseEvent):\r\n self.setFrameStyle(QtGui.QFrame.Panel | QtGui.QFrame.Raised)\r\n self.setCursor(QtGui.QCursor(QtCore.Qt.OpenHandCursor))\r\n\r\n#-----------------------------------------------------------------------------------------------------------------------\r\n\r\nclass ModuleDialogBoxBase(QtGui.QDialog):\r\n def __init__(self, pw, column, row, list_of_parameters):\r\n super(ModuleDialogBoxBase, self).__init__()\r\n self.setWindowIcon(QtGui.QIcon('asat\\\\Icons\\\\logo.png'))\r\n self.pw = pw\r\n self.column = column\r\n self.row = row\r\n self.list_of_parameters = list_of_parameters\r\n self.create_dialog()\r\n\r\n def create_dialog(self):\r\n self.layout = QtGui.QVBoxLayout()\r\n self.setLayout(self.layout)\r\n self.ok_button = QtGui.QPushButton('Ok')\r\n self.ok_button.clicked.connect(self.ok_pressed)\r\n self.cancel_button = QtGui.QPushButton('Cancel')\r\n self.cancel_button.clicked.connect(self.close)\r\n self.specific_dialog_components()\r\n bottom_layout = QtGui.QHBoxLayout()\r\n bottom_widget = QtGui.QWidget()\r\n bottom_widget.setLayout(bottom_layout)\r\n bottom_layout.addStretch(1)\r\n bottom_layout.addWidget(self.ok_button)\r\n bottom_layout.addWidget(self.cancel_button)\r\n self.layout.addWidget(bottom_widget)\r\n\r\n#-----------------------------------------------------------------------------------------------------------------------\r\n\r\nclass MSRDataSet():\r\n def __init__(self, filename):\r\n self.filename = filename\r\n self.data_start_index = -1\r\n self.num_dataset = 0\r\n self.start_time = 0\r\n self.offset = 0\r\n\r\n def initialize_dataset(self):\r\n self.data_datetime = np.ndarray(self.num_dataset, float)\r\n self.data_x = np.zeros(self.num_dataset)\r\n self.data_y = np.zeros(self.num_dataset)\r\n self.data_z = np.zeros(self.num_dataset)\r\n\r\n def get_time(self):\r\n return(range(self.num_dataset))\r\n\r\n def get_actual_time(self):\r\n return(self.data_datetime)\r\n\r\n def get_x_acceleration(self):\r\n return(self.data_x)\r\n\r\n def get_y_acceleration(self):\r\n return (self.data_y)\r\n\r\n def get_z_acceleration(self):\r\n return (self.data_z)","sub_path":"asat/Plugins/ModuleBase.py","file_name":"ModuleBase.py","file_ext":"py","file_size_in_byte":4280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"129064816","text":"# -*- coding:utf-8 -*-\nimport random\n\nfrom django.views.generic import View\nfrom django.template.response import TemplateResponse\n\nimport app.tanka100.api as tanka_api\n\n\nHIRAGANA = (u\"あいうえお\"\n u\"かきくけこ\"\n u\"さしすせそ\"\n u\"たちつてと\"\n u\"なにぬねの\"\n u\"はひふへほ\"\n u\"まみむめも\"\n u\"やゆよ\"\n u\"わをん\"\n )\n\n\nclass Index(View):\n http_method_names = [\"get\"]\n template = \"top/index.html\"\n\n def get(self, request, *args, **kwargs):\n \"\"\"ランダムにひらがな一文字を指定して百人一首を検索する。\"\"\"\n keyword = random.choice(HIRAGANA)\n rows = tanka_api.findpoem(keyword)\n context = {\"rows\": rows, \"letter\": keyword}\n return TemplateResponse(request, self.template, context)\n","sub_path":"vol3/src/app/top/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"444528920","text":"#############################################################\n#################### START ADDON IMPORTS ####################\nimport xbmc\nimport xbmcaddon\nimport xbmcgui\nimport xbmcplugin\n\nimport os\nimport re\nimport sys\nimport urllib\nimport urllib2\nimport urlparse\nimport Main\nimport list\n\n\nimport pyxbmct.addonwindow as pyxbmct\nfrom addon.common.addon import Addon\n\ndialog = xbmcgui.Dialog()\n\n\n\n#############################################################\n#################### SET ADDON ID ###########################\n_addon_id_\t= 'plugin.video.neverwalkalone'\n_self_\t\t\t= xbmcaddon.Addon(id=_addon_id_)\n\n#############################################################\n#################### SET ADDON THEME DIRECTORY ##############\n_theme_\t\t\t= _self_.getSetting('Theme')\n_images_\t\t= '/resources/' + _theme_\t\n\n#############################################################\n#################### SET ADDON THEME IMAGES #################\nBackground_Image\t= xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'livechoice.jpg'))\nLogo_Image = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'logo.png'))\nAddon_Image = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'addon.png'))\nListbg = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'listbg.png'))\nButtonaF = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'acestreamf.png'))\nButtonaNF = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'acestream.png'))\nButtonbF = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'scraperf.png'))\nButtonbNF = xbmc.translatePath(os.path.join('special://home/addons/' + _addon_id_ + _images_, 'scraper.png'))\n\n########## Function To Call That Starts The Window ##########\ndef livegameswindow(ta):\n global data\n global List\n \n data = ta\n window = nfl_window('neverwalkalone')\n window.doModal()\n del window\n\ndef Get_Data(url):\n\n req = urllib2.Request(url)\n req.add_header(\n 'User-Agent', 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.73 Safari/537.36')\n response = urllib2.urlopen(req, timeout=30)\n data = response.read()\n response.close()\n\n return data\n\ndef passed(self, title):\n pass\n\n\ndef acestream(self):\n self.close\n list.listwindow('Acestream')\n \ndef scraper(self):\n self.close\n list.listwindow('Scraper')\n\n\n#############################################################\n######### Class Containing the GUi Code / Controls ##########\nclass nfl_window(pyxbmct.AddonFullWindow):\n\n xbmc.executebuiltin(\"Dialog.Close(busydialog)\")\n\n def __init__(self, title='neverwalkalone'):\n super(nfl_window, self).__init__(title)\n\n self.setGeometry(1280, 720, 100, 50)\n\n Background = pyxbmct.Image(Background_Image)\n\n self.placeControl(Background, -10, -1, 123, 52)\n \n Logo = pyxbmct.Image(Logo_Image)\n\n self.placeControl(Logo, -1, 4, 10, 20)\n\n self.set_info_controls()\n\n self.set_active_controls()\n\n self.set_navigation()\n\n self.connect(pyxbmct.ACTION_NAV_BACK, self.close)\n self.connect(self.button1, lambda:acestream(self))\n self.connect(self.button2, lambda:scraper(self))\n self.setFocus(self.button1)\n\n\n def set_info_controls(self):\n self.Hello = pyxbmct.Label('', textColor='0xFFF44248', font='font60', alignment=pyxbmct.ALIGN_CENTER)\n self.placeControl(self.Hello, -4, 1, 1, 50)\n\n\n def set_active_controls(self):\n self.button1 = pyxbmct.Button('', focusTexture=ButtonaF, noFocusTexture=ButtonaNF)\n self.placeControl(self.button1, 28, 6, 78 , 21)\n self.button2 = pyxbmct.Button('', focusTexture=ButtonbF, noFocusTexture=ButtonbNF)\n self.placeControl(self.button2, 42, 26, 50 , 15)\n\n\n\n def set_navigation(self):\n self.button1.controlRight(self.button2)\n self.button2.controlLeft(self.button1)\n \n\n","sub_path":"plugin.video.neverwalkalone/livegames.py","file_name":"livegames.py","file_ext":"py","file_size_in_byte":4091,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"624260484","text":"from django.conf.urls import url\n\nfrom schedule.utils_console import ConsoleManual, ConsoleSchedule\n\nurlpatterns = [\n url(r'(?P\\d+)/clone/(?P\\d{2,4}-\\d{1,2}-\\d{1,2})/'\n r'(?P\\d{1,2}:\\d{1,2})/'\n r'(?P[\\w|-]+)/(?P\\d+)/$',\n ConsoleManual.as_view(), name='clone'),\n url(r'set/$', ConsoleSchedule.as_view(),\n name='set'),\n]","sub_path":"schedule/urls_console.py","file_name":"urls_console.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"555604442","text":"#code projet 02 web scraping\nimport requests\nfrom bs4 import BeautifulSoup\n\nurl = \"http://books.toscrape.com/catalogue/eragon-the-inheritance-cycle-1_153/index.html\"\n\npage = requests.get(url)\n\n\"\"\"\nif page.ok:\n\tsoupe = BeautifulSoup(page.text, \"html.parser\")\n\ttitle = soupe.find('title')\n\tprint(title.text)\n\n\"\"\"\n\n\n\ndef title_one():\n\t\"\"\"Give the title of the book\"\"\"\n\tsoupe = BeautifulSoup(page.text, \"html.parser\")\n\ttitle = soupe.find('title')\n\treturn title\n\n#\n","sub_path":"Code_projet_02.py","file_name":"Code_projet_02.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"297746714","text":"import traceback\nfrom common import *\nimport login_page as LoginPage\nimport devices_page as DevicesPage\nimport event_console_page as EventConsolePage\nimport navigation_page as Navigation\nimport device_detail_page as DeviceDetailPage\n\nclass CheckDevice(Workflow):\n def __init__(self, ip):\n Workflow.__init__(self)\n self.ip = ip\n\n @timed\n def run(self, user):\n result = WorkflowResult(self.name)\n\n if not user.loggedIn:\n result.fail(\"user is not logged in\")\n return result\n\n takeAction(result, Navigation.goToDevicesPage, user)\n if not result.success:\n return result\n\n user.think(1)\n\n actionResult = takeAction(result, DevicesPage.filterByIp, user, self.ip)\n if not result.success:\n return result\n\n if actionResult.data['filterByIp.devices']:\n takeAction(result, DevicesPage.goToDeviceDetailPage, user, self.ip)\n if not result.success:\n return result\n\n takeAction(result, DeviceDetailPage.getEvents, user, None, True)\n if not result.success:\n return result\n\n takeAction(result, DeviceDetailPage.lookAtGraphs, user)\n if not result.success:\n return result\n\n takeAction(result, DeviceDetailPage.lookAtComponentGraphs, user)\n\n return result\n\n","sub_path":"workflows/check_device_workflow.py","file_name":"check_device_workflow.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"73554702","text":"import mysql.connector\nfrom mysql.connector import errorcode\n\nclass Database:\n\n def __init__(self):\n # init mysql connection\n self.connection = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"12345678\",\n database=\"csmart_faces\"\n )\n\n def query(self, q, arg=()):\n cursor = self.connection.cursor(buffered=True)\n cursor.execute(q, arg)\n results = cursor.fetchall()\n cursor.close()\n return results\n\n def insert(self, q, arg=()):\n cursor = self.connection.cursor(buffered=True)\n cursor.execute(q, arg)\n self.connection.commit()\n result = cursor.lastrowid\n cursor.close()\n return result\n\n def select(self, q, arg=()):\n cursor = self.connection.cursor(buffered=True)\n cursor.execute(q, arg)\n return cursor.fetchall()\n\n def delete(self, q, arg=()):\n cursor = self.connection.cursor(buffered=True)\n result = cursor.execute(q, arg)\n self.connection.commit()\n return result","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"439071070","text":"from django.http import JsonResponse\nfrom django.shortcuts import render, HttpResponseRedirect\nfrom django.contrib import auth\nfrom datetime import date\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.hashers import make_password\nfrom mapping.forms import CountryForm, DepartmentForm, TaskForm, SubtaskForm, DescriptionForm, UserForm, UserEditForm\nfrom mapping.models import Country, CustomUser, Department, Task, Subtask, Description\nfrom mapping.filters import DescriptionFilter\n\n# Create your views here.\n\ndef home(request):\n return render(request,'mapping/home.html',{'title':'HOME'})\n\ndef login(request):\n # if user is logged in then redirect to the dashboard\n if request.user.is_authenticated():\n return HttpResponseRedirect('/department')\n\n if request.method == \"POST\":\n username = request.POST.get('username', '')\n password = request.POST.get('password', '')\n user = auth.authenticate(username=username, password=password)\n if user is not None and user.is_active:\n # Login the user\n auth.login(request, user)\n return JsonResponse({'message': 'Login Successful'}, status=200)\n else:\n return JsonResponse({'message': 'Invalid Username/Password'}, status=500)\n return render(request, 'mapping/login.html')\n\ndef logout(request):\n auth.logout(request)\n return HttpResponseRedirect('/login')\n\ndef department(request):\n departments = Department.objects.all().order_by('name')\n d = Department()\n tasks = Task.objects.all().order_by('name')\n t = Task()\n # total_depts = sum(0 == 0 for department in departments)\n # total_tasks = sum(0 == 0 for task in tasks)\n return render(request,'mapping/departments.html',{'departments': departments, 'title':'DEPARTMENTS', 'tasks': tasks})\n\ndef tasks(request, department=None, task=None):\n departments = Department.objects.all().order_by('name')\n tasks = Task.objects.all().order_by('name')\n subtasks = Subtask.objects.filter(task=task).order_by('name')\n title = Task.objects.get(id=task).name\n return render(request,'mapping/viewtask.html',{'subtasks': subtasks ,'title': title ,'departments': departments, 'tasks': tasks})\n\ndef subtasks(request, department=None, task=None, subtask=None):\n departments = Department.objects.all().order_by('name')\n tasks = Task.objects.all().order_by('name')\n subtasks = Subtask.objects.filter(task=task).order_by('name')\n title = Task.objects.get(id=task).name\n try:\n description = Description.objects.get(subtask=subtask, country=request.user.country)\n date_created = description.created_at.year\n date_today = date.today().year\n if date_today != date_created:\n description = None\n except Description.DoesNotExist:\n description = None\n if request.method == \"POST\":\n if description:\n form = DescriptionForm(request.POST, instance=description)\n else:\n form = DescriptionForm(request.POST)\n\n if form.is_valid():\n instance = form.save(commit=False)\n instance.subtask = Subtask.objects.get(id=subtask)\n instance.user = request.user\n instance.country = request.user.country\n instance.save()\n dest_url = '/department/{0}/task/{1}/subtask/{2}'.format(department, task, subtask)\n return HttpResponseRedirect(dest_url, {'success': 'Task Added', 'subtasks': subtasks ,'title': title, 'departments': departments, 'tasks': tasks , 'form': form})\n else:\n return render(request, 'mapping/viewsubtask.html', {'form': form, 'title': 'Task'})\n else:\n if description:\n form = DescriptionForm(instance=description)\n else:\n form = DescriptionForm()\n return render(request,'mapping/viewsubtask.html',{'subtasks': subtasks ,'title': title ,'departments': departments, 'tasks': tasks , 'form':form })\n\ndef listdepartment(request):\n departments = Department.objects.all().order_by('name')\n return render(request, 'mapping/department_list.html', {'departments': departments})\n\ndef newdepartment(request):\n if request.method == 'POST':\n form = DepartmentForm(request.POST)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/admin/department', {'success': 'Department Added'})\n else:\n return render(request,'mapping/newdepartment.html', {'title': 'Department', 'form': form})\n else:\n form = DepartmentForm()\n return render(request,'mapping/newdepartment.html', {'title': 'Department', 'form': form})\n\ndef editdepartment(request, id=None):\n department = Department.objects.get(id=id)\n if request.method == 'POST':\n form = DepartmentForm(request.POST, instance=department)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/admin/department', {'success': 'Department edited'})\n else:\n return render(request,'mapping/newdepartment.html', {'title': 'Department', 'form': form})\n else:\n form = DepartmentForm(instance=department)\n return render(request,'mapping/newdepartment.html', {'title': 'Department', 'form': form})\n\ndef listtask(request,id=None):\n tasks = Task.objects.filter(department=id)\n return render(request, 'mapping/task_list.html', {'tasks': tasks, 'department_id':id})\n\ndef newtask(request,id=None):\n if request.method == \"POST\":\n form = TaskForm(request.POST)\n if form.is_valid():\n instance = form.save(commit=False)\n instance.department = Department.objects.get(id=id)\n instance.save()\n return HttpResponseRedirect('/admin/department/{0}/task'.format(id), {'success': 'Task Added'})\n else:\n return render(request,'mapping/newtask.html',{'title':'NEW TASK ', 'department_id': id, 'tasks': tasks , 'form' : form})\n\n else:\n form = TaskForm()\n return render(request,'mapping/newtask.html',{ 'title':'NEW TASK ','department_id': id ,'tasks': tasks , 'form' : form})\n\ndef edittask(request, department=None,task=None):\n tasks = Task.objects.get(id=task)\n if request.method == 'POST':\n form = TaskForm(request.POST, instance=tasks)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/admin/department/{0}/task'.format(department), {'success': 'Task edited'})\n else:\n return render(request,'mapping/edittask.html', {'title': 'Department', 'form': form, 'department_id': department ,'task_id' : task })\n else:\n form = TaskForm(instance=tasks)\n return render(request,'mapping/edittask.html', {'title': 'Department', 'form': form , 'department_id': department,'task_id' : task })\n\ndef newsubtask(request):\n departments = Department.objects.all().order_by('name')\n tasks = Task.objects.all().order_by('name')\n if request.method == \"POST\":\n form = SubtaskForm(request.POST)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/newsubtask', {'success': 'Subtask Added'})\n else:\n return render(request,'mapping/newsubtask.html',{'departments': departments, 'title':'NEW SUBTASK ', 'tasks': tasks , 'form' : form})\n\n else:\n form = SubtaskForm()\n return render(request,'mapping/newsubtask.html',{'departments': departments, 'title':'NEW SUBTASK ', 'tasks': tasks , 'form' : form})\n\ndef filter(request):\n f = DescriptionFilter(request.POST, queryset=Description.objects.all())\n if request.method == 'POST':\n countries = [c.name for c in Country.objects.filter(pk__in=request.POST.getlist('country'))]\n subtasks = [s.name for s in Subtask.objects.filter(pk__in=request.POST.getlist('subtask'))]\n\n descriptions = {}\n c = 0\n for obj in f:\n if obj.country.name in descriptions:\n descriptions[obj.country.name][obj.subtask.name] = [obj.description, obj.status]\n else:\n descriptions[obj.country.name] = {obj.subtask.name: [obj.description, obj.status]}\n c += 1\n\n return render(request, 'mapping/print.html', {'descriptions': descriptions, 'countries': countries, 'subtasks': subtasks})\n return render(request, 'mapping/filter.html', {'filter': f, 'title': 'Filter'})\n\ndef user(request):\n u = CustomUser.objects.all().order_by('country')\n return render(request, 'mapping/user_list.html', {'title': 'Users', 'users': u})\n\ndef newuser(request):\n if request.method == 'POST':\n form = UserForm(request.POST)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/user')\n else:\n return render(request, 'mapping/newuser.html', {'title': 'User', 'form': form})\n else:\n form = UserForm()\n return render(request, 'mapping/newuser.html', {'title': 'User', 'form': form})\n\ndef edituser(request, id=None):\n user = CustomUser.objects.get(id=id)\n if request.method == \"POST\":\n form = UserEditForm(request.POST, instance=user)\n if form.is_valid():\n form.save()\n return HttpResponseRedirect('/user', {'success': 'User Updated'})\n else:\n return render(request, 'mapping/edituser.html', {'id': user.id, 'form': form, 'title': 'Users'})\n else:\n form = UserEditForm(instance=user)\n return render(request,'mapping/edituser.html', {'id': user.id, 'form': form, 'title': 'Users'})\n","sub_path":"mapping/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"284959559","text":"# -*- coding: utf-8 -*-\nfrom xml.dom.minidom import parseString\nimport dicttoxml\n\n\nclass Json2xml:\n def __init__(\n self, data: str,\n wrapper: str = \"all\",\n pretty: bool = True,\n attr_type: bool = True\n ):\n self.data = data\n self.pretty = pretty\n self.wrapper = wrapper\n self.attr_type = attr_type\n\n def to_xml(self):\n \"\"\"\n Convert to xml using dicttoxml.dicttoxml and then pretty print it.\n \"\"\"\n if self.data:\n xml_data = dicttoxml.dicttoxml(self.data, custom_root=self.wrapper, attr_type=self.attr_type)\n if self.pretty:\n return parseString(xml_data).toprettyxml()\n return xml_data\n return None\n","sub_path":"json2xml/json2xml.py","file_name":"json2xml.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"406590207","text":"#===================================================================#\n# Tool Name: SitkaII HWCHK FVS Tool #\n# Version: 0.1 #\n# Edit by: Chris Liu 2017/05/26 #\n#===================================================================#\nimport sys\nimport datetime\nimport subprocess\nimport os\nimport time\nimport ctypes\n\nglobal VER\nVER = \"0.1\"\n\nglobal LOG_FILE\nLOG_FILE = \"test.log\"\n\nglobal LOG_DIR\nLOG_DIR = os.path.join(os.getcwd(), \"Log\")\n\nglobal DEBUG_MODE\nDEBUG_MODE = True\n\nglobal FAIL_CONTINUE\nFAIL_CONTINUE = False\n\nFONT_NONE = 0\nFONT_WHITE = 1\nFONT_RED = 2\nFONT_GREEN = 3\nFONT_YELLOW = 4\n\nPASS_BANNER = \"\"\"\n######## ### ###### ###### #### ####\n## ## ## ## ## ## ## ## #### ####\n## ## ## ## ## ## #### ####\n######## ## ## ###### ###### ## ##\n## ######### ## ##\n## ## ## ## ## ## ## #### ####\n## ## ## ###### ###### #### ####\n\"\"\"\n\nFAIL_BANNER = \"\"\"\n######## ### #### ## #### ####\n## ## ## ## ## #### ####\n## ## ## ## ## #### ####\n###### ## ## ## ## ## ##\n## ######### ## ##\n## ## ## ## ## #### ####\n## ## ## #### ######## #### ####\n\"\"\"\n#===============================================================================\nclass Dimm:\n\tdef __init__(self, locator):\n\t\tself.locator = locator\n\t\tself.manufacturer = \"\"\n\t\tself.size = \"\"\n\t\tself.width = \"\"\n\t\tself.speed = \"\"\n\t\tself.part_number = \"\"\n\t\tself.serial_number = \"\"\n\t\tself.current_speed = \"\"\n\t\tself.current_voltage = \"\"\n#===============================================================================\ndef INIT():\n\tglobal LOG_FILE\n\tglobal LOG_DIR\n\n\tcmd = \"ifconfig -a eth0 | grep HWaddr | awk '{print $5}'\"\n\tsled_mac = subprocess.Popen(cmd, stdout = subprocess.PIPE, shell = True, universal_newlines = True).communicate()[0].split(\"\\n\")[0]\n\n\tLOG_DIR = os.path.join(\"/usr/local/scan/%s\"%(sled_mac), \"LOG\")\n\n\tif(os.path.isdir(LOG_DIR) == False):\n\t\tos.mkdir(LOG_DIR)\n\n\tLOG_FILE = \"LOG_%s_%02d%02d-%02d%02d.txt\"%(os.path.basename(__file__).split(\".\")[0], datetime.datetime.strftime(datetime.datetime.now(), \"%y%m%d-%H%M%S\"))\n#===============================================================================\ndef Banner(msg):\n\tline_0 = \"#\" + \"=\"*78 + \"#\"\n\tline_1 = \"#\" + \" \"*78 + \"#\"\n\ttmp_str = \"#\" + msg.center(78) + \"#\"\n\n\tif(sys.platform == \"win32\"):\n\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x08)\n\t\tprint(\"\")\n\t\tprint(line_0)\n\t\tprint(line_1)\n\t\tprint(tmp_str)\n\t\tprint(line_1)\n\t\tprint(line_0)\n\t\tprint(\"\")\n\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x04)\n\telse:\n\t\tprint(\"\")\n\t\tprint(\"\\033[35;1m%s\\033[0m\"%(line_0))\n\t\tprint(\"\\033[35;1m%s\\033[0m\"%(line_1))\n\t\tprint(\"\\033[35;1m%s\\033[0m\"%(tmp_str))\n\t\tprint(\"\\033[35;1m%s\\033[0m\"%(line_1))\n\t\tprint(\"\\033[35;1m%s\\033[0m\"%(line_0))\n\t\tprint(\"\")\n#===============================================================================\ndef Log(msg, color = FONT_WHITE):\n\ttmp = \"[%s] %s\\n\"%(datetime.datetime.strftime(datetime.datetime.now(), \"%y/%m/%d %H:%M:%S\"), msg)\n\n\ttry:\n\t\tf = open(os.path.join(LOG_DIR, LOG_FILE), \"a\")\n\t\tf.write(tmp)\n\t\tf.close()\n\texcept:\n\t\tprint(\"Logging Error!!\")\n\t\treturn\n\n\ttmp = tmp[:-1]\n\n\tif(color == FONT_RED):\n\t\tif(sys.platform == \"win32\"):\n\t\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x04 | 0x08)\n\t\t\tprint(tmp)\n\t\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x04)\n\t\telse:\n\t\t\tprint(\"\\033[31;1m%s\\033[0m\"%(tmp))\n\telif(color == FONT_GREEN):\n\t\tif(sys.platform == \"win32\"):\n\t\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x02 | 0x08)\n\t\t\tprint(tmp)\n\t\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x04)\n\t\telse:\n\t\t\tprint(\"\\033[32;1m%s\\033[0m\"%(tmp))\n\telif(color == FONT_YELLOW):\n\t\tif(sys.platform == \"win32\"):\n\t\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x02 | 0x04 | 0x08)\n\t\t\tprint(tmp)\n\t\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x04)\n\t\telse:\n\t\t\tprint(\"\\033[33;1m%s\\033[0m\"%(tmp))\n\telif(color == FONT_NONE):\n\t\tpass\n\telse:\n\t\ttry:\n\t\t\tprint(tmp)\n\t\texcept:\n\t\t\tprint(\"Logging Error!!\")\n#===============================================================================\ndef Show_Pass():\n\tif(sys.platform == \"win32\"):\n\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x02 | 0x08)\n\t\tprint(PASS_BANNER)\n\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x04)\n\telse:\n\t\tprint(\"\\033[32;1m%s\\033[0m\"%(PASS_BANNER))\n\n\tLog(\"Log File: %s\"%(os.path.join(LOG_DIR, LOG_FILE)), FONT_GREEN)\n\tLog(\"PASS\", FONT_GREEN)\n\tsys.exit(0)\n#===============================================================================\ndef Show_Fail(error_msg):\n\tif(sys.platform == \"win32\"):\n\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x04 | 0x08)\n\t\tprint(FAIL_BANNER)\n\t\tctypes.windll.kernel32.SetConsoleTextAttribute(ctypes.windll.kernel32.GetStdHandle(-11), 0x01 | 0x02 | 0x04)\n\telse:\n\t\tprint(\"\\033[31;1m%s\\033[0m\"%(FAIL_BANNER))\n\n\tLog(\"Log File: %s\"%(os.path.join(LOG_DIR, LOG_FILE)), FONT_RED)\n\tLog(\"Error Message: %s\"%(error_msg), FONT_RED)\n\tLog(\"FAIL\", FONT_RED)\n\tsys.exit(-1)\n#===============================================================================\ndef Input_CMD_OS(cmd):\n\tLog(\"Input OS Command: %s\"%(cmd), FONT_WHITE)\n\n\ttry:\n\t\tret = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell = True, universal_newlines = True).communicate()[0].splitlines()\n\texcept:\n\t\tLog(\"Input OS Command Fail (%s)\"%(cmd), FONT_RED)\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tret[i] = ret[i].strip()\n\t\tif(DEBUG_MODE):\n\t\t\tLog(\"ret[%02d] %s\"%(i, ret[i]), FONT_WHITE)\n\n\treturn ret\n#===============================================================================\ndef check_smbios_bios():\n\t'''Check BIOS Information (SMBIOS)'''\n\n\tflag_vendor = False\n\tflag_version = False\n\n\tret = Input_CMD_OS(\"dmidecode -t 0\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"Vendor:\" in ret[i] and \"Dell Inc.\" in ret[i]):\n\t\t\tflag_vendor = True\n\t\tif(\"Version:\" in ret[i] and \"98.0.0\" in ret[i]):\n\t\t\tflag_version = True\n\n\tif(flag_vendor and flag_version):\n\t\tLog(\"check_smbios_bios Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_vendor == False):\n\t\t\tLog(\"check_smbios_bios Fail (Vendor)\", FONT_RED)\n\t\tif(flag_version == False):\n\t\t\tLog(\"check_smbios_bios Fail (Version)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_smbios_system():\n\t'''Check System Information (SMBIOS)'''\n\n\tflag_manufacturer = False\n\tflag_product = False\n\t#Ignore Check Service Tag\n\tflag_service_tag = True\n\n\tcmd = \"ifconfig eth0 | grep HWaddr | awk '{print $5}'\"\n\tsled_mac = subprocess.Popen(cmd, stdout = subprocess.PIPE, shell = True, universal_newlines = True).communicate()[0].split(\"\\n\")[0]\n\n\tf = open(\"/usr/local/scan/%s/scan.dat\"%(sled_mac), \"r\")\n\tret1 = f.readlines()\n\tfor i in range(len(ret1)):\n\t\tret1[i] = ret1[i].strip()\n\tf.close()\n\n\tservice_tag = \"\"\n\n\tfor i in ret1:\n\t\tif(\"Service Tag\" in i):\n\t\t\tservice_tag = i.split(\"=\", 1)[1].strip()\n\n\tret = Input_CMD_OS(\"dmidecode -t 1\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"Manufacturer:\" in ret[i] and \"Dell Inc.\" in ret[i]):\n\t\t\tflag_manufacturer = True\n\t\tif(\"Product Name:\" in ret[i] and \"PowerEdge R440\" in ret[i]):\n\t\t\tflag_product = True\n\t\tif(\"Serial Number:\" in ret[i] and service_tag in ret[i]):\n\t\t\tflag_service_tag = True\n\n\tif(flag_manufacturer and flag_product and flag_service_tag):\n\t\tLog(\"check_smbios_system Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_manufacturer == False):\n\t\t\tLog(\"check_smbios_system Fail (Manufacturer)\", FONT_RED)\n\t\tif(flag_product == False):\n\t\t\tLog(\"check_smbios_system Fail (Product Name)\", FONT_RED)\n\t\tif(flag_service_tag == False):\n\t\t\tLog(\"check_smbios_system Fail (Service Tag)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_smbios_baseboard():\n\t'''Check Baseboard Information (SMBIOS)'''\n\n\tflag_manufacturer = False\n\tflag_product = False\n\tflag_version = False\n\n\tret = Input_CMD_OS(\"dmidecode -t 2\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"Manufacturer:\" in ret[i] and \"Dell Inc.\" in ret[i]):\n\t\t\tflag_manufacturer = True\n\t\tif(\"Product Name:\" in ret[i] and \"0N28XX\" in ret[i]):\n\t\t\tflag_product = True\n\t\tif(\"Version:\" in ret[i] and \"X20\" in ret[i]):\n\t\t\tflag_version = True\n\n\tif(flag_manufacturer and flag_product and flag_version):\n\t\tLog(\"check_smbios_baseboard Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_manufacturer == False):\n\t\t\tLog(\"check_smbios_baseboard Fail (Manufacturer)\", FONT_RED)\n\t\tif(flag_product == False):\n\t\t\tLog(\"check_smbios_baseboard Fail (Product Name)\", FONT_RED)\n\t\tif(flag_version == False):\n\t\t\tLog(\"check_smbios_baseboard Fail (Version)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_smbios_chassis():\n\t'''Check Chassis Information (SMBIOS)'''\n\n\tflag_manufacturer = False\n\tflag_type = False\n\t#Ignore version\n\tflag_version = True\n\n\tret = Input_CMD_OS(\"dmidecode -t 3\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"Manufacturer:\" in ret[i] and \"Dell Inc.\" in ret[i]):\n\t\t\tflag_manufacturer = True\n\t\tif(\"Type:\" in ret[i] and \"Rack Mount Chassis\" in ret[i]):\n\t\t\tflag_type = True\n\t\tif(\"Version:\" in ret[i] and \"D57D2\" in ret[i]):\n\t\t\tflag_version = True\n\n\tif(flag_manufacturer and flag_type and flag_version):\n\t\tLog(\"check_smbios_chassis Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_manufacturer == False):\n\t\t\tLog(\"check_smbios_chassis Fail (Manufacturer)\", FONT_RED)\n\t\tif(flag_type == False):\n\t\t\tLog(\"check_smbios_chassis Fail (Type)\", FONT_RED)\n\t\tif(flag_version == False):\n\t\t\tLog(\"check_smbios_chassis Fail (Version)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_cpu_info():\n\t'''Check CPU Information'''\n\n\tflag_manufacturer = False\n\tflag_version = False\n\tflag_speed = False\n\tflag_core = False\n\tflag_thread = False\n\n\tret = Input_CMD_OS(\"dmidecode -t 4\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"Manufacturer:\" in ret[i] and \"Intel\" in ret[i]):\n\t\t\tflag_manufacturer = True\n\t\tif(\"Version:\" in ret[i] and \"Intel(R) Xeon(R) Gold 6130 CPU @ 2.10GHz\" in ret[i]):\n\t\t\tflag_version = True\n\t\tif(\"Current Speed:\" in ret[i] and \"2100\" in ret[i]):\n\t\t\tflag_speed = True\n\t\tif(\"Core Count:\" in ret[i] and \"16\" in ret[i]):\n\t\t\tflag_core = True\n\t\tif(\"Thread Count:\" in ret[i] and \"32\" in ret[i]):\n\t\t\tflag_thread = True\n\n\tret = Input_CMD_OS(\"lscpu\")\n\tif(ret == False):\n\t\treturn False\n\n\tflag_socket = False\n\tflag_family = False\n\tflag_model = False\n\tflag_stepping = False\n\tflag_L1d = False\n\tflag_L1i = False\n\tflag_L2 = False\n\tflag_L3 = False\n\n\tfor i in range(len(ret)):\n\t\tif(\"Socket(s):\" in ret[i] and \"2\" in ret[i]):\n\t\t\tflag_socket = True\n\t\tif(\"CPU family:\" in ret[i] and \"6\" in ret[i]):\n\t\t\tflag_family = True\n\t\tif(\"Model:\" in ret[i] and \"85\" in ret[i]):\n\t\t\tflag_model = True\n\t\tif(\"Stepping:\" in ret[i] and \"4\" in ret[i]):\n\t\t\tflag_stepping = True\n\t\tif(\"L1d cache:\" in ret[i] and \"32K\" in ret[i]):\n\t\t\tflag_L1d = True\n\t\tif(\"L1i cache:\" in ret[i] and \"32K\" in ret[i]):\n\t\t\tflag_L1i = True\n\t\tif(\"L2 cache:\" in ret[i] and \"1024K\" in ret[i]):\n\t\t\tflag_L2 = True\n\t\tif(\"L3 cache:\" in ret[i] and \"22528K\" in ret[i]):\n\t\t\tflag_L3 = True\n\n\tif(flag_manufacturer and flag_version and flag_speed and flag_core and flag_thread and flag_socket and flag_family and flag_model and flag_stepping and flag_L1d and flag_L1i and flag_L2 and flag_L3):\n\t\tLog(\"check_cpu_info Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_manufacturer == False):\n\t\t\tLog(\"check_cpu_info Fail (Manufacturer)\", FONT_RED)\n\t\tif(flag_version == False):\n\t\t\tLog(\"check_cpu_info Fail (Version)\", FONT_RED)\n\t\tif(flag_speed == False):\n\t\t\tLog(\"check_cpu_info Fail (Speed)\", FONT_RED)\n\t\tif(flag_core == False):\n\t\t\tLog(\"check_cpu_info Fail (Core)\", FONT_RED)\n\t\tif(flag_thread == False):\n\t\t\tLog(\"check_cpu_info Fail (Thread)\", FONT_RED)\n\t\tif(flag_socket == False):\n\t\t\tLog(\"check_cpu_info Fail (Socket)\", FONT_RED)\n\t\tif(flag_family == False):\n\t\t\tLog(\"check_cpu_info Fail (Family)\", FONT_RED)\n\t\tif(flag_model == False):\n\t\t\tLog(\"check_cpu_info Fail (Model)\", FONT_RED)\n\t\tif(flag_stepping == False):\n\t\t\tLog(\"check_cpu_info Fail (Stepping)\", FONT_RED)\n\t\tif(flag_L1d == False):\n\t\t\tLog(\"check_cpu_info Fail (L1d Cache)\", FONT_RED)\n\t\tif(flag_L1i == False):\n\t\t\tLog(\"check_cpu_info Fail (L1i Cache)\", FONT_RED)\n\t\tif(flag_L2 == False):\n\t\t\tLog(\"check_cpu_info Fail (L2 Cache)\", FONT_RED)\n\t\tif(flag_L3 == False):\n\t\t\tLog(\"check_cpu_info Fail (L3 Cache)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_memory_info():\n\t'''Check Memory Information'''\n\n\tflag_total_size = False\n\tmemory_size = 0\n\n\tret = Input_CMD_OS(\"cat /proc/meminfo\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"MemTotal\" in ret[i]):\n\t\t\tmemory_size = int(ret[i].split()[1])\n\n\tif(memory_size > 32000000):\n\t\tflag_total_size = True\n\n\tret = Input_CMD_OS(\"dmidecode -t 17\")\n\tif(ret == False):\n\t\treturn False\n\n\tdimm_locator_list = [\"A1\", \"B1\"]\n\tdimm_list = []\n\n\tfor i in dimm_locator_list:\n\t\tfor index in range(len(ret)):\n\t\t\tif(\"Locator:\" in ret[index]):\n\t\t\t\tdimm_index = ret[index].split(\":\")[1].strip()\n\t\t\t\tif(i == dimm_index):\n\t\t\t\t\tdimm_list.append(Dimm(i))\n\t\t\t\t\tdimm_list[-1].manufacturer = ret[index + 5].split(maxsplit = 1)[1]\n\t\t\t\t\tdimm_list[-1].size = ret[index - 3].split(maxsplit = 1)[1]\n\t\t\t\t\tdimm_list[-1].width = ret[index - 5].split(maxsplit = 2)[2]\n\t\t\t\t\tdimm_list[-1].speed = ret[index + 4].split(maxsplit = 1)[1]\n\t\t\t\t\tdimm_list[-1].part_number = ret[index + 8].split(maxsplit = 2)[2]\n\t\t\t\t\tdimm_list[-1].serial_number = ret[index + 6].split(maxsplit = 2)[2]\n\t\t\t\t\tdimm_list[-1].current_speed = ret[index + 10].split(maxsplit = 3)[3]\n\t\t\t\t\tdimm_list[-1].current_voltage = ret[index + 13].split(maxsplit = 2)[2]\n\n\tfor i in dimm_list:\n\t\tLog(\"===================================================\", FONT_YELLOW)\n\t\tLog(\"DIMM %s Manufacturer: %s\"%(i.locator, i.manufacturer), FONT_YELLOW)\n\t\tLog(\"DIMM %s Size: %s\"%(i.locator, i.size), FONT_YELLOW)\n\t\tLog(\"DIMM %s Total Width: %s\"%(i.locator, i.width), FONT_YELLOW)\n\t\tLog(\"DIMM %s Speed: %s\"%(i.locator, i.speed), FONT_YELLOW)\n\t\tLog(\"DIMM %s Part Number: %s\"%(i.locator, i.part_number), FONT_YELLOW)\n\t\tLog(\"DIMM %s Serial Number: %s\"%(i.locator, i.serial_number), FONT_YELLOW)\n\t\tLog(\"DIMM %s Current Speed: %s\"%(i.locator, i.current_speed), FONT_YELLOW)\n\t\tLog(\"DIMM %s Current Voltage: %s\"%(i.locator, i.current_voltage), FONT_YELLOW)\n\t\tLog(\"===================================================\", FONT_YELLOW)\n\n\tflag_same_pn = True\n\n\ttemp_pn = dimm_list[0].part_number\n\tfor i in dimm_list:\n\t\tif(i.part_number != temp_pn):\n\t\t\tflag_same_pn = False\n\n\tfor i in dimm_list:\n\t\tflag_manufacturer = False\n\t\tflag_size = False\n\t\tflag_width = False\n\t\tflag_speed = False\n\t\tflag_part_number = False\n\t\tflag_current_speed = False\n\t\tflag_current_voltage = False\n\n\t\tif(i.manufacturer in [\"00CE063200CE\"]):\n\t\t\tflag_manufacturer = True\n\t\tif(i.size == \"16384 MB\"):\n\t\t\tflag_size = True\n\t\tif(i.width == \"72 bits\"):\n\t\t\tflag_width = True\n\t\tif(i.speed == \"2666 MHz\"):\n\t\t\tflag_speed = True\n\t\tif(i.part_number == \"M393A2K43BB1-CTD\"):\n\t\t\tflag_part_number = True\n\t\tif(i.current_speed == \"2666 MHz\"):\n\t\t\tflag_current_speed = True\n\t\tif(i.current_voltage == \"1.2 V\"):\n\t\t\tflag_current_voltage = True\n\n\t\tif(flag_manufacturer and flag_size and flag_width and flag_speed and flag_part_number and flag_current_speed and flag_current_voltage):\n\t\t\tLog(\"Check DIMM%s Pass\"%(i.locator), FONT_GREEN)\n\t\t\tcontinue\n\t\telse:\n\t\t\tLog(\"Check DIMM%s Fail\"%(i.locator), FONT_RED)\n\t\t\tbreak\n\n\tif(flag_total_size and flag_same_pn and flag_manufacturer and flag_size and flag_width and flag_speed and flag_part_number and flag_current_speed and flag_current_voltage):\n\t\tLog(\"check_memory_info Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_total_size == False):\n\t\t\tLog(\"check_memory_info Fail (Total Size)\", FONT_RED)\n\t\tif(flag_same_pn == False):\n\t\t\tLog(\"check_memory_info Fail (Same Part Number)\", FONT_RED)\n\t\tif(flag_manufacturer == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Manufacturer)\"%(i.locator), FONT_RED)\n\t\tif(flag_size == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Size)\"%(i.locator), FONT_RED)\n\t\tif(flag_width == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Total Width)\"%(i.locator), FONT_RED)\n\t\tif(flag_speed == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Speed)\"%(i.locator), FONT_RED)\n\t\tif(flag_part_number == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Part Number)\"%(i.locator), FONT_RED)\n\t\tif(flag_current_speed == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Current Speed)\"%(i.locator), FONT_RED)\n\t\tif(flag_current_voltage == False):\n\t\t\tLog(\"check_memory_info Fail (DIMM %s Current Voltage)\"%(i.locator), FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_pcie_perc():\n\t'''Check Dell PERC H730P Mini PCIe Information'''\n\n\tflag_id = False\n\tflag_status = False\n\n\tret = Input_CMD_OS(\"lspci -s 18:00.0 -vv -x\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"00: 00 10 5d 00\" in ret[i]):\n\t\t\tflag_id = True\n\t\tif(\"LnkSta:\" in ret[i] and \"Speed 8GT/s, Width x8\" in ret[i]):\n\t\t\tflag_status = True\n\n\tif(flag_id and flag_status):\n\t\tLog(\"check_pcie_perc Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_id == False):\n\t\t\tLog(\"check_pcie_perc Fail (VID/DID)\", FONT_RED)\n\t\tif(flag_status == False):\n\t\t\tLog(\"check_pcie_perc Fail (Link Speed/Width)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_pcie_nvme_controller():\n\t'''Check NVMe Controller PCIe Information'''\n\n\tflag_id = False\n\tflag_status = False\n\n\tret = Input_CMD_OS(\"lspci -s 83:00.0 -vv -x\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"00: b5 10 34 87\" in ret[i]):\n\t\t\tflag_id = True\n\t\tif(\"LnkSta:\" in ret[i] and \"Speed 8GT/s, Width x8\" in ret[i]):\n\t\t\tflag_status = True\n\n\tif(flag_id and flag_status):\n\t\tLog(\"check_pcie_nvme_controller Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_id == False):\n\t\t\tLog(\"check_pcie_nvme_controller Fail (VID/DID)\", FONT_RED)\n\t\tif(flag_status == False):\n\t\t\tLog(\"check_pcie_nvme_controller Fail (Link Speed/Width)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_pcie_nvme_slot0():\n\t'''Check Intel NVMe Slot0 PCIe Information'''\n\n\tflag_id = False\n\tflag_status = False\n\n\tret = Input_CMD_OS(\"lspci -s 85:00.0 -vv -x\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"00: 86 80 53 09\" in ret[i]):\n\t\t\tflag_id = True\n\t\tif(\"LnkSta:\" in ret[i] and \"Speed 8GT/s, Width x4\" in ret[i]):\n\t\t\tflag_status = True\n\n\tif(flag_id and flag_status):\n\t\tLog(\"check_pcie_nvme_slot0 Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_id == False):\n\t\t\tLog(\"check_pcie_nvme_slot0 Fail (VID/DID)\", FONT_RED)\n\t\tif(flag_status == False):\n\t\t\tLog(\"check_pcie_nvme_slot0 Fail (Link Speed/Width)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_pcie_nvme_slot1():\n\t'''Check Intel NVMe Slot1 PCIe Information'''\n\n\tflag_id = False\n\tflag_status = False\n\n\tret = Input_CMD_OS(\"lspci -s 86:00.0 -vv -x\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"00: 86 80 53 09\" in ret[i]):\n\t\t\tflag_id = True\n\t\tif(\"LnkSta:\" in ret[i] and \"Speed 8GT/s, Width x4\" in ret[i]):\n\t\t\tflag_status = True\n\n\tif(flag_id and flag_status):\n\t\tLog(\"check_pcie_nvme_slot1 Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_id == False):\n\t\t\tLog(\"check_pcie_nvme_slot1 Fail (VID/DID)\", FONT_RED)\n\t\tif(flag_status == False):\n\t\t\tLog(\"check_pcie_nvme_slot1 Fail (Link Speed/Width)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_hdd_info():\n\t'''Check HDD Information'''\n\n\thdd_list = [\"sda\", \"sdb\"]\n\n\tfor hdd in hdd_list:\n\t\tret = Input_CMD_OS(\"smartctl -a /dev/%s -d sat+megaraid,0\"%hdd)\n\t\tif(ret == False):\n\t\t\treturn False\n\n\t\tflag_presence = False\n\t\tflag_size = False\n\t\tflag_speed = False\n\t\tflag_smart = False\n\n\t\tfor i in ret:\n\t\t\tif(\"Device Model:\" in i and \"SSDSC2BB120G7R\" in i):\n\t\t\t\tflag_presence = True\n\t\t\tif(\"User Capacity:\" in i and \"120 GB\" in i):\n\t\t\t\tflag_size = True\n\t\t\tif(\"SATA Version is:\" in i and \"current: 6.0 Gb/s\" in i):\n\t\t\t\tflag_speed = True\n\t\t\tif(\"SMART overall-health self-assessment test result:\" in i and \"PASSED\" in i):\n\t\t\t\tflag_smart = True\n\n\t\tif(flag_presence and flag_size and flag_speed and flag_smart):\n\t\t\tLog(\"%s PASS\"%(hdd), FONT_GREEN)\n\t\t\tcontinue\n\t\telse:\n\t\t\tLog(\"%s FAIL\"%(hdd), FONT_RED)\n\t\t\tbreak\n\n\tif(flag_presence and flag_size and flag_speed and flag_smart):\n\t\tLog(\"check_hdd_info Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_presence == False):\n\t\t\tLog(\"check_hdd_info Fail (%s)(presence)\"%(hdd), FONT_RED)\n\t\tif(flag_size == False):\n\t\t\tLog(\"check_hdd_info Fail (%s)(size)\"%(hdd), FONT_RED)\n\t\tif(flag_speed == False):\n\t\t\tLog(\"check_hdd_info Fail (%s)(speed)\"%(hdd), FONT_RED)\n\t\tif(flag_smart == False):\n\t\t\tLog(\"check_hdd_info Fail (%s)(smart)\"%(hdd), FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_psu():\n\t'''Check PSU Status'''\n\n\tflag_psu1 = False\n\tflag_psu2 = False\n\n\tret = Input_CMD_OS(\"racadm getsensorinfo\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in range(len(ret)):\n\t\tif(\"PS1 Status\" in ret[i] and \"Present\" in ret[i]):\n\t\t\tflag_psu1 = True\n\t\tif(\"PS2 Status\" in ret[i] and \"Present\" in ret[i]):\n\t\t\tflag_psu2 = True\n\n\tif(flag_psu1 and flag_psu2):\n\t\tLog(\"check_psu Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_psu1 == False):\n\t\t\tLog(\"check_psu Fail (PSU1)\", FONT_RED)\n\t\tif(flag_psu2 == False):\n\t\t\tLog(\"check_psu Fail (PSU2)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_fan():\n\t'''Check FAN Status'''\n\n\tfan_list = [\"1A\", \"1B\", \"2A\", \"2B\", \"3A\", \"3B\", \"4A\", \"4B\", \"5A\", \"5B\", \"6A\", \"6B\"]\n\n\tfan_min = 12000\n\tfan_max = 20000\n\n\tret = Input_CMD_OS(\"racadm getsensorinfo\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor fan in fan_list:\n\n\t\tflag_presence = False\n\t\tflag_status = False\n\t\tflag_speed = False\n\n\t\tfor index in range(len(ret)):\n\t\t\tif(\"System Board Fan%s\"%(fan) in ret[index]):\n\t\t\t\tflag_presence = True\n\n\t\t\t\tspeed = int(ret[index].split()[4][:-3])\n\t\t\t\tLog(\"Fan%s Speed = %d RPM\"%(fan, speed), FONT_YELLOW)\n\n\t\t\t\tif(ret[index].split()[3] == \"Ok\"):\n\t\t\t\t\tflag_status = True\n\t\t\t\tif(fan_min < speed < fan_max):\n\t\t\t\t\tflag_speed = True\n\n\t\tif(flag_presence and flag_status and flag_speed):\n\t\t\tLog(\"Fan%s PASS\"%(fan), FONT_GREEN)\n\t\t\tcontinue\n\t\telse:\n\t\t\tLog(\"Fan%s FAIL\"%(fan), FONT_RED)\n\t\t\tbreak\n\n\tif(flag_presence and flag_status and flag_speed):\n\t\tLog(\"check_fan Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_presence == False):\n\t\t\tLog(\"check_fan Fail (%s)(presence)\"%(fan), FONT_RED)\n\t\tif(flag_status == False):\n\t\t\tLog(\"check_fan Fail (%s)(status)\"%(fan), FONT_RED)\n\t\tif(flag_speed == False):\n\t\t\tLog(\"check_fan Fail (%s)(speed)\"%(fan), FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_battery():\n\t'''Check Battery Status'''\n\n\tflag_cmos = False\n\tflag_perc = False\n\n\tret = Input_CMD_OS(\"racadm getsensorinfo\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in ret:\n\t\tif(\"System Board CMOS Battery\" in i and \"Ok\" in i and \"Present\" in i):\n\t\t\tflag_cmos = True\n\t\tif(\"PERC1 ROMB Battery\" in i and \"Ok\" in i and \"Present\" in i):\n\t\t\tflag_perc = True\n\n\tif(flag_cmos and flag_perc):\n\t\tLog(\"check_battery Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tif(flag_cmos == False):\n\t\t\tLog(\"check_battery Fail (CMOS)\", FONT_RED)\n\t\tif(flag_perc == False):\n\t\t\tLog(\"check_battery Fail (PERC)\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef check_intrusion():\n\t'''Check Intrusion Status'''\n\n\tflag = False\n\n\tret = Input_CMD_OS(\"racadm getsensorinfo\")\n\tif(ret == False):\n\t\treturn False\n\n\tfor i in ret:\n\t\tif(\"System Board Intrusion\" in i and \"Closed\" in i and \"Power ON\" in i):\n\t\t\tflag = True\n\n\tif(flag):\n\t\tLog(\"check_intrusion Pass\", FONT_GREEN)\n\t\treturn True\n\telse:\n\t\tLog(\"check_intrusion Fail\", FONT_RED)\n\t\treturn False\n#===============================================================================\ndef main():\n\tglobal VER\n\tglobal DEBUG_MODE\n\tglobal FAIL_CONTINUE\n\tglobal LOG_DIR\n\tglobal LOG_FILE\n\n\tINIT()\n\n\tBanner(\"SitkaII HWCHK FVS Tool, By Foxconn CESBG-EPDI-TE, Version: %s\"%(VER))\n\n\tif(DEBUG_MODE):\n\t\tLog(\"DEBUG_MODE\", FONT_WHITE)\n\tif(FAIL_CONTINUE):\n\t\tLog(\"FAIL_CONTINUE\", FONT_WHITE)\n\n\tLog(\"Log Directory: %s\"%(LOG_DIR), FONT_WHITE)\n\tLog(\"Log File: %s\"%(LOG_FILE), FONT_WHITE)\n\n\ttest_sequence = [\n\t\tcheck_smbios_bios,\n\t\tcheck_smbios_system,\n\t\tcheck_smbios_baseboard,\n\t\tcheck_smbios_chassis,\n\t\tcheck_cpu_info,\n\t\tcheck_memory_info,\n\t\tcheck_hdd_info,\n\t\tcheck_pcie_perc,\n\t\t# check_pcie_nvme_controller,\n\t\t# check_pcie_nvme_slot0,\n\t\t# check_pcie_nvme_slot1,\n\t\tcheck_psu,\n\t\tcheck_fan,\n\t\tcheck_battery,\n\t\tcheck_intrusion,\n\t]\n\n\ttest_result = True\n\tresult_msg = []\n\n\ttest_start = datetime.datetime.now()\n\tLog(\"Test Start...\", FONT_YELLOW)\n\tfor test_item in test_sequence:\n\t\tLog(\"=\"*58, FONT_NONE)\n\t\tBanner(test_item.__doc__)\n\t\tLog(\"Test Item: %s (%s)\"%(test_item.__doc__, test_item.__name__), FONT_YELLOW)\n\t\ttime.sleep(1)\n\t\tif(test_item() == False):\n\t\t\ttest_result = False\n\t\t\tresult_msg.append((test_item.__name__, False))\n\t\t\tif(FAIL_CONTINUE):\n\t\t\t\traw_input(\"%s Fail!! Press ENTER to Continue...\"%(test_item.__doc__))\n\t\t\telse:\n\t\t\t\tbreak\n\t\telse:\n\t\t\tresult_msg.append((test_item.__name__, True))\n\t\ttime.sleep(1)\n\t\tLog(\"=\"*58, FONT_NONE)\n\tLog(\"Test End...\", FONT_YELLOW)\n\ttest_end = datetime.datetime.now()\n\n\tprint(\"\")\n\tLog(\"Test Start: %s\"%(str(test_start)), FONT_YELLOW)\n\tLog(\"Test End: %s\"%(str(test_end)), FONT_YELLOW)\n\tLog(\"Test Time: %s\"%(str(test_end - test_start)), FONT_YELLOW)\n\tprint(\"\")\n\tfor (item_name, result) in result_msg:\n\t\tif(result):\n\t\t\tmsg = item_name.ljust(52, \"-\") + \"[PASS]\"\n\t\t\tLog(msg, FONT_GREEN)\n\t\telse:\n\t\t\tmsg = item_name.ljust(52, \"-\") + \"[FAIL]\"\n\t\t\tLog(msg, FONT_RED)\n\tprint(\"\")\n\n\tif(test_result):\n\t\tShow_Pass()\n\telse:\n\t\tShow_Fail(\"%s Fail\"%(test_item.__doc__))\n#===============================================================================\nif(__name__ == \"__main__\"):\n\ttry:\n\t\tmain()\n\texcept Exception as e:\n\t\tprint(\"ERROR: %s\"%(str(e)))\n\t\tsys.exit(-1)\n\tsys.exit(0)\n","sub_path":"SitkaII/SitkaII_HWCHK_FVS_Tool.py","file_name":"SitkaII_HWCHK_FVS_Tool.py","file_ext":"py","file_size_in_byte":27008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"54188010","text":"'''Exercício Python 037: leia um número inteiro qualquer e peça para o usuário escolher qual será a base de conversão:\r\n 1 para binário, 2 para octal e 3 para hexadecimal.'''\r\n\r\nnum = int(input('UM NUMERO: '))\r\nprint('''Escolha uma das bases: \r\n1 - Para Binario\r\n2 - Para Octal\r\n3 - Para Hexacimal''')\r\n\r\nopcao = int(input('Sua opção: '))\r\n\r\nif opcao == 1:\r\n print('{} em BINARIO fica {}'.format(num, bin(num)[2:]))\r\nelif opcao == 2:\r\n print('{} em OCTAL fica {}'.format(num, oct(num)[2:]))\r\nelif opcao == 3:\r\n print('{} em HEXADECIMAL fica {}'.format(num, hex(num)[2:]))\r\nelse:\r\n print('OPCAO INVALIDA')","sub_path":"037.py","file_name":"037.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"226077396","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport sys\nimport time\nimport tensorflow as tf\nfrom tensorflow.python import debug as tf_debug\n\nsys.path.append('../../')\nfrom models.ctc.multitask_blstm_ctc import Multitask_BLSTM_CTC\nfrom models.test.util import measure_time\nfrom models.test.data import generate_data, num2alpha, num2phone\nfrom experiments.utils.sparsetensor import sparsetensor2list\nfrom experiments.utils.parameter import count_total_parameters\n\n\nclass TestCTC(tf.test.TestCase):\n\n @measure_time\n def test_ctc(self):\n print(\"Multitask CTC Working check.\")\n self.check_training()\n\n def check_training(self):\n\n tf.reset_default_graph()\n with tf.Graph().as_default():\n # Load batch data\n batch_size = 4\n inputs, labels_true_char_st, labels_true_phone_st, inputs_seq_len = generate_data(\n label_type='multitask',\n model='ctc',\n batch_size=batch_size)\n\n # Define placeholders\n inputs_pl = tf.placeholder(tf.float32,\n shape=[None, None, inputs.shape[-1]],\n name='inputs')\n indices_pl = tf.placeholder(tf.int64, name='indices')\n values_pl = tf.placeholder(tf.int32, name='values')\n shape_pl = tf.placeholder(tf.int64, name='shape')\n labels_pl = tf.SparseTensor(indices_pl, values_pl, shape_pl)\n indices_sub_pl = tf.placeholder(tf.int64, name='indices_sub')\n values_sub_pl = tf.placeholder(tf.int32, name='values_sub')\n shape_sub_pl = tf.placeholder(tf.int64, name='shape_sub')\n labels_sub_pl = tf.SparseTensor(indices_sub_pl,\n values_sub_pl,\n shape_sub_pl)\n inputs_seq_len_pl = tf.placeholder(tf.int64,\n shape=[None],\n name='inputs_seq_len')\n keep_prob_input_pl = tf.placeholder(tf.float32,\n name='keep_prob_input')\n keep_prob_hidden_pl = tf.placeholder(tf.float32,\n name='keep_prob_hidden')\n\n # Define model graph\n num_classes_main = 26\n num_classes_sub = 61\n network = Multitask_BLSTM_CTC(\n batch_size=batch_size,\n input_size=inputs[0].shape[1],\n num_unit=256,\n num_layer_main=2,\n num_layer_sub=1,\n num_classes_main=num_classes_main,\n num_classes_sub=num_classes_sub,\n main_task_weight=0.8,\n parameter_init=0.1,\n clip_grad=5.0,\n clip_activation=50,\n dropout_ratio_input=1.0,\n dropout_ratio_hidden=1.0,\n num_proj=None,\n weight_decay=1e-8)\n\n # Add to the graph each operation\n loss_op, logits_main, logits_sub = network.compute_loss(\n inputs_pl,\n labels_pl,\n labels_sub_pl,\n inputs_seq_len_pl,\n keep_prob_input_pl,\n keep_prob_hidden_pl)\n learning_rate = 1e-3\n train_op = network.train(loss_op,\n optimizer='rmsprop',\n learning_rate_init=learning_rate,\n is_scheduled=False)\n decode_op_main, decode_op_sub = network.decoder(\n logits_main,\n logits_sub,\n inputs_seq_len_pl,\n decode_type='beam_search',\n beam_width=20)\n ler_op_main, ler_op_sub = network.compute_ler(\n decode_op_main, decode_op_sub, labels_pl, labels_sub_pl)\n\n # Add the variable initializer operation\n init_op = tf.global_variables_initializer()\n\n # Count total parameters\n parameters_dict, total_parameters = count_total_parameters(\n tf.trainable_variables())\n for parameter_name in sorted(parameters_dict.keys()):\n print(\"%s %d\" %\n (parameter_name, parameters_dict[parameter_name]))\n print(\"Total %d variables, %s M parameters\" %\n (len(parameters_dict.keys()),\n \"{:,}\".format(total_parameters / 1000000)))\n\n # Make feed dict\n feed_dict = {\n inputs_pl: inputs,\n labels_pl: labels_true_char_st,\n labels_sub_pl: labels_true_phone_st,\n inputs_seq_len_pl: inputs_seq_len,\n keep_prob_input_pl: network.dropout_ratio_input,\n keep_prob_hidden_pl: network.dropout_ratio_hidden,\n network.lr: learning_rate\n }\n\n with tf.Session() as sess:\n # Initialize parameters\n sess.run(init_op)\n\n # Wrapper for tfdbg\n # sess = tf_debug.LocalCLIDebugWrapperSession(sess)\n\n # Train model\n max_steps = 400\n start_time_global = time.time()\n start_time_step = time.time()\n ler_train_char_pre = 1\n not_improved_count = 0\n for step in range(max_steps):\n\n # Compute loss\n _, loss_train = sess.run(\n [train_op, loss_op], feed_dict=feed_dict)\n\n # Gradient check\n # grads = sess.run(network.clipped_grads, feed_dict=feed_dict)\n # for grad in grads:\n # print(np.max(grad))\n\n if (step + 1) % 10 == 0:\n # Change to evaluation mode\n feed_dict[keep_prob_input_pl] = 1.0\n feed_dict[keep_prob_hidden_pl] = 1.0\n\n # Compute accuracy\n ler_train_char, ler_train_phone = sess.run(\n [ler_op_main, ler_op_sub], feed_dict=feed_dict)\n\n duration_step = time.time() - start_time_step\n print('Step %d: loss = %.3f / cer = %.4f / per = %.4f (%.3f sec)\\n' %\n (step + 1, loss_train, ler_train_char,\n ler_train_phone, duration_step))\n start_time_step = time.time()\n\n # Visualize\n labels_pred_char_st, labels_pred_phone_st = sess.run(\n [decode_op_main, decode_op_sub],\n feed_dict=feed_dict)\n labels_true_char = sparsetensor2list(\n labels_true_char_st, batch_size=batch_size)\n labels_true_phone = sparsetensor2list(\n labels_true_phone_st, batch_size=batch_size)\n labels_pred_char = sparsetensor2list(\n labels_pred_char_st, batch_size=batch_size)\n labels_pred_phone = sparsetensor2list(\n labels_pred_phone_st, batch_size=batch_size)\n\n # character\n print('Character')\n print(' True: %s' % num2alpha(labels_true_char[0]))\n print(' Pred: %s' % num2alpha(labels_pred_char[0]))\n print('Phone')\n print(' True: %s' % num2phone(labels_true_phone[0]))\n print(' Pred: %s' % num2phone(labels_pred_phone[0]))\n print('----------------------------------------')\n\n if ler_train_char >= ler_train_char_pre:\n not_improved_count += 1\n else:\n not_improved_count = 0\n if not_improved_count >= 5:\n print('Modle is Converged.')\n break\n ler_train_char_pre = ler_train_char\n\n # Change to training mode\n network.is_training = True\n\n duration_global = time.time() - start_time_global\n print('Total time: %.3f sec' % (duration_global))\n\n\nif __name__ == \"__main__\":\n tf.test.main()\n","sub_path":"models/test/test_multitask_ctc.py","file_name":"test_multitask_ctc.py","file_ext":"py","file_size_in_byte":8672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"262457517","text":"import ROOT\nfrom pdb import set_trace\nimport numpy as np\nimport plotfactory\nimport glob\n\n# ROOT.ROOT.EnableImplicitMT()\n\ndef makeChain(samplesPath, singleFile = False):\n plotfactory.setpfstyle()\n chain = ROOT.TChain('tree')\n\n all_files = glob.glob(samplesPath + '*/HNLTreeProducer*/tree.root')\n nfiles = len(all_files)\n\n if singleFile == False:\n for file in all_files:\n chain.Add(file)\n\n elif singleFile == True:\n chain.Add('/Users/dehuazhu/SynologyDrive/PhD/5_Projects/analysis/200114_AcceptancePlots/signals_2018/HN3L_M_2_V_0p0307896086367_mu_Dirac_cc_massiveAndCKM_LO/HNLTreeProducer_mmm/tree.root')\n\n return chain, nfiles\n\ndef makeEffPlot(channel = 'mu', color = ROOT.kBlack):\n # xbins = np.logspace(-2, 1.6, 10) # 50 evenly spaced points from 10^-3 to 10^3 cm \n # xbins = np.logspace(-2, 3, 10) # 50 evenly spaced points from 10^-3 to 10^3 cm \n # xbins = np.arange(0, 100, 5) # 50 evenly spaced points from 10^-3 to 10^3 cm \n xbins = np.array([0.0,2.9,6.8,10.9,16.0,110.0,180.,280.,380.,720.,1000.])\n \n if channel == 'mu' : pdgid = 13\n if channel == 'ele': pdgid = 11\n\n sel_denominator_l1 = '(l1_gen_pdgid == %d || l1_gen_pdgid == -%d) && l1_gen_eta < 2.4 && l1_gen_eta > -2.4 && l1_gen_pt > 7'%(pdgid,pdgid)\n sel_enumerator_l1 = sel_denominator_l1 + ' && l1_good_match < 0.1 && l1_gen_match_pt > 0 && (l1_gen_match_pdgid == %d || l1_gen_match_pdgid == -%d)'%(pdgid,pdgid) #it's delta R\n sel_denominator_l2 = '(l2_gen_pdgid == %d || l2_gen_pdgid == -%d) && l2_gen_eta < 2.4 && l2_gen_eta > -2.4 && l2_gen_pt > 7'%(pdgid,pdgid)\n sel_enumerator_l2 = sel_denominator_l2 + ' && l2_good_match < 0.1 && l2_gen_match_pt > 0 && (l2_gen_match_pdgid == %d || l2_gen_match_pdgid == -%d)'%(pdgid,pdgid) #it's delta R\n\n h_denominator_l1 = dataframe.Filter(sel_denominator_l1).Histo1D(('','',len(xbins)-1,xbins),'hnl_2d_gen_disp')\n h_denominator_l1 = h_denominator_l1.Clone() \n h_enumerator_l1 = dataframe.Filter(sel_enumerator_l1 ).Histo1D(('','',len(xbins)-1,xbins),'hnl_2d_gen_disp')\n h_enumerator_l1 = h_enumerator_l1.Clone() \n\n h_denominator_l2 = dataframe.Filter(sel_denominator_l2).Histo1D(('','',len(xbins)-1,xbins),'hnl_2d_gen_disp')\n h_denominator_l2 = h_denominator_l2.Clone() \n h_enumerator_l2 = dataframe.Filter(sel_enumerator_l2 ).Histo1D(('','',len(xbins)-1,xbins),'hnl_2d_gen_disp')\n h_enumerator_l2 = h_enumerator_l2.Clone() \n\n h_denominator_mu = h_denominator_l1 + h_denominator_l2\n h_enumerator_mu = h_enumerator_l1 + h_enumerator_l2\n\n # h_enumerator_mu_l1.Divide(h_denominator_mu_l1)\n effPlot = ROOT.TEfficiency(h_enumerator_mu,h_denominator_mu)\n effPlot.SetTitle(';transverse production radius (cm); reconstruction efficiency')\n effPlot.SetMarkerColor(color)\n effPlot.SetLineColor(color)\n\n return effPlot\n\nif __name__ == \"__main__\":\n samplesPath = '/Users/dehuazhu/SynologyDrive/PhD/5_Projects/analysis/200114_AcceptancePlots/20200117_signals_2018_m/'\n singleFile = False\n chain, nfiles = makeChain(samplesPath, singleFile = singleFile)\n dataframe = ROOT.ROOT.RDataFrame(chain)\n print('created dataframe with %d entries'%(dataframe.Count().GetValue()))\n \n eff_mu = makeEffPlot(channel = 'mu', color = ROOT.kBlue + 2)\n eff_ele = makeEffPlot(channel = 'ele', color = ROOT.kRed + 2)\n\n can = ROOT.TCanvas()\n eff_mu .Draw()\n eff_ele.Draw('same')\n\n leg = ROOT.TLegend(.2,.25,.5,.38)\n leg.AddEntry(eff_mu, 'muon','EP')\n leg.AddEntry(eff_ele, 'electron', 'EP')\n leg.Draw('apez same')\n\n plotfactory.showlogopreliminary()\n\n can.SetLogx()\n can.Update()\n can.SaveAs('Acceptanceplots/LeptonAcceptance.pdf')\n can.SaveAs('Acceptanceplots/LeptonAcceptance.png')\n can.SaveAs('Acceptanceplots/LeptonAcceptance.root')\n can.SaveAs('Acceptanceplots/LeptonAcceptance.tex')\n set_trace()\n\n\n\n","sub_path":"InstantPlots/plot_LeptonAcceptance.py","file_name":"plot_LeptonAcceptance.py","file_ext":"py","file_size_in_byte":3884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"411317598","text":"\"\"\"Communicating with the flashfocus server via unix socket.\"\"\"\nfrom queue import Queue\nimport logging\nimport socket\nfrom threading import Thread\n\nfrom flashfocus.compat import get_focused_window, Window\nfrom flashfocus.display import WMEvent, WMEventType\nfrom flashfocus.sockets import init_client_socket, init_server_socket\n\n\ndef client_request_flash() -> None:\n \"\"\"Request that the server flashes the current window.\"\"\"\n logging.debug(\"Connecting to the flashfocus daemon...\")\n sock = init_client_socket()\n logging.debug(\"Connection established, sending flash request...\")\n # Just send a single byte to the server. Contents are unimportant.\n sock.sendall(bytearray(\"1\", encoding=\"UTF-8\"))\n\n\nclass ClientMonitor(Thread):\n \"\"\"Queue flash requests from clients.\"\"\"\n\n def __init__(self, queue: Queue) -> None:\n self.ready = False\n super(ClientMonitor, self).__init__()\n self.queue = queue\n self.keep_going = True\n self.sock = init_server_socket()\n self.ready = True\n\n def run(self) -> None:\n \"\"\"Queue client request flashes.\"\"\"\n while self.keep_going:\n try:\n self.sock.recv(1)\n except socket.timeout:\n continue\n logging.debug(\"Received a flash request from client...\")\n focused = get_focused_window()\n if focused is not None:\n self.queue_window(focused, WMEventType.CLIENT_REQUEST)\n else:\n logging.debug(\"Focused window is undefined, ignoring request...\")\n\n def queue_window(self, window: Window, event_type: WMEventType):\n \"\"\"Add a window to the queue.\"\"\"\n self.queue.put(WMEvent(window=window, event_type=event_type))\n\n def stop(self) -> None:\n self.keep_going = False\n self.join()\n logging.debug(\"Disconnecting socket...\")\n self.sock.close()\n","sub_path":"flashfocus/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"125567840","text":"# 测试酒精质量和剂量\n\n# 计算身体水含量\ndef TotalBodyWater(gender, age, height, weight):\n if gender == 'M':\n tbw = 2.117 - 0.09516 * age + 0.1074 * height + 0.3362 * weight\n elif gender == 'F':\n tbw = -2.097 + 0.1069 * height + 0.2466 * weight\n return tbw \n\n# 计算酒精质量\ndef alcohol_kg(bal, tbw, ddp, mr=0.015, tpb=0.5):\n '''\n input:\n BAL-Blood-Alcohol Level: 目标血液中酒精浓度 g/100ml\n TBW-Total Body Water: 身体含水量\n MR-Metabolism Rate: 代谢率，一般定为0.015g/100ml/hr\n DDP-Duration of the Drinking Period: 喝酒时间\n TPB-Time to Peak BAL: 到达BAL的时间，常设为0.5hr\n output:\n g-gram: 酒精质量\n formula:\n 酒精剂量(g) = [(10 x BAL x TBW) / 0.8] + 10 x MR x (DDP + TPB) x (TBW / 0.8)\n '''\n ddp = ddp / 60\n return ((10 * bal * tbw)/0.8) + 10 * mr * (ddp + tpb) * (tbw / 0.8)\n\n# 计算饮酒剂量\ndef alcohol_ml(aq, ac):\n '''\n input:\n AQ-Alcohol Quality: 酒精质量\n AC-Alcohol Concetration: 酒精浓度\n output:\n ml-milliliter: 酒精毫升\n '''\n return aq / 0.8 / (ac / 100)\n\n\nif __name__ == \"__main__\":\n print(\"友情提示：***请确定饮用酒的密度，及时更改程序中的密度值***\")\n sublist = list(input(\"请分别输入年龄，身高(cm)，体重(kg)，空格分隔：\").split())\n age, height, weight = [float(sublist[i]) for i in range(len(sublist))]\n gender = input(\"输入性别(男性为M，女性为F): \")\n sublist2 = list(input(\"输入目标血液酒精浓度BAL(g/100ml)和饮酒时间DDP(minutes)，空格分隔：\").split())\n bal, ddp = [float(sublist2[i]) for i in range(len(sublist2))]\n ac = int(input(\"请输入酒精浓度值：\"))\n\n tbw = TotalBodyWater(gender, age, height, weight)\n aq = alcohol_kg(bal, tbw, ddp)\n ml = alcohol_ml(aq, ac)\n print(\"当前被试的饮酒质量为：%.2f g\" % aq)\n print(\"当前被试的饮酒剂量为：%.2f ml\" % ml)\n\n\n","sub_path":"Alcohol_calculation.py","file_name":"Alcohol_calculation.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"143889906","text":"#\n# Copyright (c) 2014 NORDUnet A/S\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or\n# without modification, are permitted provided that the following\n# conditions are met:\n#\n# 1. Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# 2. Redistributions in binary form must reproduce the above\n# copyright notice, this list of conditions and the following\n# disclaimer in the documentation and/or other materials provided\n# with the distribution.\n# 3. Neither the name of the NORDUnet nor the names of its\n# contributors may be used to endorse or promote products derived\n# from this software without specific prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n# \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n# POSSIBILITY OF SUCH DAMAGE.\n#\n# Author : Fredrik Thulin \n#\nfrom __future__ import annotations\n\nimport uuid\nfrom dataclasses import asdict, dataclass, field\nfrom datetime import datetime, timezone\nfrom typing import Any, Dict, List, Mapping, NewType, Optional, Type\n\nimport bson\nfrom bson import ObjectId\n\nfrom eduid_common.misc.timeutil import utc_now\nfrom eduid_common.session.logindata import ExternalMfaData\nfrom eduid_userdb.idp import IdPUser, IdPUserDb\n\nfrom eduid_webapp.idp.idp_authn import AuthnData\n\n# A distinct type for session ids\nSSOSessionId = NewType('SSOSessionId', bytes)\n\n\n@dataclass\nclass SSOSession:\n \"\"\"\n Single Sign On sessions are used to remember a previous authentication\n performed, to avoid re-authenticating users for every Service Provider\n they visit.\n\n The references to 'authn' here are strictly about what kind of Authn\n the user has performed. The resulting SAML AuthnContext is a product\n of this, as well as other policy decisions (such as what ID-proofing\n has taken place, what AuthnContext the SP requested and so on).\n\n :param user_id: User id, typically MongoDB _id\n :param authn_request_id: SAML request id of request that caused authentication\n :param authn_credentials: Data about what credentials were used to authn\n :param authn_timestamp: Authentication timestamp, in UTC\n\n # These fields are from the 'outer' scope of the session, and are\n # duplicated here for now. Can't be changed here, since they are removed in to_dict.\n\n :param created_ts: When the database document was created\n :param eppn: User eduPersonPrincipalName\n\n \"\"\"\n\n user_id: bson.ObjectId # move away from this - use the eppn instead\n authn_request_id: str\n authn_credentials: List[AuthnData]\n eppn: str\n idp_user: IdPUser = field(repr=False) # extra info - not serialised\n _id: Optional[ObjectId] = None\n session_id: SSOSessionId = field(default_factory=lambda: create_session_id())\n created_ts: datetime = field(default_factory=utc_now)\n external_mfa: Optional[ExternalMfaData] = None\n authn_timestamp: datetime = field(default_factory=utc_now)\n\n def __str__(self) -> str:\n return f'<{self.__class__.__name__}: eppn={self.eppn}, ts={self.authn_timestamp.isoformat()}>'\n\n def to_dict(self) -> Dict[str, Any]:\n \"\"\" Return the object in dict format (serialized for storing in MongoDB).\n\n For legacy reasons, some of the attributes are stored in an 'inner' scope called 'data':\n\n {\n '_id': ObjectId('5fcde44d56cf512b51f1ac4e'),\n 'session_id': b'ZjYzOTcwNWItYzUyOS00M2U1LWIxODQtODMxYTJhZjQ0YzA1',\n 'username': 'hubba-bubba',\n 'data': {\n 'user_id': ObjectId('5fd09748c07041072b237ae0')\n 'authn_request_id': 'id-IgHyGTmxBEORfx5NJ',\n 'authn_credentials': [\n {\n 'cred_id': '5fc8b78cbdaa0bf337490db1',\n 'authn_ts': datetime.fromisoformat('2020-09-13T12:26:40+00:00'),\n }\n ],\n 'authn_timestamp': 1600000000,\n 'external_mfa': None,\n },\n 'created_ts': datetime.fromisoformat('2020-12-07T08:14:05.744+00:00'),\n }\n \"\"\"\n res = asdict(self)\n res['authn_credentials'] = [x.to_dict() for x in self.authn_credentials]\n if self.external_mfa is not None:\n res['external_mfa'] = self.external_mfa.to_session_dict()\n # Remove extra fields\n del res['idp_user']\n # Use integer format for this in the database until this code (from_dict() below) has been\n # deployed everywhere so we can switch to datetime.\n # TODO: Switch over to datetime.\n res['authn_timestamp'] = int(self.authn_timestamp.timestamp())\n # Store these attributes in an 'inner' scope (called 'data')\n _data = {}\n for this in ['user_id', 'authn_request_id', 'authn_credentials', 'authn_timestamp', 'external_mfa']:\n _data[this] = res.pop(this)\n res['data'] = _data\n # rename 'eppn' to 'username' in the database, for legacy reasons\n res['username'] = res.pop('eppn')\n return res\n\n @classmethod\n def from_dict(cls: Type[SSOSession], data: Mapping[str, Any], userdb: IdPUserDb) -> SSOSession:\n \"\"\" Construct element from a data dict in database format. \"\"\"\n\n _data = dict(data) # to not modify callers data\n if 'data' in _data:\n # move contents from 'data' to top-level of dict\n _data.update(_data.pop('data'))\n _data['authn_credentials'] = [AuthnData.from_dict(x) for x in _data['authn_credentials']]\n if 'external_mfa' in _data and _data['external_mfa'] is not None:\n _data['external_mfa'] = [ExternalMfaData.from_session_dict(x) for x in _data['external_mfa']]\n if 'user_id' in _data:\n _data['idp_user'] = userdb.lookup_user(_data['user_id'])\n if not _data['idp_user']:\n raise RuntimeError(f'User with id {repr(_data[\"user_id\"])} not found')\n # Compatibility code to convert integer format to datetime format. Keep this until nothing writes\n # authn_timestamp as integers, and all the existing sessions have expired.\n # TODO: Remove this code when all sessions in the database have datetime authn_timestamps.\n if isinstance(_data.get('authn_timestamp'), int):\n _data['authn_timestamp'] = datetime.fromtimestamp(_data['authn_timestamp'], tz=timezone.utc)\n # rename 'username' to 'eppn'\n if 'eppn' not in _data:\n _data['eppn'] = _data.pop('username')\n return cls(**_data)\n\n @property\n def public_id(self) -> str:\n \"\"\"\n Return a identifier for this session that can't be used to hijack sessions\n if leaked through a log file etc.\n \"\"\"\n return f'{self.user_id}.{self.authn_timestamp.timestamp()}'\n\n @property\n def minutes_old(self) -> int:\n \"\"\" Return the age of this SSO session, in minutes. \"\"\"\n age = (utc_now() - self.authn_timestamp).total_seconds()\n return int(age) // 60\n\n def add_authn_credential(self, authn: AuthnData) -> None:\n \"\"\" Add information about a credential successfully used in this session. \"\"\"\n if not isinstance(authn, AuthnData):\n raise ValueError(f'data should be AuthnData (not {type(authn)})')\n\n # Store only the latest use of a particular credential.\n _creds: Dict[str, AuthnData] = {x.cred_id: x for x in self.authn_credentials}\n _existing = _creds.get(authn.cred_id)\n # TODO: remove this in the future - don't have to set tz when all SSO sessions without such have expired\n if _existing and _existing.timestamp.tzinfo is None:\n _existing.timestamp = _existing.timestamp.replace(tzinfo=timezone.utc)\n # only replace if newer\n if not _existing or authn.timestamp > _existing.timestamp:\n _creds[authn.cred_id] = authn\n\n # sort on cred_id to have deterministic order in tests\n _list = list(_creds.values())\n self.authn_credentials = sorted(_list, key=lambda x: x.cred_id)\n\n\ndef create_session_id() -> SSOSessionId:\n \"\"\"\n Create a unique value suitable for use as session identifier.\n\n The uniqueness and inability to guess is security critical!\n :return: session_id as bytes (to match what cookie decoding yields)\n \"\"\"\n return SSOSessionId(bytes(str(uuid.uuid4()), 'ascii'))\n","sub_path":"src/eduid_webapp/idp/sso_session.py","file_name":"sso_session.py","file_ext":"py","file_size_in_byte":9142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"584836776","text":"import multiprocessing\nimport os\n\n\ndef foo(q):\n print('被启动的新进程: (%s)' % os.getpid())\n q.put('Python')\n\n\nif __name__ == '__main__':\n # 设置使用 fork 方式启动进程\n multiprocessing.set_start_method('spawn')\n q = multiprocessing.Queue()\n # 创建进程\n mp = multiprocessing.Process(target=foo, args=(q,))\n # 启动进程\n mp.start()\n # 获取队列中的消息 \n print(q.get())\n mp.join()\n","sub_path":"疯狂Python讲义/codes/14/14.9/start_method_test.py","file_name":"start_method_test.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"529491259","text":"import android\nimport time\n\n#this is the wifi version of smartBuggy\n#gets the lat of the phone which would allow you to\n#find the speed of the buggy as it goes around the course\n#what i first wrote and tested to see\n#how an android phone responds to programs\n\ndroid = android.Android()\ndroid.startLocating(0)\ntime.sleep(5)\nwhile True:\n location = droid.readLocation()\n location = location.result['network']\n print (\"lat \" + str(location))\n time.sleep(5)","sub_path":"location.py","file_name":"location.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"22558378","text":"from django.urls import path, include\nfrom user import views\nfrom rest_framework.routers import DefaultRouter\n\napp_name = 'user'\nrouter = DefaultRouter()\nrouter.register('', views.UserViewSet)\n\nurlpatterns = [\n path('', include(router.urls)),\n path('token/', views.CreateTokenView.as_view(), name='token'),\n path('me/', views.ManageUserView.as_view(), name='me'),\n]\n","sub_path":"backend/user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"244624251","text":"# Working from left-to-right if no digit is exceeded by the digit to its left it is called an increasing number;\n# for example, 134468.\n#\n# Similarly if no digit is exceeded by the digit to its right it is called a decreasing number; for example, 66420.\n#\n# We shall call a positive integer that is neither increasing nor decreasing a \"bouncy\" number; for example, 155349.\n#\n# Clearly there cannot be any bouncy numbers below one-hundred, but just over half of the numbers below one-thousand\n# (525) are bouncy. In fact, the least number for which the proportion of bouncy numbers first reaches 50% is 538.\n#\n# Surprisingly, bouncy numbers become more and more common and by the time we reach 21780 the proportion of bouncy\n# numbers is equal to 90%.\n#\n# Find the least number for which the proportion of bouncy numbers is exactly 99%.\n#\n# Answer:\n\nimport time\n\n\ndef main():\n start = time.time()\n\n bouncy = 0\n non_bouncy = 0\n\n i = 1\n while True:\n if is_bouncy(i):\n bouncy += 1\n else:\n non_bouncy += 1\n\n proportion = bouncy / (bouncy + non_bouncy)\n if proportion == .99:\n break\n\n i += 1\n\n print(\"\\nanswer: \" + str(i))\n print(\"\\ntook \" + str(time.time() - start) + \" seconds\")\n\n\ndef is_bouncy(n):\n inc = True\n dec = True\n n_str = str(n)\n for i in range(len(n_str)):\n if i == 0:\n continue\n\n if n_str[i] > n_str[i - 1]:\n dec = False\n if n_str[i] < n_str[i - 1]:\n inc = False\n\n if not inc and not dec:\n return True\n\n return False\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/python/Problem112.py","file_name":"Problem112.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"450135515","text":"import os\nimport math\nimport pickle\nfrom math import cos, sin\nimport numpy as np\nfrom scipy import interpolate\n\n### filenames, constants ###\n\nGDFFILE = \"temp.gdf\" \nASCIIFILE = \"outscope.txt\"\nTRANSFILE = \"trans.gdf\"\nTRANSASCII = \"trans.txt\"\nEXE = \"/home/chenyu/Software/gpt310x64/bin/gpt\"\nEXETXT = \"/home/chenyu/Software/gpt310x64/bin/gdf2a\"\nEXETRANS = \"/home/chenyu/Software/gpt310x64/bin/gdftrans\"\nMConHBAR = 2.59e12 #inverse meters\n# sampleL = 5e-10\nsampleL = 2.096e-9*4\nsampleL = 9e-9\nsampleL = 32 * 0.396e-9\nsampleScale = 1\nerrorsigmaL = 0.0\nerrorsigmaTheta = 0.0\nmaxsig = 1.0\n#H1 = 1228.5\n#S6 = 390000\n#S7 =-680186.0\n#Obj=-12180368.5\n\nparams = {\"sol1nI\" : 2.5e5,\n \"sol1cH\" : 0.0,\n \"sol1cV\" : 0.0,\n \"sol2nI\" : 2.5e5,\n \"sol2cH\" : 0.0,\n \"sol2cV\" : 0.0,\n \"hex1G\" : 899,\n \"soltnI\" : 1.199315e5,\n \"soltcH\" : 0.0,\n \"soltcV\" : 0.0,\n \"csol1nI\" : 6.48691415e5,\n \"csol1cH\" : 0.0,\n \"csol1cV\" : 0.0,\n \"csol2nI\" : -6.48691415e5,\n \"csol2cH\" : 0.0,\n \"csol2cV\" : 0.0,\n \"hex2G\" : 899,\n \"csol3nI\" : 3.9e5,\n \"csol3cH\" : 0.0,\n \"csol3cV\" : 0.0,\n \"csol4nI\" : -6.541e5,\n \"csol4cH\" : 0.0,\n \"csol4cV\" : 0.0,\n \"sol3nI\" : -9.39e5,\n \"sol3cH\" : 0.0,\n \"sol3cV\" : 0.0,\n \"sol4nI\" : 0.0,\n \"alpha\" : 1e-4,\n \"theta\" : 0.0,\n \"delta\" : 0.0}\n\neleprefix = [\"sol1\", \"sol2\", \"solt\",\"hex1\",\n \"csol1\", \"csol2\", \"hex2\",\n \"csol3\", \"csol4\", \"sol3\"]\n\nelepostfix = [\"ox\", \"oy\", \"oz\",\n \"xx\", \"xy\", \"xz\",\n \"yx\", \"yy\", \"yz\"]\n\nerrornames = [[pre + post for post in elepostfix] for pre in eleprefix]\n\n### wrapper for gpt ###\n\ndef sim(S1 = params[\"sol1nI\"],\n S1CH = params[\"sol1cH\"], \n S1CV = params[\"sol1cV\"], \n S2 = params[\"sol2nI\"],\n S2CH = params[\"sol2cH\"],\n S2CV = params[\"sol2cV\"], \n S3 = params[\"soltnI\"],\n S3CH = params[\"soltcH\"],\n S3CV = params[\"soltcV\"], \n H1 = params[\"hex1G\"],\n S4 = params[\"csol1nI\"],\n S4CH = params[\"csol1cH\"],\n S4CV = params[\"csol1cV\"],\n S5 = params[\"csol2nI\"],\n S5CH = params[\"csol2cH\"],\n S5CV = params[\"csol2cV\"],\n H2 = params[\"hex2G\"],\n S6 = params[\"csol3nI\"],\n S6CH = params[\"csol3cH\"],\n S6CV = params[\"csol3cV\"],\n S7 = params[\"csol4nI\"],\n S7CH = params[\"csol4cH\"],\n S7CV = params[\"csol4cV\"],\n Obj = params[\"sol3nI\"],\n ObjCH = params[\"sol3cH\"],\n ObjCV = params[\"sol3cV\"],\n S9 = params[\"sol4nI\"],\n alpha = params[\"alpha\"],\n theta = params[\"theta\"],\n delta = params[\"delta\"],\n seed = 0,\n erL = errorsigmaL,\n erTh = errorsigmaTheta):\n np.random.seed(seed = seed)\n rs = [np.random.normal(size = 6) for dummy in range(0, len(eleprefix))]\n errors = [[r[0]*erL, r[1]*erL, r[2]*erL,\n cos(r[3]*erTh)*cos(r[5]*erTh) - cos(r[4]*erTh)*sin(r[3]*erTh)*sin(r[5]*erTh),\n -cos(r[3]*erTh)*sin(r[5]*erTh) - cos(r[4]*erTh)*cos(r[5]*erTh)*sin(r[3]*erTh),\n sin(r[3]*erTh)*sin(r[4]*erTh),\n cos(r[5]*erTh)*sin(r[3]*erTh) + cos(r[3]*erTh)*cos(r[4]*erTh)*sin(r[5]*erTh),\n cos(r[3]*erTh)*cos(r[4]*erTh)*cos(r[5]*erTh) - sin(r[3]*erTh)*sin(r[5]*erTh),\n -cos(r[3]*erTh)*sin(r[4]*erTh)] for r in rs] \n cmdA = \"{} -o {} hexuscope.in {}{}\".format(EXE, GDFFILE, \n \"\".join([\"{}={} \".format(x,y) for x, y in zip(params.keys(), \n [S1, S1CH, S2CV, S2, S2CH, S2CV, H1, S3, \n S3CH, S3CV, S4, S4CH, S4CV, \n S5, S5CH, S5CV, H2, S6, S6CH, S6CV, S7, S7CH, S7CV, Obj, ObjCH, ObjCV, S9, alpha, theta, delta])]), \n \"\".join([\"{}={} \".format(s, t) for x, y in zip(errornames, errors) for s, t in zip(x, y)]))\n\n cmdC = \"{} -o {} {} time x y z G\".format(EXETRANS, TRANSFILE, GDFFILE)\n\n cmdB = \"{} -o {} {}\".format(EXETXT, ASCIIFILE, GDFFILE)\n\n cmdD = \"{} -o {} {}\".format(EXETXT, TRANSASCII, TRANSFILE)\n \n # cmdA,C,D to track the particles, cmdA,B to run standard screen\n os.system(cmdA)\n # os.system(cmdC)\n os.system(cmdB)\n # os.system(cmdD)\n screen = np.loadtxt(ASCIIFILE, skiprows=5)\n \n x = screen[:,0]\n y = screen[:,1]\n kx = np.divide(screen[:,4], screen[:,6])\n ky = np.divide(screen[:,5], screen[:,6])\n\n meankx = np.mean(kx)\n sigkx = np.std(kx)\n\n meanky = np.mean(ky)\n sigky = np.std(ky)\n \n N = 40\n # set a fixed kx, ky limit if necessary\n sigkx = 0.040 / maxsig\n sigky = 0.040 / maxsig\n\n x_bins = [[[] for n in range(0,N)] for m in range(0,N)]\n y_bins = [[[] for n in range(0,N)] for m in range(0,N)]\n\n x_grid = np.zeros([N, N])\n y_grid = np.zeros([N, N])\n\n kx_grid, ky_grid = np.meshgrid(sigkx*np.linspace(-maxsig, maxsig, N),\n sigky*np.linspace(-maxsig, maxsig, N))\n\n\n for xi, yi, kxi, kyi in zip(x, y, kx, ky):\n i = int(0.5*N*((kyi-meanky)/(maxsig*sigky)) + 0.5*N)\n j = int(0.5*N*((kxi-meankx)/(maxsig*sigkx)) + 0.5*N)\n if i < 0 or i > N-1 or j < 0 or j > N-1:\n continue\n x_bins[i][j].append(xi)\n y_bins[i][j].append(yi)\n\n for i in range(0, N):\n for j in range(0, N):\n x_grid[i,j] = np.mean(x_bins[i][j])\n y_grid[i,j] = np.mean(y_bins[i][j])\n\n # Remove possible nan points that would make following interpolation step fail\n y_grid[np.isnan(y_grid)]=0\n x_grid[np.isnan(x_grid)]=0\n index = np.where(x_grid != 0)\n\n xfunc = interpolate.SmoothBivariateSpline(kx_grid[index].flatten(), ky_grid[index].flatten(), x_grid[index].flatten(), kx=5, ky=5)\n yfunc = interpolate.SmoothBivariateSpline(kx_grid[index].flatten(), ky_grid[index].flatten(), y_grid[index].flatten(), kx=5, ky=5)\n\n ky_fine = np.linspace(-sigkx*maxsig, sigkx*maxsig, 201)\n kx_fine = np.linspace(-sigkx*maxsig, sigkx*maxsig, 201)\n\n FILENAME = \"/home/chenyu/Desktop/git/STEMalign/GPTrelated/trnsmssn_antialiasing.pickle\"\n\n with open(FILENAME, \"rb\") as f:\n trnsmssn = pickle.load(f)\n\n shadow = np.array([[trnsmssn((xfunc(kx, ky)[0][0]/sampleScale + sampleL/2)%sampleL, \n (yfunc(kx, ky)[0][0]/sampleScale + sampleL/2)%sampleL)[0] for kx in kx_fine] for ky in ky_fine])\n return maxsig*sigkx, maxsig*sigky, shadow\n\n","sub_path":"GPTrelated/uscope.py","file_name":"uscope.py","file_ext":"py","file_size_in_byte":6620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"638982055","text":"from cities.models import Country, City, Region\nfrom django.contrib.gis.geos import Point\nfrom django.test import TestCase\n\nfrom airports.management.commands.airports import get_location_info, create_airport\nfrom airports.models import Airport\n\n# python 2&3 compatible\ntry:\n from cStringIO import StringIO\nexcept ImportError:\n from io import StringIO\n\n\nclass TestCommandAirports3(TestCase):\n def setUp(self):\n default_format = 'airport_id,name,city_name,country_name,iata,icao,latitude,longitude,altitude,timezone,dst'\n\n twolines = \"\"\"1,\"Goroka Airport\",\"Goroka\",\"Papua New Guinea\",\"GKA\",\"AYGA\",-6.081689834590001,145.391998291,5282,10,\"U\",\"Pacific/Port_Moresby\",\"airport\",\"OurAirports\"\n 2,\"Madang Airport\",\"Madang\",\"Papua New Guinea\",\"MAG\",\"AYMD\",-5.20707988739,145.789001465,20,10,\"U\",\"Pacific/Port_Moresby\",\"airport\",\"OurAirports\" \"\"\"\n\n self.columns = default_format.split(',')\n self.csv = StringIO(twolines)\n\n self.dialect = csv.Sniffer().sniff(self.csv.read(512))\n\n self.csv.seek(0)\n self.reader = csv.DictReader(self.csv, dialect=self.dialect, fieldnames=self.columns)\n\n self.url = \"https://raw.githubusercontent.com/jpatokal/openflights/master/data/airports.dat\"\n\n def test_get_lines(self):\n lines = get_lines(self.url)\n\n self.assertTrue(len(next(lines)) > 10)\n self.assertTrue(len(next(lines)) > 10)\n self.assertTrue(len(next(lines)) > 10)\n\n def test_read_airports(self):\n airports = list(read_airports(self.reader))\n self.assertEquals(len(airports), 2)\n\n def test_airports_updated(self):\n # read them in first\n list(read_airports(self.reader))\n\n self.assertEquals(Airport.objects.count(), 2)\n self.assertEqual(Airport.objects.get(airport_id=1).name, \"Goroka Airport\")\n self.assertEqual(Airport.objects.get(airport_id=2).name, \"Madang Airport\")\n\n # update the airports\n csv_text = StringIO(\"\"\"1,\"Goro White Dog Airport\",\"Goroka\",\"Papua New Guinea\",\"GKA\",\"AYGA\",-6.081689834590001,145.391998291,5282,10,\"U\",\"Pacific/Port_Moresby\",\"airport\",\"OurAirports\"\n 2,\"Vabank Airport\",\"Madang\",\"Papua New Guinea\",\"MAG\",\"AYMD\",-5.20707988739,145.789001465,20,10,\"U\",\"Pacific/Port_Moresby\",\"airport\",\"OurAirports\" \"\"\")\n reader = csv.DictReader(csv_text, dialect=self.dialect, fieldnames=self.columns)\n\n list(read_airports(reader))\n\n self.assertEquals(Airport.objects.count(), 2)\n self.assertEqual(Airport.objects.get(airport_id=1).name, \"Goro White Dog Airport\")\n self.assertEqual(Airport.objects.get(airport_id=2).name, \"Vabank Airport\")\n\n\nclass TestCommandAirports(TestCase):\n def setUp(self):\n self.country = Country(\n slug='United-States',\n name='United States',\n code='US',\n code3='USA',\n population=310232863,\n area=9629091,\n currency='USD',\n currency_name='Dollar',\n currency_symbol='$',\n language_codes='en-US,es-US,haw,fr',\n phone='1',\n tld='us',\n capital='Washington',\n )\n self.country.save()\n self.region = Region(\n slug='California_US.CA',\n name='California',\n name_std='California',\n code='CA',\n country=self.country,\n )\n self.region.save()\n self.city = City(\n slug='5323401-Alpine',\n name='Alpine',\n name_std='Alpine',\n country=self.country,\n region=self.region,\n location=Point(-116.76641,32.83505),\n population=14236,\n elevation=559,\n kind='PPL',\n timezone='America/Los_Angeles',\n )\n self.city.save()\n self.airport = Airport(\n id=8227,\n name='On the Rocks Airport',\n city_name='Alpine',\n iata=None,\n icao='1CA6',\n local='1CA6',\n ident='1CA6',\n altitude=0.0,\n location=Point(-116.7229995727539,32.76509857177734),\n country=self.country,\n region=self.region,\n city=self.city,\n type='small_airport'\n )\n self.airport.save()\n\n def assert_location_tuple(self, country, region, city):\n self.assertEqual(country, self.country)\n self.assertEqual(region, self.region)\n self.assertEqual(city, self.city)\n\n def test_get_location_info_name(self):\n country, region, city = get_location_info('Alpine', None, None, -116.7229995727539, 32.76509857177734)\n self.assert_location_tuple(country, region, city)\n\n def test_get_location_info_wrong_name(self):\n country, region, city = get_location_info('Wrong Name', None, None, -116.7229995727539, 32.76509857177734)\n self.assert_location_tuple(country, region, city)\n\n def test_get_location_info_fix_name(self):\n country, region, city = get_location_info('', None, None, -116.7229995727539, 32.76509857177734)\n self.assert_location_tuple(country, region, city)\n\n def test_get_location_info_missing_name(self):\n country, region, city = get_location_info('', None, None, -116.7229995727539, 32.76509857177734)\n self.assert_location_tuple(country, region, city)\n\n def test_get_location_info_country(self):\n country, region, city = get_location_info('', self.country, None, -116.7229995727539, 32.76509857177734)\n self.assert_location_tuple(country, region, city)\n\n def test_get_location_info_country_region(self):\n country, region, city = get_location_info('', self.country, self.region, -116.7229995727539, 32.76509857177734)\n self.assert_location_tuple(country, region, city)\n\n def test_get_location_info_country_region_bad_coords(self):\n country, region, city = get_location_info('', self.country, self.region, -116.7, 38.0)\n self.assertEqual(country, self.country)\n self.assertEqual(region, self.region)\n self.assertIsNone(city)\n\n def test_get_location_info_country_bad_coords(self):\n country, region, city = get_location_info('', self.country, None, -106.0, 32.0)\n self.assertEqual(country, self.country)\n self.assertIsNone(region)\n self.assertIsNone(city)\n\n\nclass TestCommandAirports1(TestCase):\n def setUp(self):\n country = Country(\n name='country',\n population=0\n )\n country.save()\n region = Region(\n name_std='region',\n country=country\n )\n region.save()\n city1 = City(\n name='city1',\n name_std='city1....',\n country=country,\n region=region,\n location=Point(0, 0, srid=4326),\n population=0,\n )\n city1.save()\n self.city2 = City(\n name='city2',\n name_std='city2....',\n country=country,\n region=region,\n location=Point(100, 100, srid=4326),\n population=0,\n )\n self.city2.save()\n self.city3 = City(\n name='city3',\n name_std='city3....',\n country=country,\n region=region,\n location=Point(1000, -1000, srid=4326),\n population=0,\n )\n self.city3.save()\n\n def test_get_location_info(self):\n country, region, city = get_location_info('test', None, None, 1000, -1000)\n self.assertEqual(city, self.city3)\n\n def test_create_airport(self):\n pass\n\n def tearDown(self):\n pass\n\n\nclass TestCommandAirports2(TestCase):\n def setUp(self):\n self.guinea = Country.objects.create(\n name='Papua New Guinea',\n population=0,\n code='PG',\n code3='PNG',\n\n )\n self.city1 = City.objects.create(\n name='Goroka',\n name_std='Goroka',\n country=self.guinea,\n location=Point(145.38735, -6.08336, srid=4326),\n population=0,\n )\n\n country2 = Country.objects.create(\n name='Marshall Islands',\n population=0,\n code='MH',\n code3='MHL',\n )\n self.city2 = City.objects.create(\n name='Utrik',\n name_std='Utrik',\n country=country2,\n location=Point(169.84739, 11.22778, srid=4326),\n population=0,\n )\n self.airport_location = Point(146.725977, -6.569803, srid=4326)\n self.airport_region_name = 'Papua New Guinea'\n\n def test_get_location_info(self):\n country, region, city = get_location_info('test',\n None, None, self.airport_location.coords[0], self.airport_location.coords[1])\n self.assertIsNotNone(city)\n self.assertIsNotNone(country)\n self.assertEqual(country, self.guinea)\n","sub_path":"tests/test_commands.py","file_name":"test_commands.py","file_ext":"py","file_size_in_byte":9094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"643571057","text":"import urllib.request as nivesh\nfrom bs4 import *\n\n\ncount=0\nurl=input(\"Enter Your URL\")\nrcount=int(input(\"Enter Count: \"))\npos=int(input(\"Enter Position: \"))\n\nwhile count < rcount:\n\tprint(\"Getting: \",url)\n\thtml=nivesh.urlopen(url).read()\n\tsoup=BeautifulSoup(html,'html.parser')\n\ttags=soup('a')\n\tfor index, item in enumerate(tags):\n\t\tif index == pos-1:\n\t\t\turl=item.get('href',None)\n\t\t\tname=item.contents[0]\n\t\t\tbreak\n\t\telse:\n\t\t\tcontinue\n\tcount=count+1\n\nprint('Last Url: ',url)\n\n","sub_path":"CourseraP3/bs2.py","file_name":"bs2.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"628097028","text":"\"\"\" Звездный треугольник 🌶️\nНапишите функцию draw_triangle(), которая выводит звездный равнобедренный треугольник с основанием и высотой равными 1515 и 88 соответственно:\n\n *\n ***\n *****\n *******\n *********\n ***********\n *************\n***************\nПримечание 1 . Для вывода треугольника используйте цикл for. \n\nПримечание 2 . Справа от звездочек пробелов нет. \"\"\"\n\n# объявление функции\ndef draw_triangle():\n j = 1\n for i in range(1, 9):\n print(' ' * (8 - i), '*' * j, sep = '')\n j += 2\n\n# основная программа\ndraw_triangle() # вызов функции\n\n\"\"\" Калькулятор доставки\nИнтернет магазин осуществляет экспресс доставку для своих товаров по цене 10001000 рублей за первый товар и 120120 рублей за каждый последующий товар. Напишите функцию get_shipping_cost(quantity), которая принимает в качестве аргумента натуральное число quantity – количество товаров в заказе и возвращает стоимость доставки.\n\nПримечание. Следующий программный код:\n\nprint(get_shipping_cost(1))\nprint(get_shipping_cost(3))\nдолжен выводить:\n\n1000\n1240 \"\"\"\n\n# объявление функции\ndef get_shipping_cost(quantity):\n res = 1000 + 120 * (quantity - 1)\n return res\n\n# считываем данные\nn = int(input())\n\n# вызываем функцию\nprint(get_shipping_cost(n))\n\n\n\"\"\" Биномиальный коэффициент 🌶️\nНапишите функцию compute_binom(n, k), которая принимает в качестве аргументов два натуральных числа n и k и возвращает значение биномиального коэффициента, равного \\dfrac{n!}{k! (n-k)!} \nk!(n−k)!\nn!\n​\n .\n\nПримечание 1. Факториалом натурального числа nn, называется произведение всех натуральных чисел от 11 до nn, то есть \nn!=1\\cdot2\\cdot3\\cdot…\\cdot n\nn!=1⋅2⋅3⋅…⋅n\n\nПримечание 2. Реализуйте вспомогательную функцию factorial(n), вычисляющую факториал числа или воспользуйтесь уже готовой функцией из модуля math. \"\"\"\n# объявление функции\ndef compute_binom(n, k):\n if k > n:\n return 0\n from math import factorial\n res = int(factorial(n) / (factorial(k) * factorial(n - k)))\n return res\n\n# считываем данные\nn = int(input())\nk = int(input())\n\n# вызываем функцию\nprint(compute_binom(n, k))\n\n\"\"\" Число словами 🌶️\nНапишите функцию number_to_words(num), которая принимает в качестве аргумента натуральное число num и возвращает его словесное описание на русском языке.\n\nПримечание 1. Считайте, что число 1 \\le num \\le 991≤num ≤99.\n\nПримечание 2. Следующий программный код:\n\nprint(number_to_words(7))\nprint(number_to_words(85))\nдолжен выводить:\n\nсемь\nвосемьдесят пять \"\"\"\n\n# объявление функции\ndef number_to_words(num):\n num_1 = ['', 'один', 'два', 'три', 'четыре', 'пять', 'шесть', 'семь', 'восемь', 'девять', 'десять', 'одиннадцать', 'двенадцать', 'тринадцать', 'четырнадцать', 'пятнадцать', 'шестнадцать', 'семнадцать', 'восемнадцать', 'девятнадцать',]\n num_10 = ['', '', 'двадцать', 'тридцать', 'сорок', 'пятьдесят', 'шестьдесят', 'семьдесят', 'восемьдесят', 'девяносто']\n if 1 <= num < 20:\n res = num_1[num]\n else:\n res = num_10[num // 10] + ' ' + num_1[num % 10]\n return res\n\n# считываем данные\n# n = int(input())\n\n# провер��а вызываем функцию\nfor i in range(1, 100):\n print(number_to_words(i))\n\n\"\"\" Искомый месяц\nНапишите функцию get_month(language, number), которая принимает на вход два аргумента language – язык ru или en и number – номер месяца (от 1 до 12) и возвращает название месяца на русском или английском языке.\n\nПримечание. Следующий программный код:\n\nprint(get_month('ru', 1))\nprint(get_month('ru', 12))\nprint(get_month('en', 1))\nprint(get_month('en', 10))\nдолжен выводить:\n\nянварь\nдекабрь\njanuary\noctober \"\"\"\n\n# объявление функции\ndef get_month(language, number):\n lng_ru = ['январь', 'февраль', 'март', 'апрель', 'май', 'июнь', 'июль', 'август', 'сентябрь', 'октябрь', 'ноябрь', 'декабрь']\n\n lng_en = ['january', 'february', 'march', 'april', 'may', 'june', 'july', 'august', 'september', 'october', 'november', 'december']\n if language == 'en':\n return lng_en[number - 1]\n else:\n return lng_ru[number - 1]\n\n# считываем данные\nlan = input()\nnum = int(input())\n\n# вызываем функцию\nprint(get_month(lan, num))\n\n\"\"\" Магические даты\nМагическая дата – это дата, когда день, умноженный на месяц, равен числу образованному последними двумя цифрами года.\n\nНапишите функцию, is_magic(date) которая принимает в качестве аргумента строковое представление корректой даты и возвращает значение True если дата является магической и False в противном случае.\n\nПримечание. Следующий программный код:\n\nprint(is_magic('10.06.1960'))\nprint(is_magic('11.06.1960'))\nдолжен выводить:\n\nTrue\nFalse \"\"\"\n\n# объявление функции\ndef is_magic(date):\n list1 = [int(i) for i in date.split('.')]\n if list1[0] * list1[1] == list1[2] % 100:\n return True\n else:\n return False\n\n# считываем данные\ndate = input()\n\n# вызываем функцию\nprint(is_magic(date))\n\n\"\"\" Панграммы\nПанграмма – это фраза, содержащая в себе все буквы алфавита. \nОбычно панграммы используют для презентации шрифтов, чтобы можно было в одной фразе рассмотреть все глифы.\nНапишите функцию, is_pangram(text) которая принимает в качестве аргумента строку текста на английском языке и \nвозвращает значение True если текст является панграммой и False в противном случае.\nПримечание 1. Гарантируется, что введенная строка содержит только буквы английского алфавита.\nПримечание 2. Следующий программный код:\n\nprint(is_pangram('Jackdaws love my big sphinx of quartz'))\nprint(is_pangram('The jay pig fox zebra and my wolves quack'))\nprint(is_pangram('Hello world'))\nдолжен выводить:\n\nTrue\nTrue\nFalse \"\"\"\n\n# объявление функции\ndef is_pangram(text):\n for q in range(97, 123):\n if chr(q) not in text.lower():\n return False\n return True\n\n# считываем данные\ntext = input()\n\n# вызываем функцию\nprint(is_pangram(text))","sub_path":"exercises/examfunc.py","file_name":"examfunc.py","file_ext":"py","file_size_in_byte":8400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"100077293","text":"#!/usr/bin/env python\n\nimport csv\nimport os\nimport re\nimport argparse\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--file', help='Enter the full Path of the output file', nargs='?', default=\"\", const=\"\")\nparser.add_argument('--nThreads', help='Enter number of threads used', nargs='?', default=1, const=1)\nparser.add_argument('--expectedResult', help='Enter the expected value')\nparser.add_argument('--serialTime', help='Enter the baseline serial time taken')\nparser.add_argument('--parallelTime', help='Enter the expected parallel time taken')\n\nargs = parser.parse_args()\ninputFilePath = args.file\nexpectedResult = float(args.expectedResult)\nnThreads = int(args.nThreads)\nserialTime = float(args.serialTime)\nparallelTime = float(args.parallelTime)\n\nspacePattern = \"(\\s)*\"\nnumberOfThreadsPattern = re.compile(\"^Number of threads{}:{}(\\d+)\".format(spacePattern, spacePattern))\nheatTransferThreadStatsPattern = re.compile(\"^thread_id, start_column, end_column, time_taken\")\nresultPattern = re.compile(\"^Temp\\[400,400\\]\\=(\\d*\\.\\d*)\")\ntimeTakenPattern = re.compile(\"^Time taken \\(in seconds\\){}:{}(\\d*\\.\\d*)\".format(spacePattern, spacePattern))\n\n\nstartProcessing = False\nfinishedProcessingThreadData = False\nprocessingOverallData = False\nnumberOfColumns = 4\n\nthreadData = list()\nnThreadsRead = 0\ntotalTime = 0.0\nresult = 0.0\n\nconsoleLogContents = \"\"\nif(inputFilePath != \"\"):\n with open(inputFilePath, 'r') as inputFile:\n consoleLogContents = inputFile.read()\n\nconsoleLogContents = consoleLogContents.split('\\n')\n\n### Parse the output logs ###\nfor line in consoleLogContents:\n if(startProcessing == False):\n if(numberOfThreadsPattern.match(line)):\n lineSplit = line.split(':')\n lastWord = lineSplit[-1].strip()\n nThreadsRead = int(lastWord) \n continue\n if(heatTransferThreadStatsPattern.match(line)):\n startProcessing = True\n continue\n\n # // Can proceed\n if(finishedProcessingThreadData == False):\n # print(line)\n lineSplit = line.split(',') \n if(len(lineSplit) == 4):\n threadID = lineSplit[0].strip()\n start_col = lineSplit[1].strip()\n end_col = lineSplit[2].strip()\n timeTaken = lineSplit[3].strip()\n threadData.append((threadID, int(start_col), int(end_col), float(timeTaken)))\n continue\n else:\n finishedProcessingThreadData = True\n\n # Process overall data\n if(resultPattern.match(line)):\n m = resultPattern.search(line)\n l = len(m.groups())\n result = float(m.group(l))\n if(timeTakenPattern.match(line)):\n m = timeTakenPattern.search(line)\n l = len(m.groups())\n totalTime = float(m.group(l))\n\n\n\n### Validate all information is correctly parsed ###\n\n# print(\"Parsed information from the program execution\")\n# print(\"Temp[400,400]={}\".format(result))\n# print(\"Time taken (in seconds) : {}\".format(totalTime))\n# print(\"Time taken (in seconds) : {}\".format(result))\n\nvalidationFlag = True\nif nThreadsRead != nThreads:\n validationFlag = False\n print(\"VALIDATION FAILED: Incorrect number of threads used = {}\".format(nThreadsRead))\n\nif totalTime < 0.000000000000000001:\n validationFlag = False\n print(\"VALIDATION FAILED: Time taken = {}\".format(totalTime))\n\nif (result > (expectedResult+1)) or (result < (expectedResult-1)):\n validationFlag = False\n print(\"VALIDATION FAILED: Temp[400,400]({}) accuracy is very poor\".format(result))\n\nif nThreads != len(threadData):\n validationFlag = False\n print(\"VALIDATION FAILED: Thread statistics only found for {} threads\".format(len(threadData)))\n\nfor value in threadData:\n if len(value) != 4:\n validationFlag = False\n print(\"VALIDATION FAILED: Incorrect format for thread statistics logs\\n Expected format : {}\".format(ellipseAreaThreadStatsPattern.pattern))\n\nif validationFlag == True:\n print(\"Validation successful\")\nelse:\n print(\"Validation failed\")\n exit(1)\n\n\n\n### Evaluation of the processed information ###\n\nminInterval = 999999999999\nmaxInterval = 0\nminTimeTaken = 99999999999.0\nmaxTimeTaken = 0.0\nfor v in threadData:\n startc = v[1]\n endc = v[2]\n timeTaken = v[3]\n minInterval = min(minInterval, endc-startc+1)\n maxInterval = max(maxInterval, endc-startc+1)\n minTimeTaken = min(minTimeTaken, timeTaken)\n maxTimeTaken = max(maxTimeTaken, timeTaken)\n\n\nsuccessFlag=True\n\n#Validate work distribution\nif(maxInterval-minInterval > 1):\n successFlag=False\n print(\"EVALUATION FAILED : Work is poorly distributed between threads.{}\".format(expectedResult))\n \n#Validate time taken with respect to baseline serial time\nif(totalTime > serialTime):\n print(\"WARNING : Your parallel execution time ({}) is more than the serial execution time ({}).\\nIdeally, your execution time should less than {}\".format(totalTime, serialTime, serialTime))\n \n#Validate time taken with respect to expected parallel time\nspeedup = parallelTime / totalTime\n# print(\"totalTime : {}\".format(totalTime))\n# print(\"serialTime : {}\".format(serialTime))\n# print(\"parallelTime : {}\".format(parallelTime))\n# print(\"speedup : {}\".format(speedup))\nslowdown = totalTime / parallelTime\nprint(\"Your parallel execution time, T : {} seconds\".format(totalTime))\nprint(\"Expected parallel execution time, T_expected : {} seconds\".format(parallelTime))\nprint(\"Ideally, T should be equal to (or less than) T_expected\")\n\nif(slowdown > 1.5):\n print(\"WARNING : Your parallel execution time ({} seconds) is more than 1.5 times higher than the expected parallel execution time ({} seconds).\\nIdeally, your execution time should be around {} seconds\".format(totalTime, parallelTime, parallelTime))\n\n\nif successFlag == True:\n print(\"Evaluation successful\")\nelse:\n print(\"Evaluation failed.\")\n print(\"Ensure that you do not print more data than expected and the expected output format is strictly followed\")\n\n","sub_path":"CMPT431/assignment2/test_scripts/heat_transfer_evaluator.py","file_name":"heat_transfer_evaluator.py","file_ext":"py","file_size_in_byte":5980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"68615283","text":"#!/usr/bin/env python\nimport json\n\nimport pika\nimport os\n\nmq_host = os.environ.get('MQ_HOST') or 'localhost'\nconnection = pika.BlockingConnection(\n pika.ConnectionParameters(host=mq_host))\nchannel = connection.channel()\n\nchannel.exchange_declare(exchange='mail-topic', exchange_type='topic')\n\nrouting_key = 'eapx.mail'\nmessage = {\n \"mail\": \"bruce1988cn@gmail.com\",\n \"content\": \"Test\"\n}\nchannel.basic_publish(\n exchange='mail-topic', routing_key=routing_key, body=json.dumps(message))\nprint(\" [x] Sent %r:%r\" % (routing_key, message))\nconnection.close()","sub_path":"base/eapx-email-master/py/topic.py","file_name":"topic.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"213968918","text":"from lab10.Vasich.pythagorean_triples import triples\nfrom random import randint\n\nimport unittest\n\n\nclass TestTriples(unittest.TestCase):\n def _check(self, input_arr, out_value):\n work_copy = [input_arr[i] ** 2 for i in range(len(input_arr))]\n work_copy.sort()\n arr = [work_copy[0]]\n\n j = 0\n for i in range(1, len(work_copy)):\n value = work_copy[i]\n if arr[j] != value:\n arr.append(value)\n j += 1\n\n count = 0\n for i in range(len(arr) - 1, -1, -1):\n for j in range(i - 1, -1, -1):\n for k in range(j - 1, -1, -1):\n if arr[i] == arr[j] + arr[k]:\n count += 1\n return count == out_value\n\n def test_egypt(self):\n input_arr = [3, 4, 5]\n self.assertEqual(1, triples(input_arr))\n\n def test_manual1(self):\n input_arr = [20, 21, 29, 12, 16, 3]\n self.assertEqual(2, triples(input_arr))\n\n def test_manual2(self):\n input_arr = [23, 247, 19, 96, 264, 265, 132, 181]\n self.assertEqual(2, triples(input_arr))\n\n def test_manual3(self):\n input_arr = [12, 15, 24, 27, 40, 1, 42]\n self.assertEqual(0, triples(input_arr))\n\n def test_random(self):\n size = 500\n input_arr = [0] * size\n for i in range(size):\n input_arr[i] = randint(0, 1000)\n self.assertTrue(self._check(input_arr, triples(input_arr)))\n","sub_path":"lab10/Vasich/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"42673760","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 16 11:37:37 2022\n\n@author: Milena.Singletary\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as img\nimport matplotlib as mpl\nimport matplotlib.patches as mpatches\nimport numpy as np\nimport pandas as pd\n\nfrom matplotlib import cm\n\n\nim = img.imread(r'C:\\Users\\milena.singletary\\OneDrive - Boa Technology Inc\\Pictures\\Foot.PNG')\n\n# initiate foot regions\n# input the number of regions\n# RegionsList = []\n# plt.imshow(im)\n# regions = 23\n# for i in range(regions):\n# print('Select region' + str(i+1) + '.')\n# RegionsList.append(plt.ginput(1))\n \n# print('select region1')\n# rr1 = plt.ginput(n=1)\n# print('select region2')\n# rr2 = plt.ginput(n=1)\n\n\n\nrr1 = [66, 90]\nrr2 = [138, 84]\nrr3 = [158, 85.5]\nrr4 = [203.5, 60]\nrr5 = [200, 83]\nrr6 = [303, 107]\nrr7 = [305, 94]\nrr8 = [323, 102.5]\nrr9 = [340, 94]\nrr10 = [384, 19]\nrr11 = [383.5, 46]\nrr12 = [405, 25]\nrr13 = [410, 48]\n\nRegionsList = [rr1, rr2, rr3, rr4, rr5, rr6, rr7, rr8, rr9, rr10, rr11, rr12, rr13]\n\ndatt = pd.read_excel(r'C:\\Users\\milena.singletary\\Boa Technology Inc\\PFL Team - Documents\\General\\Testing Segments\\EndurancePerformance\\TrailRun_2022\\TrailRunQualFootRegions.xlsx', 'Sheet4')\n\n\n# using datt DataFrame\nPFS = pd.DataFrame(datt[datt['Config'] == 'PFS'])\nLace = pd.DataFrame(datt[datt['Config'] == 'Lace'])\ncount= pd.DataFrame(PFS.sum(0)[2:25])\ncount.columns = ['PFS']\ncount['Lace'] = pd.DataFrame(Lace.sum(0)[2:25])\ncount['Count'] = count.sum(1)\ncount = count.T\n\ncount['Total'] = count.sum(1)\ncount2 = np.array(count)\ncount3 = count.drop('Total', axis=1)\n# plot frequency\ncount3.T.plot.bar(use_index = True)\n\ndiff = count.diff(periods = - 1)\n\n\n# positive is PFS, negative Lace\ndiffList = []\nfor ii in range(len(diff.axes[1])):\n diffList.append(diff.iloc[0,ii])\n\n#removing the total column\ndiffList.pop(-1)\n\n\n# create circles\ndef createCirc (center, radius, n):\n ang = np.linspace(0,2*np.pi,n)\n x = radius*np.cos(ang) + center[0]\n y = radius*np.sin(ang) + center[1] \n return x, y\n\n# xx , yy = createCirc(region1, 10, 20)\n\n# setting alpha with freq\nlength = np.shape(count)[1]\nalph_PFS = []\nalph_L = []\nfor i in range(length-1):\n alpha = count2[0,i]/ count2[0,length-1]\n alph_PFS.append(alpha)\n alpha = count2[1,i]/ count2[1,length-1]\n alph_L.append(alpha)\n\n\n# determining face color based on difference\nfc = []\nalph = []\nfor ii, val in enumerate(diffList):\n if val > 0:\n fc.append('b')\n alph.append(abs(val)/((max(diffList)-min(diffList))/0.8))\n if val < 0:\n fc.append('r')\n alph.append(abs(val)/((max(diffList)-min(diffList))/0.8))\n if val == 0:\n fc.append('purple')\n alph.append(0)\n\n\n# Difference plot PFS blue, Lace Red\nplt.figure(2)\nax = plt.gca()\nax.axes.xaxis.set_visible(False)\nax.axes.yaxis.set_visible(False)\nfor ind, reg in enumerate(RegionsList):\n xx, yy = createCirc(reg, 10, 20)\n plt.fill_between(xx,yy,facecolor = fc[ind], alpha = alph[ind])\n plt.title('Qualitative Feedback')\n laceleg = mpatches.Patch(color = 'red' , label = 'Lace')\n PFSleg = mpatches.Patch(color = 'blue' , label = 'PFS')\n plt.legend(handles = [laceleg, PFSleg], loc = 2)\nplt.imshow(im)\n\n\n\n\n# PFS\nplt.figure(3)\nax = plt.gca()\n# ax.axis('off')\nax.axes.xaxis.set_visible(False)\nax.axes.yaxis.set_visible(False)\nfor ind, reg in enumerate(RegionsList):\n xx, yy = createCirc(reg, 10, 20)\n alpha = alph_PFS[ind]\n plt.fill_between(xx,yy,facecolor = 'b', alpha = alpha * (1/(max(alph_PFS)/0.8)))\n plt.title('PFS')\nplt.imshow(im)\n\n# Lace\nplt.figure(4)\nax = plt.gca()\n# ax.axis('off')\nax.axes.xaxis.set_visible(False)\nax.axes.yaxis.set_visible(False)\nfor ind, reg in enumerate(RegionsList):\n xx, yy = createCirc(reg, 10, 20)\n alpha = alph_L[ind]\n plt.fill_between(xx,yy,facecolor = 'r', alpha = alpha * (1/(max(alph_L)/0.8)))\n plt.title('Lace')\nplt.imshow(im)\n\n\n\n\n\n\n\n\n\n\n","sub_path":"TrailRunQualitativeRegionsVis.py","file_name":"TrailRunQualitativeRegionsVis.py","file_ext":"py","file_size_in_byte":3895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"234811352","text":"from pyplasm import *\nfrom larcc import *\nimport exercise3\ndef rgb2color(rgb):\n\tr,g,b = rgb\n\tnr = float(r)/255\n\tnb = float(b)/255\n\tng = float(g)/255\n\treturn [nr,nb,nb]\ncity = exercise3.area\n\nwoodRails_color= rgb2color([112,85,69])\nglass = [0.1,0.2,0.47,1, 0,0,0,0.48, 2,2,2,1, 0,0,0,1, 50]\nwall_color = rgb2color([149,143,140])\n\nrail = CUBOID([0.2,50,0.2])\nrails = STRUCT(NN(2) ([rail,T(1)(1)]))\nrails = T(3)(0.1)(rails)\nrails = T(1)(0.5)(rails)\nrails = COLOR(GRAY)(rails)\nwoodRail = CUBOID([0.9,0.5,0.1])\nwoodRails = STRUCT(NN(50)([woodRail,T(2)(1)]))\nwoodRails = T(1)(0.6)(woodRails)\nwoodRails = T(3)(0.15)(woodRails)\nwoodRails = COLOR(woodRails_color)(woodRails)\n\nwallRailRoad = CUBOID([0.3,50,1])\nwallRailRoad = T([1,3])([5,0.2])(wallRailRoad)\nwallRailRoad = COLOR(wall_color)(wallRailRoad)\nrailRoad = STRUCT([rails,woodRails])\nrailRoads = STRUCT(NN(2) ([railRoad,T(1)(2)]))\n\nstreetLine = CUBOID([0.5,0.8])\nstreetlines = STRUCT(NN(50)([streetLine,T(2)(1)]))\nstreetlines = T(1)(12)(streetlines)\nstreetlines = T(3)(0.3)(streetlines)\n\nrodlamps = larRod([0.07,3])([48,2])\nrodlamps = STRUCT(MKPOLS(rodlamps))\nrodlamps = COLOR(BLACK)(rodlamps)\nball = larBall(0.2)([18,36])\nballLamps = STRUCT(MKPOLS(ball))\nballLamps = T(3)(2.9)(ballLamps)\nballLamps = COLOR(YELLOW)(ballLamps)\nlamp = STRUCT([rodlamps,ballLamps])\nlamps = STRUCT(NN(10)([lamp,T(2)(5)]))\nlamps = T([1,2])([7,0.5])(lamps)\n#VIEW(lamps)\nlamp = T([1,2])([2,6.5])(lamp)\n\nbackBus = CUBOID([0.02,4,2.5])\nbackBus = MATERIAL(glass)(backBus)\n\nlatoBus = CUBOID([1.5,0.02,2.5])\nlatoBus = MATERIAL(glass)(latoBus)\n\nlatiBus = STRUCT(NN(2)([latoBus,T(2)(4)]))\ntettoBus = CUBOID([1.5,4,0.05])\ntettoBus = T(3)(2.5)(tettoBus)\n\nbench = CUBOID([0.6,3.8,0.03])\nbench = T([2,3])([0.1,0.65])(bench)\nbusStop = STRUCT([backBus,latiBus,tettoBus,bench])\n\nbusStop = R([1,2])(PI)(busStop)\nbusStop = T([1,2])([20,25])(busStop)\n#VIEW(busStop)\n\nrodtree = larRod([0.2,3])([48,2])\nrodtree = STRUCT(MKPOLS(rodtree))\nrodTreeColor = rgb2color([76,22,10])\nrodtree = COLOR(rodTreeColor)(rodtree)\n\ntreeChioma = larBall(1.2)([18,36])\ntreeChioma = STRUCT(MKPOLS(treeChioma))\ntreeChioma = T(3)(2.9)(treeChioma)\n\n\ntreeChiomaColor = rgb2color([4,186,24])\ntreeChioma = COLOR(treeChiomaColor)(treeChioma)\ntree = STRUCT([rodtree,treeChioma])\ntrees = STRUCT(NN(4)([tree,T(2)(5)]))\ntrees = T([1,2])([18.5,1.5])(trees)\n\nparkingLine = CUBOID([4,0.2])\nparkingLines = STRUCT(NN(6)([parkingLine,T(2)(3)]))\n\nparkingLines = T([1,2,3])([20,1,0.3])(parkingLines)\nparkingLineX = CUBOID([0.2,15.2])\nparkingLineX = T([1,2,3])([24,1,0.3])(parkingLineX)\nparking = STRUCT([parkingLines,parkingLineX])\n\nbusIrod =larRod([0.04,3])([48,2])\nbusIrod = STRUCT(MKPOLS(busIrod))\nbusIrod = COLOR(YELLOW)(busIrod)\nbusIrod = T(2)(0.05)(busIrod)\nbusICube = CUBOID([0.8,0.1,1])\nbusICube = COLOR(YELLOW)(busICube)\nbusICube = T(3)(2)(busICube)\n\nbusInfo = STRUCT([busIrod,busICube])\nbusInfo = T([1,2])([18,26])(busInfo)\n#VIEW(busInfo)\n\nsmallCylinder = larRod([1.5,0.3])([48,2])\nsmallCylinder = STRUCT(MKPOLS(smallCylinder))\nlargePizza = larPizza([1,0.1])([8,48])\nlargePizza = STRUCT(MKPOLS(largePizza))\nlargePizza = T(3)(0.2)(largePizza)\n\nbench = STRUCT([smallCylinder,largePizza])\nbench = COLOR(GRAY)(bench)\nbench = T([1,2,3])([25,25,1])(bench)\n\nwallsea = CUBOID([2,50,1])\nwallsea = T(1)(40)(wallsea)\n\npalo = larRod([0.07,1.2])([48,2])\npalo = STRUCT(MKPOLS(palo))\npalizzata = STRUCT(NN(2) ([palo,T(1)(1.8)]))\n\npalizzata = COLOR(rodTreeColor)(palizzata)\n# palizzata = R([2,3])(PI/2)(palizzata)\npalizzata = R([1,2])(PI/2)(palizzata)\npalizzata = T([1,2])([43,40])(palizzata)\npalizzata = STRUCT(NN(10) ([palizzata,T(1)(1.5)]))\npasserella = CUBOID([15,2.3,0.3])\npasserella = T([1,2,3])([42,39.8,1.2])(passerella)\npasserella = COLOR(rodTreeColor)(passerella)\ngradino = CUBOID([0.5,2,0.3])\ngradino = T([1,2,3])([41.5,40,1])(gradino)\ngradino = COLOR(rodTreeColor)(gradino)\ngradino2 = CUBOID([0.5,50,0.6])\ngradino2 = T([1,3])([39.5,0.1])(gradino2)\ngradino3 = CUBOID([0.5,50,0.3])\ngradino3 = T([1,3])([39,0.1])(gradino3)\nponticello = STRUCT([palizzata,passerella,gradino,gradino2,gradino3])\n\ncespuglio = larBall(0.4)([18,36])\ncespuglio = STRUCT(MKPOLS(cespuglio))\ncespuglio = T([1,2,3])([0.5,0.5,1])(cespuglio)\ncespuglio = COLOR(treeChiomaColor)(cespuglio)\nvaso = CUBOID([1,1,1])\ndiff = CUBOID([0.8,0.8,0.2])\ndiff = T([1,2,3])([0.1,0.1,0.8])(diff)\ndiff = COLOR(GREEN)(diff)\nvaso = DIFFERENCE([vaso,diff])\nvaso = STRUCT([vaso,diff,cespuglio])\nvasi = STRUCT(NN(5)([vaso,T(2)(4)]))\nvasi = T([1,2])([18,30])(vasi)\nvasi = COLOR(wall_color)(vasi)\n\n\ncity = STRUCT([city,streetlines,parking,railRoads,wallRailRoad,lamps,busStop,trees,busInfo,bench,wallsea,ponticello,vasi])\nVIEW(city)","sub_path":"python/exercise4.py","file_name":"exercise4.py","file_ext":"py","file_size_in_byte":4648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"566371746","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nclass Ui_TabWidget(object):\n def setupUi(self, TabWidget):\n TabWidget.setObjectName(\"TabWidget\") #设置创建的是\"TabWidget\"\n #TabWidget.resize(789, 619)\n TabWidget.resize(1200, 1000) # 设置主窗口的宽和高\n\n ############################################################################\n # 第一个子窗口的信息\n '''\n self.tab = QtWidgets.QWidget()\n self.tab.setObjectName(\"视频检测和识别\") #\"第一个子窗口\"\n # 创建的Button的位置信息\n\n self.pushButton = QtWidgets.QPushButton(self.tab)\n self.pushButton.setGeometry(QtCore.QRect(10, 50, 75, 50)) #Button2的位置信息\n self.pushButton.setObjectName(\"pushButton_2\") #设置创建的是\"pushButton2\"\n\n # 设置显示的区域\n self.label = QtWidgets.QLabel(self.tab)\n self.label.setGeometry(QtCore.QRect(110, 25, 640, 480))\n self.label.setText(\"\")\n self.label.setObjectName(\"label\")\n\n TabWidget.addTab(self.tab, \"\") # 第一个子窗口添加成功\n '''\n ############################################################################\n self.tab1 = QtWidgets.QWidget() # \"第二个子窗口\"\n self.tab1.setObjectName(\"图像检测和识别\")\n TabWidget.addTab(self.tab1, \"\")\n\n self.pushButton1 = QtWidgets.QPushButton(self.tab1)\n self.pushButton1.setGeometry(QtCore.QRect(10, 50, 100, 50)) #Button1的位置信息\n self.pushButton1.setObjectName(\"pushButton\") #设置创建的是\"pushButton\"\n self.pushButton12 = QtWidgets.QPushButton(self.tab1)\n self.pushButton12.setGeometry(QtCore.QRect(10, 150, 100, 50)) # Button12的位置信息\n self.pushButton12.setObjectName(\"pushButton12\") # 设置创建的是\"pushButton\"\n\n self.label1 = QtWidgets.QLabel(self.tab1)\n self.label1.setGeometry(QtCore.QRect(110, 25, 640, 480))\n self.label1.setText(\"\")\n self.label1.setObjectName(\"label2\")\n TabWidget.addTab(self.tab1, \"\") # 第一个子窗口添加成功\n\n # 其他设置\n self.retranslateUi(TabWidget)\n TabWidget.setCurrentIndex(0)\n #self.pushButton.clicked.connect(TabWidget.videoprocessing) #将按键1与事件相连\n self.pushButton12.clicked.connect(TabWidget.imageprocessing2) # 将按键1与事件相连\n self.pushButton1.clicked.connect(TabWidget.imageprocessing) #将按键2与事件相连\n QtCore.QMetaObject.connectSlotsByName(TabWidget)\n\n def retranslateUi(self, TabWidget):\n _translate = QtCore.QCoreApplication.translate\n TabWidget.setWindowTitle(_translate(\"TabWidget\", \"安检图像违禁品检测与识别软件\")) # 设置主窗口的名字\n #self.pushButton.setText(_translate(\"TabWidget\", \"打开视频\")) #设置按键的名字\n self.pushButton12.setText(_translate(\"TabWidget\", \"开始检测\")) # 设置按键的名字\n self.pushButton1.setText(_translate(\"TabWidget\", \"打开图像\")) #设置按键的名字\n # 设置三个分页的名字\n #TabWidget.setTabText(TabWidget.indexOf(self.tab), _translate(\"TabWidget\", \"视频检测和识别\"))\n TabWidget.setTabText(TabWidget.indexOf(self.tab1), _translate(\"TabWidget\", \"违禁品检测与识别\"))\n","sub_path":"yolov3-master/utils/DetectGUI.py","file_name":"DetectGUI.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"24612843","text":"import torch\nfrom torch.utils import data, model_zoo\nimport numpy as np\nimport torch.optim as optim\nimport torch.backends.cudnn as cudnn\nimport torch.nn as nn\nimport os\nimport os.path as osp\nimport random\nimport torch.nn.functional as F\nimport copy\nfrom ChangeSim_data import ChangeSimDataset\nfrom models.deeplab_multi import Deeplab_multi\nfrom utils.customized_function import load_from_checkpoint\nfrom options_train import TrainOptions\nimport re\n\n\nnum_workers = 4\nMAX_ITERS = 250000\nVAL_ITERS = 500\nSAVE_PERIOD = 5000\n\nnum_classes = 32\ninput_size = (320, 240)\npretrained_RESNET = True\nnum_steps = 150000\npower = 0.9\n\nMemory_Loss = True\n\n# data_path_train = '/media/smyoo/Backup_Data/dataset/Query_Seq_Train'\n# data_path_test = '/media/smyoo/Backup_Data/dataset/Query_Seq_Test'\n\ndata_path_train = '../dataset/smyoo/Query_Seq_Train'\ndata_path_test = '../dataset/smyoo/Query_Seq_Test'\n\n# target_mode = 'normal'\n# target_mode = 'dark'\ntarget_mode = 'dust'\n\nsaved_dir = './snapshots/AdaptSegNet_dust'\nPRE_TRAINED_SEG = './snapshots/SourceOnly/Iter_50000.pth'\n\nlistup = False\n\n\ndef main():\n args = TrainOptions().parse()\n\n seed = args.random_seed\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n np.random.seed(seed)\n random.seed(seed)\n\n cudnn.enabled = True\n\n device = torch.device(\"cuda\" if torch.cuda.is_available() else 'cpu')\n\n\n val_dataset_s = ChangeSimDataset(data_path_test, crop_size=input_size, max_iters=500, ignore_label=255,\n num_classes=num_classes, mode='normal')\n\n # DataLoader\n val_loader_s = data.DataLoader(val_dataset_s,\n batch_size=4, shuffle=False, num_workers=2,\n pin_memory=True)\n\n val_dataset_t = ChangeSimDataset(data_path_test, crop_size=input_size, max_iters=500, ignore_label=255, num_classes=num_classes, mode=target_mode)\n\n # DataLoader\n val_loader_t = data.DataLoader(val_dataset_t,\n batch_size=4, shuffle=False, num_workers=2,\n pin_memory=True)\n\n\n model = Deeplab_multi(args=args)\n\n # training model from pre-trained DeepLabV2 on source & previous target domains\n # saved_state_dict = torch.load(PRE_TRAINED_SEG, map_location=device)\n # new_params = model.state_dict().copy()\n # for i in saved_state_dict:\n # if i in new_params.keys():\n # new_params[i] = saved_state_dict[i]\n # model.load_state_dict(new_params)\n\n models_path = sorted(os.listdir(saved_dir), key = lambda x: int(x.split('_')[1].split('.')[0]))\n\n for model_path in models_path:\n # if int(model_path.split('_')[1].split('.')[0]) < 110000:\n # continue\n pre_trained = osp.join(saved_dir, model_path)\n print('Loaded from {}'.format(osp.join(saved_dir, model_path)))\n saved_state_dict = torch.load(pre_trained, map_location=device)\n model = load_from_checkpoint(model, saved_state_dict['state_dict_G'])\n loaded_iter = saved_state_dict['iter']\n model.to(device)\n interp = nn.Upsample(size=(input_size[1], input_size[0]), mode='bilinear', align_corners=True)\n model.eval()\n with torch.no_grad():\n hist = np.zeros((num_classes, num_classes))\n count = 0\n total = 0\n count_s = 0\n total_s = 0\n\n for i, test_batch in enumerate(val_loader_s):\n test_images, test_labels, _ = test_batch\n test_images = test_images.to(device)\n test_labels = test_labels.to(device)\n\n pred2, pred1, pred_ori2, pred_ori1, _, _ = model(test_images, input_size)\n\n if args.tm:\n pred = interp(pred2)\n else:\n pred = interp(pred_ori2)\n\n _, pred = pred.max(dim=1)\n\n test_labels = test_labels.cpu().numpy()\n pred = pred.cpu().detach().numpy()\n\n hist += fast_hist(test_labels.flatten(), pred.flatten(), num_classes)\n\n count_s += (pred == test_labels).sum()\n total_s += test_labels.shape[0] * test_labels.shape[1] * test_labels.shape[2]\n\n mIoUs = per_class_iu(hist)\n mIoU = round(np.nanmean(mIoUs) * 100, 2)\n if listup:\n for ind_class in range(32):\n print('==>' + val_dataset_t.seg.idx2name[ind_class] + ':\\t' + str(round(mIoUs[ind_class] * 100, 2)))\n print('===> mIoU (Source): ' + str(mIoU))\n print('===> Pixel Accuracy (Source): {}%'.format(float(count_s / total_s) * 100))\n\n for i, test_batch in enumerate(val_loader_t):\n test_images, test_labels, _ = test_batch\n test_images = test_images.to(device)\n test_labels = test_labels.to(device)\n\n pred2, pred1, pred_ori2, pred_ori1, _, _ = model(test_images, input_size)\n if args.tm:\n pred = interp(pred2)\n else:\n pred = interp(pred_ori2)\n _, pred = pred.max(dim=1)\n\n test_labels = test_labels.cpu().numpy()\n pred = pred.cpu().detach().numpy()\n\n hist += fast_hist(test_labels.flatten(), pred.flatten(), num_classes)\n\n count += (pred == test_labels).sum()\n total += test_labels.shape[0] * test_labels.shape[1] * test_labels.shape[2]\n\n mIoUs = per_class_iu(hist)\n mIoU = round(np.nanmean(mIoUs) * 100, 2)\n\n if listup:\n for ind_class in range(32):\n print('==>' + val_dataset_t.seg.idx2name[ind_class] + ':\\t' + str(round(mIoUs[ind_class] * 100, 2)))\n print('===> mIoU (Target): ' + str(mIoU))\n print('===> Pixel Accuracy (Target): {}%'.format(float(count/total) * 100))\n print('\\n')\n\n\ndef fast_hist(a, b, n):\n k = (a >= 0) & (a < n)\n return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)\n\ndef per_class_iu(hist):\n return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist))\n\n\nif __name__ == '__main__':\n # os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n # os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n main()\n","sub_path":"test_CUDA.py","file_name":"test_CUDA.py","file_ext":"py","file_size_in_byte":6329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"86838011","text":"from mesa import Agent, Model\nfrom mesa.space import Grid\nfrom mesa.time import BaseScheduler\n\nfrom mesa.visualization.TextVisualization import TextGrid\n\nimport random\n\n\nclass OneAgentPerStep(BaseScheduler):\n \"\"\" One Agent Per Step Scheduler.\n\n Select an agent at random and call its *step* method.\n \"\"\"\n\n def step(self):\n \"\"\"\n Pick an agent at random, step it, bump counts.\n \"\"\"\n\n self.agents[random.randint(0, self.get_agent_count()-1)].step()\n self.steps += 1\n self.time += 1\n\n\nclass SandTable(Grid):\n\n def __init__(self, width, height):\n super().__init__(width, height, torus=False)\n self.viz = TextGrid(self, self.val_converter)\n self.spill_q = [] # agents who are currently 'spilling'\n self.avalanches = [] # [(step, cnt) ... ]\n\n def val_converter(self, agent):\n return '%1d ' % (agent._grains)\n\n def render(self):\n print(self.viz.render())\n\n def spilling(self, cell):\n self.spill_q.append(cell)\n\n def distribute(self, model):\n cnt = 0\n while self.spill_q:\n agent = self.spill_q.pop()\n x, y = agent.unique_id\n for n in model.grid.neighbor_iter(agent.unique_id, moore=False):\n n.add_a_grain()\n agent._grains -= 4\n if agent._grains < 0:\n print('grains < 0!', agent.unique_id, agent._grains)\n exit()\n agent._spilling = False\n cnt += 1\n self.avalanches.append((model.schedule.time, cnt))\n\n\nclass SandColumn(Agent):\n\n def __init__(self, unique_id, model, spill_size=3):\n super().__init__(unique_id, model)\n self._spill_size = spill_size\n self._grains = 0\n self._spilling = False\n\n def __str__(self):\n return str(self.unique_id)\n\n @property\n def spill_size(self):\n return self._spill_size\n\n @spill_size.setter\n def spill_size(self, val):\n if not isinstance(val, int) or val < 0:\n raise TypeError('spill_size must be int type 0 or greater!')\n self._spill_size = val\n\n def add_a_grain(self):\n self._grains += 1\n if self._grains > self._spill_size and not self._spilling:\n # print('Spill: ', self.unique_id, self._grains)\n self._spilling = True\n self.model.grid.spilling(self)\n\n def step(self):\n # print('Step: ', self.unique_id)\n self.add_a_grain()\n if self._spilling:\n # Have model distribute sand\n self.model.grid.distribute(self.model)\n\n\nclass SandTableModel(Model):\n\n def __init__(self, width, height):\n super().__init__(seed=42)\n self.num_agents = width * height\n self.running = True\n self.grid = SandTable(width, height)\n self.schedule = OneAgentPerStep(self)\n\n # Create agents and fill grid\n for x in range(width):\n for y in range(height):\n a = SandColumn((x, y), self)\n self.grid.place_agent(a, (x, y))\n self.schedule.add(a)\n\n def step(self):\n self.schedule.step()\n # self.grid.render()\n\n def run_model(self, n):\n for i in range(n):\n self.step()\n\n\nm = SandTableModel(100, 100)\n\nm.run_model(100000)\n\nprint('time, slide size')\nfor entry in m.grid.avalanches:\n print('%d, %d' % (entry[0], entry[1]))\n\n# m.grid.avalanches.sort(key=lambda pair: pair[1])\n# print(m.grid.avalanches)\n\n# s = SandColumn('1', None, 5)\n# print(s.spill_size)\n# s.spill_size = 3\n# print(s.spill_size)\n\n# g.grid[2][2] = 1\n# viz = TextGrid(g, my_converter)\n# # viz = TextGrid(g, TextGrid.converter)\n# print(viz.render())\n","sub_path":"examples/Sandpile/sandpile/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"581195358","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom ..items import BookItem\nimport re\nimport requests\nimport json\n\n\nclass JingdongSpider(scrapy.Spider):\n name = 'jingdong'\n allowed_domains = ['book.jd.com']\n start_urls = ['https://book.jd.com/booksort.html']\n price_url_prefix = 'https://p.3.cn/prices/mgets?skuIds=J_'\n briefing_url_prefix = 'https://dx.3.cn/desc/'\n date_pattern = re.compile(r'\\d{4}-\\d{2}-\\d{2}')\n tag_pattern = re.compile('<[^>]+>', re.S)\n\n def start_requests(self):\n '调节数据为10000000～20000000全量爬取'\n for book_id in range(10000000,20000000):\n print(book_id / 10000000 - 1)\n yield scrapy.Request('https://item.jd.com/{}.html'.format(book_id), self.parse)\n \n def parse(self, response):\n book_id = response.url[20:-5]\n if response.css('.first > a::text').extract_first() == '图书':\n new_book = BookItem()\n new_book['title'] = response.css('.sku-name ::text').extract_first().strip()\n new_book['author'] = ' '.join(response.css('.p-author > a::attr(data-name)').extract())\n new_book['publisher'] = response.css('.p-parameter li::attr(title)').extract_first()\n # new_book['publishDate'] = self.extract_date(response.css('.p-parameter li::attr(title)').extract())\n new_book['coverUrl'] = response.css('.main-img > img::attr(src)').extract_first()[2:]\n new_book['source'] = '京东网'\n # new_book['price'] = self.get_price(book_id)\n # new_book['briefing'] = self.get_briefing(book_id)\n yield new_book\n \n def extract_date(self, li):\n for i in li:\n if self.date_pattern.search(i):\n return i\n return None\n\n def get_briefing(self,book_id):\n response = requests.get(url=self.briefing_url_prefix+book_id)\n if response.status_code == 200:\n s = response.text\n pos1 = s.find('book-detail-content')\n pos2 = s.find('',pos1)\n return self.tag_pattern.sub('',''.join(s[pos1+23:pos2].strip().split()))\n else:\n return '无简介'\n\n def get_price(self,book_id):\n response = requests.get(url=self.price_url_prefix+book_id)\n if response.status_code == 200:\n return '¥' + json.loads(response.text)[0]['p']\n else:\n return '无价格'","sub_path":"webspider/webspider/spiders/jingdong.py","file_name":"jingdong.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"569939053","text":"# 2071. 평균값 구하기\r\n\r\nT = int(input())\r\n\r\ndef mean_list(arr):\r\n total = 0\r\n for i in arr:\r\n total += i\r\n return round(total/len(arr))\r\n\r\nfor _ in range(T):\r\n arr = list(map(int, input().split()))\r\n print(f'#{_ + 1} {mean_list(arr)}')","sub_path":"SWEA/D1/swea2071.py","file_name":"swea2071.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"542811466","text":"\"\"\"Copyright (c) 2015 Kyle James Walker\n\nSee the file LICENSE for copying permission.\n\n\"\"\"\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\n\nimport os\n\nfrom yamlsettings import yamldict\n\n\ndef _locate_file(filepaths):\n '''\n Locate settings file from list of filepaths.\n '''\n if not isinstance(filepaths, list) and not isinstance(filepaths, tuple):\n filepaths = [filepaths]\n for filepath in filepaths:\n filepath = os.path.expanduser(filepath)\n if os.path.isfile(filepath):\n break\n else:\n if len(filepaths) > 1:\n raise IOError(\"unable to locate the settings file from:\\n{0}\".\n format('\\n'.join(filepaths)))\n else:\n raise IOError(\"unable to locate the settings file from: {0}\".\n format(filepaths))\n return filepath\n\n\ndef load(filepaths, fields=[]):\n '''\n Load YAML settings from a list of file paths given.\n\n - File paths in the list gets the priority by their orders\n of the list.\n - If fields are set, only the selected fields are loaded in the\n returned object. For example, fields=['users', 'hosts'] will\n eliminate all of the other loaded fields except for them.\n '''\n filepath = _locate_file(filepaths)\n\n # TODO:\n # Add support to load the selected fields only.\n # It could take some time if you had a very large amount of fields.\n # Currently, all settings are loaded and then pruned out.\n # --------------------------------------------\n # load settings into a YAMLDict object\n yaml_dict = yamldict.load(open(filepath))\n # if set, limit the YAMLDict object to only the selected fields\n if fields:\n yaml_dict.limit(fields)\n # --------------------------------------------\n\n # return YAMLDict object\n return yaml_dict\n\n\ndef load_all(filepaths):\n '''\n Load *all* YAML settings from a list of file paths given.\n\n - File paths in the list gets the priority by their orders\n of the list.\n '''\n filepath = _locate_file(filepaths)\n\n # load all settings into YAMLDict objects\n yaml_series = yamldict.load_all(open(filepath))\n yaml_dicts = []\n for yaml_dict in yaml_series:\n yaml_dicts.append(yaml_dict)\n # return YAMLDict objects\n return yaml_dicts\n\n\ndef save(yaml_dict, filepath):\n '''\n Save YAML settings to the specified file path.\n '''\n yamldict.dump(yaml_dict, open(filepath, 'w'), default_flow_style=False)\n\n\ndef save_all(yaml_dicts, filepath):\n '''\n Save *all* YAML settings to the specified file path.\n '''\n yamldict.dump_all(yaml_dicts, open(filepath, 'w'),\n default_flow_style=False)\n\n\ndef update_from_file(yaml_dict, filepaths):\n '''\n Override YAML settings with loaded values from filepaths.\n\n - File paths in the list gets the priority by their orders of the list.\n '''\n # load YAML settings with only fields in yaml_dict\n yaml_dict.update(load(filepaths, list(yaml_dict)))\n\n\ndef update_from_env(yaml_dict, prefix=\"\"):\n '''\n Override YAML settings with values from the environment variables.\n\n - The letter '_' is delimit the hierarchy of the YAML settings such\n that the value of 'config.databases.local' will be overridden\n by CONFIG_DATABASES_LOCAL.\n '''\n def _set_env_var(path, node):\n env_path = \"{0}{1}{2}\".format(\n prefix.upper(),\n '_' if prefix else '',\n '_'.join([str(key).upper() for key in path])\n )\n env_val = os.environ.get(env_path, None)\n if env_val is not None:\n # convert the value to a YAML-defined type\n env_dict = yamldict.load('val: {0}'.format(env_val))\n return env_dict.val\n else:\n return None\n\n # traverse yaml_dict with the callback function\n yaml_dict.traverse(_set_env_var)\n\n\nclass YamlSettings(object):\n ''' Note you can easily get the same effect, with more flexibility\n by using the functions above directly.\n '''\n def __init__(self, default_settings, override_settings, override_envs=True,\n default_section=None, cur_section=None,\n param_callback=None, override_required=False,\n envs_override_defaults_only=False,\n single_section_load=False):\n defaults = load(default_settings)\n\n if override_envs and envs_override_defaults_only:\n if default_section:\n prefix = default_section\n section = defaults[default_section]\n else:\n prefix = \"\"\n section = defaults\n update_from_env(section, prefix)\n self.cur_section = default_section\n\n if default_section is None:\n # No section support simply update with overrides\n self.settings = defaults\n try:\n # WAS:\n # self.settings.update_yaml(override_settings)\n self.settings.update(load(override_settings))\n except IOError:\n if override_required:\n raise\n if override_envs and not envs_override_defaults_only:\n update_from_env(self.settings, \"\")\n else:\n # Load Overrides first and merge with defaults\n try:\n self.settings = load(override_settings)\n except IOError:\n if override_required:\n raise\n # Note this will copy to itself right now, but\n # will allows for simpler logic to get environment\n # variables to work\n self.settings = defaults\n\n for cur_section in self.settings:\n cur = self.settings[cur_section]\n cur.rebase(defaults[self.cur_section])\n\n if override_envs and not envs_override_defaults_only:\n update_from_env(cur, default_section)\n\n # Make sure default section is created\n if default_section not in self.settings:\n self.settings[default_section] = defaults[default_section]\n if override_envs and not envs_override_defaults_only:\n update_from_env(self.settings[default_section],\n default_section)\n\n def get_settings(self, section_name=None):\n if section_name is None:\n section_name = self.cur_section\n\n if section_name is None:\n return self.settings\n else:\n return self.settings[section_name]\n","sub_path":"yamlsettings/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"221557698","text":"#!/usr/bin/python3\nimport ipaddress as ipa\nimport socket, struct\nimport sys,os\nfrom subprocess import call\nimport syslog\npath = os.path.abspath(os.path.realpath(__file__)+\"/../..\")\nsys.path.append(path)\nsys.path.append(\"/etc/networkmanagement\")\nimport server_config\nimport helpers\n\nsyslog.openlog(\"openvpnclient-connect\")\n\nif len(sys.argv) != 2:\n print(\"usage: \"+sys.argv[0]+\" \")\n exit(1)\ntmp_filepath = sys.argv[1]\ntmp_file = open(tmp_filepath, \"w\")\nsyslog.syslog(\"tmp_file: \"+tmp_filepath)\n\ncommon_name = os.getenv(\"common_name\")\nconfig_file = os.getenv(\"config\")\nport = os.path.basename(config_file)\nsyslog.syslog(\"CN: \"+str(common_name))\nsyslog.syslog(\"Config: \"+str(config_file))\nsyslog.syslog(\"Port: \"+str(port))\n\nresults = helpers.Device.get_where(\"connection='openvpn' AND identifier=%s\", (common_name,))\nif len(results) == 0:\n print(\"CN not found\")\n syslog.syslog(\"CN not found\")\n exit(1)\nelif len(results) > 1:\n print(\"multiple results found, aborting\")\n syslog.syslog(\"multiple devices found\")\n exit(1)\n\nif port not in results[0].portraw.split(\",\"):\n syslog.syslog(\"wrong network\")\n exit(1)\nsyslog.syslog(\"IP: \"+results[0].ip)\ntmp_file.write(\"ifconfig-push \"+results[0].ip+\" 255.255.255.0\\n\")\nif results[0].internet:\n tmp_file.write(\"push \\\"redirect-gateway def1\\\"\\n\")\ntmp_file.close()\n\ntry:\n exit(call([\"/etc/openvpn/proxyarp-connect.sh\", results[0].ip]))\nexcept FileNotFoundError:\n pass\n","sub_path":"check_access/checkopenvpnaccess.py","file_name":"checkopenvpnaccess.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"7786248","text":"class Solution:\n # @param {string} s\n # @return {string}\n #http://articles.leetcode.com/2011/11/longest-palindromic-substring-part-i.html\n # Time: O(N^2), Space: O(1)\n def expand_around_center(self, s, index1, index2):\n l = index1\n r = index2\n while(l >= 0 and r < len(s) and s[l] == s[r]):\n l -= 1\n r += 1\n return s[l+1:r]\n\n def longestPalindrome(self, s):\n if not s or len(s) == 0:\n return \"\"\n longest = s[0]\n for i in range(len(s) - 1):\n expand_on_str = self.expand_around_center(s, i, i)\n if len(expand_on_str) > len(longest):\n longest = expand_on_str\n expand_on_space = self.expand_around_center(s, i, i + 1)\n if len(expand_on_space) > len(longest):\n longest = expand_on_space\n return longest\n","sub_path":"src/main/java/com/practice/python/5_longest_palindromic_substring.py","file_name":"5_longest_palindromic_substring.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"232773623","text":"\"\"\"\nThis script determines if nonsynonymous variants are enriched for an input \nbiallelic site pattern, relative to null simulations in ms. \n\nAuthor: Mark Hibbins\n\nModified from this script written by Meng Wu for Wu et al. 2018 (Moecular Ecology):\nhttps://github.com/wum5/JaltPhylo/blob/master/python_scripts/phyloGWAS_pval.py\n\"\"\"\n\n\nimport argparse\nimport numpy as np\n\ndef parse_total(total_path):\n\n \"\"\"\n Gets relevant info from .total file, which is \n output from InferGroupSpecificAllele\n \"\"\"\n\n total_file = open(total_path, \"r\")\n\n for line in total_file:\n\n line = line.strip().split()\n \n if line[0] == \"total_nsyn_codons\":\n ns_count = int(line[1])\n elif line[0] == \"nonsynonymous_changes\":\n ns_expected = int(line[1])\n\n total_file.close()\n\n return ns_count, ns_expected\n\n\ndef parse_ms(ms_file, obs_pattern, subset_size):\n\n \"\"\"\n Gets trait patterns from ms simulations \n for samples specified in observed pattern\n \"\"\"\n\n #Parse observed trait pattern\n\n obs_pattern = obs_pattern.split(\",\")\n obs_pattern_dict = {}\n\n for pair in obs_pattern:\n pair = pair.split(\":\")\n obs_pattern_dict[int(pair[0])] = pair[1]\n\n\n #Count matching patterns in ms sims \n\n sample_counter, subset_tracker, n_matching = 0, 0, 0\n subset_counts = []\n pattern_dict = {}\n obs_keys = obs_pattern_dict.keys()\n\n with open(ms_file, \"r\") as sims:\n\n for line in sims:\n \n line = line.strip().split()\n\n if subset_tracker >= subset_size:\n subset_counts.append(n_matching)\n n_matching = 0\n subset_tracker = 0\n\n if len(line) > 0:\n\n if line[0] == \"//\":\n\n for key in list(pattern_dict):\n if key not in obs_keys:\n del pattern_dict[key]\n\n if pattern_dict == obs_pattern_dict:\n n_matching += 1\n\n sample_counter = 0\n pattern_dict = {}\n subset_tracker += 1\n\n elif line[0] == \"0\" or line[0] == \"1\":\n sample_counter += 1\n pattern_dict[sample_counter] = line[0] \n\n\n\n return(subset_counts)\n\n \ndef calc_pval(subset_counts, ns_expected):\n\n #Calculate p-value\n n_extreme = 0\n\n for count in subset_counts:\n if count >= int(ns_expected):\n n_extreme += 1\n\n rank = float(n_extreme)/len(subset_counts)\n pval = 1 - (2*abs(0.5 - rank))\n\n return(pval, len(subset_counts), subset_counts)\n\ndef main():\n\n parser = argparse.ArgumentParser(description = \"PhyloGWAS p-value\")\n parser.add_argument(\"-i\", \"--total_file\", required = True, \n help = \".total file that InferGroupSpecificAllele writes as output\")\n parser.add_argument(\"-m\", \"--ms_file\", required = True,\n help = \"ms simulation output file\")\n parser.add_argument(\"-p\", \"--pattern\", required = True,\n help = \"Series of comma-separated ID:state pairs, specifying the trait pattern to be tested\")\n args = parser.parse_args()\n\n ns_count, ns_expected = parse_total(args.total_file)\n\n subset_counts = parse_ms(args.ms_file, args.pattern, ns_count)\n \n pval, subset_length, subset_counts = calc_pval(subset_counts, ns_expected)\n\n max_val = max(subset_counts)\n min_val = min(subset_counts)\n\n print(\"Expected \" + str(ns_expected) + \" variants\")\n print(\"In \" + str(subset_length) + \" simulations:\")\n print(\"Saw as many as \" + str(max_val) + \" and as few as \" + str(min_val))\n\n if pval > 0:\n print(\"p = \" + str(pval))\n elif pval == 0:\n print(\"p < \" + str(1/len(subset_counts)))\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"phylo_calls_snakemake/workflow/scripts/new_phyloGWAS_pval_v2.py","file_name":"new_phyloGWAS_pval_v2.py","file_ext":"py","file_size_in_byte":3803,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"469558964","text":"# -*- utf-8 -*-\n\"\"\"\n build nginx.conf and uwsgi.ini\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n \n\"\"\"\nimport os\nimport sys\nfrom jinja2 import Environment,BaseLoader,TemplateNotFound\nfrom jinja2.utils import open_if_exists\n\nbase_path = os.path.dirname(os.path.abspath(__name__))\n\nclass FileTplLoader(BaseLoader):\n def get_source(self,environment,template):\n path = template\n #print(path)\n if not os.path.exists(path):\n raise TemplateNotFound(path)\n mtime = os.path.getmtime(path)\n f = open_if_exists(path)\n try:\n source = f.read().decode('utf-8')\n finally:\n f.close()\n return source,path,lambda: mtime == os.path.getmtime(path)\n\nenv = Environment(loader=FileTplLoader())\n\nnginx_tpl = env.get_template(os.path.join(base_path,'nginx.tpl'))\nuwsgi_tpl = env.get_template(os.path.join(base_path,'uwsgi.tpl'))\n\ndef main(pyenv,svrname,path=base_path):\n try:\n nginx = nginx_tpl.render(pyenv=pyenv,svrname=svrname,appdir=path)\n uwsgi = uwsgi_tpl.render(pyenv=pyenv)\n with open('nginx.conf','w') as f:\n print(nginx,file=f)\n print('nginx.conf')\n with open('uwsgi.ini','w') as i:\n print(uwsgi,file=i)\n print('uwsgi.ini')\n except IOError as err:\n print(\"Error: \"+str(err))\n\n\nif __name__ == '__main__':\n try:\n args = sys.argv[1:]\n svrname = 'localhost'\n #pyenv = os.path.dirname(sys.executable)\n pyenv = sys.prefix\n for arg in args:\n (key,value) = arg.split('=',1)\n if key=='-pyenv':\n pyenv = value\n if key=='-svrname':\n svrname= value\n #print(pyenv)\n \n #print(svrname)\n main(pyenv,svrname)\n except Exception as err:\n print(str(err))\n","sub_path":"configure.py","file_name":"configure.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"456667988","text":"import unittest\nfrom datetime import datetime\nfrom contextlib import contextmanager\nimport zap_common\n\n\nclass _MockHooks(object):\n\n def __init__(self):\n self.called = 0\n \n\n def zap_started(self, zap, target):\n self.called += 1\n return zap,\n\n\nclass TestZapHooks(unittest.TestCase):\n\n def test_trigger_hook_mismatch_exception(self):\n \"\"\" If the hook signature doesn't match the hook the exception bubbles up \"\"\"\n zap_common.zap_hooks = _MockHooks()\n with self.assertRaises(Exception):\n zap_common.trigger_hook('zap_started')\n self.assertEqual(zap_common.zap_hooks.called, 0)\n\n\n def test_trigger_hook_verify_calls(self):\n \"\"\" Verify the hook gets called if it matches signature \"\"\"\n zap_common.zap_hooks = _MockHooks()\n args = ['zap', 'http://127.0.0.1']\n zap_common.trigger_hook('zap_started', *args)\n zap_common.trigger_hook('zap_started', *args)\n zap_common.trigger_hook('zap_started', *args)\n zap_common.trigger_hook('zap_started', *args)\n zap_common.trigger_hook('zap_started', *args)\n self.assertEqual(zap_common.zap_hooks.called, 5)\n\n\n def test_trigger_hook_maintain_signature(self):\n \"\"\" Should return original args if there is a mismatch on the return signature \"\"\"\n zap_common.zap_hooks = _MockHooks()\n args = ['zap', 'http://127.0.0.1']\n # The defined hook method only returns 1 item\n return_direct = zap_common.zap_hooks.zap_started(*args)\n self.assertTrue(len(return_direct) == 1)\n self.assertNotEqual(len(return_direct), len(args))\n\n # However, when called in hook, if there is a different\n # return signature, ignore the hook return\n return_args = zap_common.trigger_hook('zap_started', *args)\n self.assertEqual(len(args), len(return_args))\n self.assertEqual(args, list(return_args))\n\n","sub_path":"docker/tests/test_zap_hooks.py","file_name":"test_zap_hooks.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"189445156","text":"from classes.tick import Tick\n\n\nif __name__ == '__main__':\n \n arquivo = open('input.txt', 'r')\n\n lst = [int(word) for word in arquivo.readlines()] #lendo as linhas\n\n arquivo.close()\n\n try:\n ttask = int(lst[0])\n umax = int(lst[1])\n lst = lst[2:]\n\n arquivo = open('output.txt', 'w')\n arquivo.writelines(Tick(umax, ttask, lst).execute())\n arquivo.close()\n except:\n print('Não é possível trabalhar com o arquivo, verifique se o mesmo encontra-se no padrão.')","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"395216351","text":"class Node:\r\n def __init__(self, keys, children):\r\n self.keys = keys \r\n self.children = children \r\n self.str_pos = None\r\n\r\n\r\n def num_keys(self):\r\n return len(self.keys)\r\n\r\n\r\n def num_children(self):\r\n return len(self.children)\r\n\r\n\r\n def is_leaf(self):\r\n return self.num_children() == 0\r\n\r\n\r\n def search(self, key):\r\n left = 0 \r\n right = self.num_keys()\r\n while right > left:\r\n mid = (left + right)//2\r\n if self.keys[mid] >= key:\r\n right = mid\r\n else:\r\n left = mid + 1\r\n return left\r\n\r\n\r\n def linear_search(self, key):\r\n index = 0\r\n while index < self.num_keys() and self.keys[index] < key:\r\n index += 1\r\n return index\r\n\r\n\r\n def contains_key_at(self, key, index):\r\n return index < self.num_keys() and self.keys[index] == key\r\n\r\n\r\n def deep_min(self):\r\n node = self\r\n while not node.is_leaf():\r\n node = node.children[0]\r\n return node.keys[0] if node.keys else None\r\n\r\n\r\n def deep_max(self):\r\n node = self\r\n while not node.is_leaf():\r\n node = node.children[-1]\r\n return node.keys[-1] if node.keys else None\r\n\r\n\r\n def locate_predecessor(self, key):\r\n index = self.search(key)\r\n return index-1\r\n\r\n\r\n def predecessor(self, key):\r\n index = self.locate_predecessor(key)\r\n return self.keys[index] if index >= 0 else None\r\n\r\n\r\n def deep_predecessor(self, index):\r\n return self.children[index].deep_max()\r\n\r\n\r\n def locate_successor(self, key):\r\n index = 0\r\n while index < self.num_keys() and self.keys[index] <= key:\r\n index += 1\r\n return index\r\n\r\n\r\n def successor(self, key):\r\n index = self.locate_successor(key)\r\n self.keys[index] if index < self.num_keys() else None\r\n\r\n\r\n def deep_successor(self, index):\r\n return self.children[index+1].deep_min()\r\n \r\n\r\n def insert(self, key):\r\n index = self.search(key)\r\n self.keys.insert(index, key)\r\n\r\n\r\n def delete(self, key):\r\n index = self.search(key)\r\n if self.contains_key_at(key, index):\r\n del self.keys[index]\r\n\r\n\r\n def split_child(self, index):\r\n child = self.children[index]\r\n median = (child.num_keys())//2\r\n median_key = child.keys[median]\r\n\r\n left = Node(child.keys[:median], child.children[:median + 1])\r\n right = Node(child.keys[median + 1:], child.children[median + 1:])\r\n\r\n self.keys.insert(index, median_key)\r\n self.children[index:index+1] = [left, right]\r\n\r\n\r\n def merge_children(self, index):\r\n median_key = self.keys[index]\r\n left, right = self.children[index : index+2]\r\n\r\n left.keys.append(median_key)\r\n left.keys.extend(right.keys)\r\n\r\n if not right.is_leaf():\r\n left.children.extend(right.children)\r\n\r\n del self.keys[index]\r\n del self.children[index+1]\r\n\r\n merged = left\r\n\r\n if self.num_keys() == 0:\r\n self.keys = left.keys\r\n self.children = left.children\r\n merged = self\r\n\r\n return merged \r\n\r\n\r\n def grow_child(self, index, min_num_keys):\r\n child = self.children[index]\r\n left_sibling = (index > 0) and self.children[index-1]\r\n right_sibling = (index < self.num_keys()) and self.children[index+1]\r\n\r\n if left_sibling and left_sibling.num_keys() > min_num_keys:\r\n self.transfer_key_clockwise(index-1)\r\n\r\n elif right_sibling and right_sibling.num_keys() > min_num_keys:\r\n self.transfer_key_counter_clockwise(index)\r\n\r\n else:\r\n shared_key_index = (index - 1) if left_sibling else index\r\n child = self.merge_children(shared_key_index)\r\n\r\n return child \r\n\r\n\r\n def transfer_key_clockwise(self, index):\r\n left, right = self.children[index : index+2]\r\n right.keys.insert(0, self.keys[index])\r\n\r\n if left.children:\r\n right.children.insert(0, left.children[-1])\r\n del left.children[-1]\r\n\r\n self.keys[index] = left.keys[-1]\r\n del left.keys[-1]\r\n\r\n\r\n def transfer_key_counter_clockwise(self, index):\r\n left, right = self.children[index : index+2]\r\n left.keys.append(self.keys[index])\r\n\r\n if not right.is_leaf():\r\n left.children.append(right.children[0])\r\n del right.children[0]\r\n\r\n self.keys[index] = right.keys[0]\r\n del right.keys[0]\r\n\r\n\r\n\r\n\r\nclass B_Tree:\r\n \"\"\"\r\n B-Tree data structure.\r\n \"\"\"\r\n\r\n def __init__(self, degree):\r\n self.root = Node([], [])\r\n self.min_num_keys = degree - 1 \r\n self.max_num_keys = 2*degree - 1\r\n\r\n\r\n def insert(self, key):\r\n print(\"The key \"+ str(key) + \" is insert into B_Tree\")\r\n if self.root.num_keys() == self.max_num_keys:\r\n self.root = Node([], [self.root])\r\n self.root.split_child(0)\r\n\r\n node = self.root \r\n while not node.is_leaf():\r\n index = node.search(key)\r\n child = node.children[index]\r\n if child.num_keys() == self.max_num_keys:\r\n node.split_child(index)\r\n\r\n if node.keys[index] < key:\r\n index += 1\r\n\r\n node = node.children[index] \r\n\r\n node.insert(key)\r\n\r\n def delete(self, key):\r\n print (\"The value \" + str(key)+\" is delete as shown below\")\r\n node = self.root\r\n while not node.is_leaf():\r\n index = node.search(key)\r\n\r\n if node.contains_key_at(key, index):\r\n left, right = node.children[index : index+2]\r\n\r\n if left.num_keys() > self.min_num_keys:\r\n node.keys[index] = node.deep_predecessor(index)\r\n (node, key) = (left, node.keys[index])\r\n\r\n elif right.num_keys() > self.min_num_keys:\r\n node.keys[index] = node.deep_successor(index) \r\n (node, key) = (right, node.keys[index])\r\n\r\n else:\r\n node = node.merge_children(index)\r\n\r\n else:\r\n child = node.children[index]\r\n if child.num_keys() <= self.min_num_keys:\r\n child = node.grow_child(index, self.min_num_keys)\r\n node = child\r\n \r\n node.delete(key)\r\n\r\n def inorder(self):\r\n\r\n print(\"--------------------- Veiw of b- tree------------------------\")\r\n \"\"\"\r\n Generates the keys of the b-tree in non-decreasing order.\r\n \"\"\"\r\n queue = []\r\n node = self.root\r\n index = 0\r\n while node:\r\n\r\n if node.is_leaf():\r\n print(\"The value recived is \" + str(node.keys))\r\n print(\"----\")\r\n # return (node.keys)\r\n\r\n if not queue:\r\n node = None\r\n\r\n else:\r\n node, index = queue.pop()\r\n print(\"The value reciving is \" + str(node.keys[index]))\r\n print(\"----\")\r\n # return node.keys[index]\r\n index = index + 1\r\n\r\n else:\r\n if index < node.num_keys():\r\n queue.append((node, index))\r\n # print(queue)\r\n\r\n node = node.children[index]\r\n index = 0\r\n\r\n\r\nbt = B_Tree(2)\r\nbt.insert(25)\r\nbt.inorder()\r\nbt.insert(30)\r\nbt.inorder()\r\nbt.insert(35)\r\nbt.inorder()\r\nbt.insert(40)\r\nbt.inorder()\r\nbt.insert(45)\r\nbt.inorder()\r\nbt.delete(25)\r\nbt.inorder()\r\n","sub_path":"Python related task/b_tree.py","file_name":"b_tree.py","file_ext":"py","file_size_in_byte":7689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"647823202","text":"import csv\nimport MySQLdb\n#import mysql.connector\n\n\ndef UpSQL(BS,TG):\n mydb = MySQLdb.connect(\"localhost\",\"root\",\"12345678\",\"test\") \n mycursor = mydb.cursor()\n sql = \"INSERT INTO bienso (BienSo, ThoiGian) VALUES (%s, %s)\"\n val = (BS, TG)\n mycursor.execute(sql, val)\n mydb.commit()\n print(\"1 record inserted \")\n\n \n\n\n\n\n","sub_path":"DB1.py","file_name":"DB1.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"276812691","text":"from ml_tools.transformers import *\nfrom ml_tools.models import *\nfrom ml_tools.helpers import *\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, KFold, StratifiedKFold, cross_val_score\nfrom sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder\nfrom sklearn.metrics import log_loss\nfrom sklearn.compose import ColumnTransformer\nfrom typing import Dict, Any\nimport logging\nimport pandas as pd\nimport numpy as np\nimport yaml\nimport os, sys\nfrom shutil import copyfile\nfrom hyperopt import hp, fmin, tpe, Trials, space_eval\n\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\n\n\ntry:\n project_dir = os.path.dirname(__file__)\n config_file = os.path.join(project_dir, 'config.yaml')\n\n with open (config_file, 'r') as file:\n config = yaml.safe_load(file)\nexcept yaml.YAMLError as exc:\n logging.error(exec)\n sys.exit(1)\nexcept Exception as e:\n logging.error('Error reading the config file')\n sys.exit(1)\n\n\ntrain = pd.read_csv(config['data']['train'])\ntest = pd.read_csv(config['data']['test'])\n\nX = train[train.columns[~train.columns.isin([config['data']['target']])]]\nX = X.astype('float32')\nX.drop(['id'], axis=1, inplace=True)\ny = train[config['data']['target']]\ny = pd.Series(y.str.split('_', expand=True)[1], dtype=np.int64, name='target')\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=config['data']['train_size'], \\\n random_state=config['model']['random_state'])\n\nout = pd.DataFrame(data={'id': test['id'].astype(int)})\ntest.drop(['id'], axis=1, inplace=True)\n\nif config['model']['f_ext']['to_use']:\n f_ext_columns_num = config['model']['f_ext']['columns']\n f_ext_columns = ['feature_' + str(num) for num in f_ext_columns_num]\n config['model']['f_ext']['params']['columns'] = f_ext_columns\n\nfor column in X_train.columns:\n X['{}_enc'.format(column)] = OrdinalEncoder().fit_transform( np.array(X[column].astype(int)).reshape(-1, 1) )\n X_train['{}_enc'.format(column)] = OrdinalEncoder().fit_transform( np.array(X_train[column].astype(int)).reshape(-1, 1) )\n X_test['{}_enc'.format(column)] = OrdinalEncoder().fit_transform( np.array(X_test[column].astype(int)).reshape(-1, 1) )\n test['{}_enc'.format(column)] = OrdinalEncoder().fit_transform( np.array(test[column].astype(int)).reshape(-1, 1) )\n\n\npipeline_steps_list: Dict[str, Any] = {\n 'f_ext': FeatureExtractionTransformer,\n 'f_sel': FeatureSelectionTransformer,\n 'f_int': FeatureInteractionTransformer,\n 'anomaly': AnomalyDetectionTransformer,\n 'model': MetaClassifier,\n}\npipeline_steps = []\nfor step_name, transformer in pipeline_steps_list.items():\n if config['model'][step_name]['to_use']:\n if step_name != 'model':\n step = (step_name, transformer(**config['model'][step_name]['params'],\n random_state=config['model']['random_state']))\n else:\n step = (step_name, transformer(model=config['model'][step_name]['method'],\n params=config['model'][step_name]['params'],\n verbose=config['model'][step_name]['verbose'],\n random_state = config['model']['random_state']))\n pipeline_steps.append(step)\nfull_pipeline = Pipeline(steps=pipeline_steps)\n\n\nif config['model']['strategy'] == 'grid':\n if config['model']['grid_random']:\n full_pipeline = RandomizedSearchCV(full_pipeline, config['model']['param_grid'], **config['model']['grid'],\\\n random_state=config['model']['random_state'])\n else:\n full_pipeline = GridSearchCV(full_pipeline, config['model']['param_grid'], **config['model']['grid'])\n full_pipeline.fit(X, y)\n print(full_pipeline.best_params_)\nelif config['model']['strategy'] == 'cv':\n cv = StratifiedKFold(n_splits=config['model']['cv']['folds'], shuffle=True, random_state=config['model']['random_state'])\n scores = cross_val_score(full_pipeline, X_train, y_train, scoring='neg_log_loss', cv=cv)\n logging.info('Cross-validation scores: [%s]', ', '.join(scores))\n logging.info('Cross-validation average score: %s', np.round(np.mean(scores), 6))\n full_pipeline.fit(X_train, y_train)\nelif config['model']['strategy'] == 'model':\n full_pipeline.fit(X_train, y_train)\nelif config['model']['strategy'] == 'hyperopt':\n def objective(params):\n full_pipeline.set_params(**params)\n cv = StratifiedKFold(n_splits=config['model']['cv']['folds'], shuffle=False)\n score = cross_val_score(full_pipeline, X_train, y_train, cv=cv, scoring='neg_log_loss', n_jobs=-1)\n return -score.mean()\n\n space = {}\n for param, values in config['model']['param_hyperopt'].items():\n # space[param] = values if len(values) > 3 else np.arange(*values)\n space[param] = hp.uniform(param, values[0], values[1])\n\n trials = Trials()\n print('Space')\n print(space)\n best = fmin(objective,\n space,\n algo=tpe.suggest,\n max_evals=config['model']['hyperopt']['max_evals'],\n trials=trials\n )\n print('Best')\n print(best)\n # Get the values of the optimal parameters\n best_params = space_eval(space, best)\n print('Best params')\n print(best_params)\n # Fit the model with the optimal hyperparamters\n full_pipeline.set_params(**best_params)\n full_pipeline.fit(X, y)\n\ny_pred = full_pipeline.predict_proba(X_test)\ntest_score = log_loss(y_test, y_pred)\nlogging.info('Log-loss: %s', test_score)\n\n\nif config['output']['save']:\n out[['Class_1', 'Class_2', 'Class_3', 'Class_4']] = full_pipeline.predict_proba(test)\n\n output_folder = gen_submit(config, test_score)\n output_path = os.path.join(project_dir, 'data/submissions', output_folder)\n os.mkdir(output_path)\n\n out.to_csv( os.path.join(output_path, 'output.csv'), index=False)\n copyfile( config_file, os.path.join(output_path, 'config.yaml') )\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"182600062","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 2 13:00:00 2019\n\n@author: daniel\n\"\"\"\n\n\nimport numpy as np\n\nimport timeit\n\n# from assignment1 import Subset\nfrom assignment1 import import_data\nfrom assignment1 import import_best_solutions\nfrom assignment1 import compute_feasibility\nfrom assignment1 import compute_fitness\nfrom assignment1 import remove_redundant_sets\nfrom assignment1 import ch1\nfrom assignment1 import ch2\nfrom assignment1 import ch3\n\nfrom assignment1rand import ch1rand\nfrom assignment1rand import ch2rand\nfrom assignment1rand import ch3rand\n\nfrom assignment2 import oneFlipBestImprovementLocalSearch\nfrom assignment2 import oneFlipFirstImprovementLocalSearch\nfrom assignment2 import twoOneFlipBestImprovementLocalSearch\nfrom assignment2 import twoOneFlipFirstImprovementLocalSearch\n#from assignment2 import oneTwoFlipBestImprovementLocalSearch\n#from assignment2 import oneTwoFlipFirstImprovementLocalSearch\n\nfrom assignment3 import grasp\nfrom assignment3 import iteratedLocalSearch\n\nimport random\n\n# Set a seed to get always the same numbers from random()\nrandom.seed(23)\n\n# instFile = input(\"What instance file should be used? \")\n# import data from an instance file\n\nfiles = [\"scp42.txt\",\n \"scp43.txt\",\n \"scp44.txt\",\n \"scp45.txt\",\n \"scp46.txt\",\n \"scp47.txt\",\n \"scp48.txt\",\n \"scp49.txt\",\n \"scp51.txt\",\n \"scp52.txt\",\n \"scp53.txt\",\n \"scp54.txt\",\n \"scp55.txt\",\n \"scp56.txt\",\n \"scp57.txt\",\n \"scp58.txt\",\n \"scp59.txt\",\n \"scp61.txt\",\n \"scp62.txt\",\n \"scp63.txt\",\n \"scp64.txt\",\n \"scp65.txt\",\n \"scpa1.txt\",\n \"scpa2.txt\",\n \"scpa3.txt\",\n \"scpa4.txt\",\n \"scpa5.txt\",\n \"scpb1.txt\",\n \"scpb2.txt\",\n \"scpb3.txt\",\n \"scpb4.txt\",\n \"scpb5.txt\",\n \"scpc1.txt\",\n \"scpc2.txt\",\n \"scpc3.txt\",\n \"scpc4.txt\",\n \"scpc5.txt\",\n \"scpd1.txt\",\n \"scpd2.txt\",\n \"scpd3.txt\",\n \"scpd4.txt\",\n \"scpd5.txt\"]\n\nbestSolutions = import_best_solutions('SCP Best Solutions.txt')\n\nn_runs = 5\n\ndev_ils = np.zeros((n_runs, len(files)))\ntime_ils = np.zeros((n_runs, len(files)))\n\n# Iterated Local Search\nfor j in range(n_runs):\n for i in range(len(files)):\n \n print(files[i])\n \n nSubsets, nAttributes, subsets = import_data('scp_insts/' + files[i])\n \n bestSolution = bestSolutions[i][1]\n \n start_time = timeit.default_timer()\n sol = iteratedLocalSearch(nAttributes, subsets, 200, ch3, oneFlipFirstImprovementLocalSearch)\n time_ils[j,i] = timeit.default_timer() - start_time\n \n fit = compute_fitness(subsets, sol)\n #print(fit)\n dev_ils[j,i] = (float(fit - bestSolution)/bestSolution)*100\n\ndev_max = dev_ils.max(axis=0)\ndev_min = dev_ils.min(axis=0)\ndev_mean = dev_ils.mean(axis=0)\ntime_mean = time_ils.mean(axis=0)\n\n#np.savetxt(\"ils_times.csv\", time_ils, delimiter=\",\")\n#np.savetxt(\"ils_devs.csv\", dev_ils, delimiter=\",\")\n","sub_path":"SCP/assignment3/ils.py","file_name":"ils.py","file_ext":"py","file_size_in_byte":3130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"113485332","text":"import timeit\nimport re\n\n\nakash_karan_setup = '''\nfrom __main__ import akash_karan_day20\n'''\n\ndef akash_karan_day20(string):\n fl=0\n s=[]\n for i in string:\n if(i==\")\" and fl==0 ):\n return False\n elif(i==\"(\"):\n s.append(\"(\")\n fl=1\n elif(i==\")\"):\n try:\n s.pop()\n except(IndexError):\n return False\n fl=1\n return not len(s)\n\nTEST_CODE_akash_karan = '''\nresult = akash_karan_day20(\"(())((()())())\")\n'''\n\nccquiel_setup = '''\nfrom __main__ import ccquiel_day20\n'''\n\ndef ccquiel_day20(string):\n valsum =0 \n for s in string:\n if s == '(':\n valsum += 1\n elif s == ')':\n valsum -= 1\n if valsum < 0:\n return False\n if valsum == 0:\n return True\n else:\n return False\n\nTEST_CODE_ccquiel = '''\nresult = ccquiel_day20(\"(())((()())())\")\n'''\n\ncharlie_ang_setup = '''\nfrom __main__ import charlie_ang_day20\n'''\n\ndef charlie_ang_day20(string):\n #print(\"Working with {}\".format(string))\n pattern = re.compile(r'\\(\\w*\\)')\n working = string\n while pattern.search(working):\n working = pattern.sub(\"\", working)\n return not re.search(r'\\(|\\)', working)\n\nTEST_CODE_charlie_ang = '''\nresult = charlie_ang_day20(\"(())((()())())\")\n'''\n\ndiana_henninger_setup = '''\nfrom __main__ import diana_henninger_day20\n'''\n\ndef diana_henninger_day20(string):\n stack = []\n for c in string:\n if c == '(': stack.append(c)\n elif c == ')': \n if len(stack)==0: return False\n else: stack.pop()\n return len(stack)==0\n\nTEST_CODE_diana_henninger = '''\nresult = diana_henninger_day20(\"(())((()())())\")\n'''\n\nJens_setup = '''\nfrom __main__ import Jens_day20\n'''\n\ndef Jens_day20(string):\n open_parentheses = 0\n for letter in string:\n if letter == '(':\n open_parentheses += 1\n elif letter == ')':\n open_parentheses -= 1\n if open_parentheses < 0:\n return False\n return True if open_parentheses == 0 else False\n\nTEST_CODE_Jens = '''\nresult = Jens_day20(\"(())((()())())\")\n'''\n\nJose_Catela_setup = '''\nfrom __main__ import Jose_Catela_day20\n'''\n\ndef Jose_Catela_day20(string):\n string2 = ''\n for character in string:\n if character == '(' or character == ')':\n string2 += character\n stack = []\n for character in string2:\n if character == '(':\n stack.append(character)\n else:\n if len(stack) == 0:\n return False\n stack.pop()\n return len(stack) == 0\n\nTEST_CODE_Jose_Catela = '''\nresult = Jose_Catela_day20(\"(())((()())())\")\n'''\n\nkilian_setup = '''\nfrom __main__ import kilian_day20\n'''\n\ndef kilian_day20(string):\n count = 0\n for paranthese in string:\n if paranthese == '(':\n count += 1\n elif paranthese == ')':\n count -= 1\n if count < 0: return False\n return count == 0\n\nTEST_CODE_kilian = '''\nresult = kilian_day20(\"(())((()())())\")\n'''\n\nKrzysztof_Blach_setup = '''\nfrom __main__ import Krzysztof_Blach_day20\n'''\n\ndef Krzysztof_Blach_day20(string):\n if len(string) == 0:\n return True\n paren_str = ''\n for char in string:\n if char == '(' or char == ')':\n paren_str += char\n for i in range(len(paren_str) - 1):\n if paren_str[i] == '(' and paren_str[i + 1] == ')':\n return Krzysztof_Blach_day20(paren_str[:i] + paren_str[i + 2:])\n return False\n\nTEST_CODE_Krzysztof_Blach = '''\nresult = Krzysztof_Blach_day20(\"(())((()())())\")\n'''\n\nKurt_Hinderer_setup = '''\nfrom __main__ import Kurt_Hinderer_day20\n'''\n\ndef Kurt_Hinderer_day20(string):\n paren_level = 0\n for i in range(len(string)):\n if string[i] == '(':\n paren_level += 1\n elif string[i] == ')':\n if paren_level < 1:\n paren_level = -1000\n else:\n paren_level -= 1\n return paren_level == 0\n\nTEST_CODE_Kurt_Hinderer = '''\nresult = Kurt_Hinderer_day20(\"(())((()())())\")\n'''\n\nOleksandra_Chmel_setup = '''\nfrom __main__ import Oleksandra_Chmel_day20\n'''\n\ndef Oleksandra_Chmel_day20(string):\n ans = ''\n for i in string:\n if i in ('(',')'):\n ans += i\n while '()' in ans:\n ans = ans.replace('()','')\n return ans == ''\n\nTEST_CODE_Oleksandra_Chmel = '''\nresult = Oleksandra_Chmel_day20(\"(())((()())())\")\n'''\n\nSamrat_Mukherjee_setup = '''\nfrom __main__ import Samrat_Mukherjee_day20\n'''\n\ndef Samrat_Mukherjee_day20(string):\n #your code here\n stack = []\n for char in string:\n if char == '(':\n stack.append(char)\n else:\n try:\n if char == ')':\n del stack[-1]\n except:\n return False\n if len(stack) == 0:\n return True\n else:\n return False\n\nTEST_CODE_Samrat_Mukherjee = '''\nresult = Samrat_Mukherjee_day20(\"(())((()())())\")\n'''\n\nsjay_setup = '''\nfrom __main__ import sjay_day20\n'''\n\ndef sjay_day20(string):\n flag = False\n openpara = list(numb for numb,item in enumerate(string) if item==\"(\")\n closepara = list(numb for numb,item in enumerate(string) if item==\")\")\n data = zip(openpara,closepara)\n #print(openpara,closepara)\n for item in data:\n #print(item)\n if isinstance(item[0],int)and isinstance(item[1],int):\n if item[0] < item[1]:\n flag = True\n else:\n flag = False\n break\n else:\n flag=False\n break\n if len(openpara)==0 and len(closepara)==0:\n flag=True\n elif len(openpara)!= len(closepara):\n flag=False\n return flag\n\nTEST_CODE_sjay = '''\nresult = sjay_day20(\"(())((()())())\")\n'''\n\nVanessa_G_setup = '''\nfrom __main__ import Vanessa_G_day20\n'''\n\ndef Vanessa_G_day20(string):\n count_left = 0\n count_right = 0\n for c in string:\n if count_left < count_right: return False\n elif c == '(': count_left += 1\n elif c == ')': count_right += 1\n return count_left == count_right\n\nTEST_CODE_Vanessa_G = '''\nresult = Vanessa_G_day20(\"(())((()())())\")\n'''\n\nprint(\"Time for akash_karan test code: \" + str(timeit.timeit(stmt=TEST_CODE_akash_karan, setup=akash_karan_setup, number=100000)) + \" seconds\")\nprint(\"Time for ccquiel test code: \" + str(timeit.timeit(stmt=TEST_CODE_ccquiel, setup=ccquiel_setup, number=100000)) + \" seconds\")\nprint(\"Time for charlie_ang test code: \" + str(timeit.timeit(stmt=TEST_CODE_charlie_ang, setup=charlie_ang_setup, number=100000)) + \" seconds\")\nprint(\"Time for diana_henninger test code: \" + str(timeit.timeit(stmt=TEST_CODE_diana_henninger, setup=diana_henninger_setup, number=100000)) + \" seconds\")\nprint(\"Time for Jens test code: \" + str(timeit.timeit(stmt=TEST_CODE_Jens, setup=Jens_setup, number=100000)) + \" seconds\")\nprint(\"Time for Jose_Catela test code: \" + str(timeit.timeit(stmt=TEST_CODE_Jose_Catela, setup=Jose_Catela_setup, number=100000)) + \" seconds\")\nprint(\"Time for kilian test code: \" + str(timeit.timeit(stmt=TEST_CODE_kilian, setup=kilian_setup, number=100000)) + \" seconds\")\nprint(\"Time for Krzysztof_Blach test code: \" + str(timeit.timeit(stmt=TEST_CODE_Krzysztof_Blach, setup=Krzysztof_Blach_setup, number=100000)) + \" seconds\")\nprint(\"Time for Kurt_Hinderer test code: \" + str(timeit.timeit(stmt=TEST_CODE_Kurt_Hinderer, setup=Kurt_Hinderer_setup, number=100000)) + \" seconds\")\nprint(\"Time for Oleksandra_Chmel test code: \" + str(timeit.timeit(stmt=TEST_CODE_Oleksandra_Chmel, setup=Oleksandra_Chmel_setup, number=100000)) + \" seconds\")\nprint(\"Time for Samrat_Mukherjee test code: \" + str(timeit.timeit(stmt=TEST_CODE_Samrat_Mukherjee, setup=Samrat_Mukherjee_setup, number=100000)) + \" seconds\")\nprint(\"Time for sjay test code: \" + str(timeit.timeit(stmt=TEST_CODE_sjay, setup=sjay_setup, number=100000)) + \" seconds\")\nprint(\"Time for Vanessa_G test code: \" + str(timeit.timeit(stmt=TEST_CODE_Vanessa_G, setup=Vanessa_G_setup, number=100000)) + \" seconds\")\n","sub_path":"day20/day20.py","file_name":"day20.py","file_ext":"py","file_size_in_byte":8047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"234278383","text":"import requests\nimport json\n\ndef github_request(username: str) -> list:\n \"\"\"\n Sends the request to GitHub.\n :param username: The name of a GitHub user.\n :return: JSON data as a Python list.\n \"\"\"\n\n url = f\"https://api.github.com/users/{username}/repos\"\n headers = {'accept': 'application/vnd.github.v3+json'}\n response = requests.get(url, headers=headers)\n\n return response.json() # list\n\n\ndef prepare_repos_list(json_list: list) -> str:\n \"\"\"\n Extracts the necessary data (names and stars of repos).\n :param json_list: JSON data (list) to extract the necessary data.\n :return: JSON data (str).\n \"\"\"\n\n repos_stars = list(map(\n lambda repo: {\n 'name': repo['name'],\n 'stars': repo['stargazers_count']\n }, json_list))\n\n return json.dumps(repos_stars)\n\n\ndef prepare_stars_sum(json_list: list) -> str:\n \"\"\"\n Extracts the necessary data (sum of stars in repos).\n :param json_list: JSON data (list).\n :return: JSON data (str).\n \"\"\"\n\n starssum = sum(repo[\"stargazers_count\"] for repo in json_list)\n\n return json.dumps({\"starssum\": starssum})\n\n\n#\n","sub_path":"services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"488046119","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n#Read File\ndf = pd.read_csv('classics.csv')\n#Sort Values according to the downloads column\ndf = df.sort_values(\n \"downloads\",ascending=False)\n#fetch requried records from the record having the highest \"downloads\" column value\ndata_set = df.loc[0][\"downloads\"];\nname = df.loc[0][\"name\"]\ntitle = df.loc[0][\"title\"]\ndownload = data_set\n#Printing the output\nprint(\"Maximum download:\", data_set)\n\nprint(str(title) + \" by \" + str(name) + \" has \" + str(download) + \" downloads\")\n","sub_path":"June 2018/Python Course/End Course/exam3.py","file_name":"exam3.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"516046761","text":"import os\nimport json\nimport time\nfrom copy import copy\nfrom importlib import import_module\nfrom collections import Mapping\nfrom datetime import datetime\n\nfrom pulsar import asyncio\nfrom pulsar.utils.structures import OrderedDict, mapping_iterator\nfrom pulsar.apps.http import HttpClient\nfrom pulsar.apps import wsgi\n\n__all__ = ['Css', 'Variable', 'Symbol', 'Mixin',\n 'px', 'em', 'pc', 'size', 'as_value', 'Lazy',\n 'spacing', 'Variables', 'as_params']\n\nnan = float('nan')\n\n\ndef smart_round(value, ndigits):\n value = round(value, ndigits)\n ivalue = int(value)\n return ivalue if ivalue == value else value\n\n\ndef clamp(val, maxval=1):\n return min(maxval, max(0, val))\n\n\ndef alltags(tags):\n '''Generator of all tags from a string.'''\n for tag in tags.split(','):\n t = 0\n # Remove front white spaces and keep count of how many\n while tag and tag.startswith(' '):\n t += 1\n tag = tag[1:]\n if tag:\n if tag.startswith('.'):\n yield ' %s' % tag if t else tag\n elif tag.startswith(':'):\n yield tag\n else:\n yield ' %s' % tag\n\n\ndef as_value(value):\n '''Internal function used to convert any ``value`` into a suitable\n string to include into the css rules.'''\n if hasattr(value, 'value'):\n return value.value()\n else:\n return value\n\n\ndef as_params(value, default_name=None):\n '''Convert ``value`` into a dictionary.\n '''\n if isinstance(value, Variables):\n return value.params()\n elif value is None:\n return {}\n elif isinstance(value, dict):\n return value\n elif default_name:\n return {default_name: value}\n else:\n raise TypeError('\"%s\" is not a mapping' % value)\n\n\ndef addition(a, b):\n return a+b\n\n\ndef subtraction(a, b):\n return a-b\n\n\ndef multiplication(a, b):\n return a*b\n\n\ndef division(a, b):\n return a/b\n\n\ndef floordivision(a, b):\n return a//b\n\n\nclass Symbolic(object):\n '''Base class for :class:`Variable` and :class:`Unit`.\n '''\n def __str__(self):\n return self.__repr__()\n\n def __add__(self, other):\n return self._op(other, addition)\n\n def __sub__(self, other):\n return self._op(other, subtraction)\n\n def __rsub__(self, other):\n return self._op(other, addition)\n\n def __rsub__(self, other):\n return self._op(other, subtraction, True)\n\n def __mul__(self, other):\n return self._sp(other, multiplication)\n\n def __floordiv__(self, other):\n return self._sp(other, floordivision)\n\n def __rmul__(self, other):\n return self.__mul__(other)\n\n def __truediv__(self, other):\n return self._sp(other, division)\n\n def _op(self, other, op, right=False):\n raise NotImplementedError\n\n def _sp(self, other, op):\n raise NotImplementedError\n\n\nclass Variable(Symbolic):\n '''Base class for :class:`Variable` which can be stored in\n a :class:`Variables` container.\n '''\n def value(self):\n '''The current value of this :class:`Variable`.'''\n raise NotImplementedError\n\n def __repr__(self):\n v = self.value()\n return str(v) if v is not None else ''\n\n def tojson(self):\n return str(self)\n\n def _op(self, other, op, right=False):\n return LazyOp(other, self, op) if right else LazyOp(self, other, op)\n\n def _sp(self, other, op):\n return LazyOp(self, other, op)\n\n\nclass Symbol(Variable):\n '''A :class:`Variable` with a :attr:`name` and an underlying value which\n can be another :class:`Variable`.\n\n .. attribute:: value\n\n The value of this variable. It can be another Variable\n '''\n def __init__(self, name, value=None):\n self.name = name\n self._value = value\n\n def value(self, *val):\n if val:\n if len(val) == 1:\n self._value = val[0]\n else:\n raise TypeError('value() takes zero or one argument (%s given)'\n % len(val))\n else:\n if isinstance(self._value, Variable):\n return self._value.value()\n else:\n return self._value\n\n\nclass Lazy(Variable):\n '''A lazy :class:`.Variable`.\n\n :param callable: the callable invoked when accessing the\n :meth:`Variable.value` method of this lazy variable.\n '''\n def __init__(self, callable, *args, **kwargs):\n if not hasattr(callable, '__call__'):\n raise TypeError('First argument must be a callable')\n self.callable = callable\n self.args = args\n self.kwargs = kwargs\n\n def value(self):\n return self.callable(*self.args, **self.kwargs)\n\n\nclass LazyOp(Variable):\n '''A :class:`Variable` representing a lazy operation.'''\n def __init__(self, a, b, op):\n self.v1 = a\n self.v2 = b\n self._calculate = op\n\n def value(self):\n return self._calculate(as_value(self.v1), as_value(self.v2))\n\n\nclass Unit(Symbolic):\n '''Base class for :class:`Size` and :class:`Spacing`.'''\n unit = nan\n\n\nclass Size(Unit):\n '''Don't Initialise directly. Use the :func:`size` function instead.'''\n def __init__(self, value, unit=None):\n if value == 'auto':\n self._value = value\n self.unit = nan\n else:\n self.unit = unit or 'px'\n self._value = smart_round(value, 0 if self.unit == 'px' else 4)\n\n def __repr__(self):\n if self._value and self.unit == self.unit:\n return '%s%s' % (self._value, self.unit)\n else:\n return '%s' % self._value\n\n @property\n def top(self):\n return self\n\n @property\n def bottom(self):\n return self\n\n @property\n def left(self):\n return self\n\n @property\n def right(self):\n return self\n\n def __eq__(self, other):\n try:\n other = size(other)\n except:\n return False\n if self.unit == other.unit:\n return self._value == other._value\n else:\n return False\n\n def _op(self, other, op, right=False):\n other = size(other)\n if self.unit == other.unit:\n return self.__class__(op(\n other._value, self._value) if right else op(\n self._value, other._value), self.unit)\n else:\n raise TypeError('Cannot perform operation between %s and %s.'\n ' Different units.' % (self, other))\n\n def _sp(self, other, op):\n if isinstance(other, (int, float)):\n return self.__class__(op(self._value, other), self.unit)\n else:\n raise TypeError('Cannot perform operation between %s and %s.'\n % (self, other))\n\n\nclass Spacing(Unit):\n '''Css spacing with same unit. It can be used to specify padding,\n margin or any other css parameters which requires spacing box of\n the form (top, right, bottom, left).'''\n def __init__(self, *top_right_bottom_left):\n if not top_right_bottom_left:\n raise TypeError('Spacing() takes at least 1 argument (0 given)')\n elif len(top_right_bottom_left) > 4:\n raise TypeError('Spacing() takes at most 4 argument (%d given)'\n % len(top_right_bottom_left))\n self._value = top_right_bottom_left\n\n def __repr__(self):\n return ' '.join((str(size(b)) for b in self._value))\n\n @property\n def unit(self):\n unit = size(self.top).unit\n for v in self._value[1:]:\n if size(v).unit != unit:\n return nan\n return unit\n\n @property\n def top(self):\n return self._value[0]\n\n @property\n def right(self):\n return self._value[1] if len(self._value) > 1 else self.top\n\n @property\n def bottom(self):\n return self._value[2] if len(self._value) > 2 else self.top\n\n @property\n def left(self):\n return self._value[3] if len(self._value) > 3 else self.right\n\n def __eq__(self, other):\n try:\n other = spacing(other)\n except:\n return False\n if self.unit == other.unit:\n return self._value == other._value\n else:\n return False\n\n def __add__(self, other):\n return self._op(other, lambda a, b: a+b)\n\n def __sub__(self, other):\n return self._op(other, lambda a, b: a-b)\n\n def __mul__(self, other):\n return self.__class__(*[v*other for v in self._value])\n\n def _div(self, other):\n return self.__class__(*[v/other for v in self._value])\n\n def __floordiv__(self, other):\n return self.__class__(*[v//other for v in self._value])\n\n def _op(self, other, op, right=False):\n other = spacing(other)\n if right:\n return self.__class__(*[op(a, b) for a, b in zip(other, self)])\n else:\n return self.__class__(*[op(a, b) for a, b in zip(self, other)])\n\n def _sp(self, other, op):\n if isinstance(other, (int, float)):\n return self.__class__(*[op(a, other) for a in self._value])\n else:\n raise TypeError('Cannot perform operation between %s and %s.'\n % (self, other))\n\n\n############################################################################\n# factory functions\ndef px(v):\n return size(v, unit='px')\n\n\ndef pc(v):\n return size(v, unit='%')\n\n\ndef em(v):\n return size(v, unit='em')\n\n\ndef size(s, unit=None):\n if isinstance(s, Size):\n return s\n elif not s:\n return 0\n else:\n v = str(s)\n if v == 'auto':\n return Size('auto')\n else:\n try:\n try:\n v = float(v)\n except:\n if v.endswith('px') or v.endswith('em'):\n v, unit = float(v[:-2]), v[-2:]\n elif v.endswith('%'):\n v, unit = float(v[:-1]), v[-1:]\n else:\n raise\n except:\n raise TypeError('\"%s\" not a valid size' % s)\n ivalue = int(v)\n v = ivalue if ivalue == v else v\n return Size(v, unit) if v else 0\n\n\ndef spacing(v, *vals):\n '''Create a :class:`Spacing` element.'''\n return v if isinstance(v, Spacing) and not vals else Spacing(v, *vals)\n\n\nclass CssBase(object):\n _spacings = ('top', 'right', 'bottom', 'left')\n\n @property\n def code(self):\n return '%s-%s' % (self.__class__.__name__, id(self))\n\n def __repr__(self):\n return self.code\n __str__ = __repr__\n\n def set_parent(self, parent):\n raise NotImplementedError\n\n def clone(self):\n return self\n\n\nclass Mixin(CssBase):\n '''A css *Mixin* is a generator of :class:`css` and other\n :class:`Mixin` elements. All :class:`Mixin` must implement the\n callable method which receives the :class:`css` element which\n contains them.\n '''\n def __call__(self, elem):\n return self.apply(elem, self.value())\n\n def value(self):\n pass\n\n def apply(self, elem, value):\n pass\n\n def set_parent(self, parent):\n if parent.rendered:\n return self(parent)\n else:\n parent.add_child(self)\n\n\n# ################################################ Media\nclass Media(Mixin):\n '''Add @media queries to css.'''\n def __init__(self, type, query):\n self.type = type\n self.query = query\n self.container = Css()\n\n def css(self, tag, *components, **attributes):\n '''Add a `css`` rule for tag.\n\n Return ``self`` for chaining more rules\n '''\n self.container.css(tag, *components, **attributes)\n return self\n\n def __call__(self, elem):\n self.container.variables = elem.root.variables\n media = self.type\n if self.query:\n query = ' and '.join(('(%s:%s)' % (k.replace('_', '-'), v)\n for k, v in self.query.items()))\n media = '%s and %s' % (media, query)\n stream = '\\n'.join(('@media %s {' % media,\n self.container.render(' '),\n '}'))\n elem.add_stream(stream)\n\n\nclass Css(CssBase):\n '''A :class:`css` element in python.\n\n .. attribute:: attributes\n\n List of css attributes for the css element.\n\n .. attribute:: children\n\n An ordered dictionary of children for this :class:`css` element.\n Children are either other :class:`css` elements or :class:`Mixin`.\n\n .. attribute:: parent\n\n The :class:`css` ancestor for this :class:`css` element.\n\n '''\n rendered = False\n theme = None\n _app = None\n _css_libs = None\n\n def __init__(self, tag=None, vars=None, app=None):\n self._tag = tag\n self._http = None\n self._parent = None\n self._children = OrderedDict()\n self._attributes = []\n if app:\n assert tag is None, 'app should be passed to the root element only'\n self._app = app\n if self._tag is None:\n self._css_libs = wsgi.Links(self.config('MEDIA_URL', '/media/'))\n self.variables = Variables() if vars is None else vars\n self.classes = Variables()\n self.classes.hover = 'hover'\n self.classes.active = 'active'\n elif not tag:\n raise ValueError('A tag must be defined')\n\n def clone(self):\n c = copy(self)\n c._parent = None\n c._children = OrderedDict(((name, [c.clone() for c in children])\n for name, children in\n self._children.items()))\n c._attributes = copy(self._attributes)\n return c\n\n @property\n def tag(self):\n '''The tag for this :class:`Css` element.\n\n Always defined unless this is the root instance.\n '''\n tag = self._tag\n if self._parent:\n ptag = self._parent.tag\n if ptag:\n tag = '%s%s' % (ptag, tag)\n if tag:\n return tag[1:] if tag.startswith(' ') else tag\n\n @property\n def code(self):\n '''The code for this css tag.'''\n return self._tag or 'ROOT'\n\n @property\n def attributes(self):\n '''Css attributes for this element.'''\n return self._attributes\n\n @property\n def children(self):\n ''':class:`Css` children of this element.'''\n return self._children\n\n @property\n def parent(self):\n return self._parent\n\n @property\n def root(self):\n if self._parent:\n return self._parent.root\n else:\n return self\n\n @property\n def app(self):\n return self.root._app\n\n @property\n def http(self):\n if self._parent:\n return self._parent.http\n else:\n if self._http is None:\n self._http = HttpClient(loop=asyncio.new_event_loop())\n return self._http\n\n def __setitem__(self, name, value):\n if value is None or isinstance(value, Variables):\n return\n if isinstance(value, Mixin):\n raise TypeError('Cannot assign a Mixin to {0}. Use add instead.'\n .format(name))\n name = name.replace('_', '-')\n self._attributes.append((name, value))\n\n def __getitem__(self, name):\n raise NotImplementedError('cannot get item')\n\n def config(self, name, default=None):\n return self.app.config.get(name, default) if self.app else default\n\n def css(self, tag, *components, **attributes):\n '''A child :class:`Css` elements.'''\n if tag:\n elems = [Css(t) for t in alltags(tag)]\n else:\n elems = [Css(tag)]\n for clone, css in enumerate(elems):\n for name, value in attributes.items():\n css[name] = value\n css.set_parent(self)\n # Loop over components to add them to self\n for cl in components:\n if not isinstance(cl, list):\n cl = (cl,)\n for c in cl:\n css.add(c.clone() if clone else c)\n return elems[0] if len(elems) == 1 else elems\n\n def media(self, *type, **query):\n assert len(type) <= 1\n media = Media(type[0] if type else 'all', query)\n self.add(media)\n return media\n\n def get_media_url(self, path):\n '''Build the url for a media path.\n '''\n libs = self.root._css_libs\n if libs:\n path = libs.absolute_path(path)\n if not path.startswith('http'):\n path = 'http:%s' % path\n return path\n else:\n raise RuntimeError('No css libs configured')\n\n def update(self, iterable):\n for name, value in mapping_iterator(iterable):\n self[name] = value\n\n def add(self, c):\n '''Add a child :class:`css` or a class:`Mixin`.'''\n if isinstance(c, CssBase):\n c.set_parent(self)\n\n def add_child(self, child):\n clist = self._children.get(child.code)\n if isinstance(clist, list) and child not in clist:\n clist.append(child)\n else:\n self._children[child.code] = [child]\n\n def add_stream(self, stream):\n '''Add css text to the element.'''\n self._children[stream] = stream\n\n def set_parent(self, parent):\n # Get the element if available\n if getattr(self, 'tag', False) is None:\n if parent:\n raise ValueError('Body cannot have parent')\n return self\n assert parent is not self, 'cannot set self as parent'\n # When switching parents, remove itself from current parent children\n if self._parent and self._parent is not parent:\n self._parent.remove(self)\n self._parent = parent\n self._parent.add_child(self)\n\n def destroy(self):\n '''Safely this :class:`css` from the body tree.'''\n parent = self.parent\n if parent:\n parent.remove(self)\n\n def remove(self, child):\n '''Safely remove *child* form this :class:`css` element.'''\n clist = self._children.get(child.code)\n if clist:\n try:\n clist.remove(child)\n except ValueError:\n pass\n if not clist:\n self._children.pop(child.code)\n\n def extend(self, elem):\n '''Extend by adding *elem* attributes and children.'''\n self._attributes.extend(elem._attributes)\n for child_list in elem._children.values():\n for child in child_list:\n child.set_parent(self)\n\n def stream(self, whitespace=''):\n '''This function convert the :class:`css` element into a string.'''\n # First we execute mixins\n if self.rendered:\n raise RuntimeError('%s already rendered' % self)\n self.rendered = True\n children = self._children\n self._children = OrderedDict()\n for tag, clist in children.items():\n for c in clist:\n c._parent = None\n s = c.set_parent(self)\n if s: # the child (mixin) has return a string, added it.\n yield (None, s)\n data = []\n for k, v in self._attributes:\n v = as_value(v)\n if v is not None:\n data.append('%s %s: %s;' % (whitespace, k, v))\n if data:\n yield (self.tag, '\\n'.join(data))\n # Mixins and children\n for child_list in self._children.values():\n if isinstance(child_list, list):\n child = child_list[0]\n for c in child_list[1:]:\n child.extend(c)\n for s in child.stream(whitespace):\n yield s\n else:\n yield None, child_list\n\n def render(self, whitespace=''):\n '''Render the :class:`css` component and all its children'''\n od = OrderedDict()\n for tag, data in self.stream(whitespace):\n if data not in od:\n od[data] = []\n if tag:\n od[data].append(tag)\n\n def _():\n for data, tags in od.items():\n if tags:\n yield ',\\n'.join(('%s%s' % (whitespace, t) for t in tags)\n ) + ' {'\n yield data\n yield whitespace + '}\\n'\n else:\n yield data\n return '\\n'.join(_())\n\n def render_all(self, media_url=None, charset='utf-8'):\n root = self.root\n if media_url:\n root.variables.MEDIAURL = media_url\n start = time.time()\n body = root.render()\n created = datetime.fromtimestamp(int(start))\n nice_dt = round(time.time() - start, 2)\n intro = '''@charset \"UTF-8\";\n/*\n------------------------------------------------------------------\nCreated by lux in {1} seconds\nDate: {0}\n\nhttp://quantmind.github.io/lux/\n------------------------------------------------------------------ */\n\n'''.format(created.isoformat(' '), nice_dt)\n return intro + body\n\n def dump(self, theme=None, dump_variables=False):\n root = self.root\n root.theme = theme\n app = root.app\n if app:\n module = None\n # Import applications styles if available\n exclude = app.config['EXCLUDE_EXTENSIONS_CSS'] or ()\n for extension in app.config['EXTENSIONS']:\n if extension in exclude:\n continue\n try:\n module = import_module(extension)\n if hasattr(module, 'add_css'):\n module.add_css(root)\n app.write('Imported style from \"%s\".' % extension)\n except ImportError as e:\n app.write_err('Cannot import style %s: \"%s\".' %\n (extension, e))\n if dump_variables:\n data = root.variables.tojson()\n return json.dumps(data, indent=4)\n else:\n return root.render_all()\n\n\nclass Variables(object):\n '''A container of :class:`Variable` with name-spaces::\n\n v = Variables()\n v.body.height = px(16)\n\n If the body name-space is not available is automatically created.\n '''\n reserved = (None, '_reserved', 'reserved', 'name', 'parent')\n MEDIAURL = '/media/'\n\n def __init__(self, parent=None, name=None):\n self.__dict__.update({'_reserved': {'name': name,\n 'parent': parent},\n '_data': OrderedDict()})\n\n def __repr__(self):\n return repr(self._data)\n __str__ = __repr__\n\n @property\n def name(self):\n '''The name of this container of :class:`Variable`.'''\n if self.parent is None:\n return 'root'\n else:\n return self._reserved['name']\n\n @property\n def parent(self):\n '''The parent :class:`Variables` container.'''\n return self._reserved['parent']\n\n def value(self):\n '''Provide the value method which returns ``None``.'''\n pass\n\n def get(self, name):\n if name not in self._data:\n return Variables(self, name)\n else:\n return self._data[name]\n\n def __iter__(self):\n return iter(self._data.values())\n\n def __len__(self):\n return len(self._data)\n\n def __contains__(self, name):\n return name.lower() in self._data\n\n def tojson(self):\n return OrderedDict(((v.name, v.tojson()) for v in self))\n\n def params(self, recursive=False):\n '''Return this :class:`Variables` container as a dictionary\nof named variables.'''\n return dict(((name, value) for name, value in self._stream(recursive)\n if value is not None))\n\n def _stream(self, recursive, prefix=None):\n d = self._data\n for name in d:\n if name not in self.reserved:\n value = d[name]\n if prefix:\n name = '%s%s' % (prefix, name)\n if isinstance(value, Variables) and recursive:\n pfix = '%s_' % name\n for name, value in value._stream(recursive, pfix):\n yield name, value\n else:\n yield name, value\n\n def __setattr__(self, name, value):\n if name not in self.reserved:\n if isinstance(value, Mapping):\n items = value.items()\n value = self.get(name)\n if isinstance(value, Variables):\n for k, val in items:\n setattr(value, k, val)\n else:\n raise ValueError('Cannot set attribute %s' % name)\n if isinstance(value, Variables):\n v = value\n v._reserved.update({'parent': self, 'name': name})\n else:\n v = self._data.get(name)\n if v is None:\n v = Symbol(name, value)\n else:\n v.value(value)\n self._data[name] = v\n if self.parent is not None and self.name not in self.parent:\n setattr(self.parent, self.name, self)\n\n def __getattr__(self, name):\n return self.get(name)\n\n def __getitem__(self, name):\n return self.get(name)\n","sub_path":"lux/extensions/ui/lib/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":25574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"201872640","text":"#modul fungsction login required\nfrom django.contrib.auth.decorators import login_required\n#modul fungsi staff member only \nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.shortcuts import render, get_object_or_404, redirect\n#impot model blog\nfrom .models import BlogPost\n#import excepting handling errro 404\nfrom django.http import Http404\n#import django form \nfrom .forms import *\n\n# # function show data\n# def blog_post_detail_page(request, slug):\n# # try:\n# obj = BlogPost.objects.get(slug=slug) # query -> database -> django render data\n# # except BlogPost.DoesNotExist: \n# # raise Http404\n# # except ValueError:\n# # raise Http404\n# # SLUG QUERY Filter \n# # queryset = BlogPost.objects.filter(slug=slug)\n# # if queryset.count() == 0:\n# # raise Http404\n# # obj = queryset.first()\n# template_name = \"blog_post_detail.html\"\n# context = {\"object\" : obj } # { \"title\" =obj.title }\n# return render(request, template_name, context)\n\n\ndef blog_post_list_view(request):\n qs = BlogPost.objects.all() #queryset -> list of python objects\n return render(request, 'blog_post_list.html', {'object_list' : qs })\n\n\n# login member required\n@staff_member_required\ndef blog_post_create(request):\n #create objects\n # ? use form\n # make exception if not user \n # if not request.user.is_authenticated:\n # return render(request, \"not-user.html\", {})\n form = BlogPostModelForm(request.POST or None)\n if form.is_valid():\n #obj = BlogPost.objects.create(**form.c\n # leaned_data)\n # fungsi save menggunakan form modelpost\n obj = form.save(commit=False)\n obj.save()\n form = BlogPostModelForm()\n return render(request, 'blog_post_create.html', {'form' : form })\n\ndef blog_post_detail(request, slug):\n # 1 object Detail View\n obj = BlogPost.objects.get(slug=slug)\n template_name = \"blog_post_detail.html\"\n context = {\"object\" : obj } # { \"title\" =obj.title }\n return render(request, template_name, context)\n\ndef blog_post_update(request, slug):\n obj = get_object_or_404(BlogPost, slug=slug)\n form = BlogPostForm(request.POST or None, instance=obj)\n template_name = 'form.html'\n context = {'form': form, \"title\": f\"Update {obj.title}\"} # { \"title\" =obj.title }\n return render(request, template_name, context)\n\ndef blog_post_delete(request, slug):\n obj = BlogPost.objects.get(slug=slug)\n template_name = \"blog_post_delete.html\"\n context = {\"object\" : obj } # { \"title\" =obj.title }\n return render(request, template_name, context)","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"234665683","text":"from urllib import request\nfrom urllib import error\n\n\ndef download_page(url, num_retries=2):\n print('downloading:'+url)\n try:\n html = request.urlopen(url).read()\n except error.URLError as e:\n print('download error:'+e.reason)\n html = None\n if num_retries > 0:\n if hasattr(e, 'code') and 500 <= e.code < 600:\n # recursively retry 5xx Http errors\n return download_page(url, num_retries-1)\n return html\n\nprint(download_page('http://httpstat.us/500'))","sub_path":"downloadPage.py","file_name":"downloadPage.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"442882446","text":"import matplotlib.pyplot as plt\n\ninput_values = range(1, 1001)\nsquares = [x**2 for x in input_values]\n\nplt.style.use('seaborn')\n\nfig, ax = plt.subplots()\n\n#ax.plot(input_values, squares, linewidth=3, c='red')\nax.scatter(input_values, squares, s=5, c=squares, cmap=plt.cm.Blues)\n\nax.set_title(\"Square Numbers\", fontsize=24)\nax.set_xlabel(\"Value\", fontsize=14)\nax.set_ylabel(\"Square of Value\", fontsize=14)\n\nax.tick_params(axis=\"both\", labelsize=14)\n\nax.axis([0, 1100, 0, 1100000]) #axis ranges\n\nplt.savefig('squares_plot.png', bbox_inches='tight')","sub_path":"mpl_squares.py","file_name":"mpl_squares.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"40663942","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 11 05:39:23 2019\n\n@author: xingg\n\"\"\"\n\nfrom os import listdir\nfrom os.path import isfile, join\nfrom collections import defaultdict\n\n'''\nget all attributes with frequency\nget category <--> attributes with frequency\ncategory attribute1 attribute2...\n'''\n\n\nallAttribute = {}\n\n\n# open the category data --label\nfile = open('../CleanData/category_s.csv','r', encoding = 'utf-8')\ncategories = {}\nfor c in file.readlines():\n c = (c.replace('\\n','').replace('_',' ')).split('\\t')\n categories[c[0]] = {}\nfile.close()\n\n\n# open article <--> category\nfile = open('../CleanData/article_category.csv', 'r', encoding = 'utf-8')\narticle_category = []\nfor cg in file.readlines():\n cg = cg.replace('\\n','').replace('_',' ').split('\\t')\n # 0 article, 1 category\n if cg[0] in categories.keys():\n article_category.append([cg[1],cg[0]])\n elif cg[1] in categories.keys():\n article_category.append([cg[0],cg[1]])\nfile.close()\n\n# read all attribute files\nmypath = '../CleanData/attribute'\nfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n\ndef merge_dict(a,b):\n for k,v in a.items():\n if k in b.keys():\n b[k] += v\n else:\n b[k] = v\n return b\n\n\nfor f in files:\n anum = f.split('_')[0]\n file = open(join(mypath, f), 'r', encoding = 'utf-8') \n lines = file.readlines()\n file.close()\n defdesc = {}\n for l in lines:\n tempkv = (l.replace('\\n','')).split('\\t') \n # already in attr, count ++\n if tempkv[0] in allAttribute.keys():\n allAttribute[tempkv[0]] += 1\n else: # add a new attr\n allAttribute[tempkv[0]] = 1\n ''' \n # already in defdesc, count ++\n if tempkv[0] in defdesc.keys():\n defdesc[tempkv[0]] += 1\n else:# add a new attr\n defdesc[tempkv[0]] = 1\n \n #if article has category, add these attributes to the category dict\n for ac in article_category:\n if anum == ac[0] and ac[1] in categories.keys(): #ac[0] is article\n categories[ac[1]] = merge_dict(defdesc,categories[ac[1]])#{**defdesc, **categories[ac[1]]}\n \n elif anum == ac[1] and ac[0] in categories.keys(): #ac[1] is article\n categories[ac[0]] = merge_dict(defdesc,categories[ac[0]])#{**defdesc, **categories[ac[0]]}\n '''\noutput = open('../CleanData/attribute.csv', 'w', encoding = 'utf-8')\nsorted_by_value = sorted(allAttribute.items(), key=lambda x: (-x[1], x[0])) \n\nfor s in sorted_by_value:\n output.write(s[0]+'\\t'+str(s[1])+'\\n')\noutput.close()\n\n'''\noutput = open('../CleanData/category_attribute.csv', 'w', encoding = 'utf-8') \nsorted_by_key = sorted(categories.items(), key=lambda kv: kv[0]) \nfor sk in sorted_by_key:\n sorted_by_value = sorted(sk[1].items(), key=lambda kv: kv[1], reverse=True) \n for s in sorted_by_value:\n output.write(sk[0]+'\\t'+s[0]+'\\t'+str(s[1])+'\\n')\noutput.close()\n'''","sub_path":"SilmPy/DataProcess/get_all_attributes.py","file_name":"get_all_attributes.py","file_ext":"py","file_size_in_byte":2971,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"292974858","text":"import torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n\ndef _l2_normalize(d):\n if isinstance(d, Variable):\n d = d.data.numpy()\n elif isinstance(d, torch.Tensor):\n d = d.numpy()\n d /= (np.sqrt(np.sum(d ** 2, axis=(1, 2, 3))).reshape((-1, 1, 1, 1)) + 1e-16)\n return torch.from_numpy(d)\n\n\ndef vat_loss(model, X, xi=0.1, eps=1.0, Ip=1, use_gpu=True):\n \"\"\"VAT loss function\n :param model: networks to train\n :param X: Variable, input\n :param xi: hyperparameter of VAT (default: 1.0)\n :param eps: hyperparameter of VAT (default: 1.0)\n :param Ip: iteration times of computing adv noise (default: 1)\n :param use_gpu: use gpu or not (default: True)\n :return: LDS, model prediction (for classification-loss calculation)\n \"\"\"\n kl_div = nn.KLDivLoss()\n if use_gpu:\n kl_div.cuda()\n\n pred = model(X)\n\n # prepare random unit tensor\n d = torch.rand(X.shape)\n d = Variable(_l2_normalize(d))\n if use_gpu:\n d = d.cuda()\n \n # calc adversarial direction\n for ip in range(Ip):\n d.requires_grad = True\n pred_hat = model(X + d / xi)\n adv_distance = kl_div(F.log_softmax(pred_hat, dim=1), pred.detach())\n adv_distance.backward()\n d = Variable(_l2_normalize(d.grad.data))\n model.zero_grad()\n\n # calc LDS\n r_adv = d * eps\n pred_hat = model(X + r_adv)\n pred = model(X)\n LDS = kl_div(F.log_softmax(pred_hat, dim=1), pred.detach())\n return LDS, pred\n","sub_path":"vat.py","file_name":"vat.py","file_ext":"py","file_size_in_byte":1549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"638695853","text":"import numpy as np\nimport os\nimport cv2\nimport pickle\n\nIMG_WIDTH = 45\nIMG_HEIGHT = 45\n\n\ndef create_dataset(img_folder, tweak=False):\n \"\"\"\n Saves dataset in form of pickle files\n :param img_folder: path to folder with directories of image samples for building model\n :param tweak: If true images will be dilated and eroded once\n \"\"\"\n erode_kernel = np.ones(shape=(2, 2), dtype=np.uint8)\n for folder in os.listdir(img_folder):\n img_data = []\n if folder == \"results\":\n continue\n print(folder)\n for file in os.listdir(os.path.join(img_folder, folder)):\n image_path = os.path.join(img_folder, folder, file)\n image = cv2.imread(image_path, cv2.COLOR_BGR2RGB)\n image = cv2.resize(image, (IMG_HEIGHT, IMG_WIDTH), interpolation=cv2.INTER_AREA)\n if tweak:\n image = cv2.dilate(image, erode_kernel, iterations=1)\n image = cv2.erode(image, erode_kernel, iterations=1)\n image = cv2.bitwise_not(image)\n image = np.array(image)\n image = image.astype('float32')\n image /= 255\n img_data.append([image, folder])\n with open(\"./dataset/results/\" + folder + \".pickle\", \"wb\") as pickle_file:\n pickle.dump(img_data, pickle_file)\n\n\ndef load_dataset(dataset_folder):\n \"\"\"\n :param dataset_folder: path to pickle files that contain dataset made by function create_dataset()\n :return: test_images and labels as numpy array\n \"\"\"\n dataset = []\n for file in os.listdir(dataset_folder):\n with open(dataset_folder + file, \"rb\") as pickle_file:\n data = pickle.load(pickle_file)\n dataset.extend(data)\n\n dataset = np.array(dataset)\n X, y = dataset[:, 0], dataset[:, 1]\n\n Xn = np.zeros(shape=(len(X), len(X[0]), len(X[0][0]), 1))\n for i in range(len(X)):\n Xn[i] = np.expand_dims(X[i], axis=(0, 3))\n return Xn, y\n\n\ndef encode_labels(labels):\n encoder = {\n \"0\": 0,\n \"1\": 1,\n \"2\": 2,\n \"3\": 3,\n \"4\": 4,\n \"5\": 5,\n \"6\": 6,\n \"7\": 7,\n \"8\": 8,\n \"9\": 9,\n \"+\": 10,\n \"-\": 11,\n \"div\": 12,\n \"times\": 13,\n \"(\": 14,\n \")\": 15\n }\n res = np.zeros(len(labels))\n for i in range(len(labels)):\n res[i] = encoder.get(labels[i])\n return res\n\n\ndef decode_predictions(predictions):\n result = \"\"\n decoder = {\n 0: \"0\",\n 1: \"1\",\n 2: \"2\",\n 3: \"3\",\n 4: \"4\",\n 5: \"5\",\n 6: \"6\",\n 7: \"7\",\n 8: \"8\",\n 9: \"9\",\n 10: \"+\",\n 11: \"-\",\n 12: \"/\",\n 13: \"*\",\n 14: \"(\",\n 15: \")\"\n }\n for p in predictions:\n result += decoder.get(p)\n return result\n\n\ndef predict_images(model, images, verbose=False, decoded=True):\n \"\"\"\n Predicts list of images while using given model\n :param model: model for predicting\n :param images: list of images to be predicted\n :param verbose: if true shows images\n :param decoded: if true returns predicted symbols instead of labels\n :return:\n \"\"\"\n predictions = []\n for i, crop in enumerate(images):\n img = np.array(crop)\n img = img.astype('float32')\n img /= 255\n # img[img < 0.4] = 0\n # img[img >= 0.7] = 1\n if verbose:\n cv2.imshow(\"test\", img)\n cv2.waitKey(0)\n p = np.argmax(model.predict(np.expand_dims(img, axis=(0, 3))))\n predictions.append(p)\n if decoded:\n return decode_predictions(predictions)\n return predictions\n\n\ndef solve(task):\n \"\"\"\n :param task: string of the task to be evaluated\n :return: if solved returns solution, else returns None\n \"\"\"\n try:\n solution = eval(task)\n return solution\n except SyntaxError:\n return None\n","sub_path":"src/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":3877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"29879422","text":"#!/usr/bin/env python3\nimport os\nimport re\nimport telnetlib\nimport os\n\ndirectory_name = \"dir/ospfv3.initial/\"\nfile_name = \"R3b.txt\"\nhost = \"192.168.21.103\"\nPORT = \"23\"\n\npath = directory_name + file_name\nprint(\"Sending {} to {}\\n\".format(directory_name + file_name, host))\n\ntry:\n with open(path, 'r') as handle:\n for line in handle:\n print(line, end=\"\")\nexcept IOError as e:\n print(e)\n\nexit()\n\ntry:\n tn = telnetlib.Telnet(host, PORT, 10)\n try:\n with open(directory_name + file_name, 'r') as handle:\n for line in handle:\n print(\"Sending:\")\n print(line)\n tn.write(line.encode(\"ascii\\b\"))\n\n except IOError as e:\n print(e)\n tn.write(b\"\\n\\nshow ip interface br\\n\")\n tn.write(b\"exit\\n\")\n print(tn.read_all().decode(\"ascii\"))\n tn.close()\n\nexcept IOError:\n print(\"IOError, could not open a connection to {}\".format(host))\n\nprint(\"Upload Complete.\\n\")\n","sub_path":"ine.ccie/test-upload.py","file_name":"test-upload.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"430597813","text":"\n\n\n# 复习day01\n\n\n#\n# 1.python定义：是一门免费 开源 跨平台 动态 面向对象 的编程语言\n# 2.执行方式： 交互式 文件式\n# 3.执行过程： 编译 解释\n# 运行时 python 源代码 --> 编译 --> 字节码 -->解释 --> 机器码\n# 4.函数 ： 功能 定义者 调用者\n# print : 打印 print(‘内容’) 将内容输出到控制台\n# input : Input('提示信息') 从控制台获取信息\n\n\n\n\nstr = \"abcada\"\nlist = str.split('-',2)\nprint(list)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"xiaojian/xiaojian/first_phase/day01/reverse.py","file_name":"reverse.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"114367993","text":"import os\nimport pickle\nimport pandas as pd\n\nfrom ffpred3_test import get_cafa_mappings\nfrom train import ProteinAnnotationDataset\nfrom model.utils import prepare_annotations\nfrom test import evaluate_predictions_fmax, evaluate_predictions_smin, evaluate_predictions_average_auc\n\n\ndef prepare_hpo2go_predictions():\n \"\"\" Prepare HPO2GO predictions for proteins in the intersection of test set and CAFA3 protein sets\n\n :return: prediction for intersection of test set and CAFA3 proteins of HPO2GO method\n :rtype: dict\n \"\"\"\n mappings = get_cafa_mappings()\n if not os.path.exists('data/benchmark/HPO_cafa3_test.txt'):\n with open('data/benchmark/HPO_test.txt', 'r') as f:\n test_protein_ids = f.read().split('\\n')[:-1]\n test_protein_ids = set(list(mappings.keys())).intersection(test_protein_ids)\n else:\n with open('data/benchmark/HPO_cafa3_test.txt', 'r') as f:\n test_protein_ids = f.read().split('\\n')[:-1]\n\n hpo2go_predictions = pd.read_csv('data/predictions/CAFA3_HPO_target_predictions.txt',\n sep='\\t', header=None, names=['protein', 'term', 'score'])\n predictions = {}\n for protein in test_protein_ids:\n functions = hpo2go_predictions[hpo2go_predictions['protein'] == mappings[protein]]['term'].values\n if len(functions) == 0:\n continue\n scores = hpo2go_predictions[hpo2go_predictions['protein'] == mappings[protein]]['score'].values\n predictions[protein] = {'scores': scores, 'functions': functions}\n\n with open('data/benchmark/HPO_cafa3_test.txt', 'w') as f:\n for protein in list(predictions.keys()):\n f.write(f'{protein}\\n')\n\n return predictions\n\n\ndef evaluate_hpo2go_method():\n \"\"\" Evaluates the HPO2GO method\n\n :return: None\n \"\"\"\n hpo2go_predictions_file = 'data/predictions/hpo2go_predictions.pkl'\n if not os.path.exists(hpo2go_predictions_file):\n predictions = prepare_hpo2go_predictions()\n with open(hpo2go_predictions_file, 'wb') as f:\n pickle.dump(predictions, f, pickle.HIGHEST_PROTOCOL)\n else:\n with open(hpo2go_predictions_file, 'rb') as f:\n predictions = pickle.load(f)\n\n with open('data/benchmark/HPO_cafa3_test.txt', 'r') as f:\n test_protein_ids = f.read().split('\\n')[:-1]\n anno_file = 'data/hpo/hpo_protein_anno.tab'\n terms_file = 'data/ontology/HPO_terms.txt'\n with open('data/ontology/hpo_ancestors.pkl', 'rb') as f:\n ancestors = pickle.load(f)\n\n with open(terms_file, 'r') as f:\n terms = f.read().split('\\n')[:-1]\n _, annotations = prepare_annotations(terms, ancestors, test_protein_ids, anno_file)\n\n test_data = ProteinAnnotationDataset(test_protein_ids, {},\n annotations, dict(), 0)\n\n fmax = evaluate_predictions_fmax(test_data, predictions)\n print('Fmax:')\n print(fmax)\n\n corrected_predictions = dict()\n for protein in predictions.keys():\n mask = [func in test_data.term_mappings.keys()\n for func in predictions[protein]['functions'].tolist()]\n corrected_predictions[protein] = {'scores': predictions[protein]['scores'][mask],\n 'functions': predictions[protein]['functions'][mask]}\n smin = evaluate_predictions_smin('HP', test_data, corrected_predictions, ancestors)\n print('Smin:')\n print(smin)\n\n auc = evaluate_predictions_average_auc(test_data, predictions)\n print('Average AUC:')\n print(auc)\n\n\nif __name__ == '__main__':\n evaluate_hpo2go_method()\n","sub_path":"hpo2go_test.py","file_name":"hpo2go_test.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"458656169","text":"#coding:utf-8\n\n'''\nread log files and calculate results\n'''\n\nimport os\nimport sys\nimport numpy as np\n\ndef confusionMat2numpy(confusionMatS,nr,nc):\n confusionMatN=list()\n temp=''\n for s in confusionMatS:\n if(s>='0' and s<='9'):temp+=s\n else:\n if(len(temp)>0):confusionMatN.append(int(temp))\n temp=''\n confusionMatN=np.array(confusionMatN).reshape((nr,nc))\n return confusionMatN\n\nbasename=sys.argv[1]\nacc=list()\nloss=list()\naccClass=list()\n\nfor i in range(5):\n fname=basename+\"{}.log\".format(i)\n with open(fname,'r') as f:\n lines=f.readlines()\n lastline=lines[-17].strip()\n confuseMat=confusionMat2numpy('\\n'.join(lines[-15:]),15,15)\n confuseArr=np.sum(confuseMat,axis=1)\n for c in range(15):\n if(c==0):\n classAcc=[float(confuseMat[c][c])/float(confuseArr[c]),]\n else:\n classAcc.append(float(confuseMat[c][c])/float(confuseArr[c]),)\n acc.append(float(lastline.split(\"(\")[1][0:5]))\n loss.append(float(lastline.split(\"loss: \")[1][0:6]))\n accClass.append(classAcc)\n\nprint(\"Test\")\nfor i in range(15):print(accClass[0][i])\nprint(\"CV\")\nfor i in range(15):print(0.25*(accClass[1][i]+accClass[2][i]+accClass[3][i]+accClass[4][i]))\n\nacc0=acc[0]\nacc1=np.mean(acc[1:])\nstd=np.std(acc[1:],ddof=1)\nprint('Test loss {:.4e}'.format(loss[0]))\nprint('CV loss {:.4e}+-{:.4e}'.format(np.mean(loss[1:]),np.std(loss[1:],ddof=1)))\nprint('CV acc {:.2f}+-{:.2f}'.format(acc1,std))\nprint('Test acc {:.2f}'.format(acc0))\n","sub_path":"script/metric/count.py","file_name":"count.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"261588187","text":"import imageTest_pb2\n\nimport imageTest_pb2_grpc\n\nimport numpy as np\n\nimport grpc\n\nfrom concurrent import futures\n\nimport time\n\n#import opencv as cv2\n\n\n_ONE_DAY_IN_SECONDS = 60 * 60 * 24\n\n\n\nclass Greeter(imageTest_pb2_grpc.ImageTestServicer):\n\n def Analyse(self, request_iterator, context):\n for req in request_iterator:\n frame = np.array(list(req.img))\n frame = frame.reshape( (576,704) )\n frame = np.array(frame, dtype = np.uint8 )\n\n #cv2.imshow('Processed Image', frame)\n #cv2.waitKey(1)\n return imageTest_pb2.MsgReply(reply = 1)\n\n\ndef serve():\n server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))\n imageTest_pb2_grpc.add_ImageTestServicer_to_server(Greeter(), server)\n server.add_insecure_port('[::]:50051')\n server.start()\n try:\n while True:\n time.sleep(_ONE_DAY_IN_SECONDS)\n except KeyboardInterrupt:\n server.stop(0)\n\nif __name__ == '__main__':\n serve()","sub_path":"imageTest_server.py","file_name":"imageTest_server.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"124241558","text":"# XGBoost for python should be installed.\n\nimport pandas as pd\nimport os\nimport numpy as np\nfrom sklearn.cross_validation import train_test_split\nimport xgboost as xgb\nimport sys\nfrom sklearn.utils import shuffle\n\n# Evaluation\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import r2_score\n\n# Custom \nfrom idora import settings\n# FUNCTIONS\n\n# Split the data into train and test\ndef train_test(data): \n np.random.seed(100)\n data = shuffle(data)\n data.index = range(0,len(data))\n # ^ Instead of imputing - drop nans !\n data.dropna(inplace=True)\n # Do the split\n train = data.sample(frac=0.8,random_state=200)\n test = data.drop(train.index)\n return train, test\n\ndef set_params(n_classes,loss=\"binary:logistic\",**kawrgs):\n # Defaults: \n params ={} ;\n if loss == \"binary:logistic\":\n params = {\"objective\": loss,\n \"booster\" : \"gbtree\",\n \"eta\": 0.3,\n \"max_depth\": 10,\n \"subsample\": 0.9,\n \"colsample_bytree\": 0.7,\n \"silent\": 1,\n \"seed\": 1300 }\n if loss == \"multi:softmax\":\n print(\"Number of classes should be stated: give n_classes argument\")\n params = {\"objective\": \"multi:softmax\",\n \"booster\" : \"gbtree\",\n \"eta\": 0.3,\n \"max_depth\": 10,\n \"subsample\": 0.9,\n \"colsample_bytree\": 0.7,\n \"silent\": 1,\n \"seed\": 1300,\n \"num_class\": n_classes}\n if loss == \"reg:linear\":\n params = {\"objective\": \"reg:linear\",\n \"booster\" : \"gbtree\",\n \"eta\": 0.3,\n \"max_depth\": 10,\n \"subsample\": 0.9,\n \"colsample_bytree\": 0.7,\n \"silent\": 1,\n \"seed\": 1300}\n \n return params\n\n# Train test will be split into train and validation\n# def gmb_xgb_binary(train,test, params=params,features=features,all_features=all_features,target=target):\ndef gmb_xgb_binary(train,test,params,features,all_features,target):\n num_boost_round = 300\n print(\"Train a XGBoost model\")\n X_train, X_valid = train_test_split(train[all_features], test_size=0.20, random_state=10)\n y_train = X_train[target]\n y_valid = X_valid[target]\n dtrain = xgb.DMatrix(X_train[features], y_train)\n dvalid = xgb.DMatrix(X_valid[features], y_valid)\n X_test = test[all_features]\n y_test = test[target]\n dtest = xgb.DMatrix(test[features],y_test)\n watchlist = [(dtrain, 'train'), (dvalid, 'eval')]\n gbm = xgb.train(params, dtrain, num_boost_round, evals=watchlist, \\\n early_stopping_rounds=10, verbose_eval=True)\n \n if params['objective'] == \"binary:logistic\":\n _evaluate_binary(gbm=gbm,dvalid=dvalid,y_valid=y_valid,target=target,dtest=dtest,y_test=y_test) \n elif params['objective'] == \"multi:softmax\":\n _evaluate_multi(gbm,dvalid=dvalid,y_valid=y_valid,y_test=y_test,dtest=dtest)\n if params['objective'] == \"reg:linear\":\n _evaluate_reg(gbm,dvalid=dvalid,y_valid=y_valid,dtest=dtest,y_test=y_test)\n \n return gbm\n\n# def _evaluate_binary(gmb,X_valid=X_valid[features],target=target,dtest=dtest,y_test=test[target]):\ndef _evaluate_binary(gbm,dvalid,y_valid,target,dtest,y_test):\n # Do evaluation of the model\n print(\"Validating (Evaluation of validation split)\")\n yhat = gbm.predict(dvalid)\n yhat = [1 if a>0.5 else 0 for a in yhat]\n pr = precision_score(y_valid, yhat)\n re = recall_score(y_valid, yhat)\n F1 = 2*(pr*re)/(pr+re)\n print(\"the precision is:\", pr)\n print(\"the recall is:\", re)\n print(\"the F-score is:\", F1)\n print(\"Evaluation on test: make predictions on the test set\")\n yhat_test = gbm.predict(dtest)\n yhat_test = [1 if a>0.5 else 0 for a in yhat_test]\n pr = precision_score(y_test, yhat_test)\n re = recall_score(y_test, yhat_test)\n F1 = 2*(pr*re)/(pr+re)\n print(\"the precision is:\", pr)\n print(\"the recall is:\", re)\n print(\"the F-score is:\", F1)\n \n return \n\n\ndef _evaluate_multi(gbm,dvalid,y_valid,y_test,dtest):\n # EVALUATE\n print(\"Validating (Evaluation of validation split)\")\n yhat = gbm.predict(dvalid)\n F1 = f1_score(y_valid, yhat, average='weighted')\n re = recall_score(y_valid, yhat, average='weighted')\n pr = precision_score(y_valid, yhat, average='weighted')\n print(\"the precision is:\", pr)\n print(\"the recall is:\", re)\n print(\"the F-score is:\", F1)\n print()\n print(\"Evaluation on test: make predictions on the test set\")\n yhat_test = gbm.predict(dtest)\n F1 = f1_score(y_test, yhat_test, average='weighted')\n re = recall_score(y_test, yhat_test, average='weighted')\n pr = precision_score(y_test, yhat_test, average='weighted')\n print(\"the precision is:\", pr)\n print(\"the recall is:\", re)\n print(\"the F-score is:\", F1)\n \n return\n\n\ndef _evaluate_reg(gbm,dvalid,y_valid,dtest,y_test):\n def rmspe(y, yhat):\n return np.sqrt(np.mean((yhat/y-1) ** 2))\n def squared_error(ys_orig,ys_line):\n return sum((ys_line - ys_orig) * (ys_line - ys_orig))\n def R2(ys_orig,ys_line):\n y_mean_line = [mean(ys_orig) for y in ys_orig]\n squared_error_regr = squared_error(ys_orig, ys_line)\n squared_error_y_mean = squared_error(ys_orig, y_mean_line)\n return 1 - (squared_error_regr/squared_error_y_mean)\n def R2_sklearn(ys_orig,ys_line):\n return r2_score(ys_orig,ys_line)\n # Do evaluation of the model\n print(\" Validating (Evaluation of validation split) \")\n yhat = gbm.predict(dvalid)\n error = rmspe( y_valid.values, np.expm1(yhat) )\n print( 'RMSPE: {:.6f}'.format(error) )\n r2 = R2_sklearn( y_valid.values, yhat) \n print( 'R2: {:.6f}\\n'.format(r2) )\n print(\"Evaluation on test: Make predictions on the test set\")\n yhat_test = gbm.predict(dtest)\n error = rmspe( y_test.values, yhat_test) \n print( 'RMSPE: {:.6f}'.format(error) )\n r2 = R2_sklearn( y_test.values, yhat_test) \n print( 'R2: {:.6f}'.format(r2) )\n \n return\n\n\n\n\n\n\n","sub_path":"idora/idora/models/gbm.py","file_name":"gbm.py","file_ext":"py","file_size_in_byte":6163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"234501722","text":"\"\"\"Get assignment data from the db.\"\"\"\nfrom graph_curation.db import db_nomenclature as _db_nomenclature\nfrom graph_curation.db import db_objects as _db_objects\n\n\ndef get_tasks_by_chapter_query(chapter_key):\n \"\"\"Query users, chapters, tasks in the db.\n\n Returns\n -------\n dict\n return all users, chapters, tasks.\n\n \"\"\"\n return \"\"\"\n LET tasks = (\n FOR task IN {tasks_collection}\n FILTER task.chapter_key == \"{chapter_key}\"\n RETURN {{\n \"task_key\": task._key,\n \"status\": task.status,\n \"assigned_to\": task.assigned_to,\n \"chapter_key\": task.chapter_key,\n \"chapter\": task.chapter,\n \"assigned_time\": task.assigned_time\n }}\n )\n RETURN {{\n tasks: tasks\n }}\n \"\"\".format(\n tasks_collection=_db_nomenclature.TASK_COLLECTION,\n chapter_key=chapter_key\n )\n\n\ndef get_tasks_by_chapter_response(chapter_key):\n \"\"\"Query all assignment in the db.\n\n Returns\n -------\n api_output_pb2.GetAllUsers\n return all users username,first name,last name,email.\n\n \"\"\"\n query_response = _db_objects.graph_db().AQLQuery(\n get_tasks_by_chapter_query(chapter_key)\n ).response\n if query_response['error'] or len(query_response['result']) is 0:\n return {\"is_successful_execution\": False}\n return query_response['result'][0]\n","sub_path":"graph_curation/apis/get_tasks_by_chapter.py","file_name":"get_tasks_by_chapter.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"61969936","text":"#!/usr/bin/env python\n#coding:utf-8\n\n\"\"\"UDP昨日时间服务器\"\"\"\nfrom socket import socket\nfrom socket import AF_INET, SOCK_DGRAM\nfrom socket import SOL_SOCKET, SO_REUSEADDR\nimport time\nimport struct\nimport traceback\n\n\nhost = \"\"\nport = 12345\n\n# 步骤一\ns = socket(AF_INET, SOCK_DGRAM)#设定通信类型和\n\n# 步骤二\ns.setsockopt(SOL_SOCKET, SO_REUSEADDR,1)\n\n# 步骤三\ns.bind((host,port))\nprint(\"等待连接\")\n\nwhile True:\n try:\n msg,addr = s.recvfrom(8192)\n print(\"连接来自\")\n print(addr)\n #实现时间功能\n secs = int(time.time())\n secs -= 60*60*24\n secs += 2208988800\n reply = struct.pack(\"!I\",secs)\n #实现时间功能结束\n print(\"准备返回发送\")\n s.sendto(reply,addr)\n print(\"发送结束\")\n except (KeyboardInterrupt,SystemExit):\n raise\n except:\n traceback.print_exc()","sub_path":"Python_STD_LIB/网络编程/socket编程/C:S模型网络/socket/exp6.1/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"503790682","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 7 15:33:36 2020\n\n@author: kemcrimmins\n\"\"\"\n\ndef dotProduct(listA, listB):\n '''\n listA: a list of numbers\n listB: a list of numbers of the same length as listA\n '''\n # Your code here\n \n answer = 0\n \n for pair in range(len(listA)):\n answer += listA[pair] * listB[pair]\n \n return answer\n","sub_path":"6.00.1x/midterm/dot_product.py","file_name":"dot_product.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"268940006","text":"import cv2\r\nimport argparse\r\nimport numpy as np\r\n\r\n\r\nap = argparse.ArgumentParser()\r\nap.add_argument(\"-i\", \"--image\", help=\"Path to input image\", required=True)\r\nap.add_argument(\"-p\", \"--pivot-point\", help=\"Pivot point coordinates x, y separated by comma (,)\", required=True)\r\nap.add_argument(\"-s\", \"--scale\", help=\"Scale to zoom\", type=int, required=True)\r\nargs = vars(ap.parse_args())\r\n\r\nimage_path = args[\"image\"]\r\nx, y = map(int, args[\"pivot_point\"].split(\",\"))\r\nscale = args[\"scale\"]\r\nimage = cv2.imread(image_path)\r\n#image = image.tolist()\r\n\r\n#################################################################################################################\r\n#My Code Starts here\r\n#################################################################################################################\r\n\r\n\r\n\r\nb = image\r\nm,n,_ = b.shape\r\n\r\n'''\r\nFind the offset of the image, where it should be cropped. \r\n'''\r\n\r\nrow_delta = (m/scale)/2\r\ncolumn_delta = (n/scale)/2\r\nx_offset_top = x - row_delta - 2\r\nx_offset_bottom = x + row_delta + 2\r\ny_offset_left = y - column_delta - 2\r\ny_offset_right = y + column_delta + 2\r\n\r\n\r\n'''\r\nIf the corners of the crop fall beyond the dimensions of the original image, push the pivot appropriately so that corner is covered. \r\n'''\r\n\r\nif x_offset_top<0:\r\n x_offset_bottom += -1*(x_offset_top)\r\n x_offset_top = 0 \r\nif x_offset_bottom>m:\r\n x_offset_top -= (x_offset_bottom-m)\r\n x_offset_bottom = m\r\nif y_offset_left<0:\r\n y_offset_right += -1*(y_offset_left)\r\n y_offset_left = 0\r\nif y_offset_right>n:\r\n y_offset_left -= (y_offset_right-n)\r\n y_offset_right = n\r\n\r\n\r\n'''\r\nImage is cropped, so that only pixels surrounding the pivot is included. Once scaled, the cropped image should be of same dimensions as original.\r\nCalculations are localised to only those pixels which are required for scaling the image at the pivot.\r\n'''\r\ncrop = b[x_offset_top:x_offset_bottom,y_offset_left:y_offset_right]\r\ncrop_m,crop_n,_ = crop.shape\r\n\r\n\r\n'''\r\nx,y will remain same as given pivot if no overflow happens. \r\nElse, the pivot is shifted to include the corners of the image, as explained above.\r\n'''\r\nx,y = (x_offset_bottom+x_offset_top)/2,(y_offset_left+y_offset_right)/2\r\n\r\n\r\n\r\n'''\r\nK-TIMES ZOOMING ALGO\r\n'''\r\n\r\nout = np.zeros((crop_m,scale*(crop_n-1)+1,3),dtype=np.int16)\r\nout[:,::scale] = crop\r\n\r\nfor row in range(0,out.shape[0]):\r\n for column in range(0,out.shape[1]-scale,scale):\r\n for channel in range(3):\r\n diff = out[row,column,channel] - out[row,column+scale,channel]\r\n if diff>0:\r\n op = diff//scale\r\n for mid_elem in range(0,scale-1):\r\n out[row,column+mid_elem+1,channel] = out[row,column+mid_elem,channel] - op\r\n else:\r\n op = -1*diff//scale\r\n for mid_elem in range(0,scale-1):\r\n out[row,column+mid_elem+1,channel] = out[row,column+mid_elem,channel] + op\r\n\r\n\r\nout_final = np.zeros((scale*(crop_m-1)+1,scale*(crop_n-1)+1,3),dtype=np.int16)\r\nout_final[::scale,:,:] = out\r\n\r\nfor column in range(0,out_final.shape[1]):\r\n for row in range(0,out_final.shape[0]-scale,scale):\r\n for channel in range(0,3):\r\n diff = out_final[row,column,channel] - out_final[row+scale,column,channel]\r\n if diff>0:\r\n op = diff//scale\r\n for mid_elem in range(0,scale-1):\r\n out_final[row+mid_elem+1,column,channel] = out_final[row+mid_elem,column,channel] - op\r\n else:\r\n op = -1*diff//scale\r\n for mid_elem in range(0,scale-1):\r\n out_final[row+mid_elem+1,column,channel] = out_final[row+mid_elem,column,channel] + op\r\n\r\n\r\n'''\r\nAll co-ordinates whose value is equal to pixel values for given pivot.\r\n'''\r\nfinal_shape = out_final.shape\r\npivot_points = []\r\nfor i in range(out_final.shape[0]):\r\n for j in range(out_final.shape[1]):\r\n if out_final[i,j,0] == b[x,y,0] and out_final[i,j,1] == b[x,y,1] and out_final[i,j,2] == b[x,y,2]: \r\n pivot_points.append((i,j))\r\n\r\n\r\n'''\r\nFind the co-ordinate of the value (equal to pivot pixel) closest to the center of the image. \r\n'''\r\nimage_center = (m/2,n/2)\r\nmin = 100000\r\nfor index in range(len(pivot_points)):\r\n pivot = pivot_points[index]\r\n diff_y,diff_x = pivot[0] - image_center[0],pivot[1] - image_center[1]\r\n diff_y,diff_x = diff_y*diff_y,diff_x*diff_x\r\n if (diff_x+diff_y) x_offset:\r\n out_final = out_final[y_offset:,x_offset:-1*(final_shape[1]-n-x_offset)] \r\n if (final_shape[1] - n) == x_offset and (final_shape[0] - m) > y_offset:\r\n out_final = out_final[y_offset:-1*(final_shape[0]-m-y_offset),x_offset:]\r\n if (final_shape[1] - n) > x_offset and (final_shape[0] - m) > y_offset:\r\n out_final = out_final[y_offset:-1*(final_shape[0]-m-y_offset),x_offset:-1*(final_shape[1]-n-x_offset)]\r\n\r\n\r\n#################################################################################################################\r\n#My Code Ends here\r\n#################################################################################################################\r\n\r\ncv2.imwrite(\"zoomed_image.jpg\", np.array(out_final, dtype=\"uint8\"))\r\n","sub_path":"Image_zooming.py","file_name":"Image_zooming.py","file_ext":"py","file_size_in_byte":5566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"568377067","text":"# https://github.com/tpope/vim-unimpaired\nfrom sublime_plugin import TextCommand\n\nfrom NeoVintageous.lib.api import plugin\n\n\n__all__ = [\n 'NeovintageousUnimpairedBlankUpCommand',\n 'NeovintageousUnimpairedBlankDownCommand',\n 'NeovintageousUnimpairedMoveUpCommand',\n 'NeovintageousUnimpairedMoveDownCommand'\n]\n\n\n# LINE OPERATIONS *unimpaired-lines*\n# ------------------------------------------------------------------\n\n@plugin.register(seq='[', modes=(plugin.modes.NORMAL,))\nclass UnimpairedBlankUp(plugin.ViOperatorDef):\n def translate(self, state):\n return {\n 'action': 'neovintageous_unimpaired_blank_up',\n 'action_args': {\n 'mode': state.mode,\n 'count': state.count\n }\n }\n\n\n@plugin.register(seq=']', modes=(plugin.modes.NORMAL,))\nclass UnimpairedBlankDown(plugin.ViOperatorDef):\n def translate(self, state):\n return {\n 'action': 'neovintageous_unimpaired_blank_down',\n 'action_args': {\n 'mode': state.mode,\n 'count': state.count\n }\n }\n\n\n@plugin.register(seq='[e', modes=(plugin.modes.NORMAL,))\nclass UnimpairedMoveUp(plugin.ViOperatorDef):\n def translate(self, state):\n return {\n 'action': 'neovintageous_unimpaired_move_up',\n 'action_args': {\n 'mode': state.mode,\n 'count': state.count\n }\n }\n\n\n@plugin.register(seq=']e', modes=(plugin.modes.NORMAL,))\nclass UnimpairedMoveDown(plugin.ViOperatorDef):\n def translate(self, state):\n return {\n 'action': 'neovintageous_unimpaired_move_down',\n 'action_args': {\n 'mode': state.mode,\n 'count': state.count\n }\n }\n\n\n# Add [count] blank lines above the cursor.\nclass NeovintageousUnimpairedBlankUpCommand(TextCommand):\n def run(self, edit, mode=None, count=1):\n new_sels = []\n for sel in self.view.sel():\n line = self.view.line(sel)\n new_sels.append(self.view.find('[^\\\\s]', line.begin()).begin() + count)\n self.view.insert(\n edit,\n line.begin() - 1 if line.begin() > 0 else 0,\n '\\n' * count\n )\n\n self.view.sel().clear()\n self.view.sel().add_all(new_sels)\n\n\n# Add [count] blank lines below the cursor.\nclass NeovintageousUnimpairedBlankDownCommand(TextCommand):\n def run(self, edit, mode=None, count=1):\n end_point = self.view.size()\n new_sels = []\n for sel in self.view.sel():\n line = self.view.line(sel)\n new_sels.append(self.view.find('[^\\\\s]', line.begin()).begin())\n self.view.insert(\n edit,\n line.end() + 1 if line.end() < end_point else end_point,\n '\\n' * count\n )\n\n self.view.sel().clear()\n self.view.sel().add_all(new_sels)\n\n\n# Exchange the current line with [count] lines above it.\nclass NeovintageousUnimpairedMoveUpCommand(TextCommand):\n def run(self, edit, mode=None, count=1):\n for i in range(count):\n self.view.run_command('swap_line_up')\n\n\n# Exchange the current line with [count] lines below it.\nclass NeovintageousUnimpairedMoveDownCommand(TextCommand):\n def run(self, edit, mode=None, count=1):\n for i in range(count):\n self.view.run_command('swap_line_down')\n","sub_path":"lib/extras/unimpaired.py","file_name":"unimpaired.py","file_ext":"py","file_size_in_byte":3488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"529720561","text":"# -*- coding: utf-8 -*-\n# !@time: 2020/6/10 下午10:30\n# !@author: superMC @email: 18758266469@163.com\n# !@fileName: video.py\nimport time\n\nimport cv2\nimport torch\nfrom torch.backends import cudnn\nfrom torchreid.utils import FeatureExtractor\n\nfrom fid.insightFace.faceNet import FaceNet\nfrom fid.mtcnn.mtcnn import MTCNN\nfrom fid.retinaFace.detector import Detector as RetinaFace\nfrom pid.yolov5.yolov5 import YoloV5\nfrom self_utils.person_utils import generate_person, compression_person, update_person\nfrom self_utils.image_tool import plot_boxes\n\ncudnn.benchmark = True\ntorch.set_grad_enabled(False)\n\n\ndef main():\n yolo = YoloV5()\n reid = FeatureExtractor(\n model_name='osnet_x1_0',\n model_path='pid/deep_person_reid/checkpoints/osnet_x1_0_market_256x128_amsgrad_ep150_stp60_lr0.0015_b64_fb10_softmax_labelsmooth_flip.pth',\n verbose=False)\n detector = RetinaFace(image_size=(720, 1280))\n # detector = MTCNN()\n faceNet = FaceNet()\n person_cache = []\n cap = cv2.VideoCapture('data/1080p.mp4')\n fps = cap.get(cv2.CAP_PROP_FPS)\n speed = 1\n size = (\n int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),\n int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n )\n\n videoWriter = cv2.VideoWriter(\n \"data/output.avi\",\n cv2.VideoWriter_fourcc(*'MJPG'), # 编码器\n fps / speed,\n size\n )\n frame_num = 0\n index = 0\n compress_time = 1000\n vis = True\n while cap.isOpened():\n start_time = time.time() # start time of the loop\n frame_num += 1\n ret, frame = cap.read()\n if frame_num % speed != 0:\n continue\n\n if not ret:\n break\n\n person_images, person_boxes = yolo(frame)\n if person_boxes:\n face_features, face_boxes = None, None\n person_features = reid(person_images).cpu().detach()\n face_images, face_boxes = detector(frame)\n if len(face_boxes) > 0:\n face_features = faceNet(face_images)\n cur_person_dict = generate_person(person_features, person_boxes, face_features, face_boxes)\n person_cache, cur_person_dict, index = update_person(index, person_cache, cur_person_dict)\n frame = plot_boxes(frame, cur_person_dict, fps)\n\n if frame_num % compress_time == 0:\n person_cache = compression_person(person_cache)\n \n # q键退出\n if vis:\n cv2.imshow('frame', frame)\n k = cv2.waitKey(1)\n if k & 0xff == ord('q'):\n break\n videoWriter.write(frame)\n if frame_num % (10 * speed) == 0:\n print(\"FPS: \", 1.0 / (time.time() - start_time)) # FPS = 1 / time to process loop\n\n cap.release()\n videoWriter.release()\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":2833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"74896175","text":"'''\nCreated on 5 mai 2015\n\n@author: SkynaCrow\n'''\n\n\nclass SQLite3Crud(object):\n '''\n classdocs\n '''\n\n\n def __init__(self, dbAdaptater):\n '''\n Constructor\n '''\n #assert isinstance(sqlite_db, SQLite3DB)\n self.sqlite_db = dbAdaptater\n \n def insert(self, table_name, **column_and_values):\n sql = \"INSERT INTO %s \" % table_name.lower() \n first = True\n column = '('\n values_pattern = 'VALUES ('\n values = []\n for col, val in column_and_values.items():\n if first:\n column += col\n values_pattern += '?'\n first = False\n else:\n column += ', ' + col\n values_pattern += ', ?'\n values.append(val)\n sql+= column + ') ' + values_pattern + ')'\n values = tuple(values)\n print(sql)\n self.sqlite_db.execute(sql, values)\n \n def update(self, table_name, list_column_maj, **column_and_values):\n column_maj = ', '.join(list_column_maj)\n \n sql = \"UPDATE \" + table_name + \" SET \" + column_maj + \" WHERE \"\n values = []\n first = True\n for col, val in column_and_values.items():\n if not(first) :\n sql += ' AND '\n sql += col + ' = ?'\n values.append(val)\n first = False\n values = tuple(values)\n print(sql)\n self.sqlite_db.execute(sql, values)\n\n \n def delete(self, table_name, **column_and_values):\n sql = \"DELETE FROM %s WHERE \" % table_name\n values = []\n first = True\n \n for col, val in column_and_values.items():\n if not(first):\n sql += ' AND '\n sql += col + ' = ?'\n values.append(val)\n first = False\n\n values = tuple(values)\n print(sql)\n self.sqlite_db.execute(sql, values)\n \n \n\n#insert into site values (\"S1\",\"desc 1\");\n#insert into unite_stratigraphique values(\"US1\",\"S1\"); \n#if __name__ == '__main__':\n \n #test = SQLite3Crud()\n #test.update('test', ['name=\\'veis\\''], id=1)\n #test.insert('test', name='veis', age='20', codepostal='62440')\n \n #test.delete('test', id=2)\n ","sub_path":"src/onicer/model/orm/sqlite3/sqlite3_crud.py","file_name":"sqlite3_crud.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"342616063","text":"from datetime import datetime\n\nfrom django.contrib.gis.db import models\nfrom django.contrib.gis.geos import Point\nfrom django.core.urlresolvers import reverse\nfrom django.db import IntegrityError\nfrom django.db.models import Q\nfrom django.forms.models import model_to_dict\n\nimport pickle\nimport utils\n\n\nclass ProposalManager(models.GeoManager):\n def latest(self):\n results = self.order_by(\"-created\")\n return results and results[0]\n\n def between(self, start=None, end=None):\n q = None\n\n if start:\n q = Q(created__gte=start)\n\n if end:\n endQ = Q(closed_lte=end)\n\n if q:\n q = Q & endQ\n else:\n q = endQ\n\n return self.filter(q)\n\n def build_query(self, params):\n \"Construct a query from parameters\"\n pass\n\n def for_parcel(self, parcel):\n return self.filter(location__within=parcel.shape)\n\n\ndef make_property_map():\n def _g(p):\n return lambda d: d.get(p, \"\")\n\n def _G(p):\n return lambda d: d[p]\n\n return [(\"address\", _G(\"address\")),\n (\"location\", lambda d: Point(d[\"long\"], d[\"lat\"])),\n (\"summary\", lambda d: d.get(\"summary\", \"\")[0:1024]),\n (\"description\", _g(\"description\")),\n (\"source\", _g(\"source\")),\n (\"region_name\", _g(\"region_name\")),\n (\"updated\", _G(\"updated_date\")),\n (\"complete\", _G(\"complete\"))]\n\nproperty_map = make_property_map()\n\n\nclass Proposal(models.Model):\n case_number = models.CharField(max_length=64,\n unique=True,\n help_text=(\"The unique case number \"\n \"assigned by the city\"))\n address = models.CharField(max_length=128,\n help_text=\"Street address\")\n location = models.PointField(help_text=\"The latitude and longitude\")\n region_name = models.CharField(max_length=128,\n default=\"Somerville, MA\",\n null=True,\n help_text=\"\")\n # The time when the proposal was last saved:\n modified = models.DateTimeField(auto_now=True)\n # The last time that the source was changed:\n updated = models.DateTimeField()\n created = models.DateTimeField(auto_now_add=True)\n summary = models.CharField(max_length=1024, default=\"\")\n description = models.TextField(default=\"\")\n source = models.URLField(null=True,\n help_text=\"The data source for the proposal.\")\n status = models.CharField(max_length=64)\n\n # A proposal can be associated with a Project:\n project = models.ForeignKey(\"project.Project\", blank=True, null=True)\n # A misnomer; if True, indicates that the proposal has been approved:\n complete = models.BooleanField(default=False)\n\n # To enable geo queries\n objects = ProposalManager()\n\n def get_absolute_url(self):\n return reverse(\"view-proposal\", kwargs={\"pk\": self.pk})\n\n def document_for_field(self, field):\n return self.document_set.filter(field=field)\n\n @classmethod\n def create_or_update_proposal_from_dict(kls, p_dict):\n \"\"\"\n Constructs a Proposal from a dictionary. If an existing proposal has a\n matching case number, update it from p_dict.\"\"\"\n try:\n proposal = kls.objects.get(case_number=p_dict[\"case_number\"])\n created = False\n except kls.DoesNotExist:\n proposal = kls(case_number=p_dict[\"case_number\"])\n created = True\n\n changed = not created\n if changed:\n prop_changes = []\n\n for p, fn in property_map:\n old_val = changed and getattr(proposal, p)\n try:\n val = fn(p_dict)\n if changed and val != old_val:\n prop_changes.append({\"name\": p,\n \"new\": val,\n \"old\": old_val})\n setattr(proposal, p, fn(p_dict))\n except Exception as exc:\n if old_val:\n continue\n raise Exception(\"Missing required property: %s\\n Reason: %s\" %\n (p, exc))\n\n proposal.save()\n\n # Create associated documents:\n for field, val in p_dict.items():\n if not isinstance(val, dict) or not val.get(\"links\"):\n continue\n\n for link in val[\"links\"]:\n try:\n doc = proposal.document_set.get(url=link[\"url\"])\n except Document.DoesNotExist:\n doc = Document(proposal=proposal)\n\n doc.url = link[\"url\"]\n doc.title = link[\"title\"]\n doc.field = field\n doc.published = p_dict[\"updated_date\"]\n\n doc.save()\n\n if changed:\n attr_changes = []\n for attr_name, attr_val in p_dict.get(\"attributes\", []):\n try:\n handle = utils.normalize(attr_name)\n attr = proposal.attributes.get(handle=handle)\n old_val = attr.text_value\n except Attribute.DoesNotExist:\n proposal.attributes.create(name=attr_name,\n handle=handle,\n text_value=attr_val,\n published=p_dict[\"updated_date\"])\n old_val = None\n if changed:\n attr_changes.append({\"name\": attr_name,\n \"old\": old_val,\n \"new\": attr_val})\n\n if changed:\n changeset = Changeset.from_changes(proposal,\n {\"properties\": prop_changes,\n \"attributes\": attr_changes})\n changeset.save()\n\n return (created, proposal)\n\n\nclass Attribute(models.Model):\n \"\"\"\n Arbitrary attributes associated with a particular proposal.\n \"\"\"\n proposal = models.ForeignKey(Proposal, related_name=\"attributes\")\n name = models.CharField(max_length=128)\n handle = models.CharField(max_length=128, db_index=True)\n\n # Either the date when the source document was published or the date\n # when the attribute was observed:\n published = models.DateTimeField()\n text_value = models.TextField(null=True)\n date_value = models.DateTimeField(null=True)\n\n # class Meta:\n # unique_together = (\"proposal\", \"handle\")\n\n def to_dict(self):\n return {\"name\": self.name,\n \"handle\": self.handle,\n \"value\": self.text_value or self.date_value}\n\n def set_value(self, v):\n if isinstance(v, str):\n self.text_value = v\n elif isinstance(v, datetime):\n self.date_value = v\n\n def clear_value(self):\n self.text_value = None\n self.date_value = None\n\n @property\n def value(self):\n return self.text_value or \\\n self.date_value\n\n\nclass Event(models.Model):\n \"\"\"\n Meeting or hearing associated with a proposal.\n \"\"\"\n title = models.CharField(max_length=256, db_index=True)\n date = models.DateTimeField(db_index=True)\n duration = models.DurationField(null=True)\n description = models.TextField()\n proposals = models.ManyToManyField(Proposal, related_name=\"proposals\")\n\n def to_dict(self):\n return model_to_dict(self, exclude=[\"proposals\"])\n\n\nclass Document(models.Model):\n \"\"\"\n A document associated with a proposal.\n \"\"\"\n proposal = models.ForeignKey(Proposal)\n event = models.ForeignKey(Event, null=True,\n help_text=\"Event associated with this document\")\n url = models.URLField()\n title = models.CharField(max_length=256,\n help_text=\"The name of the document\")\n field = models.CharField(max_length=256,\n help_text=(\"The field in which the document\"\n \" was found\"))\n # Record when the document was first observed:\n created = models.DateTimeField(auto_now_add=True)\n\n # If available: when the document was published.\n published = models.DateTimeField(null=True)\n\n # If the document has been copied to the local filesystem:\n document = models.FileField(null=True)\n\n # File containing extracted text of the document:\n fulltext = models.FileField(null=True)\n encoding = models.CharField(max_length=20, default=\"\")\n # File containing a thumbnail of the document:\n thumbnail = models.FileField(null=True)\n\n class Meta:\n # Ensure at the DB level that documents are not duplicated:\n unique_together = ((\"proposal\", \"url\"))\n\n def get_absolute_url(self):\n return reverse(\"view-document\", kwargs={\"pk\": self.pk})\n\n def to_dict(self):\n d = model_to_dict(self, exclude=[\"event\", \"document\",\n \"fulltext\", \"thumbnail\"])\n if self.thumbnail:\n d[\"thumb\"] = self.thumbnail.url\n\n if self.document:\n d[\"url\"] = self.document.url\n\n return d\n\n def get_text(self):\n with open(self.fulltext.path, \"r\", encoding=self.encoding) as f:\n return f.read()\n\n @property\n def local_path(self):\n return self.document and self.document.path or \"\"\n\n move_file = utils.make_file_mover(\"document\")\n\n\nclass Image(models.Model):\n \"\"\"An image associated with a document.\n \"\"\"\n proposal = models.ForeignKey(Proposal, related_name=\"images\")\n document = models.ForeignKey(Document, null=True,\n help_text=\"Source document for image\")\n image = models.FileField(null=True)\n thumbnail = models.FileField(null=True)\n url = models.URLField(null=True, unique=True, max_length=512)\n # Crude way to specify that an image should not be copied to the\n # local filesystem:\n skip_cache = models.BooleanField(default=False)\n priority = models.IntegerField(default=0, db_index=True)\n source = models.CharField(max_length=64, default=\"document\")\n created = models.DateTimeField(auto_now_add=True)\n\n class Meta:\n unique_together = ((\"proposal\", \"image\"))\n\n def get_url(self):\n return self.image and self.image.url or self.url\n\n def to_dict(self):\n return {\"id\": self.pk,\n \"src\": self.get_url(),\n \"thumb\": self.thumbnail.url if self.thumbnail else None}\n\n\nclass Changeset(models.Model):\n \"\"\"\n Model used to record the changes to a Proposal over time.\n \"\"\"\n proposal = models.ForeignKey(Proposal, related_name=\"changes\")\n created = models.DateTimeField(auto_now_add=True)\n change_blob = models.BinaryField()\n\n @classmethod\n def from_changes(kls, proposal, changes):\n instance = kls(proposal=proposal)\n instance.changes = changes\n return instance\n\n @property\n def changes(self):\n # { \"properties\": [ { } ] ,\n # \"attributes\": [ { } ] }\n d = getattr(self, \"_change_dict\", None)\n if not d:\n d = pickle.loads(self.change_blob) if self.change_blob else {}\n self._change_dict = d\n return d\n\n @changes.setter\n def changes(self, d):\n self._change_dict = d\n self.change_blob = pickle.dumps(d)\n","sub_path":"server/proposal/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"653778940","text":"\"\"\"toolbar.py:\n\nAdd toolbar\n\n\"\"\"\n\n__author__ = \"Me\"\n__copyright__ = \"Copyright 2016, Me\"\n__credits__ = [\"NCBS Bangalore\"]\n__license__ = \"GNU GPL\"\n__version__ = \"1.0.0\"\n__maintainer__ = \"Me\"\n__email__ = \"\"\n__status__ = \"Development\"\n\nimport sys\nimport os\nimport logging\n\ntry:\n import Tkinter as tk\n import ttk\nexcept Exception as e:\n import tkinter as tk\n import tkinter.ttk as ttk\n\nfrom PIL import Image, ImageTk\n\nimport moosegui._globals\n\n# After each section, put a separator.\ntoolbar = [['Cylinder', 'Cube'], ['Pool', 'BufPool'], ['Move', 'Exit']]\n\n\ndef iconRead(name):\n imgFilePath = os.path.join('icons', '%s.png' % name)\n img = Image.open(imgFilePath)\n img = img.resize((20, 20), Image.ANTIALIAS)\n # return tk.PhotoImage( file = imgFilePath )\n return ImageTk.PhotoImage(img)\n\ndef btnCallback( btn ):\n if btn.lower( ) == 'exit':\n raise SystemExit\n if btn.lower( ) == 'cube':\n logging.debug( \"Drawing cube and changing cursor \" )\n _globals.root_.config( cursor = 'wait' )\n print( btn )\n\nclass ToolBar(tk.Frame):\n\n def __init__(self, root):\n logging.info(\"Creating a toolbar\")\n tk.Frame.__init__(self, root )\n i = 0\n for tools in toolbar:\n for tool in tools:\n i += 1\n btnImg = iconRead(tool)\n btn = tk.Button( self\n , image=btnImg\n , command = lambda x = tool : btnCallback( x )\n )\n btn.image = btnImg\n btn.grid(row=i, column=0)\n i += 1\n sep = ttk.Separator( root , orient = tk.VERTICAL )\n sep.grid(row=i, column=0 )\n\n\ndef main(parent):\n toolbar = ToolBar( parent ) \n toolbar.grid( row = 1, column = 0, sticky = 'ns' )\n _globals.toolbar_ = toolbar\n","sub_path":"build/lib/moosegui/toolbar.py","file_name":"toolbar.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"191892476","text":"from __future__ import division\n\nfrom flipd.dsply import Dsply\nfrom flipd.dotbf import Dotbf\n\nclass Qst: # question state\n qscroll = 1\n postqpause = 2\n vscroll = 3\n vpause = 4\n nvscroll = 5\n preqpause = 6\n noq = 7\n\nclass Qdsply:\n\n def __init__(self):\n\n # left & right displays\n self.lftd = Dsply([1, 2, 3, 4])\n self.rtd = Dsply([5, 6, 7, 8])\n\n # buffers\n self.lqbf = None # left question buffer\n self.rqbf = None\n self.lftbf = Dotbf(self.lftd.wdth, self.lftd.hght)\n self.rtbf = Dotbf(self.rtd.wdth, self.rtd.hght)\n self.abf = None\n self.bbf = None\n self.lftbgbf = Dotbf(self.lftd.wdth, self.lftd.hght * 2)\n self.rtbgbf = Dotbf(self.rtd.wdth, self.rtd.hght * 2)\n self.lftrtiobf = Dotbf(self.lftd.wdth, self.lftd.hght)\n self.rtrtiobf = Dotbf(self.rtd.wdth, self.rtd.hght)\n\n ### states\n self.qst = Qst.noq # question state\n self.vst = False # vote buzzer state\n\n self.adpth = 0 # depth of a vote buzzer\n self.bdpth = 0 # depth of b vote buzzer\n self.rtio = 0.5\n\n self.qscroll = 0\n self.mxqscroll = 0\n self.vscroll = 0\n self.mxvscroll = 7\n self.pause = 8\n self.mxpause = 8\n\n def step(self, srl):\n \"\"\"step animation forwards\"\"\"\n\n if self.qst == Qst.noq:\n self.wipe(srl, False)\n return\n\n if self.vst:\n if self.adpth > 0 or self.bdpth > 0:\n self._render_ratio()\n else:\n self.vst = False\n self.qst = Qst.nvscroll\n self.vscroll = self.mxvscroll\n self.qscroll = 0\n self._writeq()\n return\n \n if self.adpth > 0:\n self.adpth -= 1\n self._buzza()\n\n if self.bdpth > 0:\n self.bdpth -= 1\n self._buzzb()\n\n self.lftd.render(srl, self.lftrtiobf)\n self.rtd.render(srl, self.rtrtiobf)\n return\n\n if self.qst == Qst.preqpause:\n if self.pause > 0:\n self.pause -= 1\n else:\n self.qst = Qst.qscroll\n self.qscroll = 0\n self._writeq()\n self.pause = self.mxpause\n\n if self.qst == Qst.qscroll:\n if self.qscroll >= self.mxqscroll:\n self.qst = Qst.postqpause\n else:\n self.qscroll += 1\n self._writeq()\n\n self.lftd.render(srl, self.lftbgbf, 0, 0)\n self.rtd.render(srl, self.rtbgbf, 0, 0)\n return\n\n if self.qst == Qst.postqpause:\n if self.pause > 0:\n self.pause -= 1\n else:\n self.qst = Qst.vscroll\n self.pause = self.mxpause\n\n if self.qst == Qst.vscroll:\n if self.vscroll == self.mxvscroll:\n self.qscroll = 0\n self._writeq()\n self.qst = Qst.vpause\n return\n\n self.vscroll += 1\n self.lftd.render(srl, self.lftbgbf, 0, self.vscroll)\n self.rtd.render(srl, self.rtbgbf, 0, self.vscroll)\n return\n\n if self.qst == Qst.vpause:\n if self.pause > 0:\n self.pause -= 1\n else:\n self.qst = Qst.nvscroll\n self.pause = self.mxpause\n \n if self.qst == Qst.nvscroll:\n if self.vscroll == 0:\n self.qst = Qst.preqpause\n return\n\n self.vscroll -= 1\n self.lftd.render(srl, self.lftbgbf, 0, self.vscroll)\n self.rtd.render(srl, self.rtbgbf, 0, self.vscroll)\n return \n\n\n def _buzza(self):\n aw = self.abf.wdth\n hlw = self.lftd.wdth // 2\n lap = (hlw - aw) // 2\n self.abf.flipmask(self.lftrtiobf, lap, 0)\n\n hrw = self.rtd.wdth // 2\n rap = hrw + ((hrw - aw) // 2)\n self.abf.flipmask(self.rtrtiobf, rap, 0)\n #print(\"lap:\", lap, \"rap:\", rap)\n\n def _buzzb(self):\n bw = self.bbf.wdth\n hlw = self.lftd.wdth // 2\n lbp = hlw + ((hlw - bw) // 2)\n self.bbf.flipmask(self.lftrtiobf, lbp, 0)\n\n hrw = self.rtd.wdth // 2\n rbp = (hrw - bw) // 2\n self.bbf.flipmask(self.rtrtiobf, rbp, 0)\n\n def _writeq(self):\n self.lqbf.writebf(\n self.lftbgbf, 0, 0, \n self.qscroll, 0, self.lftbgbf.wdth, self.lqbf.hght)\n self.rqbf.writebf(\n self.rtbgbf, 0, 0, \n self.qscroll, 0, self.rtbgbf.wdth, self.rqbf.hght)\n\n def _render_ratio(self):\n llst = int(self.lftd.wdth * self.rtio)\n for x in range(self.lftd.wdth):\n on = x < llst\n for y in range(self.lftd.hght):\n self.lftrtiobf[x, y] = on\n\n rlst = self.rtd.wdth - int(self.rtd.wdth * self.rtio)\n for x in range(self.rtd.wdth):\n on = x >= rlst\n for y in range(self.rtd.hght):\n self.rtrtiobf[x, y] = on\n\n\n def ask(self, q, a, b):\n \"\"\"ask new question\"\"\"\n\n # wipe old question\n self.lftbgbf.wipe()\n self.rtbgbf.wipe()\n\n # set left & right question buffers\n lq = rq = q\n if q == \"?\":\n lq = a + \" or \" + b + \"?\"\n rq = b + \" or \" + a + \"?\"\n\n lqbf = Dotbf(txt=lq)\n rqbf = Dotbf(txt=rq)\n if lqbf.wdth < self.lftd.wdth:\n self.lqbf = Dotbf(self.lftd.wdth)\n self.rqbf = Dotbf(self.lftd.wdth)\n dlta = (self.lqbf.wdth - lqbf.wdth) // 2\n lqbf.writebf(self.lqbf, dlta, 0)\n rqbf.writebf(self.rqbf, dlta, 0)\n self.mxqscroll = 0\n else:\n self.lqbf = lqbf\n self.rqbf = rqbf\n self.mxqscroll = self.lqbf.wdth - self.lftd.wdth\n print(\"mxqscroll:\", self.mxqscroll)\n\n self.qscroll = 0\n self._writeq()\n\n hld = self.lftd.wdth // 2\n self.abf = Dotbf(txt=a, txtmx=hld)\n self.bbf = Dotbf(txt=b, txtmx=hld)\n\n lap = (hld - self.abf.wdth) // 2\n self.abf.writebf(self.lftbgbf, lap, self.lftd.hght)\n lbp = hld + ((hld - self.bbf.wdth) // 2)\n self.bbf.writebf(self.lftbgbf, lbp, self.lftd.hght)\n\n hrd = self.rtd.wdth // 2\n rap = hrd + ((hrd - self.abf.wdth) // 2)\n self.abf.writebf(self.rtbgbf, rap, self.rtd.hght)\n rbp = (hrd - self.bbf.wdth) // 2\n self.bbf.writebf(self.rtbgbf, rbp, self.rtd.hght)\n \n self.qst = Qst.qscroll # start scrolling!\n\n def vote(self, a, ratio, dpth=20): # a -> bool, true if vote is for a\n \"\"\"add depth to a or b vote buzzer\"\"\"\n self.rtio = ratio\n self.vst = True\n if a:\n self.adpth += dpth\n else:\n self.bdpth += dpth\n\n def wipe(self, srl, white=True):\n Dsply.WIPE(srl, white)\n","sub_path":"qdsply.py","file_name":"qdsply.py","file_ext":"py","file_size_in_byte":6993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"328343169","text":"import json\nimport functools\nimport re\nfrom bottle import Bottle, redirect, jinja2_view, request\nfrom pymongo import MongoClient\nfrom bson.json_util import dumps\n\napp = Bottle()\nclient = MongoClient('localhost', 27017)\ndb = client['ro']\nview = functools.partial(jinja2_view, template_lookup=['templates'])\n\n\n@app.route('/', method='GET')\n@view('index.html')\ndef index():\n return {}\n\n@app.route('/api/search/')\ndef search():\n if request.GET.get('search_by'):\n filters = {}\n if request.GET.get('search_by') == 'queue':\n if request.GET.get('queue_id') and request.GET.get('year'):\n if request.GET.get('queue_id'):\n filters['queue_id'] = request.GET.get('queue_id')\n if request.GET.get('year'):\n filters['year'] = request.GET.get('year')\n else:\n return json.dumps({'status': 'error', 'message': 'Trebuie mai multa data'})\n elif request.GET.get('search_by') == 'name':\n if request.GET.get('first_name') or request.GET.get('last_name'):\n if request.GET.get('first_name'):\n filters['first_name'] = re.compile('.*' + re.escape(request.GET.get('first_name').decode('utf-8')) + '.*', re.IGNORECASE)\n if request.GET.get('last_name'):\n filters['last_name'] = re.compile('.*' + re.escape(request.GET.get('last_name').decode('utf-8')) + '.*', re.IGNORECASE)\n else:\n return json.dumps({'status': 'error', 'message': 'Trebuie mai multa data'})\n else:\n return json.dumps({'status': 'error', 'message': 'Trebuie mai multa data'})\n\n result = json.loads(dumps(db.record.find(filters)))\n\n return json.dumps({'status': 'success', 'results': result})\n else:\n return json.dumps({'status': 'error', 'message': 'Trebuie mai multa data'})\n\n\nif __name__ == \"__main__\":\n app.run(host='localhost', port=8000, debug=True, reloader=True)\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"146176527","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 18 mars 2017\n\n@author: Robin LERUTH ; Thibault PAYERNE\n'''\n\nfrom random import shuffle\nfrom random import randint\nfrom enum import IntEnum\n\n\nclass Card:\n\n def __init__(self, color, value):\n self._color = color\n self._value = value\n\n def __str__(self):\n return (\"\", \"\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"J\", \"Q\", \"K\", \"A\")[self._value] + (\"\\u2660\", \"\\u2665\", \"\\u2666\", \"\\u2663\")[self._color]\n\n def __repr__(self):\n return self.__str__()\n\nclass Deck:\n\n def __init__(self):\n self._liste = []\n for color in range(0, 4) :\n for value in range(2, 15) :\n card = Card(color, value)\n self._liste.append(card)\n\n def shuffle(self):\n shuffle(self._liste)\n\n def remove(self, cards):\n for i in range(0,len(self._liste)):\n if(str(cards) == str(self._liste[i])):\n self._liste.remove(self._liste[i])\n break\n\n def get_card(self):\n card = self._liste.pop()\n return card\n\n def __str__(self):\n ret = \"[\"\n for i in range(0,len(self._liste)):\n ret += \" \" + str(self._liste[i])\n return ret\n\ndef compare_games(game1, game2):\n # TODO\n a = Hand(game1)\n b = Hand(game2)\n #print()\n #print(a._game)\n #print(b._game)\n #print(a._score)\n #print(b._score)\n #print(a._force)\n #print(b._force)\n #try:\n # print(a._kicker)\n # print(b._kicker)\n #except:\n # pass\n\n if(a._score > b._score):\n #print(\"a gagne\")\n return -1\n elif(a._score < b._score):\n #print(\"b gagne\")\n return 1\n else:\n if a._force > b._force:\n #print(\"a gagne\")\n return -1\n elif a._force < b._force:\n return 1\n #print(\"b gagne\")\n else:\n try:\n if a._kicker > b._kicker:\n return -1\n elif a._kicker < b._kicker:\n return 1\n else:\n return 0\n except:\n return 0\n\ndef monte_carlo_probability(my_cards, flop=None, turn = None, river = None):\n\n #2 cartes + 3 cartes + 2X1 cartes\n counter_win=0\n counter_equal=0\n N=1000\n if (flop==None):\n f=1\n else:\n f=0\n\n if (turn==None):\n t=1\n else:\n t=0\n\n if (river==None):\n r=1\n else:\n r=0\n\n #Simulation de N événements\n for i in range (0, N) :\n\n #on crée un Deck\n deck = Deck()\n # On remove les cartes given in parameters\n deck.remove(my_cards[0])\n deck.remove(my_cards[1])\n\n if (flop!=None):\n for i in range (0, 3) :\n deck.remove(flop[i])\n if (turn!=None):\n deck.remove(turn)\n if (river!=None):\n deck.remove(river)\n\n #on shuffle le deck\n deck.shuffle()\n\n if (flop==None or f==1):\n flop=[]\n for i in range (0, 3) :\n c=deck.get_card()\n flop.append(c)\n if (turn==None or t==1):\n c=deck.get_card()\n turn=c\n\n if (river==None or r==1):\n c=deck.get_card()\n river=c\n\n\n #set randomly 2 cards for the opponent\n cards_opponent=[]\n for i in range (0, 2) :\n c=deck.get_card()\n cards_opponent.append(c)\n\n\n #4. Then use the compare _games function with these two lists\n game1=[]\n game1.append(my_cards[0])\n game1.append(my_cards[1])\n game1.append(flop[0])\n game1.append(flop[1])\n game1.append(flop[2])\n game1.append(turn)\n game1.append(river)\n\n game2=[]\n game2.append(cards_opponent[0])\n game2.append(cards_opponent[1])\n game2.append(flop[0])\n game2.append(flop[1])\n game2.append(flop[2])\n game2.append(turn)\n game2.append(river)\n\n\n #on compte le nb de victories\n #game1 -> notre jeu\n #si on notre jeu est meilleur\n #print(game1)\n #print(game2)\n #print()\n if (compare_games(game1,game2)==-1):\n counter_win=counter_win+1\n\n #si on notre jeu est moins bon\n if (compare_games(game1,game2)==0):\n counter_equal=counter_equal+1\n\n #end de la simulation de N événements\n #Probability to win\n proba_win=counter_win/N\n proba_equal=counter_equal/N\n result=[]\n result.append(proba_win)\n result.append(proba_equal)\n #on return le result\n return result\n\nclass PokerPlayer:\n\n '''\n\n DESCRIBE YOUR VERY SMALL STRATEGY HERE\n\n Stratégie :\n L'idée principale est de jouer en jouer en fonction de notre probabilité de gagner\n Autrement dit : c'est du 1vs1, si on a un avantage on le pousse en étant agressif\n De manière assez safe, nous avons fixé le seuil à une probabilité de 0.70\n Si l'on a une proba de gagner supérieure à 0.70, on mise en fonction de notre bankroll :\n Si bankroll <= 50 -> bet * 1\n Si bankroll >= 50 -> bet * 1.5\n Si bankroll >= 125 -> double les bet\n Si bankroll >= 150 -> triple les bet\n Sinon, si notre proba de Win est < à 0.70, on joue par défaut avec la\n Minimal strategy qui consiste à jouer si (pot + bet)* probaWin >= bet\n\n Cas particuliers : Bluff ou très bonne proba de l'adversaire de gagner :\n si notre proba < 0.70, on suit, sinon on fold\n Grande chance d'égalité, on fait Tapis # Thug Life\n '''\n\n def __init__(self):\n self._bankroll = 0\n self._winStreak = 0\n self._probaWin = 0\n self._probaEqual = 0\n\n def play(self, my_cards, my_bankroll, flop, turn, river, pot, bet):\n self._probaWin, self.probaEqual = monte_carlo_probability(my_cards, flop, turn, river)\n if self.probaWin >= 0.70:\n if bet > my_bankroll*(1/2) :\n return bet\n\n if my_bankroll <= 50:\n return bet\n\n if my_bankroll > 50 and my_bankroll <= 125:\n return bet*1.5\n\n if my_bankroll >125 and my_bankroll <= 150:\n return bet*2\n\n if my_bankroll > 150:\n return bet*3\n\n else :\n 'Si notre proba de gagner est <0.70 '\n if bet > my_bankroll*(1/2) :\n return bet\n\n if self._probaEqual >= 0.70:\n return my_bankroll\n\n 'Par défaut : minimal strategy '\n if minimal_strategy(pot, bet, my_cards, flop, turn, river):\n return bet\n\n else:\n return 0\n\n def __str__(self):\n return \"Robin Leruth and Thibault Payerne\"\n\n def minimal_strategy(pot, bet, my_cards, flop, turn, river):\n self._probaWin, self._probaEqual = monte_carlo_probability(my_cards, flop, turn, river)\n expectation = (pot + bet) * proba_win\n if bet < expectation:\n return True #on suit\n else:\n return False #on fold\n\nclass Hand():\n def __init__(self, game):\n self._force = 0\n self._score = 0\n self._handTool = [ 0 for i in range(0,15)]\n self._game = game\n self.__set_tool()\n self.__determine_hand()\n\n def __set_tool(self):\n for i in range(0, len(self._game)):\n if self._game[i]._value >= 1:\n self._handTool[self._game[i]._value] += 1\n if self._game[i]._value == 14:\n self._handTool[1] += 1 # c'est l'as\n #print(self._handTool)\n\n def __genereValue(self):\n for i in range(0,15):\n yield i\n\n #def __check_occurence(self):\n # cardValue = self.__genereValue()\n # compteur = 0\n # higherCompteur = 0\n # for value in cardValue:\n # for card in self._game:\n # if card._value == value:\n # compteur += 1\n # if compteur >= higherCompteur:\n # higherCompteur = compteur\n # if(compteur >= 2):\n # self._force = value\n # compteur = 0\n # return higherCompteur\n\n def __check_pair(self):\n pair = False\n for i in range(0, len(self._handTool)):\n if self._handTool[i] == 2:\n pair = True\n self._force = i\n return pair\n\n def __check_doublepair(self):\n doublepair = False\n compteur = 0\n for i in range(0, len(self._handTool)):\n if self._handTool[i] == 2:\n compteur += 1\n if compteur == 1:\n self._kicker = i\n self._force = i\n if compteur >= 2:\n #print(\"kicker :\" +str(self._kicker))\n doublepair = True\n else:\n self._kicker = 0\n return doublepair\n\n def __check_brelan(self):\n brelan = False\n for i in range(0, len(self._handTool)):\n if self._handTool[i] == 3:\n brelan = True\n self._force = i\n return brelan\n\n# def __check_brelan(self):\n# a = self.__check_occurence()\n# if a == 3:\n# return True\n# else:\n# return False\n\n def __check_quinte(self):\n quinte = False\n for i in range(0, len(self._handTool) - 5):\n if self._handTool[i] >= 1 and self._handTool[i+1] >= 1 and self._handTool[i+2] >= 1 and self._handTool[i+3] >= 1 and self._handTool[i+4] >= 1:\n self._force = i+4\n quinte = True\n return quinte\n\n #def __check_quinte(self):\n # compteur = 0\n # for i in range(0, len(self._game)-1):\n # if self._game[i+1]._value == self._game[i]._value + 1:\n # compteur += 1\n # self._force = self._game[i+1]._value\n # if compteur >= 5:\n # return True\n # else:\n # return False\n\n def __check_couleur(self):\n carreau = 0\n pique = 0\n coeur = 0\n trefle = 0\n for i in range(0, len(self._game)):\n if self._game[i]._color == 0:\n pique += 1\n if pique == 5:\n self._force = self._game[i]._value\n self._couleur = \"pique\"\n elif self._game[i]._color == 1:\n carreau += 1\n if carreau == 5:\n self._force = self._game[i]._value\n self._couleur = \"carreau\"\n elif self._game[i]._color == 2:\n trefle += 1\n if trefle == 5:\n self._force = self._game[i]._value\n self._couleur = \"trefle\"\n elif self._game[i]._color == 3:\n coeur += 1\n if coeur == 5:\n self._force = self._game[i]._value\n self._couleur = \"coeur\"\n\n if carreau >= 5 or pique >= 5 or coeur >= 5 or trefle >= 5:\n return True\n else:\n return False\n\n\n def __check_full(self):\n if self.__check_brelan() and self.__check_pair():\n return True\n else:\n return False\n #def __check_full(self):\n # full = False\n # compteurDouble = 0\n # compteurBrelan = 0\n # for i in range(0, len(self._handTool)):\n # if self._handTool[i] == 2:\n # compteurDouble += 1\n # self._force = i\n # if self._handTool[i] == 3:\n # compteurDouble += 1\n # self._force = i\n # if compteurDouble >= 1 and compteurBrelan >= 1:\n # full = True\n # return full\n\n def __check_carre(self):\n carre = False\n for i in range(0, len(self._handTool)):\n if self._handTool[i] == 4:\n carre = True\n self._force = i\n return carre\n# def __check_carre(self):\n# a = self.__check_occurence()\n# if a == 4:\n# return True\n# else:\n# return False\n\n\n def __check_quinteflush(self):\n compteur = 1\n for i in range(0, len(self._game) - 1):\n if self._game[i]._value == self._game[i+1]._value - 1:\n if self._game[i]._color == self._game[i+1]._color:\n compteur += 1\n self._force = self._game[i+1]._value\n if compteur >= 5:\n return True\n else:\n return False\n\n #def __check_quinteflush(self):\n # compteur = 0\n # for i in range(1, len(self._game)):\n # if self._game[i-1]._value == self._game[i]._value - 1:\n # if self._game[i-1]._color == self._game[i]._color:\n # compteur += 1\n # self._force = self._game[i]._value\n # print(self._game[i]._value)\n # elif self._game[i-1]._value == self._game[i]._value:\n # try:\n # if self._game[i-1]._value == self._game[i+1]._value -1:\n # if self._game[i-1]._color == self._game[i+1]._color:\n # compteur += 1\n # self._force = self._game[i+1]._value\n # except:\n # pass\n\n # if compteur >= 5:\n # return True\n # else:\n # return False\n\n# def __check_quinteflush(self):\n# if self.__check_couleur() and self.__check_quinte():\n# return True\n# else:\n# return False\n\n def __check_quinteflushroyal(self):\n if self.__check_quinteflush() and self._force == 14:\n return True\n else:\n return False\n\n def __sort_game(self):\n self._game = sorted(self._game, key=lambda game: game._value)\n\n def __determine_hand(self):\n #self._score = Hand_Enum()\n self.__sort_game()\n if self.__check_quinteflushroyal():\n self._score = Hand_Enum.quinte_flush_royal\n return\n elif self.__check_quinteflush():\n self._score = Hand_Enum.quinte_flush\n return\n elif self.__check_carre():\n self._score = Hand_Enum.carre\n return\n elif self.__check_full():\n self._score = Hand_Enum.full\n return\n elif self.__check_couleur():\n self._score = Hand_Enum.couleur\n return\n elif self.__check_quinte():\n self._score = Hand_Enum.quinte\n return\n elif self.__check_brelan():\n self._score = Hand_Enum.brelan\n return\n elif self.__check_doublepair():\n self._score = Hand_Enum.two_pair\n return\n elif self.__check_pair():\n self._score = Hand_Enum.pair\n return\n else:\n self._force = self._game[len(self._game) - 1]._value\n self._score = Hand_Enum.high\n return\n\n def __sort_hands(self):\n pass\n\nclass Hand_Enum(IntEnum):\n high = 1\n pair = 2\n two_pair = 3\n brelan = 4\n quinte = 5\n couleur = 6\n full = 7\n carre = 8\n quinte_flush = 9\n quinte_flush_royal = 10\n\ndef genere_quinteflushroyal():\n game = []\n for i in range(6,14):\n card = Card(2, i)\n game.append(card)\n return game\n\nif __name__ == \"__main__\":\n\n # Exemple of Card class tests\n #liste = []\n\n #for color in range(0, 4) :\n # for value in range(2, 15) :\n # card = Card(color, value)\n # #print(card)\n # liste.append(card)\n\n #print(liste)\n\n # Write here your tests\n # Deck Class Test\n #a = Deck()\n #Shuffle\n #a.shuffle()\n #print(a)\n\n #Remove a card\n #a = Deck()\n #card = Card(0, 5)\n #print(card)\n #a.remove(card)\n\n #Get a card\n #a = Deck()\n #print(a)\n #card2 = a.get_card()\n #print(card2)\n #print(a)\n\n #compare_game test\n #game1 = []\n #game2 = []\n #for i in range(0,7):\n # x = randint(0,3)\n # y = randint(2,14)\n # card = Card(x,y)\n # game1.append(card)\n\n #for i in range(0,7):\n # x = randint(0,3)\n # y = randint(2,14)\n # card = Card(x,y)\n # game2.append(card)\n\n #print(game1)\n #print(game2)\n #game = genere_quinteflushroyal()\n #print(game)\n #a = Hand(game)\n #print(a._score)\n\n #Hand test\n #b = Hand(game1)\n #print(b._score)\n #c = Hand(game2)\n #print(c._score)\n #print(compare_games(game1, game2))\n\n #MonteCarlo test\n #deck = Deck()\n #card1=Card(2,11)\n #card2=Card(3,11)\n #my_cards=[]\n #my_cards.append(card1)\n #my_cards.append(card2)\n\n #deck.get_card()\n #deck.shuffle()\n #list_result=[]\n #list_result=monte_carlo_probability(my_cards)\n #print(list_result)\n\n '''\n Deck remove test\n '''\n deck = Deck()\n deck.shuffle()\n deck.remove(Card(2, 2))\n other_cards = []\n for i in range(0, 51) :\n other_cards.append(deck.get_card())\n deleted_card = [c for c in other_cards if c._color == 2 and c._value == 2]\n if len(deleted_card) != 0 :\n print(\"[Deck test] Your delete method does not work\")\n '''\n Test on compare_games\n \"the method returns -1 if game1 is better than game2, 0 if\n game1 is as strong as game2, and 1 if game2 is better than\n game1\"\n '''\n if not -1 == compare_games([Card(1, 2), Card(3, 13), Card(0,2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)],[Card(1, 11), Card(1, 14), Card(0, 2), Card(0,3), Card(1, 4),Card(1, 10), Card(2, 6)]):\n print(\"Error : pair is better than high card\")\n if not -1 == compare_games([Card(1, 2), Card(3, 10), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(1, 13), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : two pair is better than one pair\")\n if not -1 == compare_games([Card(1, 2), Card(3, 2), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(1, 10), Card(0, 2), Card(0,3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Three of a kind is better than two pair\")\n if not -1 == compare_games([Card(1, 2), Card(3, 5), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(1, 2), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Straight is better than Three of a kind\")\n if not -1 == compare_games([Card(0, 2), Card(0, 5), Card(0, 2), Card(0, 3), Card(0, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(3, 5), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Flush is better than straight\")\n if not -1 == compare_games([Card(0, 2), Card(0, 5), Card(0, 2), Card(0, 5), Card(0, 4), Card(1, 10), Card(2, 2)], [Card(0, 2), Card(0, 5), Card(0, 2), Card(0, 3), Card(0, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Full house is better than flush\")\n if not -1 == compare_games([Card(1, 2), Card(3, 2), Card(0, 2), Card(0, 2), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(0, 2), Card(0, 5), Card(0, 2), Card(0, 5), Card(0, 4), Card(1, 10), Card(2, 2)]):\n print(\"Error : Four of a kind is better than flush\")\n if not -1 == compare_games([Card(0, 2), Card(0, 3), Card(0, 4), Card(0, 5), Card(0, 6), Card(1, 10), Card(2, 2)], [Card(1, 2), Card(3, 2), Card(0, 2), Card(0, 2), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Straight flush is better than Four of a kind\")\n if not -1 == compare_games([Card(1, 3), Card(3, 13), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(3, 13), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : pair of 3 is better than pair of 2\")\n if not -1 == compare_games([Card(1, 2), Card(3, 10), Card(0, 3), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(3, 10), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : two pair of 10, 3 is better than two pair of 10, 2\")\n if not -1 == compare_games([Card(1, 4), Card(3, 4), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)], [Card(1, 2), Card(3, 2), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Three of a kind of 4 is better than three of a kind of 2\")\n if not -1 == compare_games([Card(1, 2), Card(3, 5), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 7), Card(2, 6)], [Card(1, 2), Card(3, 5), Card(0, 2), Card(0, 3), Card(1, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Straight of 7 is better than straight of 6\")\n if not -1 == compare_games([Card(0, 13), Card(0, 5), Card(0, 2), Card(0, 3), Card(0, 4), Card(1, 10), Card(2, 6)], [Card(0, 2), Card(0, 5), Card(0, 2), Card(0, 3), Card(0, 4), Card(1, 10), Card(2, 6)]):\n print(\"Error : Flush of King is better than flush of 5\")\n if not -1 == compare_games([Card(0, 2), Card(0, 6), Card(0, 2), Card(0, 6), Card(0, 4), Card(1, 10), Card(2, 2)], [Card(0, 2), Card(0, 5), Card(0, 2), Card(0, 5), Card(0, 4), Card(1, 10), Card(2, 2)]):\n print(\"Error : Full house of 2, 6 is better than full house of 2, 5\")\n '''\n Monte carlo probability\n '''\n# Should be between 0.80 and 0.90\n print(\"Proba paire As : \" + str(monte_carlo_probability([Card(1, 14), Card(0, 14)])))\n# Sh#ould be between 0.30 and 0.40\n print(\"Proba 2, 7 : \" + str(monte_carlo_probability([Card(1, 2), Card(0, 7)])))\n# Sh#ould be between 0.70 and 0.80\n print(\"Proba 10, 10 : \" + str(monte_carlo_probability([Card(1, 10), Card(0, 10)])))\n# Equality test : quinte flush is on table, proba is 1 to have an equality, (0 to win), whatever your hand\n print(\"Equality : \" + str(monte_carlo_probability([Card(1, 2), Card(0, 7)], [Card(1, 10), Card(1, 11), Card(1, 12)], Card(1, 13), Card(1, 14))))\n\n #game1 = []\n #game2 = []\n #game1.append(Card(0,14))\n #game1.append(Card(0,13))\n #game1.append(Card(0,12))\n #game1.append(Card(0,11))\n #game1.append(Card(0,10))\n #game1.append(Card(3,2))\n #game1.append(Card(3,8))\n\n #game2.append(Card(0,2))\n #game2.append(Card(0,3))\n #game2.append(Card(2,4))\n #game2.append(Card(2,5))\n #game2.append(Card(2,6))\n #game2.append(Card(2,9))\n #game2.append(Card(2,8))\n\n #print(compare_games(game1,game2))\n","sub_path":"poker_ia.py","file_name":"poker_ia.py","file_ext":"py","file_size_in_byte":22276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"319110005","text":"from tkinter import *\nfrom tkinter import ttk\nfrom PIL import ImageTk, Image\nfrom tkinter import filedialog\nimport os\n\n\nimport driver_backend\n\nfilename=''\nsave_dir='driver_images/'\nsave_img=''\nimage_path=''\n\n\"\"\" check for folder exist otherwise create it \"\"\"\nif not os.path.exists('driver_images'):\n os.makedirs('driver_images')\n\ndef save_image():\n #if not str(os.path.exists(filena)):\n #print(\"image path is\"+image_path)\n save_img.save(image_path,'JPEG')\n\n\n\ndef add_command():\n #print(\"image path = \"+image_path)\n driver_backend.insert(ID_text.get(),name_text.get(),vehicle_text.get(),plate_text.get(),location_text.get(),image_path)\n view_command()\n save_image()\n clear_textfield()\n\ndef view_command():\n tree.delete(*tree.get_children())\n for row in driver_backend.view():\n tree.insert(\"\",END,values=row)\n tree.column(\"#1\", width=0)\n listnames = list(row)\n\n\ndef get_selected_row(event):\n\n clear_textfield()\n global selected_tuple\n for selected_tuple in tree.selection():\n global id\n id,emp_id,name,vehicle,plate_no,work_location,image_path = tree.item(selected_tuple, 'values')\n e1.insert(END, emp_id)\n e2.insert(END, name)\n e3.insert(END, vehicle)\n e4.insert(END, plate_no)\n e5.insert(END, work_location)\n display_image(image_path)\n\ndef clear_textfield():\n e1.delete(0,END)\n e2.delete(0,END)\n e3.delete(0,END)\n e4.delete(0,END)\n e5.delete(0,END)\n\ndef update_command():\n #print(image_path)\n driver_backend.update(id,ID_text.get(),name_text.get(),vehicle_text.get(),plate_text.get(),location_text.get(),image_path)\n save_image()\n view_command()\n clear_textfield()\n\n\ndef search_command():\n tree.delete(*tree.get_children())\n for row in driver_backend.search(ID_text.get(),name_text.get(),vehicle_text.get(),plate_text.get(),location_text.get()):\n tree.insert(\"\",END,values=row)\n\ndef delete_command():\n driver_backend.delete(id)\n view_command()\n\nwindow=Tk()\n\n\nwindow.wm_title(\"Drivers Details\")\n\ntab_parent = ttk.Notebook(window)\n\ntab1 = ttk.Frame(tab_parent)\ntab2 = ttk.Frame(tab_parent)\ntab3 = ttk.Frame(tab_parent)\n\n\ntab_parent.add(tab1, text=\"Driver Details\")\ntab_parent.add(tab2, text=\"Duty Schedule\")\ntab_parent.add(tab3, text=\"vacation\")\n\ntab_parent.grid(row=0,column=0)\n\nl1=Label(tab1,text=\"ID No:\")\nl1.grid(row=0,column=0)\n\nl2=Label(tab1,text=\"Name\")\nl2.grid(row=0,column=2)\n\nl3=Label(tab1,text=\"Vehicle\")\nl3.grid(row=1,column=0)\n\nl4=Label(tab1,text=\"Plate:\")\nl4.grid(row=1,column=2)\n\nl5=Label(tab1,text=\"Work Location:\")\nl5.grid(row=2,column=0)\n\nl6=Label(tab1,text=\"Upload Photo\")\nl6.grid(row=2,column=2)\n\nb1=Button(tab1,text=\"view All\", width=12,command=view_command)\nb1.grid(row=4,column=0)\n\nb2=Button(tab1,text=\"Search\", width=12,command=search_command)\nb2.grid(row=4,column=1)\n\nb3=Button(tab1,text=\"Add\", width=12,command=add_command)\nb3.grid(row=4,column=2)\n\nb4=Button(tab1,text=\"Update\", width=12,command=update_command)\nb4.grid(row=4,column=3)\n\nb5=Button(tab1,text=\"Delete\", width=12,command=delete_command)\nb5.grid(row=4,column=4)\n\nb6=Button(tab1,text=\"Close\", width=12,command=window.destroy)\nb6.grid(row=4,column=5)\n\nID_text=StringVar()\ne1=Entry(tab1,textvariable=ID_text)\ne1.grid(row=0,column=1)\n\nname_text=StringVar()\ne2=Entry(tab1,textvariable=name_text)\ne2.grid(row=0,column=3)\n\nvehicle_text=StringVar()\ne3=Entry(tab1,textvariable=vehicle_text)\ne3.grid(row=1,column=1)\n\nplate_text=StringVar()\ne4=Entry(tab1,textvariable=plate_text)\ne4.grid(row=1,column=3)\n\nlocation_text=StringVar()\ne5=Entry(tab1,textvariable=location_text)\ne5.grid(row=2,column=1)\n\n\"\"\" Treeview \"\"\"\ntree= ttk.Treeview(tab1, column=(\"ID\",\"Employee ID\", \"Name\", \"Vehicle\",\" Plate No:\",\"Work Location\"), show='headings')\ntree.heading(\"#1\", text=\"ID\")\ntree.heading(\"#2\", text=\"Employee ID\")\ntree.heading(\"#3\", text=\"Name\")\ntree.heading(\"#4\", text=\"Vehicle\")\ntree.heading(\"#5\", text=\"Plate No:\")\ntree.heading(\"#6\", text=\"Work Location\")\ntree.grid(row=6,column=0,rowspan=6,columnspan=7)\n\ntree.bind(\"<>\",get_selected_row)\n\n\"\"\" upload image\"\"\"\ndef openfn():\n global filename\n filename= filedialog.askopenfilename(title='open')\n\n\ndef open_img():\n openfn()\n global image_path, save_img\n image_path = getImagePath(filename)\n #print(\"image path = \"+image_path)\n save_img=Image.open(filename)\n img = Image.open(filename)\n #img.save(image_name,'JPEG')\n img = img.resize((55, 65), Image.ANTIALIAS)\n img = ImageTk.PhotoImage(img)\n panel = Label(tab1, image=img)\n panel.image = img\n panel.grid(row=0,column=4,rowspan=3,columnspan=2)\n\ndef display_image(image_path):\n img = Image.open(image_path)\n img = img.resize((55, 65), Image.ANTIALIAS)\n img = ImageTk.PhotoImage(img)\n panel = Label(tab1, image=img)\n panel.image = img\n panel.grid(row=0,column=4,rowspan=3,columnspan=2)\n\n\ndef getImagePath(filename):\n global filena\n filena = filename.split('/')[-1]\n new_path = save_dir+filena\n return new_path\n\n\nbtn = Button(tab1, text='open image', command=open_img).grid(row=2,column=3)\n\n\n\"\"\" tab2 - duty Schedule \"\"\"\n\nduty_l1=Label(tab2,text=\"Select Name:\")\nduty_l1.grid(row=0,column=0)\n\nduty_name_text=StringVar()\nduty_e2=Entry(tab2,textvariable=duty_name_text)\nduty_e2.grid(row=0,column=3)\n\n# label\nduty_l2=Label(tab2, text = \"Select the Month :\",\n font = (\"Times New Roman\", 10)).grid(column = 4,\n row = 0, padx = 10, pady = 25)\n\n# Combobox creation\nduty_month_text = StringVar()\nmonthchoosen = ttk.Combobox(tab2, width = 10, textvariable = duty_month_text)\n\n# Adding combobox drop down list\nmonthchoosen['values'] = (' January',\n ' February',\n ' March',\n ' April',\n ' May',\n ' June',\n ' July',\n ' August',\n ' September',\n ' October',\n ' November',\n ' December')\n\nmonthchoosen.grid(column = 5, row = 0)\nmonthchoosen.current()\n\n# label\nduty_l3=Label(tab2, text = \"Select Time :\",\n font = (\"Times New Roman\", 10)).grid(column = 6,\n row = 0, padx = 10, pady = 25)\n# Combobox creation\nduty_time_text = StringVar()\ntimechoosen = ttk.Combobox(tab2, width = 10, textvariable = duty_time_text)\n\n# Adding combobox drop down list\ntimechoosen['values'] = (' 7 AM - 4 PM',\n ' 8 AM - 5 PM',\n ' 10 AM - 7 PM',\n ' 3 PM - 12 AM',\n ' 10 PM - 7 AM')\n\ntimechoosen.grid(column = 7, row = 0)\ntimechoosen.current()\n\n# label\nduty_l4=Label(tab2, text = \"Select OFF Day :\",\n font = (\"Times New Roman\", 10)).grid(column = 8,\n row = 0, padx = 10, pady = 25)\n# Combobox creation\noffday_text = StringVar()\noffchoosen = ttk.Combobox(tab2, width = 10, textvariable = offday_text)\n\n# Adding combobox drop down list\noffchoosen['values'] = (' Sunday',\n ' Monday',\n ' Tuesday',\n ' Wednesday',\n ' Thursday',\n ' Friday')\n\noffchoosen.grid(column = 9, row = 0)\noffchoosen.current()\n\n\n\nwindow.mainloop()\n","sub_path":"drivfront_tab.py","file_name":"drivfront_tab.py","file_ext":"py","file_size_in_byte":7342,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"534763227","text":"#!env python3\n# -*- coding: utf-8 -*-\n\nclass PrimeIter:\n def __init__(self, max):\n self.max = max\n\n def __iter__(self):\n self.n = 1\n return self\n\n def __next__(self):\n is_prime = False\n self.n += 1\n while not is_prime:\n is_prime = True\n for i in range(2, self.n):\n if self.n % i == 0:\n is_prime = False\n break\n if is_prime:\n break\n self.n += 1\n if self.n >= self.max:\n raise StopIteration\n return self.n\n\n\nit = PrimeIter(100)\nfor no in it:\n print(no, end=\",\")\nprint(\"\")\n","sub_path":"python/OO/class_iterator.py","file_name":"class_iterator.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"529090841","text":"import lkml\nfrom connection import Connection\nfrom logger import Logger\nfrom view import View\nimport os\nimport re\n\nclass Model:\n def __init__(self):\n self.name = None\n self.connectionName = None\n self.connection = None\n self.label = None\n self.includes = None\n\n def setModel(self, model):\n\n if 'label' in model:\n self.label = model['label']\n self.name = \"SIGMA_\" + self.label.replace(' ', '_')\n\n if 'includes' in model:\n self.includes = model['includes']\n\n if 'connection' in model:\n self.connectionName = model['connection']\n\n self.connection = Connection(self.connectionName)\n\n def __str__(self):\n return \"\"\"\n Model: ---------------------------------------------------------------------------------------------------------------\n Label : {label}\n Name : {name}\n Connection Name : {connectionName}\n Database Name : {databaseName}\n SchemaName : {schemaName}\n includes : {includes}\n \"\"\".format(label = self.label, connectionName = self.connectionName, databaseName = self.connection.getDatabaseName(), schemaName = self.connection.getSchemaName(), name = self.name, includes = self.includes)\n\nlogging = Logger().getLogger()\n\n\ndef getFiles(dir, filesIncluded):\n filesList = []\n for dirName, subdirList, fileList in os.walk(dir):\n for fname in fileList:\n for fileIncluded in filesIncluded:\n fileIncluded = fileIncluded.replace('*.', '.*.')\n rx = re.compile(fileIncluded)\n filePath = '{}{}'.format(dirName, fname)\n if rx.match(filePath): \n modelFilesDict = {\n \"FileName\" : fname,\n \"DirName\" : dirName,\n }\n\n filesList.append(modelFilesDict)\n\n return filesList\n\n\ndef main():\n rootDir = '../data/its_sig/'\n filesIncluded = ['*.model.lkml']\n modelFilesList = getFiles(rootDir, filesIncluded)\n\n logging.info(modelFilesList)\n\n for modelFileItem in modelFilesList:\n with open('{}{}'.format(modelFileItem[\"DirName\"], modelFileItem[\"FileName\"]), 'r') as modelFile:\n modelParsed = lkml.load(modelFile)\n \n model = Model()\n model.setModel(modelParsed)\n logging.info(model)\n #print(model)\n\n\n viewList = []\n\n rootDir = modelFileItem[\"DirName\"]\n filesIncluded = model.includes\n viewFilesList = getFiles(rootDir, filesIncluded)\n\n logging.info('View----------------------------------')\n logging.info(viewFilesList)\n for viewFileItem in viewFilesList:\n\n if viewFileItem['FileName'] != 'users.view.lkml':\n continue\n\n viewFile = '{}{}'.format(viewFileItem[\"DirName\"], viewFileItem[\"FileName\"])\n msg = \"Parsing: {}\".format(viewFile)\n logging.info(msg)\n print(msg)\n \n viewObj = View()\n views = viewObj.getViewInfomationFromFile(viewFile)\n\n for view in views:\n logging.info(\"Viewinfo\")\n logging.info(view)\n \n view.schemaName = model.connection.schemaName\n view.databaseName = model.connection.databaseName\n view.targetSchema = model.name\n\n logging.info(\"-------------------------All Dimensions---------------------------------------------\")\n\n for dimension_ in view.allDimensions:\n logging.info(dimension_)\n\n logging.info(\"-------------------------Valid Dimensions---------------------------------------------\")\n\n for dimension_ in view.validDimensions:\n logging.info(dimension_)\n \n logging.info(\"-------------------------Invalid Dimensions---------------------------------------------\")\n for dimension_ in view.excludedDimensions:\n logging.info(dimension_)\n \n view.getViewSQL()\n\n view.injectViewSchema()\n\n view.setDBTModelName()\n\n viewList.append(view)\n\n for view in viewList:\n view.injectSqlTableName(viewList)\n view.injectSqlTableNameInSQLTriggerValue(viewList)\n view.writedbtModel()\n\n\n\nif __name__ == \"__main__\":\n main()\n\n\n'''\n\n\nwith open('../data/its_sig/its_sig.model.lkml', 'r') as file:\n parsed = lkml.load(file)\n #logging.info(parsed)\n\n model = Model()\n model.setModel(parsed)\n logging.info(model)\n\n viewList = []\n\n viewFile = '../data/its_sig/events_pdt.view.lkml'\n\n viewObj = View()\n views = viewObj.getViewInfomationFromFile(viewFile)\n\n for view in views:\n logging.info(view)\n\n logging.info(view.getViewSQL())\n\n view.schemaName = model.connection.schemaName\n view.databaseName = model.connection.databaseName\n view.targetSchema = model.name\n\n view.injectViewSchema()\n\n view.setDBTModelName()\n\n viewList.append(view) \n\n #view.writedbtModel()\n\n\n for view in viewList:\n view.injectSqlTableName(viewList)\n view.injectSqlTableNameInSQLTriggerValue(viewList)\n view.writedbtModel()\n\n\n'''","sub_path":"LookerToSigmaConverter/python/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"92377907","text":"# jacob clarkson\n# project euler problem 13\n# january 2015\n\n# program to find the first ten digits of the sum of 100 50-digit numbers\n\n\n# method to get the first y digits of a number x \ndef shorten(x, y):\n\twhile len(str(x))>y:\n\t\tx = x//10\n\treturn x\n\n# main loop\nsum = 0\nfor i in range(100):\n\tx = eval(input())\n\tsum += shorten(x, 11) # only need the first 11 digits of each number\n\nprint(shorten(sum, 10))\n","sub_path":"Prob13.py","file_name":"Prob13.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"319045491","text":"# This file was written by Soren and Sean in late June 2020.\n# This file is the 'main' function that will drive the program.\n# I will document the different command-line-arguments here once we get to that point.\n# Imports\nimport os\nimport boto3\nimport pandas as pd\nimport numpy as np\nfrom botocore.exceptions import NoCredentialsError\nimport sys\nfrom sklearn.preprocessing import OneHotEncoder\nfrom data_class import DataFrame\nimport mlfinlab.data_structures as ds\n\n# Initialize command line arguments\ncsv_name = sys.argv[1]\n\n# Read csv into data frame\ndf = pd.read_csv(csv_name)\n\n# Create an instance of our DataFrame class to modify data\ndata = DataFrame(df, csv_name)\n\n# Transform data (columns we want- price, date_time, volume)\ncols_to_keep = ['date_time', 'period_volume', 'high']\ndata.df = data.df[cols_to_keep]\n\n# Dollar bar\ndsb_filename = 'data_standard_bar.csv'\nds.get_dollar_bars(\n data.df,\n threshold=28000,\n batch_size=10000000,\n verbose=False,\n to_csv=True,\n output_path=dsb_filename)\nframe = pd.read_csv(dsb_filename)\nframe.to_csv(dsb_filename, index=False) # we did this because we wanted to remove the index\n\n# Ema Dollar Imbalance Bar\ndeib_filename = 'data_ema_imbalance_bar.csv'\nds.get_ema_dollar_imbalance_bars(\n data.df,\n num_prev_bars=3,\n expected_imbalance_window=10000,\n exp_num_ticks_init=2000,\n exp_num_ticks_constraints=[0, 100],\n batch_size=100,\n verbose=True,\n to_csv=True,\n output_path=deib_filename)\nframe = pd.read_csv(deib_filename)\nframe.to_csv(deib_filename, index=False) # we did this becasue we wanted to remove the index\n\n# Dumb print message. Remove this somemtime later.\nprint('Standard and EMA Imbalance Data created')\nprint(data.df)\n\n# Upload to AWS\ndata.write_df_to_csv()\ndata.upload_to_aws(data.final_name)\ndata.upload_to_aws(dsb_filename)\ndata.upload_to_aws(deib_filename)\n\n# Delete temporary data that we just uploaded to aws\nos.remove(dsb_filename)\nos.remove(deib_filename)\n\n\n\n### ADD IN FEATURE TO SPLIT STRUCTURED DATA INTO TRAIN AND TEST SETS\n\n","sub_path":"open_quant/ml pipeline/etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"36969634","text":"def solution(name):\n count=0\n alpha='ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n d={}\n indexes=[]\n current_idx=0\n n=len(name)\n for i in range(len(alpha)):\n d[alpha[i]]=min(i,26-i)\n print(d)\n for i in range(n):\n num=d[name[i]]\n count+=num\n if num !=0 :\n indexes.append(i)\n\n print(indexes)\n while True:\n if len(indexes)==0:\n break\n min_dist=99\n min_idx=0\n for it in indexes:\n min_dist2=min(abs(it-current_idx),n-abs(it-current_idx))\n if min_dist2 < min_dist:\n min_dist=min_dist2\n min_idx=it\n count+=min_dist\n indexes.remove(min_idx)\n current_idx = min_idx\n\n return count\n\nsolution('JEORDAN')","sub_path":"algorithm/programmers/greedy3.py","file_name":"greedy3.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"25140151","text":"import os\n\nimport numpy as np\nfrom pycompss.api.parameter import FILE_IN\nfrom pycompss.api.task import task\nfrom scipy.sparse import issparse\n\nfrom dislib.data import Subset, Dataset\n\n\ndef load_data(x, subset_size, y=None):\n \"\"\"\n Loads data into a Dataset.\n\n Parameters\n ----------\n x : ndarray, shape=[n_samples, n_features]\n Array of samples.\n y : ndarray, optional, shape=[n_features,]\n Array of labels.\n subset_size : int\n Subset size in number of samples.\n\n Returns\n -------\n dataset : Dataset\n A distributed representation of the data divided in Subsets of\n subset_size.\n \"\"\"\n dataset = Dataset(n_features=x.shape[1], sparse=issparse(x))\n\n for i in range(0, x.shape[0], subset_size):\n if y is not None:\n subset = Subset(x[i: i + subset_size], y[i: i + subset_size])\n else:\n subset = Subset(x[i: i + subset_size])\n dataset.append(subset)\n\n return dataset\n\n\ndef load_libsvm_file(path, subset_size, n_features, store_sparse=True):\n \"\"\" Loads a LibSVM file into a Dataset.\n\n Parameters\n ----------\n path : string\n File path.\n subset_size : int\n Subset size in lines.\n n_features : int\n Number of features.\n store_sparse : boolean, optional (default = True).\n Whether to use scipy.sparse data structures to store data. If False,\n numpy.array is used instead.\n\n Returns\n -------\n dataset : Dataset\n A distributed representation of the data divided in Subsets of\n subset_size.\n \"\"\"\n\n return _load_file(path, subset_size, fmt=\"libsvm\",\n store_sparse=store_sparse,\n n_features=n_features)\n\n\ndef load_libsvm_files(path, n_features, store_sparse=True):\n \"\"\" Loads a set of LibSVM files into a Dataset.\n\n Parameters\n ----------\n path : string\n Path to a directory containing LibSVM files.\n n_features : int\n Number of features.\n store_sparse : boolean, optional (default = True).\n Whether to use scipy.sparse data structures to store data. If False,\n numpy.array is used instead.\n\n Returns\n -------\n dataset : Dataset\n A distributed representation of the data divided in a Subset for\n each file in path.\n \"\"\"\n\n return _load_files(path, fmt=\"libsvm\", store_sparse=store_sparse,\n n_features=n_features)\n\n\ndef load_txt_file(path, subset_size, n_features, delimiter=\",\",\n label_col=None):\n \"\"\" Loads a text file into a Dataset.\n\n Parameters\n ----------\n path : string\n File path.\n subset_size : int\n Subset size in lines.\n n_features : int\n Number of features.\n delimiter : string, optional (default \",\")\n String that separates features in the file.\n label_col : int, optional (default=None)\n Column representing data labels. Can be 'first' or 'last'.\n\n Returns\n -------\n dataset : Dataset\n A distributed representation of the data divided in Subsets of\n subset_size.\n \"\"\"\n return _load_file(path, subset_size, fmt=\"txt\", n_features=n_features,\n delimiter=delimiter, label_col=label_col)\n\n\ndef load_txt_files(path, n_features, delimiter=\",\", label_col=None):\n \"\"\" Loads a set of text files into a Dataset.\n\n Parameters\n ----------\n path : string\n Path to a directory containing text files.\n n_features : int\n Number of features.\n delimiter : string, optional (default \",\")\n String that separates features in the file.\n label_col : int, optional (default=None)\n Column representing data labels. Can be 'first' or 'last'.\n\n Returns\n -------\n dataset : Dataset\n A distributed representation of the data divided in a Subset for\n each file in path.\n \"\"\"\n\n return _load_files(path, fmt=\"txt\", n_features=n_features,\n delimiter=delimiter, label_col=label_col)\n\n\ndef _load_file(path, subset_size, fmt, n_features, delimiter=None,\n label_col=None, store_sparse=False):\n lines = []\n dataset = Dataset(n_features, store_sparse)\n\n with open(path, \"r\") as f:\n for line in f:\n lines.append(line.encode())\n\n if len(lines) == subset_size:\n subset = _read_lines(lines, fmt, n_features, delimiter,\n label_col, store_sparse)\n dataset.append(subset)\n lines = []\n\n if lines:\n dataset.append(_read_lines(lines, fmt, n_features, delimiter,\n label_col, store_sparse))\n\n return dataset\n\n\ndef _load_files(path, fmt, n_features, delimiter=None, label_col=None,\n store_sparse=False):\n assert os.path.isdir(path), \"Path is not a directory.\"\n\n files = os.listdir(path)\n subsets = Dataset(n_features, store_sparse)\n\n for file_ in files:\n full_path = os.path.join(path, file_)\n subset = _read_file(full_path, fmt, n_features, delimiter, label_col,\n store_sparse)\n subsets.append(subset)\n\n return subsets\n\n\n@task(returns=1)\ndef _read_lines(lines, fmt, n_features, delimiter, label_col, store_sparse):\n if fmt == \"libsvm\":\n subset = _read_libsvm(lines, n_features, store_sparse)\n else:\n samples = np.genfromtxt(lines, delimiter=delimiter)\n\n if label_col == \"first\":\n subset = Subset(samples[:, 1:], samples[:, 0])\n elif label_col == \"last\":\n subset = Subset(samples[:, :-1], samples[:, -1])\n else:\n subset = Subset(samples)\n\n return subset\n\n\n@task(file=FILE_IN, returns=1)\ndef _read_file(file, fmt, n_features, delimiter, label_col, store_sparse):\n from sklearn.datasets import load_svmlight_file\n\n if fmt == \"libsvm\":\n x, y = load_svmlight_file(file, n_features)\n\n if not store_sparse:\n x = x.toarray()\n\n subset = Subset(x, y)\n else:\n samples = np.genfromtxt(file, delimiter=delimiter)\n\n if label_col == \"first\":\n subset = Subset(samples[:, 1:], samples[:, 0])\n elif label_col == \"last\":\n subset = Subset(samples[:, :-1], samples[:, -1])\n else:\n subset = Subset(samples)\n\n return subset\n\n\ndef _read_libsvm(lines, n_features, store_sparse):\n from tempfile import SpooledTemporaryFile\n from sklearn.datasets import load_svmlight_file\n # Creating a tmp file to use load_svmlight_file method should be more\n # efficient than parsing the lines manually\n tmp_file = SpooledTemporaryFile(mode=\"wb+\", max_size=2e8)\n tmp_file.writelines(lines)\n tmp_file.seek(0)\n x, y = load_svmlight_file(tmp_file, n_features)\n\n if not store_sparse:\n x = x.toarray()\n\n data = Subset(x, y)\n return data\n","sub_path":"dislib/data/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"140261024","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 15 23:04:23 2020\n\n@author: Marek\n\"\"\"\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as sc\n\ndata = pd.read_csv('C:\\programming\\Machine Learning\\Stanford course\\machine-learning-ex2\\machine-learning-ex2\\ex2\\ex2data1.txt', header = None)\ndata_arr = data.to_numpy()\n\nX = np.array(data_arr[:,:2])\ny = np.array([data_arr[:,2]]).T\nm, n = X.shape\nX = np.c_[np.ones((m, 1)), X]\ninitial_theta = np.zeros((n + 1, 1))\n\ndef plotData(X, y):\n \n idx_0 = [i for i in range(len(y)) if y[i,0] == 0]\n idx_1 = [i for i in range(len(y)) if y[i,0] == 1]\n\n plt.plot(X[idx_0,1], X[idx_0,2],\"ko\", markersize = 7, markerfacecolor = 'yellow')\n plt.plot(X[idx_1,1], X[idx_1,2],\"k+\", markersize = 7, linewidth = 2)\n\ndef costFunction(theta, X, y):\n theta = theta.reshape(-1, 1)\n h = np.array(1/(1 + np.exp((-1)*np.matmul(X, theta))))\n J = 1/m*np.sum(np.matmul(-y.T, np.log(h)) - np.matmul((1-y).T,np.log(1-h)))\n grad = 1/m*(np.matmul(X.T,(h-y)))\n return J, grad\n\ndef plotBoundary(theta, X, y):\n \n plot_X1 = [np.amax(X[:,1]), np.amin(X[:,1])]\n plot_X2 = (-1/theta[2])*(theta[1]*plot_X1 + theta[0])\n plt.plot(plot_X1,plot_X2)\n\ndef sigmoid(z):\n \n g = np.array(1/(1 + np.exp((-1)*z)))\n return g\n\ndef predict(theta, X, y):\n \n p = np.zeros((m,1))\n h = np.array(1/(1 + np.exp((-1)*np.matmul(X, theta))))\n idx_1 = [i for i in range(len(h)) if h[i,0] >= 0.5]\n p[idx_1,0] = 1\n acc = (np.sum([1 for i in range(len(p)) if p[i,0] == y[i,0]]))/m\n return acc\n\nplt.figure(figsize=(6,4), dpi=300)\nplotData(X, y)\ncost, grad = costFunction(initial_theta, X, y)\n\ntest_theta = np.array([[-24, 0.2, 0.2]]).T\ncost, grad = costFunction(test_theta, X, y)\n\nresult = sc.fmin_tnc(func=costFunction, x0=initial_theta, args=(X, y))\ntheta = np.array([result[0]]).T\nplotBoundary(theta, X, y)\n\ntest_prob = sigmoid(np.dot((np.array([1, 45, 85])), theta))\naccuracy = predict(theta, X, y)\n\n\n\n","sub_path":"Logistic regression 1.py","file_name":"Logistic regression 1.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"571590292","text":"import scrapy\nimport scrapy.selector\nfrom cnblogSpider.items import CnblogspiderItem\nfrom scrapy import Selector\n\n\nclass CnblogSpider(scrapy.Spider):\n name = \"cnblog_spider\" # 爬虫的名称\n allowed_domains = [\"cnblogs.com\"] # 域名范围\n start_urls = [\n \"http://www.cnblogs.com/qiyeboy/default.html?page=1\",\n ]\n\n def parse(self, response):\n # 实现网页解析\n papers = response.xpath(\".//*[@class='day']\") # 抽取所有文章栏目\n\n # 从每个文章栏目中抽取数据\n for paper in papers:\n url = paper.xpath(\".//*[@class='postTitle']/a/@href\").extract()[0]\n title = paper.xpath(\".//*[@class='postTitle']/a/text()\").extract()[0].split()\n time = paper.xpath(\".//*[@class='dayTitle']/a/text()\").extract()[0]\n content = paper.xpath(\".//*[@class='postCon']/div/text()\").extract()[0].split()\n item = CnblogspiderItem(url=url,title=title,time=time,content=content)\n yield item\n\n # 获取下一页链接，翻页\n next_page = Selector(response).re('\\s*下一页\\s*')\n if next_page:\n yield scrapy.Request(url=next_page[0], callback=self.parse)","sub_path":"《python爬虫开发与项目实战》书籍学习/12，初窥Scrapy爬虫框架/简单scrapy-cnblogSpider/cnblogSpider/build/lib/cnblogSpider/spiders/Spider_cnblogs.py","file_name":"Spider_cnblogs.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"157601218","text":"import cv2\nimport time\ncap = cv2.VideoCapture('video.avi')\ntime1 = time.time()\nwhile cap.isOpened():\n ret, frame = cap.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n cv2.imshow('frame', gray)\n key = cv2.waitKey(100)\n time2 = time.time()\n print(time2-time1)\n if key == ord('q'):\n print(key)\n break\ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"mastercar/Test/123.py","file_name":"123.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"602870496","text":"from nltk.tokenize import word_tokenize\nfrom similarity.jarowinkler import JaroWinkler\n\n\ndef devices(sentecte, theshold=80):\n jarowinkler = JaroWinkler()\n device_list = {\"fan\": \"\", \"light\": \"\"}\n names = []\n light_words = [\"light\", \"lamb\", \"bulb\", \"torch\"]\n fan_words = [\"fan\", \"blade\", \"ventilator\", \"vane\"]\n tokens = word_tokenize(sentecte)\n for token in tokens:\n flag = 0\n for word in light_words:\n percentage = jarowinkler.similarity(token, word) * 100\n if percentage >= 80:\n device_list[\"light\"] = token\n flag = 1\n names += [token]\n break\n if flag == 1:\n break\n for token in tokens:\n flag = 0\n for word in fan_words:\n percentage = jarowinkler.similarity(token, word) * 100\n if percentage >= 80:\n device_list[\"fan\"] = token\n flag = 1\n names += [token]\n break\n if flag == 1:\n break\n\n return {\n \"device_list\": device_list,\n \"names\": names\n }\n","sub_path":"jaro_winkler.py","file_name":"jaro_winkler.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"185327751","text":"import unittest\n\nfrom python import part1 as d8p1\n\n# md5 hashs takes a while in python\nclass TestDay8(unittest.TestCase):\n def test_part1_rect(self):\n grid = [['.' for i in range(7)] for j in range(3)]\n input_string = \"rect 3x2\"\n assert_string = \"###....\\n###....\\n.......\\n\"\n test_grid = \"\"\n for i in d8p1.rect(input_string, grid):\n temp_string = \"\".join(j for j in i)\n test_grid += temp_string + \"\\n\"\n self.assertEqual(test_grid, assert_string)\n\n def test_part1_rotate_column(self):\n grid_string = \"1234567\\n89ABCDE\\nFGHIJKL\"\n grid = [list(i) for i in grid_string.split('\\n')]\n input_string = \"rotate column x=2 by 1\"\n assert_string = \"12H4567\\n893BCDE\\nFGAIJKL\\n\"\n test_grid = \"\" #there must be a way to do this in one line..\n for i in d8p1.rotate(input_string, grid):\n temp_string = \"\".join(j for j in i)\n test_grid += temp_string + \"\\n\"\n self.assertEqual(test_grid, assert_string)\n\n def test_part1_rotate_row(self):\n grid_string = \"1234567\\n89ABCDE\\nFGHIJKL\"\n grid = [list(i) for i in grid_string.split('\\n')]\n input_string = \"rotate row y=0 by 4\"\n assert_string = \"4567123\\n89ABCDE\\nFGHIJKL\\n\"\n test_grid = \"\" #there must be a way to do this in one line..\n for i in d8p1.rotate(input_string, grid):\n temp_string = \"\".join(j for j in i)\n test_grid += temp_string + \"\\n\"\n self.assertEqual(test_grid, assert_string)\n\n #To check that any future changes still give the now known solution\n def test_answers(self):\n import os\n if \"day8_Two_factor\" in os.getcwd():\n path = \"python/\"\n else:\n path = \"day8_Two_factor/python/\"\n self.assertEqual(d8p1.run(path + \"input.txt\")[0], 110)\n # self.assertEqual(d8p2.run(path), 260)\n\n\ndef main():\n unittest.main()\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"day8_Two_factor/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"608938153","text":"from __future__ import absolute_import, print_function, unicode_literals\nfrom builtins import dict, str\nfrom indra.trips import trips_client\nfrom indra.trips.processor import TripsProcessor\n\ndef process_text(text, save_xml_name='trips_output.xml', save_xml_pretty=True):\n \"\"\"Return a TripsProcessor by processing text.\n\n Parameters\n ----------\n text : str\n The text to be processed.\n save_xml_name : Optional[str]\n The name of the file to save the returned TRIPS extraction knowledge\n base XML. Default: trips_output.xml\n save_xml_pretty : Optional[bool]\n If True, the saved XML is pretty-printed. Some third-party tools\n require non-pretty-printed XMLs which can be obtained by setting this\n to False. Default: True\n\n Returns\n -------\n tp : TripsProcessor\n A TripsProcessor containing the extracted INDRA Statements\n in tp.statements.\n \"\"\"\n html = trips_client.send_query(text)\n xml = trips_client.get_xml(html)\n if save_xml_name:\n trips_client.save_xml(xml, save_xml_name, save_xml_pretty)\n return process_xml(xml)\n\n\ndef process_xml(xml_string):\n \"\"\"Return a TripsProcessor by processing a TRIPS EKB XML string.\n\n Parameters\n ----------\n xml_string : str\n A TRIPS extraction knowledge base (EKB) string to be processed.\n http://trips.ihmc.us/parser/api.html\n\n Returns\n -------\n tp : TripsProcessor\n A TripsProcessor containing the extracted INDRA Statements\n in tp.statements.\n \"\"\"\n tp = TripsProcessor(xml_string)\n if tp.tree is None:\n return None\n tp.get_activations_causal()\n tp.get_activations_stimulate()\n tp.get_complexes()\n tp.get_modifications()\n tp.get_active_forms()\n tp.get_active_forms_state()\n tp.get_activations()\n tp.get_translocation()\n tp.get_regulate_amounts()\n tp.get_degradations()\n tp.get_syntheses()\n return tp\n","sub_path":"indra/trips/trips_api.py","file_name":"trips_api.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"628266318","text":"# encoding=utf-8\nimport jieba\nimport os\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n# print(dir(TfidfVectorizer))\nimport jieba.posseg\nfrom collections import Counter\n\njieba.load_userdict(\"医学名词.txt\") # 加载用户自定义词典\ndef cut(txt_name1, txt_name2):\n l = []\n with open(txt_name1, 'r',encoding='utf-8') as f1: # 以只读方式打开文件\n txt = f1.read()\n txt_encode = txt.encode('utf-8')\n txt_cut = jieba.posseg.cut(txt_encode) # 切词\n # result = ' '.join(txt_cut)\n\n for i in txt_cut:\n if (i.flag == 'n')|( i.flag=='v'):\n l.append(i.word)\n # print(l)\n with open(txt_name2, 'w') as f2: # 分词结果写入文件保存\n for i in l:\n f2.write(i+' ')\n f1.close()\n f2.close()\ncorpus = []\nrootdir = 'files'\nllist = os.listdir(rootdir) #列出文件夹下所有的目录与文件\nfor i in range(0,len(llist)):\n path = os.path.join(rootdir,llist[i])\n print(path)\n if os.path.isfile(path):\n cut(path, 'result/1-1.txt')\n # 分别对文件调用cut方法分词\n with open('result/1-1.txt', 'r') as f3:\n res3 = f3.read()\n corpus.append(res3)\n\n\n\n\n# 将停用词表从文件读出，并切分成一个数组备用\nstopWords_dic = open('中文停用词库.txt', 'r') # 从文件中读入停用词\nstopWords_content = stopWords_dic.read()\nstopWords_list = stopWords_content.splitlines() # 转为list备用\nstopWords_dic.close()\n\n# print(corpus)\nvector = TfidfVectorizer(stop_words=stopWords_list)\ntf_idf = vector.fit_transform(corpus)\n# print(tf_idf)\n\nword_list = vector.get_feature_names() # 获取词袋模型的所有词\nweight_list = tf_idf.toarray()\n# result1 = ''.join(word_list)\n# result2 = ''.join(weight_list)\n# print(result1, result2)\n# with open('files/wordslist.txt', 'w') as f3:\n# f3.write(result)\ntotal=[] #每篇文章数据分别存入\ndictt={}\nmun={}\n# 打印每类文本的tf-idf词语权重，第一个for遍历所有文本，第二个for便利某一类文本下的词语权重\nfor i in range(len(weight_list)):\n ddict={} #装每个文档的数据\n for j in range(len(word_list)):\n ddict[word_list[j]] = weight_list[i][j]\n\n total.append(ddict)\n for k, v in ddict.items():\n if k in dictt.keys():\n if ddict[k]!=0:\n mun[k] += 1\n dictt[k] += v\n else:\n dictt[k] = v\n mun[k]=1\nfor k,v in dictt.items():\n if mun.get(k):\n dictt[k]=round(float(dictt[k])/mun[k],2)\ndic=dict(sorted(dictt.items(), key=lambda x: x[1], reverse=True))\n# print(mun)\nprint(\"-------所有论文的词语tf-idf权重总和-------\")\n# for k in dic:\n# # print(k)\n# list(k)\n# print(k[0],' ',k[1])\nprint(dic)\n\ndi={}# 非前1000key\ncnt=0\nfor k, v in dic.items():\n cnt += 1\n # cnt后面的值代表维度数量\n if cnt > 1000:\n di[k] = v\nfor i in total:\n for k,v in di.items():\n i.pop(k)\n\nfl=open('result/dataresult.txt', 'w')\nfor i in total:\n for k,v in i.items():\n s=str(v)\n fl.write(s)\n fl.write(\"\\t\")\n fl.write(\"\\n\")\nfl.close()\nacc=0\n# for k,v in total[0].items():\n# acc +=1\n# print(acc,k)\n","sub_path":"Neusoft_TestCode/论文权重.py","file_name":"论文权重.py","file_ext":"py","file_size_in_byte":3259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"456626852","text":"#coding: utf-8\r\nimport gevent\r\nfrom gevent import monkey;monkey.patch_all()\r\nfrom gevent import lock\r\nfrom gevent.queue import Queue\r\n\r\n\r\nimport json\r\nimport logging\r\nimport traceback\r\nfrom sqlalchemy.sql import select, update, delete, insert, and_,or_, subquery, not_, null, func, text,exists\r\nfrom sqlalchemy import desc\r\n\r\nimport random,time\r\n\r\nfrom collections import Counter\r\nfrom datetime import datetime\r\nfrom datetime import date as dt_date\r\nfrom datetime import time as dt_time\r\n\r\nfrom proto.constant_pb2 import *\r\n\r\nfrom message.base import *\r\nfrom message.resultdef import *\r\n\r\nfrom goldflower.game import *\r\nfrom goldflower.gameconf import *\r\nfrom goldflower.eventsender import *\r\n\r\nfrom proto import struct_pb2 as pb2\r\nfrom db.connect import *\r\n\r\nfrom dal.core import *\r\n\r\nclass Player:\r\n def __init__(self,table,uid,user,access_service,seat):\r\n self.table = table\r\n self.uid = uid\r\n self.user = user\r\n self.user_gf = table.manager.dal.get_user_gf(uid)\r\n self.access_service = access_service\r\n self.seat = seat\r\n self.gold = 10000\r\n self.is_connected = True\r\n self.nick = user.nick\r\n self.avatar = user.avatar\r\n self.idle_time = time.time()\r\n\r\n def reload_user_gf(self):\r\n self.user_gf = self.table.manager.dal.get_user_gf(self.uid,True)\r\n\r\n def update_by_reconnected(self,user,access_service):\r\n self.user = user\r\n self.user_gf = self.table.manager.dal.get_user_gf(user.id)\r\n self.access_service = access_service\r\n self.nick = user.nick\r\n self.avatar = user.avatar\r\n self.gold = self.user.get_gold()\r\n self.is_connected = True\r\n\r\n def update_user_info(self):\r\n new_user = self.user.load_from_redis()\r\n self.nick = new_user.get('nick')\r\n self.user.nick = new_user.get('nick')\r\n self.avatar = new_user.get('avatar')\r\n self.user.avatar = new_user.get('avatar')\r\n self.gold = self.user.get_gold()\r\n self.user_gf = self.table.manager.dal.get_user_gf(self.uid, True)\r\n self.is_connected = True\r\n self.vip_exp = new_user.get('vip_exp')\r\n\r\n\r\n def update_by_disconnected(self):\r\n self.access_service = -1\r\n self.is_connected = False\r\n\r\n def has_gold(self,gold):\r\n return self.user.gold >= gold\r\n\r\n def get_gold(self):\r\n self.gold = self.user.get_gold()\r\n return self.gold\r\n\r\n def modify_gold(self,session,gold):\r\n gold = self.user.modify_gold(session,gold)\r\n return gold\r\n \r\n\r\n def __repr__(self):\r\n return \"Player[%d/%d]\" % (self.uid,self.seat)\r\n \r\n def get_brief_proto_struct(self,pb_brief = None):\r\n if pb_brief == None:\r\n pb_brief = pb2.PlayerBrief()\r\n\r\n pb_brief.uid = self.uid\r\n pb_brief.avatar = self.user.avatar\r\n pb_brief.gold = self.user.gold\r\n pb_brief.seat = self.seat\r\n pb_brief.nick = self.user.nick\r\n pb_brief.vip = self.user.vip\r\n if hasattr(self, 'vip_exp'):\r\n pb_brief.vip_exp = self.vip_exp\r\n else:\r\n pb_brief.vip_exp = 0 if self.user.vip_exp is None else self.user.vip_exp\r\n pb_brief.sex = 0 if self.user.sex == 0 else 1\r\n pb_brief.exp = self.user_gf.exp\r\n\r\n return pb_brief\r\n\r\n\r\nclass Table:\r\n def __init__(self,manager,table_id,type):\r\n self.id = table_id\r\n self.game = None\r\n self.table_type = type\r\n self.manager = manager\r\n self.players = {}\r\n self.dealer = -1\r\n\r\n self.deal_counter = 0\r\n self.deal_trigger = 0\r\n\r\n self.restart_game()\r\n self.redis = manager.redis\r\n self.table_key = \"table_\" + str(manager.service.serviceId) + \"_\" + str(table_id)\r\n\r\n self.sender = TableEventSender(self)\r\n self.lock = lock.Semaphore()\r\n\r\n self.ready_time = 0\r\n\r\n self.table_event_queue = Queue()\r\n gevent.spawn_later(1,self.real_send_event)\r\n\r\n def is_ready(self):\r\n return int(time.time()) > self.ready_time\r\n\r\n def player_reconnected(self,uid,user,access_service):\r\n player = self.get_player(uid)\r\n if player == None:\r\n return\r\n player.update_by_reconnected(user,access_service)\r\n\r\n self.redis.hset(self.table_key,uid,access_service)\r\n self.redis.hset(self.manager.room_key,uid,self.id)\r\n self.sender.send_player_connect(player,True)\r\n\r\n def player_disconnected(self,uid):\r\n player = self.get_player(uid)\r\n if player == None:\r\n return\r\n\r\n if self.game != None and self.game.is_started() and self.game.is_gambler(uid):\r\n player.update_by_disconnected()\r\n self.sender.send_player_connect(player,False)\r\n else:\r\n self.remove_player(uid)\r\n\r\n def add_player(self,uid,user,access_service):\r\n if self.is_full() or self.has_player(uid):\r\n return None\r\n\r\n used = [x.seat for x in self.players.values()]\r\n not_used = [x for x in xrange(MAX_TABLE_PLAYER) if x not in used]\r\n seat = random.choice(not_used)\r\n player = Player(self,uid,user,access_service,seat)\r\n self.players[uid] = player\r\n\r\n self.redis.hset(self.table_key,uid,access_service)\r\n self.redis.hset(self.manager.room_key,uid,self.id)\r\n self.sender.send_player_join(player)\r\n return player\r\n\r\n def update_player_info(self,uid):\r\n player = self.get_player(uid)\r\n if player != None:\r\n player.update_user_info()\r\n self.sender.send_player_updated(player)\r\n\r\n\r\n def is_gambler(self,uid):\r\n return self.game != None and self.game.is_started() and uid in self.game.gamblers\r\n\r\n\r\n def is_game_started(self):\r\n return self.game != None and self.game.is_started()\r\n\r\n def remove_player(self,uid, is_kicked = False):\r\n if uid == self.dealer:\r\n self.dealer = -1\r\n \t\r\n player = self.get_player(uid)\r\n if player == None:\r\n return False\r\n\r\n self.players.pop(uid,None)\r\n\r\n if self.game != None:\r\n self.game.leave_game(uid)\r\n\r\n self.redis.hdel(self.table_key,uid)\r\n self.redis.hdel(self.manager.room_key,uid)\r\n if not is_kicked:\r\n self.sender.send_player_leave(player)\r\n return len(self.players) == 0 \r\n\r\n def kick_player(self,kicker,uid):\r\n player = self.get_player(uid)\r\n\r\n self.remove_player(uid,is_kicked = True)\r\n self.sender.send_player_kicked(kicker,player)\r\n\r\n def has_player(self,uid):\r\n return uid in self.players\r\n\r\n def get_player(self,uid):\r\n return self.players.get(uid,None)\r\n\r\n def countof_players(self):\r\n return len(self.players)\r\n \r\n def is_empty(self):\r\n return len(self.players) == 0\r\n\r\n def is_full(self):\r\n return len(self.players) == MAX_TABLE_PLAYER\r\n \r\n \r\n def restart_game(self):\r\n config = TABLE_GAME_CONFIG[self.table_type]\r\n for player in self.players.values():\r\n if player.idle_time < 0:\r\n player.idle_time = time.time()\r\n\r\n for player in self.players.values():\r\n player.reload_user_gf()\r\n\r\n self.game = GoldFlower(self,config[2],config[3],config[4],config[5],config[6],config[7])\r\n gevent.spawn_later(3,self.kick_invalid_players())\r\n\r\n\r\n def kick_invalid_players(self):\r\n config = TABLE_GAME_CONFIG[self.table_type]\r\n for player in self.players.values():\r\n if not player.is_connected:\r\n self.remove_player(player.uid)\r\n continue\r\n \"\"\"\r\n gold = player.get_gold()\r\n if config[1] >= 0 and gold > config[1]:\r\n self.kick_player(-1,player.uid)\r\n continue\r\n \"\"\"\r\n\r\n def notify_event(self,event):\r\n event_type = event.header.command\r\n event_data = event.encode()\r\n service = self.manager.service\r\n for player in self.players.values():\r\n if player.access_service < 0:\r\n continue\r\n #service.send_client_event(player.access_service,player.uid,event_type,event_data)\r\n self.send_table_event(player.access_service,player.uid,event_type,event_data)\r\n\r\n def notify_event_player(self,event,player):\r\n if player.access_service < 0:\r\n return\r\n event_type = event.header.command\r\n event_data = event.encode()\r\n #service = self.manager.service\r\n #service.send_client_event(player.access_service,player.uid,event_type,event_data)\r\n self.send_table_event(player.access_service,player.uid,event_type,event_data)\r\n\r\n def send_table_event(self,access_service,uid,event_type,event_data):\r\n self.table_event_queue.put_nowait((access_service,uid,event_type,event_data,))\r\n\r\n def real_send_event(self):\r\n service = self.manager.service\r\n while True:\r\n access_service,uid,event_type,event_data = self.table_event_queue.get()\r\n service.send_client_event(access_service,uid,event_type,event_data)\r\n\r\n\r\n def __repr__(self):\r\n s = \"Table[\"\r\n for player in self.players.values():\r\n s += str(player) + \"|\"\r\n s += \"]\\n\"\r\n s += str(self.game)\r\n return s\r\n\r\n def get_proto_struct(self,uid,pb_table = None):\r\n if pb_table == None:\r\n pb_table = pb2.Table()\r\n\r\n pb_table.table_type = self.table_type\r\n\r\n for player in self.players.values():\r\n player.get_brief_proto_struct(pb_table.players.add())\r\n\r\n if self.game != None:\r\n self.game.get_proto_struct(uid,pb_table)\r\n return pb_table \r\n\r\nclass TableManager:\r\n def __init__(self,service):\r\n self.service = service\r\n if service != None:\r\n self.redis = service.server.redis\r\n else:\r\n self.redis = None\r\n\r\n self.tables = {}\r\n self.room_id = service.serviceId\r\n\r\n self.session = Session()\r\n self.dal = DataAccess(self.redis)\r\n\r\n if self.redis != None:\r\n self.room_key = \"room_users_\" + str(self.room_id)\r\n self.redis.delete(self.room_key)\r\n self.redis.hset(self.room_key,\"info\",\"\")\r\n\r\n keys = self.redis.keys(\"table_\" + str(service.serviceId) + \"*\")\r\n for k in keys:\r\n self.redis.delete(k)\r\n self.lock = lock.Semaphore()\r\n gevent.spawn_later(5,self.scan_idle_player)\r\n\r\n\r\n def scan_idle_player(self):\r\n while True:\r\n now = time.time()\r\n for _,t in self.tables.items():\r\n t.lock.acquire()\r\n try :\r\n for _,player in t.players.items():\r\n if player.idle_time > 0 and now - player.idle_time > 180:\r\n t.kick_player(-2,player.uid)\r\n logging.info(\"Player %d/on table %d is idle too long, so kick it out\",player.uid,t.id)\r\n finally:\r\n t.lock.release()\r\n\r\n gevent.sleep(30)\r\n\r\n\r\n def get_table(self,table_id):\r\n return self.tables.get(table_id)\r\n\r\n def new_table(self,table_type):\r\n table_id = self.redis.incr(\"table_id\")\r\n table = Table(self, table_id, table_type)\r\n self.tables[table_id] = table\r\n return table\r\n\r\n def get_player_table(self,uid):\r\n for table in self.tables.values():\r\n if table.has_player(uid):\r\n return table \r\n return None\r\n\r\n def get_players_by_access_services(self,access_services):\r\n players = []\r\n for table in self.tables.values():\r\n for player in table.players.values():\r\n if player.access_service in access_services:\r\n players.append(player)\r\n return players\r\n\r\n def check_table_type(self,table_type,gold):\r\n config = TABLE_GAME_CONFIG[table_type]\r\n if config[0] >= 0 and gold < config[0]:\r\n return RESULT_FAILED_LESS_GOLD\r\n if config[1] >= 0 and gold > config[1]:\r\n return RESULT_FAILED_MORE_GOLD\r\n return 0 \r\n \r\n def find_suitable_table_type(self,gold):\r\n table_type = -1\r\n for k,v in TABLE_GAME_CONFIG.items():\r\n if v[0] >=0 and gold < v[0]:\r\n continue\r\n if v[1] >=0 and gold > v[1]:\r\n continue\r\n table_type = k\r\n return table_type\r\n\r\n def reconnect_table_player(self,table,uid,user,access_service):\r\n if table == None:\r\n return\r\n table.lock.acquire()\r\n try:\r\n table.player_reconnected(uid,user,access_service)\r\n finally:\r\n table.lock.release()\r\n\r\n def remove_table_player(self,table,uid):\r\n if table == None:\r\n return\r\n table.lock.acquire()\r\n try:\r\n table.remove_player(uid)\r\n finally:\r\n table.lock.release()\r\n\r\n def sort_table(self,t1,t2):\r\n is_t1_started = t1.is_game_started()\r\n is_t2_started = t2.is_game_started()\r\n if is_t1_started == is_t2_started:\r\n return cmp(len(t1.players),len(t2.players))\r\n if is_t1_started:\r\n return 1\r\n else:\r\n return -1\r\n\r\n\r\n def sit_table(self,target_table_id,uid,access_service,not_table_ids,table_type):\r\n user = self.dal.get_user(uid)\r\n if user == None:\r\n return RESULT_FAILED_INVALID_USER,None\r\n\r\n if table_type < 0:\r\n table_type = self.find_suitable_table_type(user.gold)\r\n if table_type < 0:\r\n return RESULT_FAILED_LESS_GOLD,None\r\n table = None\r\n\r\n old_table = self.get_player_table(uid)\r\n if target_table_id < 0:\r\n if old_table != None:\r\n self.reconnect_table_player(old_table,uid,user,access_service)\r\n return 0, old_table\r\n\r\n all_tables = [t for t in self.tables.values() \\\r\n if t.id not in not_table_ids and t.table_type == table_type and not t.is_full() and t.is_ready()]\r\n all_tables.sort(cmp=self.sort_table,reverse = True)\r\n for t in all_tables:\r\n if t.is_gambler(uid):\r\n # if user is gambler of this table,it means that he quit this game before ,so didnot arrange this table\r\n continue\r\n table = t\r\n break\r\n if table == None:\r\n table = self.new_table(table_type)\r\n else:\r\n if old_table != None and target_table_id == old_table.id:\r\n #self.reconnect_table_player(old_table,uid,user,access_service)\r\n return 0,old_table\r\n\r\n self.remove_table_player(old_table,uid)\r\n table = self.get_table(target_table_id)\r\n if table == None or table.is_full():\r\n return RESULT_FAILED_INVALID_TABLE,None\r\n\r\n table.lock.acquire()\r\n try :\r\n check_result = self.check_table_type(table.table_type,user.gold)\r\n if check_result < 0:\r\n return check_result,None\r\n \r\n player = table.add_player(uid,user,access_service)\r\n if player == None:\r\n return RESULT_FAILED_TABLE_IS_FULL,None\r\n finally:\r\n table.lock.release()\r\n return 0,table\r\n \r\n\r\nif __name__ == '__main__':\r\n \r\n manager = TableManager(None)\r\n\r\n manager.sit_table(1, 1, [], TABLE_L)\r\n manager.sit_table(2, 1, [], TABLE_L)\r\n table = manager.sit_table(3, 1, [], TABLE_L)\r\n\r\n print(table)\r\n\r\n table.game.set_ready(1)\r\n table.game.set_ready(2)\r\n table.game.set_ready(3)\r\n\r\n gevent.sleep(1)\r\n\r\n","sub_path":"server/goldflower/table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":15831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"36195062","text":"# Importing the library\nimport pandas as pd\nimport json as js\n\n# loading the file\ndb = js.load(open('C:\\\\Users\\\\RAJEEV KHANNA\\\\Documents\\\\mohit\\\\python\\\\food_db.json'))\nlen(db)\n\n# db is a list of dictionaries \n# Every disctionary has 2 special atributes\n# Nutrients and Portion. These are dictionaries as well\ndb[0].keys()\ntype(db[0]['nutrients'][0])\nnutrients = pd.DataFrame(db[0]['nutrients'])\n\n\n","sub_path":"food_db.py","file_name":"food_db.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"324989445","text":"from datetime import datetime as dt\n\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import IsAuthenticated, IsAdminUser\nfrom rest_framework.response import Response\n\nfrom image.models import TransformedImage\nfrom image.serializers import TransformedImageSerializer\nfrom statistics.ml_api import MlApiStatsRequest\nfrom utils.dataset import format_api_dataset, format_client_dataset\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated, IsAdminUser])\ndef get_client_users_stats(request):\n user_id = request.query_params.get('user_id')\n style_id = request.query_params.get('style_id')\n from_datetime = request.query_params.get('from_datetime')\n to_datetime = request.query_params.get('to_datetime')\n\n filter_data = {}\n if user_id is not None:\n filter_data['user_id'] = user_id\n if style_id is not None:\n filter_data['style'] = style_id\n if from_datetime is not None:\n filter_data['created_on__gte'] = dt.fromisoformat(from_datetime)\n if to_datetime is not None:\n filter_data['created_on__lt'] = dt.fromisoformat(to_datetime)\n\n images = TransformedImage.objects.filter(**filter_data)\n serialized_images = [TransformedImageSerializer(image).data for image in images]\n\n dataset = []\n if len(serialized_images) > 0:\n dataset = format_client_dataset(serialized_images)\n\n return Response(dataset, status=status.HTTP_200_OK)\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated, IsAdminUser])\ndef get_api_users_stats(request):\n user_uuid = request.query_params.get('user_uuid')\n style_id = request.query_params.get('style_id')\n from_datetime = request.query_params.get('from_datetime')\n to_datetime = request.query_params.get('to_datetime')\n\n data = MlApiStatsRequest().filter(\n user_uuid=user_uuid,\n style_id=style_id,\n from_datetime=from_datetime,\n to_datetime=to_datetime\n )\n dataset = []\n if len(data) > 0:\n dataset = format_api_dataset(data)\n\n return Response(dataset, status=status.HTTP_200_OK)\n","sub_path":"client_api/statistics/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"199586575","text":"from setuptools import setup\n\nwith open(\"README.md\", 'r') as f:\n long_description = f.read()\n\nsetup(\n name='namedns',\n version='0.1',\n author='Mitch Kiah',\n author_email='mitch@mitchkiah.com',\n description=(\"Tool to manage domains and DNS records using\"\n \"Name.com API.\"),\n long_description=long_description,\n license='',\n keywords=\"namedotcom name.com namedns donutsinc\",\n # url=\"https://gitlab.com/mitchkiah/namedns\",\n packages=['namedns'],\n install_requires=[\n 'Click', 'requests'\n ],\n entry_points='''\n [console_scripts]\n namedns=namedns.cli:cli\n ''',\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"114875085","text":"from http import HTTPStatus\nimport json\nimport math\nfrom typing import Dict\nimport requests\n\nurl = 'https://api.postcodes.io/postcodes/{}'\n\n\ndef get_details(postcode: str) -> Dict:\n '''\n Get postcode data from postcode.io\n '''\n response = requests.get(url.format(postcode))\n if response.status_code == HTTPStatus.OK:\n try:\n res_dict = json.loads(response.text)\n except json.JSONDecodeError:\n return {}\n return res_dict.get('result', {})\n return {}\n\n\ndef find_the_distance(source: Dict, target: Dict) -> float:\n '''\n Find the distance between two coordinates\n '''\n d_lat = math.radians(target.get('latitude', 0) - source.get('latitude', 0))\n d_lng = math.radians(target.get('longitude', 0)\n - source.get('longitude', 0))\n lat1 = math.radians(source.get('latitude', 0))\n lat2 = math.radians(target.get('latitude', 0))\n temp = math.sin(d_lat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * \\\n math.sin(d_lng / 2) ** 2\n return 6373.0 * (2 * math.atan2(math.sqrt(temp), math.sqrt(1 - temp)))\n","sub_path":"build/lib/trails/helpers/postcodes.py","file_name":"postcodes.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"425587852","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass General2dBinningPlot():\n def __init__(self, bins, x_label='x-axis', y_label='y-axis', title='', y_label_hist='Histogram (fraction)', histogram_log=True, histogram_fraction=True,\n yscale='linear'):\n self.models_data = list()\n self.e_bins = bins\n\n self.yscale=yscale\n\n if type(bins) is not np.ndarray:\n raise ValueError(\"bins has to be numpy array\")\n\n self.x_label = x_label\n self.y_label = y_label\n self.title = title\n self.y_label_hist = y_label_hist\n self.histogram_log=histogram_log\n self.histogram_fraction=histogram_fraction\n # self.e_bins = [0, 1., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16,18, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 120,140,160,180,200]\n\n\n def _compute(self, x_values, y_values, weights=None):\n e_bins = self.e_bins\n e_bins_n = np.array(e_bins)\n e_bins_n = (e_bins_n - e_bins_n.min()) / (e_bins_n.max() - e_bins_n.min())\n\n centers = []\n mean = []\n\n lows = []\n highs = []\n\n if weights is None:\n weights = np.ones_like(y_values)\n\n for i in range(len(e_bins) - 1):\n l = e_bins[i]\n h = e_bins[i + 1]\n\n\n filter = np.argwhere(np.logical_and(x_values >= l, x_values < h))\n filtered_y_values = y_values[filter].astype(float)\n filtered_weights = weights[filter].astype(float)\n\n m = np.sum(filtered_y_values*filtered_weights)/np.sum(filtered_weights)\n mean.append(m)\n # print(np.sum(filtered_found), len(filtered_found), m, l, h)\n lows.append(l)\n highs.append(h)\n\n\n hist_values, _ = np.histogram(x_values, bins=e_bins)\n # hist_values = (hist_values / (e_bins_n[1:] - e_bins_n[:-1])).tolist()\n # hist_values = (hist_values / np.sum(hist_values))\n\n processed_data = dict()\n processed_data['bin_lower_energy'] = np.array(lows)\n processed_data['bin_upper_energy'] = np.array(highs)\n processed_data['hist_values'] = hist_values\n processed_data['mean'] = np.array(mean)\n\n return processed_data\n\n def add_raw_values(self, x_values, y_values, tags={}, weights=None):\n if type(x_values) is not np.ndarray:\n raise ValueError(\"x values has to be numpy array\")\n if type(y_values) is not np.ndarray:\n raise ValueError(\"y values has to be numpy array\")\n if weights is not None:\n if type(weights) is not np.ndarray:\n raise ValueError(\"weights has to be numpy array\")\n\n\n\n data = self._compute(x_values, y_values, weights=weights)\n data['tags'] = tags\n self.models_data.append(data)\n\n def add_processed_data(self, processed_data):\n self.models_data.append(processed_data)\n\n def draw(self, name_tag_formatter=None, return_fig=True):\n \"\"\"\n\n :param name_tag_formatter: a function to which tags dict is given and it returns the name\n :return:\n \"\"\"\n fig, ax1 = plt.subplots(1, 1, figsize=(9, 6))\n ax2 = ax1.twinx()\n\n max_of_hist_values = 0\n\n do_legend = False\n\n for model_data in self.models_data:\n error_exists = 'error' in model_data\n lows = model_data['bin_lower_energy']\n highs = model_data['bin_upper_energy']\n hist_values = model_data['hist_values']\n mean = model_data['mean']\n if error_exists:\n error = model_data['error']\n\n e_bins = self.e_bins\n e_bins_n = np.array(e_bins)\n e_bins_n = (e_bins_n - e_bins_n.min()) / (e_bins_n.max() - e_bins_n.min())\n if self.histogram_fraction:\n hist_values = (hist_values / (e_bins_n[1:] - e_bins_n[:-1])).tolist()\n hist_values = (hist_values / np.sum(hist_values))\n\n tags = model_data['tags']\n\n if name_tag_formatter is None:\n name_of_plot = ''\n else:\n name_of_plot = name_tag_formatter(tags)\n\n do_legend = do_legend or len(name_of_plot) > 0\n\n hist_values = hist_values.tolist()\n mean = mean.tolist()\n\n e_bins = np.concatenate(([lows[0]], highs), axis=0)\n max_of_hist_values = max(max_of_hist_values, np.max(hist_values))\n\n ax2.step(e_bins, [hist_values[0]] + hist_values, color='tab:gray', alpha=0)\n ax2.fill_between(e_bins, [hist_values[0]] + hist_values, step=\"pre\", alpha=0.2)\n\n ax2.set_ylabel(self.y_label_hist)\n if self.histogram_log:\n ax2.set_yscale('log')\n ax1.set_title(self.title)\n\n if error_exists:\n err_1 = ((np.array(mean) + error/2)).tolist()\n err_2 = ((np.array(mean) - error/2)).tolist()\n ax1.step(e_bins, [mean[0]] + mean, label=name_of_plot)\n ax1.fill_between(e_bins, [err_1[0]] + err_1, [err_2[0]] + err_2, alpha=0.4, step=\"pre\")\n # else:\n else:\n ax1.step(e_bins, [mean[0]] + mean, label=name_of_plot)\n\n ax1.set_xlabel(self.x_label)\n ax1.set_ylabel(self.y_label)\n ax1.set_yscale(self.yscale)\n if do_legend:\n ax1.legend(loc='center right')\n\n # ax1.set_ylim(0, 1.04)\n # ax2.set_ylim(0, max_of_hist_values * 1.3)\n if return_fig:\n return fig\n\n @classmethod\n def draw_static(cls, x_values, y_values):\n plotter = General2dBinningPlot()\n plotter.add_raw_values(x_values, y_values, tags=None)\n plotter.draw()\n\n def write_to_database(self, database_manager, table_name):\n # print(\"Iterating\")\n for model_data in self.models_data:\n # print(\"Iterating xyz\")\n lows = model_data['bin_lower_energy']\n highs = model_data['bin_upper_energy']\n hist_values = model_data['hist_values']\n mean = model_data['mean']\n tags = model_data['tags']\n\n if 'error' in model_data:\n error = model_data['error']\n\n database_data = dict()\n for i in range(len(lows)):\n # database_data['bin_lower_energy'] = lows[i]\n # database_data['bin_upper_energy'] = highs[i]\n database_data['hist_values_%d'%i] = float(hist_values[i])\n database_data['mean_%d'%i] = float(mean[i])\n if 'error' in model_data:\n database_data['error_%d'%i] = float(error[i])\n\n for tag_name, tag_value in tags.items():\n database_data[tag_name] = tag_value\n\n # print(\"Inserting \", database_data, table_name)\n database_manager.insert_experiment_data(table_name, database_data)\n\n\n def get_tags(self):\n return [x['tags'] for x in self.models_data]\n\n def read_from_database(self, database_reading_manager, table_name, experiment_name=None, condition=None):\n results_dict = database_reading_manager.get_data(table_name, experiment_name, condition_string=condition)\n num_rows = len(results_dict['experiment_name'])\n\n results_dict_copy = results_dict.copy()\n\n error_exists = 'error_0' in results_dict\n\n for i in range(len(self.e_bins) - 1):\n results_dict_copy.pop('mean_%d' % i)\n results_dict_copy.pop('hist_values_%d' % i)\n if error_exists:\n results_dict_copy.pop('error_%d' % i)\n\n tags_names = results_dict_copy.keys()\n\n # print(results_dict.keys())\n\n for row in range(num_rows):\n processed_data = dict()\n\n lows = []\n highs = []\n hist_values = []\n mean = []\n if error_exists:\n error = []\n\n for i in range(len(self.e_bins) - 1):\n l = self.e_bins[i]\n h = self.e_bins[i + 1]\n lows.append(l)\n highs.append(h)\n mean.append(float(results_dict['mean_%d'%i][row]))\n hist_values.append(float(results_dict['hist_values_%d'%i][row]))\n if error_exists:\n error.append(float(results_dict['hist_values_%d'%i][row]))\n\n processed_data['hist_values'] = np.array(hist_values)\n processed_data['mean'] = np.array(mean)\n\n if error_exists:\n processed_data['error'] = np.array(error)\n\n processed_data['bin_lower_energy'] = np.array(lows)\n processed_data['bin_upper_energy'] = np.array(highs)\n tags = dict()\n\n for tag_name in tags_names:\n tags[tag_name] = results_dict[tag_name][row]\n\n processed_data['tags'] = tags\n self.add_processed_data(processed_data)\n\n\n\n\n\n\n\n","sub_path":"modules/hplots/general_2d_plot.py","file_name":"general_2d_plot.py","file_ext":"py","file_size_in_byte":8894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"13155368","text":"# Copyright 2019 The ASReview Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom PyInquirer import prompt, Separator\n\nfrom asreview.ascii import ASCII_TEA\nfrom asreview.config import NOT_AVAILABLE\nfrom asreview.review import BaseReview\nfrom asreview.review.base import _merge_prior_knowledge\nfrom asreview.types import convert_list_type\n\n\ndef update_stats(stats, label):\n if label == 1:\n stats[\"n_included\"] += 1\n stats[\"last_inclusion\"] = 0\n else:\n stats[\"n_excluded\"] += 1\n stats[\"last_inclusion\"] += 1\n stats[\"n_reviewed\"] += 1\n stats[\"n_pool\"] -= 1\n\n\nclass ReviewOracle(BaseReview):\n \"\"\" Review class for Oracle mode on the command line. \"\"\"\n\n def __init__(self, X, as_data, *args, use_cli_colors=True,\n **kwargs):\n self.as_data = as_data\n super(ReviewOracle, self).__init__(\n X,\n y=np.tile([NOT_AVAILABLE], X.shape[0]),\n *args,\n **kwargs)\n\n self.use_cli_colors = use_cli_colors\n\n def _papers_from_finder(self):\n \"Find papers using a fuzzy finder in the available records.\"\n question = [\n {\n 'type': 'input',\n 'name': 'keywords',\n 'message': 'Find papers using keywords/authors/title:',\n }\n ]\n try:\n keywords = prompt(question)['keywords']\n except KeyError:\n return\n\n all_prior = self.prior_included + self.prior_excluded\n paper_idx = self.as_data.fuzzy_find(keywords, exclude=all_prior)\n\n # Get the (possibly) relevant papers.\n choices = []\n for idx in paper_idx:\n choices.append(self.as_data.preview_record(idx))\n choices.extend([Separator(), \"return\"])\n\n # Stay in the same menu until no more options are left\n while len(choices) > 2:\n question = [\n {\n 'type': 'list',\n 'name': 'paper',\n 'message': 'Choose a paper to review:',\n 'choices': choices\n }\n ]\n new_choice = prompt(question).get('paper', \"return\")\n if new_choice == \"return\":\n return\n choice_idx = choices.index(new_choice)\n idx = paper_idx[choice_idx]\n\n # Get the label for the selected paper.\n label = self._get_labels_paper(idx, ask_stop=False)\n if label == 1:\n self.prior_included.append(idx)\n elif label == 0:\n self.prior_excluded.append(idx)\n\n # Remove the selected choice from the list.\n del choices[choice_idx]\n del paper_idx[choice_idx]\n return\n\n def _papers_from_id(self):\n \"Get papers by a list of IDs.\"\n question = [\n {\n 'type': 'input',\n 'name': 'included',\n 'message': 'Which papers do you want to include?\\n'\n 'Separate paper indices by spaces:',\n },\n {\n 'type': 'input',\n 'name': 'excluded',\n 'message': 'Which papers do you want to exclude?\\n'\n 'Separate paper indices by spaces:',\n }\n ]\n answer = prompt(question)\n try:\n included = answer[\"included\"]\n excluded = answer[\"excluded\"]\n except KeyError:\n return\n self.prior_included.extend(convert_list_type(included.split(), int))\n self.prior_excluded.extend(convert_list_type(excluded.split(), int))\n\n def priors_from_cli(self):\n \"Get initial papers for modelling.\"\n while True:\n question = [\n {\n 'type': 'list',\n 'name': 'action',\n 'message': 'What do you want to do next?',\n 'choices': [\n \"Find papers by keywords\",\n \"Add papers from ID's\",\n Separator(),\n f\"Start review ({len(self.prior_included)} included, \"\n f\"{len(self.prior_excluded)} excluded)\",\n \"Stop\"\n ]\n }\n ]\n action = prompt(question).get(\"action\", \"Stop\")\n\n if action.startswith(\"Add papers\"):\n self._papers_from_id()\n elif action.startswith(\"Find papers\"):\n self._papers_from_finder()\n elif action.startswith(\"Stop\"):\n raise KeyboardInterrupt\n elif action.startswith(\"Start review\"):\n break\n\n def _prior_knowledge(self):\n \"\"\"Create prior knowledge from arguments.\"\"\"\n\n self.priors_from_cli()\n prior_indices, prior_labels = _merge_prior_knowledge(\n self.prior_included, self.prior_excluded)\n return np.array(prior_indices, dtype=np.int), np.array(\n prior_labels, dtype=np.int)\n\n def _prior_teach(self):\n\n print(\"\\n\\n We work, you drink tea.\\n\")\n print(ASCII_TEA)\n\n def _format_paper(self,\n title=None,\n abstract=None,\n keywords=None,\n authors=None):\n\n if self.use_cli_colors:\n title = \"\\033[95m\" + title + \"\\033[0m\"\n\n return f\"\\n{title}\\n{authors}\\n\\n{abstract}\\n\"\n\n def _get_labels_paper(self, index, stat_str=None, ask_stop=True):\n \"\"\"Ask the user for a label for a particular paper.\n\n Arguments\n ---------\n index: int\n Paper ID in the dataset.\n stat_str: str\n Display this (statistic) string under the paper.\n ask_stop: bool\n Ask for confirmation when stopping.\n \"\"\"\n # CLI paper format\n self.as_data.print_record(index)\n if stat_str is not None:\n print(stat_str + \"\\n\")\n\n def _interact():\n question = [\n {\n 'type': 'list',\n 'name': 'action',\n 'message': 'Include or Exclude?',\n 'default': 'Exclude',\n 'choices': [\n 'Exclude', 'Include', Separator(),\n 'Export', Separator(), 'Stop'\n ],\n 'filter': lambda val: val.lower()\n }\n ]\n action = prompt(question).get(\"action\", \"stop\")\n if action == \"stop\" and ask_stop:\n question = [\n {\n 'type': 'confirm',\n 'message': \"Are you sure you want to stop?\",\n 'name': 'stop',\n 'default': 'false',\n }\n ]\n stopping = prompt(question).get(\"stop\", True)\n if stopping:\n return None\n else:\n return _interact()\n elif action == \"export\":\n self._export()\n return _interact()\n return action\n\n action = _interact()\n\n if action == \"include\":\n label = 1\n elif action == \"exclude\":\n label = 0\n else:\n label = None\n\n return label\n\n def train(self, *args, **kwargs):\n print(ASCII_TEA)\n super(ReviewOracle, self).train(*args, **kwargs)\n\n def review(self, *args, instant_save=True, **kwargs):\n super(ReviewOracle, self).review(*args, instant_save=instant_save,\n **kwargs)\n\n def _export(self):\n \"\"\"Export the results to a csv file.\n\n Order of records is: [included, not reviewed (by proba), excluded]\n \"\"\"\n question = [\n {\n 'type': 'input',\n 'name': 'file_name',\n 'validate': lambda val: val.endswith((\".csv\", \".ris\")),\n 'message': 'Type file name for export ending with .csv or .ris'\n }\n ]\n try:\n file_name = prompt(question)[\"file_name\"]\n except KeyError:\n return\n pred_proba = self.query_kwargs.get('pred_proba', None)\n pool_idx = np.delete(np.arange(len(self.y)), self.train_idx)\n if pred_proba is not None:\n proba_order = np.argsort(pred_proba[pool_idx, 1])\n else:\n proba_order = np.arange(len(pool_idx))\n train_zero = self.train_idx[np.where(self.y[self.train_idx] == 0)[0]]\n train_one = self.train_idx[np.where(self.y[self.train_idx] == 1)[0]]\n df_order = np.concatenate(\n (train_one, pool_idx[proba_order], train_zero), axis=None)\n assert len(df_order) == len(self.y)\n for i in range(len(self.y)):\n assert i in df_order\n labels = np.full(len(self.y), np.nan, dtype=object)\n labels[self.train_idx] = self.y[self.train_idx]\n self.as_data.to_file(fp=file_name, labels=labels,\n df_order=df_order)\n\n def get_stats(self, stats):\n n_included = stats[\"n_included\"]\n n_papers = stats[\"n_papers\"]\n n_reviewed = stats[\"n_reviewed\"]\n perc_read = 100*(stats[\"n_reviewed\"]/stats[\"n_papers\"])\n if(n_reviewed == 0):\n perc_included = np.nan\n else:\n perc_included = 100*n_included/n_reviewed\n last_inclusion = stats[\"last_inclusion\"]\n stat_str = (f\"| {perc_read:.2f}% read | {last_inclusion} since last \"\n f\"inclusion | {perc_included:.2f}% included |\"\n f\" total papers: {n_reviewed}/{n_papers} |\")\n return stat_str\n\n def _get_labels(self, ind):\n \"Get a sequence of labels.\"\n y = np.zeros((len(ind), ), dtype=np.int)\n stats = self.statistics()\n\n for j, index in enumerate(ind):\n label = self._get_labels_paper(index,\n stat_str=self.get_stats(stats))\n if label is None:\n raise KeyboardInterrupt\n update_stats(stats, label)\n y[j] = label\n\n return y\n","sub_path":"asreview/review/oracle.py","file_name":"oracle.py","file_ext":"py","file_size_in_byte":10765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"551085097","text":"import linear_model as lm\n\ndef feature_selection(X: pd.DataFrame, y: pd.DataFrame, thresh):\n \"\"\"\n Evaluate for every feature it's pearson correlation\n against y and plot it against price for visualization\n :param thresh: min corr allowed\n :param X: dataframe of the design matrix\n :param y: response vector (price)\n :return: the two highest correlated features\n \"\"\"\n S = ['intercept']\n while True:\n w, singular = lm.fit_linear_model(X[S].to_numpy(), y.to_numpy())\n y_hat = X[S] @ w\n z = y_hat - y\n corr = lm.pearson_corr(X, z)\n\n feature = corr.abs().idxmax()\n if abs(corr[feature]) < thresh:\n break\n\n y = z\n S.append(feature)\n\n S.append('price')\n return S","sub_path":"ex2/ex2_evaluation.py","file_name":"ex2_evaluation.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"363722443","text":"# Brain Data Visualizer for ASP Brain Model output\n# by Richard Watson\n# Texas Tech Unviersity\n# Version 1.0\n# 5/6/14\n#\n# This program allows the user to view data output from a run of an ASP Brain\n# Model in a more human readable way. The program prompts the user for the\n# name of a file containing the ASP output and prints:\n# 1) For each node, what, if anything was accepted based on inline or\n# reconstitution acceptance tests.\n# 2) For each node, what meassages were sent from that node to each of the\n# nodes it is linked to. If a message was not sent, either due to\n# the node being bad or for some other reason, it is indicated as well.\n# 3) For each node, what meassagee were sent to that node from each of the\n# nodes it is linked to. If a message was not sent, either due to\n# sending node being bad or for some other reason, it is indicated as well.\n# It is assumed that the data in that file was the output of an ASP Brain\n# model that had been piped through MKATOMS before being stored.\n\n\n# Function index_of_sub takes 2 arguemnts\n# s - a string\n# bm - a list of strings\n# and returns the index of the first element of the list of which s is a\n# substring. If s is not a substring of any string within bm then -1\n# is returned. The function assumes input is of the correct types - no\n# type checking is done.\ndef index_of_sub(s,bm):\n x = 0\n for item in bm:\n if item.find(s) != -1:\n return x\n else:\n x = x + 1\n return -1\n\n# Function output_message_from takes 3 arguments\n# i & j - integers representing node numbers\n# bm - a list of strings containing the predicates of an answer set of the\n# brain model\n# and prints out one of three forms of messages about what was sent\n# from node i to node j. in accordance with the data stored in variable bm,\n# the message either states:\n# 1) that no frame was sent because node i was bad and omitted sending;\n# 2) that no frame was sent for an unknown reason (propogation didn't\n# forward); or\n# 3) the data sent in the frame, giving the inititaor id, the partner,\n# the data sent, the hop count, the integrity flag, and the pair congruency\n# flag.\ndef output_message_from(i,j,bm):\n if ('omit('+str(i)+','+str(j)+')\\n') in bm:\n print('No message sent from',i,'to',j,'due to bad node',i)\n elif ('-frame_sent('+str(i)+','+str(j)+')\\n') in bm:\n print('No Message sent from',i,'to',j,'for unknown reason')\n else:\n print('Message sent from',i,'to',j,':',end=' ')\n line = index_of_sub('id('+str(i)+','+str(j),bm)\n print(bm[line][7],end=' ')\n line = index_of_sub('partner('+str(i)+','+str(j),bm)\n print(bm[line][12],end=' ')\n line = index_of_sub('sent_to('+str(i)+','+str(j),bm)\n if bm[line][12] == 'g':\n print('good ',end=' ')\n else:\n print('bad('+bm[line][16]+')',end=' ')\n line = index_of_sub('hop_count('+str(i)+','+str(j),bm)\n print(bm[line][14],end=' ')\n line = index_of_sub('integrity('+str(i)+','+str(j),bm)\n if line != -1:\n print('T',end=' ')\n else:\n print('F',end=' ')\n line = index_of_sub('pair_cong('+str(i)+','+str(j),bm)\n if line != -1:\n print('T')\n else:\n print('F')\n\n# Function output_message_to takes 3 arguments\n# i & j - integers representing node numbers\n# bm - a list of strings containing the predicates of an answer set of the\n# brain model\n# and prints out one of three forms of messages about what was sent\n# to node i from node j. in accordance with the data stored in variable bm,\n# the message either states:\n# 1) that no frame was sent because node i was bad and omitted sending;\n# 2) that no frame was sent for an unknown reason (propogation didn't\n# forward); or\n# 3) the data sent in the frame, giving the inititaor id, the partner,\n# the data sent, the hop count, the integrity flag, and the pair congruency\n# flag.\ndef output_message_to(i,j,bm):\n if ('omit('+str(j)+','+str(i)+')\\n') in bmodel:\n print('No message sent to',i,'from',j,'due to bad node',j)\n elif ('-frame_sent('+str(j)+','+str(i)+')\\n') in bmodel:\n print('No Message sent to',i,'from',j,'for unknown reason')\n else:\n print('Message sent to',i,'from',j,':',end=' ')\n line = index_of_sub('id('+str(j)+','+str(i),bm)\n print(bm[line][7],end=' ')\n line = index_of_sub('partner('+str(j)+','+str(i),bm)\n print(bm[line][12],end=' ')\n line = index_of_sub('sent_to('+str(j)+','+str(i),bm)\n if bm[line][12] == 'g':\n print('good ',end=' ')\n else:\n print('bad('+bm[line][16]+')',end=' ')\n line = index_of_sub('hop_count('+str(j)+','+str(i),bm)\n print(bm[line][14],end=' ')\n line = index_of_sub('integrity('+str(j)+','+str(i),bm)\n if line != -1:\n print('T',end=' ')\n else:\n print('F',end=' ')\n line = index_of_sub('pair_cong('+str(j)+','+str(i),bm)\n if line != -1:\n print('T')\n else:\n print('F')\n\n# Function do_acceptances takes one argument\n# bm - a list of strings containing the predicates of an answer set of the\n# brain model\n# and prints out, in order of the non-sending nodes, from 2 - 7, two statements\n# indicating if the node passed the inline acceptance test and if the\n# node passed reconstitution acceptance test. In each case, if it passed the\n# test it indicates the value of the data that was accepted. The messages\n# are determined by the data contained in variable bm.\ndef do_acceptances(bm):\n print('_________________________________________________________')\n print('Acceptances:')\n for i in range(2,8):\n line = index_of_sub('inline_accept('+str(i),bm)\n if line != -1:\n print(bm[line],end='')\n else:\n print('inline_accept failed for node', i)\n line = index_of_sub('recon_accept('+str(i),bm)\n if line != -1:\n print(bm[line],end='')\n else:\n print('recon_accept failed for node', i)\n print('_________________________________________________________')\n \n# Function do_messages_from_nodes takes one argument\n# bm - a list of strings containing the predicates of an answer set of the\n# brain model\n# and loops through the list of nodes and calls output_message_from to\n# print messages about what each node sent to its 4 connected neighbors in\n# accordance with the data in bm. \ndef do_messages_from_nodes(bm):\n print('_________________________________________________________')\n print('Messages sent from:')\n print(' Id Pa Data HC In PC')\n for i in range(0,8):\n for j in [(i+6)%8,(i+7)%8,(i+1)%8,(i+2)%8]:\n output_message_from(i,j,bm)\n print('_________________________________________________________')\n \n\n# Function do_messages_to_nodes takes one argument\n# bm - a list of strings containing the predicates of an answer set of the\n# brain model\n# and loops through the list of nodes and calls output_message_to to\n# print messages about what each node received from its 4 connected neighbors in\n# accordance with the data in bm. \ndef do_messages_to_nodes(bm):\n print('_________________________________________________________')\n print('Messages sent to:')\n print(' Id Pa Data HC In PC')\n for i in range(0,8):\n for j in [(i+6)%8,(i+7)%8,(i+1)%8,(i+2)%8]:\n output_message_to(i,j,bm)\n print('_________________________________________________________')\n \n# The main body of the code prompts for the input file as indicated in the\n# comment at the top of the code, reads the file into bmodels, and calls the\n# funtions to output the data.\nfname = input('Enter Brain output file name - ')\nfhandle = open(fname,'r')\nbmodel = fhandle.readlines()\nfhandle.close()\ndo_acceptances(bmodel)\ndo_messages_from_nodes(bmodel)\ndo_messages_to_nodes(bmodel)\n\n\n\n \n \n \n\n\n \n \n","sub_path":"ASP/Orig/brainformat.py","file_name":"brainformat.py","file_ext":"py","file_size_in_byte":8173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"271137149","text":"\"\"\"\nImplementation of Upload view. This view takes an input image name and returns a signed URL that can be used to\nupload the image to S3\n\"\"\"\n\nfrom flask.views import MethodView\nfrom flask import request\nfrom marshmallow import ValidationError\nfrom .errors import InternalServerError\nfrom ...utils.aws_auth import AWSAuth\nfrom ...utils.allowed_extensions import FILE_CONTENT_TYPES\nfrom ..schemas.upload import UploadSchema\n\n\nclass UploadImage(MethodView):\n \"\"\" Generates a pre-signed URL that can be used to post an image to AWS S3\n ---\n post:\n url parameters:\n - Required:\n file_name: [string]\n - Optional:\n expiration: [int], default=1000\n content_size_min: [int] range=[min=1000, max=10000000], default=1000\n content_size_max: [int] range=[min=1000, max=10000000], default=10000000\n success response:\n 200\n content: {'success': True, 'post_url': post_url, 'data': fields dictionary returned by\n generate_presigned_post from boto3 SDK}\n failure response:\n 422: Bad client parameters\n 404: Endpoint doesn't exist\n \"\"\"\n def __init__(self, args):\n cfg = args.get(\"cfg\")\n self.s3_bucket_name = cfg.get(\"S3_BUCKET_NAME\")\n self.region_name = cfg.get(\"REGION_NAME\")\n self.acl = cfg.get(\"ACL\")\n self.logger = args.get('logger')\n if self.s3_bucket_name and self.region_name:\n self.aws_auth = AWSAuth()\n\n\n def post(self):\n \"\"\"\n Post implementation\n \"\"\"\n if self.aws_auth is None or self.s3_bucket_name is None:\n raise InternalServerError\n\n args = request.form\n\n try:\n vargs = UploadSchema().load(args)\n # client validation error\n except ValidationError as err:\n self.logger.warn(err.messages)\n return err.messages, 422\n\n image_name = vargs['file_name']\n content_size_min = vargs['content_size_min']\n content_size_max = vargs['content_size_max']\n expiration = vargs['expiration']\n extension = image_name.rsplit('.', 1)[1].lower()\n object_name = 'images/' + image_name\n # Note if your bucket is not publicly accessible (to be accessed by assuming an IAM role),\n # you must set the acl to private, otherwise you'll get an access denied error\n fields = {\"acl\": self.acl, \"Content-Type\": FILE_CONTENT_TYPES[extension]}\n conditions = [\n {\"acl\": \"private\"},\n {\"Content-Type\": FILE_CONTENT_TYPES[extension]},\n [\"content-length-range\", content_size_min, content_size_max]\n ]\n try:\n resp = self.aws_auth.create_presigned_post(self.region_name, self.s3_bucket_name,\n object_name, fields=fields,\n conditions=conditions, expiration=expiration)\n post_url = resp['url']\n data = resp['fields']\n return {'success': True, 'post_url': post_url, 'data': data}\n except ValueError as err:\n self.logger.warn(err)\n raise InternalServerError(\"Internal Server Error\")\n","sub_path":"bff_api/api/resources/signed_url.py","file_name":"signed_url.py","file_ext":"py","file_size_in_byte":3285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"504234506","text":"#!/usr/bin/env python\n\"\"\"\nWrite pid and stdout/stderr to a standard location before execing a command.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nimport argparse\nimport contextlib\nimport logging\nimport os\nimport subprocess\nimport sys\n\nimport yaml\n\nlog = logging.getLogger(\"tron.action_runner\")\n\n\nSTATUS_FILE = 'status'\n\n\nclass StatusFile(object):\n \"\"\"Manage a status file.\"\"\"\n\n def __init__(self, filename):\n self.filename = filename\n\n def get_content(self, run_id, command, proc):\n return {\n 'run_id': run_id,\n 'command': command,\n 'pid': proc.pid,\n 'return_code': proc.returncode,\n }\n\n @contextlib.contextmanager\n def wrap(self, command, run_id, proc):\n with open(self.filename, 'w') as fh:\n yaml.safe_dump(\n self.get_content(\n run_id=run_id,\n command=command,\n proc=proc,\n ), fh,\n )\n try:\n yield\n finally:\n yaml.safe_dump(\n self.get_content(\n run_id=run_id,\n command=command,\n proc=proc,\n ), fh,\n )\n\n\ndef get_status_file(output_path):\n if os.path.isdir(output_path):\n if not os.access(output_path, os.W_OK):\n raise OSError(\"Output dir %s not writable\" % output_path)\n return StatusFile(os.path.join(output_path, STATUS_FILE))\n else:\n try:\n os.makedirs(output_path)\n except OSError:\n raise OSError(\"Could not create output dir %s\" % output_path)\n return StatusFile(os.path.join(output_path, STATUS_FILE))\n\n\ndef run_proc(output_path, command, run_id, proc):\n status_file = get_status_file(output_path)\n with status_file.wrap(\n command=command,\n run_id=run_id,\n proc=proc,\n ):\n proc.wait()\n sys.exit(proc.returncode)\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Action Runner for Tron')\n parser.add_argument(\n 'output_dir',\n help='an integer for the accumulator',\n )\n parser.add_argument(\n 'command',\n help='the command to run',\n )\n parser.add_argument(\n 'run_id',\n help='run_id of the process',\n )\n return parser.parse_args()\n\n\ndef run_command(command):\n return subprocess.Popen(\n command, shell=True, stdout=sys.stdout, stderr=sys.stderr,\n )\n\n\nif __name__ == \"__main__\":\n logging.basicConfig()\n args = parse_args()\n proc = run_command(args.command)\n run_proc(\n output_path=args.output_dir,\n run_id=args.run_id,\n command=args.command,\n proc=proc,\n )\n","sub_path":"bin/action_runner.py","file_name":"action_runner.py","file_ext":"py","file_size_in_byte":2841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"500624331","text":"from game2048.game import Game\nfrom game2048.displays import Display,IPythonDisplay\nfrom game2048.agents import Agent, RandomAgent, ExpectiMaxAgent\nimport numpy as np\nimport pandas as pd\n\ndisplay1=Display()\ndisplay2=IPythonDisplay()\n\n\n\ndef log2(board):\n for i in range(16):\n if board[i]!=0:\n board[i]=np.log2(board[i])\n return board\n\nfor i in range(0,100):\n print(i,\"is running\")\n game=Game(4,2048,random=False)\n agent=ExpectiMaxAgent(game,display=display2)\n n_iter=0\n max_iter=np.inf\n data=np.zeros((0,17),dtype=float)\n while(n_iter= 0):\r\n b2[\"state\"] = \"active\"\r\n else:\r\n b2[\"state\"] = \"disabled\"\r\n\r\n root.mainloop()\r\n\r\n\r\ndef Add():\r\n global stuInfo1, stuInfo2, stuInfo3, stuInfo4,stuInfo5, Canvas1, con, cur, bookTable, root\r\n\r\n headingFrame1 = Frame(root, bg=\"#FFBB00\", bd=5)\r\n headingFrame1.place(relx=0.25, rely=0.1, relwidth=0.5, relheight=0.13)\r\n\r\n headingLabel = Label(headingFrame1, text=\"ADD STUDENTS\", bg='black', fg='white', font=('Courier', 15))\r\n headingLabel.place(relx=0, rely=0, relwidth=1, relheight=1)\r\n\r\n labelFrame = Frame(root, bg='black')\r\n labelFrame.place(relx=0.15, rely=0.3, relwidth=0.7, relheight=0.5)\r\n\r\n option=[\"CSE\",\"MECH\",\"ECE\",\"EEE\",\"CIVIL\"]\r\n option2=[\"FIRST YEAR\",\"SECOND YEAR\",\"THIRD YEAR\",\"FINAL YEAR\"]\r\n\r\n lb1 = Label(labelFrame, text=\"Student ID : \", bg='black', fg='white')\r\n lb1.place(relx=0.05, rely=0.1, relheight=0.08)\r\n\r\n stuInfo1 = Entry(labelFrame)\r\n stuInfo1.place(relx=0.3, rely=0.1, relwidth=0.62, relheight=0.08)\r\n\r\n lb2 = Label(labelFrame, text=\"Student Name : \", bg='black', fg='white')\r\n lb2.place(relx=0.05, rely=0.25, relheight=0.08)\r\n\r\n stuInfo2 = Entry(labelFrame)\r\n stuInfo2.place(relx=0.3, rely=0.25, relwidth=0.62, relheight=0.08)\r\n\r\n lb3 = Label(labelFrame, text=\"Department Name \", bg='black', fg='white')\r\n lb3.place(relx=0.05, rely=0.40, relheight=0.08)\r\n stuInfo3 = ttk.Combobox(labelFrame, value=option, width=\"37\")\r\n stuInfo3.set(\"---Select Department Name---\")\r\n stuInfo3.place(relx=0.3, rely=0.40)\r\n\r\n lb4 = Label(labelFrame, text=\"Year:\", bg='black', fg='white')\r\n lb4.place(relx=0.05, rely=0.55, relheight=0.08)\r\n stuInfo4 = ttk.Combobox(labelFrame, value=option2, width=\"37\")\r\n stuInfo4.set(\"---Select Year---\")\r\n stuInfo4.place(relx=0.3, rely=0.55)\r\n\r\n lb5 = Label(labelFrame, text=\"Phone No. : \", bg='black', fg='white')\r\n lb5.place(relx=0.05, rely=0.7, relheight=0.08)\r\n stuInfo5 = Entry(labelFrame)\r\n stuInfo5.place(relx=0.3, rely=0.7, relwidth=0.62, relheight=0.08)\r\n\r\n SubmitBtnr = Button(labelFrame, text=\"OK\", bg='white', fg='black', width=10, height=2, command=Addstudent)\r\n SubmitBtnr.place(x=300, y=200)\r\n\r\ndef Addstudent():\r\n sid = stuInfo1.get()\r\n name = stuInfo2.get()\r\n dep = stuInfo3.get()\r\n year= stuInfo4.get()\r\n phone=stuInfo5.get()\r\n\r\n print(name,sid,dep,year,phone)\r\n #status = status.lower()\r\n\r\n insertstu = \"insert into students values('\" + sid + \"','\" + name + \"','\" + year + \"','\" + phone + \"','\" + dep + \"')\"\r\n if(sid.isnumeric()==False):\r\n messagebox.showinfo(\"Error\",\"Please check StudentID\")\r\n elif (sid == \"\" or name == \"\" or dep == \"\" or year == \"\" or phone == \"\"):\r\n messagebox.showinfo(\"Error\", \"Please add the required details\")\r\n elif (len(phone)!=10):\r\n messagebox.showinfo(\"Error\",\"Please check your phonenumber\")\r\n else:\r\n try:\r\n cur.execute(insertstu)\r\n con.commit()\r\n messagebox.showinfo('Success', \"Student added successfully\")\r\n except:\r\n messagebox.showinfo(\"Error\", \"Can't add data into Database\")\r\n\r\n\r\ndef studentb():\r\n global stuInfo1, stuInfo2, stuInfo3, stuInfo4, Canvas1, con, cur, bookTable, root\r\n\r\n root = Tk()\r\n root.title(\"Library\")\r\n root.minsize(width=400, height=400)\r\n root.geometry(\"1000x500\")\r\n\r\n mypass = \"850222Ass\"\r\n mydatabase = \"db\"\r\n con = pymysql.connect(host=\"localhost\", user=\"root\", password=mypass, database=mydatabase)\r\n\r\n '''mydatabase = \"mydatabase\"\r\n con = pymysql.connect(host=\"localhost\",user=\"root\",password=\"\",database=mydatabase)''' \r\n cur = con.cursor()\r\n\r\n bookTable = \"books\"\r\n Canvas1 = Canvas(root)\r\n Canvas1.config(bg=\"#ff6e40\")\r\n Canvas1.pack(expand=True, fill=BOTH)\r\n\r\n headingFrame1 = Frame(root, bg=\"#FFBB00\", bd=5)\r\n headingFrame1.place(relx=0.25, rely=0.1, relwidth=0.5, relheight=0.13)\r\n\r\n headingLabel = Label(headingFrame1, text=\"STUDENTS\", bg='black', fg='white', font=('Courier', 15))\r\n headingLabel.place(relx=0, rely=0, relwidth=1, relheight=1)\r\n\r\n\r\n # Submit Button\r\n SubmitBtn = Button(root, text=\"ADD\", bg='white', fg='black', width=10, height=2, command=Add)\r\n # SubmitBtn.place(relx=0.28,rely=0.9, relwidth=0.18,relheight=0.08)\r\n SubmitBtn.place(x=20, y=150)\r\n\r\n SubmitBtn1 = Button(root, text=\"DELETE\", bg='white', fg='black', width=10, height=2, command=delete)\r\n # SubmitBtn.place(relx=0.28,rely=0.9, relwidth=0.18,relheight=0.08)\r\n SubmitBtn1.place(x=20, y=200)\r\n\r\n SubmitBtn1 = Button(root, text=\"SEARCH\", bg='white', fg='black', width=10, height=2, command=search)\r\n # SubmitBtn.place(relx=0.28,rely=0.9, relwidth=0.18,relheight=0.08)\r\n SubmitBtn1.place(x=20, y=250)\r\n\r\n SubmitBtn1 = Button(root, text=\"UPDATE\", bg='white', fg='black', width=10, height=2, command=update)\r\n # SubmitBtn.place(relx=0.28,rely=0.9, relwidth=0.18,relheight=0.08)\r\n SubmitBtn1.place(x=20, y=300)\r\n\r\n SubmitBtn2 = Button(root, text=\"VIEW\", bg='white', fg='black', width=10, height=2, command=lambda:View2(0))\r\n # SubmitBtn.place(relx=0.28,rely=0.9, relwidth=0.18,relheight=0.08)\r\n SubmitBtn2.place(x=20, y=350)\r\n\r\n quitBtn = Button(root, text=\"Quit\", bg='white', fg='black', width=10, height=2, command=root.destroy)\r\n quitBtn.place(x=450, y=430)\r\n\r\n labelFrame = Frame(root, bg='black')\r\n labelFrame.place(relx=0.15, rely=0.3, relwidth=0.7, relheight=0.5)\r\n\r\n root.mainloop()\r\n","sub_path":"Student.py","file_name":"Student.py","file_ext":"py","file_size_in_byte":14318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"406958632","text":"import pytest\r\nfrom rating_app.app.models import Rating\r\n\r\n\r\nclass TestsRate:\r\n @pytest.mark.django_db\r\n def test_rate(self):\r\n rate = Rating.objects.create(name=\"test_1\", surname=\"test_2\", middlename=\"test_3\", rating=\"100.0\")\r\n assert rate.name == \"test_1\"\r\n assert rate.surname == \"test_2\"\r\n assert rate.middlename == \"test_3\"\r\n assert rate.rating == '100.0'","sub_path":"StudentsRating (laba 3-4)/rating_app/app/tests/test_rate.py","file_name":"test_rate.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"418317329","text":"import dataclasses\nimport time\nimport uuid\nimport warnings\nfrom abc import ABCMeta\nfrom collections.abc import Sequence\nfrom dataclasses import dataclass\nfrom dataclasses import field\nfrom enum import Flag\nfrom typing import Literal\n\nfrom mitmproxy import certs\nfrom mitmproxy.coretypes import serializable\nfrom mitmproxy.net import server_spec\nfrom mitmproxy.proxy import mode_specs\nfrom mitmproxy.utils import human\n\n\nclass ConnectionState(Flag):\n \"\"\"The current state of the underlying socket.\"\"\"\n\n CLOSED = 0\n CAN_READ = 1\n CAN_WRITE = 2\n OPEN = CAN_READ | CAN_WRITE\n\n\nTransportProtocol = Literal[\"tcp\", \"udp\"]\n\n\n# practically speaking we may have IPv6 addresses with flowinfo and scope_id,\n# but type checking isn't good enough to properly handle tuple unions.\n# this version at least provides useful type checking messages.\nAddress = tuple[str, int]\n\nkw_only = {\"kw_only\": True}\n\n\n# noinspection PyDataclass\n@dataclass(**kw_only)\nclass Connection(serializable.SerializableDataclass, metaclass=ABCMeta):\n \"\"\"\n Base class for client and server connections.\n\n The connection object only exposes metadata about the connection, but not the underlying socket object.\n This is intentional, all I/O should be handled by `mitmproxy.proxy.server` exclusively.\n \"\"\"\n\n peername: Address | None\n \"\"\"The remote's `(ip, port)` tuple for this connection.\"\"\"\n sockname: Address | None\n \"\"\"Our local `(ip, port)` tuple for this connection.\"\"\"\n\n state: ConnectionState = field(\n default=ConnectionState.CLOSED, metadata={\"serialize\": False}\n )\n \"\"\"The current connection state.\"\"\"\n\n # all connections have a unique id. While\n # f.client_conn == f2.client_conn already holds true for live flows (where we have object identity),\n # we also want these semantics for recorded flows.\n id: str = field(default_factory=lambda: str(uuid.uuid4()))\n \"\"\"A unique UUID to identify the connection.\"\"\"\n transport_protocol: TransportProtocol = field(default=\"tcp\")\n \"\"\"The connection protocol in use.\"\"\"\n error: str | None = None\n \"\"\"\n A string describing a general error with connections to this address.\n\n The purpose of this property is to signal that new connections to the particular endpoint should not be attempted,\n for example because it uses an untrusted TLS certificate. Regular (unexpected) disconnects do not set the error\n property. This property is only reused per client connection.\n \"\"\"\n\n tls: bool = False\n \"\"\"\n `True` if TLS should be established, `False` otherwise.\n Note that this property only describes if a connection should eventually be protected using TLS.\n To check if TLS has already been established, use `Connection.tls_established`.\n \"\"\"\n certificate_list: Sequence[certs.Cert] = ()\n \"\"\"\n The TLS certificate list as sent by the peer.\n The first certificate is the end-entity certificate.\n\n > [RFC 8446] Prior to TLS 1.3, \"certificate_list\" ordering required each\n > certificate to certify the one immediately preceding it; however,\n > some implementations allowed some flexibility. Servers sometimes\n > send both a current and deprecated intermediate for transitional\n > purposes, and others are simply configured incorrectly, but these\n > cases can nonetheless be validated properly. For maximum\n > compatibility, all implementations SHOULD be prepared to handle\n > potentially extraneous certificates and arbitrary orderings from any\n > TLS version, with the exception of the end-entity certificate which\n > MUST be first.\n \"\"\"\n alpn: bytes | None = None\n \"\"\"The application-layer protocol as negotiated using\n [ALPN](https://en.wikipedia.org/wiki/Application-Layer_Protocol_Negotiation).\"\"\"\n alpn_offers: Sequence[bytes] = ()\n \"\"\"The ALPN offers as sent in the ClientHello.\"\"\"\n # we may want to add SSL_CIPHER_description here, but that's currently not exposed by cryptography\n cipher: str | None = None\n \"\"\"The active cipher name as returned by OpenSSL's `SSL_CIPHER_get_name`.\"\"\"\n cipher_list: Sequence[str] = ()\n \"\"\"Ciphers accepted by the proxy server on this connection.\"\"\"\n tls_version: str | None = None\n \"\"\"The active TLS version.\"\"\"\n sni: str | None = None\n \"\"\"\n The [Server Name Indication (SNI)](https://en.wikipedia.org/wiki/Server_Name_Indication) sent in the ClientHello.\n \"\"\"\n\n timestamp_start: float | None = None\n timestamp_end: float | None = None\n \"\"\"*Timestamp:* Connection has been closed.\"\"\"\n timestamp_tls_setup: float | None = None\n \"\"\"*Timestamp:* TLS handshake has been completed successfully.\"\"\"\n\n @property\n def connected(self) -> bool:\n \"\"\"*Read-only:* `True` if Connection.state is ConnectionState.OPEN, `False` otherwise.\"\"\"\n return self.state is ConnectionState.OPEN\n\n @property\n def tls_established(self) -> bool:\n \"\"\"*Read-only:* `True` if TLS has been established, `False` otherwise.\"\"\"\n return self.timestamp_tls_setup is not None\n\n def __eq__(self, other):\n if isinstance(other, Connection):\n return self.id == other.id\n return False\n\n def __hash__(self):\n return hash(self.id)\n\n def __repr__(self):\n attrs = {\n # ensure these come first.\n \"id\": None,\n \"address\": None,\n }\n for f in dataclasses.fields(self):\n val = getattr(self, f.name)\n if val != f.default:\n if f.name == \"cipher_list\":\n val = f\"<{len(val)} ciphers>\"\n elif f.name == \"id\":\n val = f\"…{val[-6:]}\"\n attrs[f.name] = val\n return f\"{type(self).__name__}({attrs!r})\"\n\n @property\n def alpn_proto_negotiated(self) -> bytes | None: # pragma: no cover\n \"\"\"*Deprecated:* An outdated alias for Connection.alpn.\"\"\"\n warnings.warn(\n \"Connection.alpn_proto_negotiated is deprecated, use Connection.alpn instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return self.alpn\n\n\n# noinspection PyDataclass\n@dataclass(eq=False, repr=False, **kw_only)\nclass Client(Connection):\n \"\"\"A connection between a client and mitmproxy.\"\"\"\n\n peername: Address\n \"\"\"The client's address.\"\"\"\n sockname: Address\n \"\"\"The local address we received this connection on.\"\"\"\n\n mitmcert: certs.Cert | None = None\n \"\"\"\n The certificate used by mitmproxy to establish TLS with the client.\n \"\"\"\n\n proxy_mode: mode_specs.ProxyMode = field(\n default=mode_specs.ProxyMode.parse(\"regular\")\n )\n \"\"\"The proxy server type this client has been connecting to.\"\"\"\n\n timestamp_start: float = field(default_factory=time.time)\n \"\"\"*Timestamp:* TCP SYN received\"\"\"\n\n def __str__(self):\n if self.alpn:\n tls_state = f\", alpn={self.alpn.decode(errors='replace')}\"\n elif self.tls_established:\n tls_state = \", tls\"\n else:\n tls_state = \"\"\n state = self.state.name\n assert state\n return f\"Client({human.format_address(self.peername)}, state={state.lower()}{tls_state})\"\n\n @property\n def address(self): # pragma: no cover\n \"\"\"*Deprecated:* An outdated alias for Client.peername.\"\"\"\n warnings.warn(\n \"Client.address is deprecated, use Client.peername instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return self.peername\n\n @address.setter\n def address(self, x): # pragma: no cover\n warnings.warn(\n \"Client.address is deprecated, use Client.peername instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n self.peername = x\n\n @property\n def cipher_name(self) -> str | None: # pragma: no cover\n \"\"\"*Deprecated:* An outdated alias for Connection.cipher.\"\"\"\n warnings.warn(\n \"Client.cipher_name is deprecated, use Client.cipher instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return self.cipher\n\n @property\n def clientcert(self) -> certs.Cert | None: # pragma: no cover\n \"\"\"*Deprecated:* An outdated alias for Connection.certificate_list[0].\"\"\"\n warnings.warn(\n \"Client.clientcert is deprecated, use Client.certificate_list instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n if self.certificate_list:\n return self.certificate_list[0]\n else:\n return None\n\n @clientcert.setter\n def clientcert(self, val): # pragma: no cover\n warnings.warn(\n \"Client.clientcert is deprecated, use Client.certificate_list instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n if val:\n self.certificate_list = [val]\n else:\n self.certificate_list = []\n\n\n# noinspection PyDataclass\n@dataclass(eq=False, repr=False, **kw_only)\nclass Server(Connection):\n \"\"\"A connection between mitmproxy and an upstream server.\"\"\"\n\n address: Address | None # type: ignore\n \"\"\"The server's `(host, port)` address tuple. The host can either be a domain or a plain IP address.\"\"\"\n\n peername: Address | None = None\n \"\"\"\n The server's resolved `(ip, port)` tuple. Will be set during connection establishment.\n May be `None` in upstream proxy mode when the address is resolved by the upstream proxy only.\n \"\"\"\n sockname: Address | None = None\n\n timestamp_start: float | None = None\n \"\"\"\n *Timestamp:* Connection establishment started.\n\n For IP addresses, this corresponds to sending a TCP SYN; for domains, this corresponds to starting a DNS lookup.\n \"\"\"\n timestamp_tcp_setup: float | None = None\n \"\"\"*Timestamp:* TCP ACK received.\"\"\"\n\n via: server_spec.ServerSpec | None = None\n \"\"\"An optional proxy server specification via which the connection should be established.\"\"\"\n\n def __str__(self):\n if self.alpn:\n tls_state = f\", alpn={self.alpn.decode(errors='replace')}\"\n elif self.tls_established:\n tls_state = \", tls\"\n else:\n tls_state = \"\"\n if self.sockname:\n local_port = f\", src_port={self.sockname[1]}\"\n else:\n local_port = \"\"\n state = self.state.name\n assert state\n return f\"Server({human.format_address(self.address)}, state={state.lower()}{tls_state}{local_port})\"\n\n def __setattr__(self, name, value):\n if name in (\"address\", \"via\"):\n connection_open = (\n self.__dict__.get(\"state\", ConnectionState.CLOSED)\n is ConnectionState.OPEN\n )\n # assigning the current value is okay, that may be an artifact of calling .set_state().\n attr_changed = self.__dict__.get(name) != value\n if connection_open and attr_changed:\n raise RuntimeError(f\"Cannot change server.{name} on open connection.\")\n return super().__setattr__(name, value)\n\n @property\n def ip_address(self) -> Address | None: # pragma: no cover\n \"\"\"*Deprecated:* An outdated alias for `Server.peername`.\"\"\"\n warnings.warn(\n \"Server.ip_address is deprecated, use Server.peername instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n return self.peername\n\n @property\n def cert(self) -> certs.Cert | None: # pragma: no cover\n \"\"\"*Deprecated:* An outdated alias for `Connection.certificate_list[0]`.\"\"\"\n warnings.warn(\n \"Server.cert is deprecated, use Server.certificate_list instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n if self.certificate_list:\n return self.certificate_list[0]\n else:\n return None\n\n @cert.setter\n def cert(self, val): # pragma: no cover\n warnings.warn(\n \"Server.cert is deprecated, use Server.certificate_list instead.\",\n DeprecationWarning,\n stacklevel=2,\n )\n if val:\n self.certificate_list = [val]\n else:\n self.certificate_list = []\n\n\n__all__ = [\"Connection\", \"Client\", \"Server\", \"ConnectionState\"]\n","sub_path":"mitmproxy/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":12264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"564961395","text":"import xml.dom.minidom\r\nimport re\r\nimport os\r\nre.UNICODE\r\n\r\ndef get_text_from_xml_files(folder):\r\n thread_files = {}\r\n for filename in os.listdir(folder):\r\n file = folder + \"/\" + filename\r\n text = get_text_from_xml(file)\r\n thread_files[filename] = text\r\n return thread_files\r\n\r\n\r\ndef get_text_from_xml(file):\r\n\r\n alle_text = {}\r\n\r\n DOMTree = xml.dom.minidom.parse(file)\r\n collection = DOMTree.documentElement\r\n\r\n text_nodelist = collection.getElementsByTagName(\"text\")\r\n postid_nodelist = collection.getElementsByTagName(\"postid\")\r\n\r\n for text_node, postid_node in zip(text_nodelist, postid_nodelist):\r\n postid = postid_node.childNodes[0].data\r\n text = text_node.childNodes[0].data\r\n\r\n alle_text[postid] = text\r\n\r\n return(alle_text)","sub_path":"get_text_from_xml_3.py","file_name":"get_text_from_xml_3.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"547569507","text":"#BBN_LICENSE_START -- DO NOT MODIFY BETWEEN LICENSE_{START,END} Lines\n# Copyright (c) <2017,2018,2019,2020,2021>, \n# To be applied to the DCOMP/MAP Public Source Code Release dated 2018-04-19, with\n# the exception of the dcop implementation identified below (see notes).\n# \n# Dispersed Computing (DCOMP)\n# Mission-oriented Adaptive Placement of Task and Data (MAP) \n# \n# All rights reserved.\n# \n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n# \n# Redistributions of source code must retain the above copyright\n# notice, this list of conditions and the following disclaimer.\n# Redistributions in binary form must reproduce the above copyright\n# notice, this list of conditions and the following disclaimer in the\n# documentation and/or other materials provided with the distribution.\n# \n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\n# IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\n# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\n# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n#BBN_LICENSE_END\nimport warnings\nwith warnings.catch_warnings():\n import datetime\n import re\n import logging\n import os\n import os.path\n import json\n import json\n import csv\n\n\nclass Base(object):\n def __str__(self):\n return str(self.__dict__)\n \n def __repr__(self):\n type_ = type(self)\n module = type_.__module__\n qualname = type_.__qualname__ \n return f\"<{module}.{qualname} {str(self)}>\"\n \n \ndef timestamp_to_minutes(timestamp):\n \"\"\"\n Convert a Java timestamp to fractional minutes\n \n Args:\n timestamp (int): Java timestamp\n\n Return:\n float: fractional minutes\n \"\"\"\n return timestamp_to_seconds(timestamp) / 60.0\n\n\ndef timestamp_to_seconds(timestamp):\n \"\"\"\n Convert a Java timestamp to fractional seconds\n \n Args:\n timestamp (int): Java timestamp\n\n Return:\n float: fractional seconds\n \"\"\"\n return float(timestamp) / 1000.0\n\n\ndef find_ncp_folder(node_base):\n \"\"\"\n Find the folder under node_base that contains the timestamped folders.\n This may be node_base or a directory under it.\n This method exists to handle the differences in the directory structure between lo-fi and hi-fi.\n\n Args:\n node_base (Path): where to look\n\n Return:\n Path: The path to use or None if not found\n \"\"\"\n lofi_file = node_base / 'agent-configuration.json'\n if lofi_file.exists():\n return node_base\n \n agent_dir = node_base / 'agent'\n if not agent_dir.is_dir():\n return None\n for node_name_dir in agent_dir.iterdir():\n if node_name_dir.is_dir():\n check = node_name_dir / 'agent-configuration.json'\n if check.exists():\n return node_name_dir\n return None\n\n\ndef fill_missing_times(all_times, time_data):\n \"\"\"\n Create a numpy array that contains values for all_times based on time_data.\n Any times before the first time in time_data are filled with zero.\n Any times after the last time in time_data are filled with zero.\n Any times in between are filled with the value to the left.\n\n Args:\n time_data (dict): time to value\n all_times (list): All times that need values\n\n Returns:\n numpy.array: filled values\n \"\"\"\n import numpy as np\n\n # make a copy so that the original value isn't modified \n modified_time_data = time_data.copy()\n\n # Add NaN values for all missing times\n for time in all_times:\n if time not in modified_time_data:\n modified_time_data[time] = None\n\n times, values = zip(*sorted(modified_time_data.items()))\n\n # fill the values with the value to the left\n values_ar = np.asarray(values, dtype=np.float32)\n indicies = np.arange(len(values_ar))\n\n # make all nans on the right be 0\n max_non_nan_idx = max(np.where(np.isnan(values_ar), 0, indicies))\n np.put(values_ar, np.arange(max_non_nan_idx+1, len(values_ar)), 0)\n\n mask = np.isnan(values_ar)\n non_nan_idx = np.where(~mask, indicies, 0)\n\n # use left most non-nan value\n values_ar = values_ar[np.maximum.accumulate(non_nan_idx)]\n\n # replace nans on left end with 0\n values_ar = np.nan_to_num(values_ar)\n\n return values_ar\n\n\ndef log_timestamp_to_datetime(log_timestamp_str):\n \"\"\"\n Arguments:\n log_timestamp_str(str): the timestamp from a log file as a string\n Returns:\n datetime.datetime: the parsed datetime\n \"\"\"\n return datetime.datetime.strptime(log_timestamp_str, '%Y-%m-%d/%H:%M:%S.%f/%z')\n\n\ndef log_line_to_time(line, time_zone):\n \"\"\"\n Arguments:\n line (str): line to parse the timestamp on\n time_zone (timezone): If not None, then used to determine the timzone of the log line\n\n Returns:\n datetime: the datetime or None if one cannot be found in the line\n \"\"\"\n bracket_index = line.find(\" [\")\n if -1 == bracket_index:\n return None\n \n log_file_reference_time = line[0:bracket_index]\n\n try:\n if time_zone is None:\n # if a time zone was not given, look for time zone information in the log message timestamp\n time = datetime.datetime.strptime(log_file_reference_time, '%Y-%m-%d/%H:%M:%S.%f/%z')\n else:\n # if a time zone is given, use the old log timestamp format and the given time zone\n time = datetime.datetime.strptime(log_file_reference_time, '%Y-%m-%d %H:%M:%S,%f')\n time = time_zone.localize(time)\n return time;\n except ValueError:\n return None\n\n\ndef datetime_to_string(date_time):\n \"\"\"\n Output a datetime in our preferred format\n\n Arguments:\n date_time (datetime): the datetime to convert\n\n Returns:\n str: string representation of the datetime\n \"\"\"\n\n if date_time is None:\n return \"None\"\n else:\n return date_time.strftime('%Y-%m-%d/%H:%M:%S.%f/%z')\n\ndef node_name_from_dir(dir):\n \"\"\"\n Determine the name of a node given a path. The last path element\n is assumed to be either the node name or .map.dcomp.\n \n Arguments:\n dir (Path): path to convert to a node name\n Returns:\n str: the name of the node\n \"\"\"\n match = re.match(r'^(.*)\\.map\\.dcomp$', dir.name)\n if not match:\n return dir.name\n else:\n return match.group(1)\n\n \ndef setup_logging(\n default_path='logging.json',\n default_level=logging.INFO,\n env_key='LOG_CFG'\n):\n \"\"\"\n Setup logging configuration\n \"\"\"\n path = default_path\n value = os.getenv(env_key, None)\n if value:\n path = value\n if os.path.exists(path):\n with open(path, 'r') as f:\n config = json.load(f)\n logging.config.dictConfig(config)\n else:\n logging.basicConfig(level=default_level)\n\n # quiet down matplotlib font manager\n logging.getLogger('matplotlib.font_manager').setLevel(logging.WARNING)\n\n\ndef skip_null_lines(f):\n \"\"\"\n Arguments:\n f(file handle): the file to read a line at a time\n Returns:\n iterator: the lines that do not contain null characters\n \"\"\"\n for line in f:\n if not re.search('\\0', line):\n yield line\n \n \ndef set_figure_size(fig):\n \"\"\"\n Used to set the size of all graphs to the same size.\n\n Arguments:\n fig(Figure): matplotlib figure to set the size on\n \"\"\"\n \n fig.set_size_inches(10, 6)\n \n\ndef get_plot_colors():\n \"\"\"\n Colors used for plots. This can be used when one needs to directly assign colors to series.\n\n Returns:\n list(color tuples): colors to use for plots\n \"\"\"\n # import matplotlib\n # import matplotlib.cm\n \n #return matplotlib.cm.get_cmap(\"tab20\").colors\n\n # import matplotlib.pyplot as plt\n # import matplotlib.cm as mplcm\n # import matplotlib.colors as colors\n # import numpy as np\n\n # NUM_COLORS = 30\n # \n # cm = plt.get_cmap('gist_rainbow')\n # cNorm = colors.Normalize(vmin=0, vmax=NUM_COLORS-1)\n # scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)\n # colors = [scalarMap.to_rgba(i) for i in range(NUM_COLORS)]\n\n colors = [\"#eea100\",\n \"#5f56dd\",\n \"#74d546\",\n \"#bd0096\",\n \"#aad53b\",\n \"#015ec6\",\n \"#c4b100\",\n \"#9f9eff\",\n \"#00b35f\",\n \"#ff75db\",\n \"#05611f\",\n \"#ff3b5f\",\n \"#01d3da\",\n \"#cb2119\",\n \"#01b1fc\",\n \"#f35c29\",\n \"#92217a\",\n \"#b3d07e\",\n \"#c80049\",\n \"#f8bb62\",\n \"#ff92df\",\n \"#8f5300\",\n \"#f7b0e6\",\n \"#923401\",\n \"#996891\",\n \"#ff935e\",\n \"#a21344\",\n \"#be836a\",\n \"#ff8f9e\",\n \"#8a3933\"]\n return colors\n\n\ndef set_color_cycle(ax):\n \"\"\"\n Used to set the color cycle on all graphs to the same colors.\n\n Arguments:\n ax(Axes): matplotlib Axes\n \"\"\"\n \n ax.set_prop_cycle(color=get_plot_colors())\n \n\ndef subplots():\n \"\"\"\n Call matplotlib.pyplot.subplots() and then set the standard color cycle and figure size.\n\n Returns:\n Figure: matplotlib figure\n Axes: matplotlib axes\n \"\"\"\n import matplotlib.pyplot as plt\n \n fig, ax = plt.subplots()\n set_figure_size(fig)\n set_color_cycle(ax)\n ax.grid(alpha=0.5, axis='y')\n \n return fig, ax\n\n\ndef gather_all_services(scenario_dir):\n \"\"\"\n Arguments:\n scenario_dir(Path): path to where the scenario is\n Returns:\n set: all services in the scenario\n \"\"\"\n location = scenario_dir / 'service-configurations.json'\n if not location.exists():\n raise RuntimeError(\"Cannot find path to service-configurations.json\")\n \n with open(location) as f:\n services = json.load(f)\n\n all_services = set()\n for service_config in services:\n service = service_config['service']\n app = service['artifact']\n all_services.add(app)\n return all_services\n\n\ndef get_service_artifact(service_str):\n \"\"\"\n Arguments:\n service_str(str): service as a string\n Returns:\n str: the artifact from the string or None if parsing failed\n \"\"\"\n match = re.match(r'^AppCoordinates {\\S+,\\s*(\\S+),\\s*\\S+}$', service_str)\n if match:\n return match.group(1)\n else:\n return None\n\n\n\ndef is_general_service_domain_name(domain_name):\n \"\"\"\n Arguments:\n domain_name(str): domain name for service\n Returns:\n boolean: True is the domain name has a service but no region and False otherwise\n \"\"\"\n match = re.match(r'([^\\.]+)\\.map\\.dcomp', domain_name)\n if match:\n return True\n else:\n return False\n\n\ndef get_service_artifact_from_domain_name(domain_name):\n \"\"\"\n Arguments:\n domain_name(str): domain name for service\n Returns:\n str: the artifact from the domain name or None if parsing failed\n \"\"\"\n match = re.match(r'(.+?)(\\.[^\\.]+)?\\.map\\.dcomp', domain_name)\n if match:\n return match.group(1)\n else:\n return None\n\n\ndef gather_region_info(scenario_dir):\n \"\"\"\n Arguments:\n scenario_dir(Path): path to where the scenario is\n Returns:\n dict: node name (str) to region name (str)\n \"\"\"\n node_regions = dict()\n if not scenario_dir.exists():\n return node_regions\n \n for node_info in scenario_dir.glob('*.json'):\n if not node_info.is_file():\n continue\n\n try:\n with open(node_info, 'r') as f:\n node_data = json.load(f)\n except json.decoder.JSONDecodeError:\n get_logger().warning(\"Problem reading node information %s, skipping\", node_info)\n if 'region' in node_data:\n node_name = node_info.stem\n node_regions[node_name] = node_data['region']\n node_regions[node_name.lower()] = node_data['region']\n \n return node_regions\n\n\ndef gather_ip_to_node_info(scenario_dir):\n \"\"\"\n Arguments:\n scenario_dir(Path): path to where the scenario is\n Returns:\n dict: ip name (str) to node name (str)\n \"\"\"\n ip_to_node_name_dict = dict()\n\n host_ip_file = scenario_dir / 'host-ip.csv'\n if not host_ip_file.exists():\n return ip_to_node_name_dict\n\n with open(host_ip_file) as f:\n reader = csv.DictReader(f) \n for row in reader:\n host = row['host'].lower()\n ip = row['ip']\n\n ip_to_node_name_dict[ip] = host\n\n return ip_to_node_name_dict\n\n \n \n","sub_path":"src/MAP-ChartGeneration/scripts/map_utils.py","file_name":"map_utils.py","file_ext":"py","file_size_in_byte":13330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"62484224","text":"#head first flask page 203,change on page 227\n#todays date 9-16-18\n#lower case flask module upper case Flask class\n\nfrom flask import Flask, render_template,request\nfrom vsearch import search4letters\n\napp = Flask(__name__)\n\n\n\n\t\n@app.route('/search4',methods=['POST'])\ndef do_search() -> 'html':\n\tphrase = request.form['phrase']\n\tletters = request.form['letters']\n\ttitle = 'Here are your results'\n\tresults = str(search4letters(phrase,letters))\n\treturn render_template('results.html',\n\t\t\t\t\t\t\tthe_phrase = phrase,\n\t\t\t\t\t\t\tthe_letters = letters,\n\t\t\t\t\t\t\tthe_title = title,\n\t\t\t\t\t\t\tthe_results=results,)\n\n@app.route('/')\t\t\t\t\t\t\t\n@app.route('/entry')\ndef entry_page() -> 'html':\n\treturn render_template('entry.html',\n\t\t\t\t\t\t\tthe_title='Welcome to search4letters on the web!')\n\nif __name__ == '__main__':\t\n\tapp.run(debug=True)\n\n","sub_path":"vsearch4web.py","file_name":"vsearch4web.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"640173965","text":"#!/usr/bin/python3\n\"\"\" New module for a view to Place amenities relationship\n\"\"\"\nimport models\nfrom api.v1.views import app_views\nfrom flask import jsonify\nfrom flask import abort\nfrom models import storage\nfrom models.place import Place\nfrom models.amenity import Amenity\nfrom flask import request\n\n\n@app_views.route(\"places//amenities/\", methods=[\"POST\"],\n strict_slashes=False)\ndef new_amenity_for_place(place_id, amenity_id):\n \"\"\"Creates a new amenity for a place\n \"\"\"\n place = storage.get(Place, place_id)\n if place is None:\n abort(404)\n\n amenity = storage.get(Amenity, amenity_id)\n if amenity is None:\n abort(404)\n\n if amenity in place.amenities:\n return jsonify(amenity.to_dict()), 200\n\n if models.storage_t == 'db':\n place.amenities.append(amenity)\n else:\n place.amenity_ids.append(amenity_id)\n storage.save()\n storage.reload()\n return jsonify(amenity.to_dict()), 201\n\n\n@app_views.route(\"places//amenities\", methods=[\"GET\"],\n strict_slashes=False)\ndef amenities_by_place(place_id):\n \"\"\"Retrieves list of all amenities of a place\n \"\"\"\n list_res = []\n\n place = storage.get(Place, place_id)\n if place is None:\n abort(404)\n\n amenities = place.amenities\n for amenity in amenities:\n list_res.append(amenity.to_dict())\n return jsonify(list_res)\n\n\n@app_views.route(\"places//amenities/\",\n methods=[\"DELETE\"],\n strict_slashes=False)\ndef delete_amenity_in_place(place_id, amenity_id):\n \"\"\"Deletes an amenity by place and its id\n \"\"\"\n place = storage.get(Place, place_id)\n if place is None:\n abort(404)\n amenity = storage.get(Amenity, amenity_id)\n if amenity is None:\n abort(404)\n\n if amenity not in place.amenities:\n abort(404)\n\n if models.storage_t != 'db':\n place.amenity_ids.remove(amenity)\n else:\n place.amenities.remove(amenity)\n storage.save()\n storage.reload()\n return jsonify({}), 200\n","sub_path":"api/v1/views/places_amenities.py","file_name":"places_amenities.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"606491480","text":"# Bungeni Parliamentary Information System - http://www.bungeni.org/\n# Copyright (C) 2010 - Africa i-Parliaments - http://www.parliaments.info/\n# Licensed under GNU GPL v2 - http://www.gnu.org/licenses/gpl-2.0.txt\n\n\"\"\"Versioning of Domain Objects\n\n$Id$\n\n\"\"\"\nlog = __import__(\"logging\").getLogger(\"bungeni.ui.versions\")\n\nfrom zope import interface\nfrom zope import schema\nfrom zope import formlib\n\nfrom zope.security.proxy import removeSecurityProxy\nfrom zope.security import canWrite\nfrom zope.security.interfaces import ForbiddenAttribute\nfrom zope.security.management import getInteraction\nfrom zope.app.pagetemplate import ViewPageTemplateFile\nfrom zope.i18n import translate\nfrom zope.dublincore.interfaces import IDCDescriptiveProperties\nfrom zope.annotation.interfaces import IAnnotations\nfrom zope.publisher.interfaces.browser import IBrowserPublisher\n\nfrom sqlalchemy import orm\n\nfrom bungeni.alchemist.interfaces import IIModelInterface\nfrom bungeni.alchemist.ui import getSelected\n\nfrom bungeni.core import version\nfrom bungeni.models.interfaces import IFeatureVersion\nfrom bungeni.ui.interfaces import IWorkspaceOrAdminSectionLayer\nfrom bungeni.ui.i18n import _\nfrom bungeni.ui.utils import url\nfrom bungeni.ui import browser\nfrom bungeni.ui import forms\nfrom bungeni.utils import register\nfrom bungeni.ui.htmldiff import htmldiff\nfrom bungeni.ui import audit\nfrom zc.table import column\n\n\n'''\nfrom zope.publisher.browser import BrowserView\nclass VersionsView(BrowserView):\n \"\"\"To-Do: Find out why this class isn't hooked up.\"\"\"\n \n def __call__(self):\n context = self.context.__parent__.__parent__\n ifaces = filter(IIModelInterface.providedBy, interface.providedBy(context))\n \n class Form(formlib.form.DisplayForm):\n \n template = ViewPageTemplateFile(\"templates/form.pt\")\n form_fields = formlib.form.FormFields(*ifaces)\n form_name = _(u\"View\")\n \n @property\n def description(self):\n return _(u\"Currently displaying version ${version}\",\n mapping={\"version\": self.context.version_id})\n \n def setUpWidgets(self, ignore_request=False):\n self.adapters = dict(\n [(iface, self.context) for iface in ifaces])\n\n self.widgets = formlib.form.setUpEditWidgets(\n self.form_fields, self.prefix, self.context, self.request,\n adapters=self.adapters, for_display=True,\n ignore_request=ignore_request\n )\n \n view = Form(self.context, self.request)\n \n return view()\n'''\n#!+zc.table the selection column below overrides the base class\n# that generates id's using base64 resulting in invalid HTML ids\n# eg. they contain = sign.\nclass CustomSelectionColumn(column.SelectionColumn):\n def makeId(self, item):\n return ''.join(self.idgetter(item).split())\n\n\n# versions are a special \"audit\" case\n\n\nclass VersionDataDescriptor(audit.ChangeDataDescriptor):\n \n # !+bungeni_custom\n def columns(self):\n return [\n CustomSelectionColumn(\n lambda item:str(item.audit_id), name=\"selection\"),\n column.GetterColumn(title=_(\"version\"),\n getter=lambda i,f:\"%s\" % (i.audit_id),\n cell_formatter=lambda g,i,f:'%s'\n % (f.url, i.__name__, i.seq)),\n column.GetterColumn(title=_(\"procedure\"), \n getter=lambda i,f:i.procedure),\n column.GetterColumn(title=_(\"modified\"), \n getter=lambda i,f:self.date_formatter.format(i.date_active)),\n column.GetterColumn(title=_(\"by\"), \n getter=lambda i,f:IDCDescriptiveProperties(i.user).title),\n column.GetterColumn(title=_(\"message\"),\n getter=lambda i,f:i.note),\n ]\n\nclass VersionLogMixin(object):\n \"\"\"Base handling of version log listing for a context.\n \"\"\"\n formatter_factory = audit.TableFormatter\n prefix = \"container_contents_versions\"\n \n _message_no_data = _(\"No Version Data\")\n @property\n def message_no_data(self):\n return translate(self.__class__._message_no_data)\n \n _columns = None\n def columns(self):\n if self._columns is None:\n self._columns = VersionDataDescriptor(self.context, self.request\n ).columns()\n return self._columns\n \n @property\n def selection_column(self): \n return self.columns()[0]\n \n _data_items = None\n def version_data_items(self):\n # version log is only concerned with own versions (not of any child \n # objects) i.e. only own \"version changes\"; as \"data_provider\" we \n # simply use the versions attribute on context:\n if self._data_items is None:\n interaction = getInteraction()\n # sorted desc by sqlalchemy, so following sorting not necessary:\n self._data_items = [\n removeSecurityProxy(v) for v in self.context.versions\n if interaction.checkPermission(\"zope.View\", v) ]\n return self._data_items\n \n @property\n def has_data(self):\n return bool(self.version_data_items)\n \n def listing(self):\n formatter = self.formatter_factory(self.context, self.request,\n self.version_data_items(), # formatter.items\n visible_column_names=[ c.name for c in self.columns() ], #!+self.visible_column_names, \n prefix=self.prefix,\n columns=self.columns()\n )\n # visible_column_names & columns -> formatter.visible_columns\n formatter.url = url.absoluteURL(self.context, self.request)\n formatter.cssClasses[\"table\"] = \"listing grid\"\n return formatter()\n\n@register.view(IFeatureVersion, layer=IWorkspaceOrAdminSectionLayer, \n name=\"version-log\", \n protect={\"zope.Public\": \n dict(attributes=[\"publishTraverse\", \"browserDefault\", \"__call__\"])})\nclass VersionLogView(VersionLogMixin, \n browser.BungeniBrowserView, \n forms.common.BaseForm,\n ):\n \"\"\"Version Log View for an object\n \"\"\"\n interface.implements(IBrowserPublisher)\n \n class IVersionEntry(interface.Interface):\n commit_message = schema.Text(title=_(\"Change Message\"))\n form_fields = formlib.form.Fields(IVersionEntry)\n \n render = ViewPageTemplateFile(\"templates/version.pt\")\n \n __name__ = \"version-log\"\n _page_title = _(\"Version Log\")\n \n diff_view = None\n \n def __init__(self, context, request):\n browser.BungeniBrowserView.__init__(self, context, request)\n VersionLogMixin.__init__(self)\n self._page_title = translate(self.__class__._page_title)\n if hasattr(self.context, \"short_title\"):\n self._page_title = \"%s: %s\" % (\n self._page_title, translate(self.context.short_title))\n \n def publishTraverse(self, request, ver_seq):\n seq = int(ver_seq[len(\"ver-\"):])\n for ver in self.context.versions:\n if ver.seq == seq:\n removeSecurityProxy(ver).__parent__ = self\n return ver\n \n def has_write_permission(self, context):\n \"\"\"Check that the user has the rights to edit the object, if not we \n assume he has no rights to make a version assumption is here that if \n he has the rights on any of the fields he may create a version.\n \"\"\"\n trusted = removeSecurityProxy(self.context)\n # !+extended attributes? get complete list of attribuites off kls, as \n # in core.audit...get_field_names_to_audit(kls)\n # !+ replace with a more explict permission check?\n table = orm.class_mapper(trusted.__class__).mapped_table\n for column in table.columns:\n try:\n if canWrite(self.context, column.name):\n return True\n else:\n return False\n except ForbiddenAttribute:\n pass\n else:\n return False\n \n # !+action_url(mr, jul-2010) - throughout bungeni UI, defined only here\n @property\n def action_url(self):\n # this avoids that \"POST\"ed forms get a \"@@index\" appended to action URL\n return \"\"\n # !+action_method(mr, jul-2010) - throughout bungeni UI, defined only here\n @property\n def action_method(self):\n # XXX - for forms that only View information, this should return \"get\"\n # e.g. business / questions / / versions / Show Differences\n return \"post\"\n \n @formlib.form.action(label=_(\"New Version\"), name=\"new_version\",\n condition=has_write_permission)\n def handle_new_version(self, action, data):\n # !+ change_data not yet initialized for version requests\n change_data = IAnnotations(self.request)[\"change_data\"] = {}\n change_data[\"note\"] = data[\"commit_message\"]\n change_data[\"procedure\"] = \"m\"\n version.create_version(self.context)\n self.status = _(\"New Version Created\")\n \n @formlib.form.action(label=_(\"Revert To\"), name=\"revert_to\",\n condition=has_write_permission)\n def handle_revert_version(self, action, data):\n # !+REVERSION must be reviewed, probably obsoleted\n selected_audit_ids = getSelected(self.selection_column, self.request)\n if len(selected_audit_ids) != 1:\n self.status = _(\"Select one item to revert to\")\n return\n selected_audit = self.get_version_change(selected_audit_ids[0])\n # !+ change_data not yet initialized for version requests\n change_data = IAnnotations(self.request)[\"change_data\"] = {}\n # !+polymorphic_identity_multi adding action \"qualifier\" to note...\n # there could be a case for an additional column on change table.\n change_data[\"note\"] = \"%s [reversion %s]\" % (\n data[\"commit_message\"], selected_audit_ids[0])\n change_data[\"procedure\"] = \"m\"\n version.create_reversion(selected_audit)\n self.status = _(u\"Reverted to Previous Version %s\") % (\n removeSecurityProxy(selected_audit).audit_id)\n \n @formlib.form.action(label=_(\"Show Differences\"), name=\"diff\",\n validator=lambda form, action, data: ())\n def handle_diff_version(self, action, data):\n self.status = _(\"Displaying differences\")\n selected_audit_ids = sorted(getSelected(self.selection_column, self.request))\n if len(selected_audit_ids) not in (1, 2):\n self.status = _(\"Select one or two items to show differences\")\n return\n source = self.get_version_change(selected_audit_ids[0])\n try:\n target = self.get_version_change(selected_audit_ids[1])\n except IndexError:\n target = removeSecurityProxy(self.context)\n diff_view = DiffView(source, target, self.request)\n self.diff_view = diff_view(\n *filter(IIModelInterface.providedBy, interface.providedBy(self.context)))\n log.debug(\"handle_diff_version: source=%s target=%s \\n%s\" % (\n source, target, self.diff_view))\n \n def get_version_change(self, audit_id):\n for c in self.context.versions:\n c = removeSecurityProxy(c)\n if c.audit_id == audit_id:\n return c\n \n def setUpWidgets(self, ignore_request=False):\n # setup widgets in data entry mode not bound to context\n actions = self.actions\n self.actions = []\n for action in actions:\n if getattr(action, \"condition\", None):\n if action.condition(self, self.context):\n self.actions.append(action) \n else:\n self.actions.append(action)\n if not self.has_write_permission(self.context):\n self.form_fields = self.form_fields.omit(\"commit_message\")\n super(VersionLogView, self).setUpWidgets(self)\n \n def __call__(self):\n self.update()\n return self.render()\n\n\n#### \n# Handling of version diffs (implementation of Issue 588)\n# \n# The DiffView class is adapted from: z3c.schemadiff.browser.py\n# The diff() utility is adapted from: z3c.schemadiff.schema.py\n# \n# The DiffView here is different than the one in schemadiff.browser, as:\n# - the result of a diff is now always being obtained via from\n# htmldiff.htmldiff(),so it is all much simpler\n# note that z3c.schemadiff.schema.diff() was\n# anyway shortcutting any and all adapter genericity (for IFieldDiff) by\n# hard-wiring explicit checks on whether not to call IFieldDiff.html_diff()!\n# \n# This implementation also removes all dependencies on the z3c.schemadiff\n# package, that may therefore be removed.\n# \nclass DiffView(object):\n\n template = ViewPageTemplateFile(\"templates/diff.pt\")\n context = None\n \n def __init__(self, source, target, request):\n self.source = source\n self.target = target\n self.request = request\n \n def __call__(self, *interfaces):\n results = diff(self.source, self.target, *interfaces)\n tables = []\n content_changed = False\n for (field, changed, hresult) in results:\n tables.append({\n \"name\": field.__name__,\n \"title\": field.title,\n \"changed\": changed,\n \"html\": hresult})\n if changed:\n content_changed = True\n return self.template(tables=tables, content_changed=content_changed)\n\n\ndef diff(source, target, *interfaces):\n \"\"\"Get a list of (field, changed, result) 3-tuples, for \"diff-able\" fields.\n \"\"\"\n if not len(interfaces):\n interfaces = interface.providedBy(source)\n results = []\n for iface in interfaces:\n # the order is locked on the order returned by of interface.names()\n for name in iface.names():\n #!+VERSIONS(miano, 2 may 2012) something changed in the last couple\n # of weeks that makes removeSecurityPolicy below required yet\n # it wasn't before.\n field = removeSecurityProxy(iface[name])\n # only consider for diffing fields of this type\n #!+VERSIONS(miano, 2 May 2012) This was an isinstance check before.\n # switched it to check on interfaces.\n if set((schema.interfaces.IText, schema.interfaces.ITextLine,\n schema.interfaces.ISet)).isdisjoint(\n set(interface.providedBy(field))):\n continue\n bound = field.bind(source)\n source_value = bound.query(source, field.default)\n target_value = bound.query(target, field.default)\n if source_value is None or target_value is None:\n continue\n hresult = htmldiff(source_value, target_value)\n results.append((field, bool(hresult!=source_value), hresult))\n return results\n\n","sub_path":"bungeni.main/branches/miano-hansard/bungeni/ui/versions.py","file_name":"versions.py","file_ext":"py","file_size_in_byte":14981,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"216207588","text":"import numpy as np\nimport requests\nfrom sklearn.naive_bayes import GaussianNB\n\n\n\ndef load_australian_dataset():\n print('h')\n r = requests.get(r\"http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/australian/australian.dat\")\n L = str(r.content).replace(\"b'\",\"\").replace(\"\\\\r\",\"\").replace(\"'\",\"\").split(r'\\n')\n X = []\n for i in L:\n try:\n K = np.array(i.split(' ')).astype(\"float\")\n X.append (K)\n except:\n continue\n D = np.array(X[:-1])\n D = D.transpose()\n Y = D[-1]\n X = np.array(X)[:-2]\n Y = Y[:-1]\n return X,Y\n\ndef save_dataset_on_disc():\n try:\n with open(\"australian_dataset.txt\",\"w\") as f:\n r = requests.get (r\"http://archive.ics.uci.edu/ml/machine-learning-databases/statlog/australian/australian.dat\")\n f.write(str(r.content).replace(\"\\n\",\"\\n\"))\n f.close()\n except(Exception):\n print(\"retry\")\n save_dataset_on_disc()\n\n\n\n#train,target = load_australian_dataset()\n#print(train.shape)\n#print(target.shape)\n#print(target)\n#E = Evaluateur_Precision(train,target)\n#E.train(GaussianNB())\n#print(E.vecteur_precision())","sub_path":"DataSets/australian_dataset.py","file_name":"australian_dataset.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"495809204","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport os\nimport sys\nimport time\n\nimport tensorflow as tf\nfrom sat_model import *\n\nFLAGS = None\nNUM_CLASSES = 6\n\nseed = 10\nnp.random.seed(seed)\n\ntrain_file = 'sat_train.txt'\ntest_file = 'sat_train.txt'\n\ndef placehoder_inputs(batch_size):\n features_placeholder = tf.placeholder(tf.float32, [None, NUM_FEATURES])\n labels_placeholder = tf.placeholder(tf.float32, [None, NUM_CLASSES])\n return features_placeholder, labels_placeholder\n\ndef main(_):\n trainX, trainY = process_inputs(train_file)\n testX, testY = process_inputs(test_file)\n\n trainX = scale(trainX, np.min(trainX, axis=0), np.max(trainX, axis=0))\n\n batch_size = FLAGS.batch_size\n hidden1 = FLAGS.hidden1\n hidden2 = FLAGS.hidden2\n learning_rate = FLAGS.learning_rate\n epochs = FLAGS.epochs\n beta = FLAGS.beta\n\n with tf.Session() as sess:\n x, y_ = placehoder_inputs(batch_size)\n\n y, loss = inference(x, y_, hidden1, beta)\n \n train_op = training(loss, learning_rate)\n\n accuracy = evaluation(y, y_)\n\n sess.run(tf.global_variables_initializer())\n\n test_acc = []\n N = len(trainX)\n idx = np.arange(N)\n for i in range(epochs):\n np.random.shuffle(idx)\n trainX = trainX[idx]\n trainY = trainY[idx]\n\n for start, end in zip(range(0, N, batch_size), range(batch_size, N, batch_size)):\n train_op.run(feed_dict={x: trainX[start: end], y_: trainY[start: end]})\n \n test_acc.append(accuracy.eval(feed_dict={x: testX, y_: testY}))\n if i % 100 == 0:\n print('iter %d: test accuracy %g' % (i, test_acc[i]))\n\n # plot learning curves\n plt.figure(1)\n plt.plot(range(epochs), test_acc)\n plt.xlabel(str(epochs) + ' iterations')\n plt.ylabel('Train accuracy')\n plt.show()\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--batch_size',\n type=int,\n default=32,\n help='Batch size'\n )\n parser.add_argument(\n '--learning_rate',\n type=float,\n default=0.01,\n help='Initial learning rate.'\n )\n parser.add_argument(\n '--beta',\n type=float,\n default=10e-6,\n help='beta for L2 regulation'\n )\n parser.add_argument(\n '--epochs',\n type=int,\n default=200,\n help='Epochs'\n )\n parser.add_argument(\n '--hidden1',\n type=int,\n default=10,\n help='Number of units in hidden layer 1'\n )\n parser.add_argument(\n '--hidden2',\n type=int,\n default=10,\n help='Number of units in hidden layer 2'\n )\n FLAGS, unparsed = parser.parse_known_args()\n tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\n\n ","sub_path":"Project1/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"393050875","text":"# /usr/bin/python\n# encoding:utf-8\nimport csv\nimport os\nimport time\n\n\n# 控制类\nclass Controller(object):\n def __init__(self, count):\n self.counter = count\n self.alldata = [(\"timestamp\", \"cpustatus\")]\n\n # 单次测试过程\n def testprocess(self):\n result = os.popen(\"adb shell top -n 1 | findstr com.huawei.heal+\")\n # 运行命令得到： 30464 u0_a129 10 -10 22% S 44 1823308K 158792K fg ...\n for line in result.readlines():\n cpuvalue = line.split(\"%\")[0] # 30464 u0_a129 10 -10 24 for line in result.readlines():\n result1 = line.split(\"%\")[0] # 得到 30464 u0_a129 10 -10 24\n result2 = result1.split(\" \") # 根据空格隔开\n cpuvalue = result2[len(result2) - 1] # 获取数组最后一个，即为cpu%\n currenttime = self.getCurrentTime()\n self.alldata.append((currenttime, cpuvalue))\n\n\n # 多次执行测试过程\n def run(self):\n while self.counter > 0:\n self.testprocess()\n self.counter = self.counter - 1\n time.sleep(3)\n\n # 获取当前的时间戳\n def getCurrentTime(self):\n currentTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n return currentTime\n\n # 数据的存储\n def SaveDataToCSV(self):\n csvfile = open('cpustatus.csv', 'w')\n writer = csv.writer(csvfile)\n writer.writerows(self.alldata)\n csvfile.close()\n\n\nif __name__ == \"__main__\":\n controller = Controller(10)\n controller.run()\n controller.SaveDataToCSV()\n","sub_path":"learn/python_all/app/recordCup.py","file_name":"recordCup.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"508386886","text":"import pandas as pd\nimport numpy as np\nimport os\nimport re\nfrom sklearn.neural_network import BernoulliRBM\n# %matplotlib inline\n\nexecfile('/Users/franciscojavierarceo/GitHub/K/Ponpare/1_Code/FirstStep.py')\nexecfile('/Users/franciscojavierarceo/GitHub/Python/My_Functions.py')\ndef import_data():\n wd = 'Users/franciscojavierarceo/GitHub/K/Ponpare/2_Input'\n files = [x for x in os.listdir(wd) if re.search('.csv$', x) is not None]\n full_dir = [wd + '\\\\' + x for x in files]\n names = [n[:-4] for n in files]\n raw_dfs = {x: pd.DataFrame.from_csv(full_dir[i], index_col=None) for i, x in enumerate(names)}\n df = pd.merge(\n left=raw_dfs['coupon_visit_train'],\n right=raw_dfs['coupon_detail_train'],\n on='PURCHASEID_hash',\n suffixes=['', '_r']\n )\n df = pd.merge(\n left=df,\n right=raw_dfs['coupon_list_train'],\n on='COUPON_ID_hash',\n suffixes=['', '_r']\n )\n df = pd.merge(\n left=df,\n right=raw_dfs['coupon_area_train'],\n on='COUPON_ID_hash',\n suffixes=['', '_r']\n )\n df = pd.merge(\n left=df,\n right=raw_dfs['user_list'],\n on='USER_ID_hash',\n suffixes=['', '_r']\n )\n return df\n \n\ndf = import_data()\ndf['test'] = np.in1d(df.USER_ID_hash.str[-1], ['4', '2', '0', '6', '9']).astype('int')\nptable(df,'PURCHASE_FLG')\nnp.sum(df['PURCHASE_FLG'])\n","sub_path":"2_KP_ProcessData.py","file_name":"2_KP_ProcessData.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"72454400","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# pysine.py\n# \n# Copyright 2016 Erickson \n# \n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n# \n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n# \n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software\n# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n# MA 02110-1301, USA.\n# \n# \n\nimport time,argparse\nfrom urllib.request import urlopen\ntry:\n\tfrom bs4 import BeautifulSoup\nexcept:\n\tprint('''\nBeautifulSoup4 não instalado\nFaça a instalação do BeautifulSoup\n[+] Link Download\nBeautifulSoup: https://pypi.python.org/pypi/beautifulsoup4\n\n''')\t\n\n\n#Adicionando mensagens na variavel\n######################################################################\nauthor = \"\"\"\n\n\t\t\t\t\t\t\t\t\t\t\t \n[+]\tAuthor: Erickson Douglas\t\t\t\t \n[+]\tFacebook: erickshow.mattos\n[+]\tTwitter: @erickshowplay \n\n\"\"\"\nexemplos = \"\"\"\nExemplos\n\n[+] Modo Interativo\n./pysine \n\n[+] Mostrando o resultado do emprego Estagiario na Cidade Crato/PE\npython3 pysine -e Estagiario --cidades Crato/CE -v\n\n[+] Salvando o resultado dos empregos da cidade Juazeiro do norte no arquivo vendedor.txt \n./pysine -c juazeiro-do-norte/ce --empregos Vendedor,vendedor-externo --salvar vendedor.txt\n\n[+] Criando e Salvando resultado dos empregos nas Cidades Fortaleza/CE e Recife/pe no arquivo programador.txt dentro da pasta empregos \npython3 pysine -c FORTALEZA/CE,recife/pe -e web-programador,desenvolvedor,analista-desenvolvimento-de-sistemas -s ~/empregos/programador.txt\"\"\"\nerrorAcento =\"\"\"\n[-]Erro no argumento\n[-]-------------------------------------------------------\n[-]não precisa colocar acento no nome da Cidade e/ou Emprego\n[-]Tente novamente!\n__________________________________________________________\"\"\"\nerrorArgumento =\"\"\"\n[-]Esqueceu de algum argumento!\n[-]-----------------------------\n[-]Tente novamente\"\"\"\nresultado=\"\"\"\n<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n{Empresadt} : {Empresadd}\n{Salariodt} : {Salariodd}\n{Cidadedt} : {Cidadedd}\n{Descricdt} : {Descricdd}\n\nLink : {url}\n\"\"\"\n\n\n#pegando as informação de cada link\n######################################################################\ndef mostraremprego(complemento_link,salvar):\n\tdt,dd = list(),list()\n\tlink = \"http://www.sine.com.br{complemento}\".format(complemento=complemento_link)\n\ttry:\n\t\tsoup = BeautifulSoup(urlopen(link),\"html.parser\")\n\texcept ConnectionResetError:\n\t\ttime.sleep(3)\n\t\tsoup = BeautifulSoup(urlopen(link),\"html.parser\")\n\tfor row in soup.find(\"dl\",{\"class\":\"dl-horizontal\"}).find_all(\"dt\"):\n\t\tdt.append(row.get_text().rstrip().strip())\n\tfor row in soup.find(\"dl\",{\"class\":\"dl-horizontal\"}).find_all(\"dd\"):\n\t\tdd.append(row.get_text().rstrip().strip())\n\t\n\tif salvar == \" \":\n\t\tprint(resultado.format(Empresadt=dt[0],Empresadd=dd[0],Salariodt=dt[1],Salariodd=dd[1],Cidadedt=dt[2],Cidadedd=dd[2],Descricdt=dt[3],Descricdd=dd[3],url=link))\n\telse:\n\t\tarq = open(salvar)\n\t\ttemp = arq.readlines()\n\t\ttemp.append(resultado.format(Empresadt=dt[0],Empresadd=dd[0],Salariodt=dt[1],Salariodd=dd[1],Cidadedt=dt[2],Cidadedd=dd[2],Descricdt=dt[3],Descricdd=dd[3],url=link))\n\t\tarq = open(salvar,\"w+\")\n\t\tarq.writelines(temp)\n\t\tarq.close()\n\n#buscando as vargas disponivel\n##################################################################################\ndef procurar(cidade,estado,emprego,salvar,Verbose):\n\tlink =\"http://www.sine.com.br/vagas-empregos-em-\"+cidade+\"-\"+estado+\"/\"+emprego\n\ttry:\n\t\tsoup = BeautifulSoup(urlopen(link),\"html.parser\")\n\t\tif Verbose:\n\t\t\tprint(link)\n\t\t\n\texcept:\n\t\ttime.sleep(3)\n\t\tsoup = BeautifulSoup(urlopen(link),\"html.parser\")\n\t\tif Verbose:\n\t\t\tprint(\"Tentando conectar novamente!\")\n\t\t\tprint(link)\n\tlinks = soup.find(\"div\",{\"class\":\"row jobs\"}).find_all(\"a\")\n\tif len(links) != 0:\n\t\tfor row in links:\n\t\t\tmostraremprego(row.attrs[\"href\"],salvar)\n\telse:\n\t\tif Verbose:\n\t\t\tprint(\"[-]-----------------------\")\n\t\t\tprint(\"[-]Emprego não encontrado: \")\n\t\t\tprint(\"[-]-----------------------\")\n\t\t\tprint(link)\n\n#Menu interativo\n########################################################################\ndef mainInterativo():\n\twhile True:\n\t\ttry:\n\t\t\n\t\t\tCidades = str(input(\"Exemplos: Crato/CE,Juazeiro-do-Norte/CE\\nDigite os nomes das Cidades: \") or (print(\"\\nNão pode deixar nenhum campo vazio\\nTente novamente!\\n\"),time.sleep(3))).lower().replace(\" \",\"-\").split(\",\")\n\t\t\tEmpregos = str(input(\"Exemplos: Desenvolvedor,Estagiario\\nDigite os nomes dos Empregos: \") or (print(\"\\nNão pode deixar nenhum campo vazio\\nTente novamente!\\n\"),time.sleep(3))).lower().replace(\" \",\"-\").split(\",\")\n\t\t\tSalvar = str(input(\"Deseja Salvar? S/n \").lower())\n\t\t\tVerbose = False\n\t\t\t\n\t\t\tif Salvar == \"s\" or Salvar == \"sim\":\n\t\t\t\tSalvar = str(input(\"exemplos: ~/empregos/programador.txt ou estagiario.txt\\nDeseja Salvar aonde? \") or \"sineEmpregos.txt\")\n\t\t\t\tarq = open(Salvar,\"w+\")\n\t\t\t\tarq.writelines(author) \n\t\t\t\tarq.close()\n\t\t\t\n\t\t\telif Salvar == \"n\" or Salvar == \"nao\" or Salvar == \"não\":\n\t\t\t\tSalvar = \" \"\n\t\t\t\tVerbose = True\n\t\t\telse:\n\t\t\t\tprint(\"\\nTente novamente\\n\")\n\t\t\t\tmainInterativo()\n\t\t\t\n\t\t\tif not Cidades == \"\" and not Empregos == \"\":\n\t\t\t\tfor cidade in Cidades:\n\t\t\t\t\tcidade,estado = cidade.split(\"/\")[0],cidade.split(\"/\")[1]\n\t\t\t\t\tfor emprego in Empregos:\n\t\t\t\t\t\tprocurar(cidade,estado,emprego,Salvar,Verbose)\n\t\t\n\t\t\tparar = str(input(\"Deseja fazer outra pesquisa? S/n \").lower())\n\t\t\tif parar == \"s\" or parar == \"sim\":\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tprint(\"\\nSaindo!\\n\")\n\t\t\t\ttime.sleep(2)\n\t\t\t\tbreak\n\t\texcept KeyboardInterrupt:\n\t\t\tprint(\"\\nCancelado com Sucesso\\n\")\n\t\t\tbreak\n#######################################################################\ndef main():\n\tparser = argparse.ArgumentParser(prog=\"./pysine\",description=\" Esse script foi feito para fazer busca de emprego do jeito rápido e eficaz\")\n\tparser.add_argument(\"-c\",\"--cidades\", type=str, help=\"Pode colocar mais de uma cidade separado por vírgula, Exemplo: -c Recife/PE,Fortaleza/CE \")\n\tparser.add_argument(\"-e\",\"--empregos\",type=str, help=\"Pode colocar mais de um emprego separado por vígula, Exemplo: -s Estagiario,desenvolvedor\")\n\tparser.add_argument(\"-s\",\"--salvar\", default=\" \", help=\"Para salvar os resultados\")\n\tparser.add_argument(\"-v\",\"--verbose\", action=\"store_true\",help=\"Para mostrar oque está acontecendo\")\n\targs = parser.parse_args()\n\t\n\t\n\ttry:\n\t\tCidades = list(args.cidades.lower().split(\",\"))\n\t\tEmpregos = list(args.empregos.lower().split(\",\"))\n\t\tSalvar = args.salvar.lower()\n\t\tVerbose = args.verbose\n\t\t\n\t\t\n\t\tif not Salvar == \" \":\n\t\t\tarq = open(Salvar,\"w+\")\n\t\t\tarq.writelines(author) \n\t\t\tarq.close()\n\t\t\t\n\t\tif not Cidades == \"\" and not Empregos == \"\":\n\t\t\tfor cidade in Cidades:\n\t\t\t\tcidade,estado = cidade.split(\"/\")[0],cidade.split(\"/\")[1]\n\t\t\t\tfor emprego in Empregos:\n\t\t\t\t\tprocurar(cidade,estado,emprego,Salvar,Verbose)\n\t\n\texcept AttributeError:\n\t\tparser.print_help()\n\t\tprint(exemplos)\n\t\ttime.sleep(3)\n\t\tprint(\"\\n\\nEntrando no modo Interativo\\n\")\n\t\ttime.sleep(2)\n\t\tmainInterativo()\n\texcept UnicodeEncodeError:\n\t\tprint(errorAcento)\n\t\ttime.sleep(5)\n\t\tparser.print_help()\n\texcept IndexError:\n\t\tprint(exemplos)\n\t\ttime.sleep(5)\n\t\tparser.print_help()\n\texcept KeyboardInterrupt:\n\t\tprint(\"\\nCancelado com sucesso\\n\")\n\t\ttime.sleep(3)\n\t\tparser.print_help()\n\t\n\t\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"pysine.py","file_name":"pysine.py","file_ext":"py","file_size_in_byte":7711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"181810693","text":"import json\n\nfrom flask import Flask, request, jsonify, Blueprint\nfrom flask_restx import Resource, Api, fields, inputs, reqparse, Namespace\nimport sqlite3\n\nfrom db import *\n\nbp = Blueprint('labels', __name__, url_prefix='/labels')\napi = Namespace(\"labels\", \"Operations for labels\")\n\n\n# get labels for a user\n@api.route('/', methods=['GET'])\nclass Users(Resource):\n @api.response(200, 'Successfully retrieved label info')\n @api.response(404, 'Not Found')\n @api.doc(description=\"Gets all labels for a user given their id. Returns \\\n \")\n def get(self, user):\n conn = sqlite3.connect('clickdown.db')\n c = conn.cursor()\n\n query = f\"\"\"\n SELECT labels\n FROM users\n WHERE id = ?;\n \"\"\"\n\n c.execute(query, [f'{user}'])\n data = []\n\n try:\n data = c.fetchone()[0]\n except:\n return []\n\n c.close()\n conn.close()\n\n return json.dumps(data)\n\n\n# add new labels\nlabel_payload = api.model('task', {\n \"labels\": fields.String\n})\n# post labels for a user\n@api.route('/', methods=['POST'])\nclass Users(Resource):\n @api.response(200, 'Successfully created label info')\n @api.response(404, 'Not Found')\n @api.doc(description=\"Stores label data\")\n @api.expect(label_payload)\n def post(self, user):\n parser = reqparse.RequestParser()\n parser.add_argument('labels', required=True)\n args = parser.parse_args()\n\n conn = sqlite3.connect('clickdown.db')\n c = conn.cursor()\n\n query = f\"\"\"\n SELECT labels\n FROM users\n WHERE id = ?;\n \"\"\"\n c.execute(query, [f'{user}'])\n existing = ''\n query = ''\n try:\n existing = c.fetchone()[0]\n query = f\"\"\"\n UPDATE users\n SET labels = ?\n WHERE id = ?;\n \"\"\"\n c.execute(query, (f\"{existing + ', ' + args.labels}\", f'{user}'))\n\n except:\n existing = ''\n query = f\"\"\"\n UPDATE users\n SET labels = ?\n WHERE id = ?;\n \"\"\"\n c.execute(query, (f'{args.labels}', f'{user}'))\n\n conn.commit()\n c.close()\n conn.close()\n\n return {'value': True}\n\n# Extract only the label names into a list\ndef rawStrToList(dbStr):\n if dbStr == \"\" or dbStr == 'None':\n return []\n \n rawLabelList = json.loads(dbStr)\n \n labelList = []\n for l in rawLabelList:\n labelList.append(l[\"label\"])\n \n return labelList","sub_path":"backend/labels.py","file_name":"labels.py","file_ext":"py","file_size_in_byte":2728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"478057234","text":"# Reference:\n# https://towardsdatascience.com/in-12-minutes-stocks-analysis-with-pandas-and-scikit-learn-a8d8a7b50ee7\n\n\n################################ LIBRARIES ####################################\nimport pandas as pd\nimport datetime\nimport pandas_datareader.data as web\nfrom pandas import Series, DataFrame\nimport math\nimport numpy as np\nimport datetime\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom matplotlib import style\n\n\nfrom sklearn import preprocessing\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.linear_model import Ridge\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.pipeline import make_pipeline\n\nstyle.use('seaborn-whitegrid')\n\n\n################################### MAIN ######################################\n\nif __name__ == \"__main__\":\n\t\n\t# Load historical prices dataframe\n\tstart = datetime.datetime(2010, 1, 1)\n\tend = datetime.datetime(2017, 1, 11)\n\tdf = web.DataReader(\"AAPL\", 'yahoo', start)\n\t#print(df.head())\n\t#print(df.tail())\n\n\t# ------------------------- Format Data ------------------------------ #\n\t\n\t# High-Low Percentage\n\tdf['HL_PCT'] \t = (df['High'] - df['Low']) / df['Close'] * 100.0\t\n\t# Percentage Change\n\tdf['PCT_change'] = (df['Close'] - df['Open']) / df['Open'] * 100.0\n\t\n\t\n\t# Redifine our dataframe with the formated data\n\tdf = df.loc[:, ['Adj Close', 'HL_PCT', 'PCT_change', 'Volume']]\n\n\t# Fill missing data\n\tdf.fillna(value=-99999, inplace=True)\n\n\n\n\t# ------------------------ Pick Label ------------------------------ #\n\t# We want to predict the Adj. Close price in certain amount of time in \n\t# the future based on the past information of:\n\t# 'Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume'\n\t# This means our features are all the past and present of these 4\n\t# columns.\n\t# Our labels will be the Adj. Close in the future.\n\n\t# Define Label column IF we have it in the data frame.\n\tforecast_col = 'Adj Close'\n\n\t# Amount of time in the future where we want to predict the Adj. Close\n\t# 5% of the total in this case.\n\tforecast_out = int(math.ceil(0.05 * len(df)))\n\n\t# Create the label:\n\tlabels = df[forecast_col].shift(-forecast_out)\n\n\n\t# ----------------------- Define features -------------------------- #\n\t# Create features array.\n\tfeatures = np.array(df) \n\tfeatures = preprocessing.scale(features)\n\n\t\t# Features that we'll use for training/test\n\tX = features[:-forecast_out]\n\n\t# Features that we'll use to predict values of Adj. Close in the future.\n\tX_predict = features[-forecast_out :]\n\n\n\t# ------------------------ Define Labels --------------------------- #\n\tlabels.dropna(inplace=True)\n\ty = np.array(labels)\n\n\t# ------------- Create Training and Testing datasets ---------------- #\n\t# Use 20% of the data for validation\n\tX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n\n\t# -------------------------- Training ------------------------------- #\n\t# Linear regression\n\tclf_reg = LinearRegression(n_jobs=-1)\n\tclf_reg.fit(X_train, y_train)\n\n\t# Quadratic Regression 2\n\tclf_poly2 = make_pipeline(PolynomialFeatures(2), Ridge())\n\tclf_poly2.fit(X_train, y_train)\n\n\t# Quadratic Regression 3\n\tclf_poly3 = make_pipeline(PolynomialFeatures(3), Ridge())\n\tclf_poly3.fit(X_train, y_train)\n\n\t# KNN Regression\n\tclf_knn = KNeighborsRegressor(n_neighbors=2)\n\tclf_knn.fit(X_train, y_train)\n\n\n\t# ------------------------- Validation ------------------------------- #\n\tacc_reg \t= clf_reg.score(X_test, y_test)\n\tacc_poly2 \t= clf_poly2.score(X_test,y_test)\n\tacc_poly3\t= clf_poly3.score(X_test,y_test)\n\tacc_knn\t \t= clf_knn.score(X_test, y_test)\n\n\tprint('Accuracy with Linear Regression:', acc_reg)\n\tprint('Accuracy with poly2:'\t\t\t, acc_poly2)\n\tprint('Accuracy with poly3:'\t\t\t, acc_poly3)\n\tprint('Accuracy with KNN:'\t\t\t\t, acc_knn)\n\n\t\n\t# ------------------------- Predictions ------------------------------- #\n\tprediction_reg \t\t= clf_reg.predict(X_predict)\n\tprediction_poly2 \t= clf_poly2.predict(X_predict)\n\tprediction_poly3 \t= clf_poly3.predict(X_predict)\n\tprediction_knn \t\t= clf_knn.predict(X_predict)\n\t\t\t\t\t\t\n\n\t# ---------------------------- Plots ---------------------------------- #\n\t# Plot data from the past and present\n\tdf['Adj Close'].plot()\n\n\n\t# List the prediction models that you wish to plot and its legends\n\tpredictions = [\tprediction_reg, \n\t\t\t\t\tprediction_poly2, \n\t\t\t\t\tprediction_poly3, \n\t\t\t\t\tprediction_knn ]\n\n\tpredictions_legends = [\t'prediction_reg', \n\t\t\t\t\t\t\t'prediction_poly2', \n\t\t\t\t\t\t\t'prediction_poly3', \n\t\t\t\t\t\t\t'prediction_knn' ]\n\n\n\t# Grab the last day in the dataframe, and begin assigning each new \n\t# prediction to a new day.\n\tlast_date = df.iloc[-1].name\n\tlast_unix = last_date.timestamp()\n\tone_day = 86400\n\tnext_unix_start = last_unix + one_day\n\n\tfor prediction, legend in zip(predictions, predictions_legends):\n\t\t\n\t\t# Add new column in the data frame with the prediction method\n\t\tdf[legend] = np.nan\n\t\tnext_unix = next_unix_start\n\t\t\n\t\tfor i in prediction:\n\t\t\tnext_date = datetime.datetime.fromtimestamp(next_unix)\n\t\t\tnext_unix += 86400\n\t\t\tdf.loc[next_date] = [np.nan for _ in range(len(df.columns)-1)] + [i]\n\n\n\t\tdf[legend].plot(label=legend)\n\n\n\tplt.legend(loc=4)\n\tplt.xlabel('Date')\n\tplt.ylabel('Close Price')\n\tplt.show()\n","sub_path":"02 - Finance - Stock Prediction/stock_prediction.py","file_name":"stock_prediction.py","file_ext":"py","file_size_in_byte":5291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"85641092","text":"#This is part of the cloud manager project I am working on. https://github.com/dylanpoll/cloud-management-system\r\nimport re\r\nimport subprocess\r\nimport time\r\nimport requests # importing the requests library\r\ndylanStrike = 0\r\nlewisStrike = 0\r\nurl = \"http://***.***.*.***:9001/homereporting/\" # express address for posting to the database I am using this with.\r\nheaders = {\r\n 'Content-Type': 'application/json'\r\n}\r\nwhile True:\r\n dylan = 1\r\n lewis = 1\r\n postDylan = 'no'\r\n postLewis = 'no'\r\n adr = []\r\n # this runs commands in terminal that use nmap to print all terminal results into output.txt\r\n with open('output.txt', 'w') as f:\r\n p1 = subprocess.run(['sudo', 'nmap', '-sP', '***.***.*.1/24'], stdout=f, text=True) #i use stars to blank out my actual IP, replace with your own\r\n with open('output.txt', 'r') as searchFile:\r\n for line in searchFile:\r\n if 'MAC' in line:\r\n adr.append(line.rstrip('\\n'))\r\n with open('macAddresses.txt', 'w') as macFile:\r\n for mac in adr:\r\n macFile.write('%s\\n' %mac[:30])\r\n time.sleep(10) \r\n with open('macAddresses.txt', 'r') as macFile:\r\n time.sleep(2)\r\n with open('macAddresses.txt', 'r') as macFile:\r\n print('checking who is home...')\r\n for line in macFile:\r\n if '**:**:**:**:**:**' in line: #i use **:**:**:**:**:** to replace a real MAC address, replace with you own\r\n postDylan = \"{\\r\\n \\\"name\\\": \\\"Dylan\\\",\\r\\n \\\"isHome\\\": \\\"Yes\\\"\\r\\n}\"\r\n #print('Dylan is here') #change this to whatever you want to happen or comment out to save on processing the print, I have done so at this point as I no longer need to monitor it as its working fine.\r\n dylan = 0\r\n dylanStrike = 0\r\n if dylan == 1 :\r\n dylanStrike = dylanStrike + 1\r\n #print('Dylan may not be here running check....') \r\n if dylanStrike == 10 :\r\n #print('Dylan has not been detected for 10 minutes. Marking not here') \r\n postDylan = \"{\\r\\n \\\"name\\\": \\\"Dylan\\\",\\r\\n \\\"isHome\\\": \\\"No\\\"\\r\\n}\"\r\n r = requests.patch(url,headers=headers,data=postDylan)\r\n time.sleep(2)\r\n with open('macAddresses.txt', 'r') as macFile:\r\n for line in macFile:\r\n if '**:**:**:**:**:**' in line:\r\n postLewis = \"{\\r\\n \\\"name\\\": \\\"Lewis\\\",\\r\\n \\\"isHome\\\": \\\"Yes\\\"\\r\\n}\"\r\n #print('Lewis is here')\r\n lewis = 0\r\n lewisStrike = 0\r\n if lewis == 1:\r\n lewisStrike = lewisStrike + 1\r\n #print('Lewis may not be here running check...')\r\n if lewisStrike == 10 :\r\n #print('Lewis has not been detected for 10 minutes. Marking not here')\r\n postLewis =\"{\\r\\n \\\"name\\\": \\\"Lewis\\\",\\r\\n \\\"isHome\\\": \\\"No\\\"\\r\\n}\"\r\n \r\n r = requests.patch(url,headers=headers,data=postLewis)\r\n time.sleep(2)\r\n #print('end of loop')\r\n time.sleep(5)\r\n","sub_path":"NetworkMacAdressReportingWithDB.py","file_name":"NetworkMacAdressReportingWithDB.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"274695939","text":"\"\"\"LipCbrAnalysis class.\"\"\"\nimport Job\nimport systematics\n\n# import sys\nimport os\nimport glob\nimport shutil\nimport time\nimport subprocess\nimport random\n\n\nclass LipCbrAnalysis(Job.Job):\n \"\"\"Class that makes the LipCbrAnalysis submit files and runs them.\"\"\"\n\n job_name = \"LipCbrAnalysis\"\n\n required_options = [\"Output\", \"Description\", \"CutFile\", \"Samples\"]\n\n possible_trees = ['nominal', 'particleLevel', 'all']\n samples_variables = [\"HistoFile\", \"SampleCode\", \"Type\"]\n\n analysis_code_path = \"../../analysis_deploy/AnalysisCode/\"\n analysis_code_to_results = \"../../results/\"\n scratch_path = \"../../analysis_deploy/scratch/\"\n max_cuts_file = analysis_code_path + \"MaxCuts.cxx\"\n cut_file_path = analysis_code_path + \"DoCuts/\"\n cut_file_symlink = analysis_code_path + \"docuts.cxx\"\n cut_symlink_relative_path = \"DoCuts/\"\n\n def __init__(self):\n \"\"\"Initialize optional option values and adds possible options.\"\"\"\n super().__init__()\n self.possible_options += [\"CutFile\", \"Samples\", \"Tree\", \"SystTree\",\n \"SystWeight\", \"SamplesToRun\", \"RegionsToRun\",\n \"Compile\"]\n self.syst_weight = []\n self.tree = 'nominal'\n self.samples_to_run = 'all'\n self.regions_to_run = 'all'\n self.compile = True\n\n def set_options(self):\n super().set_options()\n\n # Required options\n self.cut_file = self.options_dict[\"CutFile\"]\n self.samples_file = self.options_dict[\"Samples\"]\n\n # Not required options\n if \"Tree\" in self.options_dict:\n self.tree = self.options_dict[\"Tree\"]\n if self.tree not in self.possible_trees:\n raise ValueError(\"Tree '{}' not valid. Choose from {}\".format(\n self.tree, self.possible_trees))\n\n if \"SystTree\" in self.options_dict:\n if self.options_dict[\"SystTree\"] == 'all':\n self.tree = systematics.syst_trees\n else:\n self.tree = [syst.strip()\n for syst in\n self.options_dict[\"SystTree\"].split(',')]\n\n self.check_systematics(self.tree, 'tree')\n\n if \"SystWeight\" in self.options_dict:\n if self.tree != \"nominal\" and self.tree != 'all':\n error_string = \"Tree '{}' \".format(self.tree)\n error_string += \"incompatible with systematic weights. \"\n error_string += \"Only 'nominal' and 'all' are compatible.\"\n raise ValueError(error_string)\n\n if \"weight\" in self.options_dict[\"SystWeight\"]:\n error_string = \"Remove 'weight' from systematic weights.\"\n raise ValueError(error_string)\n\n if self.options_dict[\"SystWeight\"] == 'all':\n self.syst_weight = systematics.syst_weights\n else:\n self.syst_weight = [syst.strip()\n for syst in\n self.options_dict[\"SystWeight\"].split(',')]\n\n self.check_systematics(self.syst_weight, 'weight')\n\n if \"SamplesToRun\" in self.options_dict:\n if 'all' not in self.options_dict[\"SamplesToRun\"]:\n self.samples_to_run = \\\n [sample.strip()\n for sample in\n self.options_dict[\"SamplesToRun\"].split(',')]\n for i, sample in enumerate(self.samples_to_run):\n if sample == \"data\":\n continue\n try:\n self.samples_to_run[i] = int(self.samples_to_run[i])\n except ValueError:\n raise ValueError(\"SampleToRun {} not valid\".format(\n self.samples_to_run[i]))\n\n if \"RegionsToRun\" in self.options_dict:\n if 'all' not in self.options_dict[\"RegionsToRun\"]:\n self.regions_to_run = \\\n [region.strip()\n for region in\n self.options_dict[\"RegionsToRun\"].split(',')]\n\n if \"Compile\" in self.options_dict:\n aux = self.options_dict[\"Compile\"].upper()\n if aux != \"TRUE\" and aux != \"FALSE\":\n raise ValueError(self.make_invalid_argument_string(\"Compile\",\n aux))\n if aux == \"FALSE\":\n self.compile = False\n\n def get_regions_and_max_cuts(self):\n \"\"\"Get regions name and code from CutFile as well as MaxCuts.\"\"\"\n self.files_to_copy.append(self.cut_file_path + self.cut_file)\n self.max_cuts, self.regions = self.parse_cut_file(\n self.cut_file_path + self.cut_file)\n\n if self.regions_to_run != 'all':\n self.regions = {k:v for (k,v) in self.regions.items()\n if v in self.regions_to_run}\n\n if not self.regions:\n error_string = \"Invalid RegionsToRun arguments. Regions list if\"\n error_string += \" empty.\"\n raise ValueError(error_string)\n\n with open(self.analysis_code_path+\"MaxCuts.cxx\", 'w') as f:\n f.write(\"MaxCuts = {};\".format(self.max_cuts))\n\n def link_cutfile_and_compile(self):\n \"\"\"Creates soft links to the cut file and changes the name of the\n FCNCqzl executable. This makes it possible for different executables\n to be run at the same time.\"\"\"\n try:\n os.unlink(self.cut_file_symlink)\n except FileNotFoundError:\n pass\n os.symlink(self.cut_symlink_relative_path + self.cut_file,\n self.cut_file_symlink)\n subprocess.run([\"make\", \"clean\"],\n cwd=self.analysis_code_path)\n proc = subprocess.run([\"make\"], stderr=subprocess.PIPE,\n cwd=self.analysis_code_path)\n\n if proc.stderr:\n err = proc.stderr.decode('utf8')\n raise RuntimeError(\"Error when compiling AnalysisCode:\\n {}\".\n format(err))\n\n shutil.move(self.analysis_code_path + \"FCNCqzl\",\n self.analysis_code_path + \"FCNCqzl_\" + self.output)\n\n def make_submit_job(self, sample, region, syst_weight=\"\", syst_tree=\"\"):\n option_string = \"time ./FCNCqzl_{} --User=\\\"DataYear={}\\\" \".format(\n self.output, self.year)\n option_string += \"--User=\\\"LepType=00\\\" \"\n\n if syst_weight and syst_tree:\n error_string = \"Only systematic weight or systematic tree possible\"\n error_string += \". Never both at the same time.\"\n raise RuntimeError(error_string)\n\n if sample[\"SampleCode\"] == 1:\n option_string += \"--isData=1 \"\n name = 'nominal'\n elif self.tree == \"particleLevel\":\n option_string += \"--isTruth=1 \"\n option_string += \"--Sample={} \".format(sample[\"SampleCode\"])\n option_string += \"--MCYear={} \".format(self.mc_year)\n name = 'nominal'\n else:\n option_string += \"--Sample={} \".format(sample[\"SampleCode\"])\n option_string += \"--MCYear={} \".format(self.mc_year)\n name = 'nominal'\n if syst_weight != \"\":\n option_string += \"--SystWeight={} \".format(syst_weight)\n name = syst_weight\n if syst_tree != \"\":\n option_string += \"--SystTree={} \".format(syst_tree)\n name = syst_tree\n\n option_string += \"--Region={} \".format(region[0])\n scratch_name = \"../scratch/{}/MC{}_{}_{}_{}.txt\".format(\n self.output, self.mc_year, self.year,\n region[1], sample[\"HistoFile\"])\n option_string += \"--SetSystematicsFileName={} \".format(scratch_name)\n option_string += \"--OutputFileName={}\\n\".format(name)\n\n return option_string\n\n def make_submit_files(self):\n self.submit_dir_path = self.output_folder + \"submit_files/\"\n\n # Delete submit folder if it exists\n if self.compile:\n if os.path.exists(self.submit_dir_path):\n shutil.rmtree(self.submit_dir_path)\n os.makedirs(self.submit_dir_path)\n else:\n os.makedirs(self.submit_dir_path)\n\n submit_file_name = \"submit_MC{}_{}\".format(self.mc_year, self.year)\n submit_file_name += \"_{}_{}.sh\"\n\n initial_string = \"#!/bin/bash\\n\"\n initial_string += \"#$ -l h_rt=20:00:00\\n\"\n initial_string += \"#$ -V\\n\"\n initial_string += \"#$ -cwd\\n\"\n # From results/output/submit_files\n initial_string += \"cd ../\" + self.analysis_code_path + \"\\n\"\n\n # Filter the samples from samples_to_run variable\n if type(self.samples_to_run) == list:\n self.samples_list = list(filter(\n lambda x: x[\"SampleCode\"] in self.samples_to_run,\n self.samples_list))\n if 'data' in self.samples_to_run:\n self.samples_list.append({\"HistoFile\": 'data',\n \"SampleCode\": 1,\n \"Type\": \"data\"})\n # if samples_to_run == 'all'\n else:\n self.samples_list.append({\"HistoFile\": 'data',\n \"SampleCode\": 1,\n \"Type\": \"data\"})\n\n syst_trees_submit = []\n syst_weights_submit = []\n\n if self.tree == 'all':\n syst_trees_submit = systematics.syst_trees\n syst_weights_submit = systematics.syst_weights\n else:\n if type(self.tree) == list:\n syst_trees_submit = self.tree\n if self.syst_weight:\n syst_weights_submit = self.syst_weight\n\n # Makes the submit files\n for sample in self.samples_list:\n submit_file_number = 1\n for r in self.regions:\n folder_string = \"mkdir -p {}{}/MC{}/{}/{}/{}\\n\".format(\n self.analysis_code_to_results, self.output,\n self.mc_year, self.year, self.regions[r],\n sample[\"HistoFile\"])\n region_l = [r, self.regions[r]]\n cond = self.tree == 'all' or self.tree == 'particleLevel'\n cond = cond or \\\n (self.tree == 'nominal' and not syst_weights_submit)\n if cond:\n with open(\n self.submit_dir_path + submit_file_name.format(\n sample[\"HistoFile\"],\n submit_file_number), 'w') as f:\n f.write(initial_string)\n f.write(folder_string)\n f.write(self.make_submit_job(sample, region_l))\n submit_file_number += 1\n\n cond = sample[\"HistoFile\"] != \"data\"\n cond = cond and sample[\"Type\"] != \"SYSTEMATIC\"\n if cond:\n for weight in syst_weights_submit:\n with open(\n self.submit_dir_path + submit_file_name.format(\n sample[\"HistoFile\"],\n submit_file_number), 'w') as f:\n f.write(initial_string)\n f.write(folder_string)\n f.write(self.make_submit_job(sample, region_l,\n syst_weight=weight))\n submit_file_number += 1\n\n for tree in syst_trees_submit:\n with open(\n self.submit_dir_path + submit_file_name.format(\n sample[\"HistoFile\"],\n submit_file_number), 'w') as f:\n f.write(initial_string)\n f.write(folder_string)\n f.write(self.make_submit_job(sample, region_l,\n syst_tree=tree))\n submit_file_number += 1\n\n def make_scratch_files(self):\n scratch_dir = self.scratch_path + self.output + \"/\"\n\n if self.compile:\n if os.path.exists(scratch_dir):\n shutil.rmtree(scratch_dir)\n os.makedirs(scratch_dir)\n else:\n os.makedirs(scratch_dir)\n\n for sample in self.samples_list:\n for r in self.regions.values():\n scratch_name = \"MC{}_{}_{}_{}.txt\".format(\n self.mc_year, self.year, r, sample[\"HistoFile\"])\n with open(scratch_dir + scratch_name, 'w') as f:\n f.write(\"000000 {}{}/MC{}/{}/{}/{}/\".format(\n self.analysis_code_to_results, self.output,\n self.mc_year, self.year, r, sample[\"HistoFile\"]))\n\n def prepare_and_run_jobs(self):\n self.make_submit_files()\n self.make_scratch_files()\n\n time.sleep(15)\n\n submit_list = glob.glob(self.submit_dir_path + \"submit*.sh\")\n # I think sometimes there are errors related to too many processes\n # trying to access the same file (in the case if syst_weights and\n # syst_trees). This might fix them\n random.shuffle(submit_list)\n for submit_file in submit_list:\n\n # When you want to run different sample files with the\n # same compiled exec, or when you want to run nominal +\n # systematic weights (hence the data exception)\n if not self.compile:\n is_to_run = False\n for sample in self.samples_list:\n if sample[\"HistoFile\"] == \"data\":\n continue\n if sample[\"HistoFile\"] in submit_file:\n is_to_run = True\n break\n\n if not is_to_run:\n continue\n\n index = submit_file.find(\"submit_files/\")\n while True:\n proc = subprocess.run(['qstat'], stdout=subprocess.PIPE)\n out = proc.stdout.decode('utf8')\n number_of_jobs = out.count('\\n')\n if number_of_jobs > 80:\n time.sleep(300)\n else:\n break\n subprocess.run([\"qsub\", submit_file[index+13:]],\n cwd=self.submit_dir_path)\n time.sleep(5)\n\n time.sleep(10)\n\n def run(self, test=False):\n super().run(test)\n self.get_samples_variables(test)\n self.get_regions_and_max_cuts()\n\n if self.compile:\n self.link_cutfile_and_compile()\n\n self.prepare_and_run_jobs()\n\n self.copy_files_to_output_folder()\n","sub_path":"src/LipCbrAnalysis.py","file_name":"LipCbrAnalysis.py","file_ext":"py","file_size_in_byte":14827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"267534218","text":"#!/usr/bin/env python3\n\n\"\"\"Trains a new neural network to identify different flower categories.\n\nThis script accepts a set of images of different flowers, which are used to\ntrain a neural network to identify different flower categories. The script\nuses transfer learning to speed the training process and reduce the amount of\ntraining data required.\n\nUsage:\n train.py --save_dir --arch --learning_rate \n --hidden_units --epochs --gpu\n\nArgs:\n : The directory where the training and validation data is\n located. The data must be structured as per the generic data loader for\n the torchvision.datasets.ImageFolder class.\n --save_dir : The directory to save the model checkpoint files.\n --arch : The feature model to use for transfer learning. The\n of the feature model must be one of the following:\n densenet: Densenet-121 (default)\n resnet: ResNet-101\n vgg: VGG-11\n --learning_rate : The learning rate used to train the network.\n Default is 0.03.\n --hidden_units : The number of nodes in the hidden layer of the\n classifier. Default is 256.\n --epochs : The number of epochs to run the training.\n Default is 15.\n --gpu: Use the GPU for training, if available.\n\nExample:\n $ python train.py flowers --save_dir checkpoints --arch \"densenet\"\n --learning_rate 0.0005 --hidden_units 256 --epochs 15 --gpu\n\n Parameters for training...\n Architecture: densenet\n Hidden units: 256\n Learning rate: 0.0005\n Epochs: 15\n\n Training model...\n Epoch: 1/15 Trng loss: 0.06174 Valn loss: 0.04596 Acc: 0.468 Dur: 53.9s\n Epoch: 2/15 Trng loss: 0.03889 Valn loss: 0.02523 Acc: 0.707 Dur: 53.1s\n Epoch: 3/15 Trng loss: 0.02599 Valn loss: 0.01608 Acc: 0.809 Dur: 53.2s\n ...\n Epoch: 15/15 Trng loss: 0.00788 Valn loss: 0.00412 Acc: 0.935 Dur: 53.3s\n \n Training complete.\n Model saved to 'checkpoints\\densenet_h256_e15_a94.pth'.\n\"\"\"\n\n\nimport time\nimport argparse\nimport torch\nfrom torch import optim\nfrom torch import nn\nfrom torchvision import datasets\nfrom pathlib import Path\nimport flowernet\n\n\nTRNG_FOLDER = 'train'\nVALN_FOLDER = 'valid'\nCHKP_FILE_EXT = 'pth'\n\nDEFAULT_ARCH = 'densenet'\nDEFAULT_LEARNING_RATE = 0.0005\nDEFAULT_HIDDEN_UNITS = 256\nDEFAULT_EPOCHS = 15\nDEFAULT_SAVE_DIR = '.'\n\n\ndef get_input_args():\n \"\"\"Parse and return the input arguments to the script.\n \n Checks the paths provided to the input arguments and, if they don't exist an\n exception will be raised.\n \n Returns:\n argparse.Namespace\n \"\"\"\n \n argp = argparse.ArgumentParser(\n formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n description=\"\"\"Trains a new neural network to identify different\n flower categories.\"\"\")\n\n argp.add_argument('data_path', type=Path,\n help=\"\"\"The directory path where the training and\n validation data is located.\"\"\")\n argp.add_argument('--save_dir', type=Path, \n default=DEFAULT_SAVE_DIR,\n help=\"\"\"The directory to save the model checkpoint\n files.\"\"\")\n argp.add_argument('--arch', choices=['densenet', 'resnet', 'vgg'],\n default=DEFAULT_ARCH,\n help=\"\"\"The feature model to use for transfer\n learning.\"\"\")\n argp.add_argument('--learning_rate', type=float, \n default=DEFAULT_LEARNING_RATE,\n help=\"\"\"The learning rate used to train the network.\"\"\")\n argp.add_argument('--hidden_units', type=int, \n default=DEFAULT_HIDDEN_UNITS,\n help=\"\"\"The number of nodes in the hidden layer of the\n classifier.\"\"\")\n argp.add_argument('--epochs', type=int, \n default=DEFAULT_EPOCHS,\n help=\"\"\"The number of epochs to run the training.\"\"\")\n argp.add_argument('--gpu', action=\"store_true\",\n help=\"\"\"Use the GPU for training, if available.\"\"\")\n\n args = argp.parse_args()\n\n if not args.data_path.exists():\n raise FileNotFoundError(\n \"Data path '{}' doesn't exist.\".format(args.data_path))\n if not args.save_dir.exists():\n raise FileNotFoundError(\n \"Save path '{}' doesn't exist.\".format(args.save_dir))\n\n return args\n\n\ndef create_idx_to_cat(cat_to_idx):\n \"\"\"Reverse the mapping of a cat_to_idx dictionary so that the internal index\n numbers provided by the classifier can be mapped to the actual flower \n category labels.\n \n Args:\n cat_to_idx: Dictionary mapping flower categories to classifier indexes.\n returns\n Dictionary mapping classifier indexes to flower categories.\n \"\"\"\n\n return {val: key for key, val in cat_to_idx.items()}\n\n\ndef create_dataloaders(data_dir):\n \"\"\"Create the training and validation dataloaders.\n \n The data files must be structured as per the generic data loader for the \n torchvision.datasets.ImageFolder class.\n \n Args:\n data_dir: A pathlib.Path object containing the location of the \n data files.\n Returns\n trng_dataloader: A torch.utils.data.DataLoader\n valn_dataloader: A torch.utils.data.DataLoader\n \"\"\"\n\n trng_dataset = datasets.ImageFolder(data_dir / TRNG_FOLDER,\n transform=flowernet.trng_transform)\n trng_dataloader = torch.utils.data.DataLoader(trng_dataset,\n batch_size=64,\n shuffle=True)\n\n valn_dataset = datasets.ImageFolder(data_dir / VALN_FOLDER,\n transform=flowernet.pred_transform)\n valn_dataloader = torch.utils.data.DataLoader(valn_dataset,\n batch_size=64,\n shuffle=True)\n\n return trng_dataloader, valn_dataloader\n\n\ndef train(fnm, trng_dataloader, valn_dataloader, max_epochs, learning_rate,\n on_gpu):\n \"\"\"Train a flowernet model.\n \n Args:\n fnm: A FlowerNetModule to train.\n trng_dataloader: A torch.utils.data.DataLoader containing the \n training dataset.\n valn_dataloader: A torch.utils.data.DataLoader containing the\n validation dataset.\n max_epochs: The number of epochs to run the training.\n learning_rate: The learning rate used to train the network.\n on_gpu: If True, use an available GPU for training.\n Returns:\n accuracy: The final accuracy of model, based on evaluation against the\n validation dataset.\n \"\"\"\n\n device = flowernet.get_device(on_gpu)\n\n optimiser = optim.Adam(fnm.classifier.parameters(), learning_rate)\n criterion = nn.NLLLoss()\n\n fnm.model.to(device)\n\n trng_losses, valn_losses = [], []\n for epoch in range(max_epochs):\n\n epoch_start_time = time.time()\n\n total_trng_loss = 0\n fnm.model.train()\n for images, exp_results in trng_dataloader:\n images, exp_results = images.to(device), exp_results.to(device)\n optimiser.zero_grad()\n\n log_pred_results = fnm.model(images)\n loss = criterion(log_pred_results, exp_results)\n total_trng_loss += loss.item()\n\n loss.backward()\n optimiser.step()\n\n total_valn_loss = 0\n total_correct = 0\n fnm.model.eval()\n with torch.no_grad():\n for images, exp_results in valn_dataloader:\n images, exp_results = images.to(device), exp_results.to(device)\n\n log_pred_results = fnm.model(images)\n\n loss = criterion(log_pred_results, exp_results)\n total_valn_loss += loss.item()\n\n predicted_results = torch.exp(log_pred_results)\n _, top_results = predicted_results.topk(1, dim=1)\n correct_results = top_results == exp_results.view(\n *top_results.shape)\n total_correct += correct_results.sum().item()\n\n mean_trng_loss = total_trng_loss / len(trng_dataloader.dataset)\n mean_valn_loss = total_valn_loss / len(valn_dataloader.dataset)\n accuracy = total_correct / len(valn_dataloader.dataset)\n\n trng_losses.append(mean_trng_loss)\n valn_losses.append(mean_valn_loss)\n\n epoch_duration = time.time() - epoch_start_time\n\n print('Epoch: {}/{} '.format(epoch + 1, max_epochs),\n 'Trng loss: {:.5f} '.format(mean_trng_loss),\n 'Valn loss: {:.5f} '.format(mean_valn_loss),\n 'Acc: {:.3f} '.format(accuracy),\n 'Dur: {:.1f}s'.format(epoch_duration))\n\n return accuracy\n\n\ndef main():\n \"\"\"Main function.\n \n Performs the following steps:\n 1. Load the input arguments\n 2. Create the dataloaders and a new untrained flowernet model.\n 3. Trains the model\n 4. Saves the model to a checkpoint file.\n \"\"\"\n\n args = get_input_args()\n\n trng_dataloader, valn_dataloader = create_dataloaders(args.data_path)\n idx_to_cat = create_idx_to_cat(trng_dataloader.dataset.class_to_idx)\n fnm = flowernet.create(args.arch, args.hidden_units, idx_to_cat)\n\n print()\n print(\"Parameters for training...\")\n print(\"Architecture: {}\".format(args.arch))\n print(\"Hidden units: {}\".format(args.hidden_units))\n print(\"Learning rate: {}\".format(args.learning_rate))\n print(\"Epochs: {}\".format(args.epochs))\n print()\n print(\"Training model...\")\n\n accuracy = train(fnm, trng_dataloader, valn_dataloader, args.epochs,\n args.learning_rate, args.gpu)\n\n chkp_file_name = '{}_h{}_e{}_a{:02.0f}.{}'.format(args.arch,\n args.hidden_units,\n args.epochs,\n accuracy * 100,\n CHKP_FILE_EXT)\n chkp_file_path = args.save_dir / chkp_file_name\n flowernet.save(fnm, chkp_file_path)\n \n print()\n print(\"Training complete.\")\n print(\"Model saved to '{}'.\".format(chkp_file_path))\n print()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":10514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"443011896","text":"from operator import itemgetter\nfrom decimal import (Decimal, ROUND_UP)\nimport statistics\n\nfrom django.db import models\nfrom django.contrib.auth.models import User\nfrom django.db.models import (Count, Avg)\n\n\nclass UsersWeight(models.Model):\n\n \"\"\"\n\n Extending users model. Users weight.\n\n \"\"\"\n\n user = models.OneToOneField(User,\n on_delete=models.CASCADE,\n related_name='users_weight')\n weight = models.IntegerField(default=0)\n\n def __str__(self):\n return 'User: {0}. Weight: {1}'.format(self.user.username,\n self.weight)\n\n\nclass Question(models.Model):\n\n \"\"\"\n\n Surveys question\n\n \"\"\"\n\n caption = models.CharField(max_length=255)\n question_type = models.ForeignKey('QuestionType', related_name='question')\n answers = models.ManyToManyField('Answer', related_name='questions')\n survey = models.ForeignKey('Survey',\n related_name='questions')\n\n\nclass Survey(models.Model):\n\n \"\"\"\n\n Users survey\n\n \"\"\"\n\n name = models.CharField(max_length=255)\n slug = models.CharField(max_length=255, null=True, blank=True)\n weight = models.IntegerField(default=0)\n user = models.ForeignKey(User, related_name='surveys')\n\n def __str__(self):\n return '{0}. Author: {1}'.format(self.name, self.user.username)\n\n\nclass QuestionType(models.Model):\n\n \"\"\"\n\n Questions type: option or select\n\n \"\"\"\n\n question_type = models.CharField(max_length=64)\n\n def __str__(self):\n return '{}'.format(self.question_type)\n\n\nclass Answer(models.Model):\n\n \"\"\"\n\n All answers for questions\n\n \"\"\"\n\n caption = models.CharField(max_length=255)\n\n def __str__(self):\n return '{}'.format(self.caption)\n\n\nclass UserAnswered(models.Model):\n\n \"\"\"\n\n Answered question with related data\n\n \"\"\"\n\n question = models.ForeignKey('Question', related_name='answered_questions')\n user = models.ForeignKey(User, related_name='answered_questions')\n users_answers = models.ManyToManyField('Answer',\n related_name='answered_questions')\n\n\nclass ModelsManager(object):\n\n def create_answer(self, caption):\n\n \"\"\"\n\n Create answer.\n\n Args: caption: answers text;\n\n Return: instance;\n\n \"\"\"\n\n answer, created = Answer.objects.get_or_create(caption=caption)\n return answer, created\n\n def get_or_create_answers(self, answers_string):\n\n \"\"\"\n\n Get or create answers.\n\n Args: answers_string: answers;\n\n Return: of ;\n\n \"\"\"\n\n answers = []\n for answer_str in answers_string.split('|'):\n answer, created = self.create_answer(answer_str)\n answers.append(answer)\n return answers\n\n def create_survey(self, name, user, weight):\n\n \"\"\"\n\n Create survey.\n\n Args: name: surveys name/caption;\n user: instance;\n\n Return: instance;\n True if created False otherwise.\n\n \"\"\"\n\n survey = Survey.objects.create(name=name, user=user, weight=weight)\n survey.slug = \"{0}{1}\".format(user.username, str(survey.id))\n survey.save()\n return survey\n\n def create_question(self, caption, question_type, survey, answers):\n\n \"\"\"\n\n Create question.\n\n Args: caption: questions text;\n question_type: object;\n survey: object;\n answers: of objects;\n\n Return: instance;\n\n \"\"\"\n\n question = Question.objects.create(caption=caption,\n question_type=question_type,\n survey=survey)\n if answers and isinstance(answers, list):\n question.answers.add(*answers)\n question.save()\n return question\n\n def create_user_answered(self, question, user, users_answers):\n\n \"\"\"\n\n Add users answers.\n\n Args: question: object;\n user: user answering;\n user_answers: of objects;\n\n Return: instance;\n\n \"\"\"\n\n answered = UserAnswered.objects.create(question=question,\n user=user)\n if users_answers and isinstance(users_answers, list):\n answered.users_answers.add(*users_answers)\n answered.save()\n return answered\n\n def update_users_weight(self, username, weight):\n\n \"\"\"\n\n Update users weight.\n\n Args: username: user answering;\n weight: new users weight;\n\n Return: instance;\n\n \"\"\"\n\n user = self.get_user_by_username(username)\n defaults = {\n 'user': user,\n 'weight': weight,\n }\n user_weight, created = UsersWeight.objects.update_or_create(\n user=user,\n defaults=defaults)\n return user_weight\n\n def get_user_by_username(self, username):\n\n \"\"\"\n\n Get user instance by username.\n\n Args: username: user answering;\n\n Return: instance;\n\n \"\"\"\n\n return User.objects.get(username=username)\n\n def get_all_surveys(self, sort=False):\n\n \"\"\"\n\n Select all surveys. Ordered by surveys and users weight.\n\n Return: of ;\n\n \"\"\"\n\n surveys = Survey.objects\n if sort:\n surveys = surveys.order_by(\n '-user__users_weight__weight', '-weight')\n\n return surveys.all()\n\n def get_surveys_questions(self, slug):\n\n \"\"\"\n\n Select all added questions by surveys slug.\n\n Args: slug: surveys slug;\n\n Return: of ;\n\n \"\"\"\n\n survey = Survey.objects.get(slug=slug)\n\n return survey.questions.all()\n\n def get_survey_by_slug(self, slug):\n\n \"\"\"\n\n Get survey by surveys slug.\n\n Args: slug: surveys slug;\n\n Return: instance;\n\n \"\"\"\n\n return Survey.objects.get(slug=slug)\n\n def get_surveys_by_username(self, username, sort=False):\n\n \"\"\"\n\n Get surveys by username. Ordered by users weight.\n\n Args: username: username;\n\n Return: of ;\n\n \"\"\"\n\n surveys = Survey.objects.filter(user__username=username)\n if sort:\n surveys = surveys.order_by('-weight')\n\n return surveys.all()\n\n def get_surveys_by_popularity(self, username):\n\n \"\"\"\n\n Get surveys by popularity.\n\n Args: username: username;\n\n Return: of ;\n\n \"\"\"\n\n users_surveys = Survey.objects.filter(user__username=username)\n popular_surveys = users_surveys.all().annotate(\n count_answers=Count(\n 'questions__answered_questions',\n distinct=True),\n count_users=Count(\n 'questions__answered_questions__user',\n distinct=True)).order_by(\n '-count_answers')\n return popular_surveys\n\n def get_users_by_popularity(self):\n\n \"\"\"\n\n Get users by popularity.\n\n Return: of ;\n\n \"\"\"\n\n count_users_popularity = User.objects.all().annotate(\n count_surveys=Count('surveys', distinct=True),\n count_answered=Count('surveys__questions__answered_questions',\n distinct=True)).order_by(\n '-count_surveys', '-count_answered')\n return count_users_popularity\n\n def get_surveys_by_answer_popularity(self, username):\n\n \"\"\"\n\n Get surveys by percent answer popularity.\n\n Args: username: username;\n\n Return: of :\n\n [{\n 'max_question': instance,\n 'answers_percents': ,\n 'num_questions': ,\n 'survey': instance,\n },\n ...]\n\n \"\"\"\n\n # a bit hardcoded :)\n users_surveys = Survey.objects.filter(user__username=username)\n survey_list = []\n for survey in users_surveys:\n question_list = []\n\n # no questions -> next survey\n if not survey.questions.all():\n continue\n\n # creating dictionary with sufficient questions data\n for question in survey.questions.all():\n question_list.append({\n 'question': question,\n 'count_answers': question.answered_questions.count(),\n })\n\n # have questions but no answers -> next survey\n if all(dict_['count_answers'] == 0 for dict_ in question_list):\n continue\n\n # sum all answers for question\n total_answered = sum(\n [dict_['count_answers'] for dict_ in question_list])\n\n # calculating answers percentage for each questions in each surveys\n remain = Decimal(100.00)\n for dict_ in question_list:\n val = dict_['count_answers'] / total_answered\n val = Decimal(val).quantize(\n Decimal('.01'), rounding=ROUND_UP) * Decimal(100.00)\n dict_['count_answers'] = val\n remain -= val\n dict_['count_answers'] += remain\n\n # sort surveys questions by 'count_answers'\n question_list = sorted(question_list,\n key=itemgetter('count_answers'),\n reverse=True)\n\n # form list of surveys for output\n survey_list.append({\n 'max_question': question_list[0]['question'],\n 'answers_percents': question_list[0]['count_answers'],\n 'num_questions': len(question_list),\n 'survey': survey,\n })\n\n # sort surveys by 'answers_percents'\n sorted_list = sorted(survey_list,\n key=itemgetter('answers_percents'),\n reverse=True)\n\n # then by 'num_questions'\n sorted_survey_list = sorted(sorted_list,\n key=itemgetter('num_questions'))\n\n return sorted_survey_list\n\n def get_surveys_name(self, slug):\n\n \"\"\"\n\n Get surveys name by surveys slug.\n\n Args: slug: surveys slug;\n\n Return: surveys name;\n\n \"\"\"\n\n survey = self.get_survey_by_slug(slug)\n\n return survey.name\n\n def get_question_by_id(self, id_):\n\n \"\"\"\n\n Get question by id.\n\n Args: id: questions id;\n\n Return: instance;\n\n \"\"\"\n\n return Question.objects.get(id=id_)\n\n def get_next_question(self, slug, user):\n\n \"\"\"\n\n Get next question for survey.\n\n Args: slug: surveys slug;\n user: user answering;\n\n Return: instance;\n\n \"\"\"\n\n next_question = self._get_surveys_unanswered_questions(\n slug, user)\n\n if next_question is None:\n return []\n else:\n return next_question\n\n def get_users_total(self):\n\n \"\"\"\n\n Count users.\n\n Return: total users;\n\n \"\"\"\n\n return User.objects.count()\n\n def get_surveys_total(self):\n\n \"\"\"\n\n Count surveys.\n\n Return: total surveys;\n\n \"\"\"\n\n return Survey.objects.count()\n\n def get_answered_total(self):\n\n \"\"\"\n\n Count answered questions (same questions with different users\n included).\n\n Return: total answered questions;\n\n \"\"\"\n\n return UserAnswered.objects.count()\n\n def get_avg_users_surveys(self):\n\n \"\"\"\n\n Get average users surveys.\n\n Return: average;\n\n \"\"\"\n\n count_users_surveys = User.objects.all().annotate(count_surveys=Count(\n 'surveys'))\n if not count_users_surveys:\n return None\n count_list = [item.count_surveys for item in count_users_surveys]\n avg = statistics.mean(count_list)\n avg = Decimal(avg).quantize(Decimal('.01'), rounding=ROUND_UP)\n return avg\n\n def get_avg_users_answers(self):\n\n \"\"\"\n\n Get average users answers.\n\n Return: average;\n\n \"\"\"\n\n count_users_answers = User.objects.all().annotate(\n count_answered=Count('answered_questions'))\n if not count_users_answers:\n return None\n count_list = [item.count_answered for item in count_users_answers]\n avg = statistics.mean(count_list)\n avg = Decimal(avg).quantize(Decimal('.01'), rounding=ROUND_UP)\n return avg\n\n def get_avg_answers(self):\n\n \"\"\"\n\n Get average answers by surveys.\n\n Return: average;\n\n \"\"\"\n\n count_answers = Survey.objects.all().annotate(\n count_answers=Count('questions__answered_questions'))\n if not count_answers:\n return None\n count_list = [item.count_answers for item in count_answers]\n avg = statistics.mean(count_list)\n avg = Decimal(avg).quantize(Decimal('.01'), rounding=ROUND_UP)\n return avg\n\n def _get_surveys_unanswered_questions(self, slug, user):\n\n \"\"\"\n\n Get surveys unanswered questions by user.\n\n Args: slug: surveys slug;\n user: user answering;\n\n Return: of ;\n\n \"\"\"\n\n survey = self.get_survey_by_slug(slug)\n\n answered_question_list = UserAnswered.objects.filter(\n user=user, question__survey=survey).values_list(\n 'question__id', flat=True)\n unanswered_questions_query = Question.objects.filter(\n survey=survey).exclude(id__in=answered_question_list)\n\n question = self._get_least_answered_question(\n unanswered_questions_query)\n\n return question\n\n def _get_least_answered_question(self, query):\n\n \"\"\"\n\n Get least answered question from list.\n\n Args: query: ;\n\n Return: instance, None if doesn't exist;\n\n \"\"\"\n\n sorted_questions = query.all().annotate(\n count_answered=Count('answered_questions')).order_by(\n 'count_answered')\n if sorted_questions:\n return sorted_questions[0]\n else:\n return None\n\n\n\n\n\n","sub_path":"mighty_survey/surveys/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":14752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"600463676","text":"from BytesUnit import BytesUnit\nimport os\n\nclass Ngram(BytesUnit):\n def __init__(self):\n BytesUnit.__init__(self, self.__class__.__name__)\n\n self.fileName = None\n self.result = None\n\n def _cvtNgram(self, byteSeq, N=2):\n gramDict = dict()\n\n for idx in range(len(byteSeq)-N+1):\n gram = ''.join(byteSeq[idx:idx+N])\n\n if gram in gramDict:\n gramDict[gram] += 1\n else:\n gramDict[gram] = 1\n\n return gramDict\n\n def _extractByteSeq(self, inputPath):\n fd = open(inputPath)\n\n byteSequence = list()\n\n for line in fd:\n line = line.strip()\n byteSequence += line.split()[1:]\n\n fd.close()\n\n return byteSequence\n\n def saveResult(self):\n BytesUnit.saveResult(self) #if result is None, throw Exception\n resultPath = os.path.join(self.resDir,self.fileName)\n\n fd = open(resultPath+'.'+self.symbol, 'w')\n resultGram = [self.fileName]\n for key in self.result.keys():\n resultGram.append('%s:%d'%(key, self.result[key]))\n\n fd.write(' '.join(resultGram))\n fd.close()\n\n def fit_transform(self, inputPath):\n self.fileName = os.path.basename(inputPath).split('.')[0]\n\n byteSequence = self._extractByteSeq(inputPath)\n gramDict = self._cvtNgram(byteSequence)\n\n self.result = gramDict\n\n return self.result\n","sub_path":"malware_for_ML/src/preprocessing/bytes/Ngram.py","file_name":"Ngram.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"205632612","text":"#!/bin/python\nimport os\nimport time\nimport warnings\nimport numpy as np\nimport keras\nimport tensorflow as tf\nimport random\nimport csv\nfrom numpy import newaxis\nfrom keras.layers.core import Dense, Activation, Dropout\nfrom keras.layers import BatchNormalization\nfrom keras.models import Sequential, load_model\nfrom keras.utils import plot_model\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA, KernelPCA\nfrom datetime import datetime, timedelta, date\nimport matplotlib.pyplot as plt\n\ndef standardizeInputs(data):\n # create scaler\n scaler = StandardScaler()\n # fit scaler on data\n scaler.fit(data)\n # apply transform\n standardized = scaler.transform(data)\n return standardized\n\ndef stripQuotes(data):\n answer = []\n for entry in data:\n# print(entry)\n curRow = []\n for i in range(len(entry)):\n# print(entry[i])\n curEntry = entry[i].replace(\"\\\"\",\"\")\n curRow.append(curEntry)\n answer.append(curRow)\n answer = np.array(answer)\n return answer\n\ndef createXYImpliedProbs(data):\n XY, ImpliedProbs = np.split(data,[-2],axis=1)\n gIY, ScoreX = np.split(XY,[2],axis=1)\n Score, X = np.split(ScoreX,[2],axis=1)\n gameIDs, Y = np.split(gIY,[1],axis=1)\n# print(X[0])\n return X, Y, ImpliedProbs, gameIDs\n\ndef build_model(layers, batch_size, modelWeightFile, dropRate=0.5, loadModel = 1,learning_rate = 0.1, decay_rate = 0.005, epochs=100):\n model = Sequential()\n startWeights = keras.initializers.RandomNormal(mean=0, stddev=(1/100)**0.5, seed=-1)\n totalLayers = len(layers)\n model.add(Dense(layers[0], input_shape=(layers[0],), kernel_initializer=(startWeights)))\n model.add(BatchNormalization())\n model.add(Dropout(dropRate))\n for i in range(1,totalLayers-1):\n stddev = (1/layers[i-1])**0.5\n model.add(Dense(layers[i], activation='relu', kernel_initializer= keras.initializers.RandomNormal(mean=0, stddev=stddev, seed=-1)))\n model.add(BatchNormalization())\n model.add(Dropout(dropRate))\n model.add(Dense(layers[-1], activation='relu'))\n model.add(Dense(1, activation='sigmoid' )) # linear for spread, sigmoid for win/loss\n if loadModel > 0:\n model.load_weights(modelWeightFile)\n start = time.time()\n\t# model.compile(loss=\"mse\", optimizer=\"adam\") #for spread\n decay_steps = 1.0\n decay_rate = decay_rate\n learning_rate_fn = keras.optimizers.schedules.InverseTimeDecay(learning_rate, decay_steps, decay_rate)\n model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\") #for win/loss\n print(\"> Compilation Time : \", time.time() - start)\n return model\n\ninFileData = \"gameData\" + datetime.today().strftime('%Y-%m-%d') + \".csv\"\ndata = np.loadtxt(open(inFileData, \"r\"),dtype=np.unicode_, delimiter=\",\")\ndataStripped = stripQuotes(data)\nX_str, Y_str, ImpliedProbs_str, homeIDs = createXYImpliedProbs(dataStripped)\nprint(homeIDs)\nX = X_str.astype(np.float)\nX = standardizeInputs(X)\nY = Y_str.astype(np.float)\nImpliedProbs = ImpliedProbs_str.astype(np.float)\nX = np.concatenate((X,ImpliedProbs), axis=1)\n\nepochs = 0\nbatch_size = 256\ndrop_rate = 0.64\nlearning_rate = 0.24\ndecay_rate = 0.0025\nmodelWeightFile1 = \"modelWeights26.h5\"\nmodel = build_model([1408, 500, 240, 60, 25, 13, 1], batch_size, modelWeightFile1, dropRate= drop_rate, loadModel=1, learning_rate = learning_rate, decay_rate = decay_rate,epochs= epochs)\n\n\npredictions = model.predict(X, batch_size=batch_size, verbose=0)\npredictionFile = datetime.today().strftime('%Y-%m-%d') + \"Preds.csv\"\npredFile = open(predictionFile, \"w\")\n\nwriter = csv.writer(predFile, delimiter=',', quotechar='\"', quoting=csv.QUOTE_ALL)\npredictionLen = len(predictions)\ncombinedMatrix = []\nfor i in range(predictionLen):\n\trow = []\n\thomeID = homeIDs[i][0]\n\trow.append(homeID)\n\trow.append(predictions[i][0])\n\twriter.writerow(row)\npredFile.close()\n\n\n","sub_path":"makePredictions.py","file_name":"makePredictions.py","file_ext":"py","file_size_in_byte":3996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"158144562","text":"def get_type_day(day):\n days = {\n (1, 7): 'Weekend',\n tuple(range(2, 7)): 'Workdays'\n }\n\n choosen_day = (_type for numbers, _type in days.items() if day in numbers)\n return next(choosen_day, '*** Invalid day ***')\n\n\nif __name__ == '__main__':\n for day in range(0,9):\n print(f'{day}: {get_type_day(day)}')\n","sub_path":"comprehension/comprehension_v6.py","file_name":"comprehension_v6.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"256637130","text":"import os, sys, tkinter, tkinter.filedialog, tkinter.messagebox, ffmpeg, json\r\n\r\nroot = tkinter.Tk()\r\nroot.withdraw()\r\n\r\nfTyp = [(\"\",\".webm .mkv .mp4\")]\r\niDir = os.path.abspath(os.path.dirname(__file__))\r\nfile = tkinter.filedialog.askopenfilename(filetypes = fTyp,initialdir = iDir)\r\nfilename = os.path.splitext(file)[0]\r\nfile_ext = os.path.splitext(file)[1][1:]\r\n\r\nif len(file) == 0:\r\n tkinter.messagebox.showerror('エラー', 'ファイルが選択されていません')\r\n sys.exit()\r\n\r\nif file_ext == \"mkv\":\r\n (\r\n ffmpeg\r\n .input(file)\r\n .output(filename+\".mp4\", vcodec='copy')\r\n .overwrite_output()\r\n .run()\r\n )\r\n\r\nelif file_ext == \"webm\":\r\n (\r\n ffmpeg\r\n .input(file)\r\n .output(filename+\".mp4\")\r\n .overwrite_output()\r\n .run()\r\n )\r\n\r\nelif file_ext == \"mp4\":\r\n (\r\n ffmpeg\r\n .input(file)\r\n .output(filename+\".mp3\", acodec=\"libmp3lame\", ab=\"128k\")\r\n .overwrite_output()\r\n .run()\r\n )\r\n","sub_path":"converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"182396476","text":"from collections import defaultdict\n\n\ndef solve(row, col, d):\n x_map = defaultdict()\n\n for i in range(row):\n a_i = list(map(int, input().split()))\n for j in range(col):\n x_map[a_i[j]] = (i, j)\n\n q = int(input())\n\n dp = [0]*(row*col+1)\n for j in range(d+1, row*col+1):\n x_map_y = abs(x_map[j][0] - x_map[j-d][0])\n x_map_x = abs(x_map[j][1] - x_map[j-d][1])\n dp[j] = dp[j-d]+x_map_y+x_map_x\n print(\"{0} -> {1} | x_map_y : {2} x_map_x : {3} dp[j] : {4}\".format(\n j-d, j, x_map_y, x_map_x, dp[j]))\n\n for _ in range(q):\n l, r = map(int, input().split())\n print(dp[r]-dp[l])\n\n\nif __name__ == '__main__':\n row, col, d = map(int, input().split())\n solve(row, col, d)\n","sub_path":"Python/PracticalSkillTest.py","file_name":"PracticalSkillTest.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"224450658","text":"#Defining variables required for the program\nproductNames = []\nproductPrices = []\nsumOfPriceList = int(0)\n\n#Looping the input of item and price input 5 times\ndef userInput():\n currentProductPrice = int()\n currentProductName = str()\n while len(productNames) <5 and len(productPrices) <5:\n currentProductName = input(\"Please enter the product name - \")\n productNames.append(currentProductName)\n currentProductPrice = int(input(\"Please enter the price of that product - £\"))\n productPrices.append(currentProductPrice)\n\n\n#Bubble Sort for price listing. This also sorts the productNames so they match up with their associated cost.\ndef bubbleSort(productPrices,productNames):\n for i in range (0, len(productPrices) - 1):\n for j in range (0, len(productPrices) - 1 - i):\n if productPrices[j] > productPrices[j+1]:\n productPrices[j], productPrices[j+1] = productPrices[j+1], productPrices[j]\n productNames[j], productNames[j+1] = productNames[j+1], productNames[j]\n return productPrices\n\n#Creates the final print to the user, showing the products, prices, total including discount on cheapest product\ndef finalPrint():\n print(\"Your basket is as follows - \")\n print((productNames[0]), \"at £\", (productPrices[0]))\n print((productNames[1]), \"at £\", (productPrices[1]))\n print((productNames[2]), \"at £\", (productPrices[2]))\n print((productNames[3]), \"at £\", (productPrices[3]))\n print((productNames[4]), \"at £\", (productPrices[4]))\n print(\"The total price is £\" + str(sumOfPriceList) + \", including the 100% discount on '\" + productNames[4] + \"'.\")\n\n#Calls the userInput function\nuserInput()\n\n#Calling the bubbleSort function, then reversing the lists so highest price is first.\nbubbleSort(productPrices, productNames)\nproductPrices.reverse()\nproductNames.reverse()\n\n#Calculating the total, missing the last entry to apply the discount\nsumOfPriceList = productPrices[0] + productPrices[1] + productPrices[2] + productPrices[3]\n\n#Calling the finalPrint function\nfinalPrint()","sub_path":"assessment2.py","file_name":"assessment2.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"439820884","text":"import json\nfrom decimal import Decimal\n\nimport gittip\nfrom aspen.utils import utcnow\nfrom gittip.testing import Harness\nfrom gittip.models.participant import Participant\nfrom gittip.testing.client import TestClient\n\n\nclass Tests(Harness):\n\n def make_alice(self):\n return self.make_participant('alice', claimed_time=utcnow())\n\n def change_goal(self, goal, goal_custom=\"\", user=\"alice\"):\n if isinstance(user, Participant):\n user = user.username\n else:\n self.make_alice()\n\n client = TestClient()\n response = client.get('/')\n csrf_token = response.request.context['csrf_token']\n\n response = client.post( \"/alice/goal.json\"\n , { 'goal': goal\n , 'goal_custom': goal_custom\n , 'csrf_token': csrf_token\n }\n , user=user\n )\n return response\n\n\n def test_participant_can_set_their_goal_to_null(self):\n response = self.change_goal(\"null\")\n actual = json.loads(response.body)['goal']\n assert actual == None, actual\n\n def test_participant_can_set_their_goal_to_zero(self):\n response = self.change_goal(\"0\")\n actual = json.loads(response.body)['goal']\n assert actual == \"0.00\", actual\n\n def test_participant_can_set_their_goal_to_a_custom_amount(self):\n response = self.change_goal(\"custom\", \"100.00\")\n actual = json.loads(response.body)['goal']\n assert actual == \"100.00\", actual\n\n def test_custom_amounts_can_include_comma(self):\n response = self.change_goal(\"custom\", \"1,100.00\")\n actual = json.loads(response.body)['goal']\n assert actual == \"1,100.00\", actual\n\n def test_wonky_custom_amounts_are_standardized(self):\n response = self.change_goal(\"custom\", \",100,100.00000\")\n actual = json.loads(response.body)['goal']\n assert actual == \"100,100.00\", actual\n\n def test_anonymous_gets_404(self):\n response = self.change_goal(\"100.00\", user=None)\n assert response.code == 404, response.code\n\n def test_invalid_is_400(self):\n response = self.change_goal(\"cheese\")\n assert response.code == 400, response.code\n\n def test_invalid_custom_amount_is_400(self):\n response = self.change_goal(\"custom\", \"cheese\")\n assert response.code == 400, response.code\n\n\n # Exercise the log_goal_changes rule.\n\n def test_last_goal_is_stored_in_participants_table(self):\n alice = self.make_alice()\n self.change_goal(\"custom\", \"100\", alice)\n self.change_goal(\"custom\", \"200\", alice)\n self.change_goal(\"custom\", \"300\", alice)\n self.change_goal(\"null\", \"\", alice)\n self.change_goal(\"custom\", \"400\", alice)\n actual = gittip.db.fetchone(\"SELECT goal FROM participants\")['goal']\n assert actual == Decimal(\"400.00\"), actual\n\n def test_all_goals_are_stored_in_goals_table(self):\n alice = self.make_alice()\n self.change_goal(\"custom\", \"100\", alice)\n self.change_goal(\"custom\", \"200\", alice)\n self.change_goal(\"custom\", \"300\", alice)\n self.change_goal(\"null\", \"\", alice)\n self.change_goal(\"custom\", \"400\", alice)\n goals = gittip.db.fetchall(\"SELECT goal FROM goals \"\n \"ORDER BY mtime DESC\")\n actual = [rec['goal'] for rec in goals]\n assert actual == [400, None, 300, 200, 100], actual\n","sub_path":"tests/test_goal_json.py","file_name":"test_goal_json.py","file_ext":"py","file_size_in_byte":3536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"328016682","text":"def transpile(self, trainer_id, program=None, pservers='127.0.0.1:6174', trainers=1, slice_var_up=True, split_method=RoundRobin, sync_mode=True):\n '\\n Run the transpiler.\\n\\n Args:\\n trainer_id (int): id for current trainer worker, if you have\\n n workers, the id may range from 0 ~ n-1\\n program (Program|None): program to transpile,\\n default is fluid.default_main_program().\\n pservers (str): comma separated ip:port string for the pserver\\n list.\\n trainers (int): number of trainers in the distributed job.\\n slice_var_up (bool): Do Tensor slice for pservers, default is True.\\n split_method (PSDispatcher): RoundRobin or HashName can be used\\n try to choose the best method to balance loads for pservers.\\n sync_mode (bool): Do sync training or not, default is True.\\n '\n assert (split_method.__bases__[0] == PSDispatcher)\n if (program is None):\n program = default_main_program()\n self.origin_program = program\n self.trainer_num = trainers\n self.sync_mode = sync_mode\n self.trainer_id = trainer_id\n pserver_endpoints = pservers.split(',')\n self.pserver_endpoints = pserver_endpoints\n (self.optimize_ops, self.params_grads) = self._get_optimize_pass()\n ps_dispatcher = split_method(self.pserver_endpoints)\n self.has_distributed_lookup_table = self._has_distributed_lookup_table()\n self._init_splited_vars(slice_var_up)\n ps_dispatcher.reset()\n send_vars = []\n grad_var_mapping_items = self.grad_var_mapping.items()\n if (not slice_var_up):\n np.random.shuffle(grad_var_mapping_items)\n for (orig_varname, splited_vars) in grad_var_mapping_items:\n eplist = ps_dispatcher.dispatch(splited_vars)\n if (not slice_var_up):\n assert (len(splited_vars) == 1)\n if (len(splited_vars) == 1):\n orig_varname = splited_vars[0].name\n index = find_op_by_output_arg(program.global_block(), orig_varname)\n elif (len(splited_vars) > 1):\n orig_var = program.global_block().vars[orig_varname]\n index = find_op_by_output_arg(program.global_block(), orig_varname)\n self._insert_split_op(program, orig_var, index, splited_vars)\n index += 1\n else:\n AssertionError('Can not insert the send op by original variable name :', orig_varname)\n program.global_block().insert_op(index=(index + 1), type='send', inputs={\n 'X': splited_vars,\n }, outputs={\n \n }, attrs={\n 'epmap': eplist,\n RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,\n })\n for (_, var) in enumerate(splited_vars):\n send_vars.append(var)\n if self.sync_mode:\n program.global_block().append_op(type='send_barrier', inputs={\n \n }, outputs={\n \n }, attrs={\n 'endpoints': pserver_endpoints,\n 'sync_mode': self.sync_mode,\n RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,\n })\n recv_vars = []\n for (_, var) in enumerate(send_vars):\n recv_vars.append(self.grad_param_mapping[var])\n ps_dispatcher.reset()\n eplist = ps_dispatcher.dispatch(recv_vars)\n for (i, ep) in enumerate(eplist):\n self.param_grad_ep_mapping[ep]['params'].append(recv_vars[i])\n self.param_grad_ep_mapping[ep]['grads'].append(send_vars[i])\n for (varname, splited_var) in self.param_var_mapping.iteritems():\n eps = []\n for var in splited_var:\n index = [v.name for v in recv_vars].index(var.name)\n eps.append(eplist[index])\n program.global_block().append_op(type='recv', inputs={\n \n }, outputs={\n 'Out': splited_var,\n }, attrs={\n 'epmap': eps,\n RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,\n })\n program.global_block().append_op(type='fetch_barrier', inputs={\n \n }, outputs={\n \n }, attrs={\n 'endpoints': pserver_endpoints,\n RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,\n })\n for (varname, splited_var) in self.param_var_mapping.iteritems():\n if (len(splited_var) <= 1):\n continue\n orig_param = program.global_block().vars[varname]\n program.global_block().append_op(type='concat', inputs={\n 'X': splited_var,\n }, outputs={\n 'Out': [orig_param],\n }, attrs={\n 'axis': 0,\n })\n if self.has_distributed_lookup_table:\n self._replace_lookup_table_op_with_prefetch(program, pserver_endpoints)\n self._split_table_grad_and_add_send_vars(program, pserver_endpoints)","sub_path":"Data Set/bug-fixing-5/f02a4da6cd6ee0dd9d8a04d60ec20e87b539b4dc--bug.py","file_name":"f02a4da6cd6ee0dd9d8a04d60ec20e87b539b4dc--bug.py","file_ext":"py","file_size_in_byte":4759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"342335754","text":"#Define a function, enrollment_stats() , that takes, as an input, a list of lists where each\n#individual list contains three elements: (a) the name of a university, (b) the total number\n#of enrolled students, and (c) the annual tuition fees.\n#Sample list:\n#universities = [\n#['California Institute of Technology', 2175, 37704],\n#['Harvard', 19627, 39849],\n#['Massachusetts Institute of Technology', 10566, 40732],\n#['Princeton', 7802, 37000],\n#['Rice', 5879, 35551],\n#['Stanford', 19535, 40569],\n#['Yale', 11701, 40500]\n#]\n#This function should return two lists: the first containing all of the student enrollment\n#values and the second containing all of the tuition fees.\n#Next, define a mean() and a median() function. Both functions should take a single list\n#as an argument and return the mean and median from the values in each list. Use the\n#return values from enrollment_stats() as arguments for each function. Challenge\n#yourself by finding a way to sum all the values in a list without using the built-in\n#'sum()' function for calculating the mean.\n#At some point you should calculate the total students enrolled and the total tuition paid.\n#Finally, output all values:\n\ndef enrollment_stats(students_tuition_lists):\n enrollment_numbers = []\n tuition_fees = []\n\n for n in range(0, len(students_tuition_lists)):\n enrollment_numbers.append(students_tuition_lists[n][1])\n tuition_fees.append(students_tuition_lists[n][2]) \n return enrollment_numbers, tuition_fees\n\ndef mean(to_calc_list):\n sum = 0\n for n in range(0, len(to_calc_list)):\n sum += to_calc_list[n]\n return sum/len(to_calc_list)\n \ndef median(to_calc_list):\n median_index = (len(to_calc_list) + 1)/2\n to_calc_list.sort()\n return to_calc_list[int(median_index)-1]\n \nuniversities = [\n['California Institute of Technology', 2175, 37704],\n['Harvard', 19627, 39849],\n['Massachusetts Institute of Technology', 10566, 40732],\n['Princeton', 7802, 37000],\n['Rice', 5879, 35551],\n['Stanford', 19535, 40569],\n['Yale', 11701, 40500]\n]\nenrolled_students_list, tuition_fees_list = enrollment_stats(universities)\ntotal_enrolled_students = sum(enrolled_students_list)\nsum_tuition_fees = sum(tuition_fees_list)\n\naverage_number_of_students_enrolled = int(mean(enrolled_students_list))\nmedian_students = median(enrolled_students_list)\n\naverage_tuition_fees = mean(tuition_fees_list)\nmedian_tuition_fees = median(tuition_fees_list)\n\n\nprint(f\"Enrolled students list : {enrolled_students_list}\")\nprint(f\"Tuition Fees list : {tuition_fees_list}\")\nprint(f\"Total Enrolled students : {total_enrolled_students}\")\nprint(f\"Sum of fees : {sum_tuition_fees}\")\nprint(f\"Avg # of students : {average_number_of_students_enrolled}\")\nprint(f\"Median students : {median_students}\")\nprint(f\"Average fees : {average_tuition_fees}\")\nprint(f\"Median fees : {median_tuition_fees}\")\n","sub_path":"learnpy/list_enrollment.py","file_name":"list_enrollment.py","file_ext":"py","file_size_in_byte":2865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"231591345","text":"from model.Fridge import Fridge\nfrom model.Computer import Computer\nfrom model.TVSet import TVSet\nfrom random import *\n\nclass Engineer:\n @staticmethod\n def create():\n cl = randint(1,3)\n if cl == 1:\n equipment = Fridge(randint(10, 20)*10, choice(('Silver', 'Black', 'White')))\n elif cl == 2:\n equipment = Computer(randint(10, 100)*10, choice((True, False)))\n elif cl == 3:\n equipment = TVSet(randint(10,40)*10, choice(('LED', 'Plasma', 'Box')))\n return equipment","sub_path":"model/Engineer.py","file_name":"Engineer.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"527977847","text":"\"\"\"\nTransformation Convariant Feature Detector Implementation\nhttps://github.com/ColumbiaDVMM/Transform_Covariant_Detector\noriginal code author: Zhange et. Al\ncompiler: Alex Butenko\n\"\"\"\nimport cv2\nimport numpy as np\nfrom features.DetectorDescriptorTemplate import DetectorAndDescriptor\nimport features.feature_utils\nimport sys\n\nimport tensorflow as tf\nfrom tensorflow.contrib import rnn\nimport numpy as np\nimport pickle\nimport cv2\nimport os\nfrom skimage.transform import pyramid_gaussian\n\ndirname = os.path.dirname(__file__)\nclass transform_covariant(DetectorAndDescriptor):\n def __init__(self):\n super(\n transform_covariant,\n self).__init__(\n name='transform_covariant',\n is_detector=True,\n is_descriptor=False,\n is_both=False)\n\n CNNConfig = {\n \"patch_size\": 32,\n \"descriptor_dim\" : 2,\n \"batch_size\" : 128,\n \"alpha\" : 1.0,\n \"train_flag\" : False\n }\n self.model = PatchCNN(CNNConfig)\n file = open(os.path.join(dirname, 'transform_covariant_misc/stats_mexico_tilde_p24_Mexico_train_point.pkl'), 'rb')\n self.mean, self.std = pickle.load(file, encoding='latin1')\n gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)\n self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n saver = tf.train.Saver()\n try:\n saver.restore(self.sess, os.path.join(dirname, 'transform_covariant_misc/mexico_tilde_p24_Mexico_train_point_translation_iter_20_model.ckpt'))\n print(\"Model restored.\")\n except:\n print('No model found')\n exit()\n\n def detect_feature(self, image):\n image = features.feature_utils.all_to_BGR(image)\n\n #build image pyramid\n pyramid = pyramid_gaussian(image, max_layer = 4, downscale=np.sqrt(2))\n\n #predict transformation\n output_list = list()\n for (j, resized) in enumerate(pyramid) :\n fetch = {\n \"o1\": self.model.o1\n }\n\n resized = np.asarray(resized)\n\n resized = (resized-self.mean)/self.std\n resized = resized.reshape((1,resized.shape[0],resized.shape[1],resized.shape[2]))\n result = self.sess.run(fetch, feed_dict={self.model.patch: resized})\n result_mat = result[\"o1\"].reshape((result[\"o1\"].shape[1],result[\"o1\"].shape[2],result[\"o1\"].shape[3]))\n output_list.append(result_mat)\n\n\n pts = np.array(output_list)\n\n return pts\n\n def close(self):\n self.sess.close()\n\nclass PatchCNN:\n def __init__(self, CNNConfig):\n self.patch = tf.placeholder(\"float32\", [1, None, None, 3])\n\n self.alpha = CNNConfig[\"alpha\"]\n self.descriptor_dim = CNNConfig[\"descriptor_dim\"]\n self._patch_size = CNNConfig[\"patch_size\"]\n\n with tf.variable_scope(\"siamese\") as scope:\n self.o1 = self.model(self.patch)\n\n self.o1_flat = tf.reshape(self.o1, [-1, self.descriptor_dim])\n\n def weight_variable(self, name, shape):\n weight = tf.get_variable(name = name+'_W', shape = shape, initializer = tf.random_normal_initializer(0, 1.0))\n return weight\n\n def bias_variable(self,name, shape):\n bias = tf.get_variable(name = name + '_b', shape = shape, initializer = tf.constant_initializer(0.0))\n return bias\n\n def conv2d(self, x, W):\n return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='VALID')\n\n def conv2d_layer(self, name, shape, x):\n weight_init = tf.uniform_unit_scaling_initializer(factor=0.5)\n weight = self._variable_with_weight_decay(name=name + '_W', shape = shape, wd = 1e-5);\n bias = self._variable_with_weight_decay(name=name + '_b', shape = [shape[3]], wd = 1e-5);\n\n conv_val = tf.nn.relu(tf.nn.conv2d(x, weight, strides=[1, 1, 1, 1], padding='VALID')+bias)\n return conv_val\n\n def conv2d_layer_no_relu(self, name, shape, x):\n weight = self._variable_with_weight_decay(name=name + '_W', shape = shape, wd = 1e-5);\n bias = self._variable_with_weight_decay(name=name + '_b', shape = [shape[3]], wd = 1e-5);\n\n conv_val = tf.nn.conv2d(x, weight, strides=[1, 1, 1, 1], padding='VALID')+bias\n return conv_val\n\n def fc_layer(self, name, shape, x):\n weight = self._variable_with_weight_decay(name=name + '_W', shape = shape, wd = 1e-5);\n bias = self._variable_with_weight_decay(name=name + '_b', shape = [shape[1]], wd = 1e-5);\n\n fc_val = tf.matmul(x, weight)+bias\n return fc_val\n\n def _variable_with_weight_decay(self, name, shape, wd):\n dtype = tf.float32\n weight_init = tf.uniform_unit_scaling_initializer(factor=0.5)\n #weight_init = tf.truncated_normal_initializer(stddev=1.0)\n var = tf.get_variable(name=name, dtype = tf.float32, \\\n shape=shape, initializer = weight_init)\n if wd is not None:\n weight_decay = tf.multiply(tf.nn.l2_loss(var), wd, name='weight_loss')\n tf.add_to_collection('losses', weight_decay)\n return var\n\n def max_pool_2x2(self, x):\n return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n strides=[1, 2, 2, 1], padding='VALID')\n\n def model(self, x):\n if self._patch_size==16:\n h_conv1 = self.conv2d_layer('conv1', [5, 5, 3, 32], x)\n h_conv2 = self.conv2d_layer('conv2', [5, 5, 32, 32], h_conv1)\n h_conv3 = self.conv2d_layer('conv3', [5, 5, 32, 64], h_conv2)\n h_conv4 = self.conv2d_layer('conv4', [3, 3, 64, 64], h_conv3)\n h_conv5 = self.conv2d_layer('conv5', [2, 2, 64, 128], h_conv4)\n conv5_flatten = tf.reshape(h_conv5, [-1, 128])\n output = self.conv2d_layer_no_relu('fc1',[128,self.descriptor_dim],conv5_flatten)\n elif self._patch_size == 32:\n h_conv1 = self.conv2d_layer('conv1', [5, 5, 3, 32], x)\n h_pool1 = self.max_pool_2x2(h_conv1)\n h_conv2 = self.conv2d_layer('conv2', [5, 5, 32, 128], h_pool1)\n h_pool2 = self.max_pool_2x2(h_conv2)\n h_conv3 = self.conv2d_layer('conv3', [3, 3, 128, 128], h_pool2)\n h_conv4 = self.conv2d_layer('conv4', [3, 3, 128, 256], h_conv3)\n output = self.conv2d_layer_no_relu('fc1',[1, 1, 256, self.descriptor_dim],h_conv4)\n else:\n output = []\n return output\n","sub_path":"python/features/transform_covariant.py","file_name":"transform_covariant.py","file_ext":"py","file_size_in_byte":6448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"483760905","text":"#!/usr/bin/python3\n\nimport pygame\nimport random\nfrom pygame.locals import *\n\nmove = [50,100]\nclock = pygame.time.Clock()\nfps = 8\nsentido = 3\nhitbox = 8\nscreen_size = 520\nrock = [0,0]\ntail = []\nin_play = True\nrestart = False\n\n\npygame.init()\nscreen = pygame.display.set_mode((screen_size,screen_size), 0, 32)\npygame.display.set_caption('Snake')\n\ndef print_text(surface, position, text, size, colour):\n font = pygame.font.get_default_font()\n font_layer = pygame.font.Font(font, size)\n font_surface = font_layer.render(text, True, colour)\n surface.blit(font_surface, position)\n return\n\ndef change_rock(t):\n global fps\n rock[0] = random.randint(0,510)\n rock[1] = random.randint(0,510)\n if t:\n tail.append([0,0])\n fps += 1\n\ndef set_tail():\n if len(tail) > 0:\n\n i=0\n while i < len(tail)-1:\n tail[i][0] = tail[i+1][0]\n tail[i][1] = tail[i+1][1]\n\n i += 1\n tail[-1][0] = move [0]\n tail[-1][1] = move [1]\n\n for i in range(len(tail)):\n pygame.draw.rect(screen, (255, 255, 255), [tail[i][0], tail[i][1]] + [10, 10], 0)\n\n\ndef run():\n\n set_tail()\n\n if sentido == 1:\n move[1] -= 10\n elif sentido == 2:\n move[1] += 10\n elif sentido == 3:\n move[0] += 10\n elif sentido == 4:\n move[0] -= 10\n\n\ndef game_over():\n if move in tail:\n return True\n else:\n if (move[0] > 1 and move[0] < screen_size-10) and (move[1] > 1 and move[1] < screen_size-10):\n return False\n else:\n return True\n\nchange_rock(0)\n\nwhile True:\n clock.tick(fps)\n\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n exit()\n\n if event.type == KEYDOWN:\n if event.key == 273: #up\n sentido = 1\n\n if event.key == 274: # down\n sentido = 2\n\n if event.key == 275: # right\n sentido = 3\n\n if event.key == 276: # left\n sentido = 4\n\n if event.key == 32: # space\n restart = True\n\n screen.fill([0, 0, 0])\n if game_over():\n print_text(screen, (screen_size//2 - 80,screen_size//2- 20), 'GAME OVER', 25, (0, 255, 0))\n print_text(screen, (screen_size//2 - 80,screen_size- 15), 'Press space to restart ', 15, (255, 255, 255))\n in_play = False\n\n else:\n run()\n\n if not in_play:\n if restart:\n move = [50, 100]\n fps = 8\n sentido = 3\n rock = [0, 0]\n tail = []\n in_play = True\n restart = False\n change_rock(0)\n\n print_text(screen, (screen_size-120, 10), 'score:'+str(len(tail)), 25, (0, 255, 0))\n pygame.draw.rect(screen, (255,255,255), [move[0], move[1]] + [10, 10], 0)\n pygame.draw.rect(screen, (255,0,0), [rock[0], rock[1]] + [10, 10], 0)\n pygame.display.update()\n\n if (move[0] >= rock[0]-hitbox and move[0] <= rock[0]+hitbox) and (move[1] >= rock[1]-hitbox and move[1] <= rock[1]+hitbox):\n change_rock(1)\n","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":3094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"628719637","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\n# \n\n# mdd importer by Bill L.Nieuwendorp\n# conversion to blender 2.5: Ivo Grigull (loolarge)\n#\n# Warning if the vertex order or vertex count differs from the\n# origonal model the mdd was Baked out from their will be Strange\n# behavior\n#\n# vertex animation to ShapeKeys with ipo and gives the frame a value of 1.0\n# A modifier to read mdd files would be Ideal but thats for another day :)\n#\n# Please send any fixes,updates,bugs to Slow67_at_Gmail.com\n# Bill Niewuendorp\n\nimport bpy\nfrom struct import unpack\n\ndef obj_update_frame(file, scene, obj, fr, step):\n\n # Insert new shape key\n new_shapekey = obj.shape_key_add()\n new_shapekey.name = (\"frame_%.4d\" % fr)\n\n obj.active_shape_key_index = len(obj.data.shape_keys.key_blocks) - 1\n index = len(obj.data.shape_keys.key_blocks) - 1\n obj.show_only_shape_key = True\n\n verts = obj.data.shape_keys.key_blocks[len(obj.data.shape_keys.key_blocks) - 1].data\n\n for v in verts: # 12 is the size of 3 floats\n v.co[:] = unpack('>3f', file.read(12))\n\n # me.update()\n obj.show_only_shape_key = False\n\n # insert keyframes\n shape_keys = obj.data.shape_keys\n\n scene.frame_current -= step\n obj.data.shape_keys.key_blocks[index].value = 0.0\n shape_keys.key_blocks[len(obj.data.shape_keys.key_blocks) - 1].keyframe_insert(\"value\")\n\n scene.frame_current += step\n obj.data.shape_keys.key_blocks[index].value = 1.0\n shape_keys.key_blocks[len(obj.data.shape_keys.key_blocks) - 1].keyframe_insert(\"value\")\n\n scene.frame_current += step\n obj.data.shape_keys.key_blocks[index].value = 0.0\n shape_keys.key_blocks[len(obj.data.shape_keys.key_blocks) - 1].keyframe_insert(\"value\")\n\n obj.data.update()\n\n\ndef load(operator, context, filepath, frame_start=0, frame_step=1):\n\n scene = context.scene\n obj = context.object\n\n print('\\n\\nimporting mdd %r' % filepath)\n\n if bpy.ops.object.mode_set.poll():\n bpy.ops.object.mode_set(mode='OBJECT')\n\n file = open(filepath, 'rb')\n frames, points = unpack(\">2i\", file.read(8))\n time = unpack((\">%df\" % frames), file.read(frames * 4))\n\n print('\\tpoints:%d frames:%d' % (points, frames))\n print('\\tstart frame:%d step:%d' % (frame_start, frame_step))\n\n # If target object doesn't have Basis shape key, create it.\n if not obj.data.shape_keys:\n basis = obj.shape_key_add()\n basis.name = \"Basis\"\n obj.data.update()\n\n scene.frame_current = frame_start\n\n for i in range(frames):\n obj_update_frame(file, scene, obj, i, frame_step)\n\n return {'FINISHED'}\n","sub_path":"io_shape_mdd/import_mdd.py","file_name":"import_mdd.py","file_ext":"py","file_size_in_byte":3363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"110670492","text":"def countRec(solution, coins, target):\n\n \n if target == 0:\n return 1\n\n\n if target < 0:\n return 0\n\n if coins <= 0 and target >= 1:\n return 0\n\n return countRec(solution, coins - 1, target) + countRec(solution, coins, target-solution[coins-1])\n\ndef countDynamic(solution, coins, target):\n\n table = [[0 for x in range(coins)] for x in range(target+1)]\n\n for i in range(coins):\n table[0][i] = 1\n\n for i in range(1, target+1):\n for j in range(coins):\n x = table[i - solution[j]][j] if i-solution[j] >= 0 else 0\n y = table[i][j-1] if j >= 1 else 0\n table[i][j] = x + y\n\n return table[target][coins-1]\n\n\n'''arr = [1, 2, 3]\ncoins = len(arr)\nprint(countRec(arr, coins, 4))\n'''\ndef main():\n\n i = 1\n\n coins = list()\n test = int(input())\n #print(test)\n\n while i <= test:\n\n #print(i)\n num_coin = int(input())\n target = int(input())\n #print(num_coin, target)\n #print(target)\n\n for j in range(num_coin):\n x = int(input())\n coins.insert(j, x)\n j += 1\n #print(coins)\n\n #coinLength = len(coins)\n #print(coinLength)\n print(\"Case \", i, \": \", countRec(coins, num_coin, target), countDynamic(coins, num_coin, target))\n i += 1\n\n\nmain()","sub_path":"Basics/CoinChange.py","file_name":"CoinChange.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"365793077","text":"import csv\nimport pyautogui as pag\nimport time\nimport cv2\nimport mss\nimport mss.tools\n\npag.FAILSAFE = True\npwd = 'takut'\n\n#useful countdown timer\ndef countdown(n):\n\twhile n > 0:\n\t\tprint(f'wait {n}s...')\n\t\tn = n - 1\ntime.sleep(1)\n\n#button locations\nbtninventory = (1797, 853)\nbtnmaster = (20, 31)\nbtnitemmaster = (58, 56)\n\ntabonlinestore = (516, 293)\ntabtechnology = (838, 293)\n\nbtngender = (929, 334)\nbtnupdate = (586, 626)\nbtncolorsize = (961, 626)\n\nsizechart = (747, 170)\nbtncolorsizeupdate = (606, 474)\n\n\n#Open Inventory\npag.click(btninventory)\ntime.sleep(3)\npag.click(btnmaster)\ntime.sleep(0.5)\npag.click(btnitemmaster)\n\nwith open('./upload-shoe.csv', 'r') as _filehandler: #live filename\n# with open('./test_this_shoe_upload.csv', 'r') as _filehandler: #temp test filename\n\tcsv_file_reader = csv.reader(_filehandler, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n\t# loop every row\n\tfor row in csv_file_reader:\n\t\titemcode = row[0]\n\t\tgender = row[1]\n\t\titemdesc = row[2]\n\t\tcushion = row[3]\n\t\twidth = row[4]\n\t\tshoetype = row[5]\n\t\tcat1 = row[6]\n\t\tcat2 = row[7]\n\t\tcat3 = row[8]\n\t\tcat4 = row[9]\n\t\tweight = row[10]\n\t\tyear = row[11]\n\t\tcolor = row[12]\n\t\tcolortext = row[13]\n\t\titemcodenocolor = row[16]\n\n\t\tprint(f'processing itemcode: {itemcode} ...')\n\t\tprint('\\n')\n\n\t\t#paste itemcode\n\t\tpag.typewrite(itemcode)\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(1) #watch this sleep\n\t\t\n\t\t#online store tab\n\t\tpag.click(tabonlinestore)\n\t\ttime.sleep(1)\n\n\t\t#enter gender\n\t\tpag.click(btngender)\n\t\ttime.sleep(0.5)\n\t\tpag.hotkey('backspace')\n\t\ttime.sleep(0.5)\n\t\tpag.typewrite(gender)\n\t\ttime.sleep(0.5)\n\n\t\t#click pending for upload\n\t\tfor i in range(4):\n\t\t\tpag.hotkey('tab')\n\t\t\ttime.sleep(0.1)\n\n\t\twith mss.mss() as sct:\n\t\t\t# The screen part to capture\n\t\t\tregion = {'top': 350, 'left': 720, 'width': 15, 'height': 15}\n\n\t\t\t# Grab the data\n\t\t\timg = sct.grab(region)\n\n\t\t\t# Save to the picture file\n\t\t\tmss.tools.to_png(img.rgb, img.size, output='pendingforupload.png')\n\n\t\timg = cv2.imread('pendingforupload.png', 0)\n\t\tshould_we_click_upload = img[7,8]\n\n\t\tif should_we_click_upload != 0:\n\t\t\t# original without check\n\t\t\tpag.hotkey('space')\n\t\t\ttime.sleep(0.5)\n\t\t\n\t\t#enter title\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tpag.typewrite(itemdesc)\n\t\ttime.sleep(0.2)\n\n\t\t#enter cushion\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tpag.typewrite(cushion)\n\t\ttime.sleep(0.2)\n\n\t\t#enter width\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tpag.typewrite(width)\n\t\ttime.sleep(0.2)\n\n\t\t#enter shoetype\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tpag.typewrite(shoetype)\n\t\ttime.sleep(0.3)\n\n\t\t#append season to itemcode\n\t\tpag.hotkey('tab')\n\t\tfor i in range(4):\n\t\t\tpag.hotkey('ctrl', 'c')\n\t\tfor i in range(8):\n\t\t\tpag.hotkey('shift', 'tab')\n\t\t\ttime.sleep(0.1)\n\t\tpag.typewrite(itemcodenocolor)\n\t\ttime.sleep(0.2)\n\t\tpag.typewrite('_')\n\t\ttime.sleep(0.2)\n\t\tpag.typewrite(year)\n\t\ttime.sleep(0.2)\n\t\tpag.hotkey('ctrl', 'v')\n\n\t\t#enter categories\n\t\tfor i in range(9):\n\t\t\tpag.hotkey('tab')\n\t\t\ttime.sleep(0.1)\n\t\tif cat1 != '':\n\t\t\tpag.typewrite(cat1)\n\t\t\ttime.sleep(0.1)\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tif cat2 != '':\n\t\t\tpag.typewrite(cat2)\n\t\t\ttime.sleep(0.1)\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tif cat3 != '':\n\t\t\tpag.typewrite(cat3)\n\t\t\ttime.sleep(0.1)\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tif cat4 != '':\n\t\t\tpag.typewrite(cat4)\n\t\t\ttime.sleep(0.1)\n\n\t\t#enter weight\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.1)\n\t\tpag.typewrite(weight)\n\t\ttime.sleep(0.1)\n\n\t\t#click update\n\t\tpag.click(btnupdate)\n\t\ttime.sleep(1)\n\t\tpag.hotkey('space')\n\t\ttime.sleep(1)\n\n\t\t#click color & size\n\t\tpag.click(btncolorsize)\n\t\ttime.sleep(0.5)\n\t\tfor i in range(3):\n\t\t\tpag.hotkey('tab')\n\t\t\ttime.sleep(0.1)\n\t\tpag.hotkey('space')\n\n\t\t#select the color\n\t\tif color == '': #na\n\t\t\tfor i in range(1):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'beige': #beige\n\t\t\tfor i in range(2):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'black': #black\n\t\t\tfor i in range(3):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '09' or color =='9': #black1\n\t\t\tfor i in range(4):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '94': #black10\n\t\t\tfor i in range(5):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '95': #black11\n\t\t\tfor i in range(6):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '96': #black12\n\t\t\tfor i in range(7):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '97': #black13\n\t\t\tfor i in range(8):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '99': #black14\n\t\t\tfor i in range(9):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '10': #black2\n\t\t\tfor i in range(10):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '71': #black3\n\t\t\tfor i in range(11):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '77': #black4\n\t\t\tfor i in range(12):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '79': #black5\n\t\t\tfor i in range(13):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '90': #black6\n\t\t\tfor i in range(14):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '91': #black7\n\t\t\tfor i in range(15):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '92': #black8\n\t\t\tfor i in range(16):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '93': #black9\n\t\t\tfor i in range(17):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'blue': #blue\n\t\t\tfor i in range(18):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '12': #blue1\n\t\t\tfor i in range(19):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '24': #blue10\n\t\t\tfor i in range(20):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '25': #blue11\n\t\t\tfor i in range(21):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '26': #blue12\n\t\t\tfor i in range(22):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '27': #blue13\n\t\t\tfor i in range(23):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '28': #blue14\n\t\t\tfor i in range(24):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '72': #blue15\n\t\t\tfor i in range(25):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '73': #blue16\n\t\t\tfor i in range(26):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '76': #blue17\n\t\t\tfor i in range(27):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '80': #blue18\n\t\t\tfor i in range(28):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '82': #blue19\n\t\t\tfor i in range(29):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '13': #blue2\n\t\t\tfor i in range(30):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '83': #blue20\n\t\t\tfor i in range(31):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '86': #blue21\n\t\t\tfor i in range(32):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'TU': #blue22\n\t\t\tfor i in range(33):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '18': #blue3\n\t\t\tfor i in range(34):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '14': #blue4\n\t\t\tfor i in range(35):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '19': #blue5\n\t\t\tfor i in range(36):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '20': #blue6\n\t\t\tfor i in range(37):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '21': #blue7\n\t\t\tfor i in range(38):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '22': #blue8\n\t\t\tfor i in range(39):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '23': #blue9\n\t\t\tfor i in range(40):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'brown': #brown\n\t\t\tfor i in range(41):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'chgrey': #chgrey\n\t\t\tfor i in range(42):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'crimson': #crimson\n\t\t\tfor i in range(43):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'dkblue': #dkblue\n\t\t\tfor i in range(44):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'gold': #gold\n\t\t\tfor i in range(45):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'green': #green\n\t\t\tfor i in range(46):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '33': #green1\n\t\t\tfor i in range(47):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '34': #green2\n\t\t\tfor i in range(48):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '35': #green3\n\t\t\tfor i in range(49):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '36': #green4\n\t\t\tfor i in range(50):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '37': #green5\n\t\t\tfor i in range(51):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '38': #green6\n\t\t\tfor i in range(52):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'grey': #grey\n\t\t\tfor i in range(53):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '02' or color =='2': #grey1\n\t\t\tfor i in range(54):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '03' or color =='3': #grey2\n\t\t\tfor i in range(55):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '04' or color =='4': #grey3\n\t\t\tfor i in range(56):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '05' or color =='5': #grey4\n\t\t\tfor i in range(57):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '06' or color =='6': #grey5\n\t\t\tfor i in range(58):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '07' or color =='7': #grey6\n\t\t\tfor i in range(59):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '08' or color =='8': #grey7\n\t\t\tfor i in range(60):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '98': #grey8\n\t\t\tfor i in range(61):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'lhtblue': #lhtblue\n\t\t\tfor i in range(62):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'lhtgrey': #lhtgrey\n\t\t\tfor i in range(63):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'magenta': #magenta\n\t\t\tfor i in range(64):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'navy': #navy\n\t\t\tfor i in range(65):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'orange': #orange\n\t\t\tfor i in range(66):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'OR': #orange1\n\t\t\tfor i in range(67):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'pink': #pink\n\t\t\tfor i in range(68):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '64': #pink1\n\t\t\tfor i in range(69):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '65': #pink2\n\t\t\tfor i in range(70):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '62B': #pink3\n\t\t\tfor i in range(71):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '64B': #pink4\n\t\t\tfor i in range(72):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'PI': #pink5\n\t\t\tfor i in range(73):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'purple': #purple\n\t\t\tfor i in range(74):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '67': #purple1\n\t\t\tfor i in range(75):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '68': #purple2\n\t\t\tfor i in range(76):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '87': #purple3\n\t\t\tfor i in range(77):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'red': #red\n\t\t\tfor i in range(78):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '55': #red1\n\t\t\tfor i in range(79):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '58': #red2\n\t\t\tfor i in range(80):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '61': #red3\n\t\t\tfor i in range(81):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '62': #red4\n\t\t\tfor i in range(82):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '63': #red5\n\t\t\tfor i in range(83):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '66': #red6\n\t\t\tfor i in range(84):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'silver': #silver\n\t\t\tfor i in range(85):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'white': #white\n\t\t\tfor i in range(86):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '01' or color =='1': #white1\n\t\t\tfor i in range(87):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'WH': #white2\n\t\t\tfor i in range(88):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == 'yellow': #yellow\n\t\t\tfor i in range(89):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '16': #yellow1\n\t\t\tfor i in range(90):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '31': #yellow2\n\t\t\tfor i in range(91):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '32': #yellow3\n\t\t\tfor i in range(92):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '44': #yellow4\n\t\t\tfor i in range(93):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '45': #yellow5\n\t\t\tfor i in range(94):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '49': #yellow6\n\t\t\tfor i in range(95):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '50': #yellow7\n\t\t\tfor i in range(96):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '53': #yellow8\n\t\t\tfor i in range(97):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tif color == '54': #yellow9\n\t\t\tfor i in range(98):\n\t\t\t\tpag.hotkey('down')\n\t\t\t\t#time.sleep(0.01)\n\t\tpag.hotkey('tab')\n\t\ttime.sleep(0.5)\n\t\t\n\t\t# #click sizes\n\t\t# pag.click(sizechart)\n\t\t# for i in range(25):\n\t\t# \tpag.hotkey('down')\n\t\t# for i in range(3):\n\t\t# \tpag.hotkey('tab') #goes to bottom of size chart\n\t\t# \ttime.sleep(0.1)\n\n\t\t# pag.hotkey('space') #ticks first one\n\t\t# for i in range(25): #ticks the rest\n\t\t# \tfor i in range(3):\n\t\t# \t\tpag.hotkey('left')\n\t\t# \tpag.hotkey('space')\n\t\t# \ttime.sleep(0.1)\n\n\t\tpag.click(btncolorsizeupdate) #click on 'update' in color/size page\n\t\ttime.sleep(0.5)\n\t\tpag.hotkey('space')\n\t\ttime.sleep(0.5)\n\t\tpag.hotkey('esc')\n\t\ttime.sleep(2)\n\n\t\t# #technology tab\n\t\t# pag.click(tabtechnology)\n\n\t\t\n\n\n\n\n#click inventory tab\n# pag.moveTo(115, 32, duration = 0.60)\n# pag.click(button='left')\n# click stock enquiry\n# pag.moveRel(0, 22, duration=0.40)\n# pag.click(button='left')\n# time.sleep(1)\n","sub_path":"old-shoe-uploader.py","file_name":"old-shoe-uploader.py","file_ext":"py","file_size_in_byte":14028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"435386182","text":"from gensim.test.utils import datapath\nfrom gensim.models.fasttext import load_facebook_vectors\nimport numpy as np\nfrom konlpy.tag import Mecab\nmecab = Mecab()\n\ncap_path = datapath(\"/home/ubuntu/seungho/fastText/build/run8_chat_mecab.bin\")\nmodel = load_facebook_vectors(cap_path)\nexample = model['안녕']\n\ndef get_sentence_vec(A):\n res = mecab.morphs(A)\n vec = np.zeros_like(example)\n for morph in res:\n vec += model[morph]\n return vec\n\ndef sentence_sim(list_of_sent):\n list_of_sent_vec = list()\n for sent in list_of_sent:\n list_of_sent_vec.append(get_sentence_vec(sent))\n A = np.array(list_of_sent_vec)\n inner = np.matmul(A, A.T)\n B = np.linalg.norm(A, axis=1, keepdims=True)\n norm = np.matmul(B, B.T)\n sim = inner / norm\n return sim","sub_path":"3.ChatClusterer/SentenceSimMecab.py","file_name":"SentenceSimMecab.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"516029615","text":"#!/usr/bin/env python\n\"\"\"\nFor annotating a point cloud that corresponds to demonstration. Select which point clouds are the verb \"arguments\"\n\"\"\"\n\n\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument(\"infile\")\nparser.add_argument(\"outfile\")\nargs = parser.parse_args()\n\nassert args.outfile.endswith(\"npy\") or args.outfile.endswith(\".h5\")\n\nfrom point_clouds import tabletop\nimport numpy as np\nimport rospy\nimport roslib\nroslib.load_manifest('tf')\nroslib.load_manifest('snazzy_msgs')\nimport tf\nfrom brett2.ros_utils import pc2xyzrgb\nimport sensor_msgs.msg as sm\nfrom brett2 import ros_utils\nfrom snazzy_msgs.srv import *\n\nrospy.init_node(\"manually_segment_point_cloud\")\nlistener = tf.TransformListener()\nseg_svc = rospy.ServiceProxy(\"interactive_segmentation\", ProcessCloud)\n\nf = np.load(args.infile)\nn_obj = int(raw_input(\"number of objects? \"))\nobject_clusters = []\n\n\npc = rospy.wait_for_message(\"/drop/points\", sm.PointCloud2)\npc_tf = ros_utils.transformPointCloud2(pc, listener, \"base_footprint\", pc.header.frame_id)\n\nfor _ in xrange(n_obj):\n pc_sel = seg_svc.call(ProcessCloudRequest(cloud_in = pc_tf)).cloud_out\n xyz, rgb = ros_utils.pc2xyzrgb(pc_sel)\n object_clusters.append(xyz.reshape(-1,3))\n \n\n\n\nif args.outfile.endswith(\"npy\"): \n np.save(args.outfile, np.array(object_clusters,dtype=object))\nelse:\n import h5py\n out = h5py.File(args.outfile, \"w\")\n for clu in object_clusters:\n out[str(clu)] = clu\n out.close()","sub_path":"lfd/scripts/manually_segment_point_cloud.py","file_name":"manually_segment_point_cloud.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"419539555","text":"import pytest # noqa\nimport json\n\n\ndef test_get_all_birds(test_client, setup_test_db):\n \"\"\"\n Get all birds from the test db\n \"\"\"\n url = '/vespeer/v1/birds'\n resp = test_client.get(url)\n\n assert resp.status_code == 200\n assert resp.json['count'] == 3\n assert resp.json['items']\n assert isinstance(resp.json['items'], list)\n\n assert resp.json['items'][0]['name'] == 'Eagle'\n\n\ndef test_post_bird(test_client, setup_test_db):\n url = '/vespeer/v1/birds'\n headers = {'content-type': 'application/json'}\n data = {\n 'name': 'Kingfisher',\n 'family': 'Alcedinidae',\n 'order': 'Coraciiformes'\n }\n\n resp = test_client.post(url, data=json.dumps(data), headers=headers)\n\n inserted = list(setup_test_db.birds.find({'name': 'Kingfisher'}))\n\n assert resp.status_code == 201\n assert resp.json['_id'] == str(inserted[0]['_id'])\n\n\ndef test_update_bird_by_id(test_client, setup_test_db):\n to_update = list(setup_test_db.birds.find({'name': 'Eagle'}))\n id_to_update = str(to_update[0]['_id'])\n\n url = '/vespeer/v1/birds/{}'.format(id_to_update)\n headers = {'content-type': 'application/json'}\n data = {\n 'name': 'Eagle',\n 'family': 'Paok',\n 'order': 'Coraciiformes'\n }\n\n resp = test_client.put(url, data=json.dumps(data), headers=headers)\n\n assert resp.status_code == 204\n\n updated = list(setup_test_db.birds.find({'name': 'Eagle'}))\n id_of_updated = str(to_update[0]['_id'])\n\n assert id_of_updated == id_to_update\n assert updated[0]['family'] == 'Paok'\n\n\ndef test_update_bird_by_id_non_existent(test_client, setup_test_db):\n non_existing_id = '985de42dac0440a6a384d3fc'\n\n url = '/vespeer/v1/birds/{}'.format(non_existing_id)\n headers = {'content-type': 'application/json'}\n data = {\n 'name': 'Eagle',\n 'family': 'Paok',\n 'order': 'Coraciiformes'\n }\n\n resp = test_client.put(url, data=json.dumps(data), headers=headers)\n assert resp.status_code == 403\n\n\ndef test_delete_bird_by_id(test_client, setup_test_db):\n to_delete = list(setup_test_db.birds.find({'name': 'Eagle'}))\n id_to_delete = str(to_delete[0]['_id'])\n\n url = '/vespeer/v1/birds/{}'.format(id_to_delete)\n\n resp = test_client.delete(url)\n\n assert resp.status_code == 204\n\n deleted = list(setup_test_db.birds.find({'name': 'Eagle'}))\n\n assert not deleted\n\n\ndef test_delete_bird_by_id_non_existent(test_client, setup_test_db):\n non_existing_id = '985de42dac0440a6a384d3fc'\n url = '/vespeer/v1/birds/{}'.format(non_existing_id)\n\n resp = test_client.delete(url)\n\n assert resp.status_code == 403\n","sub_path":"tests/integration/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":2638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"8220915","text":"# model.py\nimport os\nfrom scipy.io import wavfile\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport keras\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, LSTM, Activation\nfrom keras.layers import Dropout, Dense, TimeDistributed\nfrom keras.models import Sequential\nfrom keras.utils import to_categorical, plot_model\nfrom sklearn.utils.class_weight import compute_class_weight\nfrom tqdm import tqdm\nfrom python_speech_features import mfcc\nimport pickle\nfrom keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\nfrom cfg import Config\nimport time\n\nconfig = Config(mode ='conv')\n\n\ndef check_data():\n if os.path.isfile(config.p_path):\n print('Loading existing data for {} model'.format(config.mode))\n with open(config.p_path, 'rb') as handle:\n tmp = pickle.load(handle)\n return tmp\n else:\n return None\n\n\ndef build_rand_feat():\n tmp = check_data()\n if tmp:\n return tmp.data[0], tmp.data[1]\n \n x = []\n y = []\n\n _min, _max = float('inf'), -float('inf')\n for _ in tqdm(range(n_samples)):\n rand_class = np.random.choice(class_dist.index, p=prob_dist)\n file = np.random.choice(df[df.label == rand_class].index)\n rate, wav = wavfile.read('clean_data/' + file)\n label = df.at[file, 'label']\n rand_index = np.random.randint(0, wav.shape[0] - config.step)\n sample = wav[rand_index: rand_index + config.step]\n x_sample = mfcc(sample, rate, numcep=config.nfeat, \n nfilt=config.nfilt, nfft=config.nfft)\n\n _min = min(np.amin(x_sample), _min)\n _max = max(np.amax(x_sample), _max)\n x.append(x_sample)\n y.append(classes.index(label))\n # print(classes.index(label))\n config.min = _min\n config.max = _max\n x, y = np.array(x), np.array(y)\n # y = np.array(y)\n x = (x - _min) / (_max - _min)\n\n if config.mode == 'conv':\n x = x.reshape(x.shape[0], x.shape[1], x.shape[2], 1)\n elif config.mode == 'time':\n x = x.reshape(x.shape[0], x.shape[1], x.shape[2])\n y = to_categorical(y, num_classes=3)\n # print(y)\n config.data = (x, y)\n\n with open(config.p_path, 'wb') as handle:\n pickle.dump(config, handle, protocol=2)\n\n return x, y\n\n\ndef get_conv_model():\n model = Sequential()\n # CONV2D 1 \n model.add(Conv2D(32, (3, 3), padding='same',\n input_shape=input_shape))\n model.add(Activation('relu'))\n \n # CONV2D 2 \n model.add(Conv2D(32, (3, 3)))\n model.add(Activation('relu'))\n model.add(Dropout(0.25))\n\n # CONV2D 3 \n model.add(Conv2D(64, (3, 3), padding='same'))\n model.add(Activation('relu'))\n \n # CONV2D 4 \n model.add(Conv2D(64, (3, 3)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D(pool_size=(2, 2)))\n model.add(Dropout(0.25))\n \n # Flatten layer\n model.add(Flatten())\n model.add(Dense(512))\n model.add(Activation('relu'))\n model.add(Dropout(0.5))\n model.add(Dense(3))\n model.add(Activation('softmax'))\n\n model.summary()\n model.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['acc'])\n \n return model\n\n\ndf = pd.read_csv('csv_file/df_train.csv')\ndf.set_index('fname', inplace=True)\n\nfor f in df.index:\n rate, signal = wavfile.read('clean_data/' + f)\n df.at[f, 'length'] = signal.shape[0] / rate\n\nclasses = list(np.unique(df.label))\nclass_dist = df.groupby(['label'])['length'].mean()\n\nn_samples = 2 * int(df['length'].sum() / 0.1)\nprob_dist = class_dist / class_dist.sum()\nchoices = np.random.choice(class_dist.index, p=prob_dist)\n\n\nif config.mode == 'conv':\n x, y = build_rand_feat()\n y_flat = np.argmax(y, axis=1)\n input_shape = (x.shape[1], x.shape[2], 1)\n model = get_conv_model()\n\n\nclass_weight = compute_class_weight('balanced', np.unique(y_flat), y_flat)\n\ncheckpoint = ModelCheckpoint(config.model_path, monitor='val_acc', verbose=1, mode='max',\n save_best_only=True, save_weights_only=False, period=1)\n\nes = EarlyStopping(monitor='val_loss', mode='min', verbose=0, patience=5)\n\nhistory = model.fit(x, y, epochs=300,\n batch_size=32,\n shuffle=True,\n validation_split=0.2,\n callbacks=[checkpoint],\n class_weight=class_weight\n )\n\nmodel.save(config.model_path)\n\n# Plot training & validation accuracy values\nplt.plot(history.history['acc'])\nplt.plot(history.history['val_acc'])\nplt.title('Model accuracy')\nplt.ylabel('Accuracy')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='lower right')\nplt.tight_layout()\n# plt.savefig('fig/acc14.png', dpi=600)\nplt.show()\n\n# Plot training & validation loss values\nplt.plot(history.history['loss'])\nplt.plot(history.history['val_loss'])\nplt.title('Model loss')\nplt.ylabel('Loss')\nplt.xlabel('Epoch')\nplt.legend(['Train', 'Validation'], loc='lower right')\nplt.tight_layout()\n# plt.savefig('fig/loss14.png', dpi=600)\nplt.show()\n\n\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"5228941","text":"# 6.0001/6.00 Problem Set 5 - RSS Feed Filter\n# Name: Lynn Zhang\n# Collaborators: Tao Wu\n# Time: 241min (4h) + 32min\n# 2021-03-31 06:20 - 07:00 40min class problem 1 reading and realization\n# 2021-03-31 08:00 - 09:10 70min problem 2 & 3, stuck in class concept and class method debugging\n# some basic errors - list, variable manipulation\n# class - subclass - what attributes to put in __init__\n# 21:31 - 21:39 8min Problem 3 TitleTrigger done.\n# issue was caused by wrong use of finding index\n# and not considering to put phrase to lower case\n# punctuation exceptions purple@#$%cow not considered -> need to think fully\n# 22:20 - 23:00 40min Problem 4 DescriptionTrigger\n# and Problem 5&6 TimeTrigger - how to set up timezone on datetime\n# 2021-04-01 05:31 - 05:54 23min Problem 7,8,9\n# 05:55 - 06:55 60min Problem 10-12\n# unsolved: Polling...object has no attribute 'description'\n# 2021-04-01 21:00 - 21:30 Tao instruction - use the new version of feedparser online\n# 2021-04-02 05:54 - 05:56 modified the .txt and success!\n\nimport string\nimport time\nimport threading\nfrom project_util import translate_html\nfrom mtTkinter import *\nfrom datetime import datetime\nimport pytz\nimport feedparser\n\n#-----------------------------------------------------------------------\n\n#======================\n# Code for retrieving and parsing\n# Google and Yahoo News feeds\n# Do not change this code\n#======================\n\ndef process(url):\n \"\"\"\n Fetches news items from the rss url and parses them.\n Returns a list of NewsStory-s.\n \"\"\"\n d = feedparser.parse('http://news.google.com/news?output=rss')\n print(d.feed.title)\n feed = feedparser.parse(url)\n # d = feedparser.parse('http://news.google.com/news?output=rss')\n # d = feedparser.parse('http://news.google.com/news?output=rss')\n entries = feed.entries\n ret = []\n for entry in entries:\n guid = entry.guid\n title = translate_html(entry.title)\n link = entry.link\n description = translate_html(entry.description)\n pubdate = translate_html(entry.published)\n\n try:\n pubdate = datetime.strptime(pubdate, \"%a, %d %b %Y %H:%M:%S %Z\")\n pubdate.replace(tzinfo=pytz.timezone(\"GMT\"))\n # pubdate = pubdate.astimezone(pytz.timezone('EST'))\n # pubdate.replace(tzinfo=None)\n except ValueError:\n pubdate = datetime.strptime(pubdate, \"%a, %d %b %Y %H:%M:%S %z\")\n\n newsStory = NewsStory(guid, title, description, link, pubdate)\n ret.append(newsStory)\n return ret\n\n#======================\n# Data structure design\n#======================\n\n# Problem 1\n\n# write the class - NewsStory\nclass NewsStory(object):\n def __init__(self, guid, title, description, link, pubdate):\n self.guid = guid\n self.title = title\n self.description = description\n self.link = link\n self.pubdate = pubdate\n\n def get_guid(self):\n return self.guid\n\n def get_title(self):\n return self.title\n\n def get_description(self):\n return self.description\n\n def get_link(self):\n return self.link\n\n def get_pubdate(self):\n return self.pubdate\n\n#======================\n# Triggers\n#======================\n\nclass Trigger(object):\n\n def evaluate(self, story):\n \"\"\"\n Returns True if an alert should be generated\n for the given news item, or False otherwise.\n \"\"\"\n # DO NOT CHANGE THIS!\n raise NotImplementedError\n\n# PHRASE TRIGGERS\n\n# Problem 2\n# subclass - PhraseTrigger\nclass PhraseTrigger(Trigger):\n\n def __init__(self, phrase):\n self.phrase = phrase\n\n def is_phrase_in(self, text):\n # not case sensitive - make string into lower case\n text_lowercase = text.lower()\n phrase_lower = self.phrase.lower()\n # remove the punctuations in the text - string.punctuation\n for c in string.punctuation:\n if c in text_lowercase:\n text_lowercase = text_lowercase.replace(c,' ')\n # convert the text string into list\n text_list = text_lowercase.split()\n\n # check validity of the phrase?\n\n # if phrase is valid, continue:\n # return true if phrase is present in the string text\n # split the phrase into a list of words\n phrase_list = phrase_lower.split()\n index = 0 # keep track of the index of the word in phrase list\n # starting search from the first word in the phrase,\n # if found, then starting where the first word was found, check whether the rest matches the phrase\n if phrase_list[index] in text_list: #if find the word,\n # search the next word starting from where the first word was found\n index_in_text = text_list.index(phrase_list[index])\n # if the rest words in text is less than phrase, then return false\n if len(text_list) - index_in_text < len(phrase_list) - index:\n return False\n # else, check whether the rest matches\n index += 1 # index in phrase list\n index_in_text += 1\n while index < len(phrase_list):\n # one mismatch return false\n if phrase_list[index] != text_list[index_in_text]:\n return False\n index += 1\n index_in_text += 1\n return True\n\n\n# Problem 3\n# Subclass TitleTrigger\nclass TitleTrigger(PhraseTrigger):\n def __init__(self, phrase):\n PhraseTrigger.__init__(self, phrase)\n\n def evaluate(self, story):\n title = story.get_title()\n if self.is_phrase_in(title):\n return True\n else:\n return False\n\n\n# Problem 4\n# subclass: DescriptionTrigger\nclass DescriptionTrigger(PhraseTrigger):\n def __int__(self, phrase):\n PhraseTrigger.__init__(self, phrase)\n\n def evaluate(self, story):\n description = story.get_description()\n if self.is_phrase_in(description):\n return True\n else:\n return False\n\n# TIME TRIGGERS\n\n# Problem 5\n# SUBCLASS TimeTrigger\n# Constructor:\n# Input: Time has to be in EST and in the format of \"%d %b %Y %H:%M:%S\".\n# Convert time from string to a datetime before saving it as an attribute.\nclass TimeTrigger(Trigger):\n def __init__(self, time_string):\n self.time = datetime.strptime(time_string, \"%d %b %Y %H:%M:%S\").replace(tzinfo=pytz.timezone(\"EST\"))\n\n# Problem 6\n# SUBCLASS BeforeTrigger and AfterTrigger\nclass BeforeTrigger(TimeTrigger):\n def __init__(self, time_string):\n TimeTrigger.__init__(self, time_string)\n\n def evaluate(self, story):\n story_date = story.get_pubdate().replace(tzinfo=pytz.timezone(\"EST\"))\n if story_date < self.time:\n return True\n else:\n return False\n\nclass AfterTrigger(TimeTrigger):\n def __init__(self, time_string):\n TimeTrigger.__init__(self, time_string)\n\n def evaluate(self, story):\n story_date = story.get_pubdate().replace(tzinfo=pytz.timezone(\"EST\"))\n if story_date > self.time:\n return True\n else:\n return False\n\n# COMPOSITE TRIGGERS\n\n# Problem 7\n# Subclass: NotTrigger\nclass NotTrigger(Trigger):\n def __init__(self, T):\n self.the_other_trigger = T\n\n def evaluate(self, story):\n if self.the_other_trigger.evaluate(story):\n return False\n else:\n return True\n\n# Problem 8\n# SUBCLASS: AndTrigger\nclass AndTrigger(Trigger):\n def __init__(self, trigger1, trigger2):\n self.trigger1 = trigger1\n self.trigger2 = trigger2\n\n def evaluate(self, story):\n if self.trigger1.evaluate(story) and self.trigger2.evaluate(story):\n return True\n else:\n return False\n\n# Problem 9\n# SUBCLASS: OrTrigger\nclass OrTrigger(Trigger):\n def __init__(self, trigger1, trigger2):\n self.trigger1 = trigger1\n self.trigger2 = trigger2\n\n def evaluate(self, story):\n if self.trigger1.evaluate(story) or self.trigger2.evaluate(story):\n return True\n else:\n return False\n\n#======================\n# Filtering\n#======================\n\n# Problem 10\ndef filter_stories(stories, triggerlist):\n \"\"\"\n Takes in a list of NewsStory instances.\n\n Returns: a list of only the stories for which a trigger in triggerlist fires.\n \"\"\"\n # Problem 10\n stories_filtered = []\n for trigger in triggerlist:\n for story in stories:\n if trigger.evaluate(story):\n stories_filtered.append(story)\n\n return stories_filtered\n\n#======================\n# User-Specified Triggers\n#======================\n# Problem 11\ndef read_trigger_config(filename):\n \"\"\"\n filename: the name of a trigger configuration file\n\n Returns: a list of trigger objects specified by the trigger configuration\n file.\n \"\"\"\n # We give you the code to read in the file and eliminate blank lines and\n # comments. You don't need to know how it works for now!\n trigger_file = open(filename, 'r')\n lines = []\n for line in trigger_file:\n line = line.rstrip()\n if not (len(line) == 0 or line.startswith('//')):\n lines.append(line)\n\n # Problem 11\n # line is the list of lines that you need to parse and for which you need\n # to build triggers\n trigger_dict = {}\n for line in lines:\n # split each line into words\n word_list = line.split(',')\n print(word_list)\n # if the first word is not ADD, then it is the trigger -> store that trigger\n key = word_list[0]\n if key != 'ADD':\n type = word_list[1]\n if type == 'TITLE':\n trigger_dict[key] = TitleTrigger(word_list[2])\n elif type == 'DESCRIPTION':\n trigger_dict[key] = DescriptionTrigger(word_list[2])\n elif type == 'AFTER':\n trigger_dict[key] = AfterTrigger(word_list[2])\n elif type == 'BEFORE':\n trigger_dict[key] = BeforeTrigger(word_list[2])\n elif type == 'AND':\n trigger_dict[key] = AndTrigger(trigger_dict[word_list[2]], trigger_dict[word_list[3]])\n elif type == 'OR':\n trigger_dict[key] = OrTrigger(trigger_dict[word_list[2]], trigger_dict[word_list[3]])\n elif type == 'NOT':\n trigger_dict[key] = NotTrigger(trigger_dict[word_list[2]])\n\n # else it defines the trigger list\n else:\n trigger_list_len = len(word_list) - 1 # the length of trigger list\n trigger_list = []\n for i in range(trigger_list_len):\n trigger_list.append(trigger_dict[word_list[i+1]])\n\n print(trigger_list)\n #print(trigger_dict)\n #print(lines)\n return trigger_list\n\nSLEEPTIME = 120 #seconds -- how often we poll\n\ndef main_thread(master):\n # A sample trigger list - you might need to change the phrases to correspond\n # to what is currently in the news\n try:\n # t1 = TitleTrigger(\"election\")\n # t2 = DescriptionTrigger(\"Trump\")\n # t3 = DescriptionTrigger(\"Clinton\")\n # t4 = AndTrigger(t2, t3)\n # triggerlist = [t1, t4]\n\n # Problem 11\n triggerlist = read_trigger_config('debate_triggers.txt')\n \n # HELPER CODE - you don't need to understand this!\n # Draws the popup window that displays the filtered stories\n # Retrieves and filters the stories from the RSS feeds\n frame = Frame(master)\n frame.pack(side=BOTTOM)\n scrollbar = Scrollbar(master)\n scrollbar.pack(side=RIGHT,fill=Y)\n\n t = \"Google & Yahoo Top News\"\n title = StringVar()\n title.set(t)\n ttl = Label(master, textvariable=title, font=(\"Helvetica\", 18))\n ttl.pack(side=TOP)\n cont = Text(master, font=(\"Helvetica\",14), yscrollcommand=scrollbar.set)\n cont.pack(side=BOTTOM)\n cont.tag_config(\"title\", justify='center')\n button = Button(frame, text=\"Exit\", command=root.destroy)\n button.pack(side=BOTTOM)\n guidShown = []\n def get_cont(newstory):\n if newstory.get_guid() not in guidShown:\n cont.insert(END, newstory.get_title()+\"\\n\", \"title\")\n cont.insert(END, \"\\n---------------------------------------------------------------\\n\", \"title\")\n cont.insert(END, newstory.get_description())\n cont.insert(END, \"\\n*********************************************************************\\n\", \"title\")\n guidShown.append(newstory.get_guid())\n\n while True:\n\n print(\"Polling . . .\", end=' ')\n # Get stories from Google's Top Stories RSS news feed\n stories = process(\"http://news.google.com/news?output=rss\")\n\n # Get stories from Yahoo's Top Stories RSS news feed\n # stories.extend(process(\"http://news.yahoo.com/rss/topstories\"))\n print(triggerlist)\n\n stories = filter_stories(stories, triggerlist)\n\n list(map(get_cont, stories))\n scrollbar.config(command=cont.yview)\n\n\n print(\"Sleeping...\")\n time.sleep(SLEEPTIME)\n\n except Exception as e:\n print(e)\n\n\nif __name__ == '__main__':\n root = Tk()\n root.title(\"Some RSS parser\")\n t = threading.Thread(target=main_thread, args=(root,))\n t.start()\n root.mainloop()","sub_path":"Assignment PS5/ps5.py","file_name":"ps5.py","file_ext":"py","file_size_in_byte":13613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"298146400","text":"import numpy as np\n\n### Input ###\nclass Box:\n#moving box\n\tdef __init__(self,x,y,r,v,theta):\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.r = r\n\t\tself.v = v\n\t\tself.theta = theta\n\t\tself.left = x-r\n\t\tself.right = x+r\n\t\tself.up = y+r\n\t\tself.down = y-r\n\tdef update(self):\n\t\tself.bounce()\n\t\tdx = np.cos(self.theta)*self.v\n\t\tdy = np.sin(self.theta)*self.v\n\t\tself.x = self.x+dx\n\t\tself.y = self.y+dy\n\t\tself.left = self.x-self.r\n\t\tself.right = self.x+self.r\n\t\tself.up = self.y+self.r\n\t\tself.down = self.y-self.r \n\tdef bounce(self):\n\t\tif (self.right>1):\n\t\t\tself.theta = np.pi-self.theta\n\t\tif (self.left<-1):\n\t\t\tself.theta = -(self.theta-np.pi)\n\t\tif (self.up>1):\n\t\t\tself.theta = (2*np.pi-self.theta)\n\t\tif (self.down<-1):\n\t\t\tself.theta = -(self.theta-2*np.pi) \n\nclass Stim:\n#full field stimulus\n\tdef __init__(self,x,y,v,theta):\n\t\tself.X = x\n\t\tself.Y = y\n\t\tself.theta = theta\n\t\tself.v = v;\n\tdef update(self,box,r,v):\n\t\tself.M = np.double((self.X>=box.left)*(self.X<=box.right)*(self.Y>=box.down)*(self.Y<=box.up))\n\t\tself.v = v*self.M\n\t\tself.theta = box.theta*self.M\n\n### Brain Areas ###\nclass MT:\n# MT\n\tdef __init__(self,x,y,v,theta,dt):\t\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.v = v\n\t\tself.theta = theta\n\t\tself.dt = dt\n\t\tself.l = np.size(x)\n\t\tself.W = 25./(.91*np.sqrt(pow(x,2)+pow(y,2))+1.)\n\t\tself.netm = np.zeros(self.l)\n\t\tself.netp = np.zeros(self.l)\n\t\tself.min = np.zeros(self.l)\n\t\tself.plus = np.zeros(self.l)\n\tdef activate(self,stim):\n\t\tfor i in range(0,self.l):\n\t\t\tGcp = np.exp(-self.W*(pow(stim.X-self.x[i],2)+pow(stim.Y-self.y[i],2)))\n\t\t\tGsp = np.exp(-self.W*(pow(stim.X-self.x[i],2)+pow(stim.Y-self.y[i],2))/25)\n\t\t\tGv = np.exp(-10*(pow(stim.v-self.v[i],2)))\n\t\t\tGd = np.exp(-6*(pow(stim.theta-self.theta[i],2)))\n\t\t\tself.netm[i] = np.max(sum(Gcp*Gv*Gd)-sum(Gsp*Gv*Gd),0)\n\t\t\tself.netp[i] = sum(Gcp*Gv*Gd)+sum(Gsp*Gv*Gd)\n\tdef update(self,stim):\n\t\tself.activate(stim)\n\t\tself.min += self.dt*(-self.min+(1-self.min)*(self.netm)) # add MST and LIP contribution\n\t\tself.plus += self.dt*(-self.plus+(1-self.plus)*(self.netp))\t\n\n","sub_path":"SEM_SG/Grossberg_2012.py","file_name":"Grossberg_2012.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"339316758","text":"import os\nfrom resistics.ioHandlers.calibrationIO import CalibrationIO\nfrom resistics.ioHandlers.calibrationIO import CalibrationData\n\n# we can write a template into which to paste our data\nwritepath = os.path.join(\"calData\", \"ascii.txt\")\ncalIO = CalibrationIO()\ncalIO.writeInternalTemplate(writepath, 307, \"MFS06\", 1)\n\n# read back the internal template file\nfilepath = os.path.join(\"calData\", \"asciiWithData.txt\")\ncalIO.refresh(filepath, \"induction\", extend=False)\ncalData = calIO.read()\ncalData.printInfo()\n\n# plot\nimport matplotlib.pyplot as plt\n\nfig = plt.figure(figsize=(8, 8))\ncalData.view(fig=fig, label=\"Internal ASCII format\", legend=True)\nfig.tight_layout(rect=[0, 0.02, 1, 0.96])\nplt.show()\nfig.savefig(os.path.join(\"images\", \"calibrationASCII.png\"))","sub_path":"examples/formats/calibrationInductionExample.py","file_name":"calibrationInductionExample.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"533575445","text":"\n# coding: utf-8\n\n# Licence CC BY-NC-NDFrançois Rechenmann & Thierry Parmentelat 
\n\n# # `next_start_codon` et `next_stop_codon`\n\n# Souvenons-nous à présent de l'algorithme de recherche de régions codantes que nous avons vu dans la séquence précédente, et dans lequel nous avions eu besoin de deux fonctions accessoires pour rechercher les codons **Start** et **Stop**. Voici le code que nous avions utilisé à ce moment-là, et qui utilise les concepts que nous venons de voir dans le complément sur la recherche de chaines en python.\n\n# Mais n'oublions pas notre cellule usuelle :\n\n# In[ ]:\n\n# la formule magique pour utiliser print() en python2 et python3\nfrom __future__ import print_function\n# pour que la division se comporte en python2 comme en python3\nfrom __future__ import division\n\n\n# ### L'opérateur `%` pour le calcul des modulos\n\n# Dans le code suivant nous utilisons l'opérateur `%` qui calcule le modulo (ou le reste de la division) entre deux nombres. Par exemple\n\n# In[ ]:\n\n# l'opérateur % permet de calculer le modulo \n###OFF print(\"le reste de la division de 25 par 10 vaut\", 25 % 10)\n\n\n# ### L'instruction `continue`\n\n# Notre code utilise également l'instruction `continue`, qui permet d'interrompre le code de boucle courant (ici nous sommes dans un `for`) et de passer directement à l'itération suivante de la boucle.\n\n# In[ ]:\n\n# un exemple d'instruction `continue`\n# une boucle englobante qui itère sur les nombres de 0 à 4\n###OFF for i in range(5):\n###OFF # mais on ignore les multiples de 3\n###OFF # et dans ce cas on passe directement au i suivant\n###OFF if i % 3 == 0:\n###OFF continue\n###OFF print(\"traitement de\", i) \n\n\n# ### Recherche dans l'ADN ou dans l'ARN\n\n# Dans les vidéos on a vu les valeurs des codons **Start** et **Stop** comme contenant des `U` - et pas de `T`. C'est bien sûr équivalent de chercher `AUG` dans un ARN ou `ATG` dans l'ADN correspondant.\n# \n# Pour des raisons pratiques, puisque les brins de matériel génétique dont nous partons sont presque toujours de l'ARN, aussi nous allons ici chercher des codons contenant des `T` et non des `U`.\n\n# ### `next_start_codon` et la recherche d'un triplet sur une phase\n\n# Nous pouvons à présent écrire le code de `next_start_codon` :\n\n# In[ ]:\n\n# rappelons quel est le codon START\nstart_codon = \"ATG\"\n\n\n# In[ ]:\n\n# la fonction utilisée dans la recherche de régions codantes\ndef next_start_codon(adn, indice):\n \"\"\"\n localise le prochain START en commençant à \n indice et sur la même phase \n renvoie None s'il n'y en a plus\n \"\"\"\n # on commencer à l'indice en question\n courant = indice\n # tant qu'on trouve un START\n while True:\n # on cherche un START à partir de la position\n courant = adn.find(start_codon, courant)\n # il n'y a plus rien à chercher\n if courant == -1:\n return None\n # si on n'est pas sur la même phase que `indice`\n # on ignore cet endroit\n if (courant-indice) % 3 != 0:\n # dans ce cas il faut incrémenter sinon \n # on reste sur place\n courant += 3\n # et on recherche plus loin\n continue\n # sinon, il y a un match sur la bonne phase\n return courant\n # si on est ici c'est qu'il n'y a plus rien à trouver\n return None\n\n\n# Pour nous convaincre que cette fonction fait bien ce qu'on attend d'elle, voici un petit test qui devrait couvrir la majorité des cas :\n\n# In[ ]:\n\nadn = \"ATGCGATGTATGCGTGCAGCTGCTAGCTCGTAATGTCGTCATGGATCATCGATCATGG\"\n\n###OFF for phase in 0, 1, 2:\n###OFF print(\"PHASE\", phase)\n###OFF next = phase\n###OFF while next is not None:\n###OFF next = next_start_codon(adn, next)\n###OFF if next is not None:\n###OFF print(\"trouvé à l'indice\", next, adn[next:next+3])\n###OFF next += 3\n\n\n# ##### `next_stop_codon` et la recherche de 3 triplets sur une phase\n\n# Sur un modèle très similaire, nous pouvons écrire à présent `next_stop_codon` :\n\n# In[ ]:\n\nimport re\n# on rappelle la définition de re_stop\n# pour chercher 'TAA' ou 'TAG' ou 'TGA', on utilise le ou logique |\nre_stop = re.compile(\"TAA|TAG|TGA\")\n\n\n# In[ ]:\n\ndef next_stop_codon(adn, indice):\n \"\"\"\n localise le prochain START en commençant à \n indice et sur la même phase \n renvoie None s'il n'y en a plus\n \"\"\"\n # on commencer à l'indice en question\n courant = indice\n # tant qu'on trouve un START\n while True:\n # on cherche un STOP à partir de la position\n match = re_stop.search(adn, courant)\n # il n'y a plus rien à chercher\n if match is None:\n return None\n # si on n'est pas sur la même phase que `indice`\n # on ignore cet endroit\n courant = match.start()\n if (courant-indice) % 3 != 0:\n courant += 3\n continue\n # sinon, il y a un match sur la bonne phase\n return courant\n # si on est ici c'est qu'il n'y a plus rien à trouver\n return None\n\n\n# Et à nouveau on peut tester cette fonction sommairement :\n\n# In[ ]:\n\n###OFF print(adn)\n###OFF for phase in 0, 1, 2:\n###OFF print(\"PHASE\", phase)\n###OFF next = phase\n###OFF while next is not None:\n###OFF next = next_stop_codon(adn, next)\n###OFF if next is not None:\n###OFF print(\"trouvé à l'indice\", next, adn[next:next+3])\n###OFF next += 3\n\n","sub_path":"modules/w3_s03_c2_next_codon.py","file_name":"w3_s03_c2_next_codon.py","file_ext":"py","file_size_in_byte":5604,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"240800789","text":"import tkinter as tk\n\nclass ToolTip(object):\n \"\"\"\n create a tooltip for a given widget\n \"\"\"\n def __init__(self, root, widget, text='widget info'):\n self.root = root\n self.waittime = 500 #miliseconds\n self.wraplength = 180 #pixels\n self.widget = widget\n self.text = text\n self.widget.bind(\"\", self.enter)\n self.widget.bind(\"\", self.leave)\n self.widget.bind(\"\", self.leave)\n self.id = None\n self.tw = None\n\n def enter(self, event=None):\n self.schedule()\n\n def leave(self, event=None):\n self.unschedule()\n self.hidetip()\n\n def schedule(self):\n self.unschedule()\n self.id = self.widget.after(self.waittime, self.showtip)\n\n def unschedule(self):\n id_ = self.id\n self.id = None\n if id_:\n self.widget.after_cancel(id_)\n\n def showtip(self, event=None):\n root = self.root\n x, y, cx, cy = self.widget.bbox(\"insert\") #@unusedvariable\n x += self.widget.winfo_rootx() + 25\n y += self.widget.winfo_rooty() + 20\n # creates a toplevel window\n self.tw = tk.Toplevel(self.widget)\n # Leaves only the label and removes the app window\n self.tw.wm_overrideredirect(True)\n self.tw.wm_geometry(\"+%d+%d\" % (x, y))\n label = tk.Label(self.tw, text=self.text, justify=tk.LEFT, background=root.color['bbg'], foreground=root.color['fg'],\\\n activebackground=root.color['bbg'],activeforeground=root.color['bbg'],disabledforeground=root.color['bbg'],\n relief=tk.FLAT, borderwidth=2, highlightcolor=\"red\", font=(\"TkDefaultFont\", \"10\", \"normal\"))\n label.pack(ipadx=5, ipady=5)\n\n def hidetip(self):\n tw = self.tw\n self.tw= None\n if tw:\n tw.destroy()\n \n ","sub_path":"stellapy/GUI/graph_tools/ToolTip.py","file_name":"ToolTip.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"265862751","text":"\"\"\"\n843. Guess the Word (Hard)\n\nThis problem is an interactive problem new to the LeetCode platform.\n\nWe are given a word list of unique words, each word is 6 letters long, and one word in this list is chosen as secret.\n\nYou may call master.guess(word) to guess a word. The guessed word should have type string and must be from the original list with 6 lowercase letters.\n\nThis function returns an integer type, representing the number of exact matches (value and position) of your guess to the secret word. Also, if your guess is not in the given wordlist, it will return -1 instead.\n\nFor each test case, you have 10 guesses to guess the word. At the end of any number of calls, if you have made 10 or less calls to master.guess and at least one of these guesses was the secret, you pass the testcase.\n\nBesides the example test case below, there will be 5 additional test cases, each with 100 words in the word list. The letters of each word in those testcases were chosen independently at random from 'a' to 'z', such that every word in the given word lists is unique.\n\nExample 1:\nInput: secret = \"acckzz\", wordlist = [\"acckzz\",\"ccbazz\",\"eiowzz\",\"abcczz\"]\n\nExplanation:\n\nmaster.guess(\"aaaaaa\") returns -1, because \"aaaaaa\" is not in wordlist.\nmaster.guess(\"acckzz\") returns 6, because \"acckzz\" is secret and has all 6 matches.\nmaster.guess(\"ccbazz\") returns 3, because \"ccbazz\" has 3 matches.\nmaster.guess(\"eiowzz\") returns 2, because \"eiowzz\" has 2 matches.\nmaster.guess(\"abcczz\") returns 4, because \"abcczz\" has 4 matches.\n\nWe made 5 calls to master.guess and one of them was the secret, so we pass the test case.\nNote: Any solutions that attempt to circumvent the judge will result in disqualification.\n\"\"\"\n\n# \"\"\"\n# This is Master's API interface.\n# You should not implement it, or speculate about its implementation\n# \"\"\"\nclass Master(object):\n def __init__(self, secret):\n self.secret = secret\n\n def guess(self, word):\n \"\"\"\n :type word: str\n :rtype int\n \"\"\"\n res = 0\n for i in range(6):\n if word[i] == self.secret[i]:\n res += 1\n return res\n\n\nfrom collections import Counter\n\n\nclass Solution(object):\n def findSecretWord(self, wordlist, master):\n \"\"\"\n :type wordlist: List[Str]\n :type master: Master\n :rtype: None\n \"\"\"\n\n def distance(word1, word2):\n res = 0\n for i in range(6):\n if word1[i] == word2[i]:\n res += 1\n return res\n\n n = len(wordlist)\n dis = []\n for i in range(n):\n dis.append([-1] * n)\n for i in range(n):\n for j in range(i + 1, n):\n tmpd = distance(wordlist[i], wordlist[j])\n dis[i][j] = tmpd\n dis[j][i] = tmpd\n candidates = [i for i in range(n)]\n for i in range(10):\n # pick the one with min(max(#group))\n minidx, minmax_ = -1, n\n for idx in candidates:\n tmpdis = [dis[idx][j] for j in candidates]\n memo = Counter(tmpdis)\n tmp_max = max(memo.values())\n if tmp_max < minmax_:\n minidx = idx\n minmax_ = tmp_max\n tmp_w = wordlist[minidx]\n res = master.guess(tmp_w)\n if res == 6:\n return\n new_candidates = []\n for idx in candidates:\n if dis[minidx][idx] == res:\n new_candidates.append(idx)\n candidates = new_candidates\n if len(candidates) == 0:\n return\n\n\nif __name__ == \"__main__\":\n a = Solution()\n master = Master(\"acckzz\")\n a.findSecretWord([\"acckzz\", \"ccbazz\", \"eiowzz\", \"abcczz\"], master)\n","sub_path":"python/leetcode/divide_conquer/843_guess_word.py","file_name":"843_guess_word.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"463136999","text":"import random\nimport logging\nfrom typing import Optional\n\nimport aiohttp\nimport discord\nfrom discord.ext import commands, tasks\nfrom discord_slash import SlashContext, cog_ext\nfrom discord_slash.model import SlashCommandOptionType\nfrom discord_slash.utils.manage_commands import create_option\n\nfrom bot.helpers import tools\n\n\nclass Colors(commands.Cog, name=\"colors\"):\n COLORS = [\n (255, 62, 62),\n (255, 147, 62),\n (255, 215, 62),\n (133, 255, 62),\n (56, 255, 202),\n (56, 167, 255),\n (173, 56, 255),\n (243, 56, 255),\n ]\n\n def __init__(self, bot: commands.Bot):\n self.bot = bot\n self.color = 0\n _logger = logging.getLogger(__name__)\n self.logger = logging.LoggerAdapter(_logger, {\"botname\": self.bot.name})\n self.change_color.start()\n\n def get_rainbow_role(self, guild: discord.Guild) -> Optional[discord.Role]:\n rainbow_role = None\n for role in guild.roles:\n if role.name == \"Rainbow\":\n rainbow_role = role\n return rainbow_role\n\n @tasks.loop(seconds=5.0)\n async def change_color(self):\n for guild in self.bot.guilds:\n rainbow_role = self.get_rainbow_role(guild)\n if rainbow_role:\n try:\n await rainbow_role.edit(\n colour=discord.Colour.from_rgb(\n self.COLORS[self.color][0],\n self.COLORS[self.color][1],\n self.COLORS[self.color][2],\n )\n )\n except:\n self.logger.error(\"woops\", exc_info=True)\n\n self.color = self.color + 1 if self.color + 1 <= 7 else 0\n\n @cog_ext.cog_slash(\n name=\"createrainbowrole\",\n description=\"Create the rainbow role for the bot to use!\",\n )\n @commands.has_permissions(manage_roles=True)\n async def createrainbowrole(self, ctx: SlashContext) -> None:\n rainbow_role = self.get_rainbow_role(ctx.guild)\n if rainbow_role:\n embed = tools.create_error_embed(\n ctx, desc=\"The rainbow role already exists.\"\n )\n await ctx.send(embed=embed)\n return\n\n try:\n await ctx.guild.create_role(\n name=\"Rainbow\",\n colour=discord.Colour.from_rgb(\n self.COLORS[self.color][0],\n self.COLORS[self.color][1],\n self.COLORS[self.color][2],\n ),\n )\n except:\n embed = tools.create_error_embed(ctx, \"Could not create the rainbow role.\")\n await ctx.send(embed=embed)\n else:\n embed = tools.create_embed(\n ctx,\n \"Rainbow Role\",\n desc='The rainbow role has been created. Do not rename or delete the role named \"Rainbow\". It will update every 30 seconds.\\n**Make sure to move the bot\\'s role above the rainbow role, otherwise it will not be able to change colors.**',\n )\n await ctx.send(embed=embed)\n\n @cog_ext.cog_slash(\n name=\"deleterainbowrole\",\n description=\"Delete the rainbow role for the server.\",\n )\n @commands.has_permissions(manage_roles=True)\n async def deleterainbowrole(self, ctx: SlashContext) -> None:\n rainbow_role = self.get_rainbow_role(ctx.guild)\n if not rainbow_role:\n embed = tools.create_error_embed(ctx, \"The rainbow role does not exist.\")\n await ctx.send(embed=embed)\n return\n try:\n await rainbow_role.delete()\n except:\n embed = tools.create_error_embed(ctx, \"Could not delete the rainbow role.\")\n await ctx.send(embed=embed)\n else:\n embed = tools.create_embed(\n ctx,\n \"Rainbow Role\",\n desc=\"The rainbow role has been deleted.\",\n )\n await ctx.send(embed=embed)\n\n @cog_ext.cog_slash(\n name=\"botrainbowrole\",\n description=\"Give the bot the rainbow role.\",\n )\n @commands.has_permissions(manage_roles=True)\n async def botrole(self, ctx: SlashContext) -> None:\n rainbow_role = self.get_rainbow_role(ctx.guild)\n try:\n await ctx.guild.me.add_roles(rainbow_role)\n except:\n embed = tools.create_error_embed(\n ctx,\n desc=\"The bot could not be given the rainbow role.\",\n )\n await ctx.send(embed=embed)\n else:\n embed = tools.create_embed(\n ctx,\n \"Bot Rainbow\",\n desc=\"The bot has been given the rainbow role.\",\n )\n await ctx.send(embed=embed)\n\n @cog_ext.cog_slash(\n name=\"giverainbowrole\",\n description=\"Give yourself the rainbow role.\",\n )\n async def giverainbowrole(self, ctx: SlashContext) -> None:\n rainbow_role = self.get_rainbow_role(ctx.guild)\n try:\n await ctx.author.add_roles(rainbow_role)\n except Exception as e:\n print(e)\n embed = tools.create_error_embed(\n ctx,\n desc=\"You could not be given the rainbow role.\",\n )\n await ctx.send(embed=embed)\n else:\n embed = tools.create_embed(\n ctx,\n \"User Rainbow\",\n desc=\"You have been given the rainbow role.\",\n )\n await ctx.send(embed=embed)\n\n @cog_ext.cog_slash(\n name=\"removerainbowrole\",\n description=\"Remove the rainbow role from yourself.\",\n )\n async def removerainbowrole(self, ctx: SlashContext) -> None:\n rainbow_role = self.get_rainbow_role(ctx.guild)\n try:\n await ctx.author.remove_roles(rainbow_role)\n except:\n embed = tools.create_error_embed(\n ctx,\n desc=\"The rainbow role could not be removed from you.\",\n )\n await ctx.send(embed=embed)\n else:\n embed = tools.create_embed(\n ctx,\n \"User Rainbow\",\n desc=\"The rainbow role has been removed from you.\",\n )\n await ctx.send(embed=embed)\n\n @cog_ext.cog_slash(\n name=\"invite\",\n description=\"Invite the bot to another server!\",\n )\n async def invite(self, ctx: SlashContext) -> None:\n embed = tools.create_embed(\n ctx,\n \"Colors+ Invite\",\n desc=\"Here's an invite for Colors+ (with slash commands and Manage Roles).\",\n )\n await ctx.send(\n content=\"https://discord.com/api/oauth2/authorize?client_id=851852969770090516&permissions=268435456&scope=bot%20applications.commands\",\n embed=embed,\n )\n\n\ndef setup(bot: commands.Bot) -> None:\n bot.add_cog(Colors(bot))\n","sub_path":"bot/cogs/wip/colors.py","file_name":"colors.py","file_ext":"py","file_size_in_byte":6945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"451930975","text":"\n#dir\ndb_dir = \"/home/kobeyu/workspace/dataset/\"\npos_dir = db_dir + \"AFLW/aflw/data/flicker/\"\nneg_dir = db_dir + \"val2017/\"\ntest_dir = db_dir + \"FDDB/\"\nfig_dir = db_dir + \"result/fig/\"\nmodel_dir = '/home/kobeyu/workspace/model/AConvNNCascade4FD/'\n\n#db parameters\ninput_channel = 3\nimg_size_12 = 12\nimg_size_24 = 24\nimg_size_48 = 48\nneg_per_img = 35\nfold_num = 10\ndim_12 = img_size_12 * img_size_12 * input_channel\ndim_24 = img_size_24 * img_size_24 * input_channel\ndim_48 = img_size_48 * img_size_48 * input_channel\n\n#network parameters\nb_init = 0.0\nw_std = 0.1\nlr = 5e-2\nepoch_num = 3\npos_batch = 32\nneg_batch = 96\nmini_batch = 128\nbatch_iter = int(1e4)\n\n#result parameters\nthr_12 = 3e-3\nthr_24 = 1e-9\nthr_48 = 1e-2\n\n#training parameters\nwindow_stride = 4\nface_minimum = 20\ndownscale = 1.18\npyramid_num = 16\n\ncali_scale = [0.83, 0.91, 1.0, 1.10, 1.21]\ncali_off_x = [-0.17, 0., 0.17]\ncali_off_y = [-0.17, 0., 0.17]\ncali_patt_num = len(cali_scale) * len(cali_off_x) * len(cali_off_y)\n","sub_path":"param.py","file_name":"param.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"538585294","text":"class Vec:\n \"\"\"A simple vector in 2D. Also use for points (position vector)\"\"\"\n def __init__(self, x, y):\n self.x = x\n self.y = y\n \n def __add__(self, other):\n return Vec(self.x + other.x, self.y + other.y)\n \n def __sub__(self, other):\n return Vec(self.x - other.x, self.y - other.y)\n \n def dot(self, other):\n return self.x * other.x + self.y * other.y\n \n def lensq(self):\n return self.dot(self)\n \n \ndef is_ccw(a, b, c):\n \"\"\"True iff triangle abc is counter-clockwise or degenerate\"\"\"\n p = b - a\n q = c - a\n area = p.x * q.y - q.x * p.y\n\t # May want to throw an exception if area == 0\n return area > 0 \n \n \ndef is_strictly_convex(vertices):\n \"\"\"Return True iff the given vertices define a strictly convex polygon\n with all vertices in counter-clockwise order. The function returns\n false if there are fewer than 3 vertices or if any interior angle is\n less than 180 degrees or the vertices are not in counter-clockwise\n order\n \"\"\"\n if len(vertices) < 3:\n return False\n for i in range(len(vertices)):\n a = vertices[i]\n b = vertices[(i + 1) % len(vertices)]\n c = vertices[(i + 2) % len(vertices)]\n if (not is_ccw(a, b, c)):\n return False\n return True\n\n\nverts = [\n (0, 0),\n (100, 0),\n (100, 100),\n (0, 100)]\npoints = [Vec(v[0], v[1]) for v in verts]\nprint(is_strictly_convex(points))\n\nverts = [\n (0, 0),\n (0, 100),\n (100, 100),\n (100, 0)]\npoints = [Vec(v[0], v[1]) for v in verts]\nprint(is_strictly_convex(points))","sub_path":"Lab9/Lab9_Q6.py","file_name":"Lab9_Q6.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"124113077","text":"import sys\nimport logging\nimport traceback\n\nfrom ext_api.models.extensions import get_extensions, update_extension\nfrom ext_api.github import (get_project_path, get_json, validate_versions,\n JsonFileNotFoundError, VersionsValidationError)\n\nlogger = logging.getLogger(__name__)\n\n\ndef sync_ext_versions():\n for ext in get_extensions():\n update_ext_versions(ext)\n\n\ndef update_ext_versions(ext):\n project_path = get_project_path(ext['GithubUrl'])\n try:\n versions = get_json(project_path, 'master', 'versions')\n valid_versions = validate_versions(versions)\n versions_only = [v['required_api_version'] for v in valid_versions]\n except JsonFileNotFoundError:\n versions_only = ['^1.0.0']\n except VersionsValidationError:\n versions_only = []\n\n try:\n update_extension(ext['ID'], SupportedVersions=versions_only)\n except Exception as e:\n logger.error('%s: %s', type(e).__name__, e)\n traceback.print_exc(file=sys.stderr)\n\n logger.info('Extension %s is synced. SupportedVersions: %s', ext['ID'], versions_only)\n","sub_path":"ext_api/sync_ext_versions.py","file_name":"sync_ext_versions.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"59"} +{"seq_id":"593047216","text":"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport sys\n\nsrc=input(\"Enter Source Image Location: \") #taking image location\ntarget=input(\"Enter Target Image Location: \") #taking image location\n\n#---------------reading images----------------------------------------\nimg = cv2.imread(src) #reading the source image\ngray_img = cv2.imread(target) #reading the target image\n#---------------converting images to Lab----------------------------\norig_img = cv2.cvtColor(img,cv2.COLOR_BGR2Lab) # converting source to Lab space\ngray_img = cv2.cvtColor(gray_img,cv2.COLOR_BGR2Lab) # converting target to Lab space\nnew_img = np.copy(orig_img) # making a copy to redraw the image\n\n#---------making a mask of 5*5 neighborhood--------------------------------\nmask = np.array([[1,1,1,1,1],[1,1,1,1,1],[1,1,1,1,1],[1,1,1,1,1],[1,1,1,1,1]])\n#------------------some variables------------------------\nm = orig_img.shape[0] # no of rows\nn = orig_img.shape[1] # no of columns\nchannel = orig_img.shape[2] # no of channels\nno_of_samples = 400 # no of samples user feed\nsample_size = np.sqrt((m*n)/no_of_samples).astype(int)\n\n#----added a padding of (4,4,4,4) top,bottom,left,right pixel of zeros-----------------\npadded_orig_img = np.pad(orig_img,((4,4),(4,4),(0,0)),'constant') \npadded_gray_img = np.pad(gray_img,((4,4),(4,4),(0,0)),'constant')\n\ncolor_map=[] #store the samples color averaged intensity of mask 5*5\n\n#-------making 200 samples and selecting a random pixel from each cell --------------\ndef make_color_map_for_global_image_matching():\n i=5;\n j=5;\n global color_map\n while(i=np.absolute(intensity-rows[0])):\n dist = np.absolute(intensity-rows[0])\n index = i;\n return index\ndef form_colorization_of_gray_image_by_global_coloring():\n r_ind = 5;\n c_ind = 5;\n global new_img\n while(r_ind