\nwarnings.filterwarnings('ignore')\n\n\ndef get_data(train_file):\n target = []\n data = []\n with open(train_file, 'r', encoding='utf-8') as f:\n for line in f.readlines():\n line = line.strip().split('\\t')\n if len(line) == 1:\n continue\n target.append(int(line[0]))\n data.append(line[1])\n data = list(map(jieba.lcut, data))\n data = [' '.join(d) for d in data]\n return data, target\n\n\ndef train(cls, data, target, model_path):\n cls = cls.fit(data, target)\n with open(model_path, 'wb') as f:\n pickle.dump(cls, f)\n\n\ndef trans(data, matrix_path, stopword_path):\n with open(stopword_path, 'r', encoding='utf-8') as fs:\n stop_words = [line.strip() for line in fs.readline()]\n tfidf = TfidfVectorizer(token_pattern='(?u)\\\\b\\\\w+\\\\b', stop_words=\n stop_words)\n features = tfidf.fit_transform(data)\n with open(matrix_path, 'wb') as f:\n pickle.dump(tfidf, f)\n return features\n\n\ndef load_models(matrix_path, model_path):\n tfidf, cls = None, None\n if os.path.isfile(model_path):\n with open(model_path, 'rb') as f:\n cls = pickle.load(f)\n if os.path.isfile(matrix_path):\n with open(matrix_path, 'rb') as f:\n tfidf = pickle.load(f)\n return tfidf, cls\n\n\ndef test(matrix_path, model_path, data_path, outdir):\n curr_time = datetime.datetime.now()\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\n out_path = outdir + '/%s/' % time_str\n out_file = os.path.join(out_path, 'results.txt')\n if not os.path.exists(out_path):\n os.makedirs(out_path)\n data, target = get_data(data_path)\n tfidf, cls = load_models(matrix_path, model_path)\n if tfidf == None or cls == None:\n print('cannot load models........')\n return\n feature = tfidf.transform(data)\n predicted = cls.predict(feature)\n acc = metrics.accuracy_score(target, predicted)\n pre = metrics.precision_score(target, predicted)\n recall = metrics.recall_score(target, predicted)\n f1 = metrics.f1_score(target, predicted)\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\n auc = metrics.auc(fpr, tpr)\n print('accuracy_score: ', acc)\n print('precision_score: ', pre)\n print('recall_score: ', recall)\n print('f1_score: ', f1)\n print('auc: ', auc)\n with open(out_file, 'w', encoding='utf-8') as f:\n for label in predicted:\n f.write(str(label) + '\\n')\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--train', type=str, default='./data/train.txt',\n help='training data')\n parser.add_argument('--test', type=str, default='./data/test.txt', help\n ='test data')\n parser.add_argument('--stopwords', type=str, default=\n './data/hit_stopwords.txt', help='stop words')\n parser.add_argument('--model', type=str, default=\n './model/svm_model.pkl', help='classification model')\n parser.add_argument('--matrix', type=str, default='./model/tfidf.pkl',\n help='tfidf model')\n parser.add_argument('--outpath', type=str, default='./results/', help=\n 'out path')\n args = parser.parse_args()\n print('data processing.......')\n data, target = get_data(args.train)\n print('transform data.......')\n features = trans(data, args.matrix, args.stopwords)\n print('training model.......')\n cls = svm.LinearSVC()\n train(cls, features, target, args.model)\n print('test.......')\n test(args.matrix, args.model, args.test, args.outpath)\n","<import token>\n<code token>\n\n\ndef get_data(train_file):\n target = []\n data = []\n with open(train_file, 'r', encoding='utf-8') as f:\n for line in f.readlines():\n line = line.strip().split('\\t')\n if len(line) == 1:\n continue\n target.append(int(line[0]))\n data.append(line[1])\n data = list(map(jieba.lcut, data))\n data = [' '.join(d) for d in data]\n return data, target\n\n\ndef train(cls, data, target, model_path):\n cls = cls.fit(data, target)\n with open(model_path, 'wb') as f:\n pickle.dump(cls, f)\n\n\ndef trans(data, matrix_path, stopword_path):\n with open(stopword_path, 'r', encoding='utf-8') as fs:\n stop_words = [line.strip() for line in fs.readline()]\n tfidf = TfidfVectorizer(token_pattern='(?u)\\\\b\\\\w+\\\\b', stop_words=\n stop_words)\n features = tfidf.fit_transform(data)\n with open(matrix_path, 'wb') as f:\n pickle.dump(tfidf, f)\n return features\n\n\ndef load_models(matrix_path, model_path):\n tfidf, cls = None, None\n if os.path.isfile(model_path):\n with open(model_path, 'rb') as f:\n cls = pickle.load(f)\n if os.path.isfile(matrix_path):\n with open(matrix_path, 'rb') as f:\n tfidf = pickle.load(f)\n return tfidf, cls\n\n\ndef test(matrix_path, model_path, data_path, outdir):\n curr_time = datetime.datetime.now()\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\n out_path = outdir + '/%s/' % time_str\n out_file = os.path.join(out_path, 'results.txt')\n if not os.path.exists(out_path):\n os.makedirs(out_path)\n data, target = get_data(data_path)\n tfidf, cls = load_models(matrix_path, model_path)\n if tfidf == None or cls == None:\n print('cannot load models........')\n return\n feature = tfidf.transform(data)\n predicted = cls.predict(feature)\n acc = metrics.accuracy_score(target, predicted)\n pre = metrics.precision_score(target, predicted)\n recall = metrics.recall_score(target, predicted)\n f1 = metrics.f1_score(target, predicted)\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\n auc = metrics.auc(fpr, tpr)\n print('accuracy_score: ', acc)\n print('precision_score: ', pre)\n print('recall_score: ', recall)\n print('f1_score: ', f1)\n print('auc: ', auc)\n with open(out_file, 'w', encoding='utf-8') as f:\n for label in predicted:\n f.write(str(label) + '\\n')\n\n\n<code token>\n","<import token>\n<code token>\n\n\ndef get_data(train_file):\n target = []\n data = []\n with open(train_file, 'r', encoding='utf-8') as f:\n for line in f.readlines():\n line = line.strip().split('\\t')\n if len(line) == 1:\n continue\n target.append(int(line[0]))\n data.append(line[1])\n data = list(map(jieba.lcut, data))\n data = [' '.join(d) for d in data]\n return data, target\n\n\ndef train(cls, data, target, model_path):\n cls = cls.fit(data, target)\n with open(model_path, 'wb') as f:\n pickle.dump(cls, f)\n\n\n<function token>\n\n\ndef load_models(matrix_path, model_path):\n tfidf, cls = None, None\n if os.path.isfile(model_path):\n with open(model_path, 'rb') as f:\n cls = pickle.load(f)\n if os.path.isfile(matrix_path):\n with open(matrix_path, 'rb') as f:\n tfidf = pickle.load(f)\n return tfidf, cls\n\n\ndef test(matrix_path, model_path, data_path, outdir):\n curr_time = datetime.datetime.now()\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\n out_path = outdir + '/%s/' % time_str\n out_file = os.path.join(out_path, 'results.txt')\n if not os.path.exists(out_path):\n os.makedirs(out_path)\n data, target = get_data(data_path)\n tfidf, cls = load_models(matrix_path, model_path)\n if tfidf == None or cls == None:\n print('cannot load models........')\n return\n feature = tfidf.transform(data)\n predicted = cls.predict(feature)\n acc = metrics.accuracy_score(target, predicted)\n pre = metrics.precision_score(target, predicted)\n recall = metrics.recall_score(target, predicted)\n f1 = metrics.f1_score(target, predicted)\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\n auc = metrics.auc(fpr, tpr)\n print('accuracy_score: ', acc)\n print('precision_score: ', pre)\n print('recall_score: ', recall)\n print('f1_score: ', f1)\n print('auc: ', auc)\n with open(out_file, 'w', encoding='utf-8') as f:\n for label in predicted:\n f.write(str(label) + '\\n')\n\n\n<code token>\n","<import token>\n<code token>\n\n\ndef get_data(train_file):\n target = []\n data = []\n with open(train_file, 'r', encoding='utf-8') as f:\n for line in f.readlines():\n line = line.strip().split('\\t')\n if len(line) == 1:\n continue\n target.append(int(line[0]))\n data.append(line[1])\n data = list(map(jieba.lcut, data))\n data = [' '.join(d) for d in data]\n return data, target\n\n\ndef train(cls, data, target, model_path):\n cls = cls.fit(data, target)\n with open(model_path, 'wb') as f:\n pickle.dump(cls, f)\n\n\n<function token>\n\n\ndef load_models(matrix_path, model_path):\n tfidf, cls = None, None\n if os.path.isfile(model_path):\n with open(model_path, 'rb') as f:\n cls = pickle.load(f)\n if os.path.isfile(matrix_path):\n with open(matrix_path, 'rb') as f:\n tfidf = pickle.load(f)\n return tfidf, cls\n\n\n<function token>\n<code token>\n","<import token>\n<code token>\n\n\ndef get_data(train_file):\n target = []\n data = []\n with open(train_file, 'r', encoding='utf-8') as f:\n for line in f.readlines():\n line = line.strip().split('\\t')\n if len(line) == 1:\n continue\n target.append(int(line[0]))\n data.append(line[1])\n data = list(map(jieba.lcut, data))\n data = [' '.join(d) for d in data]\n return data, target\n\n\n<function token>\n<function token>\n\n\ndef load_models(matrix_path, model_path):\n tfidf, cls = None, None\n if os.path.isfile(model_path):\n with open(model_path, 'rb') as f:\n cls = pickle.load(f)\n if os.path.isfile(matrix_path):\n with open(matrix_path, 'rb') as f:\n tfidf = pickle.load(f)\n return tfidf, cls\n\n\n<function token>\n<code token>\n","<import token>\n<code token>\n<function token>\n<function token>\n<function token>\n\n\ndef load_models(matrix_path, model_path):\n tfidf, cls = None, None\n if os.path.isfile(model_path):\n with open(model_path, 'rb') as f:\n cls = pickle.load(f)\n if os.path.isfile(matrix_path):\n with open(matrix_path, 'rb') as f:\n tfidf = pickle.load(f)\n return tfidf, cls\n\n\n<function token>\n<code token>\n","<import token>\n<code token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"],"string":"[\n \"# -*- coding: utf-8 -*-\\r\\n# caixinjun\\r\\n\\r\\nimport argparse\\r\\nfrom sklearn import metrics\\r\\nimport datetime\\r\\nimport jieba\\r\\nfrom sklearn.feature_extraction.text import TfidfVectorizer\\r\\nimport pickle\\r\\nfrom sklearn import svm\\r\\nimport os\\r\\nimport warnings\\r\\nwarnings.filterwarnings('ignore')\\r\\n\\r\\n\\r\\ndef get_data(train_file):\\r\\n target = []\\r\\n data = []\\r\\n with open(train_file, 'r', encoding='utf-8') as f:\\r\\n for line in f.readlines():\\r\\n line = line.strip().split(\\\"\\\\t\\\")\\r\\n if len(line) == 1:\\r\\n continue\\r\\n target.append(int(line[0]))\\r\\n data.append(line[1])\\r\\n data = list(map(jieba.lcut, data))\\r\\n data = [\\\" \\\".join(d) for d in data]\\r\\n return data, target\\r\\n\\r\\n\\r\\ndef train(cls, data, target, model_path):\\r\\n cls = cls.fit(data, target)\\r\\n with open(model_path, 'wb') as f:\\r\\n pickle.dump(cls, f)\\r\\n\\r\\ndef trans(data, matrix_path, stopword_path):\\r\\n with open(stopword_path, 'r', encoding='utf-8') as fs:\\r\\n stop_words = [line.strip() for line in fs.readline()]\\r\\n tfidf = TfidfVectorizer(token_pattern=r\\\"(?u)\\\\b\\\\w+\\\\b\\\", stop_words=stop_words)\\r\\n features = tfidf.fit_transform(data)\\r\\n with open(matrix_path, 'wb') as f:\\r\\n pickle.dump(tfidf, f)\\r\\n return features\\r\\n\\r\\n\\r\\ndef load_models(matrix_path, model_path):\\r\\n tfidf, cls = None, None\\r\\n if os.path.isfile(model_path):\\r\\n with open(model_path, 'rb') as f:\\r\\n cls = pickle.load(f)\\r\\n if os.path.isfile(matrix_path):\\r\\n with open(matrix_path, 'rb') as f:\\r\\n tfidf = pickle.load(f)\\r\\n return tfidf, cls\\r\\n\\r\\ndef test(matrix_path, model_path, data_path, outdir):\\r\\n\\r\\n curr_time = datetime.datetime.now()\\r\\n time_str = curr_time.strftime(\\\"%Y-%m-%d %H-%M-%S\\\")\\r\\n out_path = outdir + '/%s/' % time_str\\r\\n out_file = os.path.join(out_path, \\\"results.txt\\\")\\r\\n if not os.path.exists(out_path):\\r\\n os.makedirs(out_path)\\r\\n data, target = get_data(data_path)\\r\\n tfidf, cls = load_models(matrix_path, model_path)\\r\\n if tfidf==None or cls==None:\\r\\n print(\\\"cannot load models........\\\")\\r\\n return\\r\\n\\r\\n feature = tfidf.transform(data)\\r\\n predicted = cls.predict(feature)\\r\\n\\r\\n acc = metrics.accuracy_score(target, predicted)\\r\\n pre = metrics.precision_score(target, predicted)\\r\\n recall = metrics.recall_score(target, predicted)\\r\\n f1 = metrics.f1_score(target, predicted)\\r\\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\\r\\n auc = metrics.auc(fpr, tpr)\\r\\n\\r\\n print(\\\"accuracy_score: \\\", acc)\\r\\n print(\\\"precision_score: \\\", pre)\\r\\n print(\\\"recall_score: \\\", recall)\\r\\n print(\\\"f1_score: \\\", f1)\\r\\n print(\\\"auc: \\\", auc)\\r\\n\\r\\n with open(out_file, 'w', encoding='utf-8') as f:\\r\\n for label in predicted:\\r\\n f.write(str(label) + '\\\\n')\\r\\n\\r\\n\\r\\nif __name__ == '__main__':\\r\\n parser = argparse.ArgumentParser()\\r\\n parser.add_argument('--train', type=str, default='./data/train.txt', help='training data')\\r\\n parser.add_argument('--test', type=str, default='./data/test.txt', help='test data')\\r\\n parser.add_argument('--stopwords', type=str, default='./data/hit_stopwords.txt', help='stop words')\\r\\n parser.add_argument('--model', type=str, default='./model/svm_model.pkl', help='classification model')\\r\\n parser.add_argument('--matrix', type=str, default='./model/tfidf.pkl', help='tfidf model')\\r\\n parser.add_argument('--outpath', type=str, default='./results/', help='out path')\\r\\n args = parser.parse_args()\\r\\n\\r\\n print(\\\"data processing.......\\\")\\r\\n data, target = get_data(args.train)\\r\\n\\r\\n print(\\\"transform data.......\\\")\\r\\n features = trans(data, args.matrix, args.stopwords)\\r\\n\\r\\n print(\\\"training model.......\\\")\\r\\n cls = svm.LinearSVC()\\r\\n train(cls, features, target, args.model)\\r\\n\\r\\n print(\\\"test.......\\\")\\r\\n test(args.matrix, args.model, args.test, args.outpath)\\r\\n\\r\\n\",\n \"import argparse\\nfrom sklearn import metrics\\nimport datetime\\nimport jieba\\nfrom sklearn.feature_extraction.text import TfidfVectorizer\\nimport pickle\\nfrom sklearn import svm\\nimport os\\nimport warnings\\nwarnings.filterwarnings('ignore')\\n\\n\\ndef get_data(train_file):\\n target = []\\n data = []\\n with open(train_file, 'r', encoding='utf-8') as f:\\n for line in f.readlines():\\n line = line.strip().split('\\\\t')\\n if len(line) == 1:\\n continue\\n target.append(int(line[0]))\\n data.append(line[1])\\n data = list(map(jieba.lcut, data))\\n data = [' '.join(d) for d in data]\\n return data, target\\n\\n\\ndef train(cls, data, target, model_path):\\n cls = cls.fit(data, target)\\n with open(model_path, 'wb') as f:\\n pickle.dump(cls, f)\\n\\n\\ndef trans(data, matrix_path, stopword_path):\\n with open(stopword_path, 'r', encoding='utf-8') as fs:\\n stop_words = [line.strip() for line in fs.readline()]\\n tfidf = TfidfVectorizer(token_pattern='(?u)\\\\\\\\b\\\\\\\\w+\\\\\\\\b', stop_words=\\n stop_words)\\n features = tfidf.fit_transform(data)\\n with open(matrix_path, 'wb') as f:\\n pickle.dump(tfidf, f)\\n return features\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\ndef test(matrix_path, model_path, data_path, outdir):\\n curr_time = datetime.datetime.now()\\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\\n out_path = outdir + '/%s/' % time_str\\n out_file = os.path.join(out_path, 'results.txt')\\n if not os.path.exists(out_path):\\n os.makedirs(out_path)\\n data, target = get_data(data_path)\\n tfidf, cls = load_models(matrix_path, model_path)\\n if tfidf == None or cls == None:\\n print('cannot load models........')\\n return\\n feature = tfidf.transform(data)\\n predicted = cls.predict(feature)\\n acc = metrics.accuracy_score(target, predicted)\\n pre = metrics.precision_score(target, predicted)\\n recall = metrics.recall_score(target, predicted)\\n f1 = metrics.f1_score(target, predicted)\\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\\n auc = metrics.auc(fpr, tpr)\\n print('accuracy_score: ', acc)\\n print('precision_score: ', pre)\\n print('recall_score: ', recall)\\n print('f1_score: ', f1)\\n print('auc: ', auc)\\n with open(out_file, 'w', encoding='utf-8') as f:\\n for label in predicted:\\n f.write(str(label) + '\\\\n')\\n\\n\\nif __name__ == '__main__':\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--train', type=str, default='./data/train.txt',\\n help='training data')\\n parser.add_argument('--test', type=str, default='./data/test.txt', help\\n ='test data')\\n parser.add_argument('--stopwords', type=str, default=\\n './data/hit_stopwords.txt', help='stop words')\\n parser.add_argument('--model', type=str, default=\\n './model/svm_model.pkl', help='classification model')\\n parser.add_argument('--matrix', type=str, default='./model/tfidf.pkl',\\n help='tfidf model')\\n parser.add_argument('--outpath', type=str, default='./results/', help=\\n 'out path')\\n args = parser.parse_args()\\n print('data processing.......')\\n data, target = get_data(args.train)\\n print('transform data.......')\\n features = trans(data, args.matrix, args.stopwords)\\n print('training model.......')\\n cls = svm.LinearSVC()\\n train(cls, features, target, args.model)\\n print('test.......')\\n test(args.matrix, args.model, args.test, args.outpath)\\n\",\n \"<import token>\\nwarnings.filterwarnings('ignore')\\n\\n\\ndef get_data(train_file):\\n target = []\\n data = []\\n with open(train_file, 'r', encoding='utf-8') as f:\\n for line in f.readlines():\\n line = line.strip().split('\\\\t')\\n if len(line) == 1:\\n continue\\n target.append(int(line[0]))\\n data.append(line[1])\\n data = list(map(jieba.lcut, data))\\n data = [' '.join(d) for d in data]\\n return data, target\\n\\n\\ndef train(cls, data, target, model_path):\\n cls = cls.fit(data, target)\\n with open(model_path, 'wb') as f:\\n pickle.dump(cls, f)\\n\\n\\ndef trans(data, matrix_path, stopword_path):\\n with open(stopword_path, 'r', encoding='utf-8') as fs:\\n stop_words = [line.strip() for line in fs.readline()]\\n tfidf = TfidfVectorizer(token_pattern='(?u)\\\\\\\\b\\\\\\\\w+\\\\\\\\b', stop_words=\\n stop_words)\\n features = tfidf.fit_transform(data)\\n with open(matrix_path, 'wb') as f:\\n pickle.dump(tfidf, f)\\n return features\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\ndef test(matrix_path, model_path, data_path, outdir):\\n curr_time = datetime.datetime.now()\\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\\n out_path = outdir + '/%s/' % time_str\\n out_file = os.path.join(out_path, 'results.txt')\\n if not os.path.exists(out_path):\\n os.makedirs(out_path)\\n data, target = get_data(data_path)\\n tfidf, cls = load_models(matrix_path, model_path)\\n if tfidf == None or cls == None:\\n print('cannot load models........')\\n return\\n feature = tfidf.transform(data)\\n predicted = cls.predict(feature)\\n acc = metrics.accuracy_score(target, predicted)\\n pre = metrics.precision_score(target, predicted)\\n recall = metrics.recall_score(target, predicted)\\n f1 = metrics.f1_score(target, predicted)\\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\\n auc = metrics.auc(fpr, tpr)\\n print('accuracy_score: ', acc)\\n print('precision_score: ', pre)\\n print('recall_score: ', recall)\\n print('f1_score: ', f1)\\n print('auc: ', auc)\\n with open(out_file, 'w', encoding='utf-8') as f:\\n for label in predicted:\\n f.write(str(label) + '\\\\n')\\n\\n\\nif __name__ == '__main__':\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--train', type=str, default='./data/train.txt',\\n help='training data')\\n parser.add_argument('--test', type=str, default='./data/test.txt', help\\n ='test data')\\n parser.add_argument('--stopwords', type=str, default=\\n './data/hit_stopwords.txt', help='stop words')\\n parser.add_argument('--model', type=str, default=\\n './model/svm_model.pkl', help='classification model')\\n parser.add_argument('--matrix', type=str, default='./model/tfidf.pkl',\\n help='tfidf model')\\n parser.add_argument('--outpath', type=str, default='./results/', help=\\n 'out path')\\n args = parser.parse_args()\\n print('data processing.......')\\n data, target = get_data(args.train)\\n print('transform data.......')\\n features = trans(data, args.matrix, args.stopwords)\\n print('training model.......')\\n cls = svm.LinearSVC()\\n train(cls, features, target, args.model)\\n print('test.......')\\n test(args.matrix, args.model, args.test, args.outpath)\\n\",\n \"<import token>\\n<code token>\\n\\n\\ndef get_data(train_file):\\n target = []\\n data = []\\n with open(train_file, 'r', encoding='utf-8') as f:\\n for line in f.readlines():\\n line = line.strip().split('\\\\t')\\n if len(line) == 1:\\n continue\\n target.append(int(line[0]))\\n data.append(line[1])\\n data = list(map(jieba.lcut, data))\\n data = [' '.join(d) for d in data]\\n return data, target\\n\\n\\ndef train(cls, data, target, model_path):\\n cls = cls.fit(data, target)\\n with open(model_path, 'wb') as f:\\n pickle.dump(cls, f)\\n\\n\\ndef trans(data, matrix_path, stopword_path):\\n with open(stopword_path, 'r', encoding='utf-8') as fs:\\n stop_words = [line.strip() for line in fs.readline()]\\n tfidf = TfidfVectorizer(token_pattern='(?u)\\\\\\\\b\\\\\\\\w+\\\\\\\\b', stop_words=\\n stop_words)\\n features = tfidf.fit_transform(data)\\n with open(matrix_path, 'wb') as f:\\n pickle.dump(tfidf, f)\\n return features\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\ndef test(matrix_path, model_path, data_path, outdir):\\n curr_time = datetime.datetime.now()\\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\\n out_path = outdir + '/%s/' % time_str\\n out_file = os.path.join(out_path, 'results.txt')\\n if not os.path.exists(out_path):\\n os.makedirs(out_path)\\n data, target = get_data(data_path)\\n tfidf, cls = load_models(matrix_path, model_path)\\n if tfidf == None or cls == None:\\n print('cannot load models........')\\n return\\n feature = tfidf.transform(data)\\n predicted = cls.predict(feature)\\n acc = metrics.accuracy_score(target, predicted)\\n pre = metrics.precision_score(target, predicted)\\n recall = metrics.recall_score(target, predicted)\\n f1 = metrics.f1_score(target, predicted)\\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\\n auc = metrics.auc(fpr, tpr)\\n print('accuracy_score: ', acc)\\n print('precision_score: ', pre)\\n print('recall_score: ', recall)\\n print('f1_score: ', f1)\\n print('auc: ', auc)\\n with open(out_file, 'w', encoding='utf-8') as f:\\n for label in predicted:\\n f.write(str(label) + '\\\\n')\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n\\n\\ndef get_data(train_file):\\n target = []\\n data = []\\n with open(train_file, 'r', encoding='utf-8') as f:\\n for line in f.readlines():\\n line = line.strip().split('\\\\t')\\n if len(line) == 1:\\n continue\\n target.append(int(line[0]))\\n data.append(line[1])\\n data = list(map(jieba.lcut, data))\\n data = [' '.join(d) for d in data]\\n return data, target\\n\\n\\ndef train(cls, data, target, model_path):\\n cls = cls.fit(data, target)\\n with open(model_path, 'wb') as f:\\n pickle.dump(cls, f)\\n\\n\\n<function token>\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\ndef test(matrix_path, model_path, data_path, outdir):\\n curr_time = datetime.datetime.now()\\n time_str = curr_time.strftime('%Y-%m-%d %H-%M-%S')\\n out_path = outdir + '/%s/' % time_str\\n out_file = os.path.join(out_path, 'results.txt')\\n if not os.path.exists(out_path):\\n os.makedirs(out_path)\\n data, target = get_data(data_path)\\n tfidf, cls = load_models(matrix_path, model_path)\\n if tfidf == None or cls == None:\\n print('cannot load models........')\\n return\\n feature = tfidf.transform(data)\\n predicted = cls.predict(feature)\\n acc = metrics.accuracy_score(target, predicted)\\n pre = metrics.precision_score(target, predicted)\\n recall = metrics.recall_score(target, predicted)\\n f1 = metrics.f1_score(target, predicted)\\n fpr, tpr, thresholds = metrics.roc_curve(target, predicted)\\n auc = metrics.auc(fpr, tpr)\\n print('accuracy_score: ', acc)\\n print('precision_score: ', pre)\\n print('recall_score: ', recall)\\n print('f1_score: ', f1)\\n print('auc: ', auc)\\n with open(out_file, 'w', encoding='utf-8') as f:\\n for label in predicted:\\n f.write(str(label) + '\\\\n')\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n\\n\\ndef get_data(train_file):\\n target = []\\n data = []\\n with open(train_file, 'r', encoding='utf-8') as f:\\n for line in f.readlines():\\n line = line.strip().split('\\\\t')\\n if len(line) == 1:\\n continue\\n target.append(int(line[0]))\\n data.append(line[1])\\n data = list(map(jieba.lcut, data))\\n data = [' '.join(d) for d in data]\\n return data, target\\n\\n\\ndef train(cls, data, target, model_path):\\n cls = cls.fit(data, target)\\n with open(model_path, 'wb') as f:\\n pickle.dump(cls, f)\\n\\n\\n<function token>\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n\\n\\ndef get_data(train_file):\\n target = []\\n data = []\\n with open(train_file, 'r', encoding='utf-8') as f:\\n for line in f.readlines():\\n line = line.strip().split('\\\\t')\\n if len(line) == 1:\\n continue\\n target.append(int(line[0]))\\n data.append(line[1])\\n data = list(map(jieba.lcut, data))\\n data = [' '.join(d) for d in data]\\n return data, target\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<function token>\\n<function token>\\n<function token>\\n\\n\\ndef load_models(matrix_path, model_path):\\n tfidf, cls = None, None\\n if os.path.isfile(model_path):\\n with open(model_path, 'rb') as f:\\n cls = pickle.load(f)\\n if os.path.isfile(matrix_path):\\n with open(matrix_path, 'rb') as f:\\n tfidf = pickle.load(f)\\n return tfidf, cls\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<code token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5876,"cells":{"index":{"kind":"number","value":5876,"string":"5,876"},"blob_id":{"kind":"string","value":"a8341bf422a4d31a83ff412c6aac75e5cb8c5e0f"},"code":{"kind":"string","value":"# Задание 1\n# Выучите основные стандартные исключения, которые перечислены в данном уроке.\n# Задание 2\n# Напишите программу-калькулятор, которая поддерживает следующие операции: сложение, вычитание,\n# умножение, деление и возведение в степень. Программа должна выдавать сообщения об ошибке и\n# продолжать работу при вводе некорректных данных, делении на ноль и возведении нуля в\n# отрицательную степень.\n\n\n# Функция преобразования текстового списка в float\ndef list_convertion(user_list):\n for index, item in enumerate(user_list):\n user_list[index]=float(item)\n\n return user_list\n\n# Умножение\ndef multiplication(user_list):\n multnum=1\n for item in user_list:\n multnum *= item\n\n return multnum\n\n# Деление. Здесь долго мучился, т.к. нужно первое число оставть и делить его на второе, т.е цикл со второго индекса\n# пока не нашел в Интернете запись через slice напр.: for i in collection[1:]\ndef division(user_list):\n divnum=user_list[0]\n for item in user_list[1:]:\n divnum /= item\n\n return divnum\n\n# Сложение\ndef adding(user_list):\n sumnum=0\n for item in user_list:\n sumnum += item\n\n return sumnum\n\n# Вычитание. Логика та же, что и в делении\ndef subtraction(user_list):\n subtractnum=user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n\n return subtractnum\n\n# Возведение в степень. Логика та же, что в делении\ndef powering(user_list):\n pownum=user_list[0]\n for item in user_list[1:]:\n pownum **= item\n\n return pownum\n\nwhile True:\n operation = input(\"Enter operation sign, please (*), (/), (+), (-), (^). \\nTo quit, please enter 'done' > \")\n if operation.lower() == \"done\": # Ключевым словом выхода из цикла будет done\n print(\"Thank you for using the program!\")\n break\n else:\n try:\n numbers_list = input(\"Enter the numbers separated by space > \").split(\" \")\n if len(numbers_list) < 2:\n raise IndexError(\"You have entered less than 2 numbers\")\n else:\n numbers_list = list_convertion(numbers_list) # Конвертация списка из str в float\n try:\n if \"*\" in operation: # Защита от ввода типа \"*\" или (*), т.е. проверяем есть ли во всей строке операция\n print(f\"Your multiplication result is {multiplication(numbers_list)}\")\n elif \"^\" in operation:\n print(f\"Your putting into power result is {powering(numbers_list)}\")\n elif \"-\" in operation:\n print(f\"Your subtraction result is {subtraction(numbers_list)}\")\n elif \"+\" in operation:\n print(f\"Your sum result is {adding(numbers_list)}\")\n elif \"/\" in operation:\n print(f\"Your division result is {division(numbers_list)}\")\n else:\n raise ValueError(\"Unsupported operation, please try again\")\n except (ValueError, ZeroDivisionError) as e:\n print(f\"We have an issue. {e}\")\n except Exception as e:\n print(f\"We have an issue. {e}\")\n"},"steps":{"kind":"list like","value":["# Задание 1\n# Выучите основные стандартные исключения, которые перечислены в данном уроке.\n# Задание 2\n# Напишите программу-калькулятор, которая поддерживает следующие операции: сложение, вычитание,\n# умножение, деление и возведение в степень. Программа должна выдавать сообщения об ошибке и\n# продолжать работу при вводе некорректных данных, делении на ноль и возведении нуля в\n# отрицательную степень.\n\n\n# Функция преобразования текстового списка в float\ndef list_convertion(user_list):\n for index, item in enumerate(user_list):\n user_list[index]=float(item)\n\n return user_list\n\n# Умножение\ndef multiplication(user_list):\n multnum=1\n for item in user_list:\n multnum *= item\n\n return multnum\n\n# Деление. Здесь долго мучился, т.к. нужно первое число оставть и делить его на второе, т.е цикл со второго индекса\n# пока не нашел в Интернете запись через slice напр.: for i in collection[1:]\ndef division(user_list):\n divnum=user_list[0]\n for item in user_list[1:]:\n divnum /= item\n\n return divnum\n\n# Сложение\ndef adding(user_list):\n sumnum=0\n for item in user_list:\n sumnum += item\n\n return sumnum\n\n# Вычитание. Логика та же, что и в делении\ndef subtraction(user_list):\n subtractnum=user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n\n return subtractnum\n\n# Возведение в степень. Логика та же, что в делении\ndef powering(user_list):\n pownum=user_list[0]\n for item in user_list[1:]:\n pownum **= item\n\n return pownum\n\nwhile True:\n operation = input(\"Enter operation sign, please (*), (/), (+), (-), (^). \\nTo quit, please enter 'done' > \")\n if operation.lower() == \"done\": # Ключевым словом выхода из цикла будет done\n print(\"Thank you for using the program!\")\n break\n else:\n try:\n numbers_list = input(\"Enter the numbers separated by space > \").split(\" \")\n if len(numbers_list) < 2:\n raise IndexError(\"You have entered less than 2 numbers\")\n else:\n numbers_list = list_convertion(numbers_list) # Конвертация списка из str в float\n try:\n if \"*\" in operation: # Защита от ввода типа \"*\" или (*), т.е. проверяем есть ли во всей строке операция\n print(f\"Your multiplication result is {multiplication(numbers_list)}\")\n elif \"^\" in operation:\n print(f\"Your putting into power result is {powering(numbers_list)}\")\n elif \"-\" in operation:\n print(f\"Your subtraction result is {subtraction(numbers_list)}\")\n elif \"+\" in operation:\n print(f\"Your sum result is {adding(numbers_list)}\")\n elif \"/\" in operation:\n print(f\"Your division result is {division(numbers_list)}\")\n else:\n raise ValueError(\"Unsupported operation, please try again\")\n except (ValueError, ZeroDivisionError) as e:\n print(f\"We have an issue. {e}\")\n except Exception as e:\n print(f\"We have an issue. {e}\")\n","def list_convertion(user_list):\n for index, item in enumerate(user_list):\n user_list[index] = float(item)\n return user_list\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\ndef division(user_list):\n divnum = user_list[0]\n for item in user_list[1:]:\n divnum /= item\n return divnum\n\n\ndef adding(user_list):\n sumnum = 0\n for item in user_list:\n sumnum += item\n return sumnum\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n return subtractnum\n\n\ndef powering(user_list):\n pownum = user_list[0]\n for item in user_list[1:]:\n pownum **= item\n return pownum\n\n\nwhile True:\n operation = input(\n \"\"\"Enter operation sign, please (*), (/), (+), (-), (^). \nTo quit, please enter 'done' > \"\"\"\n )\n if operation.lower() == 'done':\n print('Thank you for using the program!')\n break\n else:\n try:\n numbers_list = input('Enter the numbers separated by space > '\n ).split(' ')\n if len(numbers_list) < 2:\n raise IndexError('You have entered less than 2 numbers')\n else:\n numbers_list = list_convertion(numbers_list)\n try:\n if '*' in operation:\n print(\n f'Your multiplication result is {multiplication(numbers_list)}'\n )\n elif '^' in operation:\n print(\n f'Your putting into power result is {powering(numbers_list)}'\n )\n elif '-' in operation:\n print(\n f'Your subtraction result is {subtraction(numbers_list)}'\n )\n elif '+' in operation:\n print(f'Your sum result is {adding(numbers_list)}')\n elif '/' in operation:\n print(\n f'Your division result is {division(numbers_list)}'\n )\n else:\n raise ValueError(\n 'Unsupported operation, please try again')\n except (ValueError, ZeroDivisionError) as e:\n print(f'We have an issue. {e}')\n except Exception as e:\n print(f'We have an issue. {e}')\n","def list_convertion(user_list):\n for index, item in enumerate(user_list):\n user_list[index] = float(item)\n return user_list\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\ndef division(user_list):\n divnum = user_list[0]\n for item in user_list[1:]:\n divnum /= item\n return divnum\n\n\ndef adding(user_list):\n sumnum = 0\n for item in user_list:\n sumnum += item\n return sumnum\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n return subtractnum\n\n\ndef powering(user_list):\n pownum = user_list[0]\n for item in user_list[1:]:\n pownum **= item\n return pownum\n\n\n<code token>\n","def list_convertion(user_list):\n for index, item in enumerate(user_list):\n user_list[index] = float(item)\n return user_list\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\n<function token>\n\n\ndef adding(user_list):\n sumnum = 0\n for item in user_list:\n sumnum += item\n return sumnum\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n return subtractnum\n\n\ndef powering(user_list):\n pownum = user_list[0]\n for item in user_list[1:]:\n pownum **= item\n return pownum\n\n\n<code token>\n","def list_convertion(user_list):\n for index, item in enumerate(user_list):\n user_list[index] = float(item)\n return user_list\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\n<function token>\n<function token>\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n return subtractnum\n\n\ndef powering(user_list):\n pownum = user_list[0]\n for item in user_list[1:]:\n pownum **= item\n return pownum\n\n\n<code token>\n","<function token>\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\n<function token>\n<function token>\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n return subtractnum\n\n\ndef powering(user_list):\n pownum = user_list[0]\n for item in user_list[1:]:\n pownum **= item\n return pownum\n\n\n<code token>\n","<function token>\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\n<function token>\n<function token>\n\n\ndef subtraction(user_list):\n subtractnum = user_list[0]\n for item in user_list[1:]:\n subtractnum -= item\n return subtractnum\n\n\n<function token>\n<code token>\n","<function token>\n\n\ndef multiplication(user_list):\n multnum = 1\n for item in user_list:\n multnum *= item\n return multnum\n\n\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n","<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"],"string":"[\n \"# Задание 1\\n# Выучите основные стандартные исключения, которые перечислены в данном уроке.\\n# Задание 2\\n# Напишите программу-калькулятор, которая поддерживает следующие операции: сложение, вычитание,\\n# умножение, деление и возведение в степень. Программа должна выдавать сообщения об ошибке и\\n# продолжать работу при вводе некорректных данных, делении на ноль и возведении нуля в\\n# отрицательную степень.\\n\\n\\n# Функция преобразования текстового списка в float\\ndef list_convertion(user_list):\\n for index, item in enumerate(user_list):\\n user_list[index]=float(item)\\n\\n return user_list\\n\\n# Умножение\\ndef multiplication(user_list):\\n multnum=1\\n for item in user_list:\\n multnum *= item\\n\\n return multnum\\n\\n# Деление. Здесь долго мучился, т.к. нужно первое число оставть и делить его на второе, т.е цикл со второго индекса\\n# пока не нашел в Интернете запись через slice напр.: for i in collection[1:]\\ndef division(user_list):\\n divnum=user_list[0]\\n for item in user_list[1:]:\\n divnum /= item\\n\\n return divnum\\n\\n# Сложение\\ndef adding(user_list):\\n sumnum=0\\n for item in user_list:\\n sumnum += item\\n\\n return sumnum\\n\\n# Вычитание. Логика та же, что и в делении\\ndef subtraction(user_list):\\n subtractnum=user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n\\n return subtractnum\\n\\n# Возведение в степень. Логика та же, что в делении\\ndef powering(user_list):\\n pownum=user_list[0]\\n for item in user_list[1:]:\\n pownum **= item\\n\\n return pownum\\n\\nwhile True:\\n operation = input(\\\"Enter operation sign, please (*), (/), (+), (-), (^). \\\\nTo quit, please enter 'done' > \\\")\\n if operation.lower() == \\\"done\\\": # Ключевым словом выхода из цикла будет done\\n print(\\\"Thank you for using the program!\\\")\\n break\\n else:\\n try:\\n numbers_list = input(\\\"Enter the numbers separated by space > \\\").split(\\\" \\\")\\n if len(numbers_list) < 2:\\n raise IndexError(\\\"You have entered less than 2 numbers\\\")\\n else:\\n numbers_list = list_convertion(numbers_list) # Конвертация списка из str в float\\n try:\\n if \\\"*\\\" in operation: # Защита от ввода типа \\\"*\\\" или (*), т.е. проверяем есть ли во всей строке операция\\n print(f\\\"Your multiplication result is {multiplication(numbers_list)}\\\")\\n elif \\\"^\\\" in operation:\\n print(f\\\"Your putting into power result is {powering(numbers_list)}\\\")\\n elif \\\"-\\\" in operation:\\n print(f\\\"Your subtraction result is {subtraction(numbers_list)}\\\")\\n elif \\\"+\\\" in operation:\\n print(f\\\"Your sum result is {adding(numbers_list)}\\\")\\n elif \\\"/\\\" in operation:\\n print(f\\\"Your division result is {division(numbers_list)}\\\")\\n else:\\n raise ValueError(\\\"Unsupported operation, please try again\\\")\\n except (ValueError, ZeroDivisionError) as e:\\n print(f\\\"We have an issue. {e}\\\")\\n except Exception as e:\\n print(f\\\"We have an issue. {e}\\\")\\n\",\n \"def list_convertion(user_list):\\n for index, item in enumerate(user_list):\\n user_list[index] = float(item)\\n return user_list\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\ndef division(user_list):\\n divnum = user_list[0]\\n for item in user_list[1:]:\\n divnum /= item\\n return divnum\\n\\n\\ndef adding(user_list):\\n sumnum = 0\\n for item in user_list:\\n sumnum += item\\n return sumnum\\n\\n\\ndef subtraction(user_list):\\n subtractnum = user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n return subtractnum\\n\\n\\ndef powering(user_list):\\n pownum = user_list[0]\\n for item in user_list[1:]:\\n pownum **= item\\n return pownum\\n\\n\\nwhile True:\\n operation = input(\\n \\\"\\\"\\\"Enter operation sign, please (*), (/), (+), (-), (^). \\nTo quit, please enter 'done' > \\\"\\\"\\\"\\n )\\n if operation.lower() == 'done':\\n print('Thank you for using the program!')\\n break\\n else:\\n try:\\n numbers_list = input('Enter the numbers separated by space > '\\n ).split(' ')\\n if len(numbers_list) < 2:\\n raise IndexError('You have entered less than 2 numbers')\\n else:\\n numbers_list = list_convertion(numbers_list)\\n try:\\n if '*' in operation:\\n print(\\n f'Your multiplication result is {multiplication(numbers_list)}'\\n )\\n elif '^' in operation:\\n print(\\n f'Your putting into power result is {powering(numbers_list)}'\\n )\\n elif '-' in operation:\\n print(\\n f'Your subtraction result is {subtraction(numbers_list)}'\\n )\\n elif '+' in operation:\\n print(f'Your sum result is {adding(numbers_list)}')\\n elif '/' in operation:\\n print(\\n f'Your division result is {division(numbers_list)}'\\n )\\n else:\\n raise ValueError(\\n 'Unsupported operation, please try again')\\n except (ValueError, ZeroDivisionError) as e:\\n print(f'We have an issue. {e}')\\n except Exception as e:\\n print(f'We have an issue. {e}')\\n\",\n \"def list_convertion(user_list):\\n for index, item in enumerate(user_list):\\n user_list[index] = float(item)\\n return user_list\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\ndef division(user_list):\\n divnum = user_list[0]\\n for item in user_list[1:]:\\n divnum /= item\\n return divnum\\n\\n\\ndef adding(user_list):\\n sumnum = 0\\n for item in user_list:\\n sumnum += item\\n return sumnum\\n\\n\\ndef subtraction(user_list):\\n subtractnum = user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n return subtractnum\\n\\n\\ndef powering(user_list):\\n pownum = user_list[0]\\n for item in user_list[1:]:\\n pownum **= item\\n return pownum\\n\\n\\n<code token>\\n\",\n \"def list_convertion(user_list):\\n for index, item in enumerate(user_list):\\n user_list[index] = float(item)\\n return user_list\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\n<function token>\\n\\n\\ndef adding(user_list):\\n sumnum = 0\\n for item in user_list:\\n sumnum += item\\n return sumnum\\n\\n\\ndef subtraction(user_list):\\n subtractnum = user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n return subtractnum\\n\\n\\ndef powering(user_list):\\n pownum = user_list[0]\\n for item in user_list[1:]:\\n pownum **= item\\n return pownum\\n\\n\\n<code token>\\n\",\n \"def list_convertion(user_list):\\n for index, item in enumerate(user_list):\\n user_list[index] = float(item)\\n return user_list\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef subtraction(user_list):\\n subtractnum = user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n return subtractnum\\n\\n\\ndef powering(user_list):\\n pownum = user_list[0]\\n for item in user_list[1:]:\\n pownum **= item\\n return pownum\\n\\n\\n<code token>\\n\",\n \"<function token>\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef subtraction(user_list):\\n subtractnum = user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n return subtractnum\\n\\n\\ndef powering(user_list):\\n pownum = user_list[0]\\n for item in user_list[1:]:\\n pownum **= item\\n return pownum\\n\\n\\n<code token>\\n\",\n \"<function token>\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\n<function token>\\n<function token>\\n\\n\\ndef subtraction(user_list):\\n subtractnum = user_list[0]\\n for item in user_list[1:]:\\n subtractnum -= item\\n return subtractnum\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<function token>\\n\\n\\ndef multiplication(user_list):\\n multnum = 1\\n for item in user_list:\\n multnum *= item\\n return multnum\\n\\n\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\",\n \"<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5877,"cells":{"index":{"kind":"number","value":5877,"string":"5,877"},"blob_id":{"kind":"string","value":"26fb607623fda333c37e254470ca6d07708671a8"},"code":{"kind":"string","value":"from app.request import send_tor_signal\r\nfrom app.utils.session_utils import generate_user_keys\r\nfrom app.utils.gen_ddg_bangs import gen_bangs_json\r\nfrom flask import Flask\r\nfrom flask_session import Session\r\nimport json\r\nimport os\r\nfrom stem import Signal\r\n\r\napp = Flask(__name__, static_folder=os.path.dirname(\r\n os.path.abspath(__file__)) + '/static')\r\napp.user_elements = {}\r\napp.default_key_set = generate_user_keys()\r\napp.no_cookie_ips = []\r\napp.config['SECRET_KEY'] = os.urandom(32)\r\napp.config['SESSION_TYPE'] = 'filesystem'\r\napp.config['VERSION_NUMBER'] = '0.3.1'\r\napp.config['APP_ROOT'] = os.getenv(\r\n 'APP_ROOT',\r\n os.path.dirname(os.path.abspath(__file__)))\r\napp.config['LANGUAGES'] = json.load(open(\r\n os.path.join(app.config['APP_ROOT'], 'misc/languages.json')))\r\napp.config['COUNTRIES'] = json.load(open(\r\n os.path.join(app.config['APP_ROOT'], 'misc/countries.json')))\r\napp.config['STATIC_FOLDER'] = os.getenv(\r\n 'STATIC_FOLDER',\r\n os.path.join(app.config['APP_ROOT'], 'static'))\r\napp.config['CONFIG_PATH'] = os.getenv(\r\n 'CONFIG_VOLUME',\r\n os.path.join(app.config['STATIC_FOLDER'], 'config'))\r\napp.config['DEFAULT_CONFIG'] = os.path.join(\r\n app.config['CONFIG_PATH'],\r\n 'config.json')\r\napp.config['SESSION_FILE_DIR'] = os.path.join(\r\n app.config['CONFIG_PATH'],\r\n 'session')\r\napp.config['BANG_PATH'] = os.getenv(\r\n 'CONFIG_VOLUME',\r\n os.path.join(app.config['STATIC_FOLDER'], 'bangs'))\r\napp.config['BANG_FILE'] = os.path.join(\r\n app.config['BANG_PATH'],\r\n 'bangs.json')\r\n\r\nif not os.path.exists(app.config['CONFIG_PATH']):\r\n os.makedirs(app.config['CONFIG_PATH'])\r\n\r\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\r\n os.makedirs(app.config['SESSION_FILE_DIR'])\r\n\r\n# Generate DDG bang filter, and create path if it doesn't exist yet\r\nif not os.path.exists(app.config['BANG_PATH']):\r\n os.makedirs(app.config['BANG_PATH'])\r\nif not os.path.exists(app.config['BANG_FILE']):\r\n gen_bangs_json(app.config['BANG_FILE'])\r\n\r\nSession(app)\r\n\r\n# Attempt to acquire tor identity, to determine if Tor config is available\r\nsend_tor_signal(Signal.HEARTBEAT)\r\n\r\nfrom app import routes # noqa\r\n"},"steps":{"kind":"list like","value":["from app.request import send_tor_signal\r\nfrom app.utils.session_utils import generate_user_keys\r\nfrom app.utils.gen_ddg_bangs import gen_bangs_json\r\nfrom flask import Flask\r\nfrom flask_session import Session\r\nimport json\r\nimport os\r\nfrom stem import Signal\r\n\r\napp = Flask(__name__, static_folder=os.path.dirname(\r\n os.path.abspath(__file__)) + '/static')\r\napp.user_elements = {}\r\napp.default_key_set = generate_user_keys()\r\napp.no_cookie_ips = []\r\napp.config['SECRET_KEY'] = os.urandom(32)\r\napp.config['SESSION_TYPE'] = 'filesystem'\r\napp.config['VERSION_NUMBER'] = '0.3.1'\r\napp.config['APP_ROOT'] = os.getenv(\r\n 'APP_ROOT',\r\n os.path.dirname(os.path.abspath(__file__)))\r\napp.config['LANGUAGES'] = json.load(open(\r\n os.path.join(app.config['APP_ROOT'], 'misc/languages.json')))\r\napp.config['COUNTRIES'] = json.load(open(\r\n os.path.join(app.config['APP_ROOT'], 'misc/countries.json')))\r\napp.config['STATIC_FOLDER'] = os.getenv(\r\n 'STATIC_FOLDER',\r\n os.path.join(app.config['APP_ROOT'], 'static'))\r\napp.config['CONFIG_PATH'] = os.getenv(\r\n 'CONFIG_VOLUME',\r\n os.path.join(app.config['STATIC_FOLDER'], 'config'))\r\napp.config['DEFAULT_CONFIG'] = os.path.join(\r\n app.config['CONFIG_PATH'],\r\n 'config.json')\r\napp.config['SESSION_FILE_DIR'] = os.path.join(\r\n app.config['CONFIG_PATH'],\r\n 'session')\r\napp.config['BANG_PATH'] = os.getenv(\r\n 'CONFIG_VOLUME',\r\n os.path.join(app.config['STATIC_FOLDER'], 'bangs'))\r\napp.config['BANG_FILE'] = os.path.join(\r\n app.config['BANG_PATH'],\r\n 'bangs.json')\r\n\r\nif not os.path.exists(app.config['CONFIG_PATH']):\r\n os.makedirs(app.config['CONFIG_PATH'])\r\n\r\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\r\n os.makedirs(app.config['SESSION_FILE_DIR'])\r\n\r\n# Generate DDG bang filter, and create path if it doesn't exist yet\r\nif not os.path.exists(app.config['BANG_PATH']):\r\n os.makedirs(app.config['BANG_PATH'])\r\nif not os.path.exists(app.config['BANG_FILE']):\r\n gen_bangs_json(app.config['BANG_FILE'])\r\n\r\nSession(app)\r\n\r\n# Attempt to acquire tor identity, to determine if Tor config is available\r\nsend_tor_signal(Signal.HEARTBEAT)\r\n\r\nfrom app import routes # noqa\r\n","from app.request import send_tor_signal\nfrom app.utils.session_utils import generate_user_keys\nfrom app.utils.gen_ddg_bangs import gen_bangs_json\nfrom flask import Flask\nfrom flask_session import Session\nimport json\nimport os\nfrom stem import Signal\napp = Flask(__name__, static_folder=os.path.dirname(os.path.abspath(\n __file__)) + '/static')\napp.user_elements = {}\napp.default_key_set = generate_user_keys()\napp.no_cookie_ips = []\napp.config['SECRET_KEY'] = os.urandom(32)\napp.config['SESSION_TYPE'] = 'filesystem'\napp.config['VERSION_NUMBER'] = '0.3.1'\napp.config['APP_ROOT'] = os.getenv('APP_ROOT', os.path.dirname(os.path.\n abspath(__file__)))\napp.config['LANGUAGES'] = json.load(open(os.path.join(app.config['APP_ROOT'\n ], 'misc/languages.json')))\napp.config['COUNTRIES'] = json.load(open(os.path.join(app.config['APP_ROOT'\n ], 'misc/countries.json')))\napp.config['STATIC_FOLDER'] = os.getenv('STATIC_FOLDER', os.path.join(app.\n config['APP_ROOT'], 'static'))\napp.config['CONFIG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\n config['STATIC_FOLDER'], 'config'))\napp.config['DEFAULT_CONFIG'] = os.path.join(app.config['CONFIG_PATH'],\n 'config.json')\napp.config['SESSION_FILE_DIR'] = os.path.join(app.config['CONFIG_PATH'],\n 'session')\napp.config['BANG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\n config['STATIC_FOLDER'], 'bangs'))\napp.config['BANG_FILE'] = os.path.join(app.config['BANG_PATH'], 'bangs.json')\nif not os.path.exists(app.config['CONFIG_PATH']):\n os.makedirs(app.config['CONFIG_PATH'])\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\n os.makedirs(app.config['SESSION_FILE_DIR'])\nif not os.path.exists(app.config['BANG_PATH']):\n os.makedirs(app.config['BANG_PATH'])\nif not os.path.exists(app.config['BANG_FILE']):\n gen_bangs_json(app.config['BANG_FILE'])\nSession(app)\nsend_tor_signal(Signal.HEARTBEAT)\nfrom app import routes\n","<import token>\napp = Flask(__name__, static_folder=os.path.dirname(os.path.abspath(\n __file__)) + '/static')\napp.user_elements = {}\napp.default_key_set = generate_user_keys()\napp.no_cookie_ips = []\napp.config['SECRET_KEY'] = os.urandom(32)\napp.config['SESSION_TYPE'] = 'filesystem'\napp.config['VERSION_NUMBER'] = '0.3.1'\napp.config['APP_ROOT'] = os.getenv('APP_ROOT', os.path.dirname(os.path.\n abspath(__file__)))\napp.config['LANGUAGES'] = json.load(open(os.path.join(app.config['APP_ROOT'\n ], 'misc/languages.json')))\napp.config['COUNTRIES'] = json.load(open(os.path.join(app.config['APP_ROOT'\n ], 'misc/countries.json')))\napp.config['STATIC_FOLDER'] = os.getenv('STATIC_FOLDER', os.path.join(app.\n config['APP_ROOT'], 'static'))\napp.config['CONFIG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\n config['STATIC_FOLDER'], 'config'))\napp.config['DEFAULT_CONFIG'] = os.path.join(app.config['CONFIG_PATH'],\n 'config.json')\napp.config['SESSION_FILE_DIR'] = os.path.join(app.config['CONFIG_PATH'],\n 'session')\napp.config['BANG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\n config['STATIC_FOLDER'], 'bangs'))\napp.config['BANG_FILE'] = os.path.join(app.config['BANG_PATH'], 'bangs.json')\nif not os.path.exists(app.config['CONFIG_PATH']):\n os.makedirs(app.config['CONFIG_PATH'])\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\n os.makedirs(app.config['SESSION_FILE_DIR'])\nif not os.path.exists(app.config['BANG_PATH']):\n os.makedirs(app.config['BANG_PATH'])\nif not os.path.exists(app.config['BANG_FILE']):\n gen_bangs_json(app.config['BANG_FILE'])\nSession(app)\nsend_tor_signal(Signal.HEARTBEAT)\n<import token>\n","<import token>\n<assignment token>\nif not os.path.exists(app.config['CONFIG_PATH']):\n os.makedirs(app.config['CONFIG_PATH'])\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\n os.makedirs(app.config['SESSION_FILE_DIR'])\nif not os.path.exists(app.config['BANG_PATH']):\n os.makedirs(app.config['BANG_PATH'])\nif not os.path.exists(app.config['BANG_FILE']):\n gen_bangs_json(app.config['BANG_FILE'])\nSession(app)\nsend_tor_signal(Signal.HEARTBEAT)\n<import token>\n","<import token>\n<assignment token>\n<code token>\n<import token>\n"],"string":"[\n \"from app.request import send_tor_signal\\r\\nfrom app.utils.session_utils import generate_user_keys\\r\\nfrom app.utils.gen_ddg_bangs import gen_bangs_json\\r\\nfrom flask import Flask\\r\\nfrom flask_session import Session\\r\\nimport json\\r\\nimport os\\r\\nfrom stem import Signal\\r\\n\\r\\napp = Flask(__name__, static_folder=os.path.dirname(\\r\\n os.path.abspath(__file__)) + '/static')\\r\\napp.user_elements = {}\\r\\napp.default_key_set = generate_user_keys()\\r\\napp.no_cookie_ips = []\\r\\napp.config['SECRET_KEY'] = os.urandom(32)\\r\\napp.config['SESSION_TYPE'] = 'filesystem'\\r\\napp.config['VERSION_NUMBER'] = '0.3.1'\\r\\napp.config['APP_ROOT'] = os.getenv(\\r\\n 'APP_ROOT',\\r\\n os.path.dirname(os.path.abspath(__file__)))\\r\\napp.config['LANGUAGES'] = json.load(open(\\r\\n os.path.join(app.config['APP_ROOT'], 'misc/languages.json')))\\r\\napp.config['COUNTRIES'] = json.load(open(\\r\\n os.path.join(app.config['APP_ROOT'], 'misc/countries.json')))\\r\\napp.config['STATIC_FOLDER'] = os.getenv(\\r\\n 'STATIC_FOLDER',\\r\\n os.path.join(app.config['APP_ROOT'], 'static'))\\r\\napp.config['CONFIG_PATH'] = os.getenv(\\r\\n 'CONFIG_VOLUME',\\r\\n os.path.join(app.config['STATIC_FOLDER'], 'config'))\\r\\napp.config['DEFAULT_CONFIG'] = os.path.join(\\r\\n app.config['CONFIG_PATH'],\\r\\n 'config.json')\\r\\napp.config['SESSION_FILE_DIR'] = os.path.join(\\r\\n app.config['CONFIG_PATH'],\\r\\n 'session')\\r\\napp.config['BANG_PATH'] = os.getenv(\\r\\n 'CONFIG_VOLUME',\\r\\n os.path.join(app.config['STATIC_FOLDER'], 'bangs'))\\r\\napp.config['BANG_FILE'] = os.path.join(\\r\\n app.config['BANG_PATH'],\\r\\n 'bangs.json')\\r\\n\\r\\nif not os.path.exists(app.config['CONFIG_PATH']):\\r\\n os.makedirs(app.config['CONFIG_PATH'])\\r\\n\\r\\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\\r\\n os.makedirs(app.config['SESSION_FILE_DIR'])\\r\\n\\r\\n# Generate DDG bang filter, and create path if it doesn't exist yet\\r\\nif not os.path.exists(app.config['BANG_PATH']):\\r\\n os.makedirs(app.config['BANG_PATH'])\\r\\nif not os.path.exists(app.config['BANG_FILE']):\\r\\n gen_bangs_json(app.config['BANG_FILE'])\\r\\n\\r\\nSession(app)\\r\\n\\r\\n# Attempt to acquire tor identity, to determine if Tor config is available\\r\\nsend_tor_signal(Signal.HEARTBEAT)\\r\\n\\r\\nfrom app import routes # noqa\\r\\n\",\n \"from app.request import send_tor_signal\\nfrom app.utils.session_utils import generate_user_keys\\nfrom app.utils.gen_ddg_bangs import gen_bangs_json\\nfrom flask import Flask\\nfrom flask_session import Session\\nimport json\\nimport os\\nfrom stem import Signal\\napp = Flask(__name__, static_folder=os.path.dirname(os.path.abspath(\\n __file__)) + '/static')\\napp.user_elements = {}\\napp.default_key_set = generate_user_keys()\\napp.no_cookie_ips = []\\napp.config['SECRET_KEY'] = os.urandom(32)\\napp.config['SESSION_TYPE'] = 'filesystem'\\napp.config['VERSION_NUMBER'] = '0.3.1'\\napp.config['APP_ROOT'] = os.getenv('APP_ROOT', os.path.dirname(os.path.\\n abspath(__file__)))\\napp.config['LANGUAGES'] = json.load(open(os.path.join(app.config['APP_ROOT'\\n ], 'misc/languages.json')))\\napp.config['COUNTRIES'] = json.load(open(os.path.join(app.config['APP_ROOT'\\n ], 'misc/countries.json')))\\napp.config['STATIC_FOLDER'] = os.getenv('STATIC_FOLDER', os.path.join(app.\\n config['APP_ROOT'], 'static'))\\napp.config['CONFIG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\\n config['STATIC_FOLDER'], 'config'))\\napp.config['DEFAULT_CONFIG'] = os.path.join(app.config['CONFIG_PATH'],\\n 'config.json')\\napp.config['SESSION_FILE_DIR'] = os.path.join(app.config['CONFIG_PATH'],\\n 'session')\\napp.config['BANG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\\n config['STATIC_FOLDER'], 'bangs'))\\napp.config['BANG_FILE'] = os.path.join(app.config['BANG_PATH'], 'bangs.json')\\nif not os.path.exists(app.config['CONFIG_PATH']):\\n os.makedirs(app.config['CONFIG_PATH'])\\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\\n os.makedirs(app.config['SESSION_FILE_DIR'])\\nif not os.path.exists(app.config['BANG_PATH']):\\n os.makedirs(app.config['BANG_PATH'])\\nif not os.path.exists(app.config['BANG_FILE']):\\n gen_bangs_json(app.config['BANG_FILE'])\\nSession(app)\\nsend_tor_signal(Signal.HEARTBEAT)\\nfrom app import routes\\n\",\n \"<import token>\\napp = Flask(__name__, static_folder=os.path.dirname(os.path.abspath(\\n __file__)) + '/static')\\napp.user_elements = {}\\napp.default_key_set = generate_user_keys()\\napp.no_cookie_ips = []\\napp.config['SECRET_KEY'] = os.urandom(32)\\napp.config['SESSION_TYPE'] = 'filesystem'\\napp.config['VERSION_NUMBER'] = '0.3.1'\\napp.config['APP_ROOT'] = os.getenv('APP_ROOT', os.path.dirname(os.path.\\n abspath(__file__)))\\napp.config['LANGUAGES'] = json.load(open(os.path.join(app.config['APP_ROOT'\\n ], 'misc/languages.json')))\\napp.config['COUNTRIES'] = json.load(open(os.path.join(app.config['APP_ROOT'\\n ], 'misc/countries.json')))\\napp.config['STATIC_FOLDER'] = os.getenv('STATIC_FOLDER', os.path.join(app.\\n config['APP_ROOT'], 'static'))\\napp.config['CONFIG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\\n config['STATIC_FOLDER'], 'config'))\\napp.config['DEFAULT_CONFIG'] = os.path.join(app.config['CONFIG_PATH'],\\n 'config.json')\\napp.config['SESSION_FILE_DIR'] = os.path.join(app.config['CONFIG_PATH'],\\n 'session')\\napp.config['BANG_PATH'] = os.getenv('CONFIG_VOLUME', os.path.join(app.\\n config['STATIC_FOLDER'], 'bangs'))\\napp.config['BANG_FILE'] = os.path.join(app.config['BANG_PATH'], 'bangs.json')\\nif not os.path.exists(app.config['CONFIG_PATH']):\\n os.makedirs(app.config['CONFIG_PATH'])\\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\\n os.makedirs(app.config['SESSION_FILE_DIR'])\\nif not os.path.exists(app.config['BANG_PATH']):\\n os.makedirs(app.config['BANG_PATH'])\\nif not os.path.exists(app.config['BANG_FILE']):\\n gen_bangs_json(app.config['BANG_FILE'])\\nSession(app)\\nsend_tor_signal(Signal.HEARTBEAT)\\n<import token>\\n\",\n \"<import token>\\n<assignment token>\\nif not os.path.exists(app.config['CONFIG_PATH']):\\n os.makedirs(app.config['CONFIG_PATH'])\\nif not os.path.exists(app.config['SESSION_FILE_DIR']):\\n os.makedirs(app.config['SESSION_FILE_DIR'])\\nif not os.path.exists(app.config['BANG_PATH']):\\n os.makedirs(app.config['BANG_PATH'])\\nif not os.path.exists(app.config['BANG_FILE']):\\n gen_bangs_json(app.config['BANG_FILE'])\\nSession(app)\\nsend_tor_signal(Signal.HEARTBEAT)\\n<import token>\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n<import token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5878,"cells":{"index":{"kind":"number","value":5878,"string":"5,878"},"blob_id":{"kind":"string","value":"d429f03c0f0c241166d6c0a5a45dc1101bcaec16"},"code":{"kind":"string","value":"#!/usr/bin/env python3\n\nimport matplotlib\nfrom matplotlib.colors import to_hex\nfrom matplotlib import cm\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools as it\nfrom pathlib import Path\nimport subprocess\nfrom tqdm import tqdm\n\nfrom koala import plotting as pl\nfrom koala import phase_diagrams as pd\nfrom koala import pointsets, voronization, flux_finder, graph_color\nfrom koala import example_graphs as eg\n\nimport functools\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a,b: a^b, [set(s) for s in sets]))\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].adjacent_plaquettes for p in plaquettes))\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\n# imports just for this plot\n\ncolumn_width = 3.375\nw = 3.375\nblack_line_widths = 1.5\n\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True, 'font.family': 'serif', 'font.serif': ['Computer Modern']})\nmatplotlib.rcParams.update({\"axes.linewidth\": black_line_widths})\n\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\n\nrng = np.random.default_rng(seed = 10)\nl, coloring, ujk = eg.make_amorphous(8, rng = rng)\n# l, coloring, ujk = eg.make_honeycomb(8)\n\nplaquettes = [40,]\nvertices = [78,]\n\nsubprocess.run([\"mkdir\", \"-p\", \"./animation\"])\n\nfor n in tqdm(range(15)):\n fig, axes = plt.subplots(nrows=1, ncols=2)\n fig.set_size_inches(2 * w, 2/2 * w)\n for a in axes: a.set(xticks = [], yticks = [])\n\n # pl.plot_vertex_indices(l, ax = ax)\n # pl.plot_edge_indices(l, ax = ax)\n # pl.plot_plaquette_indices(l, ax = ax)\n \n if n > 0:\n vertices = flood_iteration_vertices(l, vertices)\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\n \n ax = axes[0]\n \n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges[v] for v in vertices])\n \n if multi_edges: pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], subset = multi_edges)\n pl.plot_edges(l, ax = ax, color = 'k', subset = multi_edges)\n pl.plot_vertices(l, ax = ax, subset = list(vertices), s = 5)\n\n pl.plot_edges(l, ax = ax, alpha = 0.1)\n pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], alpha = 0.1)\n\n ax.set(xticks = [], yticks = [])\n \n ax = axes[1]\n\n plaquette_boolean = np.array([i in plaquettes for i in range(l.n_plaquettes)])\n\n fluxes = 1 - 2*plaquette_boolean\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\n\n pl.plot_edges(l, ax = ax, alpha = 0.1)\n pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], alpha = 0.1)\n \n pl.plot_edges(l, ax = ax, subset = (ujk == -1))\n if len(plaquettes) > 1: pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], subset = (ujk == -1), )\n pl.plot_plaquettes(l, subset = fluxes == -1, ax = ax, color_scheme = [\"orange\", \"white\"], alpha = 0.5);\n ax.set(xticks = [], yticks = [])\n \n fig.tight_layout()\n if n == 3: \n fig.savefig(f'./{Path.cwd().name}.svg', transparent = True)\n fig.savefig(f'./{Path.cwd().name}.pdf')\n fig.savefig(f\"animation/iteration_{n:03}.svg\")\n plt.close(fig)\n\nsubprocess.run([\"magick\", \"animation/*.svg\", f'./{Path.cwd().name}.gif'])\nsubprocess.run([\"convert\", \"-delay\", \"100\", f'./{Path.cwd().name}.gif', f'./{Path.cwd().name}.gif'])\nsubprocess.run([\"rm\", \"-r\", \"./animation\"])"},"steps":{"kind":"list like","value":["#!/usr/bin/env python3\n\nimport matplotlib\nfrom matplotlib.colors import to_hex\nfrom matplotlib import cm\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools as it\nfrom pathlib import Path\nimport subprocess\nfrom tqdm import tqdm\n\nfrom koala import plotting as pl\nfrom koala import phase_diagrams as pd\nfrom koala import pointsets, voronization, flux_finder, graph_color\nfrom koala import example_graphs as eg\n\nimport functools\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a,b: a^b, [set(s) for s in sets]))\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].adjacent_plaquettes for p in plaquettes))\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\n# imports just for this plot\n\ncolumn_width = 3.375\nw = 3.375\nblack_line_widths = 1.5\n\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True, 'font.family': 'serif', 'font.serif': ['Computer Modern']})\nmatplotlib.rcParams.update({\"axes.linewidth\": black_line_widths})\n\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\n\nrng = np.random.default_rng(seed = 10)\nl, coloring, ujk = eg.make_amorphous(8, rng = rng)\n# l, coloring, ujk = eg.make_honeycomb(8)\n\nplaquettes = [40,]\nvertices = [78,]\n\nsubprocess.run([\"mkdir\", \"-p\", \"./animation\"])\n\nfor n in tqdm(range(15)):\n fig, axes = plt.subplots(nrows=1, ncols=2)\n fig.set_size_inches(2 * w, 2/2 * w)\n for a in axes: a.set(xticks = [], yticks = [])\n\n # pl.plot_vertex_indices(l, ax = ax)\n # pl.plot_edge_indices(l, ax = ax)\n # pl.plot_plaquette_indices(l, ax = ax)\n \n if n > 0:\n vertices = flood_iteration_vertices(l, vertices)\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\n \n ax = axes[0]\n \n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges[v] for v in vertices])\n \n if multi_edges: pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], subset = multi_edges)\n pl.plot_edges(l, ax = ax, color = 'k', subset = multi_edges)\n pl.plot_vertices(l, ax = ax, subset = list(vertices), s = 5)\n\n pl.plot_edges(l, ax = ax, alpha = 0.1)\n pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], alpha = 0.1)\n\n ax.set(xticks = [], yticks = [])\n \n ax = axes[1]\n\n plaquette_boolean = np.array([i in plaquettes for i in range(l.n_plaquettes)])\n\n fluxes = 1 - 2*plaquette_boolean\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\n\n pl.plot_edges(l, ax = ax, alpha = 0.1)\n pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], alpha = 0.1)\n \n pl.plot_edges(l, ax = ax, subset = (ujk == -1))\n if len(plaquettes) > 1: pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], subset = (ujk == -1), )\n pl.plot_plaquettes(l, subset = fluxes == -1, ax = ax, color_scheme = [\"orange\", \"white\"], alpha = 0.5);\n ax.set(xticks = [], yticks = [])\n \n fig.tight_layout()\n if n == 3: \n fig.savefig(f'./{Path.cwd().name}.svg', transparent = True)\n fig.savefig(f'./{Path.cwd().name}.pdf')\n fig.savefig(f\"animation/iteration_{n:03}.svg\")\n plt.close(fig)\n\nsubprocess.run([\"magick\", \"animation/*.svg\", f'./{Path.cwd().name}.gif'])\nsubprocess.run([\"convert\", \"-delay\", \"100\", f'./{Path.cwd().name}.gif', f'./{Path.cwd().name}.gif'])\nsubprocess.run([\"rm\", \"-r\", \"./animation\"])","import matplotlib\nfrom matplotlib.colors import to_hex\nfrom matplotlib import cm\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools as it\nfrom pathlib import Path\nimport subprocess\nfrom tqdm import tqdm\nfrom koala import plotting as pl\nfrom koala import phase_diagrams as pd\nfrom koala import pointsets, voronization, flux_finder, graph_color\nfrom koala import example_graphs as eg\nimport functools\n\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\n adjacent_plaquettes for p in plaquettes))\n\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\ncolumn_width = 3.375\nw = 3.375\nblack_line_widths = 1.5\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True,\n 'font.family': 'serif', 'font.serif': ['Computer Modern']})\nmatplotlib.rcParams.update({'axes.linewidth': black_line_widths})\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\nrng = np.random.default_rng(seed=10)\nl, coloring, ujk = eg.make_amorphous(8, rng=rng)\nplaquettes = [40]\nvertices = [78]\nsubprocess.run(['mkdir', '-p', './animation'])\nfor n in tqdm(range(15)):\n fig, axes = plt.subplots(nrows=1, ncols=2)\n fig.set_size_inches(2 * w, 2 / 2 * w)\n for a in axes:\n a.set(xticks=[], yticks=[])\n if n > 0:\n vertices = flood_iteration_vertices(l, vertices)\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\n ax = axes[0]\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges\n [v] for v in vertices])\n if multi_edges:\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=multi_edges\n )\n pl.plot_edges(l, ax=ax, color='k', subset=multi_edges)\n pl.plot_vertices(l, ax=ax, subset=list(vertices), s=5)\n pl.plot_edges(l, ax=ax, alpha=0.1)\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\n ax.set(xticks=[], yticks=[])\n ax = axes[1]\n plaquette_boolean = np.array([(i in plaquettes) for i in range(l.\n n_plaquettes)])\n fluxes = 1 - 2 * plaquette_boolean\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\n pl.plot_edges(l, ax=ax, alpha=0.1)\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\n pl.plot_edges(l, ax=ax, subset=ujk == -1)\n if len(plaquettes) > 1:\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=ujk == -1)\n pl.plot_plaquettes(l, subset=fluxes == -1, ax=ax, color_scheme=[\n 'orange', 'white'], alpha=0.5)\n ax.set(xticks=[], yticks=[])\n fig.tight_layout()\n if n == 3:\n fig.savefig(f'./{Path.cwd().name}.svg', transparent=True)\n fig.savefig(f'./{Path.cwd().name}.pdf')\n fig.savefig(f'animation/iteration_{n:03}.svg')\n plt.close(fig)\nsubprocess.run(['magick', 'animation/*.svg', f'./{Path.cwd().name}.gif'])\nsubprocess.run(['convert', '-delay', '100', f'./{Path.cwd().name}.gif',\n f'./{Path.cwd().name}.gif'])\nsubprocess.run(['rm', '-r', './animation'])\n","<import token>\n\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\n adjacent_plaquettes for p in plaquettes))\n\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\ncolumn_width = 3.375\nw = 3.375\nblack_line_widths = 1.5\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True,\n 'font.family': 'serif', 'font.serif': ['Computer Modern']})\nmatplotlib.rcParams.update({'axes.linewidth': black_line_widths})\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\nrng = np.random.default_rng(seed=10)\nl, coloring, ujk = eg.make_amorphous(8, rng=rng)\nplaquettes = [40]\nvertices = [78]\nsubprocess.run(['mkdir', '-p', './animation'])\nfor n in tqdm(range(15)):\n fig, axes = plt.subplots(nrows=1, ncols=2)\n fig.set_size_inches(2 * w, 2 / 2 * w)\n for a in axes:\n a.set(xticks=[], yticks=[])\n if n > 0:\n vertices = flood_iteration_vertices(l, vertices)\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\n ax = axes[0]\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges\n [v] for v in vertices])\n if multi_edges:\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=multi_edges\n )\n pl.plot_edges(l, ax=ax, color='k', subset=multi_edges)\n pl.plot_vertices(l, ax=ax, subset=list(vertices), s=5)\n pl.plot_edges(l, ax=ax, alpha=0.1)\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\n ax.set(xticks=[], yticks=[])\n ax = axes[1]\n plaquette_boolean = np.array([(i in plaquettes) for i in range(l.\n n_plaquettes)])\n fluxes = 1 - 2 * plaquette_boolean\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\n pl.plot_edges(l, ax=ax, alpha=0.1)\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\n pl.plot_edges(l, ax=ax, subset=ujk == -1)\n if len(plaquettes) > 1:\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=ujk == -1)\n pl.plot_plaquettes(l, subset=fluxes == -1, ax=ax, color_scheme=[\n 'orange', 'white'], alpha=0.5)\n ax.set(xticks=[], yticks=[])\n fig.tight_layout()\n if n == 3:\n fig.savefig(f'./{Path.cwd().name}.svg', transparent=True)\n fig.savefig(f'./{Path.cwd().name}.pdf')\n fig.savefig(f'animation/iteration_{n:03}.svg')\n plt.close(fig)\nsubprocess.run(['magick', 'animation/*.svg', f'./{Path.cwd().name}.gif'])\nsubprocess.run(['convert', '-delay', '100', f'./{Path.cwd().name}.gif',\n f'./{Path.cwd().name}.gif'])\nsubprocess.run(['rm', '-r', './animation'])\n","<import token>\n\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\n adjacent_plaquettes for p in plaquettes))\n\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\n<assignment token>\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True,\n 'font.family': 'serif', 'font.serif': ['Computer Modern']})\nmatplotlib.rcParams.update({'axes.linewidth': black_line_widths})\n<assignment token>\nsubprocess.run(['mkdir', '-p', './animation'])\nfor n in tqdm(range(15)):\n fig, axes = plt.subplots(nrows=1, ncols=2)\n fig.set_size_inches(2 * w, 2 / 2 * w)\n for a in axes:\n a.set(xticks=[], yticks=[])\n if n > 0:\n vertices = flood_iteration_vertices(l, vertices)\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\n ax = axes[0]\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges\n [v] for v in vertices])\n if multi_edges:\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=multi_edges\n )\n pl.plot_edges(l, ax=ax, color='k', subset=multi_edges)\n pl.plot_vertices(l, ax=ax, subset=list(vertices), s=5)\n pl.plot_edges(l, ax=ax, alpha=0.1)\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\n ax.set(xticks=[], yticks=[])\n ax = axes[1]\n plaquette_boolean = np.array([(i in plaquettes) for i in range(l.\n n_plaquettes)])\n fluxes = 1 - 2 * plaquette_boolean\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\n pl.plot_edges(l, ax=ax, alpha=0.1)\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\n pl.plot_edges(l, ax=ax, subset=ujk == -1)\n if len(plaquettes) > 1:\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=ujk == -1)\n pl.plot_plaquettes(l, subset=fluxes == -1, ax=ax, color_scheme=[\n 'orange', 'white'], alpha=0.5)\n ax.set(xticks=[], yticks=[])\n fig.tight_layout()\n if n == 3:\n fig.savefig(f'./{Path.cwd().name}.svg', transparent=True)\n fig.savefig(f'./{Path.cwd().name}.pdf')\n fig.savefig(f'animation/iteration_{n:03}.svg')\n plt.close(fig)\nsubprocess.run(['magick', 'animation/*.svg', f'./{Path.cwd().name}.gif'])\nsubprocess.run(['convert', '-delay', '100', f'./{Path.cwd().name}.gif',\n f'./{Path.cwd().name}.gif'])\nsubprocess.run(['rm', '-r', './animation'])\n","<import token>\n\n\ndef multi_set_symmetric_difference(sets):\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\n adjacent_plaquettes for p in plaquettes))\n\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n","<import token>\n<function token>\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\n adjacent_plaquettes for p in plaquettes))\n\n\ndef flood_iteration_vertices(l, vertices):\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\n\n\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n","<import token>\n<function token>\n\n\ndef flood_iteration_plaquettes(l, plaquettes):\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\n adjacent_plaquettes for p in plaquettes))\n\n\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n","<import token>\n<function token>\n<function token>\n<function token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"],"string":"[\n \"#!/usr/bin/env python3\\n\\nimport matplotlib\\nfrom matplotlib.colors import to_hex\\nfrom matplotlib import cm\\n\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport itertools as it\\nfrom pathlib import Path\\nimport subprocess\\nfrom tqdm import tqdm\\n\\nfrom koala import plotting as pl\\nfrom koala import phase_diagrams as pd\\nfrom koala import pointsets, voronization, flux_finder, graph_color\\nfrom koala import example_graphs as eg\\n\\nimport functools\\n\\ndef multi_set_symmetric_difference(sets):\\n return list(functools.reduce(lambda a,b: a^b, [set(s) for s in sets]))\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].adjacent_plaquettes for p in plaquettes))\\n\\ndef flood_iteration_vertices(l, vertices):\\n return set(vertices) | set(it.chain.from_iterable(i for v in set(vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\\n\\n\\n# imports just for this plot\\n\\ncolumn_width = 3.375\\nw = 3.375\\nblack_line_widths = 1.5\\n\\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True, 'font.family': 'serif', 'font.serif': ['Computer Modern']})\\nmatplotlib.rcParams.update({\\\"axes.linewidth\\\": black_line_widths})\\n\\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\\n\\nrng = np.random.default_rng(seed = 10)\\nl, coloring, ujk = eg.make_amorphous(8, rng = rng)\\n# l, coloring, ujk = eg.make_honeycomb(8)\\n\\nplaquettes = [40,]\\nvertices = [78,]\\n\\nsubprocess.run([\\\"mkdir\\\", \\\"-p\\\", \\\"./animation\\\"])\\n\\nfor n in tqdm(range(15)):\\n fig, axes = plt.subplots(nrows=1, ncols=2)\\n fig.set_size_inches(2 * w, 2/2 * w)\\n for a in axes: a.set(xticks = [], yticks = [])\\n\\n # pl.plot_vertex_indices(l, ax = ax)\\n # pl.plot_edge_indices(l, ax = ax)\\n # pl.plot_plaquette_indices(l, ax = ax)\\n \\n if n > 0:\\n vertices = flood_iteration_vertices(l, vertices)\\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\\n \\n ax = axes[0]\\n \\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges[v] for v in vertices])\\n \\n if multi_edges: pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], subset = multi_edges)\\n pl.plot_edges(l, ax = ax, color = 'k', subset = multi_edges)\\n pl.plot_vertices(l, ax = ax, subset = list(vertices), s = 5)\\n\\n pl.plot_edges(l, ax = ax, alpha = 0.1)\\n pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], alpha = 0.1)\\n\\n ax.set(xticks = [], yticks = [])\\n \\n ax = axes[1]\\n\\n plaquette_boolean = np.array([i in plaquettes for i in range(l.n_plaquettes)])\\n\\n fluxes = 1 - 2*plaquette_boolean\\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\\n\\n pl.plot_edges(l, ax = ax, alpha = 0.1)\\n pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], alpha = 0.1)\\n \\n pl.plot_edges(l, ax = ax, subset = (ujk == -1))\\n if len(plaquettes) > 1: pl.plot_dual(l, ax = ax, color_scheme = line_colors[1:], subset = (ujk == -1), )\\n pl.plot_plaquettes(l, subset = fluxes == -1, ax = ax, color_scheme = [\\\"orange\\\", \\\"white\\\"], alpha = 0.5);\\n ax.set(xticks = [], yticks = [])\\n \\n fig.tight_layout()\\n if n == 3: \\n fig.savefig(f'./{Path.cwd().name}.svg', transparent = True)\\n fig.savefig(f'./{Path.cwd().name}.pdf')\\n fig.savefig(f\\\"animation/iteration_{n:03}.svg\\\")\\n plt.close(fig)\\n\\nsubprocess.run([\\\"magick\\\", \\\"animation/*.svg\\\", f'./{Path.cwd().name}.gif'])\\nsubprocess.run([\\\"convert\\\", \\\"-delay\\\", \\\"100\\\", f'./{Path.cwd().name}.gif', f'./{Path.cwd().name}.gif'])\\nsubprocess.run([\\\"rm\\\", \\\"-r\\\", \\\"./animation\\\"])\",\n \"import matplotlib\\nfrom matplotlib.colors import to_hex\\nfrom matplotlib import cm\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport itertools as it\\nfrom pathlib import Path\\nimport subprocess\\nfrom tqdm import tqdm\\nfrom koala import plotting as pl\\nfrom koala import phase_diagrams as pd\\nfrom koala import pointsets, voronization, flux_finder, graph_color\\nfrom koala import example_graphs as eg\\nimport functools\\n\\n\\ndef multi_set_symmetric_difference(sets):\\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\\n\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\\n adjacent_plaquettes for p in plaquettes))\\n\\n\\ndef flood_iteration_vertices(l, vertices):\\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\\n\\n\\ncolumn_width = 3.375\\nw = 3.375\\nblack_line_widths = 1.5\\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True,\\n 'font.family': 'serif', 'font.serif': ['Computer Modern']})\\nmatplotlib.rcParams.update({'axes.linewidth': black_line_widths})\\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\\nrng = np.random.default_rng(seed=10)\\nl, coloring, ujk = eg.make_amorphous(8, rng=rng)\\nplaquettes = [40]\\nvertices = [78]\\nsubprocess.run(['mkdir', '-p', './animation'])\\nfor n in tqdm(range(15)):\\n fig, axes = plt.subplots(nrows=1, ncols=2)\\n fig.set_size_inches(2 * w, 2 / 2 * w)\\n for a in axes:\\n a.set(xticks=[], yticks=[])\\n if n > 0:\\n vertices = flood_iteration_vertices(l, vertices)\\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\\n ax = axes[0]\\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges\\n [v] for v in vertices])\\n if multi_edges:\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=multi_edges\\n )\\n pl.plot_edges(l, ax=ax, color='k', subset=multi_edges)\\n pl.plot_vertices(l, ax=ax, subset=list(vertices), s=5)\\n pl.plot_edges(l, ax=ax, alpha=0.1)\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\\n ax.set(xticks=[], yticks=[])\\n ax = axes[1]\\n plaquette_boolean = np.array([(i in plaquettes) for i in range(l.\\n n_plaquettes)])\\n fluxes = 1 - 2 * plaquette_boolean\\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\\n pl.plot_edges(l, ax=ax, alpha=0.1)\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\\n pl.plot_edges(l, ax=ax, subset=ujk == -1)\\n if len(plaquettes) > 1:\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=ujk == -1)\\n pl.plot_plaquettes(l, subset=fluxes == -1, ax=ax, color_scheme=[\\n 'orange', 'white'], alpha=0.5)\\n ax.set(xticks=[], yticks=[])\\n fig.tight_layout()\\n if n == 3:\\n fig.savefig(f'./{Path.cwd().name}.svg', transparent=True)\\n fig.savefig(f'./{Path.cwd().name}.pdf')\\n fig.savefig(f'animation/iteration_{n:03}.svg')\\n plt.close(fig)\\nsubprocess.run(['magick', 'animation/*.svg', f'./{Path.cwd().name}.gif'])\\nsubprocess.run(['convert', '-delay', '100', f'./{Path.cwd().name}.gif',\\n f'./{Path.cwd().name}.gif'])\\nsubprocess.run(['rm', '-r', './animation'])\\n\",\n \"<import token>\\n\\n\\ndef multi_set_symmetric_difference(sets):\\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\\n\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\\n adjacent_plaquettes for p in plaquettes))\\n\\n\\ndef flood_iteration_vertices(l, vertices):\\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\\n\\n\\ncolumn_width = 3.375\\nw = 3.375\\nblack_line_widths = 1.5\\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True,\\n 'font.family': 'serif', 'font.serif': ['Computer Modern']})\\nmatplotlib.rcParams.update({'axes.linewidth': black_line_widths})\\nline_colors = [to_hex(a) for a in cm.inferno([0.25, 0.5, 0.75])]\\nrng = np.random.default_rng(seed=10)\\nl, coloring, ujk = eg.make_amorphous(8, rng=rng)\\nplaquettes = [40]\\nvertices = [78]\\nsubprocess.run(['mkdir', '-p', './animation'])\\nfor n in tqdm(range(15)):\\n fig, axes = plt.subplots(nrows=1, ncols=2)\\n fig.set_size_inches(2 * w, 2 / 2 * w)\\n for a in axes:\\n a.set(xticks=[], yticks=[])\\n if n > 0:\\n vertices = flood_iteration_vertices(l, vertices)\\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\\n ax = axes[0]\\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges\\n [v] for v in vertices])\\n if multi_edges:\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=multi_edges\\n )\\n pl.plot_edges(l, ax=ax, color='k', subset=multi_edges)\\n pl.plot_vertices(l, ax=ax, subset=list(vertices), s=5)\\n pl.plot_edges(l, ax=ax, alpha=0.1)\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\\n ax.set(xticks=[], yticks=[])\\n ax = axes[1]\\n plaquette_boolean = np.array([(i in plaquettes) for i in range(l.\\n n_plaquettes)])\\n fluxes = 1 - 2 * plaquette_boolean\\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\\n pl.plot_edges(l, ax=ax, alpha=0.1)\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\\n pl.plot_edges(l, ax=ax, subset=ujk == -1)\\n if len(plaquettes) > 1:\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=ujk == -1)\\n pl.plot_plaquettes(l, subset=fluxes == -1, ax=ax, color_scheme=[\\n 'orange', 'white'], alpha=0.5)\\n ax.set(xticks=[], yticks=[])\\n fig.tight_layout()\\n if n == 3:\\n fig.savefig(f'./{Path.cwd().name}.svg', transparent=True)\\n fig.savefig(f'./{Path.cwd().name}.pdf')\\n fig.savefig(f'animation/iteration_{n:03}.svg')\\n plt.close(fig)\\nsubprocess.run(['magick', 'animation/*.svg', f'./{Path.cwd().name}.gif'])\\nsubprocess.run(['convert', '-delay', '100', f'./{Path.cwd().name}.gif',\\n f'./{Path.cwd().name}.gif'])\\nsubprocess.run(['rm', '-r', './animation'])\\n\",\n \"<import token>\\n\\n\\ndef multi_set_symmetric_difference(sets):\\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\\n\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\\n adjacent_plaquettes for p in plaquettes))\\n\\n\\ndef flood_iteration_vertices(l, vertices):\\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\\n\\n\\n<assignment token>\\nmatplotlib.rcParams.update({'font.size': 13, 'text.usetex': True,\\n 'font.family': 'serif', 'font.serif': ['Computer Modern']})\\nmatplotlib.rcParams.update({'axes.linewidth': black_line_widths})\\n<assignment token>\\nsubprocess.run(['mkdir', '-p', './animation'])\\nfor n in tqdm(range(15)):\\n fig, axes = plt.subplots(nrows=1, ncols=2)\\n fig.set_size_inches(2 * w, 2 / 2 * w)\\n for a in axes:\\n a.set(xticks=[], yticks=[])\\n if n > 0:\\n vertices = flood_iteration_vertices(l, vertices)\\n plaquettes = flood_iteration_plaquettes(l, plaquettes)\\n ax = axes[0]\\n multi_edges = multi_set_symmetric_difference([l.vertices.adjacent_edges\\n [v] for v in vertices])\\n if multi_edges:\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=multi_edges\\n )\\n pl.plot_edges(l, ax=ax, color='k', subset=multi_edges)\\n pl.plot_vertices(l, ax=ax, subset=list(vertices), s=5)\\n pl.plot_edges(l, ax=ax, alpha=0.1)\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\\n ax.set(xticks=[], yticks=[])\\n ax = axes[1]\\n plaquette_boolean = np.array([(i in plaquettes) for i in range(l.\\n n_plaquettes)])\\n fluxes = 1 - 2 * plaquette_boolean\\n ujk = flux_finder.find_flux_sector(l, fluxes, ujk)\\n fluxes = flux_finder.fluxes_from_bonds(l, ujk)\\n pl.plot_edges(l, ax=ax, alpha=0.1)\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], alpha=0.1)\\n pl.plot_edges(l, ax=ax, subset=ujk == -1)\\n if len(plaquettes) > 1:\\n pl.plot_dual(l, ax=ax, color_scheme=line_colors[1:], subset=ujk == -1)\\n pl.plot_plaquettes(l, subset=fluxes == -1, ax=ax, color_scheme=[\\n 'orange', 'white'], alpha=0.5)\\n ax.set(xticks=[], yticks=[])\\n fig.tight_layout()\\n if n == 3:\\n fig.savefig(f'./{Path.cwd().name}.svg', transparent=True)\\n fig.savefig(f'./{Path.cwd().name}.pdf')\\n fig.savefig(f'animation/iteration_{n:03}.svg')\\n plt.close(fig)\\nsubprocess.run(['magick', 'animation/*.svg', f'./{Path.cwd().name}.gif'])\\nsubprocess.run(['convert', '-delay', '100', f'./{Path.cwd().name}.gif',\\n f'./{Path.cwd().name}.gif'])\\nsubprocess.run(['rm', '-r', './animation'])\\n\",\n \"<import token>\\n\\n\\ndef multi_set_symmetric_difference(sets):\\n return list(functools.reduce(lambda a, b: a ^ b, [set(s) for s in sets]))\\n\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\\n adjacent_plaquettes for p in plaquettes))\\n\\n\\ndef flood_iteration_vertices(l, vertices):\\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\\n\\n\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\",\n \"<import token>\\n<function token>\\n\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\\n adjacent_plaquettes for p in plaquettes))\\n\\n\\ndef flood_iteration_vertices(l, vertices):\\n return set(vertices) | set(it.chain.from_iterable(i for v in set(\\n vertices) for i in l.edges.indices[l.vertices.adjacent_edges[v]]))\\n\\n\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\",\n \"<import token>\\n<function token>\\n\\n\\ndef flood_iteration_plaquettes(l, plaquettes):\\n return set(plaquettes) | set(it.chain.from_iterable(l.plaquettes[p].\\n adjacent_plaquettes for p in plaquettes))\\n\\n\\n<function token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\",\n \"<import token>\\n<function token>\\n<function token>\\n<function token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5879,"cells":{"index":{"kind":"number","value":5879,"string":"5,879"},"blob_id":{"kind":"string","value":"6c6026a7ff0345c37e62de7c0aac0ee3bcde2c82"},"code":{"kind":"string","value":"import pymongo\n\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/') #We create the database object\n\nmydb = myclient['mydatabase'] #Create a database\n\nmycol = mydb['customers'] #Create a collection into my mydatabase\n\nmydict = [{\"name\": \"Eric\", \"address\": \"Highway 37\"}, {\"name\": \"Albert\", \"address\": \"Highway 37\"}, {\"name\": \"Ivan\", \"address\": \"Highway 37\"}]\n\nx = mycol.insert_many(mydict)\n\nmyquery = {'name':'Albert'}\n\nmydoc = mycol.find()\n\nprint(mydoc)\n"},"steps":{"kind":"list like","value":["import pymongo\n\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/') #We create the database object\n\nmydb = myclient['mydatabase'] #Create a database\n\nmycol = mydb['customers'] #Create a collection into my mydatabase\n\nmydict = [{\"name\": \"Eric\", \"address\": \"Highway 37\"}, {\"name\": \"Albert\", \"address\": \"Highway 37\"}, {\"name\": \"Ivan\", \"address\": \"Highway 37\"}]\n\nx = mycol.insert_many(mydict)\n\nmyquery = {'name':'Albert'}\n\nmydoc = mycol.find()\n\nprint(mydoc)\n","import pymongo\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/')\nmydb = myclient['mydatabase']\nmycol = mydb['customers']\nmydict = [{'name': 'Eric', 'address': 'Highway 37'}, {'name': 'Albert',\n 'address': 'Highway 37'}, {'name': 'Ivan', 'address': 'Highway 37'}]\nx = mycol.insert_many(mydict)\nmyquery = {'name': 'Albert'}\nmydoc = mycol.find()\nprint(mydoc)\n","<import token>\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/')\nmydb = myclient['mydatabase']\nmycol = mydb['customers']\nmydict = [{'name': 'Eric', 'address': 'Highway 37'}, {'name': 'Albert',\n 'address': 'Highway 37'}, {'name': 'Ivan', 'address': 'Highway 37'}]\nx = mycol.insert_many(mydict)\nmyquery = {'name': 'Albert'}\nmydoc = mycol.find()\nprint(mydoc)\n","<import token>\n<assignment token>\nprint(mydoc)\n","<import token>\n<assignment token>\n<code token>\n"],"string":"[\n \"import pymongo\\n\\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/') #We create the database object\\n\\nmydb = myclient['mydatabase'] #Create a database\\n\\nmycol = mydb['customers'] #Create a collection into my mydatabase\\n\\nmydict = [{\\\"name\\\": \\\"Eric\\\", \\\"address\\\": \\\"Highway 37\\\"}, {\\\"name\\\": \\\"Albert\\\", \\\"address\\\": \\\"Highway 37\\\"}, {\\\"name\\\": \\\"Ivan\\\", \\\"address\\\": \\\"Highway 37\\\"}]\\n\\nx = mycol.insert_many(mydict)\\n\\nmyquery = {'name':'Albert'}\\n\\nmydoc = mycol.find()\\n\\nprint(mydoc)\\n\",\n \"import pymongo\\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/')\\nmydb = myclient['mydatabase']\\nmycol = mydb['customers']\\nmydict = [{'name': 'Eric', 'address': 'Highway 37'}, {'name': 'Albert',\\n 'address': 'Highway 37'}, {'name': 'Ivan', 'address': 'Highway 37'}]\\nx = mycol.insert_many(mydict)\\nmyquery = {'name': 'Albert'}\\nmydoc = mycol.find()\\nprint(mydoc)\\n\",\n \"<import token>\\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/')\\nmydb = myclient['mydatabase']\\nmycol = mydb['customers']\\nmydict = [{'name': 'Eric', 'address': 'Highway 37'}, {'name': 'Albert',\\n 'address': 'Highway 37'}, {'name': 'Ivan', 'address': 'Highway 37'}]\\nx = mycol.insert_many(mydict)\\nmyquery = {'name': 'Albert'}\\nmydoc = mycol.find()\\nprint(mydoc)\\n\",\n \"<import token>\\n<assignment token>\\nprint(mydoc)\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5880,"cells":{"index":{"kind":"number","value":5880,"string":"5,880"},"blob_id":{"kind":"string","value":"fb1974ad7ac9ae54344812814cb95a7fccfefc66"},"code":{"kind":"string","value":"# - Generated by tools/entrypoint_compiler.py: do not edit by hand\n\"\"\"\nNGramHash\n\"\"\"\n\nimport numbers\n\nfrom ..utils.entrypoints import Component\nfrom ..utils.utils import try_set\n\n\ndef n_gram_hash(\n hash_bits=16,\n ngram_length=1,\n skip_length=0,\n all_lengths=True,\n seed=314489979,\n ordered=True,\n invert_hash=0,\n **params):\n \"\"\"\n **Description**\n Extracts NGrams from text and convert them to vector using hashing\n trick.\n\n :param hash_bits: Number of bits to hash into. Must be between 1\n and 30, inclusive. (settings).\n :param ngram_length: Ngram length (settings).\n :param skip_length: Maximum number of tokens to skip when\n constructing an ngram (settings).\n :param all_lengths: Whether to include all ngram lengths up to\n ngramLength or only ngramLength (settings).\n :param seed: Hashing seed (settings).\n :param ordered: Whether the position of each source column should\n be included in the hash (when there are multiple source\n columns). (settings).\n :param invert_hash: Limit the number of keys used to generate the\n slot name to this many. 0 means no invert hashing, -1 means\n no limit. (settings).\n \"\"\"\n\n entrypoint_name = 'NGramHash'\n settings = {}\n\n if hash_bits is not None:\n settings['HashBits'] = try_set(\n obj=hash_bits,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if ngram_length is not None:\n settings['NgramLength'] = try_set(\n obj=ngram_length,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if skip_length is not None:\n settings['SkipLength'] = try_set(\n obj=skip_length,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if all_lengths is not None:\n settings['AllLengths'] = try_set(\n obj=all_lengths,\n none_acceptable=True,\n is_of_type=bool)\n if seed is not None:\n settings['Seed'] = try_set(\n obj=seed,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if ordered is not None:\n settings['Ordered'] = try_set(\n obj=ordered, none_acceptable=True, is_of_type=bool)\n if invert_hash is not None:\n settings['InvertHash'] = try_set(\n obj=invert_hash,\n none_acceptable=True,\n is_of_type=numbers.Real)\n\n component = Component(\n name=entrypoint_name,\n settings=settings,\n kind='NgramExtractor')\n return component\n"},"steps":{"kind":"list like","value":["# - Generated by tools/entrypoint_compiler.py: do not edit by hand\n\"\"\"\nNGramHash\n\"\"\"\n\nimport numbers\n\nfrom ..utils.entrypoints import Component\nfrom ..utils.utils import try_set\n\n\ndef n_gram_hash(\n hash_bits=16,\n ngram_length=1,\n skip_length=0,\n all_lengths=True,\n seed=314489979,\n ordered=True,\n invert_hash=0,\n **params):\n \"\"\"\n **Description**\n Extracts NGrams from text and convert them to vector using hashing\n trick.\n\n :param hash_bits: Number of bits to hash into. Must be between 1\n and 30, inclusive. (settings).\n :param ngram_length: Ngram length (settings).\n :param skip_length: Maximum number of tokens to skip when\n constructing an ngram (settings).\n :param all_lengths: Whether to include all ngram lengths up to\n ngramLength or only ngramLength (settings).\n :param seed: Hashing seed (settings).\n :param ordered: Whether the position of each source column should\n be included in the hash (when there are multiple source\n columns). (settings).\n :param invert_hash: Limit the number of keys used to generate the\n slot name to this many. 0 means no invert hashing, -1 means\n no limit. (settings).\n \"\"\"\n\n entrypoint_name = 'NGramHash'\n settings = {}\n\n if hash_bits is not None:\n settings['HashBits'] = try_set(\n obj=hash_bits,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if ngram_length is not None:\n settings['NgramLength'] = try_set(\n obj=ngram_length,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if skip_length is not None:\n settings['SkipLength'] = try_set(\n obj=skip_length,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if all_lengths is not None:\n settings['AllLengths'] = try_set(\n obj=all_lengths,\n none_acceptable=True,\n is_of_type=bool)\n if seed is not None:\n settings['Seed'] = try_set(\n obj=seed,\n none_acceptable=True,\n is_of_type=numbers.Real)\n if ordered is not None:\n settings['Ordered'] = try_set(\n obj=ordered, none_acceptable=True, is_of_type=bool)\n if invert_hash is not None:\n settings['InvertHash'] = try_set(\n obj=invert_hash,\n none_acceptable=True,\n is_of_type=numbers.Real)\n\n component = Component(\n name=entrypoint_name,\n settings=settings,\n kind='NgramExtractor')\n return component\n","<docstring token>\nimport numbers\nfrom ..utils.entrypoints import Component\nfrom ..utils.utils import try_set\n\n\ndef n_gram_hash(hash_bits=16, ngram_length=1, skip_length=0, all_lengths=\n True, seed=314489979, ordered=True, invert_hash=0, **params):\n \"\"\"\n **Description**\n Extracts NGrams from text and convert them to vector using hashing\n trick.\n\n :param hash_bits: Number of bits to hash into. Must be between 1\n and 30, inclusive. (settings).\n :param ngram_length: Ngram length (settings).\n :param skip_length: Maximum number of tokens to skip when\n constructing an ngram (settings).\n :param all_lengths: Whether to include all ngram lengths up to\n ngramLength or only ngramLength (settings).\n :param seed: Hashing seed (settings).\n :param ordered: Whether the position of each source column should\n be included in the hash (when there are multiple source\n columns). (settings).\n :param invert_hash: Limit the number of keys used to generate the\n slot name to this many. 0 means no invert hashing, -1 means\n no limit. (settings).\n \"\"\"\n entrypoint_name = 'NGramHash'\n settings = {}\n if hash_bits is not None:\n settings['HashBits'] = try_set(obj=hash_bits, none_acceptable=True,\n is_of_type=numbers.Real)\n if ngram_length is not None:\n settings['NgramLength'] = try_set(obj=ngram_length, none_acceptable\n =True, is_of_type=numbers.Real)\n if skip_length is not None:\n settings['SkipLength'] = try_set(obj=skip_length, none_acceptable=\n True, is_of_type=numbers.Real)\n if all_lengths is not None:\n settings['AllLengths'] = try_set(obj=all_lengths, none_acceptable=\n True, is_of_type=bool)\n if seed is not None:\n settings['Seed'] = try_set(obj=seed, none_acceptable=True,\n is_of_type=numbers.Real)\n if ordered is not None:\n settings['Ordered'] = try_set(obj=ordered, none_acceptable=True,\n is_of_type=bool)\n if invert_hash is not None:\n settings['InvertHash'] = try_set(obj=invert_hash, none_acceptable=\n True, is_of_type=numbers.Real)\n component = Component(name=entrypoint_name, settings=settings, kind=\n 'NgramExtractor')\n return component\n","<docstring token>\n<import token>\n\n\ndef n_gram_hash(hash_bits=16, ngram_length=1, skip_length=0, all_lengths=\n True, seed=314489979, ordered=True, invert_hash=0, **params):\n \"\"\"\n **Description**\n Extracts NGrams from text and convert them to vector using hashing\n trick.\n\n :param hash_bits: Number of bits to hash into. Must be between 1\n and 30, inclusive. (settings).\n :param ngram_length: Ngram length (settings).\n :param skip_length: Maximum number of tokens to skip when\n constructing an ngram (settings).\n :param all_lengths: Whether to include all ngram lengths up to\n ngramLength or only ngramLength (settings).\n :param seed: Hashing seed (settings).\n :param ordered: Whether the position of each source column should\n be included in the hash (when there are multiple source\n columns). (settings).\n :param invert_hash: Limit the number of keys used to generate the\n slot name to this many. 0 means no invert hashing, -1 means\n no limit. (settings).\n \"\"\"\n entrypoint_name = 'NGramHash'\n settings = {}\n if hash_bits is not None:\n settings['HashBits'] = try_set(obj=hash_bits, none_acceptable=True,\n is_of_type=numbers.Real)\n if ngram_length is not None:\n settings['NgramLength'] = try_set(obj=ngram_length, none_acceptable\n =True, is_of_type=numbers.Real)\n if skip_length is not None:\n settings['SkipLength'] = try_set(obj=skip_length, none_acceptable=\n True, is_of_type=numbers.Real)\n if all_lengths is not None:\n settings['AllLengths'] = try_set(obj=all_lengths, none_acceptable=\n True, is_of_type=bool)\n if seed is not None:\n settings['Seed'] = try_set(obj=seed, none_acceptable=True,\n is_of_type=numbers.Real)\n if ordered is not None:\n settings['Ordered'] = try_set(obj=ordered, none_acceptable=True,\n is_of_type=bool)\n if invert_hash is not None:\n settings['InvertHash'] = try_set(obj=invert_hash, none_acceptable=\n True, is_of_type=numbers.Real)\n component = Component(name=entrypoint_name, settings=settings, kind=\n 'NgramExtractor')\n return component\n","<docstring token>\n<import token>\n<function token>\n"],"string":"[\n \"# - Generated by tools/entrypoint_compiler.py: do not edit by hand\\n\\\"\\\"\\\"\\nNGramHash\\n\\\"\\\"\\\"\\n\\nimport numbers\\n\\nfrom ..utils.entrypoints import Component\\nfrom ..utils.utils import try_set\\n\\n\\ndef n_gram_hash(\\n hash_bits=16,\\n ngram_length=1,\\n skip_length=0,\\n all_lengths=True,\\n seed=314489979,\\n ordered=True,\\n invert_hash=0,\\n **params):\\n \\\"\\\"\\\"\\n **Description**\\n Extracts NGrams from text and convert them to vector using hashing\\n trick.\\n\\n :param hash_bits: Number of bits to hash into. Must be between 1\\n and 30, inclusive. (settings).\\n :param ngram_length: Ngram length (settings).\\n :param skip_length: Maximum number of tokens to skip when\\n constructing an ngram (settings).\\n :param all_lengths: Whether to include all ngram lengths up to\\n ngramLength or only ngramLength (settings).\\n :param seed: Hashing seed (settings).\\n :param ordered: Whether the position of each source column should\\n be included in the hash (when there are multiple source\\n columns). (settings).\\n :param invert_hash: Limit the number of keys used to generate the\\n slot name to this many. 0 means no invert hashing, -1 means\\n no limit. (settings).\\n \\\"\\\"\\\"\\n\\n entrypoint_name = 'NGramHash'\\n settings = {}\\n\\n if hash_bits is not None:\\n settings['HashBits'] = try_set(\\n obj=hash_bits,\\n none_acceptable=True,\\n is_of_type=numbers.Real)\\n if ngram_length is not None:\\n settings['NgramLength'] = try_set(\\n obj=ngram_length,\\n none_acceptable=True,\\n is_of_type=numbers.Real)\\n if skip_length is not None:\\n settings['SkipLength'] = try_set(\\n obj=skip_length,\\n none_acceptable=True,\\n is_of_type=numbers.Real)\\n if all_lengths is not None:\\n settings['AllLengths'] = try_set(\\n obj=all_lengths,\\n none_acceptable=True,\\n is_of_type=bool)\\n if seed is not None:\\n settings['Seed'] = try_set(\\n obj=seed,\\n none_acceptable=True,\\n is_of_type=numbers.Real)\\n if ordered is not None:\\n settings['Ordered'] = try_set(\\n obj=ordered, none_acceptable=True, is_of_type=bool)\\n if invert_hash is not None:\\n settings['InvertHash'] = try_set(\\n obj=invert_hash,\\n none_acceptable=True,\\n is_of_type=numbers.Real)\\n\\n component = Component(\\n name=entrypoint_name,\\n settings=settings,\\n kind='NgramExtractor')\\n return component\\n\",\n \"<docstring token>\\nimport numbers\\nfrom ..utils.entrypoints import Component\\nfrom ..utils.utils import try_set\\n\\n\\ndef n_gram_hash(hash_bits=16, ngram_length=1, skip_length=0, all_lengths=\\n True, seed=314489979, ordered=True, invert_hash=0, **params):\\n \\\"\\\"\\\"\\n **Description**\\n Extracts NGrams from text and convert them to vector using hashing\\n trick.\\n\\n :param hash_bits: Number of bits to hash into. Must be between 1\\n and 30, inclusive. (settings).\\n :param ngram_length: Ngram length (settings).\\n :param skip_length: Maximum number of tokens to skip when\\n constructing an ngram (settings).\\n :param all_lengths: Whether to include all ngram lengths up to\\n ngramLength or only ngramLength (settings).\\n :param seed: Hashing seed (settings).\\n :param ordered: Whether the position of each source column should\\n be included in the hash (when there are multiple source\\n columns). (settings).\\n :param invert_hash: Limit the number of keys used to generate the\\n slot name to this many. 0 means no invert hashing, -1 means\\n no limit. (settings).\\n \\\"\\\"\\\"\\n entrypoint_name = 'NGramHash'\\n settings = {}\\n if hash_bits is not None:\\n settings['HashBits'] = try_set(obj=hash_bits, none_acceptable=True,\\n is_of_type=numbers.Real)\\n if ngram_length is not None:\\n settings['NgramLength'] = try_set(obj=ngram_length, none_acceptable\\n =True, is_of_type=numbers.Real)\\n if skip_length is not None:\\n settings['SkipLength'] = try_set(obj=skip_length, none_acceptable=\\n True, is_of_type=numbers.Real)\\n if all_lengths is not None:\\n settings['AllLengths'] = try_set(obj=all_lengths, none_acceptable=\\n True, is_of_type=bool)\\n if seed is not None:\\n settings['Seed'] = try_set(obj=seed, none_acceptable=True,\\n is_of_type=numbers.Real)\\n if ordered is not None:\\n settings['Ordered'] = try_set(obj=ordered, none_acceptable=True,\\n is_of_type=bool)\\n if invert_hash is not None:\\n settings['InvertHash'] = try_set(obj=invert_hash, none_acceptable=\\n True, is_of_type=numbers.Real)\\n component = Component(name=entrypoint_name, settings=settings, kind=\\n 'NgramExtractor')\\n return component\\n\",\n \"<docstring token>\\n<import token>\\n\\n\\ndef n_gram_hash(hash_bits=16, ngram_length=1, skip_length=0, all_lengths=\\n True, seed=314489979, ordered=True, invert_hash=0, **params):\\n \\\"\\\"\\\"\\n **Description**\\n Extracts NGrams from text and convert them to vector using hashing\\n trick.\\n\\n :param hash_bits: Number of bits to hash into. Must be between 1\\n and 30, inclusive. (settings).\\n :param ngram_length: Ngram length (settings).\\n :param skip_length: Maximum number of tokens to skip when\\n constructing an ngram (settings).\\n :param all_lengths: Whether to include all ngram lengths up to\\n ngramLength or only ngramLength (settings).\\n :param seed: Hashing seed (settings).\\n :param ordered: Whether the position of each source column should\\n be included in the hash (when there are multiple source\\n columns). (settings).\\n :param invert_hash: Limit the number of keys used to generate the\\n slot name to this many. 0 means no invert hashing, -1 means\\n no limit. (settings).\\n \\\"\\\"\\\"\\n entrypoint_name = 'NGramHash'\\n settings = {}\\n if hash_bits is not None:\\n settings['HashBits'] = try_set(obj=hash_bits, none_acceptable=True,\\n is_of_type=numbers.Real)\\n if ngram_length is not None:\\n settings['NgramLength'] = try_set(obj=ngram_length, none_acceptable\\n =True, is_of_type=numbers.Real)\\n if skip_length is not None:\\n settings['SkipLength'] = try_set(obj=skip_length, none_acceptable=\\n True, is_of_type=numbers.Real)\\n if all_lengths is not None:\\n settings['AllLengths'] = try_set(obj=all_lengths, none_acceptable=\\n True, is_of_type=bool)\\n if seed is not None:\\n settings['Seed'] = try_set(obj=seed, none_acceptable=True,\\n is_of_type=numbers.Real)\\n if ordered is not None:\\n settings['Ordered'] = try_set(obj=ordered, none_acceptable=True,\\n is_of_type=bool)\\n if invert_hash is not None:\\n settings['InvertHash'] = try_set(obj=invert_hash, none_acceptable=\\n True, is_of_type=numbers.Real)\\n component = Component(name=entrypoint_name, settings=settings, kind=\\n 'NgramExtractor')\\n return component\\n\",\n \"<docstring token>\\n<import token>\\n<function token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5881,"cells":{"index":{"kind":"number","value":5881,"string":"5,881"},"blob_id":{"kind":"string","value":"1af6e66c19078a9ee971f608daa93247911d8406"},"code":{"kind":"string","value":"from tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\nimport numpy as np\n\nmodel = ResNet50(weights='imagenet', # Learned weights on imagenet\n include_top=True)\n\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\nimg_input = image.img_to_array(img_input)\nimg_input = preprocess_input(img_input[np.newaxis, ...])\n\npreds = model.predict(img_input)\ndecoded_predictions = decode_predictions(preds, top=10)[0]\n\nprint(decoded_predictions)"},"steps":{"kind":"list like","value":["from tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\nimport numpy as np\n\nmodel = ResNet50(weights='imagenet', # Learned weights on imagenet\n include_top=True)\n\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\nimg_input = image.img_to_array(img_input)\nimg_input = preprocess_input(img_input[np.newaxis, ...])\n\npreds = model.predict(img_input)\ndecoded_predictions = decode_predictions(preds, top=10)[0]\n\nprint(decoded_predictions)","from tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\nimport numpy as np\nmodel = ResNet50(weights='imagenet', include_top=True)\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\nimg_input = image.img_to_array(img_input)\nimg_input = preprocess_input(img_input[np.newaxis, ...])\npreds = model.predict(img_input)\ndecoded_predictions = decode_predictions(preds, top=10)[0]\nprint(decoded_predictions)\n","<import token>\nmodel = ResNet50(weights='imagenet', include_top=True)\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\nimg_input = image.img_to_array(img_input)\nimg_input = preprocess_input(img_input[np.newaxis, ...])\npreds = model.predict(img_input)\ndecoded_predictions = decode_predictions(preds, top=10)[0]\nprint(decoded_predictions)\n","<import token>\n<assignment token>\nprint(decoded_predictions)\n","<import token>\n<assignment token>\n<code token>\n"],"string":"[\n \"from tensorflow.keras.applications.resnet50 import ResNet50\\nfrom tensorflow.keras.preprocessing import image\\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\\nimport numpy as np\\n\\nmodel = ResNet50(weights='imagenet', # Learned weights on imagenet\\n include_top=True)\\n\\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\\nimg_input = image.img_to_array(img_input)\\nimg_input = preprocess_input(img_input[np.newaxis, ...])\\n\\npreds = model.predict(img_input)\\ndecoded_predictions = decode_predictions(preds, top=10)[0]\\n\\nprint(decoded_predictions)\",\n \"from tensorflow.keras.applications.resnet50 import ResNet50\\nfrom tensorflow.keras.preprocessing import image\\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\\nimport numpy as np\\nmodel = ResNet50(weights='imagenet', include_top=True)\\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\\nimg_input = image.img_to_array(img_input)\\nimg_input = preprocess_input(img_input[np.newaxis, ...])\\npreds = model.predict(img_input)\\ndecoded_predictions = decode_predictions(preds, top=10)[0]\\nprint(decoded_predictions)\\n\",\n \"<import token>\\nmodel = ResNet50(weights='imagenet', include_top=True)\\nimg_input = image.load_img('my_picture.jpg', target_size=(224, 224))\\nimg_input = image.img_to_array(img_input)\\nimg_input = preprocess_input(img_input[np.newaxis, ...])\\npreds = model.predict(img_input)\\ndecoded_predictions = decode_predictions(preds, top=10)[0]\\nprint(decoded_predictions)\\n\",\n \"<import token>\\n<assignment token>\\nprint(decoded_predictions)\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5882,"cells":{"index":{"kind":"number","value":5882,"string":"5,882"},"blob_id":{"kind":"string","value":"6edb1f99ca9af01f28322cbaf13f278e79b94e92"},"code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\nc = int(input())\nt = input()\nm = []\n\nfor i in range(12):\n aux = []\n for j in range(12):\n aux.append(float(input()))\n m.append(aux)\n aux = []\n\nsoma = 0\nfor i in range(12):\n soma += m[i][c]\n\nresultado = soma / (t == 'S' and 1 or 12)\nprint('%.1f' % resultado)\n"},"steps":{"kind":"list like","value":["# -*- coding: utf-8 -*-\n\nc = int(input())\nt = input()\nm = []\n\nfor i in range(12):\n aux = []\n for j in range(12):\n aux.append(float(input()))\n m.append(aux)\n aux = []\n\nsoma = 0\nfor i in range(12):\n soma += m[i][c]\n\nresultado = soma / (t == 'S' and 1 or 12)\nprint('%.1f' % resultado)\n","c = int(input())\nt = input()\nm = []\nfor i in range(12):\n aux = []\n for j in range(12):\n aux.append(float(input()))\n m.append(aux)\n aux = []\nsoma = 0\nfor i in range(12):\n soma += m[i][c]\nresultado = soma / (t == 'S' and 1 or 12)\nprint('%.1f' % resultado)\n","<assignment token>\nfor i in range(12):\n aux = []\n for j in range(12):\n aux.append(float(input()))\n m.append(aux)\n aux = []\n<assignment token>\nfor i in range(12):\n soma += m[i][c]\n<assignment token>\nprint('%.1f' % resultado)\n","<assignment token>\n<code token>\n<assignment token>\n<code token>\n<assignment token>\n<code token>\n"],"string":"[\n \"# -*- coding: utf-8 -*-\\n\\nc = int(input())\\nt = input()\\nm = []\\n\\nfor i in range(12):\\n aux = []\\n for j in range(12):\\n aux.append(float(input()))\\n m.append(aux)\\n aux = []\\n\\nsoma = 0\\nfor i in range(12):\\n soma += m[i][c]\\n\\nresultado = soma / (t == 'S' and 1 or 12)\\nprint('%.1f' % resultado)\\n\",\n \"c = int(input())\\nt = input()\\nm = []\\nfor i in range(12):\\n aux = []\\n for j in range(12):\\n aux.append(float(input()))\\n m.append(aux)\\n aux = []\\nsoma = 0\\nfor i in range(12):\\n soma += m[i][c]\\nresultado = soma / (t == 'S' and 1 or 12)\\nprint('%.1f' % resultado)\\n\",\n \"<assignment token>\\nfor i in range(12):\\n aux = []\\n for j in range(12):\\n aux.append(float(input()))\\n m.append(aux)\\n aux = []\\n<assignment token>\\nfor i in range(12):\\n soma += m[i][c]\\n<assignment token>\\nprint('%.1f' % resultado)\\n\",\n \"<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5883,"cells":{"index":{"kind":"number","value":5883,"string":"5,883"},"blob_id":{"kind":"string","value":"9baf55eb2fb70e9fa0d92df22d307962b8d6c6d4"},"code":{"kind":"string","value":"#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport json\r\nimport urllib2\r\n#this is executed by a cron job on the pi inside the pooltable\r\nsecret ='secret'\r\nbaseurl='https://pooltable.mysite.com/'\r\nurl = baseurl + 'gettrans.php?secret=' + secret\r\nreq = urllib2.Request(url)\r\nf = urllib2.urlopen(req)\r\nresponse = f.read()\r\nf.close()\r\nprint response\r\n#check if there is a transaction\r\nif response != 'error' and response != 'false' and response != False:\r\n obj = json.loads(response)\r\n trans = str(obj['transaction_hash'])\r\n#move the transaction to the processed table and delete from unprocessed\r\n url = baseurl + 'deltrans.php?secret=' + secret + '&trans=' + trans \r\n req = urllib2.Request(url)\r\n f = urllib2.urlopen(req)\r\n response = f.read()\r\n f.close()\r\n#if transaction was moved correctly, set off the solanoid\r\n if response == '*ok*':\r\n print response\r\n #run solanoid script\r\n"},"steps":{"kind":"list like","value":["#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport json\r\nimport urllib2\r\n#this is executed by a cron job on the pi inside the pooltable\r\nsecret ='secret'\r\nbaseurl='https://pooltable.mysite.com/'\r\nurl = baseurl + 'gettrans.php?secret=' + secret\r\nreq = urllib2.Request(url)\r\nf = urllib2.urlopen(req)\r\nresponse = f.read()\r\nf.close()\r\nprint response\r\n#check if there is a transaction\r\nif response != 'error' and response != 'false' and response != False:\r\n obj = json.loads(response)\r\n trans = str(obj['transaction_hash'])\r\n#move the transaction to the processed table and delete from unprocessed\r\n url = baseurl + 'deltrans.php?secret=' + secret + '&trans=' + trans \r\n req = urllib2.Request(url)\r\n f = urllib2.urlopen(req)\r\n response = f.read()\r\n f.close()\r\n#if transaction was moved correctly, set off the solanoid\r\n if response == '*ok*':\r\n print response\r\n #run solanoid script\r\n"],"string":"[\n \"#!/usr/bin/python\\r\\n# -*- coding: utf-8 -*-\\r\\n\\r\\nimport json\\r\\nimport urllib2\\r\\n#this is executed by a cron job on the pi inside the pooltable\\r\\nsecret ='secret'\\r\\nbaseurl='https://pooltable.mysite.com/'\\r\\nurl = baseurl + 'gettrans.php?secret=' + secret\\r\\nreq = urllib2.Request(url)\\r\\nf = urllib2.urlopen(req)\\r\\nresponse = f.read()\\r\\nf.close()\\r\\nprint response\\r\\n#check if there is a transaction\\r\\nif response != 'error' and response != 'false' and response != False:\\r\\n obj = json.loads(response)\\r\\n trans = str(obj['transaction_hash'])\\r\\n#move the transaction to the processed table and delete from unprocessed\\r\\n url = baseurl + 'deltrans.php?secret=' + secret + '&trans=' + trans \\r\\n req = urllib2.Request(url)\\r\\n f = urllib2.urlopen(req)\\r\\n response = f.read()\\r\\n f.close()\\r\\n#if transaction was moved correctly, set off the solanoid\\r\\n if response == '*ok*':\\r\\n print response\\r\\n #run solanoid script\\r\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5884,"cells":{"index":{"kind":"number","value":5884,"string":"5,884"},"blob_id":{"kind":"string","value":"9f3ca0d5a10a27d926a0f306665889418f8d6a0c"},"code":{"kind":"string","value":"from src.produtos import *\r\n\r\n\r\nclass Estoque(object):\r\n def __init__(self):\r\n self.categorias = []\r\n self.subcategorias = []\r\n self.produtos = []\r\n self.menu_estoque()\r\n\r\n def save_categoria(self, categoria):\r\n pass\r\n\r\n def save_subcategorias(self, subcategoria):\r\n pass\r\n\r\n def save_produtos(self, produto):\r\n pass\r\n\r\n def create_categoria(self):\r\n \"\"\"\"\r\n Cria uma categoria através dos dados recolhidos pelo formulário.\r\n Os dados são: Codigo, nome e descrição\r\n \"\"\"\r\n print(\"- Criar CATEGORIA -\")\r\n codigo = input(\"CÓDIGO: \").strip()\r\n nome = input(\"NOME: \").strip()\r\n descrição = input(\"DESCRIÇÃO: \").strip()\r\n categoria = Categoria(codigo, nome, descrição)\r\n if categoria not in self.categorias:\r\n self.categorias.append(categoria)\r\n\r\n def create_subcategoria(self):\r\n \"\"\"\"\r\n Cria uma categoria através dos dados recolhidos pelo formulário.\r\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\r\n \"\"\"\r\n if len(self.categorias) == 0:\r\n print(\"Você deve criar pelo menos uma CATEGORIA!\\n\")\r\n self.create_categoria()\r\n print(\"- Criar SUBCATEGORIA -\")\r\n codigo = input(\"CÓDIGO: \").strip()\r\n nome = input(\"NOME: \").strip()\r\n descrição = input(\"DESCRIÇÃO: \").strip()\r\n escolhe = input(\"CATEGORIA (Nome ou Código): \")\r\n categoria = 0\r\n\r\n for cat in self.categorias:\r\n if cat.nome == escolhe or cat.codigo == escolhe:\r\n categoria = cat\r\n break\r\n else:\r\n print(\"Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!\")\r\n self.create_categoria()\r\n\r\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\r\n\r\n if subcategoria not in self.subcategorias:\r\n self.subcategorias.append(subcategoria)\r\n\r\n def create_produto(self):\r\n \"\"\"\"\r\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\r\n Produtos são itens que podem ser vendidos.\r\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\r\n\r\n TODELETE: Por enquanto foto recebe uma string qualquer\r\n\r\n \"\"\"\r\n # TODO: Implementar a foto no sistemas\r\n if not len(self.subcategorias):\r\n print(\"Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n\")\r\n self.create_subcategoria()\r\n else:\r\n print(\"- Cadastrar PRODUTO -\")\r\n escolhe = input(\"SUBCATEGORIA (Nome ou Código): \").lower()\r\n codigo = input(\"CÓDIGO: \").strip()\r\n nome = input(\"NOME: \").strip()\r\n descrição = input(\"DESCRIÇÃO: \").strip()\r\n\r\n estoquemax = input(\"Quantidade Maxima em Estoque: \")\r\n while not produtos.valida_estoque(estoquemax):\r\n print(\"Valor Inválido!\")\r\n estoquemax = input(\"Valor deve ser Numérico: \")\r\n\r\n estoquemin = input(\"Quantidade Minima em Estoque: \")\r\n while not produtos.valida_estoque(estoquemin):\r\n print(\"Valor Inválido!\")\r\n estoquemin = input(\"Valor deve ser Numérico: \")\r\n\r\n valorvenda = input(\"Preço Unitário: \")\r\n while not produtos.valida_valorvenda(valorvenda):\r\n print(\"Valor Inválido!\")\r\n estoquemax = input(\"Valor deve ser Numérico: \")\r\n\r\n valorcompra = input(\"Valor de Compra: \")\r\n while not produtos.valida_valorvenda(valorcompra):\r\n print(\"Valor Inválido!\")\r\n estoquemax = input(\"Valor deve ser Numérico: \")\r\n\r\n foto = input(\"Arquivo de foto: \")\r\n\r\n subcategoria = 0\r\n\r\n for scat in self.subcategorias:\r\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\r\n subcategoria = scat\r\n break\r\n else:\r\n print(\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\")\r\n choice = input()\r\n if choice.lower() == 's' or choice == '1':\r\n self.create_subcategoria()\r\n else:\r\n self.create_produto()\r\n\r\n produto = Produtos( subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto)\r\n\r\n if produto not in self.produtos:\r\n self.produtos.append(produto)\r\n\r\n # funcionalidade pedida na especificação\r\n\r\n def low_stock_alarm(self): # aviso de estoque baixo\r\n pass\r\n\r\n def consulta_estoque(self): # exibe itens disponiveis no estoque\r\n print(\"Exibindo estoque\")\r\n if not len(self.categorias):\r\n print(\"Não há Categorias Registrados!\")\r\n else:\r\n for categoria in self.categorias:\r\n print(categoria, end=\" \")\r\n print()\r\n if not len(self.subcategorias):\r\n print(\"Não há Subcategorias Registradas!\")\r\n else:\r\n for subcategoria in self.subcategorias:\r\n print(subcategoria, end=\" \")\r\n print()\r\n if not len(self.produtos):\r\n print(\"Não há Produtos Registrados!\")\r\n else:\r\n for produto in self.produtos:\r\n print(produto, end=\" \")\r\n\r\n self.menu_estoque()\r\n\r\n def altera_item(self): # altera um item disponivel no estoque\r\n print(\"alterando item do estoque\")\r\n self.menu_estoque()\r\n\r\n def remove_item(self): # remove um item disponivel no estoque - n remover se o item ainda tem produtos no estoque\r\n print(\"Removendo item do estoque\")\r\n self.menu_estoque()\r\n\r\n def adiciona_item(self): # adiciona novo item ao estoque\r\n print(\"Adicionando item ao estoque\")\r\n while 1:\r\n print(\"************* Menu Adicionar: ******************\")\r\n print(\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\")\r\n opcao = input()\r\n while not self.valida_opcao(opcao):\r\n print(\"Opção Inválida!\")\r\n opcao = input()\r\n if opcao == '1':\r\n self.create_categoria()\r\n elif opcao == '2':\r\n self.create_subcategoria()\r\n elif opcao == '3':\r\n pass\r\n elif opcao == '4':\r\n break\r\n self.menu_estoque()\r\n\r\n def menu_estoque(self):\r\n print(\"Sistema de Vendas ao Consumidor\")\r\n print(\"****** MENU DE ESTOQUE *****\")\r\n print(\"Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar\")\r\n opcao = input()\r\n\r\n while not self.valida_opcao(opcao):\r\n print(\"Opção Inválida!\")\r\n opcao = input()\r\n\r\n if opcao == '1':\r\n self.consulta_estoque()\r\n elif opcao == '2':\r\n self.adiciona_item()\r\n elif opcao == '3':\r\n self.remove_item()\r\n elif opcao == '4':\r\n self.altera_item()\r\n\r\n def valida_opcao(self, opcao):\r\n if opcao.isdigit():\r\n return True\r\n else:\r\n return False\r\n\r\nestoque = Estoque()\r\n"},"steps":{"kind":"list like","value":["from src.produtos import *\r\n\r\n\r\nclass Estoque(object):\r\n def __init__(self):\r\n self.categorias = []\r\n self.subcategorias = []\r\n self.produtos = []\r\n self.menu_estoque()\r\n\r\n def save_categoria(self, categoria):\r\n pass\r\n\r\n def save_subcategorias(self, subcategoria):\r\n pass\r\n\r\n def save_produtos(self, produto):\r\n pass\r\n\r\n def create_categoria(self):\r\n \"\"\"\"\r\n Cria uma categoria através dos dados recolhidos pelo formulário.\r\n Os dados são: Codigo, nome e descrição\r\n \"\"\"\r\n print(\"- Criar CATEGORIA -\")\r\n codigo = input(\"CÓDIGO: \").strip()\r\n nome = input(\"NOME: \").strip()\r\n descrição = input(\"DESCRIÇÃO: \").strip()\r\n categoria = Categoria(codigo, nome, descrição)\r\n if categoria not in self.categorias:\r\n self.categorias.append(categoria)\r\n\r\n def create_subcategoria(self):\r\n \"\"\"\"\r\n Cria uma categoria através dos dados recolhidos pelo formulário.\r\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\r\n \"\"\"\r\n if len(self.categorias) == 0:\r\n print(\"Você deve criar pelo menos uma CATEGORIA!\\n\")\r\n self.create_categoria()\r\n print(\"- Criar SUBCATEGORIA -\")\r\n codigo = input(\"CÓDIGO: \").strip()\r\n nome = input(\"NOME: \").strip()\r\n descrição = input(\"DESCRIÇÃO: \").strip()\r\n escolhe = input(\"CATEGORIA (Nome ou Código): \")\r\n categoria = 0\r\n\r\n for cat in self.categorias:\r\n if cat.nome == escolhe or cat.codigo == escolhe:\r\n categoria = cat\r\n break\r\n else:\r\n print(\"Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!\")\r\n self.create_categoria()\r\n\r\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\r\n\r\n if subcategoria not in self.subcategorias:\r\n self.subcategorias.append(subcategoria)\r\n\r\n def create_produto(self):\r\n \"\"\"\"\r\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\r\n Produtos são itens que podem ser vendidos.\r\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\r\n\r\n TODELETE: Por enquanto foto recebe uma string qualquer\r\n\r\n \"\"\"\r\n # TODO: Implementar a foto no sistemas\r\n if not len(self.subcategorias):\r\n print(\"Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n\")\r\n self.create_subcategoria()\r\n else:\r\n print(\"- Cadastrar PRODUTO -\")\r\n escolhe = input(\"SUBCATEGORIA (Nome ou Código): \").lower()\r\n codigo = input(\"CÓDIGO: \").strip()\r\n nome = input(\"NOME: \").strip()\r\n descrição = input(\"DESCRIÇÃO: \").strip()\r\n\r\n estoquemax = input(\"Quantidade Maxima em Estoque: \")\r\n while not produtos.valida_estoque(estoquemax):\r\n print(\"Valor Inválido!\")\r\n estoquemax = input(\"Valor deve ser Numérico: \")\r\n\r\n estoquemin = input(\"Quantidade Minima em Estoque: \")\r\n while not produtos.valida_estoque(estoquemin):\r\n print(\"Valor Inválido!\")\r\n estoquemin = input(\"Valor deve ser Numérico: \")\r\n\r\n valorvenda = input(\"Preço Unitário: \")\r\n while not produtos.valida_valorvenda(valorvenda):\r\n print(\"Valor Inválido!\")\r\n estoquemax = input(\"Valor deve ser Numérico: \")\r\n\r\n valorcompra = input(\"Valor de Compra: \")\r\n while not produtos.valida_valorvenda(valorcompra):\r\n print(\"Valor Inválido!\")\r\n estoquemax = input(\"Valor deve ser Numérico: \")\r\n\r\n foto = input(\"Arquivo de foto: \")\r\n\r\n subcategoria = 0\r\n\r\n for scat in self.subcategorias:\r\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\r\n subcategoria = scat\r\n break\r\n else:\r\n print(\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\")\r\n choice = input()\r\n if choice.lower() == 's' or choice == '1':\r\n self.create_subcategoria()\r\n else:\r\n self.create_produto()\r\n\r\n produto = Produtos( subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto)\r\n\r\n if produto not in self.produtos:\r\n self.produtos.append(produto)\r\n\r\n # funcionalidade pedida na especificação\r\n\r\n def low_stock_alarm(self): # aviso de estoque baixo\r\n pass\r\n\r\n def consulta_estoque(self): # exibe itens disponiveis no estoque\r\n print(\"Exibindo estoque\")\r\n if not len(self.categorias):\r\n print(\"Não há Categorias Registrados!\")\r\n else:\r\n for categoria in self.categorias:\r\n print(categoria, end=\" \")\r\n print()\r\n if not len(self.subcategorias):\r\n print(\"Não há Subcategorias Registradas!\")\r\n else:\r\n for subcategoria in self.subcategorias:\r\n print(subcategoria, end=\" \")\r\n print()\r\n if not len(self.produtos):\r\n print(\"Não há Produtos Registrados!\")\r\n else:\r\n for produto in self.produtos:\r\n print(produto, end=\" \")\r\n\r\n self.menu_estoque()\r\n\r\n def altera_item(self): # altera um item disponivel no estoque\r\n print(\"alterando item do estoque\")\r\n self.menu_estoque()\r\n\r\n def remove_item(self): # remove um item disponivel no estoque - n remover se o item ainda tem produtos no estoque\r\n print(\"Removendo item do estoque\")\r\n self.menu_estoque()\r\n\r\n def adiciona_item(self): # adiciona novo item ao estoque\r\n print(\"Adicionando item ao estoque\")\r\n while 1:\r\n print(\"************* Menu Adicionar: ******************\")\r\n print(\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\")\r\n opcao = input()\r\n while not self.valida_opcao(opcao):\r\n print(\"Opção Inválida!\")\r\n opcao = input()\r\n if opcao == '1':\r\n self.create_categoria()\r\n elif opcao == '2':\r\n self.create_subcategoria()\r\n elif opcao == '3':\r\n pass\r\n elif opcao == '4':\r\n break\r\n self.menu_estoque()\r\n\r\n def menu_estoque(self):\r\n print(\"Sistema de Vendas ao Consumidor\")\r\n print(\"****** MENU DE ESTOQUE *****\")\r\n print(\"Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar\")\r\n opcao = input()\r\n\r\n while not self.valida_opcao(opcao):\r\n print(\"Opção Inválida!\")\r\n opcao = input()\r\n\r\n if opcao == '1':\r\n self.consulta_estoque()\r\n elif opcao == '2':\r\n self.adiciona_item()\r\n elif opcao == '3':\r\n self.remove_item()\r\n elif opcao == '4':\r\n self.altera_item()\r\n\r\n def valida_opcao(self, opcao):\r\n if opcao.isdigit():\r\n return True\r\n else:\r\n return False\r\n\r\nestoque = Estoque()\r\n","from src.produtos import *\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n\n def save_categoria(self, categoria):\n pass\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n\n def low_stock_alarm(self):\n pass\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n\n def remove_item(self):\n print('Removendo item do estoque')\n self.menu_estoque()\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n\n def menu_estoque(self):\n print('Sistema de Vendas ao Consumidor')\n print('****** MENU DE ESTOQUE *****')\n print(\n 'Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar'\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.consulta_estoque()\n elif opcao == '2':\n self.adiciona_item()\n elif opcao == '3':\n self.remove_item()\n elif opcao == '4':\n self.altera_item()\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\nestoque = Estoque()\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n\n def save_categoria(self, categoria):\n pass\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n\n def low_stock_alarm(self):\n pass\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n\n def remove_item(self):\n print('Removendo item do estoque')\n self.menu_estoque()\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n\n def menu_estoque(self):\n print('Sistema de Vendas ao Consumidor')\n print('****** MENU DE ESTOQUE *****')\n print(\n 'Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar'\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.consulta_estoque()\n elif opcao == '2':\n self.adiciona_item()\n elif opcao == '3':\n self.remove_item()\n elif opcao == '4':\n self.altera_item()\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\nestoque = Estoque()\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n\n def save_categoria(self, categoria):\n pass\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n\n def low_stock_alarm(self):\n pass\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n\n def remove_item(self):\n print('Removendo item do estoque')\n self.menu_estoque()\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n\n def menu_estoque(self):\n print('Sistema de Vendas ao Consumidor')\n print('****** MENU DE ESTOQUE *****')\n print(\n 'Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar'\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.consulta_estoque()\n elif opcao == '2':\n self.adiciona_item()\n elif opcao == '3':\n self.remove_item()\n elif opcao == '4':\n self.altera_item()\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n\n def save_categoria(self, categoria):\n pass\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n\n def remove_item(self):\n print('Removendo item do estoque')\n self.menu_estoque()\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n\n def menu_estoque(self):\n print('Sistema de Vendas ao Consumidor')\n print('****** MENU DE ESTOQUE *****')\n print(\n 'Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar'\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.consulta_estoque()\n elif opcao == '2':\n self.adiciona_item()\n elif opcao == '3':\n self.remove_item()\n elif opcao == '4':\n self.altera_item()\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n\n def remove_item(self):\n print('Removendo item do estoque')\n self.menu_estoque()\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n\n def menu_estoque(self):\n print('Sistema de Vendas ao Consumidor')\n print('****** MENU DE ESTOQUE *****')\n print(\n 'Digite Ação!\\n1 - Consultar Estoque\\n2 - Adicionar\\n3 - Remover\\n4 - Alterar'\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.consulta_estoque()\n elif opcao == '2':\n self.adiciona_item()\n elif opcao == '3':\n self.remove_item()\n elif opcao == '4':\n self.altera_item()\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n\n def remove_item(self):\n print('Removendo item do estoque')\n self.menu_estoque()\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n\n def save_subcategorias(self, subcategoria):\n pass\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n\n def save_produtos(self, produto):\n pass\n\n def create_categoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição\n \"\"\"\n print('- Criar CATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n categoria = Categoria(codigo, nome, descrição)\n if categoria not in self.categorias:\n self.categorias.append(categoria)\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n\n def save_produtos(self, produto):\n pass\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n\n def valida_opcao(self, opcao):\n if opcao.isdigit():\n return True\n else:\n return False\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n\n def save_produtos(self, produto):\n pass\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n\n def consulta_estoque(self):\n print('Exibindo estoque')\n if not len(self.categorias):\n print('Não há Categorias Registrados!')\n else:\n for categoria in self.categorias:\n print(categoria, end=' ')\n print()\n if not len(self.subcategorias):\n print('Não há Subcategorias Registradas!')\n else:\n for subcategoria in self.subcategorias:\n print(subcategoria, end=' ')\n print()\n if not len(self.produtos):\n print('Não há Produtos Registrados!')\n else:\n for produto in self.produtos:\n print(produto, end=' ')\n self.menu_estoque()\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n\n def save_produtos(self, produto):\n pass\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n <function token>\n\n def altera_item(self):\n print('alterando item do estoque')\n self.menu_estoque()\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n\n def save_produtos(self, produto):\n pass\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n <function token>\n <function token>\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n <function token>\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n <function token>\n <function token>\n <function token>\n\n def adiciona_item(self):\n print('Adicionando item ao estoque')\n while 1:\n print('************* Menu Adicionar: ******************')\n print(\n \"\"\"Digite Ação!\n1 - Adicionar Categoria\n2 - Adicionar Subcategoria\n3 - Adicionar Produtos\n4 - Sair\"\"\"\n )\n opcao = input()\n while not self.valida_opcao(opcao):\n print('Opção Inválida!')\n opcao = input()\n if opcao == '1':\n self.create_categoria()\n elif opcao == '2':\n self.create_subcategoria()\n elif opcao == '3':\n pass\n elif opcao == '4':\n break\n self.menu_estoque()\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n\n def __init__(self):\n self.categorias = []\n self.subcategorias = []\n self.produtos = []\n self.menu_estoque()\n <function token>\n <function token>\n <function token>\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n\n def create_produto(self):\n \"\"\"\"\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\n Produtos são itens que podem ser vendidos.\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\n\n TODELETE: Por enquanto foto recebe uma string qualquer\n\n \"\"\"\n if not len(self.subcategorias):\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\n')\n self.create_subcategoria()\n else:\n print('- Cadastrar PRODUTO -')\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n estoquemax = input('Quantidade Maxima em Estoque: ')\n while not produtos.valida_estoque(estoquemax):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n estoquemin = input('Quantidade Minima em Estoque: ')\n while not produtos.valida_estoque(estoquemin):\n print('Valor Inválido!')\n estoquemin = input('Valor deve ser Numérico: ')\n valorvenda = input('Preço Unitário: ')\n while not produtos.valida_valorvenda(valorvenda):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n valorcompra = input('Valor de Compra: ')\n while not produtos.valida_valorvenda(valorcompra):\n print('Valor Inválido!')\n estoquemax = input('Valor deve ser Numérico: ')\n foto = input('Arquivo de foto: ')\n subcategoria = 0\n for scat in self.subcategorias:\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\n subcategoria = scat\n break\n else:\n print(\n \"\"\"Subcategoria não Encontrada!\nDeseja criar uma SUBCATEGORIA?\n1- Sim\n2 - Não\"\"\"\n )\n choice = input()\n if choice.lower() == 's' or choice == '1':\n self.create_subcategoria()\n else:\n self.create_produto()\n produto = Produtos(subcategoria, codigo, nome, descricao,\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\n if produto not in self.produtos:\n self.produtos.append(produto)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n def create_subcategoria(self):\n \"\"\"\"\n Cria uma categoria através dos dados recolhidos pelo formulário.\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\n \"\"\"\n if len(self.categorias) == 0:\n print('Você deve criar pelo menos uma CATEGORIA!\\n')\n self.create_categoria()\n print('- Criar SUBCATEGORIA -')\n codigo = input('CÓDIGO: ').strip()\n nome = input('NOME: ').strip()\n descrição = input('DESCRIÇÃO: ').strip()\n escolhe = input('CATEGORIA (Nome ou Código): ')\n categoria = 0\n for cat in self.categorias:\n if cat.nome == escolhe or cat.codigo == escolhe:\n categoria = cat\n break\n else:\n print(\n 'Categoria não Encontrada!\\nVocê deve criar uma CATEGORIA!'\n )\n self.create_categoria()\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\n if subcategoria not in self.subcategorias:\n self.subcategorias.append(subcategoria)\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n\n\nclass Estoque(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<assignment token>\n","<import token>\n<class token>\n<assignment token>\n"],"string":"[\n \"from src.produtos import *\\r\\n\\r\\n\\r\\nclass Estoque(object):\\r\\n def __init__(self):\\r\\n self.categorias = []\\r\\n self.subcategorias = []\\r\\n self.produtos = []\\r\\n self.menu_estoque()\\r\\n\\r\\n def save_categoria(self, categoria):\\r\\n pass\\r\\n\\r\\n def save_subcategorias(self, subcategoria):\\r\\n pass\\r\\n\\r\\n def save_produtos(self, produto):\\r\\n pass\\r\\n\\r\\n def create_categoria(self):\\r\\n \\\"\\\"\\\"\\\"\\r\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\r\\n Os dados são: Codigo, nome e descrição\\r\\n \\\"\\\"\\\"\\r\\n print(\\\"- Criar CATEGORIA -\\\")\\r\\n codigo = input(\\\"CÓDIGO: \\\").strip()\\r\\n nome = input(\\\"NOME: \\\").strip()\\r\\n descrição = input(\\\"DESCRIÇÃO: \\\").strip()\\r\\n categoria = Categoria(codigo, nome, descrição)\\r\\n if categoria not in self.categorias:\\r\\n self.categorias.append(categoria)\\r\\n\\r\\n def create_subcategoria(self):\\r\\n \\\"\\\"\\\"\\\"\\r\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\r\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\r\\n \\\"\\\"\\\"\\r\\n if len(self.categorias) == 0:\\r\\n print(\\\"Você deve criar pelo menos uma CATEGORIA!\\\\n\\\")\\r\\n self.create_categoria()\\r\\n print(\\\"- Criar SUBCATEGORIA -\\\")\\r\\n codigo = input(\\\"CÓDIGO: \\\").strip()\\r\\n nome = input(\\\"NOME: \\\").strip()\\r\\n descrição = input(\\\"DESCRIÇÃO: \\\").strip()\\r\\n escolhe = input(\\\"CATEGORIA (Nome ou Código): \\\")\\r\\n categoria = 0\\r\\n\\r\\n for cat in self.categorias:\\r\\n if cat.nome == escolhe or cat.codigo == escolhe:\\r\\n categoria = cat\\r\\n break\\r\\n else:\\r\\n print(\\\"Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!\\\")\\r\\n self.create_categoria()\\r\\n\\r\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\r\\n\\r\\n if subcategoria not in self.subcategorias:\\r\\n self.subcategorias.append(subcategoria)\\r\\n\\r\\n def create_produto(self):\\r\\n \\\"\\\"\\\"\\\"\\r\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\r\\n Produtos são itens que podem ser vendidos.\\r\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\r\\n\\r\\n TODELETE: Por enquanto foto recebe uma string qualquer\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n # TODO: Implementar a foto no sistemas\\r\\n if not len(self.subcategorias):\\r\\n print(\\\"Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n\\\")\\r\\n self.create_subcategoria()\\r\\n else:\\r\\n print(\\\"- Cadastrar PRODUTO -\\\")\\r\\n escolhe = input(\\\"SUBCATEGORIA (Nome ou Código): \\\").lower()\\r\\n codigo = input(\\\"CÓDIGO: \\\").strip()\\r\\n nome = input(\\\"NOME: \\\").strip()\\r\\n descrição = input(\\\"DESCRIÇÃO: \\\").strip()\\r\\n\\r\\n estoquemax = input(\\\"Quantidade Maxima em Estoque: \\\")\\r\\n while not produtos.valida_estoque(estoquemax):\\r\\n print(\\\"Valor Inválido!\\\")\\r\\n estoquemax = input(\\\"Valor deve ser Numérico: \\\")\\r\\n\\r\\n estoquemin = input(\\\"Quantidade Minima em Estoque: \\\")\\r\\n while not produtos.valida_estoque(estoquemin):\\r\\n print(\\\"Valor Inválido!\\\")\\r\\n estoquemin = input(\\\"Valor deve ser Numérico: \\\")\\r\\n\\r\\n valorvenda = input(\\\"Preço Unitário: \\\")\\r\\n while not produtos.valida_valorvenda(valorvenda):\\r\\n print(\\\"Valor Inválido!\\\")\\r\\n estoquemax = input(\\\"Valor deve ser Numérico: \\\")\\r\\n\\r\\n valorcompra = input(\\\"Valor de Compra: \\\")\\r\\n while not produtos.valida_valorvenda(valorcompra):\\r\\n print(\\\"Valor Inválido!\\\")\\r\\n estoquemax = input(\\\"Valor deve ser Numérico: \\\")\\r\\n\\r\\n foto = input(\\\"Arquivo de foto: \\\")\\r\\n\\r\\n subcategoria = 0\\r\\n\\r\\n for scat in self.subcategorias:\\r\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\r\\n subcategoria = scat\\r\\n break\\r\\n else:\\r\\n print(\\\"Subcategoria não Encontrada!\\\\nDeseja criar uma SUBCATEGORIA?\\\\n1- Sim\\\\n2 - Não\\\")\\r\\n choice = input()\\r\\n if choice.lower() == 's' or choice == '1':\\r\\n self.create_subcategoria()\\r\\n else:\\r\\n self.create_produto()\\r\\n\\r\\n produto = Produtos( subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto)\\r\\n\\r\\n if produto not in self.produtos:\\r\\n self.produtos.append(produto)\\r\\n\\r\\n # funcionalidade pedida na especificação\\r\\n\\r\\n def low_stock_alarm(self): # aviso de estoque baixo\\r\\n pass\\r\\n\\r\\n def consulta_estoque(self): # exibe itens disponiveis no estoque\\r\\n print(\\\"Exibindo estoque\\\")\\r\\n if not len(self.categorias):\\r\\n print(\\\"Não há Categorias Registrados!\\\")\\r\\n else:\\r\\n for categoria in self.categorias:\\r\\n print(categoria, end=\\\" \\\")\\r\\n print()\\r\\n if not len(self.subcategorias):\\r\\n print(\\\"Não há Subcategorias Registradas!\\\")\\r\\n else:\\r\\n for subcategoria in self.subcategorias:\\r\\n print(subcategoria, end=\\\" \\\")\\r\\n print()\\r\\n if not len(self.produtos):\\r\\n print(\\\"Não há Produtos Registrados!\\\")\\r\\n else:\\r\\n for produto in self.produtos:\\r\\n print(produto, end=\\\" \\\")\\r\\n\\r\\n self.menu_estoque()\\r\\n\\r\\n def altera_item(self): # altera um item disponivel no estoque\\r\\n print(\\\"alterando item do estoque\\\")\\r\\n self.menu_estoque()\\r\\n\\r\\n def remove_item(self): # remove um item disponivel no estoque - n remover se o item ainda tem produtos no estoque\\r\\n print(\\\"Removendo item do estoque\\\")\\r\\n self.menu_estoque()\\r\\n\\r\\n def adiciona_item(self): # adiciona novo item ao estoque\\r\\n print(\\\"Adicionando item ao estoque\\\")\\r\\n while 1:\\r\\n print(\\\"************* Menu Adicionar: ******************\\\")\\r\\n print(\\\"Digite Ação!\\\\n1 - Adicionar Categoria\\\\n2 - Adicionar Subcategoria\\\\n3 - Adicionar Produtos\\\\n4 - Sair\\\")\\r\\n opcao = input()\\r\\n while not self.valida_opcao(opcao):\\r\\n print(\\\"Opção Inválida!\\\")\\r\\n opcao = input()\\r\\n if opcao == '1':\\r\\n self.create_categoria()\\r\\n elif opcao == '2':\\r\\n self.create_subcategoria()\\r\\n elif opcao == '3':\\r\\n pass\\r\\n elif opcao == '4':\\r\\n break\\r\\n self.menu_estoque()\\r\\n\\r\\n def menu_estoque(self):\\r\\n print(\\\"Sistema de Vendas ao Consumidor\\\")\\r\\n print(\\\"****** MENU DE ESTOQUE *****\\\")\\r\\n print(\\\"Digite Ação!\\\\n1 - Consultar Estoque\\\\n2 - Adicionar\\\\n3 - Remover\\\\n4 - Alterar\\\")\\r\\n opcao = input()\\r\\n\\r\\n while not self.valida_opcao(opcao):\\r\\n print(\\\"Opção Inválida!\\\")\\r\\n opcao = input()\\r\\n\\r\\n if opcao == '1':\\r\\n self.consulta_estoque()\\r\\n elif opcao == '2':\\r\\n self.adiciona_item()\\r\\n elif opcao == '3':\\r\\n self.remove_item()\\r\\n elif opcao == '4':\\r\\n self.altera_item()\\r\\n\\r\\n def valida_opcao(self, opcao):\\r\\n if opcao.isdigit():\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n\\r\\nestoque = Estoque()\\r\\n\",\n \"from src.produtos import *\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n\\n def save_categoria(self, categoria):\\n pass\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n\\n def low_stock_alarm(self):\\n pass\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n\\n def remove_item(self):\\n print('Removendo item do estoque')\\n self.menu_estoque()\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n\\n def menu_estoque(self):\\n print('Sistema de Vendas ao Consumidor')\\n print('****** MENU DE ESTOQUE *****')\\n print(\\n 'Digite Ação!\\\\n1 - Consultar Estoque\\\\n2 - Adicionar\\\\n3 - Remover\\\\n4 - Alterar'\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.consulta_estoque()\\n elif opcao == '2':\\n self.adiciona_item()\\n elif opcao == '3':\\n self.remove_item()\\n elif opcao == '4':\\n self.altera_item()\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\nestoque = Estoque()\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n\\n def save_categoria(self, categoria):\\n pass\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n\\n def low_stock_alarm(self):\\n pass\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n\\n def remove_item(self):\\n print('Removendo item do estoque')\\n self.menu_estoque()\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n\\n def menu_estoque(self):\\n print('Sistema de Vendas ao Consumidor')\\n print('****** MENU DE ESTOQUE *****')\\n print(\\n 'Digite Ação!\\\\n1 - Consultar Estoque\\\\n2 - Adicionar\\\\n3 - Remover\\\\n4 - Alterar'\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.consulta_estoque()\\n elif opcao == '2':\\n self.adiciona_item()\\n elif opcao == '3':\\n self.remove_item()\\n elif opcao == '4':\\n self.altera_item()\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\nestoque = Estoque()\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n\\n def save_categoria(self, categoria):\\n pass\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n\\n def low_stock_alarm(self):\\n pass\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n\\n def remove_item(self):\\n print('Removendo item do estoque')\\n self.menu_estoque()\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n\\n def menu_estoque(self):\\n print('Sistema de Vendas ao Consumidor')\\n print('****** MENU DE ESTOQUE *****')\\n print(\\n 'Digite Ação!\\\\n1 - Consultar Estoque\\\\n2 - Adicionar\\\\n3 - Remover\\\\n4 - Alterar'\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.consulta_estoque()\\n elif opcao == '2':\\n self.adiciona_item()\\n elif opcao == '3':\\n self.remove_item()\\n elif opcao == '4':\\n self.altera_item()\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n\\n def save_categoria(self, categoria):\\n pass\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n\\n def remove_item(self):\\n print('Removendo item do estoque')\\n self.menu_estoque()\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n\\n def menu_estoque(self):\\n print('Sistema de Vendas ao Consumidor')\\n print('****** MENU DE ESTOQUE *****')\\n print(\\n 'Digite Ação!\\\\n1 - Consultar Estoque\\\\n2 - Adicionar\\\\n3 - Remover\\\\n4 - Alterar'\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.consulta_estoque()\\n elif opcao == '2':\\n self.adiciona_item()\\n elif opcao == '3':\\n self.remove_item()\\n elif opcao == '4':\\n self.altera_item()\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n\\n def remove_item(self):\\n print('Removendo item do estoque')\\n self.menu_estoque()\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n\\n def menu_estoque(self):\\n print('Sistema de Vendas ao Consumidor')\\n print('****** MENU DE ESTOQUE *****')\\n print(\\n 'Digite Ação!\\\\n1 - Consultar Estoque\\\\n2 - Adicionar\\\\n3 - Remover\\\\n4 - Alterar'\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.consulta_estoque()\\n elif opcao == '2':\\n self.adiciona_item()\\n elif opcao == '3':\\n self.remove_item()\\n elif opcao == '4':\\n self.altera_item()\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n\\n def remove_item(self):\\n print('Removendo item do estoque')\\n self.menu_estoque()\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n\\n def save_subcategorias(self, subcategoria):\\n pass\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n def save_produtos(self, produto):\\n pass\\n\\n def create_categoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição\\n \\\"\\\"\\\"\\n print('- Criar CATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n categoria = Categoria(codigo, nome, descrição)\\n if categoria not in self.categorias:\\n self.categorias.append(categoria)\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n def save_produtos(self, produto):\\n pass\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n\\n def valida_opcao(self, opcao):\\n if opcao.isdigit():\\n return True\\n else:\\n return False\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n def save_produtos(self, produto):\\n pass\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n\\n def consulta_estoque(self):\\n print('Exibindo estoque')\\n if not len(self.categorias):\\n print('Não há Categorias Registrados!')\\n else:\\n for categoria in self.categorias:\\n print(categoria, end=' ')\\n print()\\n if not len(self.subcategorias):\\n print('Não há Subcategorias Registradas!')\\n else:\\n for subcategoria in self.subcategorias:\\n print(subcategoria, end=' ')\\n print()\\n if not len(self.produtos):\\n print('Não há Produtos Registrados!')\\n else:\\n for produto in self.produtos:\\n print(produto, end=' ')\\n self.menu_estoque()\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n def save_produtos(self, produto):\\n pass\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n <function token>\\n\\n def altera_item(self):\\n print('alterando item do estoque')\\n self.menu_estoque()\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n def save_produtos(self, produto):\\n pass\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def adiciona_item(self):\\n print('Adicionando item ao estoque')\\n while 1:\\n print('************* Menu Adicionar: ******************')\\n print(\\n \\\"\\\"\\\"Digite Ação!\\n1 - Adicionar Categoria\\n2 - Adicionar Subcategoria\\n3 - Adicionar Produtos\\n4 - Sair\\\"\\\"\\\"\\n )\\n opcao = input()\\n while not self.valida_opcao(opcao):\\n print('Opção Inválida!')\\n opcao = input()\\n if opcao == '1':\\n self.create_categoria()\\n elif opcao == '2':\\n self.create_subcategoria()\\n elif opcao == '3':\\n pass\\n elif opcao == '4':\\n break\\n self.menu_estoque()\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n\\n def __init__(self):\\n self.categorias = []\\n self.subcategorias = []\\n self.produtos = []\\n self.menu_estoque()\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n\\n def create_produto(self):\\n \\\"\\\"\\\"\\\"\\n Cria produto a ser controlado pelo estoque. Um produto deve pertencer a uma subcategoria.\\n Produtos são itens que podem ser vendidos.\\n Possuem subcategoria, codigo, nome, descricao, estoquemax, estoquemin, valorvenda, valorcompra, foto\\n\\n TODELETE: Por enquanto foto recebe uma string qualquer\\n\\n \\\"\\\"\\\"\\n if not len(self.subcategorias):\\n print('Produto deve ter CATEGORIA ou uma SUBCATEGORIA!\\\\n')\\n self.create_subcategoria()\\n else:\\n print('- Cadastrar PRODUTO -')\\n escolhe = input('SUBCATEGORIA (Nome ou Código): ').lower()\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n estoquemax = input('Quantidade Maxima em Estoque: ')\\n while not produtos.valida_estoque(estoquemax):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n estoquemin = input('Quantidade Minima em Estoque: ')\\n while not produtos.valida_estoque(estoquemin):\\n print('Valor Inválido!')\\n estoquemin = input('Valor deve ser Numérico: ')\\n valorvenda = input('Preço Unitário: ')\\n while not produtos.valida_valorvenda(valorvenda):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n valorcompra = input('Valor de Compra: ')\\n while not produtos.valida_valorvenda(valorcompra):\\n print('Valor Inválido!')\\n estoquemax = input('Valor deve ser Numérico: ')\\n foto = input('Arquivo de foto: ')\\n subcategoria = 0\\n for scat in self.subcategorias:\\n if scat.nome.lower() == escolhe or scat.codigo == escolhe:\\n subcategoria = scat\\n break\\n else:\\n print(\\n \\\"\\\"\\\"Subcategoria não Encontrada!\\nDeseja criar uma SUBCATEGORIA?\\n1- Sim\\n2 - Não\\\"\\\"\\\"\\n )\\n choice = input()\\n if choice.lower() == 's' or choice == '1':\\n self.create_subcategoria()\\n else:\\n self.create_produto()\\n produto = Produtos(subcategoria, codigo, nome, descricao,\\n estoquemax, estoquemin, valorvenda, valorcompra, foto)\\n if produto not in self.produtos:\\n self.produtos.append(produto)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def create_subcategoria(self):\\n \\\"\\\"\\\"\\\"\\n Cria uma categoria através dos dados recolhidos pelo formulário.\\n Os dados são: Codigo, nome e descrição e a passagem de um objeto categoria\\n \\\"\\\"\\\"\\n if len(self.categorias) == 0:\\n print('Você deve criar pelo menos uma CATEGORIA!\\\\n')\\n self.create_categoria()\\n print('- Criar SUBCATEGORIA -')\\n codigo = input('CÓDIGO: ').strip()\\n nome = input('NOME: ').strip()\\n descrição = input('DESCRIÇÃO: ').strip()\\n escolhe = input('CATEGORIA (Nome ou Código): ')\\n categoria = 0\\n for cat in self.categorias:\\n if cat.nome == escolhe or cat.codigo == escolhe:\\n categoria = cat\\n break\\n else:\\n print(\\n 'Categoria não Encontrada!\\\\nVocê deve criar uma CATEGORIA!'\\n )\\n self.create_categoria()\\n subcategoria = Subcategoria(categoria, codigo, nome, descrição)\\n if subcategoria not in self.subcategorias:\\n self.subcategorias.append(subcategoria)\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n\\n\\nclass Estoque(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<assignment token>\\n\",\n \"<import token>\\n<class token>\\n<assignment token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5885,"cells":{"index":{"kind":"number","value":5885,"string":"5,885"},"blob_id":{"kind":"string","value":"6fa9dfadc60108e1718c6688f07de877b0ac0afd"},"code":{"kind":"string","value":"#!usr/bin/python\n# -*- coding:UTF-8 -*-\n\n'''\nIntroduction: \nImplementation of Stack \n\nCreated on: Oct 28, 2014\n\n@author: ICY\n'''\n\n#-------------------------FUNCTION---------------------------#\n\nclass Stack(object):\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n\n def clear(self):\n self.items = []\n\n def push(self,item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n\n def get_top(self):\n return self.items[len(self.items)-1]\n\n\n#----------------------------SELF TEST----------------------------#\n\ndef main():\n s=Stack()\n print(s.is_empty())\n s.push(4)\n s.push('dog')\n print(s.get_top())\n s.push(True)\n print(s.size())\n print(s.is_empty())\n s.push(8.4)\n print(s.pop())\n print(s.pop())\n print(s.size())\n pass\n \nif __name__ == '__main__': \n main()\n \n \n"},"steps":{"kind":"list like","value":["#!usr/bin/python\n# -*- coding:UTF-8 -*-\n\n'''\nIntroduction: \nImplementation of Stack \n\nCreated on: Oct 28, 2014\n\n@author: ICY\n'''\n\n#-------------------------FUNCTION---------------------------#\n\nclass Stack(object):\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n\n def clear(self):\n self.items = []\n\n def push(self,item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n\n def get_top(self):\n return self.items[len(self.items)-1]\n\n\n#----------------------------SELF TEST----------------------------#\n\ndef main():\n s=Stack()\n print(s.is_empty())\n s.push(4)\n s.push('dog')\n print(s.get_top())\n s.push(True)\n print(s.size())\n print(s.is_empty())\n s.push(8.4)\n print(s.pop())\n print(s.pop())\n print(s.size())\n pass\n \nif __name__ == '__main__': \n main()\n \n \n","<docstring token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n\n def clear(self):\n self.items = []\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n\n def get_top(self):\n return self.items[len(self.items) - 1]\n\n\ndef main():\n s = Stack()\n print(s.is_empty())\n s.push(4)\n s.push('dog')\n print(s.get_top())\n s.push(True)\n print(s.size())\n print(s.is_empty())\n s.push(8.4)\n print(s.pop())\n print(s.pop())\n print(s.size())\n pass\n\n\nif __name__ == '__main__':\n main()\n","<docstring token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n\n def clear(self):\n self.items = []\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n\n def get_top(self):\n return self.items[len(self.items) - 1]\n\n\ndef main():\n s = Stack()\n print(s.is_empty())\n s.push(4)\n s.push('dog')\n print(s.get_top())\n s.push(True)\n print(s.size())\n print(s.is_empty())\n s.push(8.4)\n print(s.pop())\n print(s.pop())\n print(s.size())\n pass\n\n\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n\n def clear(self):\n self.items = []\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n\n def get_top(self):\n return self.items[len(self.items) - 1]\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n <function token>\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n\n def get_top(self):\n return self.items[len(self.items) - 1]\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n <function token>\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n\n def size(self):\n return len(self.items)\n <function token>\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n return self.items == []\n <function token>\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n <function token>\n <function token>\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n <function token>\n\n def is_empty(self):\n return self.items == []\n <function token>\n\n def push(self, item):\n self.items.append(item)\n\n def pop(self):\n return self.items.pop()\n <function token>\n <function token>\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n <function token>\n\n def is_empty(self):\n return self.items == []\n <function token>\n <function token>\n\n def pop(self):\n return self.items.pop()\n <function token>\n <function token>\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n <function token>\n <function token>\n <function token>\n <function token>\n\n def pop(self):\n return self.items.pop()\n <function token>\n <function token>\n\n\n<function token>\n<code token>\n","<docstring token>\n\n\nclass Stack(object):\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n <function token>\n\n\n<function token>\n<code token>\n","<docstring token>\n<class token>\n<function token>\n<code token>\n"],"string":"[\n \"#!usr/bin/python\\n# -*- coding:UTF-8 -*-\\n\\n'''\\nIntroduction: \\nImplementation of Stack \\n\\nCreated on: Oct 28, 2014\\n\\n@author: ICY\\n'''\\n\\n#-------------------------FUNCTION---------------------------#\\n\\nclass Stack(object):\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n\\n def clear(self):\\n self.items = []\\n\\n def push(self,item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n\\n def size(self):\\n return len(self.items)\\n\\n def get_top(self):\\n return self.items[len(self.items)-1]\\n\\n\\n#----------------------------SELF TEST----------------------------#\\n\\ndef main():\\n s=Stack()\\n print(s.is_empty())\\n s.push(4)\\n s.push('dog')\\n print(s.get_top())\\n s.push(True)\\n print(s.size())\\n print(s.is_empty())\\n s.push(8.4)\\n print(s.pop())\\n print(s.pop())\\n print(s.size())\\n pass\\n \\nif __name__ == '__main__': \\n main()\\n \\n \\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n\\n def clear(self):\\n self.items = []\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n\\n def size(self):\\n return len(self.items)\\n\\n def get_top(self):\\n return self.items[len(self.items) - 1]\\n\\n\\ndef main():\\n s = Stack()\\n print(s.is_empty())\\n s.push(4)\\n s.push('dog')\\n print(s.get_top())\\n s.push(True)\\n print(s.size())\\n print(s.is_empty())\\n s.push(8.4)\\n print(s.pop())\\n print(s.pop())\\n print(s.size())\\n pass\\n\\n\\nif __name__ == '__main__':\\n main()\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n\\n def clear(self):\\n self.items = []\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n\\n def size(self):\\n return len(self.items)\\n\\n def get_top(self):\\n return self.items[len(self.items) - 1]\\n\\n\\ndef main():\\n s = Stack()\\n print(s.is_empty())\\n s.push(4)\\n s.push('dog')\\n print(s.get_top())\\n s.push(True)\\n print(s.size())\\n print(s.is_empty())\\n s.push(8.4)\\n print(s.pop())\\n print(s.pop())\\n print(s.size())\\n pass\\n\\n\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n\\n def clear(self):\\n self.items = []\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n\\n def size(self):\\n return len(self.items)\\n\\n def get_top(self):\\n return self.items[len(self.items) - 1]\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n <function token>\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n\\n def size(self):\\n return len(self.items)\\n\\n def get_top(self):\\n return self.items[len(self.items) - 1]\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n <function token>\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n\\n def size(self):\\n return len(self.items)\\n <function token>\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n\\n def __init__(self):\\n self.items = []\\n\\n def is_empty(self):\\n return self.items == []\\n <function token>\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n <function token>\\n\\n def is_empty(self):\\n return self.items == []\\n <function token>\\n\\n def push(self, item):\\n self.items.append(item)\\n\\n def pop(self):\\n return self.items.pop()\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n <function token>\\n\\n def is_empty(self):\\n return self.items == []\\n <function token>\\n <function token>\\n\\n def pop(self):\\n return self.items.pop()\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n def pop(self):\\n return self.items.pop()\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n\\n\\nclass Stack(object):\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<docstring token>\\n<class token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5886,"cells":{"index":{"kind":"number","value":5886,"string":"5,886"},"blob_id":{"kind":"string","value":"4f87c2602e3233889888e419296f67fe40a2db0f"},"code":{"kind":"string","value":"#!/usr/bin/python\n#==========================================================================\n#\n# Copyright Insight Software Consortium\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.txt\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n#\n#==========================================================================*/\n# This script is used to automate the modularization process. The following\n# steps are included:\n# 1. Move the files in the monolithic ITK into modules of the modularized ITK.\n# A manifest text file that lists all the files and their destinations is\n# required to run the script.By default, the manifest file is named as\n# \"Manifest.txt\" in the same directory of this script.\n# 2. Create CMake Files and put them into modules.\n\n# Modified by Guillaume Pasero <guillaume.pasero@c-s.fr>\n# add dependencies in otb-module.cmake\n\n# To run it, type ./modulizer.py OTB_PATH Manifest_PATH\n# from the otb-modulizer root directory.\n\nprint \"*************************************************************************\"\nprint \"WARNINGs! This modularization script is still in its experimental stage.\"\nprint \"Current OTB users should not run this script.\"\nprint \"*************************************************************************\"\n\n\nimport shutil\nimport os.path as op\nimport re\nimport sys\nimport os\nimport stat\nimport glob\nimport documentationCheck\nimport analyseAppManifest\nimport dispatchTests\nimport dispatchExamples\nfrom subprocess import call\n\ndef parseFullManifest(path):\n sourceList = []\n nbFields = 6\n fd = open(path,'rb')\n # skip first line and detect separator\n firstLine = fd.readline()\n sep = ','\n if (len(firstLine.split(sep)) != nbFields):\n sep = ';'\n if (len(firstLine.split(sep)) != nbFields):\n sep = '\\t'\n if (len(firstLine.split(sep)) != nbFields):\n print \"Unknown separator\"\n return sourceList\n \n fd.seek(0)\n \n # parse file\n for line in fd:\n if (line.strip()).startswith(\"#\"):\n continue\n words = line.split(sep)\n if (len(words) < (nbFields-1)):\n print \"Wrong number of fields, skipping this line\"\n continue\n fullPath = words[0].strip(\" ,;\\t\\n\\r\")\n groupName = words[2].strip(\" ,;\\t\\n\\r\")\n moduleName = words[3].strip(\" ,;\\t\\n\\r\")\n subDir = words[4].strip(\" ,;\\t\\n\\r\")\n sourceName = op.basename(fullPath)\n \n sourceList.append({\"path\":fullPath, \"group\":groupName, \"module\":moduleName, \"subDir\":subDir})\n fd.close()\n \n return sourceList\n\n\ndef parseDescriptions(path):\n output = {}\n sep = '|'\n nbFields = 2\n fd = open(path,'rb')\n for line in fd:\n if (line.strip()).startswith(\"#\"):\n continue\n words = line.split(sep)\n if len(words) != nbFields:\n continue\n moduleName = words[0].strip(\" \\\"\\t\\n\\r\")\n description = words[1].strip(\" \\\"\\t\\n\\r\")\n output[moduleName] = description\n fd.close()\n \n return output\n\n\nif len(sys.argv) < 4:\n print(\"USAGE: {0} monolithic_OTB_PATH OUTPUT_DIR Manifest_Path [module_dep [test_dep [mod_description]]]\".format(sys.argv[0]))\n print(\" monolithic_OTB_PATH : checkout of OTB repository (will not be modified)\")\n print(\" OUTPUT_DIR : output directory where OTB_Modular and OTB_remaining will be created \")\n print(\" Manifest_Path : path to manifest file, in CSV-like format. Fields are :\")\n print(\" source_path/current_subDir/group/module/subDir/comment\")\n print(\" module_dep : dependencies between modules\")\n print(\" test_dep : additional dependencies for tests\")\n print(\" mod_description : description for each module\")\n print(\" migration_password : password to enable MIGRATION\")\n sys.exit(-1)\n\nscriptDir = op.dirname(op.abspath(sys.argv[0]))\n\nHeadOfOTBTree = sys.argv[1]\nif (HeadOfOTBTree[-1] == '/'):\n HeadOfOTBTree = HeadOfOTBTree[0:-1]\n\nOutputDir = sys.argv[2]\nHeadOfModularOTBTree = op.join(OutputDir,\"OTB_Modular\")\n\nManifestPath = sys.argv[3]\n\nEdgePath = \"\"\nif len(sys.argv) >= 5:\n EdgePath = sys.argv[4]\n \ntestDependPath = \"\"\nif len(sys.argv) >= 6:\n testDependPath = sys.argv[5]\n\nmodDescriptionPath = \"\"\nif len(sys.argv) >= 7:\n modDescriptionPath = sys.argv[6]\n\nenableMigration = False\nif len(sys.argv) >= 8:\n migrationPass = sys.argv[7]\n if migrationPass == \"redbutton\":\n enableMigration = True\n\n# copy the whole OTB tree over to a temporary dir\nHeadOfTempTree = op.join(OutputDir,\"OTB_remaining\")\n\nif op.isdir(HeadOfTempTree):\n shutil.rmtree(HeadOfTempTree)\n\nif op.isdir(HeadOfModularOTBTree):\n shutil.rmtree(HeadOfModularOTBTree)\n\nprint(\"Start to copy\" + HeadOfOTBTree + \" to ./OTB_remaining ...\")\nshutil.copytree(HeadOfOTBTree,HeadOfTempTree, ignore = shutil.ignore_patterns('.hg','.hg*'))\nprint(\"Done copying!\")\n\n# checkout OTB-Modular\ncmd ='hg clone http://hg.orfeo-toolbox.org/OTB-Modular '+HeadOfModularOTBTree\nos.system(cmd)\n\nlogDir = op.join(OutputDir,\"logs\")\nif not op.isdir(logDir):\n os.makedirs(logDir)\n\n# read the manifest file\nprint (\"moving files from ./OTB_remaining into modules in {0}\".format(HeadOfModularOTBTree))\nnumOfMissingFiles = 0;\nmissingf = open(op.join(logDir,'missingFiles.log'),'w')\nmoduleList=[]\nmoduleDic={}\nsourceList = parseFullManifest(ManifestPath)\n\nfor source in sourceList:\n # build module list\n moduleDic[source[\"module\"]] = source[\"group\"]\n \n # create the path\n inputfile = op.abspath(op.join(HeadOfTempTree,source[\"path\"]))\n outputPath = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n if not op.isdir(outputPath):\n os.makedirs(outputPath)\n \n # copying files to the destination\n if op.isfile(inputfile):\n if op.isfile(op.join(outputPath,op.basename(inputfile))):\n os.remove(op.join(outputPath,op.basename(inputfile)))\n shutil.move(inputfile, outputPath)\n else:\n missingf.write(inputfile+'\\n')\n numOfMissingFiles = numOfMissingFiles + 1\n\nmissingf.close()\nprint (\"listed {0} missing files to logs/missingFiles.log\").format(numOfMissingFiles)\n\nmoduleList = moduleDic.keys()\n\n# after move, operate a documentation check\nfor source in sourceList:\n outputPath = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n outputFile = op.join(outputPath,op.basename(source[\"path\"]))\n if op.isfile(outputFile):\n if op.splitext(outputFile)[1] == \".h\":\n nextContent = documentationCheck.parserHeader(outputFile,source[\"module\"])\n fd = open(outputFile,'wb')\n fd.writelines(nextContent)\n fd.close()\n\n\n# get dependencies (if file is present)\ndependencies = {}\ntestDependencies = {}\nexDependencies = {}\nfor mod in moduleList:\n dependencies[mod] = []\n testDependencies[mod] = []\n exDependencies[mod] = []\n\nif op.isfile(EdgePath):\n fd = open(EdgePath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if dependencies.has_key(depFrom):\n dependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\nif op.isfile(testDependPath):\n fd = open(testDependPath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if testDependencies.has_key(depFrom):\n testDependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\n\"\"\"\nif op.isfile(exDependPath):\n fd = open(exDependPath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if exDependencies.has_key(depFrom):\n exDependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\"\"\"\nmodDescriptions = {}\nif op.isfile(modDescriptionPath):\n modDescriptions = parseDescriptions(modDescriptionPath)\n\n\n\n# list the new files\nnewf = open(op.join(logDir,'newFiles.log'),'w')\nfor (root, subDirs, files) in os.walk(HeadOfTempTree):\n for afile in files:\n newf.write(op.join(root, afile)+'\\n')\nnewf.close()\nprint (\"listed new files to logs/newFiles.log\")\n\n###########################################################################\n\nprint ('creating cmake files for each module (from the template module)')\n#moduleList = os.listdir(HeadOfModularOTBTree)\nfor moduleName in moduleList:\n moduleDir = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(moduleDic[moduleName],moduleName))\n cmakeModName = \"OTB\"+moduleName\n \n if op.isdir(moduleDir):\n \n # write CMakeLists.txt\n filepath = moduleDir+'/CMakeLists.txt'\n \n if not op.isfile(filepath):\n o = open(filepath,'w')\n \n if op.isdir(moduleDir+'/src'):\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists.txt')\n else:\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists-nosrc.txt')\n \n for line in open(template_cmakelist,'r'):\n line = line.replace('otb-template-module',cmakeModName)\n o.write(line);\n o.close()\n\n # write src/CMakeLists.txt\n # list of CXX files\n if op.isdir(moduleDir+'/src'):\n cxxFiles = glob.glob(moduleDir+'/src/*.cxx')\n cxxFileList='';\n for cxxf in cxxFiles:\n cxxFileList = cxxFileList+' '+cxxf.split('/')[-1]+'\\n'\n # build list of link dependencies\n linkLibs = \"\"\n for dep in dependencies[moduleName]:\n #verify if dep is a header-onlymodule\n depThirdParty = False\n try:\n moduleDic[dep]\n except KeyError:\n # this is a ThirdParty module\n depThirdParty = True\n \n if not depThirdParty:\n depModuleDir = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(moduleDic[dep],dep))\n depcxx = glob.glob(depModuleDir+'/src/*.cxx')\n if depcxx :\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n else:\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n \n if len(linkLibs) == 0:\n linkLibs = \" ${OTBITK_LIBRARIES}\"\n filepath = moduleDir+'/src/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/src/CMakeLists.txt'),'r'):\n line = line.replace('otb-template-module',cmakeModName)\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\n\n line = line.replace('LINK_LIBRARIES_TO_BE_REPLACED',linkLibs)\n o.write(line);\n o.close()\n\n # write app/CMakeLists.txt\n if op.isdir(moduleDir+'/app'):\n os.mkdir(moduleDir+'/test')\n srcFiles = glob.glob(moduleDir+'/app/*.cxx')\n srcFiles += glob.glob(moduleDir+'/app/*.h')\n appList = {}\n \n for srcf in srcFiles:\n # get App name\n appName = analyseAppManifest.findApplicationName(srcf)\n if len(appName) == 0:\n continue\n \n appList[appName] = {\"source\":op.basename(srcf)}\n \n # get original location\n cmakeListPath = \"\"\n for item in sourceList:\n if op.basename(item[\"path\"]) == op.basename(srcf) and \\\n moduleName == item[\"module\"]:\n appDir = op.basename(op.dirname(item[\"path\"]))\n cmakeListPath = op.join(HeadOfOTBTree,op.join(\"Testing/Applications\"),op.join(appDir,\"CMakeLists.txt\"))\n break\n \n # get App tests\n if not op.isfile(cmakeListPath):\n continue\n \n appList[appName][\"test\"] = analyseAppManifest.findTestFromApp(cmakeListPath,appName)\n \n # build list of link dependencies\n linkLibs = \"\"\n for dep in dependencies[moduleName]:\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n \n filepath = moduleDir+'/app/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n # define link libraries \n o.write(\"set(\"+cmakeModName+\"_LINK_LIBS\\n\")\n o.write(linkLibs)\n o.write(\")\\n\")\n \n for appli in appList:\n content = \"\\notb_create_application(\\n\"\n content += \" NAME \" + appli + \"\\n\"\n content += \" SOURCES \" + appList[appli][\"source\"] + \"\\n\"\n content += \" LINK_LIBRARIES ${${otb-module}_LIBRARIES})\\n\"\n o.write(content)\n o.close()\n \n filepath = moduleDir+'/test/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n o.write(\"otb_module_test()\")\n for appli in appList:\n if not appList[appli].has_key(\"test\"):\n continue\n o.write(\"\\n#----------- \"+appli+\" TESTS ----------------\\n\")\n for test in appList[appli][\"test\"]:\n if test.count(\"${\"):\n print \"Warning : test name contains a variable : \"+test\n continue\n \n testcode=appList[appli][\"test\"][test]\n testcode=[s.replace('OTB_TEST_APPLICATION', 'otb_test_application') for s in testcode]\n o.writelines(testcode)\n o.write(\"\\n\")\n o.close()\n\n # write test/CMakeLists.txt : done by dispatchTests.py\n \"\"\"\n if op.isdir(moduleDir+'/test'):\n cxxFiles = glob.glob(moduleDir+'/test/*.cxx')\n cxxFileList='';\n for cxxf in cxxFiles:\n cxxFileList = cxxFileList+cxxf.split('/')[-1]+'\\n'\n filepath = moduleDir+'/test/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open('./templateModule/otb-template-module/test/CMakeLists.txt','r'):\n # TODO : refactor for OTB\n words= moduleName.split('-')\n moduleNameMod='';\n for word in words:\n moduleNameMod=moduleNameMod + word.capitalize()\n line = line.replace('itkTemplateModule',moduleNameMod)\n line = line.replace('itk-template-module',moduleName)\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\n\n o.write(line);\n o.close()\n \"\"\"\n \n # write otb-module.cmake, which contains dependency info\n filepath = moduleDir+'/otb-module.cmake'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/otb-module.cmake'),'r'):\n # replace documentation\n if line.find(\"DESCRIPTION_TO_BE_REPLACED\") >= 0:\n docString = \"\\\"TBD\\\"\"\n if moduleName in modDescriptions:\n descPos = line.find(\"DESCRIPTION_TO_BE_REPLACED\")\n limitChar = 80\n docString = \"\\\"\"+modDescriptions[moduleName]+\"\\\"\"\n curPos = 80 - descPos\n while curPos < len(docString):\n lastSpace = docString[0:curPos].rfind(' ')\n if lastSpace > max(0,curPos-80):\n docString = docString[0:lastSpace] + '\\n' + docString[lastSpace+1:]\n else:\n docString = docString[0:curPos] + '\\n' + docString[curPos:]\n curPos += 81\n line = line.replace('DESCRIPTION_TO_BE_REPLACED',docString)\n \n # replace module name\n line = line.replace('otb-template-module',cmakeModName)\n # replace depend list\n dependTagPos = line.find(\"DEPENDS_TO_BE_REPLACED\")\n if dependTagPos >= 0:\n replacementStr = \"DEPENDS\"\n indentStr = \"\"\n for it in range(dependTagPos+2):\n indentStr = indentStr + \" \"\n if len(dependencies[moduleName]) > 0:\n deplist = dependencies[moduleName]\n else:\n deplist = [\"Common\"]\n for dep in deplist:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep\n line = line.replace('DEPENDS_TO_BE_REPLACED',replacementStr)\n # replace test_depend list\n testDependTagPos = line.find(\"TESTDEP_TO_BE_REPLACED\")\n if testDependTagPos >= 0:\n if moduleName.startswith(\"App\"):\n # for application : hardcode TestKernel and CommandLine\n indentStr = \"\"\n for it in range(testDependTagPos+2):\n indentStr = indentStr + \" \"\n replacementStr = \"TEST_DEPENDS\\n\" + indentStr + \"OTBTestKernel\\n\" + indentStr + \"OTBCommandLine\"\n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\n else:\n # standard case\n\n if len(testDependencies[moduleName]) > 0:\n indentStr = \"\"\n replacementStr = \"TEST_DEPENDS\"\n for it in range(testDependTagPos+2):\n indentStr = indentStr + \" \"\n for dep in testDependencies[moduleName]:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep \n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\n else:\n line = line.replace('TESTDEP_TO_BE_REPLACED','')\n \n # replace example_depend list\n exDependTagPos = line.find(\"EXDEP_TO_BE_REPLACED\")\n if exDependTagPos >= 0:\n if len(exDependencies[moduleName]) > 0:\n indentStr = \"\"\n replacementStr = \"EXAMPLE_DEPENDS\"\n for it in range(exDependTagPos+2):\n indentStr = indentStr + \" \"\n for dep in exDependencies[moduleName]:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep \n line = line.replace('EXDEP_TO_BE_REPLACED',replacementStr)\n else:\n line = line.replace('EXDEP_TO_BE_REPLACED','')\n o.write(line);\n \n o.close()\n\n# call dispatchTests to fill test/CMakeLists\nif op.isfile(testDependPath):\n dispatchTests.main([\"dispatchTests.py\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,testDependPath])\n\n\"\"\"\n# call dispatchExamples to fill example/CMakeLists\nif op.isfile(exDependPath):\n dispatchExamples.main([\"dispatchExamples.py\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,exDependPath])\n\"\"\"\n\n# examples\nfor i in sorted(os.listdir(HeadOfTempTree + \"/Examples\")):\n if i == \"CMakeLists.txt\" or i == \"README.txt\" or i.startswith(\"DataRepresentation\"):\n continue\n\n for j in sorted(os.listdir(HeadOfTempTree + \"/Examples/\" + i)):\n if j == \"CMakeLists.txt\" or j.startswith(\"otb\"):\n continue\n \n command = \"mv %s/Examples/%s/%s %s/Examples/%s/%s\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j)\n os.system(command)\n\nfor i in sorted(os.listdir(HeadOfTempTree + \"/Examples/DataRepresentation\")):\n if i == \"CMakeLists.txt\" or i == \"README.txt\":\n continue\n\n for j in sorted(os.listdir(HeadOfTempTree + \"/Examples/DataRepresentation/\" + i)):\n if j == \"CMakeLists.txt\" or j.startswith(\"otb\"):\n continue\n \n command = \"mv %s/Examples/DataRepresentation/%s/%s %s/Examples/DataRepresentation/%s/%s\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j) \n os.system(command)\n\n\n# save version without patches (so that we can regenerate patches later)\nos.system( \"cp -ar \" + op.join(OutputDir,\"OTB_Modular\") + \" \" + op.join(OutputDir,\"OTB_Modular-nopatch\") )\n\n# apply patches in OTB_Modular\ncurdir = op.abspath(op.dirname(__file__))\ncommand = \"cd \" + op.join(OutputDir,\"OTB_Modular\") + \" && patch -p1 < \" + curdir + \"/patches/otbmodular.patch\"\nprint \"Executing \" + command\nos.system( command )\n\n# remove Copyright files we don't want to touch later\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"Copyright\") ) )\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"RELEASE_NOTES.txt\") ) )\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"README\") ) )\n\n# PREPARE MIGRATION COMMIT ON A CLONE OF ORIGINAL CHECKOUT\nif enableMigration:\n print(\"Executing migration on a clone of original checkout\")\n HeadOfTempTree = op.abspath(HeadOfTempTree)\n OutputDir = op.abspath(OutputDir)\n \n # clone original checkout\n outputModular = op.join(OutputDir,\"OTB_Modular\")\n outputMigration = op.join(OutputDir,\"OTB_Migration\")\n if op.exists(outputMigration):\n os.removedirs(outputMigration)\n command = [\"cp\",\"-ar\",HeadOfOTBTree,outputMigration]\n call(command)\n os.chdir(outputMigration)\n \n # walk through OTB_Remaining and delete corresponding files in OTB checkout\n print(\"DELETE STEP...\")\n for dirPath, dirNames, fileNames in os.walk(HeadOfTempTree):\n currentSourceDir = dirPath.replace(HeadOfTempTree,'.')\n for fileName in fileNames:\n if op.exists(op.join(currentSourceDir,fileName)):\n command = [\"hg\",\"remove\",op.join(currentSourceDir,fileName)]\n call(command)\n else:\n print(\"Unknown file : \"+op.join(currentSourceDir,fileName))\n command = ['hg','commit','-m','ENH: Remove files not necessary after modularization']\n call(command)\n \n # walk through manifest and rename files\n print(\"MOVE STEP...\")\n for source in sourceList:\n outputPath = op.join(\"./Modules\",op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n command = ['hg','rename',source[\"path\"],op.join(outputPath,op.basename(source[\"path\"]))]\n call(command)\n command = ['hg','commit','-m','ENH: Move source and test files into their respective module']\n call(command)\n \n # add new files from OTB_Modular (files from OTB-Modular repo + generated files)\n print(\"ADD STEP...\")\n for dirPath, dirNames, fileNames in os.walk(outputModular):\n currentSourceDir = dirPath.replace(outputModular,'.')\n if currentSourceDir.startswith(\"./.hg\"):\n print(\"skip .hg\")\n continue\n for fileName in fileNames:\n # skip hg files\n if fileName.startswith(\".hg\"):\n continue\n targetFile = op.join(currentSourceDir,fileName)\n if not op.exists(targetFile):\n if not op.exists(currentSourceDir):\n command = [\"mkdir\",\"-p\",currentSourceDir]\n call(command)\n shutil.copy(op.join(dirPath,fileName),targetFile)\n command = ['hg','add']\n call(command)\n command = ['hg','commit','-m','ENH: Add new files for modular build system']\n call(command)\n \n # apply patches on OTB Checkout\n print(\"PATCH STEP...\")\n for dirPath, dirNames, fileNames in os.walk(outputModular):\n currentSourceDir = dirPath.replace(outputModular,'.')\n if currentSourceDir.startswith(\"./.hg\"):\n continue\n for fileName in fileNames:\n # skip hg files\n if fileName.startswith(\".hg\"):\n continue\n targetFile = op.join(currentSourceDir,fileName)\n if op.exists(targetFile):\n command = ['cp',op.join(dirPath,fileName),targetFile]\n call(command)\n command = ['hg','commit','-m','ENH: Apply patches necessary after modularization']\n call(command)\n\n\n"},"steps":{"kind":"list like","value":["#!/usr/bin/python\n#==========================================================================\n#\n# Copyright Insight Software Consortium\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.txt\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n#\n#==========================================================================*/\n# This script is used to automate the modularization process. The following\n# steps are included:\n# 1. Move the files in the monolithic ITK into modules of the modularized ITK.\n# A manifest text file that lists all the files and their destinations is\n# required to run the script.By default, the manifest file is named as\n# \"Manifest.txt\" in the same directory of this script.\n# 2. Create CMake Files and put them into modules.\n\n# Modified by Guillaume Pasero <guillaume.pasero@c-s.fr>\n# add dependencies in otb-module.cmake\n\n# To run it, type ./modulizer.py OTB_PATH Manifest_PATH\n# from the otb-modulizer root directory.\n\nprint \"*************************************************************************\"\nprint \"WARNINGs! This modularization script is still in its experimental stage.\"\nprint \"Current OTB users should not run this script.\"\nprint \"*************************************************************************\"\n\n\nimport shutil\nimport os.path as op\nimport re\nimport sys\nimport os\nimport stat\nimport glob\nimport documentationCheck\nimport analyseAppManifest\nimport dispatchTests\nimport dispatchExamples\nfrom subprocess import call\n\ndef parseFullManifest(path):\n sourceList = []\n nbFields = 6\n fd = open(path,'rb')\n # skip first line and detect separator\n firstLine = fd.readline()\n sep = ','\n if (len(firstLine.split(sep)) != nbFields):\n sep = ';'\n if (len(firstLine.split(sep)) != nbFields):\n sep = '\\t'\n if (len(firstLine.split(sep)) != nbFields):\n print \"Unknown separator\"\n return sourceList\n \n fd.seek(0)\n \n # parse file\n for line in fd:\n if (line.strip()).startswith(\"#\"):\n continue\n words = line.split(sep)\n if (len(words) < (nbFields-1)):\n print \"Wrong number of fields, skipping this line\"\n continue\n fullPath = words[0].strip(\" ,;\\t\\n\\r\")\n groupName = words[2].strip(\" ,;\\t\\n\\r\")\n moduleName = words[3].strip(\" ,;\\t\\n\\r\")\n subDir = words[4].strip(\" ,;\\t\\n\\r\")\n sourceName = op.basename(fullPath)\n \n sourceList.append({\"path\":fullPath, \"group\":groupName, \"module\":moduleName, \"subDir\":subDir})\n fd.close()\n \n return sourceList\n\n\ndef parseDescriptions(path):\n output = {}\n sep = '|'\n nbFields = 2\n fd = open(path,'rb')\n for line in fd:\n if (line.strip()).startswith(\"#\"):\n continue\n words = line.split(sep)\n if len(words) != nbFields:\n continue\n moduleName = words[0].strip(\" \\\"\\t\\n\\r\")\n description = words[1].strip(\" \\\"\\t\\n\\r\")\n output[moduleName] = description\n fd.close()\n \n return output\n\n\nif len(sys.argv) < 4:\n print(\"USAGE: {0} monolithic_OTB_PATH OUTPUT_DIR Manifest_Path [module_dep [test_dep [mod_description]]]\".format(sys.argv[0]))\n print(\" monolithic_OTB_PATH : checkout of OTB repository (will not be modified)\")\n print(\" OUTPUT_DIR : output directory where OTB_Modular and OTB_remaining will be created \")\n print(\" Manifest_Path : path to manifest file, in CSV-like format. Fields are :\")\n print(\" source_path/current_subDir/group/module/subDir/comment\")\n print(\" module_dep : dependencies between modules\")\n print(\" test_dep : additional dependencies for tests\")\n print(\" mod_description : description for each module\")\n print(\" migration_password : password to enable MIGRATION\")\n sys.exit(-1)\n\nscriptDir = op.dirname(op.abspath(sys.argv[0]))\n\nHeadOfOTBTree = sys.argv[1]\nif (HeadOfOTBTree[-1] == '/'):\n HeadOfOTBTree = HeadOfOTBTree[0:-1]\n\nOutputDir = sys.argv[2]\nHeadOfModularOTBTree = op.join(OutputDir,\"OTB_Modular\")\n\nManifestPath = sys.argv[3]\n\nEdgePath = \"\"\nif len(sys.argv) >= 5:\n EdgePath = sys.argv[4]\n \ntestDependPath = \"\"\nif len(sys.argv) >= 6:\n testDependPath = sys.argv[5]\n\nmodDescriptionPath = \"\"\nif len(sys.argv) >= 7:\n modDescriptionPath = sys.argv[6]\n\nenableMigration = False\nif len(sys.argv) >= 8:\n migrationPass = sys.argv[7]\n if migrationPass == \"redbutton\":\n enableMigration = True\n\n# copy the whole OTB tree over to a temporary dir\nHeadOfTempTree = op.join(OutputDir,\"OTB_remaining\")\n\nif op.isdir(HeadOfTempTree):\n shutil.rmtree(HeadOfTempTree)\n\nif op.isdir(HeadOfModularOTBTree):\n shutil.rmtree(HeadOfModularOTBTree)\n\nprint(\"Start to copy\" + HeadOfOTBTree + \" to ./OTB_remaining ...\")\nshutil.copytree(HeadOfOTBTree,HeadOfTempTree, ignore = shutil.ignore_patterns('.hg','.hg*'))\nprint(\"Done copying!\")\n\n# checkout OTB-Modular\ncmd ='hg clone http://hg.orfeo-toolbox.org/OTB-Modular '+HeadOfModularOTBTree\nos.system(cmd)\n\nlogDir = op.join(OutputDir,\"logs\")\nif not op.isdir(logDir):\n os.makedirs(logDir)\n\n# read the manifest file\nprint (\"moving files from ./OTB_remaining into modules in {0}\".format(HeadOfModularOTBTree))\nnumOfMissingFiles = 0;\nmissingf = open(op.join(logDir,'missingFiles.log'),'w')\nmoduleList=[]\nmoduleDic={}\nsourceList = parseFullManifest(ManifestPath)\n\nfor source in sourceList:\n # build module list\n moduleDic[source[\"module\"]] = source[\"group\"]\n \n # create the path\n inputfile = op.abspath(op.join(HeadOfTempTree,source[\"path\"]))\n outputPath = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n if not op.isdir(outputPath):\n os.makedirs(outputPath)\n \n # copying files to the destination\n if op.isfile(inputfile):\n if op.isfile(op.join(outputPath,op.basename(inputfile))):\n os.remove(op.join(outputPath,op.basename(inputfile)))\n shutil.move(inputfile, outputPath)\n else:\n missingf.write(inputfile+'\\n')\n numOfMissingFiles = numOfMissingFiles + 1\n\nmissingf.close()\nprint (\"listed {0} missing files to logs/missingFiles.log\").format(numOfMissingFiles)\n\nmoduleList = moduleDic.keys()\n\n# after move, operate a documentation check\nfor source in sourceList:\n outputPath = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n outputFile = op.join(outputPath,op.basename(source[\"path\"]))\n if op.isfile(outputFile):\n if op.splitext(outputFile)[1] == \".h\":\n nextContent = documentationCheck.parserHeader(outputFile,source[\"module\"])\n fd = open(outputFile,'wb')\n fd.writelines(nextContent)\n fd.close()\n\n\n# get dependencies (if file is present)\ndependencies = {}\ntestDependencies = {}\nexDependencies = {}\nfor mod in moduleList:\n dependencies[mod] = []\n testDependencies[mod] = []\n exDependencies[mod] = []\n\nif op.isfile(EdgePath):\n fd = open(EdgePath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if dependencies.has_key(depFrom):\n dependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\nif op.isfile(testDependPath):\n fd = open(testDependPath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if testDependencies.has_key(depFrom):\n testDependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\n\"\"\"\nif op.isfile(exDependPath):\n fd = open(exDependPath,'rb')\n for line in fd:\n words = line.split(',')\n if len(words) == 2:\n depFrom = words[0].strip(\" ,;\\t\\n\\r\")\n depTo = words[1].strip(\" ,;\\t\\n\\r\")\n if exDependencies.has_key(depFrom):\n exDependencies[depFrom].append(depTo)\n else:\n print(\"Bad dependency : \"+depFrom+\" -> \"+depTo)\n fd.close()\n\"\"\"\nmodDescriptions = {}\nif op.isfile(modDescriptionPath):\n modDescriptions = parseDescriptions(modDescriptionPath)\n\n\n\n# list the new files\nnewf = open(op.join(logDir,'newFiles.log'),'w')\nfor (root, subDirs, files) in os.walk(HeadOfTempTree):\n for afile in files:\n newf.write(op.join(root, afile)+'\\n')\nnewf.close()\nprint (\"listed new files to logs/newFiles.log\")\n\n###########################################################################\n\nprint ('creating cmake files for each module (from the template module)')\n#moduleList = os.listdir(HeadOfModularOTBTree)\nfor moduleName in moduleList:\n moduleDir = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(moduleDic[moduleName],moduleName))\n cmakeModName = \"OTB\"+moduleName\n \n if op.isdir(moduleDir):\n \n # write CMakeLists.txt\n filepath = moduleDir+'/CMakeLists.txt'\n \n if not op.isfile(filepath):\n o = open(filepath,'w')\n \n if op.isdir(moduleDir+'/src'):\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists.txt')\n else:\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists-nosrc.txt')\n \n for line in open(template_cmakelist,'r'):\n line = line.replace('otb-template-module',cmakeModName)\n o.write(line);\n o.close()\n\n # write src/CMakeLists.txt\n # list of CXX files\n if op.isdir(moduleDir+'/src'):\n cxxFiles = glob.glob(moduleDir+'/src/*.cxx')\n cxxFileList='';\n for cxxf in cxxFiles:\n cxxFileList = cxxFileList+' '+cxxf.split('/')[-1]+'\\n'\n # build list of link dependencies\n linkLibs = \"\"\n for dep in dependencies[moduleName]:\n #verify if dep is a header-onlymodule\n depThirdParty = False\n try:\n moduleDic[dep]\n except KeyError:\n # this is a ThirdParty module\n depThirdParty = True\n \n if not depThirdParty:\n depModuleDir = op.join(op.join(HeadOfModularOTBTree,\"Modules\"),op.join(moduleDic[dep],dep))\n depcxx = glob.glob(depModuleDir+'/src/*.cxx')\n if depcxx :\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n else:\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n \n if len(linkLibs) == 0:\n linkLibs = \" ${OTBITK_LIBRARIES}\"\n filepath = moduleDir+'/src/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/src/CMakeLists.txt'),'r'):\n line = line.replace('otb-template-module',cmakeModName)\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\n\n line = line.replace('LINK_LIBRARIES_TO_BE_REPLACED',linkLibs)\n o.write(line);\n o.close()\n\n # write app/CMakeLists.txt\n if op.isdir(moduleDir+'/app'):\n os.mkdir(moduleDir+'/test')\n srcFiles = glob.glob(moduleDir+'/app/*.cxx')\n srcFiles += glob.glob(moduleDir+'/app/*.h')\n appList = {}\n \n for srcf in srcFiles:\n # get App name\n appName = analyseAppManifest.findApplicationName(srcf)\n if len(appName) == 0:\n continue\n \n appList[appName] = {\"source\":op.basename(srcf)}\n \n # get original location\n cmakeListPath = \"\"\n for item in sourceList:\n if op.basename(item[\"path\"]) == op.basename(srcf) and \\\n moduleName == item[\"module\"]:\n appDir = op.basename(op.dirname(item[\"path\"]))\n cmakeListPath = op.join(HeadOfOTBTree,op.join(\"Testing/Applications\"),op.join(appDir,\"CMakeLists.txt\"))\n break\n \n # get App tests\n if not op.isfile(cmakeListPath):\n continue\n \n appList[appName][\"test\"] = analyseAppManifest.findTestFromApp(cmakeListPath,appName)\n \n # build list of link dependencies\n linkLibs = \"\"\n for dep in dependencies[moduleName]:\n linkLibs = linkLibs + \" ${OTB\"+dep+\"_LIBRARIES}\" + \"\\n\"\n \n filepath = moduleDir+'/app/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n # define link libraries \n o.write(\"set(\"+cmakeModName+\"_LINK_LIBS\\n\")\n o.write(linkLibs)\n o.write(\")\\n\")\n \n for appli in appList:\n content = \"\\notb_create_application(\\n\"\n content += \" NAME \" + appli + \"\\n\"\n content += \" SOURCES \" + appList[appli][\"source\"] + \"\\n\"\n content += \" LINK_LIBRARIES ${${otb-module}_LIBRARIES})\\n\"\n o.write(content)\n o.close()\n \n filepath = moduleDir+'/test/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n o.write(\"otb_module_test()\")\n for appli in appList:\n if not appList[appli].has_key(\"test\"):\n continue\n o.write(\"\\n#----------- \"+appli+\" TESTS ----------------\\n\")\n for test in appList[appli][\"test\"]:\n if test.count(\"${\"):\n print \"Warning : test name contains a variable : \"+test\n continue\n \n testcode=appList[appli][\"test\"][test]\n testcode=[s.replace('OTB_TEST_APPLICATION', 'otb_test_application') for s in testcode]\n o.writelines(testcode)\n o.write(\"\\n\")\n o.close()\n\n # write test/CMakeLists.txt : done by dispatchTests.py\n \"\"\"\n if op.isdir(moduleDir+'/test'):\n cxxFiles = glob.glob(moduleDir+'/test/*.cxx')\n cxxFileList='';\n for cxxf in cxxFiles:\n cxxFileList = cxxFileList+cxxf.split('/')[-1]+'\\n'\n filepath = moduleDir+'/test/CMakeLists.txt'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open('./templateModule/otb-template-module/test/CMakeLists.txt','r'):\n # TODO : refactor for OTB\n words= moduleName.split('-')\n moduleNameMod='';\n for word in words:\n moduleNameMod=moduleNameMod + word.capitalize()\n line = line.replace('itkTemplateModule',moduleNameMod)\n line = line.replace('itk-template-module',moduleName)\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\n\n o.write(line);\n o.close()\n \"\"\"\n \n # write otb-module.cmake, which contains dependency info\n filepath = moduleDir+'/otb-module.cmake'\n if not op.isfile(filepath):\n o = open(filepath,'w')\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/otb-module.cmake'),'r'):\n # replace documentation\n if line.find(\"DESCRIPTION_TO_BE_REPLACED\") >= 0:\n docString = \"\\\"TBD\\\"\"\n if moduleName in modDescriptions:\n descPos = line.find(\"DESCRIPTION_TO_BE_REPLACED\")\n limitChar = 80\n docString = \"\\\"\"+modDescriptions[moduleName]+\"\\\"\"\n curPos = 80 - descPos\n while curPos < len(docString):\n lastSpace = docString[0:curPos].rfind(' ')\n if lastSpace > max(0,curPos-80):\n docString = docString[0:lastSpace] + '\\n' + docString[lastSpace+1:]\n else:\n docString = docString[0:curPos] + '\\n' + docString[curPos:]\n curPos += 81\n line = line.replace('DESCRIPTION_TO_BE_REPLACED',docString)\n \n # replace module name\n line = line.replace('otb-template-module',cmakeModName)\n # replace depend list\n dependTagPos = line.find(\"DEPENDS_TO_BE_REPLACED\")\n if dependTagPos >= 0:\n replacementStr = \"DEPENDS\"\n indentStr = \"\"\n for it in range(dependTagPos+2):\n indentStr = indentStr + \" \"\n if len(dependencies[moduleName]) > 0:\n deplist = dependencies[moduleName]\n else:\n deplist = [\"Common\"]\n for dep in deplist:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep\n line = line.replace('DEPENDS_TO_BE_REPLACED',replacementStr)\n # replace test_depend list\n testDependTagPos = line.find(\"TESTDEP_TO_BE_REPLACED\")\n if testDependTagPos >= 0:\n if moduleName.startswith(\"App\"):\n # for application : hardcode TestKernel and CommandLine\n indentStr = \"\"\n for it in range(testDependTagPos+2):\n indentStr = indentStr + \" \"\n replacementStr = \"TEST_DEPENDS\\n\" + indentStr + \"OTBTestKernel\\n\" + indentStr + \"OTBCommandLine\"\n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\n else:\n # standard case\n\n if len(testDependencies[moduleName]) > 0:\n indentStr = \"\"\n replacementStr = \"TEST_DEPENDS\"\n for it in range(testDependTagPos+2):\n indentStr = indentStr + \" \"\n for dep in testDependencies[moduleName]:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep \n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\n else:\n line = line.replace('TESTDEP_TO_BE_REPLACED','')\n \n # replace example_depend list\n exDependTagPos = line.find(\"EXDEP_TO_BE_REPLACED\")\n if exDependTagPos >= 0:\n if len(exDependencies[moduleName]) > 0:\n indentStr = \"\"\n replacementStr = \"EXAMPLE_DEPENDS\"\n for it in range(exDependTagPos+2):\n indentStr = indentStr + \" \"\n for dep in exDependencies[moduleName]:\n replacementStr = replacementStr + \"\\n\" + indentStr +\"OTB\"+ dep \n line = line.replace('EXDEP_TO_BE_REPLACED',replacementStr)\n else:\n line = line.replace('EXDEP_TO_BE_REPLACED','')\n o.write(line);\n \n o.close()\n\n# call dispatchTests to fill test/CMakeLists\nif op.isfile(testDependPath):\n dispatchTests.main([\"dispatchTests.py\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,testDependPath])\n\n\"\"\"\n# call dispatchExamples to fill example/CMakeLists\nif op.isfile(exDependPath):\n dispatchExamples.main([\"dispatchExamples.py\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,exDependPath])\n\"\"\"\n\n# examples\nfor i in sorted(os.listdir(HeadOfTempTree + \"/Examples\")):\n if i == \"CMakeLists.txt\" or i == \"README.txt\" or i.startswith(\"DataRepresentation\"):\n continue\n\n for j in sorted(os.listdir(HeadOfTempTree + \"/Examples/\" + i)):\n if j == \"CMakeLists.txt\" or j.startswith(\"otb\"):\n continue\n \n command = \"mv %s/Examples/%s/%s %s/Examples/%s/%s\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j)\n os.system(command)\n\nfor i in sorted(os.listdir(HeadOfTempTree + \"/Examples/DataRepresentation\")):\n if i == \"CMakeLists.txt\" or i == \"README.txt\":\n continue\n\n for j in sorted(os.listdir(HeadOfTempTree + \"/Examples/DataRepresentation/\" + i)):\n if j == \"CMakeLists.txt\" or j.startswith(\"otb\"):\n continue\n \n command = \"mv %s/Examples/DataRepresentation/%s/%s %s/Examples/DataRepresentation/%s/%s\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j) \n os.system(command)\n\n\n# save version without patches (so that we can regenerate patches later)\nos.system( \"cp -ar \" + op.join(OutputDir,\"OTB_Modular\") + \" \" + op.join(OutputDir,\"OTB_Modular-nopatch\") )\n\n# apply patches in OTB_Modular\ncurdir = op.abspath(op.dirname(__file__))\ncommand = \"cd \" + op.join(OutputDir,\"OTB_Modular\") + \" && patch -p1 < \" + curdir + \"/patches/otbmodular.patch\"\nprint \"Executing \" + command\nos.system( command )\n\n# remove Copyright files we don't want to touch later\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"Copyright\") ) )\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"RELEASE_NOTES.txt\") ) )\nos.system( \"rm -rf %s\" % (op.join(HeadOfTempTree,\"README\") ) )\n\n# PREPARE MIGRATION COMMIT ON A CLONE OF ORIGINAL CHECKOUT\nif enableMigration:\n print(\"Executing migration on a clone of original checkout\")\n HeadOfTempTree = op.abspath(HeadOfTempTree)\n OutputDir = op.abspath(OutputDir)\n \n # clone original checkout\n outputModular = op.join(OutputDir,\"OTB_Modular\")\n outputMigration = op.join(OutputDir,\"OTB_Migration\")\n if op.exists(outputMigration):\n os.removedirs(outputMigration)\n command = [\"cp\",\"-ar\",HeadOfOTBTree,outputMigration]\n call(command)\n os.chdir(outputMigration)\n \n # walk through OTB_Remaining and delete corresponding files in OTB checkout\n print(\"DELETE STEP...\")\n for dirPath, dirNames, fileNames in os.walk(HeadOfTempTree):\n currentSourceDir = dirPath.replace(HeadOfTempTree,'.')\n for fileName in fileNames:\n if op.exists(op.join(currentSourceDir,fileName)):\n command = [\"hg\",\"remove\",op.join(currentSourceDir,fileName)]\n call(command)\n else:\n print(\"Unknown file : \"+op.join(currentSourceDir,fileName))\n command = ['hg','commit','-m','ENH: Remove files not necessary after modularization']\n call(command)\n \n # walk through manifest and rename files\n print(\"MOVE STEP...\")\n for source in sourceList:\n outputPath = op.join(\"./Modules\",op.join(source[\"group\"],op.join(source[\"module\"],source[\"subDir\"])))\n command = ['hg','rename',source[\"path\"],op.join(outputPath,op.basename(source[\"path\"]))]\n call(command)\n command = ['hg','commit','-m','ENH: Move source and test files into their respective module']\n call(command)\n \n # add new files from OTB_Modular (files from OTB-Modular repo + generated files)\n print(\"ADD STEP...\")\n for dirPath, dirNames, fileNames in os.walk(outputModular):\n currentSourceDir = dirPath.replace(outputModular,'.')\n if currentSourceDir.startswith(\"./.hg\"):\n print(\"skip .hg\")\n continue\n for fileName in fileNames:\n # skip hg files\n if fileName.startswith(\".hg\"):\n continue\n targetFile = op.join(currentSourceDir,fileName)\n if not op.exists(targetFile):\n if not op.exists(currentSourceDir):\n command = [\"mkdir\",\"-p\",currentSourceDir]\n call(command)\n shutil.copy(op.join(dirPath,fileName),targetFile)\n command = ['hg','add']\n call(command)\n command = ['hg','commit','-m','ENH: Add new files for modular build system']\n call(command)\n \n # apply patches on OTB Checkout\n print(\"PATCH STEP...\")\n for dirPath, dirNames, fileNames in os.walk(outputModular):\n currentSourceDir = dirPath.replace(outputModular,'.')\n if currentSourceDir.startswith(\"./.hg\"):\n continue\n for fileName in fileNames:\n # skip hg files\n if fileName.startswith(\".hg\"):\n continue\n targetFile = op.join(currentSourceDir,fileName)\n if op.exists(targetFile):\n command = ['cp',op.join(dirPath,fileName),targetFile]\n call(command)\n command = ['hg','commit','-m','ENH: Apply patches necessary after modularization']\n call(command)\n\n\n"],"string":"[\n \"#!/usr/bin/python\\n#==========================================================================\\n#\\n# Copyright Insight Software Consortium\\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.txt\\n#\\n# Unless required by applicable law or agreed to in writing, software\\n# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n# See the License for the specific language governing permissions and\\n# limitations under the License.\\n#\\n#\\n#==========================================================================*/\\n# This script is used to automate the modularization process. The following\\n# steps are included:\\n# 1. Move the files in the monolithic ITK into modules of the modularized ITK.\\n# A manifest text file that lists all the files and their destinations is\\n# required to run the script.By default, the manifest file is named as\\n# \\\"Manifest.txt\\\" in the same directory of this script.\\n# 2. Create CMake Files and put them into modules.\\n\\n# Modified by Guillaume Pasero <guillaume.pasero@c-s.fr>\\n# add dependencies in otb-module.cmake\\n\\n# To run it, type ./modulizer.py OTB_PATH Manifest_PATH\\n# from the otb-modulizer root directory.\\n\\nprint \\\"*************************************************************************\\\"\\nprint \\\"WARNINGs! This modularization script is still in its experimental stage.\\\"\\nprint \\\"Current OTB users should not run this script.\\\"\\nprint \\\"*************************************************************************\\\"\\n\\n\\nimport shutil\\nimport os.path as op\\nimport re\\nimport sys\\nimport os\\nimport stat\\nimport glob\\nimport documentationCheck\\nimport analyseAppManifest\\nimport dispatchTests\\nimport dispatchExamples\\nfrom subprocess import call\\n\\ndef parseFullManifest(path):\\n sourceList = []\\n nbFields = 6\\n fd = open(path,'rb')\\n # skip first line and detect separator\\n firstLine = fd.readline()\\n sep = ','\\n if (len(firstLine.split(sep)) != nbFields):\\n sep = ';'\\n if (len(firstLine.split(sep)) != nbFields):\\n sep = '\\\\t'\\n if (len(firstLine.split(sep)) != nbFields):\\n print \\\"Unknown separator\\\"\\n return sourceList\\n \\n fd.seek(0)\\n \\n # parse file\\n for line in fd:\\n if (line.strip()).startswith(\\\"#\\\"):\\n continue\\n words = line.split(sep)\\n if (len(words) < (nbFields-1)):\\n print \\\"Wrong number of fields, skipping this line\\\"\\n continue\\n fullPath = words[0].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n groupName = words[2].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n moduleName = words[3].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n subDir = words[4].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n sourceName = op.basename(fullPath)\\n \\n sourceList.append({\\\"path\\\":fullPath, \\\"group\\\":groupName, \\\"module\\\":moduleName, \\\"subDir\\\":subDir})\\n fd.close()\\n \\n return sourceList\\n\\n\\ndef parseDescriptions(path):\\n output = {}\\n sep = '|'\\n nbFields = 2\\n fd = open(path,'rb')\\n for line in fd:\\n if (line.strip()).startswith(\\\"#\\\"):\\n continue\\n words = line.split(sep)\\n if len(words) != nbFields:\\n continue\\n moduleName = words[0].strip(\\\" \\\\\\\"\\\\t\\\\n\\\\r\\\")\\n description = words[1].strip(\\\" \\\\\\\"\\\\t\\\\n\\\\r\\\")\\n output[moduleName] = description\\n fd.close()\\n \\n return output\\n\\n\\nif len(sys.argv) < 4:\\n print(\\\"USAGE: {0} monolithic_OTB_PATH OUTPUT_DIR Manifest_Path [module_dep [test_dep [mod_description]]]\\\".format(sys.argv[0]))\\n print(\\\" monolithic_OTB_PATH : checkout of OTB repository (will not be modified)\\\")\\n print(\\\" OUTPUT_DIR : output directory where OTB_Modular and OTB_remaining will be created \\\")\\n print(\\\" Manifest_Path : path to manifest file, in CSV-like format. Fields are :\\\")\\n print(\\\" source_path/current_subDir/group/module/subDir/comment\\\")\\n print(\\\" module_dep : dependencies between modules\\\")\\n print(\\\" test_dep : additional dependencies for tests\\\")\\n print(\\\" mod_description : description for each module\\\")\\n print(\\\" migration_password : password to enable MIGRATION\\\")\\n sys.exit(-1)\\n\\nscriptDir = op.dirname(op.abspath(sys.argv[0]))\\n\\nHeadOfOTBTree = sys.argv[1]\\nif (HeadOfOTBTree[-1] == '/'):\\n HeadOfOTBTree = HeadOfOTBTree[0:-1]\\n\\nOutputDir = sys.argv[2]\\nHeadOfModularOTBTree = op.join(OutputDir,\\\"OTB_Modular\\\")\\n\\nManifestPath = sys.argv[3]\\n\\nEdgePath = \\\"\\\"\\nif len(sys.argv) >= 5:\\n EdgePath = sys.argv[4]\\n \\ntestDependPath = \\\"\\\"\\nif len(sys.argv) >= 6:\\n testDependPath = sys.argv[5]\\n\\nmodDescriptionPath = \\\"\\\"\\nif len(sys.argv) >= 7:\\n modDescriptionPath = sys.argv[6]\\n\\nenableMigration = False\\nif len(sys.argv) >= 8:\\n migrationPass = sys.argv[7]\\n if migrationPass == \\\"redbutton\\\":\\n enableMigration = True\\n\\n# copy the whole OTB tree over to a temporary dir\\nHeadOfTempTree = op.join(OutputDir,\\\"OTB_remaining\\\")\\n\\nif op.isdir(HeadOfTempTree):\\n shutil.rmtree(HeadOfTempTree)\\n\\nif op.isdir(HeadOfModularOTBTree):\\n shutil.rmtree(HeadOfModularOTBTree)\\n\\nprint(\\\"Start to copy\\\" + HeadOfOTBTree + \\\" to ./OTB_remaining ...\\\")\\nshutil.copytree(HeadOfOTBTree,HeadOfTempTree, ignore = shutil.ignore_patterns('.hg','.hg*'))\\nprint(\\\"Done copying!\\\")\\n\\n# checkout OTB-Modular\\ncmd ='hg clone http://hg.orfeo-toolbox.org/OTB-Modular '+HeadOfModularOTBTree\\nos.system(cmd)\\n\\nlogDir = op.join(OutputDir,\\\"logs\\\")\\nif not op.isdir(logDir):\\n os.makedirs(logDir)\\n\\n# read the manifest file\\nprint (\\\"moving files from ./OTB_remaining into modules in {0}\\\".format(HeadOfModularOTBTree))\\nnumOfMissingFiles = 0;\\nmissingf = open(op.join(logDir,'missingFiles.log'),'w')\\nmoduleList=[]\\nmoduleDic={}\\nsourceList = parseFullManifest(ManifestPath)\\n\\nfor source in sourceList:\\n # build module list\\n moduleDic[source[\\\"module\\\"]] = source[\\\"group\\\"]\\n \\n # create the path\\n inputfile = op.abspath(op.join(HeadOfTempTree,source[\\\"path\\\"]))\\n outputPath = op.join(op.join(HeadOfModularOTBTree,\\\"Modules\\\"),op.join(source[\\\"group\\\"],op.join(source[\\\"module\\\"],source[\\\"subDir\\\"])))\\n if not op.isdir(outputPath):\\n os.makedirs(outputPath)\\n \\n # copying files to the destination\\n if op.isfile(inputfile):\\n if op.isfile(op.join(outputPath,op.basename(inputfile))):\\n os.remove(op.join(outputPath,op.basename(inputfile)))\\n shutil.move(inputfile, outputPath)\\n else:\\n missingf.write(inputfile+'\\\\n')\\n numOfMissingFiles = numOfMissingFiles + 1\\n\\nmissingf.close()\\nprint (\\\"listed {0} missing files to logs/missingFiles.log\\\").format(numOfMissingFiles)\\n\\nmoduleList = moduleDic.keys()\\n\\n# after move, operate a documentation check\\nfor source in sourceList:\\n outputPath = op.join(op.join(HeadOfModularOTBTree,\\\"Modules\\\"),op.join(source[\\\"group\\\"],op.join(source[\\\"module\\\"],source[\\\"subDir\\\"])))\\n outputFile = op.join(outputPath,op.basename(source[\\\"path\\\"]))\\n if op.isfile(outputFile):\\n if op.splitext(outputFile)[1] == \\\".h\\\":\\n nextContent = documentationCheck.parserHeader(outputFile,source[\\\"module\\\"])\\n fd = open(outputFile,'wb')\\n fd.writelines(nextContent)\\n fd.close()\\n\\n\\n# get dependencies (if file is present)\\ndependencies = {}\\ntestDependencies = {}\\nexDependencies = {}\\nfor mod in moduleList:\\n dependencies[mod] = []\\n testDependencies[mod] = []\\n exDependencies[mod] = []\\n\\nif op.isfile(EdgePath):\\n fd = open(EdgePath,'rb')\\n for line in fd:\\n words = line.split(',')\\n if len(words) == 2:\\n depFrom = words[0].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n depTo = words[1].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n if dependencies.has_key(depFrom):\\n dependencies[depFrom].append(depTo)\\n else:\\n print(\\\"Bad dependency : \\\"+depFrom+\\\" -> \\\"+depTo)\\n fd.close()\\n\\nif op.isfile(testDependPath):\\n fd = open(testDependPath,'rb')\\n for line in fd:\\n words = line.split(',')\\n if len(words) == 2:\\n depFrom = words[0].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n depTo = words[1].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n if testDependencies.has_key(depFrom):\\n testDependencies[depFrom].append(depTo)\\n else:\\n print(\\\"Bad dependency : \\\"+depFrom+\\\" -> \\\"+depTo)\\n fd.close()\\n\\n\\\"\\\"\\\"\\nif op.isfile(exDependPath):\\n fd = open(exDependPath,'rb')\\n for line in fd:\\n words = line.split(',')\\n if len(words) == 2:\\n depFrom = words[0].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n depTo = words[1].strip(\\\" ,;\\\\t\\\\n\\\\r\\\")\\n if exDependencies.has_key(depFrom):\\n exDependencies[depFrom].append(depTo)\\n else:\\n print(\\\"Bad dependency : \\\"+depFrom+\\\" -> \\\"+depTo)\\n fd.close()\\n\\\"\\\"\\\"\\nmodDescriptions = {}\\nif op.isfile(modDescriptionPath):\\n modDescriptions = parseDescriptions(modDescriptionPath)\\n\\n\\n\\n# list the new files\\nnewf = open(op.join(logDir,'newFiles.log'),'w')\\nfor (root, subDirs, files) in os.walk(HeadOfTempTree):\\n for afile in files:\\n newf.write(op.join(root, afile)+'\\\\n')\\nnewf.close()\\nprint (\\\"listed new files to logs/newFiles.log\\\")\\n\\n###########################################################################\\n\\nprint ('creating cmake files for each module (from the template module)')\\n#moduleList = os.listdir(HeadOfModularOTBTree)\\nfor moduleName in moduleList:\\n moduleDir = op.join(op.join(HeadOfModularOTBTree,\\\"Modules\\\"),op.join(moduleDic[moduleName],moduleName))\\n cmakeModName = \\\"OTB\\\"+moduleName\\n \\n if op.isdir(moduleDir):\\n \\n # write CMakeLists.txt\\n filepath = moduleDir+'/CMakeLists.txt'\\n \\n if not op.isfile(filepath):\\n o = open(filepath,'w')\\n \\n if op.isdir(moduleDir+'/src'):\\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists.txt')\\n else:\\n template_cmakelist = op.join(scriptDir,'templateModule/otb-template-module/CMakeLists-nosrc.txt')\\n \\n for line in open(template_cmakelist,'r'):\\n line = line.replace('otb-template-module',cmakeModName)\\n o.write(line);\\n o.close()\\n\\n # write src/CMakeLists.txt\\n # list of CXX files\\n if op.isdir(moduleDir+'/src'):\\n cxxFiles = glob.glob(moduleDir+'/src/*.cxx')\\n cxxFileList='';\\n for cxxf in cxxFiles:\\n cxxFileList = cxxFileList+' '+cxxf.split('/')[-1]+'\\\\n'\\n # build list of link dependencies\\n linkLibs = \\\"\\\"\\n for dep in dependencies[moduleName]:\\n #verify if dep is a header-onlymodule\\n depThirdParty = False\\n try:\\n moduleDic[dep]\\n except KeyError:\\n # this is a ThirdParty module\\n depThirdParty = True\\n \\n if not depThirdParty:\\n depModuleDir = op.join(op.join(HeadOfModularOTBTree,\\\"Modules\\\"),op.join(moduleDic[dep],dep))\\n depcxx = glob.glob(depModuleDir+'/src/*.cxx')\\n if depcxx :\\n linkLibs = linkLibs + \\\" ${OTB\\\"+dep+\\\"_LIBRARIES}\\\" + \\\"\\\\n\\\"\\n else:\\n linkLibs = linkLibs + \\\" ${OTB\\\"+dep+\\\"_LIBRARIES}\\\" + \\\"\\\\n\\\"\\n \\n if len(linkLibs) == 0:\\n linkLibs = \\\" ${OTBITK_LIBRARIES}\\\"\\n filepath = moduleDir+'/src/CMakeLists.txt'\\n if not op.isfile(filepath):\\n o = open(filepath,'w')\\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/src/CMakeLists.txt'),'r'):\\n line = line.replace('otb-template-module',cmakeModName)\\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\\\n\\n line = line.replace('LINK_LIBRARIES_TO_BE_REPLACED',linkLibs)\\n o.write(line);\\n o.close()\\n\\n # write app/CMakeLists.txt\\n if op.isdir(moduleDir+'/app'):\\n os.mkdir(moduleDir+'/test')\\n srcFiles = glob.glob(moduleDir+'/app/*.cxx')\\n srcFiles += glob.glob(moduleDir+'/app/*.h')\\n appList = {}\\n \\n for srcf in srcFiles:\\n # get App name\\n appName = analyseAppManifest.findApplicationName(srcf)\\n if len(appName) == 0:\\n continue\\n \\n appList[appName] = {\\\"source\\\":op.basename(srcf)}\\n \\n # get original location\\n cmakeListPath = \\\"\\\"\\n for item in sourceList:\\n if op.basename(item[\\\"path\\\"]) == op.basename(srcf) and \\\\\\n moduleName == item[\\\"module\\\"]:\\n appDir = op.basename(op.dirname(item[\\\"path\\\"]))\\n cmakeListPath = op.join(HeadOfOTBTree,op.join(\\\"Testing/Applications\\\"),op.join(appDir,\\\"CMakeLists.txt\\\"))\\n break\\n \\n # get App tests\\n if not op.isfile(cmakeListPath):\\n continue\\n \\n appList[appName][\\\"test\\\"] = analyseAppManifest.findTestFromApp(cmakeListPath,appName)\\n \\n # build list of link dependencies\\n linkLibs = \\\"\\\"\\n for dep in dependencies[moduleName]:\\n linkLibs = linkLibs + \\\" ${OTB\\\"+dep+\\\"_LIBRARIES}\\\" + \\\"\\\\n\\\"\\n \\n filepath = moduleDir+'/app/CMakeLists.txt'\\n if not op.isfile(filepath):\\n o = open(filepath,'w')\\n # define link libraries \\n o.write(\\\"set(\\\"+cmakeModName+\\\"_LINK_LIBS\\\\n\\\")\\n o.write(linkLibs)\\n o.write(\\\")\\\\n\\\")\\n \\n for appli in appList:\\n content = \\\"\\\\notb_create_application(\\\\n\\\"\\n content += \\\" NAME \\\" + appli + \\\"\\\\n\\\"\\n content += \\\" SOURCES \\\" + appList[appli][\\\"source\\\"] + \\\"\\\\n\\\"\\n content += \\\" LINK_LIBRARIES ${${otb-module}_LIBRARIES})\\\\n\\\"\\n o.write(content)\\n o.close()\\n \\n filepath = moduleDir+'/test/CMakeLists.txt'\\n if not op.isfile(filepath):\\n o = open(filepath,'w')\\n o.write(\\\"otb_module_test()\\\")\\n for appli in appList:\\n if not appList[appli].has_key(\\\"test\\\"):\\n continue\\n o.write(\\\"\\\\n#----------- \\\"+appli+\\\" TESTS ----------------\\\\n\\\")\\n for test in appList[appli][\\\"test\\\"]:\\n if test.count(\\\"${\\\"):\\n print \\\"Warning : test name contains a variable : \\\"+test\\n continue\\n \\n testcode=appList[appli][\\\"test\\\"][test]\\n testcode=[s.replace('OTB_TEST_APPLICATION', 'otb_test_application') for s in testcode]\\n o.writelines(testcode)\\n o.write(\\\"\\\\n\\\")\\n o.close()\\n\\n # write test/CMakeLists.txt : done by dispatchTests.py\\n \\\"\\\"\\\"\\n if op.isdir(moduleDir+'/test'):\\n cxxFiles = glob.glob(moduleDir+'/test/*.cxx')\\n cxxFileList='';\\n for cxxf in cxxFiles:\\n cxxFileList = cxxFileList+cxxf.split('/')[-1]+'\\\\n'\\n filepath = moduleDir+'/test/CMakeLists.txt'\\n if not op.isfile(filepath):\\n o = open(filepath,'w')\\n for line in open('./templateModule/otb-template-module/test/CMakeLists.txt','r'):\\n # TODO : refactor for OTB\\n words= moduleName.split('-')\\n moduleNameMod='';\\n for word in words:\\n moduleNameMod=moduleNameMod + word.capitalize()\\n line = line.replace('itkTemplateModule',moduleNameMod)\\n line = line.replace('itk-template-module',moduleName)\\n line = line.replace('LIST_OF_CXX_FILES',cxxFileList[0:-1]) #get rid of the last \\\\n\\n o.write(line);\\n o.close()\\n \\\"\\\"\\\"\\n \\n # write otb-module.cmake, which contains dependency info\\n filepath = moduleDir+'/otb-module.cmake'\\n if not op.isfile(filepath):\\n o = open(filepath,'w')\\n for line in open(op.join(scriptDir,'templateModule/otb-template-module/otb-module.cmake'),'r'):\\n # replace documentation\\n if line.find(\\\"DESCRIPTION_TO_BE_REPLACED\\\") >= 0:\\n docString = \\\"\\\\\\\"TBD\\\\\\\"\\\"\\n if moduleName in modDescriptions:\\n descPos = line.find(\\\"DESCRIPTION_TO_BE_REPLACED\\\")\\n limitChar = 80\\n docString = \\\"\\\\\\\"\\\"+modDescriptions[moduleName]+\\\"\\\\\\\"\\\"\\n curPos = 80 - descPos\\n while curPos < len(docString):\\n lastSpace = docString[0:curPos].rfind(' ')\\n if lastSpace > max(0,curPos-80):\\n docString = docString[0:lastSpace] + '\\\\n' + docString[lastSpace+1:]\\n else:\\n docString = docString[0:curPos] + '\\\\n' + docString[curPos:]\\n curPos += 81\\n line = line.replace('DESCRIPTION_TO_BE_REPLACED',docString)\\n \\n # replace module name\\n line = line.replace('otb-template-module',cmakeModName)\\n # replace depend list\\n dependTagPos = line.find(\\\"DEPENDS_TO_BE_REPLACED\\\")\\n if dependTagPos >= 0:\\n replacementStr = \\\"DEPENDS\\\"\\n indentStr = \\\"\\\"\\n for it in range(dependTagPos+2):\\n indentStr = indentStr + \\\" \\\"\\n if len(dependencies[moduleName]) > 0:\\n deplist = dependencies[moduleName]\\n else:\\n deplist = [\\\"Common\\\"]\\n for dep in deplist:\\n replacementStr = replacementStr + \\\"\\\\n\\\" + indentStr +\\\"OTB\\\"+ dep\\n line = line.replace('DEPENDS_TO_BE_REPLACED',replacementStr)\\n # replace test_depend list\\n testDependTagPos = line.find(\\\"TESTDEP_TO_BE_REPLACED\\\")\\n if testDependTagPos >= 0:\\n if moduleName.startswith(\\\"App\\\"):\\n # for application : hardcode TestKernel and CommandLine\\n indentStr = \\\"\\\"\\n for it in range(testDependTagPos+2):\\n indentStr = indentStr + \\\" \\\"\\n replacementStr = \\\"TEST_DEPENDS\\\\n\\\" + indentStr + \\\"OTBTestKernel\\\\n\\\" + indentStr + \\\"OTBCommandLine\\\"\\n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\\n else:\\n # standard case\\n\\n if len(testDependencies[moduleName]) > 0:\\n indentStr = \\\"\\\"\\n replacementStr = \\\"TEST_DEPENDS\\\"\\n for it in range(testDependTagPos+2):\\n indentStr = indentStr + \\\" \\\"\\n for dep in testDependencies[moduleName]:\\n replacementStr = replacementStr + \\\"\\\\n\\\" + indentStr +\\\"OTB\\\"+ dep \\n line = line.replace('TESTDEP_TO_BE_REPLACED',replacementStr)\\n else:\\n line = line.replace('TESTDEP_TO_BE_REPLACED','')\\n \\n # replace example_depend list\\n exDependTagPos = line.find(\\\"EXDEP_TO_BE_REPLACED\\\")\\n if exDependTagPos >= 0:\\n if len(exDependencies[moduleName]) > 0:\\n indentStr = \\\"\\\"\\n replacementStr = \\\"EXAMPLE_DEPENDS\\\"\\n for it in range(exDependTagPos+2):\\n indentStr = indentStr + \\\" \\\"\\n for dep in exDependencies[moduleName]:\\n replacementStr = replacementStr + \\\"\\\\n\\\" + indentStr +\\\"OTB\\\"+ dep \\n line = line.replace('EXDEP_TO_BE_REPLACED',replacementStr)\\n else:\\n line = line.replace('EXDEP_TO_BE_REPLACED','')\\n o.write(line);\\n \\n o.close()\\n\\n# call dispatchTests to fill test/CMakeLists\\nif op.isfile(testDependPath):\\n dispatchTests.main([\\\"dispatchTests.py\\\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,testDependPath])\\n\\n\\\"\\\"\\\"\\n# call dispatchExamples to fill example/CMakeLists\\nif op.isfile(exDependPath):\\n dispatchExamples.main([\\\"dispatchExamples.py\\\",ManifestPath,HeadOfOTBTree,HeadOfModularOTBTree,exDependPath])\\n\\\"\\\"\\\"\\n\\n# examples\\nfor i in sorted(os.listdir(HeadOfTempTree + \\\"/Examples\\\")):\\n if i == \\\"CMakeLists.txt\\\" or i == \\\"README.txt\\\" or i.startswith(\\\"DataRepresentation\\\"):\\n continue\\n\\n for j in sorted(os.listdir(HeadOfTempTree + \\\"/Examples/\\\" + i)):\\n if j == \\\"CMakeLists.txt\\\" or j.startswith(\\\"otb\\\"):\\n continue\\n \\n command = \\\"mv %s/Examples/%s/%s %s/Examples/%s/%s\\\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j)\\n os.system(command)\\n\\nfor i in sorted(os.listdir(HeadOfTempTree + \\\"/Examples/DataRepresentation\\\")):\\n if i == \\\"CMakeLists.txt\\\" or i == \\\"README.txt\\\":\\n continue\\n\\n for j in sorted(os.listdir(HeadOfTempTree + \\\"/Examples/DataRepresentation/\\\" + i)):\\n if j == \\\"CMakeLists.txt\\\" or j.startswith(\\\"otb\\\"):\\n continue\\n \\n command = \\\"mv %s/Examples/DataRepresentation/%s/%s %s/Examples/DataRepresentation/%s/%s\\\" % ( HeadOfTempTree, i, j, HeadOfModularOTBTree, i, j) \\n os.system(command)\\n\\n\\n# save version without patches (so that we can regenerate patches later)\\nos.system( \\\"cp -ar \\\" + op.join(OutputDir,\\\"OTB_Modular\\\") + \\\" \\\" + op.join(OutputDir,\\\"OTB_Modular-nopatch\\\") )\\n\\n# apply patches in OTB_Modular\\ncurdir = op.abspath(op.dirname(__file__))\\ncommand = \\\"cd \\\" + op.join(OutputDir,\\\"OTB_Modular\\\") + \\\" && patch -p1 < \\\" + curdir + \\\"/patches/otbmodular.patch\\\"\\nprint \\\"Executing \\\" + command\\nos.system( command )\\n\\n# remove Copyright files we don't want to touch later\\nos.system( \\\"rm -rf %s\\\" % (op.join(HeadOfTempTree,\\\"Copyright\\\") ) )\\nos.system( \\\"rm -rf %s\\\" % (op.join(HeadOfTempTree,\\\"RELEASE_NOTES.txt\\\") ) )\\nos.system( \\\"rm -rf %s\\\" % (op.join(HeadOfTempTree,\\\"README\\\") ) )\\n\\n# PREPARE MIGRATION COMMIT ON A CLONE OF ORIGINAL CHECKOUT\\nif enableMigration:\\n print(\\\"Executing migration on a clone of original checkout\\\")\\n HeadOfTempTree = op.abspath(HeadOfTempTree)\\n OutputDir = op.abspath(OutputDir)\\n \\n # clone original checkout\\n outputModular = op.join(OutputDir,\\\"OTB_Modular\\\")\\n outputMigration = op.join(OutputDir,\\\"OTB_Migration\\\")\\n if op.exists(outputMigration):\\n os.removedirs(outputMigration)\\n command = [\\\"cp\\\",\\\"-ar\\\",HeadOfOTBTree,outputMigration]\\n call(command)\\n os.chdir(outputMigration)\\n \\n # walk through OTB_Remaining and delete corresponding files in OTB checkout\\n print(\\\"DELETE STEP...\\\")\\n for dirPath, dirNames, fileNames in os.walk(HeadOfTempTree):\\n currentSourceDir = dirPath.replace(HeadOfTempTree,'.')\\n for fileName in fileNames:\\n if op.exists(op.join(currentSourceDir,fileName)):\\n command = [\\\"hg\\\",\\\"remove\\\",op.join(currentSourceDir,fileName)]\\n call(command)\\n else:\\n print(\\\"Unknown file : \\\"+op.join(currentSourceDir,fileName))\\n command = ['hg','commit','-m','ENH: Remove files not necessary after modularization']\\n call(command)\\n \\n # walk through manifest and rename files\\n print(\\\"MOVE STEP...\\\")\\n for source in sourceList:\\n outputPath = op.join(\\\"./Modules\\\",op.join(source[\\\"group\\\"],op.join(source[\\\"module\\\"],source[\\\"subDir\\\"])))\\n command = ['hg','rename',source[\\\"path\\\"],op.join(outputPath,op.basename(source[\\\"path\\\"]))]\\n call(command)\\n command = ['hg','commit','-m','ENH: Move source and test files into their respective module']\\n call(command)\\n \\n # add new files from OTB_Modular (files from OTB-Modular repo + generated files)\\n print(\\\"ADD STEP...\\\")\\n for dirPath, dirNames, fileNames in os.walk(outputModular):\\n currentSourceDir = dirPath.replace(outputModular,'.')\\n if currentSourceDir.startswith(\\\"./.hg\\\"):\\n print(\\\"skip .hg\\\")\\n continue\\n for fileName in fileNames:\\n # skip hg files\\n if fileName.startswith(\\\".hg\\\"):\\n continue\\n targetFile = op.join(currentSourceDir,fileName)\\n if not op.exists(targetFile):\\n if not op.exists(currentSourceDir):\\n command = [\\\"mkdir\\\",\\\"-p\\\",currentSourceDir]\\n call(command)\\n shutil.copy(op.join(dirPath,fileName),targetFile)\\n command = ['hg','add']\\n call(command)\\n command = ['hg','commit','-m','ENH: Add new files for modular build system']\\n call(command)\\n \\n # apply patches on OTB Checkout\\n print(\\\"PATCH STEP...\\\")\\n for dirPath, dirNames, fileNames in os.walk(outputModular):\\n currentSourceDir = dirPath.replace(outputModular,'.')\\n if currentSourceDir.startswith(\\\"./.hg\\\"):\\n continue\\n for fileName in fileNames:\\n # skip hg files\\n if fileName.startswith(\\\".hg\\\"):\\n continue\\n targetFile = op.join(currentSourceDir,fileName)\\n if op.exists(targetFile):\\n command = ['cp',op.join(dirPath,fileName),targetFile]\\n call(command)\\n command = ['hg','commit','-m','ENH: Apply patches necessary after modularization']\\n call(command)\\n\\n\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5887,"cells":{"index":{"kind":"number","value":5887,"string":"5,887"},"blob_id":{"kind":"string","value":"8c4006ed8f4b1744f0316a61d95458b227653fee"},"code":{"kind":"string","value":"/Users/AbbyPennington/anaconda/lib/python3.5/os.py"},"steps":{"kind":"list like","value":["/Users/AbbyPennington/anaconda/lib/python3.5/os.py"],"string":"[\n \"/Users/AbbyPennington/anaconda/lib/python3.5/os.py\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5888,"cells":{"index":{"kind":"number","value":5888,"string":"5,888"},"blob_id":{"kind":"string","value":"2a6ae615b427a7c970aacf9804865ea7952d065f"},"code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 20 14:48:56 2020\n\n@author: dhk1349\n\"\"\"\n\nn = int(input()) #목표채널 \nm = int(input())\nbroken=[int(i) for i in input().split()] #망가진 버튼 \nnormal=[i for i in range(10)] #사용가능한 버튼 \nans=abs(n-100) #시작 시 정답 \n\n\nfor i in broken:\n normal.remove(i)\n\n\ntempnum=0\niternum=1\n\ndef solve(lst, target):\n #가장 유사한 숫자를 뱉\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nwhile n!=0:\n val=n%10\n n=n/10\n \n if val not in normal:\n \n \n tempnum+=(iternum*val)\n iternum*=10\n"},"steps":{"kind":"list like","value":["# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 20 14:48:56 2020\n\n@author: dhk1349\n\"\"\"\n\nn = int(input()) #목표채널 \nm = int(input())\nbroken=[int(i) for i in input().split()] #망가진 버튼 \nnormal=[i for i in range(10)] #사용가능한 버튼 \nans=abs(n-100) #시작 시 정답 \n\n\nfor i in broken:\n normal.remove(i)\n\n\ntempnum=0\niternum=1\n\ndef solve(lst, target):\n #가장 유사한 숫자를 뱉\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\nwhile n!=0:\n val=n%10\n n=n/10\n \n if val not in normal:\n \n \n tempnum+=(iternum*val)\n iternum*=10\n"],"string":"[\n \"# -*- coding: utf-8 -*-\\n\\\"\\\"\\\"\\nCreated on Sun Dec 20 14:48:56 2020\\n\\n@author: dhk1349\\n\\\"\\\"\\\"\\n\\nn = int(input()) #목표채널 \\nm = int(input())\\nbroken=[int(i) for i in input().split()] #망가진 버튼 \\nnormal=[i for i in range(10)] #사용가능한 버튼 \\nans=abs(n-100) #시작 시 정답 \\n\\n\\nfor i in broken:\\n normal.remove(i)\\n\\n\\ntempnum=0\\niternum=1\\n\\ndef solve(lst, target):\\n #가장 유사한 숫자를 뱉\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nwhile n!=0:\\n val=n%10\\n n=n/10\\n \\n if val not in normal:\\n \\n \\n tempnum+=(iternum*val)\\n iternum*=10\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5889,"cells":{"index":{"kind":"number","value":5889,"string":"5,889"},"blob_id":{"kind":"string","value":"8a04166e091e2da348928598b2356c8ad75dd831"},"code":{"kind":"string","value":"#usage:\n#crawl raw weibo text data from sina weibo users(my followees)\n#in total, there are 20080 weibo tweets, because there is uplimit for crawler\n\n# -*- coding: utf-8 -*-\nimport weibo\n\nAPP_KEY = 'your app_key'\nAPP_SECRET = 'your app_secret'\nCALL_BACK = 'your call back url'\n\ndef run():\n\ttoken = \"your access token gotten from call_back url\"\n\tclient = weibo.APIClient(APP_KEY, APP_SECRET, CALL_BACK)\n\tclient.set_access_token(token,12345)\n\n\tfollowlist = client.friendships.friends.get(screen_name='蜀云Parallelli',count=200)\n\twb_raw = open('weibo_raw_userlistweibo_big.txt','w')\n\tweiboCnt = 0\n\tusernames = {}\n\tfor fl in followlist.users:\n\t\tpg = 1\n\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\twhile (pg <= 3):\n\t\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\t\tif fl.screen_name not in usernames:\n\t\t\t\tusernames[fl.screen_name]=1\n\n\t\t\tfor wb in wbres.statuses:\n\t\t\t\tweiboCnt += 1\n\t\t\t\twb_raw.write(wb.text.encode('utf-8')+'\\n')\n\t\t\tpg += 1\n\tfollowlist = client.friendships.friends.get(screen_name='尹欢欢欢',count=200)\n\tfor fl in followlist.users:\n\t\tpg = 1\n\t\tif fl.screen_name in usernames: \n\t\t\tcontinue\n\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\twhile (pg <= 3):\n\t\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\t\tif fl.screen_name not in usernames:\n\t\t\t\tusernames[fl.screen_name]=1\n\n\t\t\tfor wb in wbres.statuses:\n\t\t\t\tweiboCnt += 1\n\t\t\t\twb_raw.write(wb.text.encode('utf-8')+'\\n')\n\t\t\tpg += 1\n\tprint weiboCnt\n\twb_raw.close()\n\t\nif __name__ == \"__main__\":\n\trun()"},"steps":{"kind":"list like","value":["#usage:\n#crawl raw weibo text data from sina weibo users(my followees)\n#in total, there are 20080 weibo tweets, because there is uplimit for crawler\n\n# -*- coding: utf-8 -*-\nimport weibo\n\nAPP_KEY = 'your app_key'\nAPP_SECRET = 'your app_secret'\nCALL_BACK = 'your call back url'\n\ndef run():\n\ttoken = \"your access token gotten from call_back url\"\n\tclient = weibo.APIClient(APP_KEY, APP_SECRET, CALL_BACK)\n\tclient.set_access_token(token,12345)\n\n\tfollowlist = client.friendships.friends.get(screen_name='蜀云Parallelli',count=200)\n\twb_raw = open('weibo_raw_userlistweibo_big.txt','w')\n\tweiboCnt = 0\n\tusernames = {}\n\tfor fl in followlist.users:\n\t\tpg = 1\n\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\twhile (pg <= 3):\n\t\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\t\tif fl.screen_name not in usernames:\n\t\t\t\tusernames[fl.screen_name]=1\n\n\t\t\tfor wb in wbres.statuses:\n\t\t\t\tweiboCnt += 1\n\t\t\t\twb_raw.write(wb.text.encode('utf-8')+'\\n')\n\t\t\tpg += 1\n\tfollowlist = client.friendships.friends.get(screen_name='尹欢欢欢',count=200)\n\tfor fl in followlist.users:\n\t\tpg = 1\n\t\tif fl.screen_name in usernames: \n\t\t\tcontinue\n\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\twhile (pg <= 3):\n\t\t\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\n\t\t\tif fl.screen_name not in usernames:\n\t\t\t\tusernames[fl.screen_name]=1\n\n\t\t\tfor wb in wbres.statuses:\n\t\t\t\tweiboCnt += 1\n\t\t\t\twb_raw.write(wb.text.encode('utf-8')+'\\n')\n\t\t\tpg += 1\n\tprint weiboCnt\n\twb_raw.close()\n\t\nif __name__ == \"__main__\":\n\trun()"],"string":"[\n \"#usage:\\n#crawl raw weibo text data from sina weibo users(my followees)\\n#in total, there are 20080 weibo tweets, because there is uplimit for crawler\\n\\n# -*- coding: utf-8 -*-\\nimport weibo\\n\\nAPP_KEY = 'your app_key'\\nAPP_SECRET = 'your app_secret'\\nCALL_BACK = 'your call back url'\\n\\ndef run():\\n\\ttoken = \\\"your access token gotten from call_back url\\\"\\n\\tclient = weibo.APIClient(APP_KEY, APP_SECRET, CALL_BACK)\\n\\tclient.set_access_token(token,12345)\\n\\n\\tfollowlist = client.friendships.friends.get(screen_name='蜀云Parallelli',count=200)\\n\\twb_raw = open('weibo_raw_userlistweibo_big.txt','w')\\n\\tweiboCnt = 0\\n\\tusernames = {}\\n\\tfor fl in followlist.users:\\n\\t\\tpg = 1\\n\\t\\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\\n\\t\\twhile (pg <= 3):\\n\\t\\t\\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\\n\\t\\t\\tif fl.screen_name not in usernames:\\n\\t\\t\\t\\tusernames[fl.screen_name]=1\\n\\n\\t\\t\\tfor wb in wbres.statuses:\\n\\t\\t\\t\\tweiboCnt += 1\\n\\t\\t\\t\\twb_raw.write(wb.text.encode('utf-8')+'\\\\n')\\n\\t\\t\\tpg += 1\\n\\tfollowlist = client.friendships.friends.get(screen_name='尹欢欢欢',count=200)\\n\\tfor fl in followlist.users:\\n\\t\\tpg = 1\\n\\t\\tif fl.screen_name in usernames: \\n\\t\\t\\tcontinue\\n\\t\\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\\n\\t\\twhile (pg <= 3):\\n\\t\\t\\twbres = client.statuses.user_timeline.get(screen_name=fl.screen_name,page=pg)\\n\\t\\t\\tif fl.screen_name not in usernames:\\n\\t\\t\\t\\tusernames[fl.screen_name]=1\\n\\n\\t\\t\\tfor wb in wbres.statuses:\\n\\t\\t\\t\\tweiboCnt += 1\\n\\t\\t\\t\\twb_raw.write(wb.text.encode('utf-8')+'\\\\n')\\n\\t\\t\\tpg += 1\\n\\tprint weiboCnt\\n\\twb_raw.close()\\n\\t\\nif __name__ == \\\"__main__\\\":\\n\\trun()\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5890,"cells":{"index":{"kind":"number","value":5890,"string":"5,890"},"blob_id":{"kind":"string","value":"b86dedad42d092ae97eb21227034e306ca640912"},"code":{"kind":"string","value":"class mySeq:\n\tdef __init__(self):\n\t\tself.mseq = ['I','II','III','IV']\n\n\tdef __len__(self):\n\t\treturn len(self.mseq)\n\n\tdef __getitem__(self,key):\n\t\tif 0 <= key < 4 :\n\t\t\treturn self.mseq[key]\n\nif __name__ == '__main__':\n\tm = mySeq()\n\tprint('Len of mySeq : ',len(m))\n\tfor i in range(len(m.mseq)):\n\t\tprint(m.mseq[i])"},"steps":{"kind":"list like","value":["class mySeq:\n\tdef __init__(self):\n\t\tself.mseq = ['I','II','III','IV']\n\n\tdef __len__(self):\n\t\treturn len(self.mseq)\n\n\tdef __getitem__(self,key):\n\t\tif 0 <= key < 4 :\n\t\t\treturn self.mseq[key]\n\nif __name__ == '__main__':\n\tm = mySeq()\n\tprint('Len of mySeq : ',len(m))\n\tfor i in range(len(m.mseq)):\n\t\tprint(m.mseq[i])","class mySeq:\n\n def __init__(self):\n self.mseq = ['I', 'II', 'III', 'IV']\n\n def __len__(self):\n return len(self.mseq)\n\n def __getitem__(self, key):\n if 0 <= key < 4:\n return self.mseq[key]\n\n\nif __name__ == '__main__':\n m = mySeq()\n print('Len of mySeq : ', len(m))\n for i in range(len(m.mseq)):\n print(m.mseq[i])\n","class mySeq:\n\n def __init__(self):\n self.mseq = ['I', 'II', 'III', 'IV']\n\n def __len__(self):\n return len(self.mseq)\n\n def __getitem__(self, key):\n if 0 <= key < 4:\n return self.mseq[key]\n\n\n<code token>\n","class mySeq:\n <function token>\n\n def __len__(self):\n return len(self.mseq)\n\n def __getitem__(self, key):\n if 0 <= key < 4:\n return self.mseq[key]\n\n\n<code token>\n","class mySeq:\n <function token>\n\n def __len__(self):\n return len(self.mseq)\n <function token>\n\n\n<code token>\n","class mySeq:\n <function token>\n <function token>\n <function token>\n\n\n<code token>\n","<class token>\n<code token>\n"],"string":"[\n \"class mySeq:\\n\\tdef __init__(self):\\n\\t\\tself.mseq = ['I','II','III','IV']\\n\\n\\tdef __len__(self):\\n\\t\\treturn len(self.mseq)\\n\\n\\tdef __getitem__(self,key):\\n\\t\\tif 0 <= key < 4 :\\n\\t\\t\\treturn self.mseq[key]\\n\\nif __name__ == '__main__':\\n\\tm = mySeq()\\n\\tprint('Len of mySeq : ',len(m))\\n\\tfor i in range(len(m.mseq)):\\n\\t\\tprint(m.mseq[i])\",\n \"class mySeq:\\n\\n def __init__(self):\\n self.mseq = ['I', 'II', 'III', 'IV']\\n\\n def __len__(self):\\n return len(self.mseq)\\n\\n def __getitem__(self, key):\\n if 0 <= key < 4:\\n return self.mseq[key]\\n\\n\\nif __name__ == '__main__':\\n m = mySeq()\\n print('Len of mySeq : ', len(m))\\n for i in range(len(m.mseq)):\\n print(m.mseq[i])\\n\",\n \"class mySeq:\\n\\n def __init__(self):\\n self.mseq = ['I', 'II', 'III', 'IV']\\n\\n def __len__(self):\\n return len(self.mseq)\\n\\n def __getitem__(self, key):\\n if 0 <= key < 4:\\n return self.mseq[key]\\n\\n\\n<code token>\\n\",\n \"class mySeq:\\n <function token>\\n\\n def __len__(self):\\n return len(self.mseq)\\n\\n def __getitem__(self, key):\\n if 0 <= key < 4:\\n return self.mseq[key]\\n\\n\\n<code token>\\n\",\n \"class mySeq:\\n <function token>\\n\\n def __len__(self):\\n return len(self.mseq)\\n <function token>\\n\\n\\n<code token>\\n\",\n \"class mySeq:\\n <function token>\\n <function token>\\n <function token>\\n\\n\\n<code token>\\n\",\n \"<class token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5891,"cells":{"index":{"kind":"number","value":5891,"string":"5,891"},"blob_id":{"kind":"string","value":"38184ed4117b1b7dcf9e135ce8612fa13c44a99c"},"code":{"kind":"string","value":"i = 0\nwhile i >= 0: \n a=input(\"Name is: \")\n print(a)\n if a == \"Zeal\":\n print(\"\"\"Name_Zeal.\n Age_16.\n Interested in Programming.\"\"\")\n elif a==\"HanZaw\":\n print(\"\"\"Name_Han Zaw.\n Age_18.\n Studying Code at Green Hacker.\"\"\")\n elif a == \"Murphy\": \n print(\"\"\"Name_Murphy.\n Age_17.\n Insterested in Editing.\"\"\") \n elif a ==\"Ngal\":\n print(\"\"\"Name_Ngal.\n Age_17.\n In Loved with Me:p\"\"\")"},"steps":{"kind":"list like","value":["i = 0\nwhile i >= 0: \n a=input(\"Name is: \")\n print(a)\n if a == \"Zeal\":\n print(\"\"\"Name_Zeal.\n Age_16.\n Interested in Programming.\"\"\")\n elif a==\"HanZaw\":\n print(\"\"\"Name_Han Zaw.\n Age_18.\n Studying Code at Green Hacker.\"\"\")\n elif a == \"Murphy\": \n print(\"\"\"Name_Murphy.\n Age_17.\n Insterested in Editing.\"\"\") \n elif a ==\"Ngal\":\n print(\"\"\"Name_Ngal.\n Age_17.\n In Loved with Me:p\"\"\")","i = 0\nwhile i >= 0:\n a = input('Name is: ')\n print(a)\n if a == 'Zeal':\n print('Name_Zeal.\\n Age_16.\\n Interested in Programming.')\n elif a == 'HanZaw':\n print('Name_Han Zaw.\\n Age_18.\\n Studying Code at Green Hacker.')\n elif a == 'Murphy':\n print('Name_Murphy.\\n Age_17.\\n Insterested in Editing.')\n elif a == 'Ngal':\n print('Name_Ngal.\\n Age_17.\\n In Loved with Me:p')\n","<assignment token>\nwhile i >= 0:\n a = input('Name is: ')\n print(a)\n if a == 'Zeal':\n print('Name_Zeal.\\n Age_16.\\n Interested in Programming.')\n elif a == 'HanZaw':\n print('Name_Han Zaw.\\n Age_18.\\n Studying Code at Green Hacker.')\n elif a == 'Murphy':\n print('Name_Murphy.\\n Age_17.\\n Insterested in Editing.')\n elif a == 'Ngal':\n print('Name_Ngal.\\n Age_17.\\n In Loved with Me:p')\n","<assignment token>\n<code token>\n"],"string":"[\n \"i = 0\\nwhile i >= 0: \\n a=input(\\\"Name is: \\\")\\n print(a)\\n if a == \\\"Zeal\\\":\\n print(\\\"\\\"\\\"Name_Zeal.\\n Age_16.\\n Interested in Programming.\\\"\\\"\\\")\\n elif a==\\\"HanZaw\\\":\\n print(\\\"\\\"\\\"Name_Han Zaw.\\n Age_18.\\n Studying Code at Green Hacker.\\\"\\\"\\\")\\n elif a == \\\"Murphy\\\": \\n print(\\\"\\\"\\\"Name_Murphy.\\n Age_17.\\n Insterested in Editing.\\\"\\\"\\\") \\n elif a ==\\\"Ngal\\\":\\n print(\\\"\\\"\\\"Name_Ngal.\\n Age_17.\\n In Loved with Me:p\\\"\\\"\\\")\",\n \"i = 0\\nwhile i >= 0:\\n a = input('Name is: ')\\n print(a)\\n if a == 'Zeal':\\n print('Name_Zeal.\\\\n Age_16.\\\\n Interested in Programming.')\\n elif a == 'HanZaw':\\n print('Name_Han Zaw.\\\\n Age_18.\\\\n Studying Code at Green Hacker.')\\n elif a == 'Murphy':\\n print('Name_Murphy.\\\\n Age_17.\\\\n Insterested in Editing.')\\n elif a == 'Ngal':\\n print('Name_Ngal.\\\\n Age_17.\\\\n In Loved with Me:p')\\n\",\n \"<assignment token>\\nwhile i >= 0:\\n a = input('Name is: ')\\n print(a)\\n if a == 'Zeal':\\n print('Name_Zeal.\\\\n Age_16.\\\\n Interested in Programming.')\\n elif a == 'HanZaw':\\n print('Name_Han Zaw.\\\\n Age_18.\\\\n Studying Code at Green Hacker.')\\n elif a == 'Murphy':\\n print('Name_Murphy.\\\\n Age_17.\\\\n Insterested in Editing.')\\n elif a == 'Ngal':\\n print('Name_Ngal.\\\\n Age_17.\\\\n In Loved with Me:p')\\n\",\n \"<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5892,"cells":{"index":{"kind":"number","value":5892,"string":"5,892"},"blob_id":{"kind":"string","value":"f39945f35b13c0918c3ef06224bca65ae6166ebc"},"code":{"kind":"string","value":"import numpy as np\na=np.array([1,2,3])\nb=np.r_[np.repeat(a,3),np.tile(a,3)]\nprint(b)\n"},"steps":{"kind":"list like","value":["import numpy as np\na=np.array([1,2,3])\nb=np.r_[np.repeat(a,3),np.tile(a,3)]\nprint(b)\n","import numpy as np\na = np.array([1, 2, 3])\nb = np.r_[np.repeat(a, 3), np.tile(a, 3)]\nprint(b)\n","<import token>\na = np.array([1, 2, 3])\nb = np.r_[np.repeat(a, 3), np.tile(a, 3)]\nprint(b)\n","<import token>\n<assignment token>\nprint(b)\n","<import token>\n<assignment token>\n<code token>\n"],"string":"[\n \"import numpy as np\\na=np.array([1,2,3])\\nb=np.r_[np.repeat(a,3),np.tile(a,3)]\\nprint(b)\\n\",\n \"import numpy as np\\na = np.array([1, 2, 3])\\nb = np.r_[np.repeat(a, 3), np.tile(a, 3)]\\nprint(b)\\n\",\n \"<import token>\\na = np.array([1, 2, 3])\\nb = np.r_[np.repeat(a, 3), np.tile(a, 3)]\\nprint(b)\\n\",\n \"<import token>\\n<assignment token>\\nprint(b)\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5893,"cells":{"index":{"kind":"number","value":5893,"string":"5,893"},"blob_id":{"kind":"string","value":"87a62f76027e0653f6966f76a42def2ce2a26ba3"},"code":{"kind":"string","value":"#! /usr/bin/python3\r\n\r\nprint(\"content-type: text/html\")\r\nprint()\r\n\r\n\r\nimport cgi\r\nimport subprocess as sp\r\nimport requests\r\nimport xmltodict\r\nimport json\r\n\r\ndb = cgi.FieldStorage()\r\nch=db.getvalue(\"ch\")\r\nurl =(\"http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>\" .format(ch))\r\nurl=url.replace(\" \",\"%20\")\r\nr = requests.get(url)\r\nn = xmltodict.parse(r.content)\r\nk = json.dumps(n)\r\ndf = json.loads(k)\r\nl=df[\"Vehicle\"][\"vehicleJson\"]\r\np=json.loads(l)\r\noutput=\"Your car's details are:\\n\"+\"Owner name: \"+str(p['Owner'])+\"\\n\"+\"Car Company: \"+str(p['CarMake']['CurrentTextValue'])+\"\\n\"+\"Car Model: \"+str(p['CarModel']['CurrentTextValue'])+\"\\n\"+\"Fuel Type: \"+str(p['FuelType']['CurrentTextValue'])+\"\\n\"+\"Registration Year: \"+str(p['RegistrationYear'])+\"\\n\"+\"Insurance: \"+str(p['Insurance'])+\"\\n\"+\"Vehicle ID: \"+str(p['VechileIdentificationNumber'])+\"\\n\"+\"Engine No.: \"+str(p['EngineNumber'])+\"\\n\"+\"Location RTO: \"+str(p['Location'])\r\nprint(output)\r\n"},"steps":{"kind":"list like","value":["#! /usr/bin/python3\r\n\r\nprint(\"content-type: text/html\")\r\nprint()\r\n\r\n\r\nimport cgi\r\nimport subprocess as sp\r\nimport requests\r\nimport xmltodict\r\nimport json\r\n\r\ndb = cgi.FieldStorage()\r\nch=db.getvalue(\"ch\")\r\nurl =(\"http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>\" .format(ch))\r\nurl=url.replace(\" \",\"%20\")\r\nr = requests.get(url)\r\nn = xmltodict.parse(r.content)\r\nk = json.dumps(n)\r\ndf = json.loads(k)\r\nl=df[\"Vehicle\"][\"vehicleJson\"]\r\np=json.loads(l)\r\noutput=\"Your car's details are:\\n\"+\"Owner name: \"+str(p['Owner'])+\"\\n\"+\"Car Company: \"+str(p['CarMake']['CurrentTextValue'])+\"\\n\"+\"Car Model: \"+str(p['CarModel']['CurrentTextValue'])+\"\\n\"+\"Fuel Type: \"+str(p['FuelType']['CurrentTextValue'])+\"\\n\"+\"Registration Year: \"+str(p['RegistrationYear'])+\"\\n\"+\"Insurance: \"+str(p['Insurance'])+\"\\n\"+\"Vehicle ID: \"+str(p['VechileIdentificationNumber'])+\"\\n\"+\"Engine No.: \"+str(p['EngineNumber'])+\"\\n\"+\"Location RTO: \"+str(p['Location'])\r\nprint(output)\r\n","print('content-type: text/html')\nprint()\nimport cgi\nimport subprocess as sp\nimport requests\nimport xmltodict\nimport json\ndb = cgi.FieldStorage()\nch = db.getvalue('ch')\nurl = (\n 'http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>'\n .format(ch))\nurl = url.replace(' ', '%20')\nr = requests.get(url)\nn = xmltodict.parse(r.content)\nk = json.dumps(n)\ndf = json.loads(k)\nl = df['Vehicle']['vehicleJson']\np = json.loads(l)\noutput = \"Your car's details are:\\n\" + 'Owner name: ' + str(p['Owner']\n ) + '\\n' + 'Car Company: ' + str(p['CarMake']['CurrentTextValue']\n ) + '\\n' + 'Car Model: ' + str(p['CarModel']['CurrentTextValue']\n ) + '\\n' + 'Fuel Type: ' + str(p['FuelType']['CurrentTextValue']\n ) + '\\n' + 'Registration Year: ' + str(p['RegistrationYear']\n ) + '\\n' + 'Insurance: ' + str(p['Insurance']\n ) + '\\n' + 'Vehicle ID: ' + str(p['VechileIdentificationNumber']\n ) + '\\n' + 'Engine No.: ' + str(p['EngineNumber']\n ) + '\\n' + 'Location RTO: ' + str(p['Location'])\nprint(output)\n","print('content-type: text/html')\nprint()\n<import token>\ndb = cgi.FieldStorage()\nch = db.getvalue('ch')\nurl = (\n 'http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>'\n .format(ch))\nurl = url.replace(' ', '%20')\nr = requests.get(url)\nn = xmltodict.parse(r.content)\nk = json.dumps(n)\ndf = json.loads(k)\nl = df['Vehicle']['vehicleJson']\np = json.loads(l)\noutput = \"Your car's details are:\\n\" + 'Owner name: ' + str(p['Owner']\n ) + '\\n' + 'Car Company: ' + str(p['CarMake']['CurrentTextValue']\n ) + '\\n' + 'Car Model: ' + str(p['CarModel']['CurrentTextValue']\n ) + '\\n' + 'Fuel Type: ' + str(p['FuelType']['CurrentTextValue']\n ) + '\\n' + 'Registration Year: ' + str(p['RegistrationYear']\n ) + '\\n' + 'Insurance: ' + str(p['Insurance']\n ) + '\\n' + 'Vehicle ID: ' + str(p['VechileIdentificationNumber']\n ) + '\\n' + 'Engine No.: ' + str(p['EngineNumber']\n ) + '\\n' + 'Location RTO: ' + str(p['Location'])\nprint(output)\n","print('content-type: text/html')\nprint()\n<import token>\n<assignment token>\nprint(output)\n","<code token>\n<import token>\n<assignment token>\n<code token>\n"],"string":"[\n \"#! /usr/bin/python3\\r\\n\\r\\nprint(\\\"content-type: text/html\\\")\\r\\nprint()\\r\\n\\r\\n\\r\\nimport cgi\\r\\nimport subprocess as sp\\r\\nimport requests\\r\\nimport xmltodict\\r\\nimport json\\r\\n\\r\\ndb = cgi.FieldStorage()\\r\\nch=db.getvalue(\\\"ch\\\")\\r\\nurl =(\\\"http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>\\\" .format(ch))\\r\\nurl=url.replace(\\\" \\\",\\\"%20\\\")\\r\\nr = requests.get(url)\\r\\nn = xmltodict.parse(r.content)\\r\\nk = json.dumps(n)\\r\\ndf = json.loads(k)\\r\\nl=df[\\\"Vehicle\\\"][\\\"vehicleJson\\\"]\\r\\np=json.loads(l)\\r\\noutput=\\\"Your car's details are:\\\\n\\\"+\\\"Owner name: \\\"+str(p['Owner'])+\\\"\\\\n\\\"+\\\"Car Company: \\\"+str(p['CarMake']['CurrentTextValue'])+\\\"\\\\n\\\"+\\\"Car Model: \\\"+str(p['CarModel']['CurrentTextValue'])+\\\"\\\\n\\\"+\\\"Fuel Type: \\\"+str(p['FuelType']['CurrentTextValue'])+\\\"\\\\n\\\"+\\\"Registration Year: \\\"+str(p['RegistrationYear'])+\\\"\\\\n\\\"+\\\"Insurance: \\\"+str(p['Insurance'])+\\\"\\\\n\\\"+\\\"Vehicle ID: \\\"+str(p['VechileIdentificationNumber'])+\\\"\\\\n\\\"+\\\"Engine No.: \\\"+str(p['EngineNumber'])+\\\"\\\\n\\\"+\\\"Location RTO: \\\"+str(p['Location'])\\r\\nprint(output)\\r\\n\",\n \"print('content-type: text/html')\\nprint()\\nimport cgi\\nimport subprocess as sp\\nimport requests\\nimport xmltodict\\nimport json\\ndb = cgi.FieldStorage()\\nch = db.getvalue('ch')\\nurl = (\\n 'http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>'\\n .format(ch))\\nurl = url.replace(' ', '%20')\\nr = requests.get(url)\\nn = xmltodict.parse(r.content)\\nk = json.dumps(n)\\ndf = json.loads(k)\\nl = df['Vehicle']['vehicleJson']\\np = json.loads(l)\\noutput = \\\"Your car's details are:\\\\n\\\" + 'Owner name: ' + str(p['Owner']\\n ) + '\\\\n' + 'Car Company: ' + str(p['CarMake']['CurrentTextValue']\\n ) + '\\\\n' + 'Car Model: ' + str(p['CarModel']['CurrentTextValue']\\n ) + '\\\\n' + 'Fuel Type: ' + str(p['FuelType']['CurrentTextValue']\\n ) + '\\\\n' + 'Registration Year: ' + str(p['RegistrationYear']\\n ) + '\\\\n' + 'Insurance: ' + str(p['Insurance']\\n ) + '\\\\n' + 'Vehicle ID: ' + str(p['VechileIdentificationNumber']\\n ) + '\\\\n' + 'Engine No.: ' + str(p['EngineNumber']\\n ) + '\\\\n' + 'Location RTO: ' + str(p['Location'])\\nprint(output)\\n\",\n \"print('content-type: text/html')\\nprint()\\n<import token>\\ndb = cgi.FieldStorage()\\nch = db.getvalue('ch')\\nurl = (\\n 'http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>'\\n .format(ch))\\nurl = url.replace(' ', '%20')\\nr = requests.get(url)\\nn = xmltodict.parse(r.content)\\nk = json.dumps(n)\\ndf = json.loads(k)\\nl = df['Vehicle']['vehicleJson']\\np = json.loads(l)\\noutput = \\\"Your car's details are:\\\\n\\\" + 'Owner name: ' + str(p['Owner']\\n ) + '\\\\n' + 'Car Company: ' + str(p['CarMake']['CurrentTextValue']\\n ) + '\\\\n' + 'Car Model: ' + str(p['CarModel']['CurrentTextValue']\\n ) + '\\\\n' + 'Fuel Type: ' + str(p['FuelType']['CurrentTextValue']\\n ) + '\\\\n' + 'Registration Year: ' + str(p['RegistrationYear']\\n ) + '\\\\n' + 'Insurance: ' + str(p['Insurance']\\n ) + '\\\\n' + 'Vehicle ID: ' + str(p['VechileIdentificationNumber']\\n ) + '\\\\n' + 'Engine No.: ' + str(p['EngineNumber']\\n ) + '\\\\n' + 'Location RTO: ' + str(p['Location'])\\nprint(output)\\n\",\n \"print('content-type: text/html')\\nprint()\\n<import token>\\n<assignment token>\\nprint(output)\\n\",\n \"<code token>\\n<import token>\\n<assignment token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5894,"cells":{"index":{"kind":"number","value":5894,"string":"5,894"},"blob_id":{"kind":"string","value":"1c6e6394a6bd26b152b2f5ec87eb181a3387f794"},"code":{"kind":"string","value":"#!/usr/bin/python\n\n# Find minimal distances between clouds in one bin, average these per bin\n# Compute geometric and arithmetical mean between all clouds per bin\n\nfrom netCDF4 import Dataset as NetCDFFile\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom numpy import ma\nfrom scipy import stats \nfrom haversine import haversine\nfrom scipy.spatial import distance\nfrom distance_methods import distances\nfrom CSD_fit import CSD_fit\n\ncusize = NetCDFFile(\n '/home/vanlaar/HDCP2data/TA_dom4/cusize_output_time41.nc')\n\nsize = cusize.variables['size']\n\nbegin_time = 41\nend_time = 48\n\nD0_all = np.zeros((end_time-begin_time+1,len(size)))\nD1_all = np.zeros((end_time-begin_time+1,len(size)))\nnclouds_bin_all = np.zeros((end_time-begin_time+1,len(size)))\nmindistance_mean_all = np.zeros((end_time-begin_time+1,len(size)))\nmindistance_std_all = np.zeros((end_time-begin_time+1,len(size)))\nmaxdistance_all = np.zeros((end_time-begin_time+1,len(size)))\nmaxdistanceY_all = np.zeros((end_time-begin_time+1,len(size)))\nhn_normalized_all = np.zeros((end_time-begin_time+1,len(size)))\n\n\nfor time in range(begin_time,end_time+1):\n print 'time:',time\n\n cusize = NetCDFFile(\n '/home/vanlaar/HDCP2data/TA_dom4/cusize_output_time'+str(time)+'.nc')\n\n cloudlon = cusize.variables['cloud_lon'][:]\n cloudlat = cusize.variables['cloud_lat'][:]\n nclouds_cusize = cusize.variables['nclouds']\n #size = cusize.variables['size']\n cloud_bin = cusize.variables['cloud_bin'][0,:]\n hn = cusize.variables['hn']\n hn_normalized_loop = hn/nclouds_cusize[0]\n ncloud_bin = cusize.variables['ncloud_bin']\n\n ncloudsint = int(nclouds_cusize[0])\n cloud_lon = cloudlon[0,0:ncloudsint]\n cloud_lat = cloudlat[0,0:ncloudsint]\n filledbin=np.argmin(hn[0,:]) # last bin with clouds, rest is empty\n\n output_distances = distances(filledbin,cloud_lon,cloud_lat,cloud_bin,size,ncloudsint)\n\n D0_all[time-41] = output_distances[0]\n D1_all[time-41] = output_distances[1]\n mindistance_mean_all[time-41] = output_distances[2]\n mindistance_std_all[time-41] = output_distances[3]\n nclouds_bin_all[time-41] = output_distances[4]\n hn_normalized_all[time-41] = hn_normalized_loop\n\n\nmindistance_mean = np.mean(mindistance_mean_all,axis=0)/1000\nmindistance_std = np.mean(mindistance_std_all,axis=0)/1000\nD0 = np.mean(D0_all,axis=0)\nD1 = np.mean(D1_all,axis=0)\nnclouds = np.mean(nclouds_bin_all,axis=0)\nhn_normalized = np.mean(hn_normalized_all,axis=0)\n\nfilledbin_all=np.argmin(hn_normalized[:]) \nfit = CSD_fit(hn_normalized[0:10],size[0:10])\nlogfit = fit[3]\na = fit[0]\nb = fit[1]\nc = fit[2]\nprint 'a, b, c:'\nprint a, b, c\n\nprint logfit\n\nsizelog = np.log10(size)\nhnlog = ma.filled(np.log10(ma.masked_equal(hn_normalized, 0)), np.nan)\nncloudslog = ma.filled(np.log10(ma.masked_equal(nclouds, 0)), np.nan)\n\n#res = ma.filled(log2(ma.masked_equal(m, 0)), 0)\n\nmindistance_plus = mindistance_mean + mindistance_std\nmindistance_minus = mindistance_mean - mindistance_std\n\nfilledbin = np.argmin(mindistance_mean)\nslope, intercept, r_value, p_value, std_err = stats.linregress(size[0:filledbin],mindistance_mean[0:filledbin])\n\nprint 'r-squared:',r_value**2\nline = intercept + slope*size\n\nprint 'slope:',slope\nprint 'intercept:',intercept\n\n##################################################################\n### Plots\n\nthreshold = 0.005*sum(nclouds)\nmaxbin = np.min(np.where(nclouds <= threshold))\n\norange = (1.,0.38,0.01)\nblue = (0.53,0.81,1)\n\nplt.figure(figsize=(14,8))\nplt.axis([0, 5500, 0, 120])\nplt.xlabel('Cloud size [m]',fontsize=15)\nplt.ylabel('Nearest-neighbour distance [km]',fontsize=15)\nplt.fill_between(size,mindistance_plus,mindistance_minus,alpha=0.3,color=blue)\nplt.scatter(size,mindistance_mean,color='k')\n#plt.scatter(size,mindistance_plus,color='g')\n#plt.plot(size,line,color='black')\nax = plt.gca()\nax.axvspan(size[maxbin], 5500, alpha=0.2, color='grey')\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nplt.savefig('Figures/mindistance.pdf')\nplt.savefig('Figures/mindistance.png')\n\n\nplt.figure(figsize=(10,8))\n#plt.axis([50000,220000, 50000, 220000])\nplt.xlabel('D1')\nplt.ylabel('D0')\nplt.scatter(D1,D0,color='k')\nplt.savefig('Figures/D1-D0.pdf')\n\n\nplt.figure(figsize=(10,8))\nplt.xlabel('log(l) [m]')\nplt.ylabel('log(N*(l)) [m-1]')\nplt.scatter(sizelog,hnlog)\nplt.scatter(sizelog[0:10],logfit[0:10])\nplt.savefig('Figures/CSD.pdf')\n\n\nplt.figure(figsize=(10,8))\nplt.xlabel('Cloud size')\nplt.ylabel('Ratio distance/size')\nplt.axis([0, 5500, 0, 0.02])\nax = plt.gca()\nax.axvspan(size[maxbin], 5500, alpha=0.2, color='grey')\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nplt.savefig('Figures/mindistance.pdf')\nplt.scatter(size[0:filledbin],mindistance_mean[0:filledbin]/size[0:filledbin])\nplt.savefig('Figures/ratio_distance_size.pdf')\n\n\n\nplt.figure(figsize=(10,8))\nplt.xlabel('Cloud size')\nplt.ylabel('Number of clouds')\nplt.axhline(y=threshold, c='black')\nax = plt.gca()\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nplt.scatter(size[0:filledbin],nclouds[0:filledbin])\nplt.savefig('Figures/nclouds_size.pdf')\n\nplt.figure(figsize=(10,8))\nplt.xlabel('size')\nplt.ylabel('nclouds')\nplt.scatter(size[0:filledbin],ncloudslog[0:filledbin])\nplt.savefig('Figures/nclouds_size_log.pdf')\n\n"},"steps":{"kind":"list like","value":["#!/usr/bin/python\n\n# Find minimal distances between clouds in one bin, average these per bin\n# Compute geometric and arithmetical mean between all clouds per bin\n\nfrom netCDF4 import Dataset as NetCDFFile\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom numpy import ma\nfrom scipy import stats \nfrom haversine import haversine\nfrom scipy.spatial import distance\nfrom distance_methods import distances\nfrom CSD_fit import CSD_fit\n\ncusize = NetCDFFile(\n '/home/vanlaar/HDCP2data/TA_dom4/cusize_output_time41.nc')\n\nsize = cusize.variables['size']\n\nbegin_time = 41\nend_time = 48\n\nD0_all = np.zeros((end_time-begin_time+1,len(size)))\nD1_all = np.zeros((end_time-begin_time+1,len(size)))\nnclouds_bin_all = np.zeros((end_time-begin_time+1,len(size)))\nmindistance_mean_all = np.zeros((end_time-begin_time+1,len(size)))\nmindistance_std_all = np.zeros((end_time-begin_time+1,len(size)))\nmaxdistance_all = np.zeros((end_time-begin_time+1,len(size)))\nmaxdistanceY_all = np.zeros((end_time-begin_time+1,len(size)))\nhn_normalized_all = np.zeros((end_time-begin_time+1,len(size)))\n\n\nfor time in range(begin_time,end_time+1):\n print 'time:',time\n\n cusize = NetCDFFile(\n '/home/vanlaar/HDCP2data/TA_dom4/cusize_output_time'+str(time)+'.nc')\n\n cloudlon = cusize.variables['cloud_lon'][:]\n cloudlat = cusize.variables['cloud_lat'][:]\n nclouds_cusize = cusize.variables['nclouds']\n #size = cusize.variables['size']\n cloud_bin = cusize.variables['cloud_bin'][0,:]\n hn = cusize.variables['hn']\n hn_normalized_loop = hn/nclouds_cusize[0]\n ncloud_bin = cusize.variables['ncloud_bin']\n\n ncloudsint = int(nclouds_cusize[0])\n cloud_lon = cloudlon[0,0:ncloudsint]\n cloud_lat = cloudlat[0,0:ncloudsint]\n filledbin=np.argmin(hn[0,:]) # last bin with clouds, rest is empty\n\n output_distances = distances(filledbin,cloud_lon,cloud_lat,cloud_bin,size,ncloudsint)\n\n D0_all[time-41] = output_distances[0]\n D1_all[time-41] = output_distances[1]\n mindistance_mean_all[time-41] = output_distances[2]\n mindistance_std_all[time-41] = output_distances[3]\n nclouds_bin_all[time-41] = output_distances[4]\n hn_normalized_all[time-41] = hn_normalized_loop\n\n\nmindistance_mean = np.mean(mindistance_mean_all,axis=0)/1000\nmindistance_std = np.mean(mindistance_std_all,axis=0)/1000\nD0 = np.mean(D0_all,axis=0)\nD1 = np.mean(D1_all,axis=0)\nnclouds = np.mean(nclouds_bin_all,axis=0)\nhn_normalized = np.mean(hn_normalized_all,axis=0)\n\nfilledbin_all=np.argmin(hn_normalized[:]) \nfit = CSD_fit(hn_normalized[0:10],size[0:10])\nlogfit = fit[3]\na = fit[0]\nb = fit[1]\nc = fit[2]\nprint 'a, b, c:'\nprint a, b, c\n\nprint logfit\n\nsizelog = np.log10(size)\nhnlog = ma.filled(np.log10(ma.masked_equal(hn_normalized, 0)), np.nan)\nncloudslog = ma.filled(np.log10(ma.masked_equal(nclouds, 0)), np.nan)\n\n#res = ma.filled(log2(ma.masked_equal(m, 0)), 0)\n\nmindistance_plus = mindistance_mean + mindistance_std\nmindistance_minus = mindistance_mean - mindistance_std\n\nfilledbin = np.argmin(mindistance_mean)\nslope, intercept, r_value, p_value, std_err = stats.linregress(size[0:filledbin],mindistance_mean[0:filledbin])\n\nprint 'r-squared:',r_value**2\nline = intercept + slope*size\n\nprint 'slope:',slope\nprint 'intercept:',intercept\n\n##################################################################\n### Plots\n\nthreshold = 0.005*sum(nclouds)\nmaxbin = np.min(np.where(nclouds <= threshold))\n\norange = (1.,0.38,0.01)\nblue = (0.53,0.81,1)\n\nplt.figure(figsize=(14,8))\nplt.axis([0, 5500, 0, 120])\nplt.xlabel('Cloud size [m]',fontsize=15)\nplt.ylabel('Nearest-neighbour distance [km]',fontsize=15)\nplt.fill_between(size,mindistance_plus,mindistance_minus,alpha=0.3,color=blue)\nplt.scatter(size,mindistance_mean,color='k')\n#plt.scatter(size,mindistance_plus,color='g')\n#plt.plot(size,line,color='black')\nax = plt.gca()\nax.axvspan(size[maxbin], 5500, alpha=0.2, color='grey')\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nplt.savefig('Figures/mindistance.pdf')\nplt.savefig('Figures/mindistance.png')\n\n\nplt.figure(figsize=(10,8))\n#plt.axis([50000,220000, 50000, 220000])\nplt.xlabel('D1')\nplt.ylabel('D0')\nplt.scatter(D1,D0,color='k')\nplt.savefig('Figures/D1-D0.pdf')\n\n\nplt.figure(figsize=(10,8))\nplt.xlabel('log(l) [m]')\nplt.ylabel('log(N*(l)) [m-1]')\nplt.scatter(sizelog,hnlog)\nplt.scatter(sizelog[0:10],logfit[0:10])\nplt.savefig('Figures/CSD.pdf')\n\n\nplt.figure(figsize=(10,8))\nplt.xlabel('Cloud size')\nplt.ylabel('Ratio distance/size')\nplt.axis([0, 5500, 0, 0.02])\nax = plt.gca()\nax.axvspan(size[maxbin], 5500, alpha=0.2, color='grey')\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nplt.savefig('Figures/mindistance.pdf')\nplt.scatter(size[0:filledbin],mindistance_mean[0:filledbin]/size[0:filledbin])\nplt.savefig('Figures/ratio_distance_size.pdf')\n\n\n\nplt.figure(figsize=(10,8))\nplt.xlabel('Cloud size')\nplt.ylabel('Number of clouds')\nplt.axhline(y=threshold, c='black')\nax = plt.gca()\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nplt.scatter(size[0:filledbin],nclouds[0:filledbin])\nplt.savefig('Figures/nclouds_size.pdf')\n\nplt.figure(figsize=(10,8))\nplt.xlabel('size')\nplt.ylabel('nclouds')\nplt.scatter(size[0:filledbin],ncloudslog[0:filledbin])\nplt.savefig('Figures/nclouds_size_log.pdf')\n\n"],"string":"[\n \"#!/usr/bin/python\\n\\n# Find minimal distances between clouds in one bin, average these per bin\\n# Compute geometric and arithmetical mean between all clouds per bin\\n\\nfrom netCDF4 import Dataset as NetCDFFile\\nfrom matplotlib import pyplot as plt\\nimport numpy as np\\nfrom numpy import ma\\nfrom scipy import stats \\nfrom haversine import haversine\\nfrom scipy.spatial import distance\\nfrom distance_methods import distances\\nfrom CSD_fit import CSD_fit\\n\\ncusize = NetCDFFile(\\n '/home/vanlaar/HDCP2data/TA_dom4/cusize_output_time41.nc')\\n\\nsize = cusize.variables['size']\\n\\nbegin_time = 41\\nend_time = 48\\n\\nD0_all = np.zeros((end_time-begin_time+1,len(size)))\\nD1_all = np.zeros((end_time-begin_time+1,len(size)))\\nnclouds_bin_all = np.zeros((end_time-begin_time+1,len(size)))\\nmindistance_mean_all = np.zeros((end_time-begin_time+1,len(size)))\\nmindistance_std_all = np.zeros((end_time-begin_time+1,len(size)))\\nmaxdistance_all = np.zeros((end_time-begin_time+1,len(size)))\\nmaxdistanceY_all = np.zeros((end_time-begin_time+1,len(size)))\\nhn_normalized_all = np.zeros((end_time-begin_time+1,len(size)))\\n\\n\\nfor time in range(begin_time,end_time+1):\\n print 'time:',time\\n\\n cusize = NetCDFFile(\\n '/home/vanlaar/HDCP2data/TA_dom4/cusize_output_time'+str(time)+'.nc')\\n\\n cloudlon = cusize.variables['cloud_lon'][:]\\n cloudlat = cusize.variables['cloud_lat'][:]\\n nclouds_cusize = cusize.variables['nclouds']\\n #size = cusize.variables['size']\\n cloud_bin = cusize.variables['cloud_bin'][0,:]\\n hn = cusize.variables['hn']\\n hn_normalized_loop = hn/nclouds_cusize[0]\\n ncloud_bin = cusize.variables['ncloud_bin']\\n\\n ncloudsint = int(nclouds_cusize[0])\\n cloud_lon = cloudlon[0,0:ncloudsint]\\n cloud_lat = cloudlat[0,0:ncloudsint]\\n filledbin=np.argmin(hn[0,:]) # last bin with clouds, rest is empty\\n\\n output_distances = distances(filledbin,cloud_lon,cloud_lat,cloud_bin,size,ncloudsint)\\n\\n D0_all[time-41] = output_distances[0]\\n D1_all[time-41] = output_distances[1]\\n mindistance_mean_all[time-41] = output_distances[2]\\n mindistance_std_all[time-41] = output_distances[3]\\n nclouds_bin_all[time-41] = output_distances[4]\\n hn_normalized_all[time-41] = hn_normalized_loop\\n\\n\\nmindistance_mean = np.mean(mindistance_mean_all,axis=0)/1000\\nmindistance_std = np.mean(mindistance_std_all,axis=0)/1000\\nD0 = np.mean(D0_all,axis=0)\\nD1 = np.mean(D1_all,axis=0)\\nnclouds = np.mean(nclouds_bin_all,axis=0)\\nhn_normalized = np.mean(hn_normalized_all,axis=0)\\n\\nfilledbin_all=np.argmin(hn_normalized[:]) \\nfit = CSD_fit(hn_normalized[0:10],size[0:10])\\nlogfit = fit[3]\\na = fit[0]\\nb = fit[1]\\nc = fit[2]\\nprint 'a, b, c:'\\nprint a, b, c\\n\\nprint logfit\\n\\nsizelog = np.log10(size)\\nhnlog = ma.filled(np.log10(ma.masked_equal(hn_normalized, 0)), np.nan)\\nncloudslog = ma.filled(np.log10(ma.masked_equal(nclouds, 0)), np.nan)\\n\\n#res = ma.filled(log2(ma.masked_equal(m, 0)), 0)\\n\\nmindistance_plus = mindistance_mean + mindistance_std\\nmindistance_minus = mindistance_mean - mindistance_std\\n\\nfilledbin = np.argmin(mindistance_mean)\\nslope, intercept, r_value, p_value, std_err = stats.linregress(size[0:filledbin],mindistance_mean[0:filledbin])\\n\\nprint 'r-squared:',r_value**2\\nline = intercept + slope*size\\n\\nprint 'slope:',slope\\nprint 'intercept:',intercept\\n\\n##################################################################\\n### Plots\\n\\nthreshold = 0.005*sum(nclouds)\\nmaxbin = np.min(np.where(nclouds <= threshold))\\n\\norange = (1.,0.38,0.01)\\nblue = (0.53,0.81,1)\\n\\nplt.figure(figsize=(14,8))\\nplt.axis([0, 5500, 0, 120])\\nplt.xlabel('Cloud size [m]',fontsize=15)\\nplt.ylabel('Nearest-neighbour distance [km]',fontsize=15)\\nplt.fill_between(size,mindistance_plus,mindistance_minus,alpha=0.3,color=blue)\\nplt.scatter(size,mindistance_mean,color='k')\\n#plt.scatter(size,mindistance_plus,color='g')\\n#plt.plot(size,line,color='black')\\nax = plt.gca()\\nax.axvspan(size[maxbin], 5500, alpha=0.2, color='grey')\\nax.spines['right'].set_visible(False)\\nax.spines['top'].set_visible(False)\\nplt.savefig('Figures/mindistance.pdf')\\nplt.savefig('Figures/mindistance.png')\\n\\n\\nplt.figure(figsize=(10,8))\\n#plt.axis([50000,220000, 50000, 220000])\\nplt.xlabel('D1')\\nplt.ylabel('D0')\\nplt.scatter(D1,D0,color='k')\\nplt.savefig('Figures/D1-D0.pdf')\\n\\n\\nplt.figure(figsize=(10,8))\\nplt.xlabel('log(l) [m]')\\nplt.ylabel('log(N*(l)) [m-1]')\\nplt.scatter(sizelog,hnlog)\\nplt.scatter(sizelog[0:10],logfit[0:10])\\nplt.savefig('Figures/CSD.pdf')\\n\\n\\nplt.figure(figsize=(10,8))\\nplt.xlabel('Cloud size')\\nplt.ylabel('Ratio distance/size')\\nplt.axis([0, 5500, 0, 0.02])\\nax = plt.gca()\\nax.axvspan(size[maxbin], 5500, alpha=0.2, color='grey')\\nax.spines['right'].set_visible(False)\\nax.spines['top'].set_visible(False)\\nplt.savefig('Figures/mindistance.pdf')\\nplt.scatter(size[0:filledbin],mindistance_mean[0:filledbin]/size[0:filledbin])\\nplt.savefig('Figures/ratio_distance_size.pdf')\\n\\n\\n\\nplt.figure(figsize=(10,8))\\nplt.xlabel('Cloud size')\\nplt.ylabel('Number of clouds')\\nplt.axhline(y=threshold, c='black')\\nax = plt.gca()\\nax.spines['right'].set_visible(False)\\nax.spines['top'].set_visible(False)\\nplt.scatter(size[0:filledbin],nclouds[0:filledbin])\\nplt.savefig('Figures/nclouds_size.pdf')\\n\\nplt.figure(figsize=(10,8))\\nplt.xlabel('size')\\nplt.ylabel('nclouds')\\nplt.scatter(size[0:filledbin],ncloudslog[0:filledbin])\\nplt.savefig('Figures/nclouds_size_log.pdf')\\n\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5895,"cells":{"index":{"kind":"number","value":5895,"string":"5,895"},"blob_id":{"kind":"string","value":"1f0680c45afb36439c56a1d202537261df5f9afc"},"code":{"kind":"string","value":"from eventnotipy import app\nimport json\n\njson_data = open('eventnotipy/config.json')\ndata = json.load(json_data)\njson_data.close()\n\nusername = data['dbuser']\npassword = data['password']\nhost = data['dbhost']\ndb_name = data['database']\n\nemail_host = data['email_host']\nemail_localhost = data['email_localhost']\nsms_host = data['sms_host']\nsms_localhost = data['sms_localhost']\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (username,password,host,db_name)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = False\n\n\n\napp.secret_key = data['session_key']"},"steps":{"kind":"list like","value":["from eventnotipy import app\nimport json\n\njson_data = open('eventnotipy/config.json')\ndata = json.load(json_data)\njson_data.close()\n\nusername = data['dbuser']\npassword = data['password']\nhost = data['dbhost']\ndb_name = data['database']\n\nemail_host = data['email_host']\nemail_localhost = data['email_localhost']\nsms_host = data['sms_host']\nsms_localhost = data['sms_localhost']\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (username,password,host,db_name)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = False\n\n\n\napp.secret_key = data['session_key']","from eventnotipy import app\nimport json\njson_data = open('eventnotipy/config.json')\ndata = json.load(json_data)\njson_data.close()\nusername = data['dbuser']\npassword = data['password']\nhost = data['dbhost']\ndb_name = data['database']\nemail_host = data['email_host']\nemail_localhost = data['email_localhost']\nsms_host = data['sms_host']\nsms_localhost = data['sms_localhost']\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (\n username, password, host, db_name)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = False\napp.secret_key = data['session_key']\n","<import token>\njson_data = open('eventnotipy/config.json')\ndata = json.load(json_data)\njson_data.close()\nusername = data['dbuser']\npassword = data['password']\nhost = data['dbhost']\ndb_name = data['database']\nemail_host = data['email_host']\nemail_localhost = data['email_localhost']\nsms_host = data['sms_host']\nsms_localhost = data['sms_localhost']\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (\n username, password, host, db_name)\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = False\napp.secret_key = data['session_key']\n","<import token>\n<assignment token>\njson_data.close()\n<assignment token>\n","<import token>\n<assignment token>\n<code token>\n<assignment token>\n"],"string":"[\n \"from eventnotipy import app\\nimport json\\n\\njson_data = open('eventnotipy/config.json')\\ndata = json.load(json_data)\\njson_data.close()\\n\\nusername = data['dbuser']\\npassword = data['password']\\nhost = data['dbhost']\\ndb_name = data['database']\\n\\nemail_host = data['email_host']\\nemail_localhost = data['email_localhost']\\nsms_host = data['sms_host']\\nsms_localhost = data['sms_localhost']\\n\\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (username,password,host,db_name)\\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\\napp.config['SQLALCHEMY_ECHO'] = False\\n\\n\\n\\napp.secret_key = data['session_key']\",\n \"from eventnotipy import app\\nimport json\\njson_data = open('eventnotipy/config.json')\\ndata = json.load(json_data)\\njson_data.close()\\nusername = data['dbuser']\\npassword = data['password']\\nhost = data['dbhost']\\ndb_name = data['database']\\nemail_host = data['email_host']\\nemail_localhost = data['email_localhost']\\nsms_host = data['sms_host']\\nsms_localhost = data['sms_localhost']\\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (\\n username, password, host, db_name)\\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\\napp.config['SQLALCHEMY_ECHO'] = False\\napp.secret_key = data['session_key']\\n\",\n \"<import token>\\njson_data = open('eventnotipy/config.json')\\ndata = json.load(json_data)\\njson_data.close()\\nusername = data['dbuser']\\npassword = data['password']\\nhost = data['dbhost']\\ndb_name = data['database']\\nemail_host = data['email_host']\\nemail_localhost = data['email_localhost']\\nsms_host = data['sms_host']\\nsms_localhost = data['sms_localhost']\\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://%s:%s@%s/%s' % (\\n username, password, host, db_name)\\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\\napp.config['SQLALCHEMY_ECHO'] = False\\napp.secret_key = data['session_key']\\n\",\n \"<import token>\\n<assignment token>\\njson_data.close()\\n<assignment token>\\n\",\n \"<import token>\\n<assignment token>\\n<code token>\\n<assignment token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5896,"cells":{"index":{"kind":"number","value":5896,"string":"5,896"},"blob_id":{"kind":"string","value":"90324392e763ac6ea78c77b909c4bea667d45e6c"},"code":{"kind":"string","value":"from admin_tools.dashboard.modules import DashboardModule\n\nfrom nodes.models import Node\nfrom slices.models import Slice\n\nclass MyThingsDashboardModule(DashboardModule):\n \"\"\"\n Controller dashboard module to provide an overview to\n the user of the nodes and slices of its groups.\n \"\"\"\n title=\"My Things\"\n template = \"dashboard/modules/mythings.html\"\n \n def init_with_context(self, context):\n user = context['request'].user\n \n # Get user slices\n slices = Slice.objects.filter(group__in=user.groups.all().values_list('pk', flat=True))\n context['slices'] = slices\n \n # Get user nodes\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state).count())\n \n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n \n # initialize to calculate is_empty\n self.has_data = nodes or slices\n \n def is_empty(self):\n return not self.has_data\n"},"steps":{"kind":"list like","value":["from admin_tools.dashboard.modules import DashboardModule\n\nfrom nodes.models import Node\nfrom slices.models import Slice\n\nclass MyThingsDashboardModule(DashboardModule):\n \"\"\"\n Controller dashboard module to provide an overview to\n the user of the nodes and slices of its groups.\n \"\"\"\n title=\"My Things\"\n template = \"dashboard/modules/mythings.html\"\n \n def init_with_context(self, context):\n user = context['request'].user\n \n # Get user slices\n slices = Slice.objects.filter(group__in=user.groups.all().values_list('pk', flat=True))\n context['slices'] = slices\n \n # Get user nodes\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state).count())\n \n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n \n # initialize to calculate is_empty\n self.has_data = nodes or slices\n \n def is_empty(self):\n return not self.has_data\n","from admin_tools.dashboard.modules import DashboardModule\nfrom nodes.models import Node\nfrom slices.models import Slice\n\n\nclass MyThingsDashboardModule(DashboardModule):\n \"\"\"\n Controller dashboard module to provide an overview to\n the user of the nodes and slices of its groups.\n \"\"\"\n title = 'My Things'\n template = 'dashboard/modules/mythings.html'\n\n def init_with_context(self, context):\n user = context['request'].user\n slices = Slice.objects.filter(group__in=user.groups.all().\n values_list('pk', flat=True))\n context['slices'] = slices\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state)\n .count())\n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n self.has_data = nodes or slices\n\n def is_empty(self):\n return not self.has_data\n","<import token>\n\n\nclass MyThingsDashboardModule(DashboardModule):\n \"\"\"\n Controller dashboard module to provide an overview to\n the user of the nodes and slices of its groups.\n \"\"\"\n title = 'My Things'\n template = 'dashboard/modules/mythings.html'\n\n def init_with_context(self, context):\n user = context['request'].user\n slices = Slice.objects.filter(group__in=user.groups.all().\n values_list('pk', flat=True))\n context['slices'] = slices\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state)\n .count())\n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n self.has_data = nodes or slices\n\n def is_empty(self):\n return not self.has_data\n","<import token>\n\n\nclass MyThingsDashboardModule(DashboardModule):\n <docstring token>\n title = 'My Things'\n template = 'dashboard/modules/mythings.html'\n\n def init_with_context(self, context):\n user = context['request'].user\n slices = Slice.objects.filter(group__in=user.groups.all().\n values_list('pk', flat=True))\n context['slices'] = slices\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state)\n .count())\n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n self.has_data = nodes or slices\n\n def is_empty(self):\n return not self.has_data\n","<import token>\n\n\nclass MyThingsDashboardModule(DashboardModule):\n <docstring token>\n <assignment token>\n <assignment token>\n\n def init_with_context(self, context):\n user = context['request'].user\n slices = Slice.objects.filter(group__in=user.groups.all().\n values_list('pk', flat=True))\n context['slices'] = slices\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state)\n .count())\n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n self.has_data = nodes or slices\n\n def is_empty(self):\n return not self.has_data\n","<import token>\n\n\nclass MyThingsDashboardModule(DashboardModule):\n <docstring token>\n <assignment token>\n <assignment token>\n\n def init_with_context(self, context):\n user = context['request'].user\n slices = Slice.objects.filter(group__in=user.groups.all().\n values_list('pk', flat=True))\n context['slices'] = slices\n nodes = {}\n nodes_states = ['offline', 'safe', 'production']\n for group in user.groups.all():\n nodes[group] = []\n qs_nodes = Node.objects.filter(group=group)\n for state in nodes_states:\n nodes[group].append(qs_nodes.filter(state_set__value=state)\n .count())\n context['nodes_states'] = nodes_states\n context['user_nodes'] = nodes\n self.has_data = nodes or slices\n <function token>\n","<import token>\n\n\nclass MyThingsDashboardModule(DashboardModule):\n <docstring token>\n <assignment token>\n <assignment token>\n <function token>\n <function token>\n","<import token>\n<class token>\n"],"string":"[\n \"from admin_tools.dashboard.modules import DashboardModule\\n\\nfrom nodes.models import Node\\nfrom slices.models import Slice\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n \\\"\\\"\\\"\\n Controller dashboard module to provide an overview to\\n the user of the nodes and slices of its groups.\\n \\\"\\\"\\\"\\n title=\\\"My Things\\\"\\n template = \\\"dashboard/modules/mythings.html\\\"\\n \\n def init_with_context(self, context):\\n user = context['request'].user\\n \\n # Get user slices\\n slices = Slice.objects.filter(group__in=user.groups.all().values_list('pk', flat=True))\\n context['slices'] = slices\\n \\n # Get user nodes\\n nodes = {}\\n nodes_states = ['offline', 'safe', 'production']\\n for group in user.groups.all():\\n nodes[group] = []\\n qs_nodes = Node.objects.filter(group=group)\\n for state in nodes_states:\\n nodes[group].append(qs_nodes.filter(state_set__value=state).count())\\n \\n context['nodes_states'] = nodes_states\\n context['user_nodes'] = nodes\\n \\n # initialize to calculate is_empty\\n self.has_data = nodes or slices\\n \\n def is_empty(self):\\n return not self.has_data\\n\",\n \"from admin_tools.dashboard.modules import DashboardModule\\nfrom nodes.models import Node\\nfrom slices.models import Slice\\n\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n \\\"\\\"\\\"\\n Controller dashboard module to provide an overview to\\n the user of the nodes and slices of its groups.\\n \\\"\\\"\\\"\\n title = 'My Things'\\n template = 'dashboard/modules/mythings.html'\\n\\n def init_with_context(self, context):\\n user = context['request'].user\\n slices = Slice.objects.filter(group__in=user.groups.all().\\n values_list('pk', flat=True))\\n context['slices'] = slices\\n nodes = {}\\n nodes_states = ['offline', 'safe', 'production']\\n for group in user.groups.all():\\n nodes[group] = []\\n qs_nodes = Node.objects.filter(group=group)\\n for state in nodes_states:\\n nodes[group].append(qs_nodes.filter(state_set__value=state)\\n .count())\\n context['nodes_states'] = nodes_states\\n context['user_nodes'] = nodes\\n self.has_data = nodes or slices\\n\\n def is_empty(self):\\n return not self.has_data\\n\",\n \"<import token>\\n\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n \\\"\\\"\\\"\\n Controller dashboard module to provide an overview to\\n the user of the nodes and slices of its groups.\\n \\\"\\\"\\\"\\n title = 'My Things'\\n template = 'dashboard/modules/mythings.html'\\n\\n def init_with_context(self, context):\\n user = context['request'].user\\n slices = Slice.objects.filter(group__in=user.groups.all().\\n values_list('pk', flat=True))\\n context['slices'] = slices\\n nodes = {}\\n nodes_states = ['offline', 'safe', 'production']\\n for group in user.groups.all():\\n nodes[group] = []\\n qs_nodes = Node.objects.filter(group=group)\\n for state in nodes_states:\\n nodes[group].append(qs_nodes.filter(state_set__value=state)\\n .count())\\n context['nodes_states'] = nodes_states\\n context['user_nodes'] = nodes\\n self.has_data = nodes or slices\\n\\n def is_empty(self):\\n return not self.has_data\\n\",\n \"<import token>\\n\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n <docstring token>\\n title = 'My Things'\\n template = 'dashboard/modules/mythings.html'\\n\\n def init_with_context(self, context):\\n user = context['request'].user\\n slices = Slice.objects.filter(group__in=user.groups.all().\\n values_list('pk', flat=True))\\n context['slices'] = slices\\n nodes = {}\\n nodes_states = ['offline', 'safe', 'production']\\n for group in user.groups.all():\\n nodes[group] = []\\n qs_nodes = Node.objects.filter(group=group)\\n for state in nodes_states:\\n nodes[group].append(qs_nodes.filter(state_set__value=state)\\n .count())\\n context['nodes_states'] = nodes_states\\n context['user_nodes'] = nodes\\n self.has_data = nodes or slices\\n\\n def is_empty(self):\\n return not self.has_data\\n\",\n \"<import token>\\n\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n <docstring token>\\n <assignment token>\\n <assignment token>\\n\\n def init_with_context(self, context):\\n user = context['request'].user\\n slices = Slice.objects.filter(group__in=user.groups.all().\\n values_list('pk', flat=True))\\n context['slices'] = slices\\n nodes = {}\\n nodes_states = ['offline', 'safe', 'production']\\n for group in user.groups.all():\\n nodes[group] = []\\n qs_nodes = Node.objects.filter(group=group)\\n for state in nodes_states:\\n nodes[group].append(qs_nodes.filter(state_set__value=state)\\n .count())\\n context['nodes_states'] = nodes_states\\n context['user_nodes'] = nodes\\n self.has_data = nodes or slices\\n\\n def is_empty(self):\\n return not self.has_data\\n\",\n \"<import token>\\n\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n <docstring token>\\n <assignment token>\\n <assignment token>\\n\\n def init_with_context(self, context):\\n user = context['request'].user\\n slices = Slice.objects.filter(group__in=user.groups.all().\\n values_list('pk', flat=True))\\n context['slices'] = slices\\n nodes = {}\\n nodes_states = ['offline', 'safe', 'production']\\n for group in user.groups.all():\\n nodes[group] = []\\n qs_nodes = Node.objects.filter(group=group)\\n for state in nodes_states:\\n nodes[group].append(qs_nodes.filter(state_set__value=state)\\n .count())\\n context['nodes_states'] = nodes_states\\n context['user_nodes'] = nodes\\n self.has_data = nodes or slices\\n <function token>\\n\",\n \"<import token>\\n\\n\\nclass MyThingsDashboardModule(DashboardModule):\\n <docstring token>\\n <assignment token>\\n <assignment token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5897,"cells":{"index":{"kind":"number","value":5897,"string":"5,897"},"blob_id":{"kind":"string","value":"14e1af3d60efef842c72bf9b55143d0e14f3a7b8"},"code":{"kind":"string","value":"import asyncio\nimport json\nfrom functools import lru_cache\n\nfrom pyrogram import Client\n\n\nSETTINGS_FILE = \"/src/settings.json\"\nCONN_FILE = \"/src/conn.json\"\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting(\"bot_name\")\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting(\"app_id\")\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting(\"app_hash\")\n\n\nasync def initialize_client():\n app = Client(\"testing\", get_app_id(), get_app_hash())\n async with app:\n with open(CONN_FILE, \"w+\") as f:\n f.write(json.dumps({\"connection_string\": await app.export_session_string()}))\n print(\"Connection string was saved to conn.json\")\n\nif __name__ == \"__main__\":\n asyncio.get_event_loop().run_until_complete(initialize_client())\n"},"steps":{"kind":"list like","value":["import asyncio\nimport json\nfrom functools import lru_cache\n\nfrom pyrogram import Client\n\n\nSETTINGS_FILE = \"/src/settings.json\"\nCONN_FILE = \"/src/conn.json\"\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting(\"bot_name\")\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting(\"app_id\")\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting(\"app_hash\")\n\n\nasync def initialize_client():\n app = Client(\"testing\", get_app_id(), get_app_hash())\n async with app:\n with open(CONN_FILE, \"w+\") as f:\n f.write(json.dumps({\"connection_string\": await app.export_session_string()}))\n print(\"Connection string was saved to conn.json\")\n\nif __name__ == \"__main__\":\n asyncio.get_event_loop().run_until_complete(initialize_client())\n","import asyncio\nimport json\nfrom functools import lru_cache\nfrom pyrogram import Client\nSETTINGS_FILE = '/src/settings.json'\nCONN_FILE = '/src/conn.json'\n\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting('app_id')\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting('app_hash')\n\n\nasync def initialize_client():\n app = Client('testing', get_app_id(), get_app_hash())\n async with app:\n with open(CONN_FILE, 'w+') as f:\n f.write(json.dumps({'connection_string': await app.\n export_session_string()}))\n print('Connection string was saved to conn.json')\n\n\nif __name__ == '__main__':\n asyncio.get_event_loop().run_until_complete(initialize_client())\n","<import token>\nSETTINGS_FILE = '/src/settings.json'\nCONN_FILE = '/src/conn.json'\n\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting('app_id')\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting('app_hash')\n\n\nasync def initialize_client():\n app = Client('testing', get_app_id(), get_app_hash())\n async with app:\n with open(CONN_FILE, 'w+') as f:\n f.write(json.dumps({'connection_string': await app.\n export_session_string()}))\n print('Connection string was saved to conn.json')\n\n\nif __name__ == '__main__':\n asyncio.get_event_loop().run_until_complete(initialize_client())\n","<import token>\n<assignment token>\n\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting('app_id')\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting('app_hash')\n\n\nasync def initialize_client():\n app = Client('testing', get_app_id(), get_app_hash())\n async with app:\n with open(CONN_FILE, 'w+') as f:\n f.write(json.dumps({'connection_string': await app.\n export_session_string()}))\n print('Connection string was saved to conn.json')\n\n\nif __name__ == '__main__':\n asyncio.get_event_loop().run_until_complete(initialize_client())\n","<import token>\n<assignment token>\n\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting('app_id')\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting('app_hash')\n\n\n<code token>\n","<import token>\n<assignment token>\n<function token>\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting('app_id')\n\n\n@lru_cache()\ndef get_app_hash():\n return load_setting('app_hash')\n\n\n<code token>\n","<import token>\n<assignment token>\n<function token>\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n@lru_cache()\ndef get_app_id():\n return load_setting('app_id')\n\n\n<function token>\n<code token>\n","<import token>\n<assignment token>\n<function token>\n\n\n@lru_cache()\ndef get_bot_name():\n return load_setting('bot_name')\n\n\n<function token>\n<function token>\n<code token>\n","<import token>\n<assignment token>\n<function token>\n<function token>\n<function token>\n<function token>\n<code token>\n"],"string":"[\n \"import asyncio\\nimport json\\nfrom functools import lru_cache\\n\\nfrom pyrogram import Client\\n\\n\\nSETTINGS_FILE = \\\"/src/settings.json\\\"\\nCONN_FILE = \\\"/src/conn.json\\\"\\n\\ndef load_setting(setting: str):\\n with open(SETTINGS_FILE) as f:\\n return json.load(f)[setting]\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting(\\\"bot_name\\\")\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting(\\\"app_id\\\")\\n\\n\\n@lru_cache()\\ndef get_app_hash():\\n return load_setting(\\\"app_hash\\\")\\n\\n\\nasync def initialize_client():\\n app = Client(\\\"testing\\\", get_app_id(), get_app_hash())\\n async with app:\\n with open(CONN_FILE, \\\"w+\\\") as f:\\n f.write(json.dumps({\\\"connection_string\\\": await app.export_session_string()}))\\n print(\\\"Connection string was saved to conn.json\\\")\\n\\nif __name__ == \\\"__main__\\\":\\n asyncio.get_event_loop().run_until_complete(initialize_client())\\n\",\n \"import asyncio\\nimport json\\nfrom functools import lru_cache\\nfrom pyrogram import Client\\nSETTINGS_FILE = '/src/settings.json'\\nCONN_FILE = '/src/conn.json'\\n\\n\\ndef load_setting(setting: str):\\n with open(SETTINGS_FILE) as f:\\n return json.load(f)[setting]\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting('app_id')\\n\\n\\n@lru_cache()\\ndef get_app_hash():\\n return load_setting('app_hash')\\n\\n\\nasync def initialize_client():\\n app = Client('testing', get_app_id(), get_app_hash())\\n async with app:\\n with open(CONN_FILE, 'w+') as f:\\n f.write(json.dumps({'connection_string': await app.\\n export_session_string()}))\\n print('Connection string was saved to conn.json')\\n\\n\\nif __name__ == '__main__':\\n asyncio.get_event_loop().run_until_complete(initialize_client())\\n\",\n \"<import token>\\nSETTINGS_FILE = '/src/settings.json'\\nCONN_FILE = '/src/conn.json'\\n\\n\\ndef load_setting(setting: str):\\n with open(SETTINGS_FILE) as f:\\n return json.load(f)[setting]\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting('app_id')\\n\\n\\n@lru_cache()\\ndef get_app_hash():\\n return load_setting('app_hash')\\n\\n\\nasync def initialize_client():\\n app = Client('testing', get_app_id(), get_app_hash())\\n async with app:\\n with open(CONN_FILE, 'w+') as f:\\n f.write(json.dumps({'connection_string': await app.\\n export_session_string()}))\\n print('Connection string was saved to conn.json')\\n\\n\\nif __name__ == '__main__':\\n asyncio.get_event_loop().run_until_complete(initialize_client())\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\ndef load_setting(setting: str):\\n with open(SETTINGS_FILE) as f:\\n return json.load(f)[setting]\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting('app_id')\\n\\n\\n@lru_cache()\\ndef get_app_hash():\\n return load_setting('app_hash')\\n\\n\\nasync def initialize_client():\\n app = Client('testing', get_app_id(), get_app_hash())\\n async with app:\\n with open(CONN_FILE, 'w+') as f:\\n f.write(json.dumps({'connection_string': await app.\\n export_session_string()}))\\n print('Connection string was saved to conn.json')\\n\\n\\nif __name__ == '__main__':\\n asyncio.get_event_loop().run_until_complete(initialize_client())\\n\",\n \"<import token>\\n<assignment token>\\n\\n\\ndef load_setting(setting: str):\\n with open(SETTINGS_FILE) as f:\\n return json.load(f)[setting]\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting('app_id')\\n\\n\\n@lru_cache()\\ndef get_app_hash():\\n return load_setting('app_hash')\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting('app_id')\\n\\n\\n@lru_cache()\\ndef get_app_hash():\\n return load_setting('app_hash')\\n\\n\\n<code token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n@lru_cache()\\ndef get_app_id():\\n return load_setting('app_id')\\n\\n\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n\\n\\n@lru_cache()\\ndef get_bot_name():\\n return load_setting('bot_name')\\n\\n\\n<function token>\\n<function token>\\n<code token>\\n\",\n \"<import token>\\n<assignment token>\\n<function token>\\n<function token>\\n<function token>\\n<function token>\\n<code token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}},{"rowIdx":5898,"cells":{"index":{"kind":"number","value":5898,"string":"5,898"},"blob_id":{"kind":"string","value":"bf40b516e202af14469cd4012597ba412e663f56"},"code":{"kind":"string","value":"import nltk\nimport A\nfrom collections import defaultdict\nfrom nltk.align import Alignment, AlignedSent\n\nclass BerkeleyAligner():\n\n def __init__(self, align_sents, num_iter):\n\tself.t, self.q = self.train(align_sents, num_iter)\n\t\n # TODO: Computes the alignments for align_sent, using this model's parameters. Return\n # an AlignedSent object, with the sentence pair and the alignments computed.\n def align(self, align_sent):\n#\t#will return german --> english alignments\n \talignments = []\n german = align_sent.words\n english = align_sent.mots\n len_g = len(german)\n len_e = len(english)\n\n for j in range(len_g):\n\t\tg = german[j]\n\t\tbest_prob = (self.t[(g,None)] * self.q[(0,j,len_e,len_g)], None)\n\t\tbest_alignment_point = None\n\t\tfor i in range(len_e):\n \te = english[i]\n \t \t\tge_prob = (self.t[(e,g)]*self.q[(j,i,len_g,len_e)], i)\n\t\t\teg_prob = (self.t[(g,e)]*self.q[(i,j,len_e,len_g)], i)\n\t\t\tbest_prob = max(best_prob, ge_prob, eg_prob)\n\t\t\t\n\t\talignments.append((j, best_prob[1]))\n\n\treturn AlignedSent(align_sent.words, align_sent.mots, alignments)\n\n \n # TODO: Implement the EM algorithm. num_iters is the number of iterations. Returns the \n # translation and distortion parameters as a tuple.\n def train(self, aligned_sents, num_iters):\n\tMIN_PROB = 1.0e-12\n\t#INITIALIZATION\n\t#defining the vocabulary for each language:\n\t#german = words\n\t#english = mots\n\tg_vocab = set()\n\te_vocab = set()\n\tfor sentence in aligned_sents:\n\t\tg_vocab.update(sentence.words)\n\t\te_vocab.update(sentence.mots)\n\n\t# initializing translation table for english --> german and german --> english\n\tt = defaultdict(float)\n\tfor g in g_vocab:\n\t\tfor e in e_vocab:\n\t\t\tt[(g,e)] = 1.0 / float(len(g_vocab))\n\t\t\tt[(e,g)] = 1.0 / float(len(e_vocab))\n\t\n\t# initializing separate alignment tables for english --> german and german --> english\n\tq_eg = defaultdict(float)\n\tq_ge = defaultdict(float)\n\tfor sentence in aligned_sents:\n\t\tlen_e=len(sentence.mots)\n\t\tlen_g=len(sentence.words)\n\t\tfor i in range(len_e):\n\t\t\tfor j in range(len_g):\n\t\t\t\tq_eg[(i,j,len_e,len_g)] = 1.0 / float((len_e+1))\n\t\t\t\tq_ge[(j,i,len_g,len_e)] = 1.0 / float((len_g+1))\n\n\tprint 'Initialization complete'\n\t#INITIALIZATION COMPLETE\n\n\tfor i in range(num_iters):\n\t\tprint 'Iteration ' + str(i+1) + ' /' + str(num_iters)\n\t\t#E step\n\t\tcount_g_given_e = defaultdict(float)\n\t\tcount_any_g_given_e = defaultdict(float)\n\t\teg_alignment_count = defaultdict(float)\n\t\teg_alignment_count_for_any_i = defaultdict(float)\n\t\tcount_e_given_g = defaultdict(float)\n\t\tcount_any_e_given_g = defaultdict(float)\n\t\tge_alignment_count = defaultdict(float)\n\t\tge_alignment_count_for_any_j = defaultdict(float)\n\t\t\n\t\tfor sentence in aligned_sents:\n\t\t\tg_sentence = sentence.words\n\t\t\te_sentence = sentence.mots\n\t\t\tlen_e = len(sentence.mots)\n\t\t\tlen_g = len(sentence.words)\n\t\t\teg_total = defaultdict(float)\n\t\t\tge_total = defaultdict(float)\n\n\t\t\t#E step (a): compute normalization\n\t\t\tfor j in range(len_g):\n\t\t\t\t\n\t\t\t\tg = g_sentence[j]\n\t\n\t\t\t\tfor i in range(len_e):\n\t\t\t\t\t\n\t\t\t\t\te = e_sentence[i]\n\n\t\t\t\t\teg_count = (t[(g_sentence[j],e_sentence[i])] * q_eg[(i,j,len_e,len_g)])\n\t\t\t\t\teg_total[g] += eg_count\n\n\t\t\t\t\tge_count = (t[(e_sentence[i], g_sentence[j])] * q_ge[(j,i,len_g,len_e)])\n\t\t\t\t\tge_total[e] += ge_count \n\n\t\t\t# E step (b): collect fractional counts\n\t\t\tfor j in range(len_g):\n\t\t\t\t\n\t\t\t\tg = g_sentence[j]\n\n\t\t\t\tfor i in range(len_e):\n\t\t\t\t\t\n\t\t\t\t\te = e_sentence[i]\n\t\n\t\t\t\t\t#English --> German\n\t\t\t\t\teg_count = (t[(g_sentence[j],e_sentence[i])] * q_eg[(i,j,len_e,len_g)])\n\t\t\t\t\teg_normalized = eg_count / eg_total[g]\n\n\t\t\t\t\t#German --> English\n\t\t\t\t\tge_count = (t[(e_sentence[i], g_sentence[j])] * q_ge[(j,i,len_g,len_e)])\n\t\t\t\t\tge_normalized = ge_count / ge_total[e]\n\n\t\t\t\t\t#Averaging the probablities\n\t\t\t\t\tavg_normalized = (eg_normalized + ge_normalized) / 2.0\n\t\t\t\t\t#Storing counts\n\t\t\t\t\tcount_g_given_e[(g,e)] += avg_normalized\n\t\t\t\t\tcount_any_g_given_e[e] += avg_normalized\n\t\t\t\t\teg_alignment_count[(i,j,len_e,len_g)] += avg_normalized\n\t\t\t\t\teg_alignment_count_for_any_i[(j,len_e,len_g)] += avg_normalized\n\t\t\t\t\tcount_e_given_g[(e,g)] += avg_normalized\n\t\t\t\t\tcount_any_e_given_g[g] += avg_normalized\n\t\t\t\t\tge_alignment_count[(j,i,len_g,len_e)] += avg_normalized\n\t\t\t\t\tge_alignment_count_for_any_j[(i,len_g,len_e)] += avg_normalized\n\n\t\t#M step\n\t\tq = defaultdict(float)\n\t\tfor sentence in aligned_sents:\n\t\t\tfor e in sentence.mots:\n\t\t\t\tfor g in sentence.words:\n\t\t\t\t\t#eng --> germ\n\t\t\t\t\tt[(g,e)]= count_g_given_e[(g,e)] / count_any_g_given_e[e]\n\t\t\t\t\t#germ --> eng\n\t\t\t\t\tt[(e,g)]= count_e_given_g[(e,g)] / count_any_e_given_g[g]\n\n\t\t\tlen_e=len(sentence.mots)\n\t\t\tlen_g=len(sentence.words)\n\t\t\tfor i in range(len_e):\n\t\t\t\tfor j in range(len_g):\n\t\t\t\t\t#eng --> germ\n\t\t\t\t\tq[(i,j,len_e,len_g)] = eg_alignment_count[(i,j,len_e,len_g)] / eg_alignment_count_for_any_i[(j,len_e, len_g)]\n\t\t\t\t\t#germ --> eng\n\t\t\t\t\tq[(j,i,len_g,len_e)] = ge_alignment_count[(j,i,len_g,len_e)] / ge_alignment_count_for_any_j[(i,len_g,len_e)]\n\treturn (t,q)\n\n\ndef main(aligned_sents):\n ba = BerkeleyAligner(aligned_sents, 10)\n A.save_model_output(aligned_sents, ba, \"ba.txt\")\n avg_aer = A.compute_avg_aer(aligned_sents, ba, 50)\n\n print ('Berkeley Aligner')\n print ('---------------------------')\n print('Average AER: {0:.3f}\\n'.format(avg_aer))\n"},"steps":{"kind":"list like","value":["import nltk\nimport A\nfrom collections import defaultdict\nfrom nltk.align import Alignment, AlignedSent\n\nclass BerkeleyAligner():\n\n def __init__(self, align_sents, num_iter):\n\tself.t, self.q = self.train(align_sents, num_iter)\n\t\n # TODO: Computes the alignments for align_sent, using this model's parameters. Return\n # an AlignedSent object, with the sentence pair and the alignments computed.\n def align(self, align_sent):\n#\t#will return german --> english alignments\n \talignments = []\n german = align_sent.words\n english = align_sent.mots\n len_g = len(german)\n len_e = len(english)\n\n for j in range(len_g):\n\t\tg = german[j]\n\t\tbest_prob = (self.t[(g,None)] * self.q[(0,j,len_e,len_g)], None)\n\t\tbest_alignment_point = None\n\t\tfor i in range(len_e):\n \te = english[i]\n \t \t\tge_prob = (self.t[(e,g)]*self.q[(j,i,len_g,len_e)], i)\n\t\t\teg_prob = (self.t[(g,e)]*self.q[(i,j,len_e,len_g)], i)\n\t\t\tbest_prob = max(best_prob, ge_prob, eg_prob)\n\t\t\t\n\t\talignments.append((j, best_prob[1]))\n\n\treturn AlignedSent(align_sent.words, align_sent.mots, alignments)\n\n \n # TODO: Implement the EM algorithm. num_iters is the number of iterations. Returns the \n # translation and distortion parameters as a tuple.\n def train(self, aligned_sents, num_iters):\n\tMIN_PROB = 1.0e-12\n\t#INITIALIZATION\n\t#defining the vocabulary for each language:\n\t#german = words\n\t#english = mots\n\tg_vocab = set()\n\te_vocab = set()\n\tfor sentence in aligned_sents:\n\t\tg_vocab.update(sentence.words)\n\t\te_vocab.update(sentence.mots)\n\n\t# initializing translation table for english --> german and german --> english\n\tt = defaultdict(float)\n\tfor g in g_vocab:\n\t\tfor e in e_vocab:\n\t\t\tt[(g,e)] = 1.0 / float(len(g_vocab))\n\t\t\tt[(e,g)] = 1.0 / float(len(e_vocab))\n\t\n\t# initializing separate alignment tables for english --> german and german --> english\n\tq_eg = defaultdict(float)\n\tq_ge = defaultdict(float)\n\tfor sentence in aligned_sents:\n\t\tlen_e=len(sentence.mots)\n\t\tlen_g=len(sentence.words)\n\t\tfor i in range(len_e):\n\t\t\tfor j in range(len_g):\n\t\t\t\tq_eg[(i,j,len_e,len_g)] = 1.0 / float((len_e+1))\n\t\t\t\tq_ge[(j,i,len_g,len_e)] = 1.0 / float((len_g+1))\n\n\tprint 'Initialization complete'\n\t#INITIALIZATION COMPLETE\n\n\tfor i in range(num_iters):\n\t\tprint 'Iteration ' + str(i+1) + ' /' + str(num_iters)\n\t\t#E step\n\t\tcount_g_given_e = defaultdict(float)\n\t\tcount_any_g_given_e = defaultdict(float)\n\t\teg_alignment_count = defaultdict(float)\n\t\teg_alignment_count_for_any_i = defaultdict(float)\n\t\tcount_e_given_g = defaultdict(float)\n\t\tcount_any_e_given_g = defaultdict(float)\n\t\tge_alignment_count = defaultdict(float)\n\t\tge_alignment_count_for_any_j = defaultdict(float)\n\t\t\n\t\tfor sentence in aligned_sents:\n\t\t\tg_sentence = sentence.words\n\t\t\te_sentence = sentence.mots\n\t\t\tlen_e = len(sentence.mots)\n\t\t\tlen_g = len(sentence.words)\n\t\t\teg_total = defaultdict(float)\n\t\t\tge_total = defaultdict(float)\n\n\t\t\t#E step (a): compute normalization\n\t\t\tfor j in range(len_g):\n\t\t\t\t\n\t\t\t\tg = g_sentence[j]\n\t\n\t\t\t\tfor i in range(len_e):\n\t\t\t\t\t\n\t\t\t\t\te = e_sentence[i]\n\n\t\t\t\t\teg_count = (t[(g_sentence[j],e_sentence[i])] * q_eg[(i,j,len_e,len_g)])\n\t\t\t\t\teg_total[g] += eg_count\n\n\t\t\t\t\tge_count = (t[(e_sentence[i], g_sentence[j])] * q_ge[(j,i,len_g,len_e)])\n\t\t\t\t\tge_total[e] += ge_count \n\n\t\t\t# E step (b): collect fractional counts\n\t\t\tfor j in range(len_g):\n\t\t\t\t\n\t\t\t\tg = g_sentence[j]\n\n\t\t\t\tfor i in range(len_e):\n\t\t\t\t\t\n\t\t\t\t\te = e_sentence[i]\n\t\n\t\t\t\t\t#English --> German\n\t\t\t\t\teg_count = (t[(g_sentence[j],e_sentence[i])] * q_eg[(i,j,len_e,len_g)])\n\t\t\t\t\teg_normalized = eg_count / eg_total[g]\n\n\t\t\t\t\t#German --> English\n\t\t\t\t\tge_count = (t[(e_sentence[i], g_sentence[j])] * q_ge[(j,i,len_g,len_e)])\n\t\t\t\t\tge_normalized = ge_count / ge_total[e]\n\n\t\t\t\t\t#Averaging the probablities\n\t\t\t\t\tavg_normalized = (eg_normalized + ge_normalized) / 2.0\n\t\t\t\t\t#Storing counts\n\t\t\t\t\tcount_g_given_e[(g,e)] += avg_normalized\n\t\t\t\t\tcount_any_g_given_e[e] += avg_normalized\n\t\t\t\t\teg_alignment_count[(i,j,len_e,len_g)] += avg_normalized\n\t\t\t\t\teg_alignment_count_for_any_i[(j,len_e,len_g)] += avg_normalized\n\t\t\t\t\tcount_e_given_g[(e,g)] += avg_normalized\n\t\t\t\t\tcount_any_e_given_g[g] += avg_normalized\n\t\t\t\t\tge_alignment_count[(j,i,len_g,len_e)] += avg_normalized\n\t\t\t\t\tge_alignment_count_for_any_j[(i,len_g,len_e)] += avg_normalized\n\n\t\t#M step\n\t\tq = defaultdict(float)\n\t\tfor sentence in aligned_sents:\n\t\t\tfor e in sentence.mots:\n\t\t\t\tfor g in sentence.words:\n\t\t\t\t\t#eng --> germ\n\t\t\t\t\tt[(g,e)]= count_g_given_e[(g,e)] / count_any_g_given_e[e]\n\t\t\t\t\t#germ --> eng\n\t\t\t\t\tt[(e,g)]= count_e_given_g[(e,g)] / count_any_e_given_g[g]\n\n\t\t\tlen_e=len(sentence.mots)\n\t\t\tlen_g=len(sentence.words)\n\t\t\tfor i in range(len_e):\n\t\t\t\tfor j in range(len_g):\n\t\t\t\t\t#eng --> germ\n\t\t\t\t\tq[(i,j,len_e,len_g)] = eg_alignment_count[(i,j,len_e,len_g)] / eg_alignment_count_for_any_i[(j,len_e, len_g)]\n\t\t\t\t\t#germ --> eng\n\t\t\t\t\tq[(j,i,len_g,len_e)] = ge_alignment_count[(j,i,len_g,len_e)] / ge_alignment_count_for_any_j[(i,len_g,len_e)]\n\treturn (t,q)\n\n\ndef main(aligned_sents):\n ba = BerkeleyAligner(aligned_sents, 10)\n A.save_model_output(aligned_sents, ba, \"ba.txt\")\n avg_aer = A.compute_avg_aer(aligned_sents, ba, 50)\n\n print ('Berkeley Aligner')\n print ('---------------------------')\n print('Average AER: {0:.3f}\\n'.format(avg_aer))\n"],"string":"[\n \"import nltk\\nimport A\\nfrom collections import defaultdict\\nfrom nltk.align import Alignment, AlignedSent\\n\\nclass BerkeleyAligner():\\n\\n def __init__(self, align_sents, num_iter):\\n\\tself.t, self.q = self.train(align_sents, num_iter)\\n\\t\\n # TODO: Computes the alignments for align_sent, using this model's parameters. Return\\n # an AlignedSent object, with the sentence pair and the alignments computed.\\n def align(self, align_sent):\\n#\\t#will return german --> english alignments\\n \\talignments = []\\n german = align_sent.words\\n english = align_sent.mots\\n len_g = len(german)\\n len_e = len(english)\\n\\n for j in range(len_g):\\n\\t\\tg = german[j]\\n\\t\\tbest_prob = (self.t[(g,None)] * self.q[(0,j,len_e,len_g)], None)\\n\\t\\tbest_alignment_point = None\\n\\t\\tfor i in range(len_e):\\n \\te = english[i]\\n \\t \\t\\tge_prob = (self.t[(e,g)]*self.q[(j,i,len_g,len_e)], i)\\n\\t\\t\\teg_prob = (self.t[(g,e)]*self.q[(i,j,len_e,len_g)], i)\\n\\t\\t\\tbest_prob = max(best_prob, ge_prob, eg_prob)\\n\\t\\t\\t\\n\\t\\talignments.append((j, best_prob[1]))\\n\\n\\treturn AlignedSent(align_sent.words, align_sent.mots, alignments)\\n\\n \\n # TODO: Implement the EM algorithm. num_iters is the number of iterations. Returns the \\n # translation and distortion parameters as a tuple.\\n def train(self, aligned_sents, num_iters):\\n\\tMIN_PROB = 1.0e-12\\n\\t#INITIALIZATION\\n\\t#defining the vocabulary for each language:\\n\\t#german = words\\n\\t#english = mots\\n\\tg_vocab = set()\\n\\te_vocab = set()\\n\\tfor sentence in aligned_sents:\\n\\t\\tg_vocab.update(sentence.words)\\n\\t\\te_vocab.update(sentence.mots)\\n\\n\\t# initializing translation table for english --> german and german --> english\\n\\tt = defaultdict(float)\\n\\tfor g in g_vocab:\\n\\t\\tfor e in e_vocab:\\n\\t\\t\\tt[(g,e)] = 1.0 / float(len(g_vocab))\\n\\t\\t\\tt[(e,g)] = 1.0 / float(len(e_vocab))\\n\\t\\n\\t# initializing separate alignment tables for english --> german and german --> english\\n\\tq_eg = defaultdict(float)\\n\\tq_ge = defaultdict(float)\\n\\tfor sentence in aligned_sents:\\n\\t\\tlen_e=len(sentence.mots)\\n\\t\\tlen_g=len(sentence.words)\\n\\t\\tfor i in range(len_e):\\n\\t\\t\\tfor j in range(len_g):\\n\\t\\t\\t\\tq_eg[(i,j,len_e,len_g)] = 1.0 / float((len_e+1))\\n\\t\\t\\t\\tq_ge[(j,i,len_g,len_e)] = 1.0 / float((len_g+1))\\n\\n\\tprint 'Initialization complete'\\n\\t#INITIALIZATION COMPLETE\\n\\n\\tfor i in range(num_iters):\\n\\t\\tprint 'Iteration ' + str(i+1) + ' /' + str(num_iters)\\n\\t\\t#E step\\n\\t\\tcount_g_given_e = defaultdict(float)\\n\\t\\tcount_any_g_given_e = defaultdict(float)\\n\\t\\teg_alignment_count = defaultdict(float)\\n\\t\\teg_alignment_count_for_any_i = defaultdict(float)\\n\\t\\tcount_e_given_g = defaultdict(float)\\n\\t\\tcount_any_e_given_g = defaultdict(float)\\n\\t\\tge_alignment_count = defaultdict(float)\\n\\t\\tge_alignment_count_for_any_j = defaultdict(float)\\n\\t\\t\\n\\t\\tfor sentence in aligned_sents:\\n\\t\\t\\tg_sentence = sentence.words\\n\\t\\t\\te_sentence = sentence.mots\\n\\t\\t\\tlen_e = len(sentence.mots)\\n\\t\\t\\tlen_g = len(sentence.words)\\n\\t\\t\\teg_total = defaultdict(float)\\n\\t\\t\\tge_total = defaultdict(float)\\n\\n\\t\\t\\t#E step (a): compute normalization\\n\\t\\t\\tfor j in range(len_g):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tg = g_sentence[j]\\n\\t\\n\\t\\t\\t\\tfor i in range(len_e):\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\te = e_sentence[i]\\n\\n\\t\\t\\t\\t\\teg_count = (t[(g_sentence[j],e_sentence[i])] * q_eg[(i,j,len_e,len_g)])\\n\\t\\t\\t\\t\\teg_total[g] += eg_count\\n\\n\\t\\t\\t\\t\\tge_count = (t[(e_sentence[i], g_sentence[j])] * q_ge[(j,i,len_g,len_e)])\\n\\t\\t\\t\\t\\tge_total[e] += ge_count \\n\\n\\t\\t\\t# E step (b): collect fractional counts\\n\\t\\t\\tfor j in range(len_g):\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tg = g_sentence[j]\\n\\n\\t\\t\\t\\tfor i in range(len_e):\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\te = e_sentence[i]\\n\\t\\n\\t\\t\\t\\t\\t#English --> German\\n\\t\\t\\t\\t\\teg_count = (t[(g_sentence[j],e_sentence[i])] * q_eg[(i,j,len_e,len_g)])\\n\\t\\t\\t\\t\\teg_normalized = eg_count / eg_total[g]\\n\\n\\t\\t\\t\\t\\t#German --> English\\n\\t\\t\\t\\t\\tge_count = (t[(e_sentence[i], g_sentence[j])] * q_ge[(j,i,len_g,len_e)])\\n\\t\\t\\t\\t\\tge_normalized = ge_count / ge_total[e]\\n\\n\\t\\t\\t\\t\\t#Averaging the probablities\\n\\t\\t\\t\\t\\tavg_normalized = (eg_normalized + ge_normalized) / 2.0\\n\\t\\t\\t\\t\\t#Storing counts\\n\\t\\t\\t\\t\\tcount_g_given_e[(g,e)] += avg_normalized\\n\\t\\t\\t\\t\\tcount_any_g_given_e[e] += avg_normalized\\n\\t\\t\\t\\t\\teg_alignment_count[(i,j,len_e,len_g)] += avg_normalized\\n\\t\\t\\t\\t\\teg_alignment_count_for_any_i[(j,len_e,len_g)] += avg_normalized\\n\\t\\t\\t\\t\\tcount_e_given_g[(e,g)] += avg_normalized\\n\\t\\t\\t\\t\\tcount_any_e_given_g[g] += avg_normalized\\n\\t\\t\\t\\t\\tge_alignment_count[(j,i,len_g,len_e)] += avg_normalized\\n\\t\\t\\t\\t\\tge_alignment_count_for_any_j[(i,len_g,len_e)] += avg_normalized\\n\\n\\t\\t#M step\\n\\t\\tq = defaultdict(float)\\n\\t\\tfor sentence in aligned_sents:\\n\\t\\t\\tfor e in sentence.mots:\\n\\t\\t\\t\\tfor g in sentence.words:\\n\\t\\t\\t\\t\\t#eng --> germ\\n\\t\\t\\t\\t\\tt[(g,e)]= count_g_given_e[(g,e)] / count_any_g_given_e[e]\\n\\t\\t\\t\\t\\t#germ --> eng\\n\\t\\t\\t\\t\\tt[(e,g)]= count_e_given_g[(e,g)] / count_any_e_given_g[g]\\n\\n\\t\\t\\tlen_e=len(sentence.mots)\\n\\t\\t\\tlen_g=len(sentence.words)\\n\\t\\t\\tfor i in range(len_e):\\n\\t\\t\\t\\tfor j in range(len_g):\\n\\t\\t\\t\\t\\t#eng --> germ\\n\\t\\t\\t\\t\\tq[(i,j,len_e,len_g)] = eg_alignment_count[(i,j,len_e,len_g)] / eg_alignment_count_for_any_i[(j,len_e, len_g)]\\n\\t\\t\\t\\t\\t#germ --> eng\\n\\t\\t\\t\\t\\tq[(j,i,len_g,len_e)] = ge_alignment_count[(j,i,len_g,len_e)] / ge_alignment_count_for_any_j[(i,len_g,len_e)]\\n\\treturn (t,q)\\n\\n\\ndef main(aligned_sents):\\n ba = BerkeleyAligner(aligned_sents, 10)\\n A.save_model_output(aligned_sents, ba, \\\"ba.txt\\\")\\n avg_aer = A.compute_avg_aer(aligned_sents, ba, 50)\\n\\n print ('Berkeley Aligner')\\n print ('---------------------------')\\n print('Average AER: {0:.3f}\\\\n'.format(avg_aer))\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":true,"string":"true"}}},{"rowIdx":5899,"cells":{"index":{"kind":"number","value":5899,"string":"5,899"},"blob_id":{"kind":"string","value":"fbd7868a37a2270e5dc86843adff50a94436404d"},"code":{"kind":"string","value":"from openvino.inference_engine import IENetwork, IECore\nimport numpy as np\nimport time\nfrom datetime import datetime\nimport sys\nimport os\nimport cv2\n\n\nclass MotionDetect:\n # Klasse zur Erkennung von Bewegung\n def __init__(self):\n self.static_back = None\n\n def detect_motion(self, frame, reset=False):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\n if self.static_back is None or reset:\n self.static_back = gray\n return False\n diff_frame = cv2.absdiff(self.static_back, gray)\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n cnts, _ = cv2.findContours(thresh_frame.copy(),\n cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n if cnts:\n return True\n else:\n return False\n\n def reset_background(self):\n self.static_back = None\n\n\nclass InferenceModel:\n # Klasse zur Erstellung eines 'ExecInferModel' Objekts\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n\n # Anlegen der Pfade zu den Modell Dateien\n model_xml = os.path.join(\n model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(\n model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n\n # Laden der Labels aus 'classes.txt'\n labels = [line.strip() for line in open(\n os.path.join(model_dir, 'classes.txt')).readlines()]\n\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n\n # Erstellung des Modells aus IR Dateien\n net = IENetwork(model=model_xml, weights=model_bin)\n\n # In-Output Shapes des Modells aus 'net' laden\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n\n assert len(\n net.outputs) == 1, \"Demo supports only single output topologies\"\n out_blob = next(iter(net.outputs))\n\n # Modell importieren (Falls vorhanden)\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(\n model_file=exported_model, device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n # sonst erstellen und exoportieren\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(\n network=net, num_requests=num_requests, device_name=self.device)\n exec_net.export(exported_model)\n\n except:\n return False\n nchw = net.inputs[input_blob].shape\n\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n\n # ersellen und zurückgeben eines ExecInferModel Objekts, mit welchem die Inferenz ausgeführt wird\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict, nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw, labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save=20, save_all=False):\n\n # Status Variablen\n n_infered, n_detected, n_saved = 0, 0, 0\n\n # alle Inferenz Requests durchiterieren\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n\n res, frame = None, None\n\n # Status der Inferenz für aktuellen Request abfragen\n status = infer_request.wait(0)\n\n # 0: ergebnis da, -11: noch nicht gestartet\n if status != 0 and status != -11:\n continue\n\n # Ergebnis für aktuellen Request holen\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n\n # neuen Inferent Request starten\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(\n self.current_frames[inf_img_ind], (self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape(\n (self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n\n # Ergebnis verarbeiten\n if res is None or frame is None:\n continue\n\n height, width = frame.shape[:2]\n # inferenz ergebnisse für ein frame durchiterieren\n for obj in res[0][0]:\n\n # Threshold prüfen\n if obj[2] < threshhold:\n continue\n\n n_detected += 1\n\n # Boundig Box koordinalte aus Erg laden\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n\n # ID der erkannten Klasse\n class_id = int(obj[1])\n\n # Bounding Box in das Bild zeichnen\n cv2.rectangle(frame, (xmin, ymin),\n (xmax, ymax), color=(0, 255, 255), thickness=2)\n\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7),\n cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n\n # detected_objects dict anlegen mit key:class_id, value:[N, Roi, proba]\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [\n 0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n # wenn wahrscheinlichkeit höher als bei gespeicherten, ersetzen\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n\n # nach 'n_save' abspeicher\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n\n # alle aus 'detected_objects' lokal speichern\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n # Funkiont zum speichern der Bilder\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime(\"%d-%b-%Y_%H-%M-%S\")\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n # save image local\n cv2.imwrite(os.path.join(\n self.output_dir, file_name), image_array)\n"},"steps":{"kind":"list like","value":["from openvino.inference_engine import IENetwork, IECore\nimport numpy as np\nimport time\nfrom datetime import datetime\nimport sys\nimport os\nimport cv2\n\n\nclass MotionDetect:\n # Klasse zur Erkennung von Bewegung\n def __init__(self):\n self.static_back = None\n\n def detect_motion(self, frame, reset=False):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\n if self.static_back is None or reset:\n self.static_back = gray\n return False\n diff_frame = cv2.absdiff(self.static_back, gray)\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n cnts, _ = cv2.findContours(thresh_frame.copy(),\n cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n if cnts:\n return True\n else:\n return False\n\n def reset_background(self):\n self.static_back = None\n\n\nclass InferenceModel:\n # Klasse zur Erstellung eines 'ExecInferModel' Objekts\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n\n # Anlegen der Pfade zu den Modell Dateien\n model_xml = os.path.join(\n model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(\n model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n\n # Laden der Labels aus 'classes.txt'\n labels = [line.strip() for line in open(\n os.path.join(model_dir, 'classes.txt')).readlines()]\n\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n\n # Erstellung des Modells aus IR Dateien\n net = IENetwork(model=model_xml, weights=model_bin)\n\n # In-Output Shapes des Modells aus 'net' laden\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n\n assert len(\n net.outputs) == 1, \"Demo supports only single output topologies\"\n out_blob = next(iter(net.outputs))\n\n # Modell importieren (Falls vorhanden)\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(\n model_file=exported_model, device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n # sonst erstellen und exoportieren\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(\n network=net, num_requests=num_requests, device_name=self.device)\n exec_net.export(exported_model)\n\n except:\n return False\n nchw = net.inputs[input_blob].shape\n\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n\n # ersellen und zurückgeben eines ExecInferModel Objekts, mit welchem die Inferenz ausgeführt wird\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict, nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw, labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save=20, save_all=False):\n\n # Status Variablen\n n_infered, n_detected, n_saved = 0, 0, 0\n\n # alle Inferenz Requests durchiterieren\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n\n res, frame = None, None\n\n # Status der Inferenz für aktuellen Request abfragen\n status = infer_request.wait(0)\n\n # 0: ergebnis da, -11: noch nicht gestartet\n if status != 0 and status != -11:\n continue\n\n # Ergebnis für aktuellen Request holen\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n\n # neuen Inferent Request starten\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(\n self.current_frames[inf_img_ind], (self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape(\n (self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n\n # Ergebnis verarbeiten\n if res is None or frame is None:\n continue\n\n height, width = frame.shape[:2]\n # inferenz ergebnisse für ein frame durchiterieren\n for obj in res[0][0]:\n\n # Threshold prüfen\n if obj[2] < threshhold:\n continue\n\n n_detected += 1\n\n # Boundig Box koordinalte aus Erg laden\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n\n # ID der erkannten Klasse\n class_id = int(obj[1])\n\n # Bounding Box in das Bild zeichnen\n cv2.rectangle(frame, (xmin, ymin),\n (xmax, ymax), color=(0, 255, 255), thickness=2)\n\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7),\n cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n\n # detected_objects dict anlegen mit key:class_id, value:[N, Roi, proba]\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [\n 0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n # wenn wahrscheinlichkeit höher als bei gespeicherten, ersetzen\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n\n # nach 'n_save' abspeicher\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n\n # alle aus 'detected_objects' lokal speichern\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n # Funkiont zum speichern der Bilder\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime(\"%d-%b-%Y_%H-%M-%S\")\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n # save image local\n cv2.imwrite(os.path.join(\n self.output_dir, file_name), image_array)\n","from openvino.inference_engine import IENetwork, IECore\nimport numpy as np\nimport time\nfrom datetime import datetime\nimport sys\nimport os\nimport cv2\n\n\nclass MotionDetect:\n\n def __init__(self):\n self.static_back = None\n\n def detect_motion(self, frame, reset=False):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\n if self.static_back is None or reset:\n self.static_back = gray\n return False\n diff_frame = cv2.absdiff(self.static_back, gray)\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n if cnts:\n return True\n else:\n return False\n\n def reset_background(self):\n self.static_back = None\n\n\nclass InferenceModel:\n\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n\n\nclass MotionDetect:\n\n def __init__(self):\n self.static_back = None\n\n def detect_motion(self, frame, reset=False):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\n if self.static_back is None or reset:\n self.static_back = gray\n return False\n diff_frame = cv2.absdiff(self.static_back, gray)\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n if cnts:\n return True\n else:\n return False\n\n def reset_background(self):\n self.static_back = None\n\n\nclass InferenceModel:\n\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n\n\nclass MotionDetect:\n <function token>\n\n def detect_motion(self, frame, reset=False):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\n if self.static_back is None or reset:\n self.static_back = gray\n return False\n diff_frame = cv2.absdiff(self.static_back, gray)\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\n cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n if cnts:\n return True\n else:\n return False\n\n def reset_background(self):\n self.static_back = None\n\n\nclass InferenceModel:\n\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n\n\nclass MotionDetect:\n <function token>\n <function token>\n\n def reset_background(self):\n self.static_back = None\n\n\nclass InferenceModel:\n\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n\n\nclass MotionDetect:\n <function token>\n <function token>\n <function token>\n\n\nclass InferenceModel:\n\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n\n\nclass InferenceModel:\n\n def __init__(self, device='MYRIAD'):\n self.ie = IECore()\n self.device = device\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n\n\nclass InferenceModel:\n <function token>\n\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\n exported_model = os.path.join(model_dir, 'exported_model')\n labels = [line.strip() for line in open(os.path.join(model_dir,\n 'classes.txt')).readlines()]\n assert os.path.isfile(model_bin)\n assert os.path.isfile(model_xml)\n net = IENetwork(model=model_xml, weights=model_bin)\n img_info_input_blob = None\n feed_dict = {}\n for blob_name in net.inputs:\n if len(net.inputs[blob_name].shape) == 4:\n input_blob = blob_name\n elif len(net.inputs[blob_name].shape) == 2:\n img_info_input_blob = blob_name\n else:\n raise RuntimeError(\n \"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\"\n .format(len(net.inputs[blob_name].shape), blob_name))\n assert len(net.outputs\n ) == 1, 'Demo supports only single output topologies'\n out_blob = next(iter(net.outputs))\n if os.path.isfile(exported_model):\n print('found model to import')\n try:\n exec_net = self.ie.import_network(model_file=exported_model,\n device_name=self.device, num_requests=num_requests)\n except:\n return False\n else:\n print('creating exec model')\n try:\n exec_net = self.ie.load_network(network=net, num_requests=\n num_requests, device_name=self.device)\n exec_net.export(exported_model)\n except:\n return False\n nchw = net.inputs[input_blob].shape\n del net\n if img_info_input_blob:\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\n nchw, labels, output_dir)\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n\n\nclass InferenceModel:\n <function token>\n <function token>\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n<class token>\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\n =20, save_all=False):\n n_infered, n_detected, n_saved = 0, 0, 0\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\n res, frame = None, None\n status = infer_request.wait(0)\n if status != 0 and status != -11:\n continue\n if inf_img_ind in self.current_frames:\n res = infer_request.outputs[self.out_blob]\n frame = self.current_frames[inf_img_ind]\n n_infered += 1\n if len(buffer):\n self.current_frames[inf_img_ind] = buffer.pop()\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\n self.w, self.h))\n in_frame = in_frame.transpose((2, 0, 1))\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\n self.feed_dict[self.input_blob] = in_frame\n infer_request.async_infer(self.feed_dict)\n if res is None or frame is None:\n continue\n height, width = frame.shape[:2]\n for obj in res[0][0]:\n if obj[2] < threshhold:\n continue\n n_detected += 1\n xmin = int(obj[3] * width)\n ymin = int(obj[4] * height)\n xmax = int(obj[5] * width)\n ymax = int(obj[6] * height)\n class_id = int(obj[1])\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \n 255, 255), thickness=2)\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\n if not class_id in self.detected_objects:\n self.detected_objects[class_id] = [0, frame, obj[2]]\n else:\n self.detected_objects[class_id][0] += 1\n if self.detected_objects[class_id][2] < obj[2]:\n self.detected_objects[class_id][1] = frame\n self.detected_objects[class_id][2] = obj[2]\n if self.detected_objects[class_id][0] > n_save:\n n_saved += 1\n self._save(class_id)\n del self.detected_objects[class_id]\n if view_result:\n cv2.imshow('infer result', frame)\n cv2.waitKey(1)\n if save_all:\n print('saving all')\n for class_id in self.detected_objects.keys():\n self._save(class_id)\n n_saved += 1\n self.detected_objects = {}\n return n_infered, n_detected, n_saved\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n<class token>\n\n\nclass ExecInferModel:\n\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\n labels, output_dir):\n self.exec_net = exec_net\n self.labels = labels\n self.input_blob = input_blob\n self.out_blob = out_blob\n self.feed_dict = feed_dict\n self.n, self.c, self.h, self.w = nchw\n self.current_frames = {}\n self.detected_objects = {}\n self.output_dir = output_dir\n <function token>\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n<class token>\n\n\nclass ExecInferModel:\n <function token>\n <function token>\n\n def _save(self, class_id):\n class_name = self.labels[class_id - 1]\n print('saving ', class_name)\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\n file_name = time_stamp + '_' + class_name + '.jpg'\n image_array = self.detected_objects[class_id][1]\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\n","<import token>\n<class token>\n<class token>\n\n\nclass ExecInferModel:\n <function token>\n <function token>\n <function token>\n","<import token>\n<class token>\n<class token>\n<class token>\n"],"string":"[\n \"from openvino.inference_engine import IENetwork, IECore\\nimport numpy as np\\nimport time\\nfrom datetime import datetime\\nimport sys\\nimport os\\nimport cv2\\n\\n\\nclass MotionDetect:\\n # Klasse zur Erkennung von Bewegung\\n def __init__(self):\\n self.static_back = None\\n\\n def detect_motion(self, frame, reset=False):\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\\n if self.static_back is None or reset:\\n self.static_back = gray\\n return False\\n diff_frame = cv2.absdiff(self.static_back, gray)\\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\\n cnts, _ = cv2.findContours(thresh_frame.copy(),\\n cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\\n if cnts:\\n return True\\n else:\\n return False\\n\\n def reset_background(self):\\n self.static_back = None\\n\\n\\nclass InferenceModel:\\n # Klasse zur Erstellung eines 'ExecInferModel' Objekts\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n\\n # Anlegen der Pfade zu den Modell Dateien\\n model_xml = os.path.join(\\n model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(\\n model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n\\n # Laden der Labels aus 'classes.txt'\\n labels = [line.strip() for line in open(\\n os.path.join(model_dir, 'classes.txt')).readlines()]\\n\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n\\n # Erstellung des Modells aus IR Dateien\\n net = IENetwork(model=model_xml, weights=model_bin)\\n\\n # In-Output Shapes des Modells aus 'net' laden\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n\\n assert len(\\n net.outputs) == 1, \\\"Demo supports only single output topologies\\\"\\n out_blob = next(iter(net.outputs))\\n\\n # Modell importieren (Falls vorhanden)\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(\\n model_file=exported_model, device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n # sonst erstellen und exoportieren\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(\\n network=net, num_requests=num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n\\n # ersellen und zurückgeben eines ExecInferModel Objekts, mit welchem die Inferenz ausgeführt wird\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict, nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw, labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save=20, save_all=False):\\n\\n # Status Variablen\\n n_infered, n_detected, n_saved = 0, 0, 0\\n\\n # alle Inferenz Requests durchiterieren\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n\\n res, frame = None, None\\n\\n # Status der Inferenz für aktuellen Request abfragen\\n status = infer_request.wait(0)\\n\\n # 0: ergebnis da, -11: noch nicht gestartet\\n if status != 0 and status != -11:\\n continue\\n\\n # Ergebnis für aktuellen Request holen\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n\\n # neuen Inferent Request starten\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(\\n self.current_frames[inf_img_ind], (self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape(\\n (self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n\\n # Ergebnis verarbeiten\\n if res is None or frame is None:\\n continue\\n\\n height, width = frame.shape[:2]\\n # inferenz ergebnisse für ein frame durchiterieren\\n for obj in res[0][0]:\\n\\n # Threshold prüfen\\n if obj[2] < threshhold:\\n continue\\n\\n n_detected += 1\\n\\n # Boundig Box koordinalte aus Erg laden\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n\\n # ID der erkannten Klasse\\n class_id = int(obj[1])\\n\\n # Bounding Box in das Bild zeichnen\\n cv2.rectangle(frame, (xmin, ymin),\\n (xmax, ymax), color=(0, 255, 255), thickness=2)\\n\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7),\\n cv2.FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n\\n # detected_objects dict anlegen mit key:class_id, value:[N, Roi, proba]\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [\\n 0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n # wenn wahrscheinlichkeit höher als bei gespeicherten, ersetzen\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n\\n # nach 'n_save' abspeicher\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n\\n # alle aus 'detected_objects' lokal speichern\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n # Funkiont zum speichern der Bilder\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime(\\\"%d-%b-%Y_%H-%M-%S\\\")\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n # save image local\\n cv2.imwrite(os.path.join(\\n self.output_dir, file_name), image_array)\\n\",\n \"from openvino.inference_engine import IENetwork, IECore\\nimport numpy as np\\nimport time\\nfrom datetime import datetime\\nimport sys\\nimport os\\nimport cv2\\n\\n\\nclass MotionDetect:\\n\\n def __init__(self):\\n self.static_back = None\\n\\n def detect_motion(self, frame, reset=False):\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\\n if self.static_back is None or reset:\\n self.static_back = gray\\n return False\\n diff_frame = cv2.absdiff(self.static_back, gray)\\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\\n cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n if cnts:\\n return True\\n else:\\n return False\\n\\n def reset_background(self):\\n self.static_back = None\\n\\n\\nclass InferenceModel:\\n\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n\\n\\nclass MotionDetect:\\n\\n def __init__(self):\\n self.static_back = None\\n\\n def detect_motion(self, frame, reset=False):\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\\n if self.static_back is None or reset:\\n self.static_back = gray\\n return False\\n diff_frame = cv2.absdiff(self.static_back, gray)\\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\\n cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n if cnts:\\n return True\\n else:\\n return False\\n\\n def reset_background(self):\\n self.static_back = None\\n\\n\\nclass InferenceModel:\\n\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n\\n\\nclass MotionDetect:\\n <function token>\\n\\n def detect_motion(self, frame, reset=False):\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n gray = cv2.GaussianBlur(gray, (21, 21), 0)\\n if self.static_back is None or reset:\\n self.static_back = gray\\n return False\\n diff_frame = cv2.absdiff(self.static_back, gray)\\n thresh_frame = cv2.threshold(diff_frame, 50, 255, cv2.THRESH_BINARY)[1]\\n thresh_frame = cv2.dilate(thresh_frame, None, iterations=2)\\n cnts, _ = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n if cnts:\\n return True\\n else:\\n return False\\n\\n def reset_background(self):\\n self.static_back = None\\n\\n\\nclass InferenceModel:\\n\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n\\n\\nclass MotionDetect:\\n <function token>\\n <function token>\\n\\n def reset_background(self):\\n self.static_back = None\\n\\n\\nclass InferenceModel:\\n\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n\\n\\nclass MotionDetect:\\n <function token>\\n <function token>\\n <function token>\\n\\n\\nclass InferenceModel:\\n\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass InferenceModel:\\n\\n def __init__(self, device='MYRIAD'):\\n self.ie = IECore()\\n self.device = device\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass InferenceModel:\\n <function token>\\n\\n def create_exec_infer_model(self, model_dir, output_dir, num_requests=2):\\n model_xml = os.path.join(model_dir, 'frozen_inference_graph.xml')\\n model_bin = os.path.join(model_dir, 'frozen_inference_graph.bin')\\n exported_model = os.path.join(model_dir, 'exported_model')\\n labels = [line.strip() for line in open(os.path.join(model_dir,\\n 'classes.txt')).readlines()]\\n assert os.path.isfile(model_bin)\\n assert os.path.isfile(model_xml)\\n net = IENetwork(model=model_xml, weights=model_bin)\\n img_info_input_blob = None\\n feed_dict = {}\\n for blob_name in net.inputs:\\n if len(net.inputs[blob_name].shape) == 4:\\n input_blob = blob_name\\n elif len(net.inputs[blob_name].shape) == 2:\\n img_info_input_blob = blob_name\\n else:\\n raise RuntimeError(\\n \\\"Unsupported {}D input layer '{}'. Only 2D and 4D input layers are supported\\\"\\n .format(len(net.inputs[blob_name].shape), blob_name))\\n assert len(net.outputs\\n ) == 1, 'Demo supports only single output topologies'\\n out_blob = next(iter(net.outputs))\\n if os.path.isfile(exported_model):\\n print('found model to import')\\n try:\\n exec_net = self.ie.import_network(model_file=exported_model,\\n device_name=self.device, num_requests=num_requests)\\n except:\\n return False\\n else:\\n print('creating exec model')\\n try:\\n exec_net = self.ie.load_network(network=net, num_requests=\\n num_requests, device_name=self.device)\\n exec_net.export(exported_model)\\n except:\\n return False\\n nchw = net.inputs[input_blob].shape\\n del net\\n if img_info_input_blob:\\n feed_dict[img_info_input_blob] = [nchw[2], nchw[3], 1]\\n return ExecInferModel(exec_net, input_blob, out_blob, feed_dict,\\n nchw, labels, output_dir)\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n\\n\\nclass InferenceModel:\\n <function token>\\n <function token>\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n\\n def infer_frames(self, buffer, threshhold=0.6, view_result=True, n_save\\n =20, save_all=False):\\n n_infered, n_detected, n_saved = 0, 0, 0\\n for inf_img_ind, infer_request in enumerate(self.exec_net.requests):\\n res, frame = None, None\\n status = infer_request.wait(0)\\n if status != 0 and status != -11:\\n continue\\n if inf_img_ind in self.current_frames:\\n res = infer_request.outputs[self.out_blob]\\n frame = self.current_frames[inf_img_ind]\\n n_infered += 1\\n if len(buffer):\\n self.current_frames[inf_img_ind] = buffer.pop()\\n in_frame = cv2.resize(self.current_frames[inf_img_ind], (\\n self.w, self.h))\\n in_frame = in_frame.transpose((2, 0, 1))\\n in_frame = in_frame.reshape((self.n, self.c, self.h, self.w))\\n self.feed_dict[self.input_blob] = in_frame\\n infer_request.async_infer(self.feed_dict)\\n if res is None or frame is None:\\n continue\\n height, width = frame.shape[:2]\\n for obj in res[0][0]:\\n if obj[2] < threshhold:\\n continue\\n n_detected += 1\\n xmin = int(obj[3] * width)\\n ymin = int(obj[4] * height)\\n xmax = int(obj[5] * width)\\n ymax = int(obj[6] * height)\\n class_id = int(obj[1])\\n cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, \\n 255, 255), thickness=2)\\n cv2.putText(frame, self.labels[class_id - 1] + ' ' + str(\\n round(obj[2] * 100, 1)) + '%', (xmin, ymin - 7), cv2.\\n FONT_HERSHEY_COMPLEX, 0.6, (0, 255, 255), 1)\\n if not class_id in self.detected_objects:\\n self.detected_objects[class_id] = [0, frame, obj[2]]\\n else:\\n self.detected_objects[class_id][0] += 1\\n if self.detected_objects[class_id][2] < obj[2]:\\n self.detected_objects[class_id][1] = frame\\n self.detected_objects[class_id][2] = obj[2]\\n if self.detected_objects[class_id][0] > n_save:\\n n_saved += 1\\n self._save(class_id)\\n del self.detected_objects[class_id]\\n if view_result:\\n cv2.imshow('infer result', frame)\\n cv2.waitKey(1)\\n if save_all:\\n print('saving all')\\n for class_id in self.detected_objects.keys():\\n self._save(class_id)\\n n_saved += 1\\n self.detected_objects = {}\\n return n_infered, n_detected, n_saved\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass ExecInferModel:\\n\\n def __init__(self, exec_net, input_blob, out_blob, feed_dict, nchw,\\n labels, output_dir):\\n self.exec_net = exec_net\\n self.labels = labels\\n self.input_blob = input_blob\\n self.out_blob = out_blob\\n self.feed_dict = feed_dict\\n self.n, self.c, self.h, self.w = nchw\\n self.current_frames = {}\\n self.detected_objects = {}\\n self.output_dir = output_dir\\n <function token>\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass ExecInferModel:\\n <function token>\\n <function token>\\n\\n def _save(self, class_id):\\n class_name = self.labels[class_id - 1]\\n print('saving ', class_name)\\n time_stamp = datetime.now().strftime('%d-%b-%Y_%H-%M-%S')\\n file_name = time_stamp + '_' + class_name + '.jpg'\\n image_array = self.detected_objects[class_id][1]\\n cv2.imwrite(os.path.join(self.output_dir, file_name), image_array)\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n\\n\\nclass ExecInferModel:\\n <function token>\\n <function token>\\n <function token>\\n\",\n \"<import token>\\n<class token>\\n<class token>\\n<class token>\\n\"\n]","isConversational":false},"error":{"kind":"bool","value":false,"string":"false"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":58,"numItemsPerPage":100,"numTotalItems":100000,"offset":5800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NTc5NTUxMSwic3ViIjoiL2RhdGFzZXRzL0NvaGVuUXUvdGhlLXN0YWNrLXYyLWRlZHVwLVB5dGhvbiIsImV4cCI6MTc3NTc5OTExMSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.p61LbOixS8nuYOMbGLCjcU9k79oe7hATxbtblxjiYfiPglmeyBOyw7ZfCXPaN7LQYM3YTX76GHntJNcIBLOuCQ","displayUrls":true,"splitSizeSummaries":[{"config":"0_100000","split":"train","numRows":100000,"numBytesParquet":1075633227},{"config":"100000_200000","split":"train","numRows":100000,"numBytesParquet":773849529},{"config":"1900000_2000000","split":"train","numRows":100000,"numBytesParquet":905561117},{"config":"2100000_2200000","split":"train","numRows":99999,"numBytesParquet":818411254},{"config":"2700000_2800000","split":"train","numRows":100000,"numBytesParquet":1131112800},{"config":"300000_400000","split":"train","numRows":100000,"numBytesParquet":1073321998},{"config":"500000_600000","split":"train","numRows":100000,"numBytesParquet":925113316}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
index int64 0 100k | blob_id stringlengths 40 40 | code stringlengths 7 7.27M | steps listlengths 1 1.25k | error bool 2
classes |
|---|---|---|---|---|
5,800 | 30e7fc169eceb3d8cc1a4fa6bb65d81a4403f2c7 | from selenium import webdriver
from time import sleep
import os.path
import time
import datetime
driver =webdriver.Chrome(executable_path=r'C:/Users/Pathak/Downloads/chromedriver_win32/chromedriver.exe')
counter=0
while True :
driver.get("https://www.google.co.in/maps/@18.9967228,73.118955,21z/data=!5m1!... | [
"from selenium import webdriver\r\nfrom time import sleep\r\nimport os.path\r\nimport time\r\nimport datetime\r\ndriver =webdriver.Chrome(executable_path=r'C:/Users/Pathak/Downloads/chromedriver_win32/chromedriver.exe')\r\ncounter=0\r\nwhile True :\r\n\t\r\n\r\n\tdriver.get(\"https://www.google.co.in/maps/@18.9967... | false |
5,801 | 3b11d514b15775e4c818a7a2adf9a80e89dca968 | import requests
from bs4 import BeautifulSoup
from urllib.request import urlretrieve
import json
import time
#功能一：下载单一歌曲、歌词
def single_song(song_id,path,song_name): #下载单一歌曲，输入为歌曲id，保存路径，歌曲名称
song_url = "http://music.163.com/song/media/outer/url?id=%s" % song_id
down_path = path +'... | [
"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom urllib.request import urlretrieve\r\nimport json\r\nimport time\r\n\r\n#功能一：下载单一歌曲、歌词\r\n\r\ndef single_song(song_id,path,song_name): #下载单一歌曲，输入为歌曲id，保存路径，歌曲名称\r\n song_url = \"http://music.163.com/song/media/outer/url?id=%s\" % song_id... | false |
5,802 | 807e19f09f4a46b6c39457b8916714e2c54c3e8d | # -*- coding:utf-8 -*-
'''
@author:oldwai
'''
# email: frankandrew@163.com
def multipliers():
return lab1(x)
def lab1(x):
list1 = []
for i in range(4):
sum = x*i
list1.append(sum)
return list1
#print ([m(2) for m in multipliers()])
def func1(x):
list2 = []
... | [
"# -*- coding:utf-8 -*-\r\n'''\r\n@author:oldwai\r\n'''\r\n# email: frankandrew@163.com\r\n\r\n\r\ndef multipliers():\r\n return lab1(x)\r\n\r\n\r\ndef lab1(x):\r\n list1 = []\r\n for i in range(4):\r\n sum = x*i\r\n list1.append(sum)\r\n return list1\r\n\r\n#print ([m(2) for m in multiplier... | false |
5,803 | 676caabb103f67c631bc191b11ab0d2d8ab25d1e | import json
from django.core.management import call_command
from django.http import JsonResponse
from django.test import TestCase
from django.urls import reverse
URLS = ['api_v1:categories', 'api_v1:main_categories', 'api_v1:articles']
class GetJsonData(TestCase):
def test_post_not_login_no_pk(self):
f... | [
"import json\n\nfrom django.core.management import call_command\nfrom django.http import JsonResponse\nfrom django.test import TestCase\nfrom django.urls import reverse\n\n\nURLS = ['api_v1:categories', 'api_v1:main_categories', 'api_v1:articles']\n\n\nclass GetJsonData(TestCase):\n def test_post_not_login_no_pk... | false |
5,804 | 2bf057621df3b860c8f677baf54673d2da8c2bd1 | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under t... | [
"# 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# distr... | false |
5,805 | c585b1439217fff42945eeb9e02512d73f8ba19f | import DB as db
import os
from Chart import Chart
import matplotlib.pyplot as plt
import numpy as np
table = db.get_researcher_copy()
chart_path = '../charts/discipline '
def get_discipline_with_more_female():
docs = table.aggregate([
{'$match':{'gender':{'$exists':1}}},
{'$unwind':'$labels'},
{'$group':{'_id'... | [
"import DB as db\nimport os\nfrom Chart import Chart\nimport matplotlib.pyplot as plt\nimport numpy as np\ntable = db.get_researcher_copy()\nchart_path = '../charts/discipline '\n\n\ndef get_discipline_with_more_female():\n\tdocs = table.aggregate([\n\t\t{'$match':{'gender':{'$exists':1}}},\n\t\t{'$unwind':'$labels... | false |
5,806 | c69c8ba218935e5bb065b3b925cc7c5f1aa2957b |
import matplotlib.pyplot as plt
import numpy as np
x = [1, 2, 2.5, 3, 4] # x-coordinates for graph
y = [1, 4, 7, 9, 15] # y-coordinates
plt.axis([0, 6, 0, 20]) # creating my x and y axis range. 0-6 is x, 0-20 is y
plt.plot(x, y, 'ro')
# can see graph has a linear correspondence, therefore, can use linear regression ... | [
"\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nx = [1, 2, 2.5, 3, 4] # x-coordinates for graph\ny = [1, 4, 7, 9, 15] # y-coordinates\nplt.axis([0, 6, 0, 20]) # creating my x and y axis range. 0-6 is x, 0-20 is y\n\nplt.plot(x, y, 'ro')\n# can see graph has a linear correspondence, therefore, can use line... | false |
5,807 | f0fa85f240b74b003ade767ffe8642feacdfaa32 | import argparse
from train import train
from test import infer
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--mode', type=str, default='train',
help='could be either infer or train')
parser.add_argument('--model_dir', type=str, default='model',
... | [
"import argparse\nfrom train import train\nfrom test import infer\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('--mode', type=str, default='train',\n help='could be either infer or train')\n parser.add_argument('--model_dir', type=str, defa... | false |
5,808 | 2ccc5e01a3b47a77abcb32160dee74a6a74fcfbb | import socket
import sys
from datetime import datetime
from threading import Thread
import logging
class RRConnection():
def __init__(self):
self._listenerSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self._inSock = None
self._inThread = Thread(target=self.inLoop)
self... | [
"import socket\nimport sys\nfrom datetime import datetime\nfrom threading import Thread\nimport logging\n\n\nclass RRConnection():\n def __init__(self):\n self._listenerSocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self._inSock = None\n self._inThread = Thread(target=self.inLo... | false |
5,809 | 9f1cbc655a5d8f14fa45cf977bb2dcee4874b188 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 23 20:44:38 2018
@author: user
"""
import fitbit
import gather_keys_oauth2 as Oauth2
import pandas as pd
import datetime as dt
from config import CLIENT_ID, CLIENT_SECRET
#Establish connection to Fitbit API
server = Oauth2.OAuth2Server(CLIENT_ID, CLIENT_SECRET)
server... | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 23 20:44:38 2018\n\n@author: user\n\"\"\"\n\nimport fitbit\nimport gather_keys_oauth2 as Oauth2\nimport pandas as pd \nimport datetime as dt\nfrom config import CLIENT_ID, CLIENT_SECRET\n\n\n#Establish connection to Fitbit API\nserver = Oauth2.OAuth2Server(CLIENT... | false |
5,810 | 72bbd100a37a86dec7684257f2bec85d7367c009 | from rest_framework import serializers
from .models import *
class VisitaSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = Visita
fields = ('id', 'usuario', 'lugar', 'fecha_visita', 'hora_visita') | [
"from rest_framework import serializers\nfrom .models import *\n\n\nclass VisitaSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = Visita\n fields = ('id', 'usuario', 'lugar', 'fecha_visita', 'hora_visita')",
"from rest_framework import serializers\nfrom .models import *\... | false |
5,811 | c9f29a92ec8627593b54f7d9569dcfd589fa7fff | '''
Binary_to_C
Converts any binary data to an array of 'char' type to be used inside of a C program.
The reason to want to do that, is to emulate a 'Windows Resource System' on Linux.
Linux does not allow inclusion of binary data in application (I am OK with that, I like that actually).
Windows, however, does. On... | [
"'''\nBinary_to_C\n\tConverts any binary data to an array of 'char' type to be used inside of a C program.\n\tThe reason to want to do that, is to emulate a 'Windows Resource System' on Linux.\n\tLinux does not allow inclusion of binary data in application (I am OK with that, I like that actually).\n\tWindows, howe... | true |
5,812 | 9d6516ea099e035fb97e5165071103698a7ec140 | # Generated by Django 2.2.8 on 2019-12-10 10:28
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('fieldsapp', '0003_pole_avatar'),
]
operations = [
migrations.AddField(
model_name='pole',
name='email',
... | [
"# Generated by Django 2.2.8 on 2019-12-10 10:28\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('fieldsapp', '0003_pole_avatar'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='pole',\n name='e... | false |
5,813 | 6a9e18cde94258b01a37f459eceaac58118b4976 | NUM_CLASSES = 31
AUDIO_SR = 16000
AUDIO_LENGTH = 16000
LIBROSA_AUDIO_LENGTH = 22050
EPOCHS = 25
categories = {
'stop': 0,
'nine': 1,
'off': 2,
'four': 3,
'right': 4,
'eight': 5,
'one': 6,
'bird': 7,
'dog': 8,
'no': 9,
'on': 10,
'seven': 11,
'cat': 12,
'left': 1... | [
"NUM_CLASSES = 31\n\nAUDIO_SR = 16000\nAUDIO_LENGTH = 16000\nLIBROSA_AUDIO_LENGTH = 22050\n\nEPOCHS = 25\n\ncategories = {\n 'stop': 0,\n 'nine': 1,\n 'off': 2,\n 'four': 3,\n 'right': 4,\n 'eight': 5,\n 'one': 6,\n 'bird': 7,\n 'dog': 8,\n 'no': 9,\n 'on': 10,\n 'seven': 11,\n ... | false |
5,814 | f49c15dca26d987e1d578790e077501a504e560b | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import pytest
class TestMark:
@pytest.mark.demo1
def test_case1(self):
print("testcase1")
@pytest.mark.demo1
def test_case2(self):
print("testcase1")
@pytest.mark.demo2
def test_case3(self):
print("testcase1")
@pytest... | [
"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport pytest\n\n\nclass TestMark:\n @pytest.mark.demo1\n def test_case1(self):\n print(\"testcase1\")\n\n @pytest.mark.demo1\n def test_case2(self):\n print(\"testcase1\")\n\n @pytest.mark.demo2\n def test_case3(self):\n print(... | false |
5,815 | 6d80a89a47b68fd8d81739787897355671ca94e9 | '''
Функція replace() може використовуватися для заміни будь-якого слова у рядку іншим словом.
Прочитайте кожен рядок зі створеного у попередньому завданні файлу learning_python.txt і замініть слово Python назвою іншої мови,
наприклад C при виведенні на екран. Це завдання написати в окремій функції.
'''
def reader():... | [
"'''\nФункція replace() може використовуватися для заміни будь-якого слова у рядку іншим словом.\nПрочитайте кожен рядок зі створеного у попередньому завданні файлу learning_python.txt і замініть слово Python назвою іншої мови,\nнаприклад C при виведенні на екран. Це завдання написати в окремій функції.\n'''\n\n\nd... | false |
5,816 | 629353392e3a4f346f734543ae3f2b8dc616a6c3 | #https://docs.python.org/3.4/library/itertools.html#module-itertools
l = [(1, 2, 9), (1, 3, 12), (2, 3, 8), (2, 4, 4), (2, 5, 7), (3, 5, 5), (3, 6, 2), (4, 5, 2), (4, 7, 10),
(5, 6, 11), (5, 7, 2), (6, 8, 4), (7, 8, 4), (7, 9, 3), (8, 9, 13)]
b = ['America', 'Sudan', 'Srilanka', 'Pakistan', 'Nepal', 'India'... | [
"#https://docs.python.org/3.4/library/itertools.html#module-itertools\n\n\nl = [(1, 2, 9), (1, 3, 12), (2, 3, 8), (2, 4, 4), (2, 5, 7), (3, 5, 5), (3, 6, 2), (4, 5, 2), (4, 7, 10),\n (5, 6, 11), (5, 7, 2), (6, 8, 4), (7, 8, 4), (7, 9, 3), (8, 9, 13)]\n\nb = ['America', 'Sudan', 'Srilanka', 'Pakistan', 'Nepa... | false |
5,817 | b3ce17401476afe2edfda3011d5602ba492cd705 | import matplotlib.pyplot as pt
import numpy as np
from scipy.optimize import leastsq
####################################
# Setting up test data
def norm(x, media, sd):
norm = []
for i in range(x.size):
norm += [1.0/(sd*np.sqrt(2*np.pi))*np.exp(-(x[i] - media)**2/(2*sd**2))]
return np.array(norm)... | [
"import matplotlib.pyplot as pt\nimport numpy as np\nfrom scipy.optimize import leastsq\n\n####################################\n# Setting up test data\n\ndef norm(x, media, sd):\n norm = []\n\n for i in range(x.size):\n norm += [1.0/(sd*np.sqrt(2*np.pi))*np.exp(-(x[i] - media)**2/(2*sd**2))]\n retu... | false |
5,818 | ee22d6226f734c67be91a3ccf1c8c0024bb7dc08 | import numpy as np
from board_specs import *
from board_components import *
import constants
import board_test
# List of resources available to be distributed on the board
RESOURCE_NAMES = constants.RESOURCE_NAMES
# Create a dictionary of each resource and a corresponding number id
res_dict = dict(zip(RESOURCE_NAMES,... | [
"import numpy as np\nfrom board_specs import *\nfrom board_components import *\nimport constants\nimport board_test\n\n\n# List of resources available to be distributed on the board\nRESOURCE_NAMES = constants.RESOURCE_NAMES\n# Create a dictionary of each resource and a corresponding number id\nres_dict = dict(zip(... | false |
5,819 | d3f80deb72ca2bd91fc09b49ad644f54d339f962 | #! /home/joreyna/anaconda2/envs/hla/bin/python
import argparse
import os
import sys
import time
import numpy as np
import copy
import subprocess
import math
project_dir = os.path.join(sys.argv[0], '../../')
project_dir = os.path.abspath(project_dir)
output_dir = os.path.join(project_dir, 'output/', 'pipeline/', 'sa... | [
"#! /home/joreyna/anaconda2/envs/hla/bin/python \nimport argparse \nimport os\nimport sys\nimport time \nimport numpy as np\nimport copy\nimport subprocess\nimport math\n\nproject_dir = os.path.join(sys.argv[0], '../../')\nproject_dir = os.path.abspath(project_dir)\noutput_dir = os.path.join(project_dir, 'output/',... | false |
5,820 | 0ac99e2b33f676a99674c9a8e5d9d47c5bce084b |
plik=open("nowy_zad_84.txt", "w")
print(" Podaj 5 imion")
for i in range(1,6):
imie=input(f" Podaj imie nr {i} ")
# plik.write(imie)
# plik.write("\n")
plik.write(f" {imie} \n")
plik.close()
plik=open("nowy_zad_84.txt", "a")
for i in range(1,101):
plik.write(str(i))
plik.w... | [
"\r\n\r\nplik=open(\"nowy_zad_84.txt\", \"w\")\r\n\r\nprint(\" Podaj 5 imion\")\r\nfor i in range(1,6):\r\n imie=input(f\" Podaj imie nr {i} \")\r\n # plik.write(imie)\r\n # plik.write(\"\\n\")\r\n plik.write(f\" {imie} \\n\")\r\n\r\nplik.close()\r\n\r\nplik=open(\"nowy_zad_84.txt\", \"a\")\r\n\r\nfor i... | false |
5,821 | f7d0d7dda955acd07b6da010d21dc5f02254e1ed | from django.conf.urls import patterns, include, url
from django.views.generic import TemplateView
from . import views
app_name = 'produce'
urlpatterns = [
# Inbound SMS view:
url(r'^sms/$', views.sms, name='sms'),
# List and Detail Views:
url(r'^list/', views.SeasonalView.as_view(), name='list'),
url(r'^(?P<pk... | [
"from django.conf.urls import patterns, include, url\nfrom django.views.generic import TemplateView\n\nfrom . import views\n\napp_name = 'produce'\n\nurlpatterns = [\n\t# Inbound SMS view:\n\turl(r'^sms/$', views.sms, name='sms'),\n\n\t# List and Detail Views:\n\turl(r'^list/', views.SeasonalView.as_view(), name='l... | false |
5,822 | 00228facd19c72bebd9afbbe52597e390233d41e | import requests
import logging
import json
class Handler(object):
def __init__(self):
"""
This class is used to handle interaction towards coffee interface.
"""
super(Handler, self).__init__()
logging.warning('Initializing coffeeHandler....')
# get an active token ... | [
"import requests\nimport logging\nimport json\n\n\nclass Handler(object):\n def __init__(self):\n \"\"\"\n This class is used to handle interaction towards coffee interface.\n \"\"\"\n super(Handler, self).__init__()\n logging.warning('Initializing coffeeHandler....')\n\n ... | false |
5,823 | 890841c8892e89375bb022f0d469fefc27414a2b | from abc import abstractmethod
from anoncreds.protocol.repo.public_repo import PublicRepo
from anoncreds.protocol.types import ClaimDefinition, PublicKey, SecretKey, ID, \
RevocationPublicKey, AccumulatorPublicKey, Accumulator, TailsType, \
RevocationSecretKey, AccumulatorSecretKey, \
TimestampType
from an... | [
"from abc import abstractmethod\n\nfrom anoncreds.protocol.repo.public_repo import PublicRepo\nfrom anoncreds.protocol.types import ClaimDefinition, PublicKey, SecretKey, ID, \\\n RevocationPublicKey, AccumulatorPublicKey, Accumulator, TailsType, \\\n RevocationSecretKey, AccumulatorSecretKey, \\\n Timesta... | false |
5,824 | ac0f0fbb9bcb450ac24198069ef8bea8b049ef47 | '''
删除排序数组中的重复项：
给定一个排序数组，你需要在原地删除重复出现的元素，使得每个元素只出现一次，返回移除后数组的新长度。
不要使用额外的数组空间，你必须在原地修改输入数组并在使用 O(1) 额外空间的条件下完成。
示例 1:
给定数组 nums = [1,1,2],
函数应该返回新的长度 2, 并且原数组 nums 的前两个元素被修改为 1, 2。
你不需要考虑数组中超出新长度后面的元素。
示例 2:
给定 nums = [0,0,1,1,1,2,2,3,3,4],
函数应该返回新的长度 5, 并且原数组 nums 的前五个元素被修改为 0, 1, 2, 3, 4。
你不需要考虑数组中超出新长度后... | [
"'''\n 删除排序数组中的重复项：\n\n给定一个排序数组，你需要在原地删除重复出现的元素，使得每个元素只出现一次，返回移除后数组的新长度。\n\n不要使用额外的数组空间，你必须在原地修改输入数组并在使用 O(1) 额外空间的条件下完成。\n\n示例 1:\n\n给定数组 nums = [1,1,2],\n\n函数应该返回新的长度 2, 并且原数组 nums 的前两个元素被修改为 1, 2。\n\n你不需要考虑数组中超出新长度后面的元素。\n示例 2:\n\n给定 nums = [0,0,1,1,1,2,2,3,3,4],\n\n函数应该返回新的长度 5, 并且原数组 nums 的前五个元素被修改为 0, 1, 2,... | false |
5,825 | 386e491f6b10ca27f513d678c632571c29093ad2 | # -*- coding: utf-8 -*-
import numpy as np
from . import BOID_NOSE_LEN
from .utils import normalize_angle, unit_vector
class Individual:
def __init__(self, color, pos, ror, roo, roa, angle=0, speed=1.0, turning_rate=0.2):
"""Constructor of Individual.
Args:
color (Color): color for ... | [
"# -*- coding: utf-8 -*-\n\nimport numpy as np\n\nfrom . import BOID_NOSE_LEN\nfrom .utils import normalize_angle, unit_vector\n\n\nclass Individual:\n def __init__(self, color, pos, ror, roo, roa, angle=0, speed=1.0, turning_rate=0.2):\n \"\"\"Constructor of Individual.\n\n Args:\n colo... | false |
5,826 | 3ec858c04a7622ae621bf322730b6b3ba9f4d07e | import datetime
from httpx import AsyncClient
from testing.conftest import (LESSONS_PATH, REVIEWS_PATH, pytestmark,
register_and_get_token)
class TestReview:
async def test_review_add_get(self, ac, fill_db):
# Creating users first
token = await register_and_get_tok... | [
"import datetime\n\nfrom httpx import AsyncClient\n\nfrom testing.conftest import (LESSONS_PATH, REVIEWS_PATH, pytestmark,\n register_and_get_token)\n\n\nclass TestReview:\n async def test_review_add_get(self, ac, fill_db):\n\n # Creating users first\n token = await reg... | false |
5,827 | 047b3b25cb064115a46cde1f1480ce55a1256bc1 | N = int(input())
StopPoint = N
cycle = 0
ten = 0
one = 0
new_N = 0
while True:
ten = N//10
one = N%10
total = ten + one
new_N = one*10 + total%10
cycle += 1
N = new_N
if new_N == StopPoint:
break
print(cycle) | [
"N = int(input())\nStopPoint = N\ncycle = 0\nten = 0\none = 0\nnew_N = 0\nwhile True:\n ten = N//10\n one = N%10\n total = ten + one\n new_N = one*10 + total%10\n cycle += 1\n N = new_N\n if new_N == StopPoint:\n break\nprint(cycle)",
"N = int(input())\nStopPoint = N\ncycle = 0\nten = ... | false |
5,828 | d55043c2a18b935478d9be442aaf7305231edc7d | from os.path import dirname
import binwalk
from nose.tools import eq_, ok_
def test_firmware_squashfs():
'''
Test: Open hello-world.srec, scan for signatures
verify that only one signature is returned
verify that the only signature returned is Motorola S-rec data-signature
'''
expected_result... | [
"from os.path import dirname\n\nimport binwalk\nfrom nose.tools import eq_, ok_\n\n\ndef test_firmware_squashfs():\n '''\n Test: Open hello-world.srec, scan for signatures\n verify that only one signature is returned\n verify that the only signature returned is Motorola S-rec data-signature\n '''\n ... | false |
5,829 | 778ef68b5270657f75185b27dc8219b35847afa1 | import cv2
import sys
import online as API
def demo(myAPI):
myAPI.setAttr()
video_capture = cv2.VideoCapture(0)
print("Press q to quit: ")
while True:
# Capture frame-by-frame
ret, frame = video_capture.read() #np.array
frame = cv2.resize(frame, (320, 240))
key = cv2.w... | [
"import cv2\nimport sys\nimport online as API\n\ndef demo(myAPI):\n myAPI.setAttr()\n video_capture = cv2.VideoCapture(0)\n print(\"Press q to quit: \")\n while True:\n # Capture frame-by-frame\n ret, frame = video_capture.read() #np.array\n\n frame = cv2.resize(frame, (320, 240))\n... | false |
5,830 | 4c752c96b7e503ae5c9bc87a038fcf6dc176b776 | def TriSelection(S):
""" Tri par sélection
Le tableau est constitué de deux parties : la 1ère constituée des éléments triés
(initialisée avec seulement le 1er élément) et la seconde constituée des éléments
non triés (initialisée du 2ème au dernier élément) """
for i in range(0, len(S)-1):
... | [
"def TriSelection(S):\r\n \"\"\" Tri par sélection\r\n\r\n Le tableau est constitué de deux parties : la 1ère constituée des éléments triés\r\n (initialisée avec seulement le 1er élément) et la seconde constituée des éléments\r\n non triés (initialisée du 2ème au dernier élément) \"\"\"\r\n\r\n for ... | true |
5,831 | 48affa1b823a2543b6bbda615247324f5c249a69 | onfiguration name="test3" type="PythonConfigurationType" factoryName="Python" temporary="true">
<module name="hori_check" />
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
<envs>
<env name="PYTHONUNBUFFERED" value="1" />
</envs>
<opt... | [
"onfiguration name=\"test3\" type=\"PythonConfigurationType\" factoryName=\"Python\" temporary=\"true\">\n <module name=\"hori_check\" />\n <option name=\"INTERPRETER_OPTIONS\" value=\"\" />\n <option name=\"PARENT_ENVS\" value=\"true\" />\n <envs>\n <env name=\"PYTHONUNBUFFERED\" value=\... | true |
5,832 | 39197b3f9f85d94457584d7e488ca376e52207f1 | from operator import itemgetter
import math
def get_tf_idf_map(document, max_freq, n_docs, index):
tf_idf_map = {}
for term in document:
tf = 0
idf = math.log(n_docs)
if term in index and term not in tf_idf_map:
posting_list = index[term]
freq_term = sum([p... | [
"from operator import itemgetter\nimport math\n\ndef get_tf_idf_map(document, max_freq, n_docs, index):\n tf_idf_map = {}\n \n for term in document:\n tf = 0\n idf = math.log(n_docs)\n if term in index and term not in tf_idf_map: \n posting_list = index[term]\n fr... | false |
5,833 | ff331dc0c72378222db9195cce7c794f93799401 | from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
cols = ['Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',
'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',
... | [
"from matplotlib import pyplot as plt\nimport pandas as pd\nimport numpy as np\n\nfrom sklearn.cluster import KMeans\n\ncols = ['Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',\n 'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',\n ... | false |
5,834 | c33d625ebd6a40551d2ce0393fd78619601ea7ae |
# This module is used to load pascalvoc datasets (2007 or 2012)
import os
import tensorflow as tf
from configs.config_common import *
from configs.config_train import *
from configs.config_test import *
import sys
import random
import numpy as np
import xml.etree.ElementTree as ET
# Original dataset organisation.
DIR... | [
"\n# This module is used to load pascalvoc datasets (2007 or 2012)\nimport os\nimport tensorflow as tf\nfrom configs.config_common import *\nfrom configs.config_train import *\nfrom configs.config_test import *\nimport sys\nimport random\nimport numpy as np\nimport xml.etree.ElementTree as ET\n\n# Original dataset ... | false |
5,835 | edc7c74a19a272bdd6da81b3ce2d214a2b613984 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | [
"#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); yo... | false |
5,836 | a5dcc66ece4e58995fe86c3a399c45975a596b1a | from utilities import SumOneToN, RSS, MSE, R2Score
import numpy as np
import scipy.stats as st
class RidgeLinearModel:
covariance_matrix = None # covariance matrix of the model coefficients
covariance_matrix_updated = False
beta = None # coefficients of the modelfunction
var_vector = None
var_vecto... | [
"from utilities import SumOneToN, RSS, MSE, R2Score\nimport numpy as np\nimport scipy.stats as st\n\nclass RidgeLinearModel:\n covariance_matrix = None # covariance matrix of the model coefficients\n covariance_matrix_updated = False\n beta = None # coefficients of the modelfunction\n var_vector = None\... | false |
5,837 | 885e02cbf78412d77bd17eba64a8a1a52aaed0df | from slacker import Slacker
import vk_api
import time
import logging
from settings import SLACK_TOKEN, VK_LOGIN, VK_PASSWORD, GROUP_ID, TOPIC_ID, ICON_URL
slack = Slacker(SLACK_TOKEN)
class Vts:
def __init__(self):
self.last_comment_id = 0
self.vk = None
def update_vk(self):
if s... | [
"from slacker import Slacker\n\nimport vk_api\n\nimport time\n\nimport logging\n\nfrom settings import SLACK_TOKEN, VK_LOGIN, VK_PASSWORD, GROUP_ID, TOPIC_ID, ICON_URL\n\nslack = Slacker(SLACK_TOKEN)\n\n\nclass Vts:\n def __init__(self):\n self.last_comment_id = 0\n self.vk = None\n\n def update... | false |
5,838 | b051a3dbe1c695fda9a0488dd8986d587bbb24a6 | from math import log
from collections import Counter
import copy
import csv
import carTreePlotter
import re
def calEntropy(dataSet):
"""
输入：二维数据集
输出：二维数据集标签的熵
描述：
计算数据集的标签的香农熵；香农熵越大，数据集越混乱；
在计算 splitinfo 和通过计算熵减选择信息增益最大的属性时可以用到
"""
entryNum = len(dataSet)
labelsCount... | [
"from math import log\r\nfrom collections import Counter\r\nimport copy\r\nimport csv\r\nimport carTreePlotter\r\nimport re\r\n\r\ndef calEntropy(dataSet):\r\n \"\"\"\r\n 输入：二维数据集\r\n 输出：二维数据集标签的熵\r\n 描述：\r\n 计算数据集的标签的香农熵；香农熵越大，数据集越混乱；\r\n 在计算 splitinfo 和通过计算熵减选择信息增益最大的属性时可以用到\r\n \"\"\"\r\n ... | false |
5,839 | 56cae7b7a0338bd4a405cdc3cdcd9945a9df8823 | a = 2
while a == 1:
b = source()
c=function(b)
| [
"a = 2\nwhile a == 1:\n b = source()\nc=function(b)\n",
"a = 2\nwhile a == 1:\n b = source()\nc = function(b)\n",
"<assignment token>\nwhile a == 1:\n b = source()\n<assignment token>\n",
"<assignment token>\n<code token>\n<assignment token>\n"
] | false |
5,840 | 6306acd1508698687842ba6b55a839743af420cc | from extras.plugins import PluginConfig
from .version import __version__
class QRCodeConfig(PluginConfig):
name = 'netbox_qrcode'
verbose_name = 'qrcode'
description = 'Generate QR codes for the objects'
version = __version__
author = 'Nikolay Yuzefovich'
author_email = 'mgk.kolek@gmail.com'
... | [
"from extras.plugins import PluginConfig\nfrom .version import __version__\n\n\nclass QRCodeConfig(PluginConfig):\n name = 'netbox_qrcode'\n verbose_name = 'qrcode'\n description = 'Generate QR codes for the objects'\n version = __version__\n author = 'Nikolay Yuzefovich'\n author_email = 'mgk.kol... | false |
5,841 | 111186f1d45b9cf3bf9065c7fa83a8f3f796bbe1 | # -*- coding: utf-8 -*-
"""Labeled entry widget.
The goal of these widgets is twofold: to make it easier for developers
to implement dialogs with compound widgets, and to naturally
standardize the user interface presented to the user.
"""
import logging
import seamm_widgets as sw
import tkinter as tk
import tkinter.... | [
"# -*- coding: utf-8 -*-\n\n\"\"\"Labeled entry widget.\n\nThe goal of these widgets is twofold: to make it easier for developers\nto implement dialogs with compound widgets, and to naturally\nstandardize the user interface presented to the user.\n\"\"\"\n\nimport logging\nimport seamm_widgets as sw\nimport tkinter... | false |
5,842 | 33938a28aad29e996255827825a0cdb1db6b70b7 | import tkinter as tk
import random
root = tk.Tk()
main_frame = tk.Frame(root)
var = tk.StringVar()
ch = [ "hello world" , "HI Pyton", "Mar Java", "Mit Java", "Lut Java" ]
var.set("Hello world I am a Label")
label = tk.Label(main_frame,textvariable=var,
bg="black",fg="white",font=("Times New Roman",24,"bold"))
... | [
"import tkinter as tk \nimport random\nroot = tk.Tk()\nmain_frame = tk.Frame(root)\nvar = tk.StringVar()\nch = [ \"hello world\" , \"HI Pyton\", \"Mar Java\", \"Mit Java\", \"Lut Java\" ]\nvar.set(\"Hello world I am a Label\")\nlabel = tk.Label(main_frame,textvariable=var,\n bg=\"black\",fg=\"white\",font=(\... | false |
5,843 | 547844eca9eab097b814b0daa5da96d6a8ccee55 | import numpy as np
import xgboost as xgb
from sklearn.grid_search import GridSearchCV #Performing grid search
import generateVector
from sklearn.model_selection import GroupKFold
from sklearn import preprocessing as pr
positiveFile="../dataset/full_data/positive.csv"
negativeFile="../dataset/full_data/negative.csv"
... | [
"import numpy as np\nimport xgboost as xgb\nfrom sklearn.grid_search import GridSearchCV #Performing grid search\nimport generateVector\nfrom sklearn.model_selection import GroupKFold\nfrom sklearn import preprocessing as pr\n\npositiveFile=\"../dataset/full_data/positive.csv\"\nnegativeFile=\"../dataset/full_dat... | true |
5,844 | 4a546222082e2a25296e31f715baf594c974b7ad | #!/usr/bin/env python
#coding=UTF8
'''
@author: devin
@time: 2013-11-23
@desc:
timer
'''
import threading
import time
class Timer(threading.Thread):
'''
每隔一段时间执行一遍任务
'''
def __init__(self, seconds, fun, **kwargs):
'''
seconds为间隔时间，单位为秒
fun为定时执行的任务... | [
"#!/usr/bin/env python\n#coding=UTF8\n'''\n @author: devin\n @time: 2013-11-23\n @desc:\n timer\n'''\nimport threading\nimport time\n\nclass Timer(threading.Thread):\n '''\n 每隔一段时间执行一遍任务\n '''\n def __init__(self, seconds, fun, **kwargs):\n '''\n seconds为间隔时间，单位为秒\n... | true |
5,845 | 774f5d01cd274755626989c2b58bde68df349d8e | class Solution:
def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool:
h = len(matrix)
w = len(matrix[0])
for curRow in range(h) :
val = matrix[curRow][0]
i = 0
while i < h-curRow and i < w :
# print(curRow+i,i)
if mat... | [
"class Solution:\n def isToeplitzMatrix(self, matrix: List[List[int]]) -> bool:\n h = len(matrix)\n w = len(matrix[0])\n for curRow in range(h) :\n val = matrix[curRow][0]\n i = 0\n while i < h-curRow and i < w :\n # print(curRow+i,i)\n ... | false |
5,846 | 25b7af2a8036f35a0bca665867d1729b7c9c113c | from ._monitor import TMonitor as TMonitor, TqdmSynchronisationWarning as TqdmSynchronisationWarning
from ._tqdm_pandas import tqdm_pandas as tqdm_pandas
from .cli import main as main
from .gui import tqdm as tqdm_gui, trange as tgrange
from .std import TqdmDeprecationWarning as TqdmDeprecationWarning, TqdmExperimental... | [
"from ._monitor import TMonitor as TMonitor, TqdmSynchronisationWarning as TqdmSynchronisationWarning\nfrom ._tqdm_pandas import tqdm_pandas as tqdm_pandas\nfrom .cli import main as main\nfrom .gui import tqdm as tqdm_gui, trange as tgrange\nfrom .std import TqdmDeprecationWarning as TqdmDeprecationWarning, TqdmExp... | false |
5,847 | a1ebb00d7cda65cb528b2253e817d925214cdce3 | # 1.闭包
# 2.装饰圈初识
# 3.标准版装饰器 | [
"# 1.闭包\n# 2.装饰圈初识\n# 3.标准版装饰器",
""
] | false |
5,848 | c907f6b954aa3eae21a54eba9d54c116576bd40a | """
Constants to be used throughout this program
stored here.
"""
ROOT_URL = "https://api.twitter.com"
UPLOAD_URL = "https://upload.twitter.com"
REQUEST_TOKEN_URL = f'{ROOT_URL}/oauth/request_token'
AUTHENTICATE_URL = f'{ROOT_URL}/oauth/authenticate'
ACCESS_TOKEN_URL = f'{ROOT_URL}/oauth/access_token'
VERSION = '1.1'... | [
"\"\"\"\nConstants to be used throughout this program\nstored here.\n\"\"\"\nROOT_URL = \"https://api.twitter.com\"\nUPLOAD_URL = \"https://upload.twitter.com\"\n\nREQUEST_TOKEN_URL = f'{ROOT_URL}/oauth/request_token'\nAUTHENTICATE_URL = f'{ROOT_URL}/oauth/authenticate'\nACCESS_TOKEN_URL = f'{ROOT_URL}/oauth/access... | false |
5,849 | 223413918ba2a49cd13a34026d39b17fb5944572 | from selenium import webdriver
driver = webdriver.Chrome(executable_path=r'D:\Naveen\Selenium\chromedriver_win32\chromedriver.exe')
driver.maximize_window()
driver.get('http://zero.webappsecurity.com/')
parent_window_handle = driver.current_window_handle
driver.find_element_by_xpath("(//a[contains(text(),'privacy')])... | [
"from selenium import webdriver\n\ndriver = webdriver.Chrome(executable_path=r'D:\\Naveen\\Selenium\\chromedriver_win32\\chromedriver.exe')\ndriver.maximize_window()\ndriver.get('http://zero.webappsecurity.com/')\n\nparent_window_handle = driver.current_window_handle\ndriver.find_element_by_xpath(\"(//a[contains(te... | false |
5,850 | 4bd928c16cd0f06931aad5a478f8a911c5a7108b | #source: https://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/
from imutils.video import VideoStream
import argparse
import datetime
import imutils
import time
import cv2
#capture the video file
b="blood.mp4"
c="Center.avi"
d="Deformed.avi"
i="Inlet.avi"
... | [
"#source: https://www.pyimagesearch.com/2015/05/25/basic-motion-detection-and-tracking-with-python-and-opencv/\r\n\r\nfrom imutils.video import VideoStream\r\nimport argparse\r\nimport datetime\r\nimport imutils\r\nimport time\r\nimport cv2\r\n\r\n\r\n#capture the video file\r\nb=\"blood.mp4\"\r\nc=\"Center.avi\"\r... | false |
5,851 | 65b30bbe737b331447235b5c640e9c3f7f6d6f8c | def slope_distance(baseElev, elv2, dist_betwn_baseElev_elv2, projectedDistance):
# Calculate the slope and distance between two Cartesian points.
#
# Input:
# For 2-D graphs,
# dist_betwn_baseElev_elv2, Distance between two elevation points (FLOAT)
# baseElev, Elevation of first ... | [
"def slope_distance(baseElev, elv2, dist_betwn_baseElev_elv2, projectedDistance):\n\n # Calculate the slope and distance between two Cartesian points.\n #\n # Input:\n # For 2-D graphs,\n # dist_betwn_baseElev_elv2, Distance between two elevation points (FLOAT)\n # baseElev, Elevati... | false |
5,852 | a283fd1e4098ea8bb3cc3580438c90e5932ba22f | #!/usr/bin/env python
from __future__ import print_function
from __future__ import division
from __future__ import absolute_import
# Workaround for segmentation fault for some versions when ndimage is imported after tensorflow.
import scipy.ndimage as nd
import argparse
import numpy as np
from pybh import tensorpack... | [
"#!/usr/bin/env python\n\nfrom __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\n# Workaround for segmentation fault for some versions when ndimage is imported after tensorflow.\nimport scipy.ndimage as nd\n\nimport argparse\nimport numpy as np\nfrom pybh i... | false |
5,853 | 198beb5a17575d781f7bce1ab36a6213ad7331b3 | import pandas as pd
import numpy as np
import inspect
from script.data_handler.Base.df_plotterMixIn import df_plotterMixIn
from script.util.MixIn import LoggerMixIn
from script.util.PlotTools import PlotTools
DF = pd.DataFrame
Series = pd.Series
class null_clean_methodMixIn:
@staticmethod
def drop_col(df: D... | [
"import pandas as pd\nimport numpy as np\nimport inspect\n\nfrom script.data_handler.Base.df_plotterMixIn import df_plotterMixIn\nfrom script.util.MixIn import LoggerMixIn\nfrom script.util.PlotTools import PlotTools\n\nDF = pd.DataFrame\nSeries = pd.Series\n\n\nclass null_clean_methodMixIn:\n @staticmethod\n ... | false |
5,854 | e9ea48dec40e75f2fc73f8dcb3b5b975065cf8af | class Solution:
def findAndReplacePattern(self, words: List[str], pattern: str) -> List[str]:
def convert(word):
table = {}
count, converted = 0, ''
for w in word:
if w in table:
converted += table[w]
else:
... | [
"class Solution:\n def findAndReplacePattern(self, words: List[str], pattern: str) -> List[str]:\n def convert(word):\n table = {}\n count, converted = 0, ''\n \n for w in word:\n if w in table:\n converted += table[w]\n ... | false |
5,855 | 399a22450d215638051a7d643fb6d391156779c5 | /home/khang/anaconda3/lib/python3.6/tempfile.py | [
"/home/khang/anaconda3/lib/python3.6/tempfile.py"
] | true |
5,856 | 91959f6621f05b1b814a025f0b95c55cf683ded3 | from pyparsing import ParseException
from pytest import raises
from easymql.expressions import Expression as exp
class TestComparisonExpression:
def test_cmp(self):
assert exp.parse('CMP(1, 2)') == {'$cmp': [1, 2]}
with raises(ParseException):
exp.parse('CMP(1)')
with raises(P... | [
"from pyparsing import ParseException\nfrom pytest import raises\n\nfrom easymql.expressions import Expression as exp\n\n\nclass TestComparisonExpression:\n def test_cmp(self):\n assert exp.parse('CMP(1, 2)') == {'$cmp': [1, 2]}\n with raises(ParseException):\n exp.parse('CMP(1)')\n ... | false |
5,857 | f9dd21aac7915b9bbf91eeffb5fd58ffdb43c6c3 | '''
Unit test for `redi.create_summary_report()`
'''
import unittest
import os
import sys
from lxml import etree
from StringIO import StringIO
import time
import redi
file_dir = os.path.dirname(os.path.realpath(__file__))
goal_dir = os.path.join(file_dir, "../")
proj_root = os.path.abspath(goal_dir)+'/'
DEFAULT_DATA_... | [
"'''\nUnit test for `redi.create_summary_report()`\n'''\nimport unittest\nimport os\nimport sys\nfrom lxml import etree\nfrom StringIO import StringIO\nimport time\nimport redi\n\nfile_dir = os.path.dirname(os.path.realpath(__file__))\ngoal_dir = os.path.join(file_dir, \"../\")\nproj_root = os.path.abspath(goal_dir... | false |
5,858 | aa15d51760c16181907994d329fb7ceede6a539b | import re
text = "Python is an interpreted high-level general-purpose programming language."
fiveWord = re.findall(r"\b\w{5}\b", text)
print("Following are the words with five Letters:")
for strWord in fiveWord:
print(strWord)
| [
"import re\r\ntext = \"Python is an interpreted high-level general-purpose programming language.\"\r\nfiveWord = re.findall(r\"\\b\\w{5}\\b\", text)\r\nprint(\"Following are the words with five Letters:\")\r\nfor strWord in fiveWord:\r\n print(strWord)\r\n",
"import re\ntext = (\n 'Python is an interpreted hi... | false |
5,859 | d077f32061b87a4bfd6a0ac226730957a4000804 | ###
### Copyright 2009 The Chicago Independent Radio Project
### All Rights Reserved.
###
### Licensed under the Apache License, Version 2.0 (the "License");
### you may not use this file except in compliance with the License.
### You may obtain a copy of the License at
###
### http://www.apache.org/licenses/LICENS... | [
"###\n### Copyright 2009 The Chicago Independent Radio Project\n### All Rights Reserved.\n###\n### Licensed under the Apache License, Version 2.0 (the \"License\");\n### you may not use this file except in compliance with the License.\n### You may obtain a copy of the License at\n###\n### http://www.apache.org/... | false |
5,860 | f882b73645c6a280a17f40b27c01ecad7e4d85ae | import logging
from datetime import datetime
import boto3
from pytz import timezone
from mliyweb.api.v1.api_session_limiter import session_is_okay
from mliyweb.api.v1.json_view import JsonView
from mliyweb.dns import deleteDnsEntry
from mliyweb.models import Cluster
from mliyweb.resources.clusters import ClusterServi... | [
"import logging\nfrom datetime import datetime\n\nimport boto3\nfrom pytz import timezone\n\nfrom mliyweb.api.v1.api_session_limiter import session_is_okay\nfrom mliyweb.api.v1.json_view import JsonView\nfrom mliyweb.dns import deleteDnsEntry\nfrom mliyweb.models import Cluster\nfrom mliyweb.resources.clusters impo... | false |
5,861 | c80ae9d2eb07fd716a80a5e2d7b5237925fda02c | # Pose estimation and object detection: OpenCV DNN, ImageAI, YOLO, mpi, caffemodel, tensorflow
# Authors:
# Tutorial by: https://learnopencv.com/deep-learning-based-human-pose-estimation-using-opencv-cpp-python/
# Model file links collection (replace .sh script): Twenkid
# http://posefs1.perception.cs.cmu.edu/OpenPose/... | [
"# Pose estimation and object detection: OpenCV DNN, ImageAI, YOLO, mpi, caffemodel, tensorflow\n# Authors:\n# Tutorial by: https://learnopencv.com/deep-learning-based-human-pose-estimation-using-opencv-cpp-python/\n# Model file links collection (replace .sh script): Twenkid\n# http://posefs1.perception.cs.cmu.edu/... | false |
5,862 | 7d2335c956776fc5890a727d22540eabf2ea4b94 | umur = raw_input("Berapakah umurmu?")
tinggi = raw_input("Berapakah tinggimu?")
berat = raw_input("Berapa beratmu?")
print "Jadi, umurmu adalah %r, tinggumu %r, dan beratmu %r." % (umur, tinggi, berat)
| [
"umur = raw_input(\"Berapakah umurmu?\")\ntinggi = raw_input(\"Berapakah tinggimu?\")\nberat = raw_input(\"Berapa beratmu?\")\n\nprint \"Jadi, umurmu adalah %r, tinggumu %r, dan beratmu %r.\" % (umur, tinggi, berat)\n"
] | true |
5,863 | 84d154afe206fd2c7381a2203affc162c28e21c1 | import sqlite3
from flask_restful import Resource, reqparse
from flask_jwt import JWT, jwt_required
#import base64
import datetime
import psycopg2
class User:
def __init__(self, _id, username, password, user_name, address, contact):
self.id = _id
self.username = username
self.password =... | [
"import sqlite3\nfrom flask_restful import Resource, reqparse\nfrom flask_jwt import JWT, jwt_required\n#import base64\nimport datetime\nimport psycopg2\n\n\n\n\nclass User:\n def __init__(self, _id, username, password, user_name, address, contact):\n self.id = _id\n self.username = username\n ... | false |
5,864 | 3bf1b4cfce55820605653d9dc57bab839f2dea55 | #!/usr/bin/env python
###############################################################################
# \file
#
# $Id:$
#
# Copyright (C) Brno University of Technology
#
# This file is part of software developed by Robo@FIT group.
#
# Author: Tomas Lokaj
# Supervised by: Michal Spanel (spanel@fit.vutbr.cz)
# Date: 12/... | [
"#!/usr/bin/env python\n###############################################################################\n# \\file\n#\n# $Id:$\n#\n# Copyright (C) Brno University of Technology\n#\n# This file is part of software developed by Robo@FIT group.\n# \n# Author: Tomas Lokaj\n# Supervised by: Michal Spanel (spanel@fit.vutb... | true |
5,865 | 3f3ed40bf800eddb2722171d5fd94f6c292162de | #!/usr/bin/env python
import sys
def trim_reads(fastq, selection, extra_cut, orientation, output, outputType,
seqLen, trim):
# Store all read/sequence ids that did not match with KoRV
ids = []
with open(selection, 'r') as f:
for line in f:
ids.append(line.strip())
... | [
"#!/usr/bin/env python\nimport sys\n\n\ndef trim_reads(fastq, selection, extra_cut, orientation, output, outputType,\n seqLen, trim):\n\n # Store all read/sequence ids that did not match with KoRV\n ids = []\n with open(selection, 'r') as f:\n for line in f:\n ids.append(lin... | false |
5,866 | 8ec257d5dfe84e363e3c3aa5adee3470c20d1765 | import sys
import time
import numpy as np
import vii
import cnn
from cnn._utils import (FLOAT_DTYPE,
_multi_convolve_image,
_opencl_multi_convolve_image,
_relu_max_pool_image,
_opencl_relu_max_pool_image)
GROUPS = 25, 20... | [
"import sys\nimport time\nimport numpy as np\n\nimport vii\n\nimport cnn\nfrom cnn._utils import (FLOAT_DTYPE,\n _multi_convolve_image,\n _opencl_multi_convolve_image,\n _relu_max_pool_image,\n _opencl_relu_max_pool_image)\n... | false |
5,867 | 539431649e54469ddbe44fdbd17031b4449abdd9 | import boto3
import os
from trustedadvisor import authenticate_support
accountnumber = os.environ['Account_Number']
rolename = os.environ['Role_Name']
rolesession = accountnumber + rolename
def lambda_handler(event, context):
sts_client = boto3.client('sts')
assumerole = sts_client.assume_role(
Role... | [
"import boto3\nimport os\n\nfrom trustedadvisor import authenticate_support\n\naccountnumber = os.environ['Account_Number']\nrolename = os.environ['Role_Name']\nrolesession = accountnumber + rolename\n\n\ndef lambda_handler(event, context):\n sts_client = boto3.client('sts')\n assumerole = sts_client.assume_r... | false |
5,868 | 75c00eec7eacd37ff0b37d26163c2304620bb9db | from django.contrib.auth.hashers import make_password
from django.core import mail
from rest_framework import status
from django.contrib.auth.models import User
import time
from api.tests.api_test_case import CustomAPITestCase
from core.models import Member, Community, LocalCommunity, TransportCommunity, Profile, Noti... | [
"from django.contrib.auth.hashers import make_password\nfrom django.core import mail\nfrom rest_framework import status\nfrom django.contrib.auth.models import User\nimport time\n\nfrom api.tests.api_test_case import CustomAPITestCase\nfrom core.models import Member, Community, LocalCommunity, TransportCommunity, P... | false |
5,869 | e09f914f00e59124ef7d8a8f183bff3f7f74b826 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import asyncio
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from myshell.environment import Environment
from myshell.job import Job, JobState
class JobManager:
"""事务管理器，用以调度前台和后台事务"""
def __init__(self, environment: "Environment"):
self... | [
"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport asyncio\nfrom typing import TYPE_CHECKING, Optional\n\nif TYPE_CHECKING:\n from myshell.environment import Environment\n\nfrom myshell.job import Job, JobState\n\n\nclass JobManager:\n \"\"\"事务管理器，用以调度前台和后台事务\"\"\"\n\n def __init__(self, environment: \"... | false |
5,870 | 6e7cca4f766ca89d2e2f82a73f22742b0e8f92a8 | from .ros_publisher import *
| [
"from .ros_publisher import *\n",
"<import token>\n"
] | false |
5,871 | 2269e74c006833976c3a28cd52c238e2dde20051 | from .__main__ import datajson_write, datajson_read
| [
"from .__main__ import datajson_write, datajson_read\n",
"<import token>\n"
] | false |
5,872 | 0ff96b2314927d7b3e763242e554fd561f3c9343 | #!/usr/bin/env python3
# coding=utf-8
import fire
import json
import os
import time
import requests
import time
import hashlib
import random
root_path, file_name = os.path.split(os.path.realpath(__file__))
ip_list_path = ''.join([root_path, os.path.sep, 'ip_list.json'])
class ProxySwift(object):
... | [
"#!/usr/bin/env python3\r\n# coding=utf-8\r\nimport fire\r\nimport json\r\nimport os\r\nimport time\r\nimport requests\r\nimport time\r\nimport hashlib\r\nimport random\r\n\r\nroot_path, file_name = os.path.split(os.path.realpath(__file__))\r\nip_list_path = ''.join([root_path, os.path.sep, 'ip_list.json'])\r\n\r\n... | false |
5,873 | 8dbc0b9b80aae4cb5c4101007afc50ac54f7a7e7 | #!/usr/bin/python
def sumbelow(n):
multiples_of_3 = set(range(0,n,3))
multiples_of_5 = set(range(0,n,5))
return sum(multiples_of_3.union(multiples_of_5))
#one linear:
# return sum(set(range(0,n,3)).union(set(range(0,n,5)))),
# or rather,
# return sum(set(range(0,n,3) + range(0,n,5)))
if __name__ == '__ma... | [
"#!/usr/bin/python\n\ndef sumbelow(n):\n multiples_of_3 = set(range(0,n,3))\n multiples_of_5 = set(range(0,n,5))\n return sum(multiples_of_3.union(multiples_of_5))\n\n#one linear:\n# return sum(set(range(0,n,3)).union(set(range(0,n,5)))),\n# or rather,\n# return sum(set(range(0,n,3) + range(0,n,5)))\n\nif ... | true |
5,874 | 7a69a9fd6ee5de704a580e4515586a1c1d2b8017 | # (C) Datadog, Inc. 2018
# All rights reserved
# Licensed under a 3-clause BSD style license (see LICENSE)
from .agent import agent
from .clean import clean
from .config import config
from .create import create
from .dep import dep
from .env import env
from .meta import meta
from .release import release
from .run impor... | [
"# (C) Datadog, Inc. 2018\n# All rights reserved\n# Licensed under a 3-clause BSD style license (see LICENSE)\nfrom .agent import agent\nfrom .clean import clean\nfrom .config import config\nfrom .create import create\nfrom .dep import dep\nfrom .env import env\nfrom .meta import meta\nfrom .release import release\... | false |
5,875 | 199872ea459a9dba9975c6531034bdbc1e77f1db | # -*- coding: utf-8 -*-
# caixinjun
import argparse
from sklearn import metrics
import datetime
import jieba
from sklearn.feature_extraction.text import TfidfVectorizer
import pickle
from sklearn import svm
import os
import warnings
warnings.filterwarnings('ignore')
def get_data(train_file):
targ... | [
"# -*- coding: utf-8 -*-\r\n# caixinjun\r\n\r\nimport argparse\r\nfrom sklearn import metrics\r\nimport datetime\r\nimport jieba\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nimport pickle\r\nfrom sklearn import svm\r\nimport os\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\n\r\n... | false |
5,876 | a8341bf422a4d31a83ff412c6aac75e5cb8c5e0f | # Задание 1
# Выучите основные стандартные исключения, которые перечислены в данном уроке.
# Задание 2
# Напишите программу-калькулятор, которая поддерживает следующие операции: сложение, вычитание,
# умножение, деление и возведение в степень. Программа должна выдавать сообщения об ошибке и
# продолжать работу при ввод... | [
"# Задание 1\n# Выучите основные стандартные исключения, которые перечислены в данном уроке.\n# Задание 2\n# Напишите программу-калькулятор, которая поддерживает следующие операции: сложение, вычитание,\n# умножение, деление и возведение в степень. Программа должна выдавать сообщения об ошибке и\n# продолжать работ... | false |
5,877 | 26fb607623fda333c37e254470ca6d07708671a8 | from app.request import send_tor_signal
from app.utils.session_utils import generate_user_keys
from app.utils.gen_ddg_bangs import gen_bangs_json
from flask import Flask
from flask_session import Session
import json
import os
from stem import Signal
app = Flask(__name__, static_folder=os.path.dirname(
os... | [
"from app.request import send_tor_signal\r\nfrom app.utils.session_utils import generate_user_keys\r\nfrom app.utils.gen_ddg_bangs import gen_bangs_json\r\nfrom flask import Flask\r\nfrom flask_session import Session\r\nimport json\r\nimport os\r\nfrom stem import Signal\r\n\r\napp = Flask(__name__, static_folder=o... | false |
5,878 | d429f03c0f0c241166d6c0a5a45dc1101bcaec16 | #!/usr/bin/env python3
import matplotlib
from matplotlib.colors import to_hex
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import itertools as it
from pathlib import Path
import subprocess
from tqdm import tqdm
from koala import plotting as pl
from koala import phase_diagrams as pd
fr... | [
"#!/usr/bin/env python3\n\nimport matplotlib\nfrom matplotlib.colors import to_hex\nfrom matplotlib import cm\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools as it\nfrom pathlib import Path\nimport subprocess\nfrom tqdm import tqdm\n\nfrom koala import plotting as pl\nfrom koala import phas... | false |
5,879 | 6c6026a7ff0345c37e62de7c0aac0ee3bcde2c82 | import pymongo
myclient = pymongo.MongoClient('mongodb://localhost:27017/') #We create the database object
mydb = myclient['mydatabase'] #Create a database
mycol = mydb['customers'] #Create a collection into my mydatabase
mydict = [{"name": "Eric", "address": "Highway 37"}, {"name": "Albert", "address": "Highway 37... | [
"import pymongo\n\nmyclient = pymongo.MongoClient('mongodb://localhost:27017/') #We create the database object\n\nmydb = myclient['mydatabase'] #Create a database\n\nmycol = mydb['customers'] #Create a collection into my mydatabase\n\nmydict = [{\"name\": \"Eric\", \"address\": \"Highway 37\"}, {\"name\": \"Albert\... | false |
5,880 | fb1974ad7ac9ae54344812814cb95a7fccfefc66 | # - Generated by tools/entrypoint_compiler.py: do not edit by hand
"""
NGramHash
"""
import numbers
from ..utils.entrypoints import Component
from ..utils.utils import try_set
def n_gram_hash(
hash_bits=16,
ngram_length=1,
skip_length=0,
all_lengths=True,
seed=314489979,
... | [
"# - Generated by tools/entrypoint_compiler.py: do not edit by hand\n\"\"\"\nNGramHash\n\"\"\"\n\nimport numbers\n\nfrom ..utils.entrypoints import Component\nfrom ..utils.utils import try_set\n\n\ndef n_gram_hash(\n hash_bits=16,\n ngram_length=1,\n skip_length=0,\n all_lengths=True,\n ... | false |
5,881 | 1af6e66c19078a9ee971f608daa93247911d8406 | from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
model = ResNet50(weights='imagenet', # Learned weights on imagenet
include_top=True)
... | [
"from tensorflow.keras.applications.resnet50 import ResNet50\nfrom tensorflow.keras.preprocessing import image\nfrom tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\nimport numpy as np\n\nmodel = ResNet50(weights='imagenet', # Learned weights on imagenet\n include... | false |
5,882 | 6edb1f99ca9af01f28322cbaf13f278e79b94e92 | # -*- coding: utf-8 -*-
c = int(input())
t = input()
m = []
for i in range(12):
aux = []
for j in range(12):
aux.append(float(input()))
m.append(aux)
aux = []
soma = 0
for i in range(12):
soma += m[i][c]
resultado = soma / (t == 'S' and 1 or 12)
print('%.1f' % resultado)
| [
"# -*- coding: utf-8 -*-\n\nc = int(input())\nt = input()\nm = []\n\nfor i in range(12):\n aux = []\n for j in range(12):\n aux.append(float(input()))\n m.append(aux)\n aux = []\n\nsoma = 0\nfor i in range(12):\n soma += m[i][c]\n\nresultado = soma / (t == 'S' and 1 or 12)\nprint('%.1f' % resu... | false |
5,883 | 9baf55eb2fb70e9fa0d92df22d307962b8d6c6d4 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import json
import urllib2
#this is executed by a cron job on the pi inside the pooltable
secret ='secret'
baseurl='https://pooltable.mysite.com/'
url = baseurl + 'gettrans.php?secret=' + secret
req = urllib2.Request(url)
f = urllib2.urlopen(req)
response = f.read()... | [
"#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport json\r\nimport urllib2\r\n#this is executed by a cron job on the pi inside the pooltable\r\nsecret ='secret'\r\nbaseurl='https://pooltable.mysite.com/'\r\nurl = baseurl + 'gettrans.php?secret=' + secret\r\nreq = urllib2.Request(url)\r\nf = urllib2.urlopen(... | true |
5,884 | 9f3ca0d5a10a27d926a0f306665889418f8d6a0c | from src.produtos import *
class Estoque(object):
def __init__(self):
self.categorias = []
self.subcategorias = []
self.produtos = []
self.menu_estoque()
def save_categoria(self, categoria):
pass
def save_subcategorias(self, subcategoria):
pa... | [
"from src.produtos import *\r\n\r\n\r\nclass Estoque(object):\r\n def __init__(self):\r\n self.categorias = []\r\n self.subcategorias = []\r\n self.produtos = []\r\n self.menu_estoque()\r\n\r\n def save_categoria(self, categoria):\r\n pass\r\n\r\n def save_subcategorias(s... | false |
5,885 | 6fa9dfadc60108e1718c6688f07de877b0ac0afd | #!usr/bin/python
# -*- coding:UTF-8 -*-
'''
Introduction:
Implementation of Stack
Created on: Oct 28, 2014
@author: ICY
'''
#-------------------------FUNCTION---------------------------#
class Stack(object):
def __init__(self):
self.items = []
def is_empty(self):
return self.items == []
... | [
"#!usr/bin/python\n# -*- coding:UTF-8 -*-\n\n'''\nIntroduction: \nImplementation of Stack \n\nCreated on: Oct 28, 2014\n\n@author: ICY\n'''\n\n#-------------------------FUNCTION---------------------------#\n\nclass Stack(object):\n def __init__(self):\n self.items = []\n\n def is_empty(self):\n ... | false |
5,886 | 4f87c2602e3233889888e419296f67fe40a2db0f | #!/usr/bin/python
#==========================================================================
#
# Copyright Insight Software Consortium
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at... | [
"#!/usr/bin/python\n#==========================================================================\n#\n# Copyright Insight Software Consortium\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 ... | true |
5,887 | 8c4006ed8f4b1744f0316a61d95458b227653fee | /Users/AbbyPennington/anaconda/lib/python3.5/os.py | [
"/Users/AbbyPennington/anaconda/lib/python3.5/os.py"
] | true |
5,888 | 2a6ae615b427a7c970aacf9804865ea7952d065f | # -*- coding: utf-8 -*-
"""
Created on Sun Dec 20 14:48:56 2020
@author: dhk1349
"""
n = int(input()) #목표채널
m = int(input())
broken=[int(i) for i in input().split()] #망가진 버튼
normal=[i for i in range(10)] #사용가능한 버튼
ans=abs(n-100) #시작 시 정답
for i in broken:
normal.remove(i)
tempnum=0
iternum=1
def solve(ls... | [
"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 20 14:48:56 2020\n\n@author: dhk1349\n\"\"\"\n\nn = int(input()) #목표채널 \nm = int(input())\nbroken=[int(i) for i in input().split()] #망가진 버튼 \nnormal=[i for i in range(10)] #사용가능한 버튼 \nans=abs(n-100) #시작 시 정답 \n\n\nfor i in broken:\n normal.remove(i)\n\n\ntemp... | true |
5,889 | 8a04166e091e2da348928598b2356c8ad75dd831 | #usage:
#crawl raw weibo text data from sina weibo users(my followees)
#in total, there are 20080 weibo tweets, because there is uplimit for crawler
# -*- coding: utf-8 -*-
import weibo
APP_KEY = 'your app_key'
APP_SECRET = 'your app_secret'
CALL_BACK = 'your call back url'
def run():
token = "your access token got... | [
"#usage:\n#crawl raw weibo text data from sina weibo users(my followees)\n#in total, there are 20080 weibo tweets, because there is uplimit for crawler\n\n# -*- coding: utf-8 -*-\nimport weibo\n\nAPP_KEY = 'your app_key'\nAPP_SECRET = 'your app_secret'\nCALL_BACK = 'your call back url'\n\ndef run():\n\ttoken = \"yo... | true |
5,890 | b86dedad42d092ae97eb21227034e306ca640912 | class mySeq:
def __init__(self):
self.mseq = ['I','II','III','IV']
def __len__(self):
return len(self.mseq)
def __getitem__(self,key):
if 0 <= key < 4 :
return self.mseq[key]
if __name__ == '__main__':
m = mySeq()
print('Len of mySeq : ',len(m))
for i in range(len(m.mseq)):
print(m.mseq[i]) | [
"class mySeq:\n\tdef __init__(self):\n\t\tself.mseq = ['I','II','III','IV']\n\n\tdef __len__(self):\n\t\treturn len(self.mseq)\n\n\tdef __getitem__(self,key):\n\t\tif 0 <= key < 4 :\n\t\t\treturn self.mseq[key]\n\nif __name__ == '__main__':\n\tm = mySeq()\n\tprint('Len of mySeq : ',len(m))\n\tfor i in range(len(m.m... | false |
5,891 | 38184ed4117b1b7dcf9e135ce8612fa13c44a99c | i = 0
while i >= 0:
a=input("Name is: ")
print(a)
if a == "Zeal":
print("""Name_Zeal.
Age_16.
Interested in Programming.""")
elif a=="HanZaw":
print("""Name_Han Zaw.
Age_18.
Studying Code at Green Hacker.""")
elif a == "Murphy":
print("""Name_Murphy.
... | [
"i = 0\nwhile i >= 0: \n a=input(\"Name is: \")\n print(a)\n if a == \"Zeal\":\n print(\"\"\"Name_Zeal.\n Age_16.\n Interested in Programming.\"\"\")\n elif a==\"HanZaw\":\n print(\"\"\"Name_Han Zaw.\n Age_18.\n Studying Code at Green Hacker.\"\"\")\n elif a == \"Murphy\": ... | false |
5,892 | f39945f35b13c0918c3ef06224bca65ae6166ebc | import numpy as np
a=np.array([1,2,3])
b=np.r_[np.repeat(a,3),np.tile(a,3)]
print(b)
| [
"import numpy as np\na=np.array([1,2,3])\nb=np.r_[np.repeat(a,3),np.tile(a,3)]\nprint(b)\n",
"import numpy as np\na = np.array([1, 2, 3])\nb = np.r_[np.repeat(a, 3), np.tile(a, 3)]\nprint(b)\n",
"<import token>\na = np.array([1, 2, 3])\nb = np.r_[np.repeat(a, 3), np.tile(a, 3)]\nprint(b)\n",
"<import token>\n... | false |
5,893 | 87a62f76027e0653f6966f76a42def2ce2a26ba3 | #! /usr/bin/python3
print("content-type: text/html")
print()
import cgi
import subprocess as sp
import requests
import xmltodict
import json
db = cgi.FieldStorage()
ch=db.getvalue("ch")
url =("http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={}&username=<username>" .format(ch))
u... | [
"#! /usr/bin/python3\r\n\r\nprint(\"content-type: text/html\")\r\nprint()\r\n\r\n\r\nimport cgi\r\nimport subprocess as sp\r\nimport requests\r\nimport xmltodict\r\nimport json\r\n\r\ndb = cgi.FieldStorage()\r\nch=db.getvalue(\"ch\")\r\nurl =(\"http://www.regcheck.org.uk/api/reg.asmx/CheckIndia?RegistrationNumber={... | false |
5,894 | 1c6e6394a6bd26b152b2f5ec87eb181a3387f794 | #!/usr/bin/python
# Find minimal distances between clouds in one bin, average these per bin
# Compute geometric and arithmetical mean between all clouds per bin
from netCDF4 import Dataset as NetCDFFile
from matplotlib import pyplot as plt
import numpy as np
from numpy import ma
from scipy import stats
from haversin... | [
"#!/usr/bin/python\n\n# Find minimal distances between clouds in one bin, average these per bin\n# Compute geometric and arithmetical mean between all clouds per bin\n\nfrom netCDF4 import Dataset as NetCDFFile\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom numpy import ma\nfrom scipy import stats ... | true |
5,895 | 1f0680c45afb36439c56a1d202537261df5f9afc | from eventnotipy import app
import json
json_data = open('eventnotipy/config.json')
data = json.load(json_data)
json_data.close()
username = data['dbuser']
password = data['password']
host = data['dbhost']
db_name = data['database']
email_host = data['email_host']
email_localhost = data['email_localhost']
sms_host =... | [
"from eventnotipy import app\nimport json\n\njson_data = open('eventnotipy/config.json')\ndata = json.load(json_data)\njson_data.close()\n\nusername = data['dbuser']\npassword = data['password']\nhost = data['dbhost']\ndb_name = data['database']\n\nemail_host = data['email_host']\nemail_localhost = data['email_loca... | false |
5,896 | 90324392e763ac6ea78c77b909c4bea667d45e6c | from admin_tools.dashboard.modules import DashboardModule
from nodes.models import Node
from slices.models import Slice
class MyThingsDashboardModule(DashboardModule):
"""
Controller dashboard module to provide an overview to
the user of the nodes and slices of its groups.
"""
title="My Things"
... | [
"from admin_tools.dashboard.modules import DashboardModule\n\nfrom nodes.models import Node\nfrom slices.models import Slice\n\nclass MyThingsDashboardModule(DashboardModule):\n \"\"\"\n Controller dashboard module to provide an overview to\n the user of the nodes and slices of its groups.\n \"\"\"\n ... | false |
5,897 | 14e1af3d60efef842c72bf9b55143d0e14f3a7b8 | import asyncio
import json
from functools import lru_cache
from pyrogram import Client
SETTINGS_FILE = "/src/settings.json"
CONN_FILE = "/src/conn.json"
def load_setting(setting: str):
with open(SETTINGS_FILE) as f:
return json.load(f)[setting]
@lru_cache()
def get_bot_name():
return load_setting(... | [
"import asyncio\nimport json\nfrom functools import lru_cache\n\nfrom pyrogram import Client\n\n\nSETTINGS_FILE = \"/src/settings.json\"\nCONN_FILE = \"/src/conn.json\"\n\ndef load_setting(setting: str):\n with open(SETTINGS_FILE) as f:\n return json.load(f)[setting]\n\n\n@lru_cache()\ndef get_bot_name():... | false |
5,898 | bf40b516e202af14469cd4012597ba412e663f56 | import nltk
import A
from collections import defaultdict
from nltk.align import Alignment, AlignedSent
class BerkeleyAligner():
def __init__(self, align_sents, num_iter):
self.t, self.q = self.train(align_sents, num_iter)
# TODO: Computes the alignments for align_sent, using this model's parameters. Return... | [
"import nltk\nimport A\nfrom collections import defaultdict\nfrom nltk.align import Alignment, AlignedSent\n\nclass BerkeleyAligner():\n\n def __init__(self, align_sents, num_iter):\n\tself.t, self.q = self.train(align_sents, num_iter)\n\t\n # TODO: Computes the alignments for align_sent, using this model's p... | true |
5,899 | fbd7868a37a2270e5dc86843adff50a94436404d | from openvino.inference_engine import IENetwork, IECore
import numpy as np
import time
from datetime import datetime
import sys
import os
import cv2
class MotionDetect:
# Klasse zur Erkennung von Bewegung
def __init__(self):
self.static_back = None
def detect_motion(self, frame, reset=False):
... | [
"from openvino.inference_engine import IENetwork, IECore\nimport numpy as np\nimport time\nfrom datetime import datetime\nimport sys\nimport os\nimport cv2\n\n\nclass MotionDetect:\n # Klasse zur Erkennung von Bewegung\n def __init__(self):\n self.static_back = None\n\n def detect_motion(self, frame... | false |
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